The court’s partial annulment of the European Commission’s Digital Markets Act designation of Meta—limited to Facebook Marketplace—is not a revolution. It did not invalidate the DMA. It did not reject the Commission’s authority to designate large digital platforms as gatekeepers. It did not save Meta’s Messenger service—a standalone instant-messaging platform that lets users send texts, share high-definition photos and large files, and make voice or video calls without needing a phone number—from the DMA’s reach.

Formally, the decision is narrow. The court upheld the Messenger designation, while annulling the designation of Facebook Marketplace—a digital classifieds platform embedded in the Facebook app and website—because the Commission had not adequately justified its analysis.

Precisely because the judgment is narrow, it may prove significant. It suggests how European Union courts can discipline regulatory overreach without openly repudiating the political choices embodied in modern EU digital regulation.

Labels Are Not Analysis

The decision matters because it insists that even ex ante regulation—rules imposed before specific competitive harm has been proved—must receive close legal scrutiny.

The Commission may be tempted to treat gatekeeper designation as an administrative shortcut: identify a large firm, invoke the statutory thresholds, and impose behavioral obligations meant to reshape digital markets in the name of fairness and contestability. But the General Court’s Marketplace holding suggests that labels matter, statutory categories matter, evidence matters, and reasons matter.

If a service is to be treated as a core platform service, the Commission must explain why the legal category fits the economic reality. It cannot infer legal consequences from the size, notoriety, or political salience of the undertaking.

That is a modest but valuable judicial signal. The greatest danger in contemporary competition policy is not merely that regulators sometimes err. Error is inevitable. The more serious danger is that regulators will institutionalize a framework in which mistakes are cheap for the state and expensive for markets.

The DMA, like other ex ante regimes, lowers the Commission’s burden relative to traditional antitrust. It replaces case-by-case proof of competitive harm with categorical obligations triggered by designation. That makes procedural discipline especially important. When a legal regime operates before harm has been demonstrated, courts should be more—not less—demanding about whether the triggering conditions have been satisfied.

The Meta judgment therefore has implications beyond Marketplace. It may become part of an emerging judicial vocabulary for reining in excessive interventionism—not by announcing a deregulatory manifesto, but by requiring the Commission to match regulatory ambition with legal precision and economic evidence.

The court’s message is not that large platforms are immune from regulation. It is that even politically disfavored platforms deserve intelligible reasoning. In a legal order committed to the rule of law, this should be uncontroversial. In practice, it may be transformative.

When Competition Policy Becomes Product Management

The Commission’s modern competition and digital-policy agenda has increasingly moved from policing exclusionary conduct to engineering market structure and product design.

The DMA does not merely prohibit certain anticompetitive acts. It often dictates how integrated services must be unbundled, how data may be combined, how app stores must be opened, how default settings must be designed, and how interoperability must be supplied. This represents a shift from antitrust as law enforcement to competition regulation as ongoing industrial administration.

That shift should concern anyone who takes dynamic competition seriously. Digital platforms compete not only through price, but also through integration, security, convenience, network effects, data-driven improvements, and rapid product iteration. Treating seamless integration as presumptively suspect risks sacrificing real consumer benefits to a static vision of market structure.

The point is not that integration is always benign. It is that integration is often how innovation reaches consumers. Search linked to maps, messaging linked to commerce, operating systems linked to app distribution, and social networks linked to marketplaces may all create risks. They may also reduce transaction costs, improve quality, and make products easier to use.

The DMA’s core danger is that it can convert this ambiguity into a regulatory presumption against scale and integration. If a large platform improves a service by connecting it to another service, regulators may characterize the improvement as leveraging. If it designs a seamless ecosystem, they may call that self-preferencing. If it maintains a closed architecture for privacy or security reasons, they may attack that as foreclosure. If it opens the architecture, they may blame it for new security risks.

That is a difficult environment in which to innovate.

The General Court’s ruling does not solve those problems, but it offers a procedural foothold. If the Commission must justify the classification of each service, then it must conduct a matter-specific economic evaluation, rather than rely on administrative convenience.

Facebook Marketplace, for example, could not simply be swept into DMA coverage because it sat inside the Meta ecosystem. The Commission had to show why Marketplace fit the relevant statutory concept and why Meta’s arguments did not undermine that conclusion. Requiring that kind of analysis can force regulators to confront tradeoffs that broad political narratives obscure.

Five Ways Courts Can Keep the DMA Honest

Future General Court and Court of Justice cases could develop this discipline in several ways.

First, EU courts should insist on genuine service-by-service analysis. The DMA’s structure invites a dangerous conflation between the undertaking and the service. A company may be large, but not every service it offers necessarily functions as an important gateway between business users and end users.

Treating every service offered by a large digital firm as presumptively gatekeeping would collapse the statutory inquiry into a company-level judgment. Courts should resist that move. The question should not be whether Meta, Apple, Google, Amazon, or Microsoft is important. The question should be whether the specific service at issue satisfies the legal and economic conditions for designation.

Second, courts should require the Commission to address countervailing evidence meaningfully. If a platform argues that a service is declining, that users multi-home, that business users have alternative routes to customers, that the service is not monetized as the Commission assumes, or that the relevant functionality is ancillary rather than independent, the Commission should not be able to wave those points away with conclusory language.

The duty to state reasons should become a duty to confront the economic record. That would not turn DMA designation into a full Article 102 abuse case, but it would prevent designation from becoming a rubber stamp.

Third, courts should scrutinize remedy specification. The most intrusive DMA interventions may arise not at designation, but during compliance and specification proceedings. Once a firm is designated, the Commission can exert substantial pressure over product architecture.

Courts should therefore remain attentive to proportionality. A compliance measure that degrades privacy, cybersecurity, product quality, or innovation incentives should not be accepted merely because it increases the formal number of choices available to users or rivals. Choice is valuable when it improves welfare. It is not valuable when regulators manufacture it by degrading the default product or forcing users through confusing consent screens and fragmented experiences.

Fourth, courts should revive administrable limits on open-ended concepts like fairness and contestability. These terms are politically attractive but economically slippery. A market can be contestable because entrants can challenge incumbents. It can also become less efficient if regulation props up less capable rivals.

A practice can be called unfair because it disadvantages competitors, but competition law should not confuse harm to competitors with harm to competition. EU courts need not import the American consumer-welfare standard wholesale to recognize that competition policy requires limiting principles. Without them, fairness becomes a license to redistribute rents from successful firms to less successful rivals.

Fifth, the Court of Justice should use appeals to clarify that ex ante regulation remains subject to proportionality, legal certainty, and institutional competence. The Commission is not a legislature with roving authority to redesign the digital economy. Nor is it a venture capitalist charged with choosing the optimal architecture of future markets.

The Commission’s powers derive from the treaties and from legislation adopted under them. When it uses those powers to impose obligations with major economic consequences, courts should require a clear legal basis, a reasoned explanation, and a plausible relationship between the measure and the statutory objective.

Europe Does Not Need More Regulatory Swagger

This kind of judicial reining-in would not be anti-European. On the contrary, it may be essential to Europe’s economic renewal.

The Draghi report and related commentary on Europe’s competitiveness problem have highlighted a sobering reality: Europe has strong institutions, educated workers, sophisticated consumers, and deep scientific capabilities, but it has struggled to generate and scale world-leading technology firms.

The problem is not a shortage of regulatory ambition. Europe has produced the General Data Protection Regulation, the Digital Services Act, the DMA, the Artificial Intelligence Act, and numerous national initiatives. The problem is that regulatory ambition has not translated into entrepreneurial dynamism.

Innovation requires more than subsidies, research programs, and public strategies. It requires permission to experiment. It requires the possibility of scale. It requires exit opportunities for startups, including acquisition by larger firms. It requires predictable rules that allow entrepreneurs and investors to estimate risks. It requires tolerance for business models that policymakers may not fully understand at inception.

An economy that regulates first and learns later will tend to get less of the experimentation that produces transformative growth.

The Costs You Cannot Count

A market-oriented approach to EU competition law should therefore emphasize error costs. False positives—mistakenly condemning beneficial conduct—are especially damaging in innovation markets.

If regulators mistakenly prohibit or burden a practice that would have benefited consumers, the lost innovation may never be observed. A delayed product launch, an abandoned integration, a startup that cannot find a buyer, or a platform feature never introduced in Europe will not always appear in enforcement statistics.

The Commission can count investigations, workshops, designations, and fines. It is much harder to count the innovations that regulation prevented.

Those unseen costs matter. When compliance burdens fall on large platforms, some observers assume the costs fall only on wealthy foreign companies. That is a mistake. Large platforms are infrastructure for smaller firms, developers, advertisers, creators, retailers, and consumers.

If regulation makes platforms less efficient, less integrated, or slower to deploy new tools, the costs propagate through the ecosystem. European startups may find fewer distribution channels, fewer acquisition opportunities, fewer integrated services, and less access to frontier technologies. Consumers may receive products later than users in the United States or Asia. Small businesses may face more fragmented marketing and transaction systems.

A judicially enforced discipline of evidence and proportionality could help reverse this pattern. If the Commission knows courts will demand careful reasoning, it may become more selective. If it must explain how a designation or remedy improves competition rather than merely handicaps a large firm, it may focus on clearer cases of exclusion. If it must account for innovation and product-quality tradeoffs, it may avoid interventions that make digital services less useful.

Such discipline would not eliminate EU regulation. It would improve it.

Competition Is a Process, Not a Diorama

The implications extend to traditional EU competition law. The Commission’s Article 101, Article 102, and merger-control enforcement has often been more interventionist than the American approach, particularly in its suspicion of dominance, vertical integration, rebates, tying, self-preferencing, and mergers involving potential competition.

But recent decades have also seen EU courts require more rigorous effects analysis in important cases. That trend should be strengthened. The future of EU competition law should not lie in abandoning economics for administrability. It should lie in using economics to make administrability honest: clear rules where experience justifies them, but serious evidence where intervention threatens dynamic rivalry.

A more market-oriented EU competition policy would begin with several presumptions. It would presume that competition is a process, not a market-design endpoint. It would presume that scale can be efficient, especially in markets characterized by network effects, high fixed costs, data complementarities, and global rivalry.

It would presume that consumer welfare includes quality, privacy, security, convenience, and innovation—not merely the number of competitors or the formal availability of alternatives. It would presume that intervention should be justified by a theory of harm and disciplined by a theory of error costs. And it would presume that protecting competitors from hard competition is not the same as protecting competition.

This approach could enhance innovation in the EU in concrete ways.

First, it would make Europe more attractive for product launches. Firms are more likely to introduce new technologies in jurisdictions where legal risk is predictable and proportionate.

Second, it would help startups by preserving efficient integration with larger ecosystems. Many startups do not become standalone giants. They succeed by being acquired, by partnering, or by building on platform infrastructure.

Third, it would encourage European firms to scale. A policy culture that treats size as a problem will not produce many large technology firms.

Fourth, it would reduce fragmentation. One promise of EU law is the creation of a single market, but overlapping EU and national interventions can recreate fragmentation through compliance complexity.

Fifth, it would shift attention from performative enforcement to welfare-enhancing enforcement.

A More Interesting Brussels Effect

The Meta decision may also have transatlantic implications. American competition law has recently flirted with more interventionist theories, particularly through neo-Brandeisian critiques of bigness, platform integration, and merger policy. Some American commentators have cited the DMA as a model for regulating large technology platforms.

But if EU courts begin to cabin the Commission’s discretion, the lesson for the United States may change. Europe may not show that aggressive ex ante regulation is the future. It may show that judicial review remains an essential corrective to administrative enthusiasm.

American antitrust law already contains doctrinal resources that support a market-oriented approach: burdens of proof, effects analysis, skepticism toward protecting competitors rather than competition, and concern for administrable rules. A European judicial turn toward evidence, proportionality, and dynamic competition could reinforce those tendencies.

It would make it harder for American interventionists to claim that the global consensus favors structural regulation of digital markets. It would also give American courts and agencies comparative support for a humbler proposition: competition policy should be especially cautious in fast-moving markets.

There is an irony here. For years, the so-called “Brussels effect” was invoked to suggest that EU regulation would become the global default because large firms would conform worldwide to Europe’s rules. But the Meta judgment hints at a different possibility: a rule-of-law Brussels effect. If European courts insist that the Commission justify intervention with rigor, the EU could export not regulatory maximalism, but regulatory discipline.

That would be a healthier model for both sides of the Atlantic.

Small Rulings Can Cast Long Shadows

One should not overstate the likelihood of a dramatic shift. The General Court upheld Messenger’s designation, and the DMA remains politically popular in Brussels. The Commission is unlikely to abandon its view that large platforms require close supervision. The Court of Justice may also proceed cautiously on appeal, especially where the legislature has expressly chosen an ex ante regulatory model.

The path toward a more market-oriented European competition law will therefore be incremental. But incremental judicial discipline can matter. Competition policy is shaped not only by grand doctrines, but also by burdens of explanation.

If the Commission must explain more, it may assume less. If it must address tradeoffs, it may intervene more carefully. If it must classify services according to legal criteria rather than political narratives, it may narrow its targets. If it must defend product-design mandates in terms of proportionality and welfare, it may hesitate before turning competition law into engineering supervision.

The Meta Marketplace ruling is best understood in that light. It is a small legal defeat for the Commission, but potentially a larger institutional warning.

Europe’s competitiveness problem will not be solved by giving regulators more discretion to rearrange markets. It will be solved, if at all, by allowing markets to discover better arrangements, while reserving intervention for cases where evidence shows genuine competitive harm. Courts cannot create European dynamism by themselves. But they can remove some obstacles by insisting that economic regulation remain tethered to law, evidence, and proportionality.

A market-oriented reading of the decision therefore sees hope in its restraint. The court did not announce that the Commission is wrong to care about digital competition. It announced that caring is not enough. The Commission must reason. It must justify. It must respect categories. It must answer arguments.

In a legal order increasingly tempted by technocratic management of markets, that is a message worth taking seriously.

If future EU courts build on this foundation, they could help restore a better balance between competition enforcement and economic liberty. They could remind regulators that innovation often comes from the very practices—scale, integration, experimentation, and ecosystem design—that interventionist policy tends to distrust. They could help Europe move from a culture of precautionary control to one of competitive discovery.

And, in doing so, they might influence American antitrust at a crucial moment by strengthening those who argue that the goal of competition law is not to punish success, but to preserve the market process that makes success contestable.

That would be a Brussels effect worth welcoming.

The post The DMA Meets the Rule of Law appeared first on Truth on the Market.

That debate is already too narrow.

President Donald Trump’s recent executive order on “Promoting Advanced Artificial Intelligence Innovation and Security” reflects the tension. The order emphasizes maintaining American leadership in artificial intelligence while directing federal agencies to develop voluntary frameworks, reporting standards, and oversight mechanisms for advanced models. Its stated objective is laudable: encourage innovation without imposing unnecessary burdens on one of the most important technologies of the 21st century.

Yet the executive order also highlights a deeper problem in the current debate. Both sides assume the central question is who should regulate AI. Should authority reside in Washington or in the states? Should Congress act, or should state legislatures lead?

That is the wrong question.

The more fundamental question is whether either level of government possesses the knowledge, incentives, or institutional capacity to regulate a technology evolving at extraordinary speed. Before deciding who should regulate AI, we should first ask whether government can regulate it effectively at all.

The answer is no.

Critics of AI regulation often focus on innovation, competitiveness, or economic growth. Those concerns matter. But they are secondary to a more fundamental insight developed by economists associated with the Austrian and Virginia schools of political economy. The problem is not simply that regulation may slow innovation. It is that neither federal nor state regulators possess the knowledge necessary to determine what AI should become. And even if they did, the political process would steadily transform limited oversight into expansive control.

The result is a regulatory project almost certain to fail on its own terms.

The Federalism Distraction

The executive order has reignited a debate that has been simmering for months: Should AI be regulated by the federal government or by the states?

Both sides raise legitimate concerns. Advocates of federal regulation worry that a patchwork of state laws will fragment national markets, raise compliance costs, and weaken America’s ability to compete with China. Supporters of state authority respond that federal regulation could entrench incumbent firms, suppress experimentation, and impose a one-size-fits-all framework on a technology still taking shape.

Both positions have merit. But both share the same mistaken premise: that the central question is which government should regulate AI.

From an Austrian perspective, the more important question is whether any political institution has the knowledge needed to regulate a technology whose most important applications, risks, and governance mechanisms are still being discovered. Put differently, the choice between federal and state regulation assumes the relevant knowledge already exists and merely needs to be assigned to the right regulator.

But much of that knowledge does not yet exist at all.

That is why the debate is ultimately not about federalism. It is about knowledge. Whether authority sits in Washington or 50 state capitals, regulators face the same basic problem: They must make decisions today about technologies whose future uses, risks, and opportunities remain largely unknown.

Federal regulation may reduce jurisdictional variation, but it cannot solve the knowledge problem. State regulation may allow experimentation among jurisdictions, but it remains constrained by the same informational limits. Either way, policymakers are trying to govern a discovery process before the relevant discoveries have been made.

A National Center of Ignorance

More than 80 years ago, F.A. Hayek articulated one of the most important insights in economics: the knowledge needed to coordinate a complex economy is dispersed among millions of individuals. It exists in fragments. It is local, often tacit, and constantly changing. No central authority possesses all of it. Markets coordinate this dispersed knowledge through the price system.

The same insight applies with even greater force to artificial intelligence. The challenge facing policymakers is not simply that AI is evolving rapidly. It is that nobody knows what AI ultimately will become. The technology’s most valuable uses may not yet have been discovered. The most important safety mechanisms may not yet exist. The most effective governance structures may emerge years from now through experimentation and competition.

In other words, AI is not merely a product. It is a discovery process.

That distinction matters. Unlike a traditional product, whose characteristics are largely known before it reaches consumers, AI’s capabilities, limitations, and most valuable applications are being discovered through use. Every interaction between users and AI systems generates information about what works, what fails, what creates value, and what risks warrant attention. That information emerges through experimentation and experience, not centralized planning.

Governments routinely regulate products. Automobiles, pharmaceuticals, and household appliances have relatively stable characteristics. Regulators can inspect them, test them, and develop standards around known risks.

AI is different. Every day, millions of people interact with large language models, coding assistants, image generators, scientific research tools, and enterprise software. Through those interactions, users discover new applications, identify flaws, develop workarounds, and reveal preferences. The technology evolves in response.

The relevant knowledge therefore does not reside in Washington, Sacramento, Austin, or Albany. It resides with the millions of users, developers, entrepreneurs, and businesses experimenting with AI in real time.

Centralizing regulatory authority does not solve this problem. It merely moves decision-making farther away from the people who possess the relevant information.

The current debate often assumes that federal regulation is preferable because it avoids a patchwork of state rules. But a single national standard cannot overcome the knowledge problem. It simply creates a single national center of ignorance.

Replacing 50 imperfect regulators with one imperfect regulator does not eliminate the underlying difficulty.

It magnifies it.

Freezing the Future

Israel Kirzner extended Hayek’s insight by emphasizing entrepreneurial discovery. Markets are not static systems moving neatly toward equilibrium. They are dynamic processes through which entrepreneurs discover opportunities others have missed. Competition matters not because it produces a predetermined outcome, but because it reveals information nobody previously recognized.

AI development exemplifies this process. No regulator predicted the explosive growth of prompt engineering—the practice of shaping inputs to get better outputs from AI systems. Few anticipated the rapid rise of AI coding assistants. Fewer still foresaw how quickly businesses would integrate generative AI into legal services, drug discovery, customer support, software development, and scientific research. Those discoveries emerged through experimentation.

That poses a serious problem for regulators. Any reporting requirement, disclosure standard, certification process, or safety framework necessarily reflects current knowledge. It embodies policymakers’ best understanding of responsible AI development at a particular moment.

But what if that understanding is wrong? More realistically, what if it is incomplete? The danger is not merely that regulators may make mistakes. It is that regulation freezes today’s assumptions into tomorrow’s rules.

A reporting framework developed in 2026 reflects what policymakers believe AI risks and opportunities look like in 2026. Yet the market’s understanding of those risks and opportunities may look entirely different in 2028 or 2030.

The more detailed the framework becomes, the greater the risk that it will block the discovery of better alternatives. Innovation frequently comes from directions experts fail to anticipate. The history of technology is filled with entrepreneurs discovering opportunities that established firms, government agencies, and academic specialists overlooked.

That is precisely why regulatory efforts to direct innovation so often disappoint. They attempt to manage a process whose most important outcomes have not yet been discovered.

The Machinery Matters More Than the Mission

Suppose regulators somehow overcome the knowledge problem. Suppose they develop a genuinely modest framework, act with the best intentions, and remain committed to encouraging innovation.

The problem still remains.

The Virginia School of political economy teaches us to abandon what James Buchanan famously called the “romantic” view of politics. Government officials are not omniscient guardians of the public interest. They are human beings responding to incentives, just like everyone else.

That insight has profound implications for the executive order. The administration deserves credit for recognizing AI’s importance to American prosperity, innovation, and national security. The order repeatedly emphasizes the need to maintain U.S. leadership in AI and says America should not “stifle this innovation with overly burdensome regulation.” Taken at face value, those aspirations are entirely sensible.

The difficulty is that institutions often matter more than intentions. The order directs federal agencies to develop a voluntary framework for advanced AI models, establishes reporting and disclosure mechanisms, and contemplates a federal role in evaluating frontier systems—the most advanced AI models at the cutting edge of development.

Many observers view these structures as limited and reasonable. Perhaps they are. The more important question is whether they will remain limited and reasonable.

Public-choice theory suggests otherwise.

Bureaucracies have incentives to expand their authority. Agencies benefit from larger budgets, larger staffs, and broader mandates. Politicians benefit from claiming credit for solving perceived problems. Interest groups benefit from influencing rules that affect their competitors.

Reporting and disclosure requirements often represent the first stage of a broader regulatory architecture. Before government can certify, license, restrict, or otherwise supervise an activity, it must first gather information about it. Supporters understandably view these provisions as modest transparency measures designed to improve visibility into frontier AI development. Yet from a public-choice perspective, information collection is rarely the endpoint. Once reporting mechanisms exist, they create both the capacity and the temptation for future policymakers to convert information gathering into more active oversight.

The executive order should therefore be evaluated not only by what it does today, but by the machinery it creates for tomorrow.

The history of regulation is instructive. Most regulatory regimes begin modestly. They aim to address specific concerns, often with assurances that they will remain narrowly tailored. Yet American regulation is full of agencies and programs that expanded far beyond their original scope.

New reporting requirements lead to new oversight responsibilities. New oversight responsibilities justify new staff and budgets. New bureaucratic capacities create pressure to identify additional problems requiring additional intervention.

That result does not require corruption or bad faith. It is simply what happens when ordinary incentives operate inside political institutions.

Why Big AI Loves Regulation

The greatest threat posed by AI regulation may not come from government officials themselves. It may come from the firms invited to help write the rules.

Gordon Tullock’s theory of rent-seeking explains that whenever government acquires the power to distribute benefits or impose burdens, individuals and firms devote resources to influencing those decisions. Rather than competing solely in markets, they compete for political advantage.

AI regulation creates precisely these incentives. Large AI companies have compliance departments, legal teams, lobbying operations, and deep financial resources. Small firms and open-source developers often do not. As a result, incumbent firms may welcome regulatory frameworks that appear neutral while imposing costs their smaller rivals struggle to bear.

The language of safety, transparency, accountability, and security can therefore become a mechanism for raising rivals’ costs.

A federal reporting standard may sound modest. Yet every reporting requirement requires personnel, documentation, legal review, and administrative infrastructure. For a trillion-dollar technology company, those costs are manageable. For a startup operating out of a garage, they may be prohibitive.

The result is a familiar pattern: regulation that ostensibly protects the public often ends up protecting incumbents.

Milton Friedman observed this dynamic repeatedly throughout the 20th century. Regulatory agencies established to protect consumers, he argued, frequently evolved into institutions that protected established firms from competition.

There is little reason to believe AI regulation will be different. If anything, the incentives may be even stronger. The stakes are enormous, the potential rewards are vast, and the firms involved have every reason to shape the rules governing the market they already dominate.

The Interventionist Ratchet

Proponents of federal AI regulation often emphasize that current proposals are limited. That misses the point.

The central lesson of Ludwig von Mises’ analysis of interventionism is that government interventions frequently produce consequences policymakers neither anticipate nor desire. Those unintended consequences then become the justification for additional interventions. The process feeds on itself, generating ever-larger and more complex regulatory regimes.

Consider AI safety. No regulatory framework will eliminate all risks associated with AI systems. Hallucinations will occur. Security incidents are inevitable. Misuse is unavoidable.

When those events happen, policymakers will face pressure to respond. Will they conclude that regulation has failed and should be scaled back?

History suggests otherwise.

The more likely response is that the existing framework did not go far enough. Additional reporting requirements will be proposed. Expanded disclosure obligations will be considered. New certification standards will be developed. Additional oversight bodies will be created.

Each intervention generates new perceived shortcomings. Each shortcoming becomes the rationale for another intervention.

This dynamic does not require bad intentions. It emerges from the incentives embedded in political institutions. Once a regulatory system is in place, the pressure almost always runs in one direction: toward expansion.

What begins as a voluntary framework may become an expected industry practice. What begins as a disclosure requirement may evolve into a certification requirement. What begins as a reporting standard may become a licensing regime.

None of this is inevitable. But all of it becomes possible once the institutional foundation has been laid.

That is the interventionist ratchet. It turns temporary solutions into permanent structures and modest frameworks into increasingly comprehensive systems of control.

Too Big to Discipline?

Advocates of federal regulation often dismiss market discipline as inadequate because the largest AI companies are supposedly too powerful. On this view, firms such as Microsoft, Amazon, Google, Meta, OpenAI, and Anthropic have resources so vast that meaningful accountability requires government intervention.

But that argument overlooks an important reality: Even the largest firms in the AI ecosystem face intense competitive pressure.

The current AI race illustrates the point. Hyperscalers—large cloud-computing providers such as Amazon, Microsoft, and Google—and frontier-model developers are investing hundreds of billions of dollars in new infrastructure. They are not doing so because regulators require it. They are doing so because customers demand better performance, lower latency, greater reliability, and more sophisticated capabilities.

Data-center construction has accelerated because users want more computing power. Companies are pursuing nuclear-power agreements, natural gas generation, and other energy solutions because reliable, always-on electricity has become a competitive necessity. These firms are not dictating market outcomes. They are responding to them.

The same pattern appears in AI safety. Long before governments developed comprehensive regulatory frameworks, leading firms were already investing heavily in alignment research, red-teaming, model evaluations, and safety testing. Alignment research aims to make AI systems behave consistently with human goals. Red-teaming means stress-testing systems to find vulnerabilities before bad actors do.

Cynics may dismiss these efforts as public relations exercises. Some undoubtedly are. But that misses the larger point: Firms devote resources to these activities because customers, enterprise clients, investors, and the public increasingly demand them.

Enterprise customers do not want unreliable systems generating inaccurate outputs. Businesses integrating AI into critical operations care deeply about security, transparency, and predictability. Investors worry about reputational risk. Users abandon products that consistently fail to meet expectations. When allowed to function, these market pressures create powerful incentives for self-correction.

Thomas Sowell’s distinction between market participants and surrogate decision-makers is useful here. Firms and customers bear the consequences of their decisions. When an AI model fails, developers lose revenue, customers lose productivity, and investors lose capital. The costs are immediate and tangible.

Regulators, by contrast, typically bear little personal cost when their decisions prove mistaken. Their feedback mechanisms are weaker, slower, and more indirect.

This does not mean markets are perfect. Market signals can be noisy. Firms make mistakes. Consumers misjudge risks. But those imperfections are not an argument for replacing markets with political control. They are precisely why competition matters. As Hayek observed, competition serves as a discovery procedure: Different firms pursue different approaches, and the market reveals which ones work.

Government standards tend to homogenize behavior. Markets encourage variation. In a technology as uncertain as AI, that variation is essential.

The hyperscalers’ enormous investments in infrastructure, energy, and safety are not evidence that markets have failed.

They are evidence that markets are working.

The Real Regulators

If neither the states nor the federal government can effectively regulate AI, who can?

The answer is surprisingly simple: the people who use it.

Mises described this idea as consumer sovereignty. His central insight was that markets are governed not by bureaucrats, but by consumers. Producers succeed only by satisfying the preferences of the people they serve. In “Human Action,” Mises likened the consumer to the captain of a ship:

The captain is the consumer. Neither the entrepreneurs nor the farmers nor the capitalists determine what has to be produced. The consumers do that.

AI is no exception.

Every day, users evaluate AI systems based on accuracy, reliability, bias, speed, privacy, security, and usefulness. When systems perform well, users reward them with greater adoption. When they do not, users leave—or worse, switch to a competitor.

When companies fail to address legitimate concerns, they lose customers, revenue, and reputation. These feedback mechanisms operate continuously and at extraordinary scale.

Unlike government regulators, users possess direct knowledge of their own needs. Unlike bureaucratic oversight, consumer feedback generates immediate consequences. Unlike regulatory mandates, consumer preferences evolve as circumstances change.

Most importantly, consumer choice encourages experimentation. Different users value different things. Some prioritize safety. Others prioritize creativity. Others care most about speed, privacy, transparency, or cost.

Markets accommodate those differences. Regulatory frameworks often suppress them.

Put differently, consumer sovereignty allows millions of individuals to make decisions for themselves. Government regulation substitutes the judgment of a relatively small number of officials for the judgment of everyone else.

Who Do You Trust?

The debate over AI regulation ultimately reflects a deeper disagreement about how knowledge is generated and how social order emerges. The prevailing view assumes wise policymakers can identify the right balance between innovation and safety, codify that balance into rules, and steer society toward the desired outcome.

The Austrian tradition suggests something very different. Knowledge emerges through discovery. Order emerges through interaction. The future cannot be designed in advance because the most important information about that future does not yet exist.

Hayek explained why no central authority can possess the knowledge needed to direct a complex economy. Kirzner showed that entrepreneurial discovery is how new knowledge gets generated. Buchanan and Tullock reminded us that political institutions respond to incentives no less than markets do. Mises showed how interventions tend to expand over time and why consumers—not bureaucrats—ultimately determine economic outcomes.

That final point is especially important. In “Human Action,” Mises argued that the apparent captains of industry are not the true sovereigns of the market. Business leaders hold their positions only so long as they satisfy consumers. The ultimate authority rests not with producers, but with the individuals who choose what to buy, what to use, and what to reject.

The same principle applies to artificial intelligence. The future of AI will not ultimately be determined by task forces, reporting standards, advisory committees, or federal frameworks. It will be shaped by millions of users deciding which systems they trust, which capabilities they value, and which firms deserve their business. Those choices generate the feedback that guides investment, rewards innovation, punishes failure, and encourages continuous improvement.

Put simply, the debate is not really about whether AI will be regulated. It already is. Every day, users regulate AI through their choices in the marketplace. They reward firms that provide value and abandon those that do not. They encourage useful applications and reject those that fail to meet their needs. They do so continuously, dynamically, and at a scale no government institution could replicate.

The question, then, is not whether we should have regulation. It is whether we trust the decentralized judgments of millions of users or the centralized judgments of a comparatively small number of policymakers. The executive order reflects faith in the latter. The Austrian tradition counsels faith in the former.

If the goal is to promote both innovation and security, policymakers should remember a lesson economists from Hayek to Mises emphasized again and again: The knowledge needed to govern society is not concentrated in Washington. It is dispersed throughout society itself.

The challenge is not to replace that discovery process with regulation.

It is to get out of its way.

The post Artificial Intelligence, Natural Ignorance appeared first on Truth on the Market.

We—people—often have a hard time letting a bad thing go. That’s true even for those who are well acquainted with the sunk-cost fallacy and should know better. I’ve been there. I’ve felt that misguided determination, and I’ve felt the consequences. 

The same is true of the people who run federal agencies, including the two with which I’m most familiar: the Federal Trade Commission (FTC) and the Antitrust Division of the U.S. Department of Justice (DOJ). 

Which brings me to . . .

The Appeal of a Sunk Cost

On May 22, the FTC filed an appeal in FTC v. Meta Platforms, Inc.; that is, the FTC filed its opening brief appealing the agency’s loss in November 2025 before the U.S. District Court for the District of Columbia (readers might do well to consult the revised December 2025 opinion, which is largely the same as the November opinion, but with fewer redactions). As I noted back in April 2025, the FTC’s case seemed to me (among many others) an uphill battle. And as I noted in December 2025, Judge James Boasberg’s decision:

Doesn’t exactly provide the law & economics analysis I would have produced, had anyone asked for it. But it is, nonetheless, a good decision by a thoughtful, generalist trial-court judge wrestling with both the evidence before him and relevant precedent. He got key things right, not least of which was the decision for the defendant. 

For additional critiques of the FTC’s case, see Brian Albrecht, Mark Jamison, Joe Coniglio, Alden Abbott, and—for a banger Tech Policy Podcast—Geoff Manne. For additional background, see Daniel Crane and Herbert Hovenkamp’s critique of the “monopoly broth” theory applied to what was originally the Facebook case, albeit in an amicus brief filed in another matter.

I won’t recapitulate most of the case against the FTC’s position. There’s plenty to be found in the opinion itself (linked above), and I discuss many of these points in the Truth on the Market posts I identified (and linked) above.

Here, I want to focus on the FTC’s appeal, which gets a bit into the weeds of the FTC Act and the Sherman Act, both in terms of statutory construction and jurisprudence. I’m sorry about that, but that’s the law part of law & economics, and law & economics is “the business we have chosen.”

The brief advances two arguments that, on the FTC’s account, identify “fundamental and independent ways” in which the district court erred, “each of which warrants reversal.” The second seems to me to hint at the FTC’s basic challenge on appeal. There, the commission argues that the district court “misapplied fundamental antitrust law principles by failing to recognize the validity of the PSN [personal social networking] market, whether in 2020 or 2025.”

I think that’s wrong, but I’m not going to spend this blog post rehashing the better part of Judge Boasberg’s 89-page decision identifying the central reason the FTC lost at trial: the burden of proof belonged to the plaintiff, and the plaintiff did not meet it. The challenge for the FTC is that the U.S. Court of Appeals for the District of Columbia Circuit is not about to review the case de novo, nor is it likely to repudiate the trial court’s findings of fact.

I think that’s why the FTC leads with an argument about the proper reading of Section 13(b) of the FTC Act. The goal is to identify a reversible error by the trial court. In practical terms, that would have to be an error of law, and a reversible one at that. It’s all about timing, and while the issue is presented as a simple matter of statutory construction, it’s not so easy.

The FTC’s argument about Section 13(b) was nowhere in my mind when I first wrote about the case in 2023, long after the FTC filed its complaint, but well before the matter went to trial. Nor is it what interests or concerns me most about the appeal. But it is the agency’s principal argument at this stage, so here goes. 

The FTC’s Section 13(b) Detour

The heart of the commission’s appeal, as it presents the matter, can be found in this passage from the brief’s introduction:

Start with the major premise—that the FTC was obligated to prove a 2025 monopoly. The court’s cursory analysis focused on Section 13(b) of the FTC Act, which authorizes the Commission to “bring suit” in certain cases “[w]henever” it “has reason to believe” that the defendant “is violating, or is about to violate” any law the Commission enforces. 15 U.S.C. § 53(b), (b)(1). By its plain terms, this is a pleading requirement that applies at the time of the complaint. Nothing in the statutory text requires the Commission to make any substantive showing as of the time of decision. . . .

That simple observation resolves the appeal. As a result, this Court need not consider the broader question of whether Section 13(b)(1) applies here at all. The answer is no. This case was brought under the second proviso of Section 13(b), which empowers the Commission to seek permanent injunctive relief in federal court. Section 13(b)(1) is located in a different part of Section 13(b) and does not apply to second-proviso cases.

I think I see where they’re going with this, but it’s a bit confusing, not least because the latter paragraph is wrong. And the FTC repeats the mistake multiple times, in multiple sections of the brief. On page 16, it asserts that “fundamentally, Section 13(b)(1) does not apply to cases like this one, where the Commission seeks a permanent injunction . . .” directly in federal court. We find more of the same on page 20, and again on pages 30-32.

But it’s hard to see any way around the opposite conclusion. That “different part of Section 13(b)” is Section 13(b)(2), and the plain language of the statute does not permit 13(b)(1) and 13(b)(2) to be separated.

The relevant section of the FTC Act, 15 U.S.C. § 53, authorizes the commission to bring suit in federal court. Section 13(a) addresses suits involving advertisements, where the commission has “reason to believe” the ads violate Section 12 of the FTC Act.

Then comes Section 13(b), which authorizes suit under two conjunctive conditions. First is Section 13(b)(1)—the forward-looking provision. Second is Section 13(b)(2), which authorizes suit for a temporary restraining order when the commission has reason to believe there is, or is about to be, a violation of “any provision of law enforced by the Federal Trade Commission.” That same provision also states that “in proper cases the Commission may seek, and after proper proof, the court may issue, a permanent injunction.”

But the statute bundles Sections 13(b)(1) and 13(b)(2) together. It is Section 13(b)(1) and Section 13(b)(2)—both satisfied—that authorize the commission to go to court. It’s all Section 13(b), all the time. The subheading for Section 13(b) is somewhat misleading because it references “[t]emporary restraining orders; preliminary injunctions.” But there is no other relevant part of Section 13(b). You cannot get to Section 13(b)(2) without first passing through Section 13(b)(1), and both preliminary and permanent injunctions appear in Section 13(b)(2).

What about Section 13(b)(3)? Well, there “ain’t no sanity clause,” and there’s no such thing as Section 13(b)(3), either.

The whole business about “a different part of Section 13(b)” is a distraction. The claim that Section 13(b)(1) does not apply to that “different part” is wrong—or, in the language of Section 5, “deceptive” (that is, material and false or misleading).

The commission also tries to support its position with precedent, including the Supreme Court’s opinion in Alaska v. United States. The citation is strained, at best, and provides no support for the FTC’s conclusion that “[t]he permanent injunction proviso in Section 13(b) grants an additional power not contingent on anything that precedes it.” For that matter, neither Alaska nor the FTC’s accompanying citation to Beaty concerns the meaning or construction of any provision of the FTC Act, much less Section 13(b).

Enough of that aside. I don’t think the argument works. But I also think it’s something of a red herring.

The opinion does state that “[t]o win the permanent injunction that it seeks here, the FTC must prove a current or imminent legal violation.” That’s right there, in black and white, on page 23 of the 89-page memorandum opinion.

What difference that makes is a separate question. According to the FTC, Judge Boasberg’s reading “could improperly deprive courts of the power to remedy past unlawful action even if it threatens to recur or continues to cause grievous harm.”

I don’t think that’s right. And I’m not entirely sure the commission believes it, either.

The brief is clear enough—at least later in the game—that its authority to go directly to district court seeking a permanent injunction “in proper cases” comes from Section 13(b)(2). The two sitting commissioners, along with the staff in the FTC’s Bureau of Competition, are experienced attorneys and more than capable of reading the conjunction that joins Sections 13(b)(1) and 13(b)(2).

There may well be difficult questions of statutory interpretation lurking in this portion of the opinion. But Section 2 of the Sherman Act (allegedly violated by the Instagram and WhatsApp acquisitions) and Section 7 of the Clayton Act (typically at issue in merger challenges, although the FTC did not plead a Section 7 violation here) are both forward-looking. Yet neither bars challenges to consummated mergers.

As the FTC’s opening brief itself puts it, the relevant question is whether the conduct at issue is harmful and whether it “threatens to recur or continues to cause grievous harm.”

I don’t think there is any serious argument that Judge Boasberg was confused about that. Which suggests that the FTC’s appeal is really about a different question of timing.

The Timing Problem 

To get at that question, I’ll construct an abridged—if not brief (sorry)—timeline.

February 2004: Facebook launches (as “the Facebook”).

2006: Facebook continues expanding its reach, adding functionality, and developing its business model.

By the end of 2007, Facebook has surpassed MySpace in social-media traffic (a metric the FTC would later distinguish from its proposed PSN market).

2012: Facebook acquires Instagram. The proposed transaction is reported to the antitrust agencies and cleared to the FTC for review. Following an investigation, the FTC votes 5-0 to close the matter and issues a closing letter.

Yes, there were the supposed “hot docs.” But they weren’t that hot, and they didn’t—and shouldn’t—have made a case.

At the time of the acquisition, Instagram had just 13 employees, and its app was one of many photo-sharing apps on the market, including Google’s predecessor to Google Photos.

The staff—and the Commission—rightly concluded that this was a defensible buy-or-build decision to add functionality, not a “killer acquisition,” not a violation of Section 7 of the Clayton Act. I don’t recall whether any time was wasted considering a Sherman Act Section 2 complaint.

The FTC was hardly alone in that view. The UK Office of Fair Trading also reviewed the transaction, concluding that it “does not believe that it is or may be the case that the merger may be expected to result in a substantial lessening of competition within a market or markets in the United Kingdom.”

Had the commission possessed a crystal ball, it would have seen what we know now: there were still many photo-sharing apps in 2020, and there are still many today, all with far greater functionality than Instagram offered in 2012.

2014: Facebook acquires WhatsApp—roughly the same story, albeit with less drama. 

WhatsApp operated a paid messaging app. It was tiny and barely present in the U.S. market. There were many other messaging apps. 

The transaction was reviewed by the FTC, which again voted 5-0 to close the investigation. 

The European Commission likewise reviewed the deal, concluding that Facebook and WhatsApp were not close competitors and that the transaction posed no threat to competition in online advertising.

Had the commission possessed a crystal ball here, it would have found much the same result.

2020: On Dec. 8, the FTC filed a monopolization complaint alleging that the Instagram and WhatsApp acquisitions—transactions the commission itself had previously reviewed and declined to challenge—were key elements of a broader scheme of unlawful monopoly maintenance in violation of Section 2 of the Sherman Act.

FWIW, I don’t recall any economists in the bureau of economics—or anyone in my office, the Office of Policy Planning—who thought bringing the case was a good idea.

2021: Facebook moves to dismiss. On June 28, Judge James Boasberg dismisses the complaint, albeit with leave to amend. 

Reading the complaint now, it strikes me as rushed and, I’m sorry to say, not very good. Judge Boasberg was right to dismiss it.

Later in 2021, the FTC files an amended complaint and then a substitute amended complaint.

2022: Facebook again moves to dismiss. This time, Judge Boasberg concludes that the FTC has cleared the pleading bar, while cautioning that “Ultimately, whether the FTC will be able to prove its case and prevail at summary judgment and trial is anyone’s guess.” 

2024: The parties file cross-motions for summary judgment. On Nov. 13, Judge Boasberg largely denies both motions, and the case proceeds. 

2025: A six-week bench trial takes place in April and May. In November, Judge Boasberg rules for Meta. An unredacted version of the opinion follows on Dec. 2. 

AND THAT SHOULD HAVE BEEN THE END OF IT. 

But it brings us back to the timing issue, and to pages 22-24 of the opinion.

The language there, too, might be a bit confusing. The FTC is hanging its first—and primary—hat on this hook: “Throughout this case, the [district] Court has held that the agency must prove that Meta is violating the law now.” And that, the opinion says, should be distinguished from a finding that there is “lingering harm” from a past violation of the FTC Act.

The FTC and the opinion do not disagree about the high-level requirements for establishing a Section 2 violation. As the Supreme Court held in United States v. Grinnell Corp., “[t]o prove monopolization under Section 2, a plaintiff must show ‘(1) the possession of monopoly power in the relevant market and (2) the willful acquisition or maintenance of that power as distinguished from growth or development as a consequence of a superior product, business acumen, or historic accident.'”

The opinion maintains that the FTC must show monopoly power and willful (impermissible) maintenance of that monopoly as, for example, through exclusionary conduct. The FTC agrees. But while the opinion says the FTC had to make that showing in 2025—presumably at the time of trial, although the FTC conjectures that “now” means the time at which Judge Boasberg drafted or published his opinion—the FTC says no. According to the commission, the immediate implications of Section 13(b)(1) are merely pleading requirements and, in any event, do not bear on the FTC’s authority to seek a permanent injunction under Section 13(b)(2).

Rather, citing Grinnell, the FTC argues that “[t]he relevant market in a Section 2 case is the market at the time of the alleged anticompetitive conduct.”

The brief doesn’t put a date on that. Given that the Instagram and WhatsApp acquisitions were the conduct at issue, we might suppose 2012. Or 2014. Or the period between the two acquisitions. Or thereabouts. Again, the FTC doesn’t quite nail it down.

There is a line early in the brief stating that “as far as Section 13(b) is concerned, what matters is the . . . time of the complaint.” I think what the FTC means is that the time of the complaint governs pleading. If that’s right, there’s no tension between that statement and the brief’s citation to Grinnell. If that’s wrong, the appeal has a more serious problem.

That brings us to . . . 

The Minor Premise That Ate the Brief

Up top, I guessed that the FTC structured its brief as it did—and led with the argument about the proper construction of Section 13(b) of the FTC Act—because it knew that an appeal challenging findings of fact would be tough sledding. At best.

It really is a guess. I have no special insight into the thinking of the two sitting commissioners when the case first went to trial—Chairman Andrew Ferguson and former Commissioner Melissa Holyoak, now U.S. Attorney for the District of Utah. Commissioner Mark Meador was confirmed two days into the trial. And I have no special insight into the thinking of the Bureau of Competition personnel taking the laboring oar on appeal. 

But the guess might also explain why, if the “simple observation” about Section 13(b) “resolves the appeal,” the FTC spends so much time arguing against what it calls the opinion’s “minor premise”: “that Meta’s personal social networking monopoly had somehow dissipated between 2020 and 2025.” It also may explain why the FTC devotes pages 36-69 of its 69-page brief to the claim that “THE COMMISSION DEMONSTRATED THAT META HELD MONOPOLY POWER IN THE MARKET FOR PSN SERVICES.” 

That “minor premise” occupies a good deal more space than what the FTC casts as its main argument—the one that supposedly “resolves the appeal.” There is nothing improper about arguing in the alternative. But while the FTC does not quite present it as such, this second argument reads like an attempt to relitigate the merits—and the findings of fact—below.

The stated focus may be monopoly power. But the brief also addresses findings about the FTC’s personal-social-networking-services market definition, the FTC’s allegation that Facebook had monopoly power in that market, and the FTC’s allegations of competitive harm caused by the exploitation of that alleged monopoly power.

I will not rehash Judge Boasberg’s findings of fact. There were many. They are covered in some detail in the opinion, and I have sketched them already—both in anticipation, based on the pleadings, and in discussing the 2025 opinion itself. 

I do not argue that every point raised in this 30-plus-page section of the brief is wrong. But I do think key parts are wrong, that some of it misrepresents Judge Boasberg’s opinion, and that much of it is conclusory. It sketches evidence the FTC presented in its pleadings, pokes here and there at the court’s treatment of that evidence, and does little to refute or rehabilitate most of the shortcomings in the FTC’s own case. 

But to return to the timing issue so central to the first part of the FTC’s appeal: rather little in the FTC’s affirmative case seemed to address monopolization at the time the FTC says matters. As noted above, citing United States v. Grinnell Corp., the FTC argues that “[t]he relevant market in a Section 2 case is the market at the time of the alleged anticompetitive conduct.” And while the agency does not quite put a date on it, the mergers alleged to be anticompetitive under Section 2 were the chief courses of “conduct” in the FTC’s allegations, and they were  noticed, screened by the FTC, and consummated in 2012 (Facebook/Instagram) and 2014 (Facebook/WhatsApp). 

The second part of the FTC’s brief argues that the 2025 decision against the agency turns on the notion “that Meta’s personal social networking monopoly had somehow dissipated between 2020 and 2025.” But neither 2012 nor 2014 falls within that interval. Having a monopoly does not violate Section 2. And most of the allegations in the FTC’s complaint—and most of the evidence the FTC presented at trial—addressed or purported to address conditions many years after the acquisitions.

Of course, evidence about competitive conditions, including competitive harm, might bear on “lingering effects” or the long-term consequences of the acquisitions. But beyond conclusory allegations and selected “hot docs,” there is not much that goes to whether the FTC’s personal-social-networking-services market definition, first introduced in 2020, was viable—much less correct—in 2012. Nor is there much to show that Facebook had monopoly power in such a market in 2012, or that any such monopoly power was achieved by dint of the Instagram acquisition. With respect to Instagram, 2012 is “the time of the alleged anticompetitive conduct.” 

The FTC could, of course, argue that Facebook later engaged in unlawful exclusionary conduct, given the 2012 and 2014 acquisitions. And it is not as if the agency has said nothing on that score. But it did not say—never mind prove—all that much.

As I have covered before, the FTC’s economic witness, C. Scott Hemphill, raised some good points about certain issues in Meta’s defense, but he offered a mysteriously weak—indeed, dubious—account of the FTC’s case in chief. The PSN market definition seemed, and still seems, gerrymandered: the product of a hash of the subjective and long-discredited “Brown Shoe factors.” As Herbert Hovenkamp has observed, “[t]he so-called Brown Shoe factors are wrong in most cases” and, in any case, even as described by the Supreme Court in its poorly aging opinion, “there was no reason to think that it was doing anything more than summarizing fact findings for that particular case.” 

And the “indirect evidence” of monopoly power that turned on that hash was no better. Neither was the “direct evidence” of monopoly power presented in the alternative, which tried to hang a story about competitive harm on a weak claim about declining product quality, based solely on ad load—and no other nonprice factors—rather than price or output effects. 

Judge Boasberg rightly identified some key flaws in that evidence, which, not incidentally, was not about monopoly power in 2012 or 2014 either. 

As for the selective hot docs: feh. They are mostly about alleged anticompetitive intent and easily misread. For a substantial discussion of the background issue, I will point you to Geoff Manne and E. Marcellus Williamson’s article, “Hot Docs vs. Cold Economics: The Use and Misuse of Business Documents in Antitrust Enforcement and Adjudication.” For now, I will simply quote part of my earlier assessment:

The problem with email generally is that C-suite executives, like kids, can say the darndest things. Indeed, they should say and consider many things. Selected excerpts from a multitude of documents (and discovery yields multitudes) might suggest any number of things about intent. What dated excerpts show about current intent is anyone’s guess.

More than that, the excerpts in question seem suggestive of complex concerns and interests, just as one might expect. It’s not at all obvious that they signal an intent to acquire instead of competing. The deals were not “killer acquisitions”; Instagram and WhatsApp were nurtured, not killed.

And, of course, while email “hot docs” evidence “is not necessarily irrelevant, to the extent it might provide a circumstantial bolster for allegations of effects,” it is also true that:

effects matter directly, and intent is not an element of a Section 2 claim: there’s no mens rea issue here. Moreover, a decade or more post-merger, that sort of evidence can cut both ways, as it underscores the question why there isn’t better direct evidence of competitive effects. 

For a bit more, see Will Rinehart’s 2023 discussion and, while we’re at it, David Balto’s 2014 post, “Spicy Documents Serve up a Paltry Antitrust Meal.”

Knowing When to Fold

No doubt the commission can answer the question “why appeal?” by saying that it is right on both the law and the facts and, in particular, in alleging ongoing harm to competition and consumers flowing from the allegedly anticompetitive acquisitions. It can say that reversal is necessary to reach the correct liability determination and, in turn, a remedy that will set things right. 

Right. 

Then again, the puzzle of persistence is not merely that people stick with a losing hand, sunk costs notwithstanding. It is that we sometimes convince ourselves that we should stick with a losing hand, even when we should know better. 

The FTC might also argue that it seeks to correct a dangerous precedent regarding its authority under Section 13(b) of the FTC Act. But the district court’s holding in FTC v. Meta is not binding precedent on any other federal court or, for that matter, even on the U.S. District Court for the District of Columbia in future cases. It is, at most, persuasive authority. That’s not nothing, but it is less. 

Still, I’m left wondering why the commission thinks this is a good case when it really wasn’t. The case was weak on the pleadings and weaker still at trial. And it was weak quite apart from the correct construction of Section 13(b). 

I’m also left with concerns I’ve had all along.

One is that a bad liability decision—a false positive—is likely to produce remedies that do more harm than good. That means harm not merely to Meta as a competitor, but to competition itself and, critically, to consumers. 

Moreover, while the district court’s decision establishes no new legal precedent, a successful appeal could. I don’t think that’s very likely, but I don’t know enough to put a meaningful subjective “probability” on my own assessment. 

And ultimately, I think this is a substantial waste of scarce resources.

Current FTC leadership has rightly emphasized that the agency must exercise discretion in choosing cases. The FTC’s jurisdiction is astonishingly broad, and its resources are limited. That reality applies not only to bringing cases in the first instance, but also to deciding which losses are worth appealing. 

The work already devoted to this appeal could have been put to more productive use. And the appeal is not over. More time. More resources. 

And if the FTC succeeds, then what?

A reversal and remand would send the case back to the district court. More process. More resources. And all of it would carry us further down the timeline, into 2027 and perhaps beyond, in a market that has remained dynamic and differentiated, and in a case that, as Judge Boasberg rightly recognized, was always about dynamic and differentiated competition. 

What are the chances of a procompetitive remedy at such a remove from the acquisitions in question—even assuming, for the sake of argument, that the FTC was right to file the complaint and wrong to clear the mergers in 2012 and 2014? 

I think the agency should drop it.

It won’t.

But it should.

The post The FTC’s Sunk-Cost Social Network appeared first on Truth on the Market.

That is where antitrust standing does its work. In ordinary litigation, standing asks whether a plaintiff has enough of a stake in the dispute to be in court at all. Antitrust standing asks a more pointed question: Is this the kind of plaintiff, and the kind of injury, the antitrust laws allow to recover damages?

That inquiry often gets treated as a threshold pleading hurdle—a box plaintiffs satisfy almost automatically once they allege anticompetitive conduct and economic harm. But modern antitrust doctrine demands more. The question is not merely whether a plaintiff lost money. It is whether that loss reflects diminished competition in the allegedly restrained market.

Section 4 of the Clayton Act provides that any person injured in their business or property “by reason of anything forbidden in the antitrust laws” may sue and recover treble damages—three times the damages proved. Read literally, that language sounds expansive. Courts have never read it that way. 

Instead, plaintiffs must show, as the Supreme Court put it in Brunswick Corp. v. Pueblo Bowl-O-Mat (1977), that their injury is “of the type the antitrust laws were intended to prevent and that flows from that which makes defendants’ acts unlawful.” 

The reason is straightforward: antitrust law protects competition, not competitors. A business harmed by rivals acting badly has not necessarily suffered an antitrust injury. The harm must reflect damage to the competitive process in a relevant market. Antitrust standing also serves a practical purpose: limiting exposure to treble damages in private enforcement actions before liability sprawls beyond any administrable boundary. 

Two recent proceedings show how much work this doctrine still does. The first is X Corp. v. World Federation of Advertisers, in which Judge Jane Boyle dismissed Elon Musk’s lawsuit against an advertising-industry coalition, holding that X Corp. failed to plead antitrust injury despite extensive allegations of coordinated advertiser boycotts. The second is the Live Nation-Ticketmaster litigation, where a federal jury found Live Nation and Ticketmaster liable for antitrust violations in a venue-facing ticketing market. That verdict raises a harder follow-on question: Can downstream consumers—and states suing on their behalf—recover federal damages for harm that began upstream? 

The cases arise in very different settings. But they expose the same doctrinal tension: antitrust liability and antitrust recovery are not the same thing. Conduct may violate the Sherman Act while a particular plaintiff’s injury remains too remote to support damages under Section 4 of the Clayton Act. In both X Corp. and Live Nation, the central question became whether the plaintiffs’ losses flowed from harm to competition, or merely from conduct alleged to be anticompetitive. 

Not Every Wound Is an Antitrust Injury

In X Corp., X—formerly Twitter—alleged that a coalition of major advertisers, coordinated through the Global Alliance for Responsible Media (GARM), an initiative of the World Federation of Advertisers (WFA), orchestrated a group boycott of the platform after Elon Musk acquired Twitter in 2022. X claimed that GARM members, including Mars, CVS, Colgate, Nestlé, Abbott, and others, agreed collectively to withhold advertising unless X complied with brand-safety standards developed by GARM. According to X, the resulting advertising withdrawal cost the company hundreds of millions of dollars in revenue.

The district court nevertheless dismissed the case with prejudice, meaning X could not simply refile the same claims. The court did not dispute that X had suffered economic loss sufficient to establish injury in fact—the basic injury needed for constitutional standing. Instead, it held that X had not alleged the type of injury the antitrust laws are designed to remedy.

According to the court, X alleged lost advertising revenue and reduced fees for advertising placements. Those are real business harms. But citing the 5th U.S. Circuit Court of Appeals’ decision in Rx Solutions v. Caremark, the court noted that classic antitrust injuries typically involve higher prices, reduced output, diminished quality, or some other harm to the competitive process.

X argued that it need not satisfy the antitrust-injury requirement because the alleged boycott was a per se antitrust violation. A per se violation is conduct deemed so likely to harm competition that courts condemn it without the usual detailed market analysis. The court disagreed. Even per se violations require plaintiffs to show antitrust injury before they may recover damages.

Illegal Doesn’t Mean You Can Sue

Assume, for the sake of argument, that the WFA-GARM boycott satisfied the threshold conditions for per se condemnation. That is far from certain. Under Northwest Wholesale Stationers (1985), not all group boycotts are per se Sherman Act violations. But even if X cleared that hurdle, its claims faced a separate and fatal defect: X failed to allege antitrust injury.

That distinction matters. Per se treatment is a rule of liability. It shortcuts the market analysis ordinarily needed to prove that the defendant’s conduct harmed competition. It does not decide whether a particular plaintiff has standing to seek private damages.

As the Supreme Court held in Atlantic Richfield v. USA Petroleum (1990): 

A loss flowing from a per se violation of § 1 does not automatically satisfy the antitrust injury requirement, which is a distinct matter that must be shown independently. The purpose of per se analysis is to determine whether a particular restraint is unreasonable. Actions per se unlawful may nonetheless have some procompetitive effects, and private parties might suffer losses therefrom. The antitrust injury requirement, however, ensures that a plaintiff can recover only if the loss stems from a competition-reducing aspect or effect of the defendant’s behavior.

In other words, conduct may be per se unlawful while a particular plaintiff’s losses still fall outside antitrust injury. The plaintiff’s harm must flow from the anticompetitive mechanism that makes the conduct illegal.

Consider a simple example. Suppose several major steel manufacturers enter a per se unlawful price-fixing conspiracy, agreeing to raise the price of industrial steel sold to automakers. The cartel is plainly illegal under Section 1 of the Sherman Act because horizontal price fixing—competitors agreeing on price—is presumed to harm competition in the steel market.

Now suppose higher steel prices lead automakers to reduce production and buy fewer trucking services to transport finished vehicles. A trucking company loses substantial business and suffers serious economic harm.

The trucking company’s losses are real, and they are causally connected to the unlawful cartel. But the company has not suffered antitrust injury. Its harm does not flow from the anticompetitive mechanism that makes the cartel unlawful: the suppression of price competition in the steel market. 

As the Supreme Court observed in 1982’s Blue Shield of Virginia v. McCready, an antitrust violation may send ripples of harm through the economy. But liability cannot extend forever. At some point, the chain becomes too attenuated.

The Court permitted standing in McCready because the plaintiff’s injury—Blue Shield’s refusal to reimburse her for psychotherapy sessions with a psychologist rather than a psychiatrist—was not merely a side effect of the alleged conspiracy. It was the very instrument through which Blue Shield allegedly sought to achieve its anticompetitive ends.

The trucking company stands in a materially different position. Its injury is remote and consequential. That is not enough to establish antitrust standing under Associated General Contractors v. Carpenters (1983). 

Other factors point the same way. The trucking company is neither a consumer nor a competitor in the restrained market—the two categories antitrust-standing doctrine most readily recognizes. Allowing recovery also would risk duplicative damages or force courts to apportion damages across multiple layers of claimants. More direct victims exist: the automakers who bought steel at cartel-inflated prices. Their claims would more cleanly serve the deterrent goals of private antitrust enforcement.

The steel cartel may be per se unlawful. The trucking company still lacks antitrust injury.

Standing Takes Center Stage

Antitrust injury also played a central role in the Live Nation-Ticketmaster litigation. In 2024, the U.S. Justice Department (DOJ) and 33 state attorneys general sued Live Nation and Ticketmaster under Sections 1 and 2 of the Sherman Act, alleging unlawful agreements and monopolization. The DOJ proceeded under Section 4 of the Sherman Act, while the states also invoked Section 4C of the Clayton Act and various state antitrust statutes. 

By summary judgment—the stage at which a court decides whether claims can proceed to trial—the district court had substantially narrowed the case. Among other claims, it dismissed allegations involving fan-facing ticketing markets. Two theories survived: claims involving the venue-facing primary-ticketing market, in which venues purchase ticketing services directly from Ticketmaster; and claims that Live Nation unlawfully tied concert-promotion services to access to its amphitheaters. 

After the DOJ settled midway through trial, several states continued the case and secured a jury verdict finding Live Nation liable on all remaining claims. The jury awarded damages based on an estimated average overcharge of $1.72 per ticket across 22 states.

Live Nation then renewed its motion for judgment as a matter of law, asking the court to set aside the verdict. Among other arguments, Live Nation contended that the states lacked a cognizable antitrust injury and were not proper—or at least efficient—enforcers under federal antitrust-standing doctrine.

Standing in the Cheap Seats

Section 4 of the Clayton Act raises two distinct questions the Supreme Court highlighted in  Illinois Brick v. Illinois (1977). First, which plaintiffs have suffered injuries too remote to recover damages? Second, which plaintiffs paid the unlawful overcharge in the legally relevant transaction? 

To answer the first question, the district court relied heavily on McCready. There, the Supreme Court held that a plaintiff may suffer antitrust injury even without directly participating in the restrained market, so long as she falls within “that area of the economy endangered by the breakdown of competitive conditions.” The district court concluded that Live Nation was on all fours with McCready. That conclusion is difficult to sustain.

In McCready, the alleged conspiracy involved a concerted refusal to reimburse patients who sought psychotherapy from psychologists rather than psychiatrists. The Court found that the refusal was the mechanism through which Blue Shield allegedly implemented the conspiracy. McCready’s injury was therefore inextricably linked to the harm the conspirators sought to inflict on psychologists and the psychotherapy market. Her injury was not merely foreseeable. It was “integral” to the scheme.

The fans’ position is materially different. Their alleged injury is not the instrument through which Live Nation purportedly advanced its anticompetitive objectives. Nor is it an integral feature of the alleged scheme. It is downstream harm.

The distinction becomes clearer when viewed through the structure of the relevant market. Unlike McCready, ticket-buying fans are neither consumers nor competitors in the market allegedly restrained by the conspiracy. The direct purchasers of venue-ticketing services are venues. The relevant competitors are firms such as SeatGeek and AXS. Fans sit one step further away. They are end users of a product whose upstream supply conditions are the subject of the alleged restraint.

At most, fans can show consequential harm flowing from conduct directed at others. That is a much weaker basis for antitrust standing.

The Supreme Court emphasized this point in Associated General Contractors. The case for extending standing to a more remote plaintiff weakens substantially when there exists “an identifiable class of persons whose self-interest would normally motivate them to vindicate the public interest in antitrust enforcement.” Such a class is readily identifiable here. Venues, along with Live Nation’s and Ticketmaster’s direct competitors, suffered the alleged harm more directly and have both the injury and the incentive to litigate. Fans occupy a derivative position.

The existence of more direct victims also highlights the practical dangers of extending standing further down the chain. When plaintiffs closer to the alleged violation can enforce the antitrust laws, allowing more remote parties to recover creates a substantial risk of duplicative damages and the complex apportionment problems that Illinois Brick and Associated General Contractors sought to avoid.

Taken together, the remoteness of the fans’ alleged injury, the availability of more suitable plaintiffs, and the risk of duplicative recovery all weigh against recognizing standing for the states’ federal claims.

The Illinois Brick Wall

A plaintiff may satisfy McCready and still be barred by Illinois Brick because it was not the direct purchaser in the market where the anticompetitive conduct occurred. Thus, the district court’s reliance on McCready resolves only the first question: whether concertgoers suffered an injury sufficiently connected to the alleged violation to satisfy antitrust-injury principles. The second question—who was injured by the alleged overcharge for purposes of Section 4 standing—is governed by Illinois Brick and Kansas v. UtiliCorp United (1990). Live Nation and Ticketmaster arguably should have pressed that question more aggressively.

In Illinois Brick, the Supreme Court held that only direct purchasers may recover federal antitrust damages. Indirect purchasers—those who bear overcharges passed through an intermediary—cannot recover under federal law. 

The Court reaffirmed that principle in UtiliCorp, holding that Section 4C did not alter the Illinois Brick rule for parens patriae actions. A parens patriae action allows a state to sue on behalf of its residents. Section 4C, the Court explained, “did not establish any new substantive liability, but simply created a new procedural device to enforce existing rights of recovery under Section 4.” States therefore could not recover on behalf of residential utility customers who merely absorbed overcharges passed through from upstream transactions.

The Live Nation ticketing structure bears a strong resemblance to the arrangement at issue in UtiliCorp. The face value of a ticket is set by the event organizer. The facility charge is set by the venue, and Ticketmaster retains none of it. The service fee is negotiated between Ticketmaster and the venue and then divided between them. 

In other words, the all-in ticket price reflects several upstream pricing decisions. Any supracompetitive overcharge attributable to Ticketmaster would therefore appear to pass through the venue before reaching the fan. 

Milton Handler and Michael Blechman warned of precisely this problem in their influential critique of parens patriae actions, which the Supreme Court cited approvingly in Illinois Brick. They observed that many antitrust violations occur at levels of the distribution chain “so remote from consumers as to make it virtually impossible . . . to prove that they suffered any compensable injury at all.” 

Once multiple intermediaries and independent pricing decisions separate the alleged restraint from the consumer transaction, determining whether—and to what extent—an overcharge reached consumers becomes less a matter of proof than of speculation.

As Handler and Blechman explained, establishing consumer injury in such settings requires tracing how an alleged overcharge moved “from one level to another in the chain of distribution” through a series of independent economic decisions that courts are poorly equipped to reconstruct after the fact.

The Live Nation ticketing system presents many of the same difficulties. Even assuming anticompetitive conduct in the venue-facing ticketing market, the states’ damages theory requires a court to determine how much of any allegedly supracompetitive fee negotiated between Ticketmaster and venues ultimately reached consumers through a pricing structure shaped by promoters, venues, facility charges, dynamic-pricing practices, and revenue-sharing arrangements.

That is exactly the sort of speculative pass-through inquiry that Illinois Brick and UtiliCorp sought to avoid by limiting federal antitrust-damages recovery to direct purchasers.

Direct Purchasers of What, Exactly?

A possible response is that Apple v. Pepper (2018) points in the opposite direction. There, the Supreme Court held that consumers who purchased apps through Apple’s App Store were direct purchasers for purposes of Illinois Brick. 

The Court emphasized the direct transactional relationship between Apple and iPhone users. Consumers bought apps directly from Apple, paid Apple directly, and Apple collected the allegedly monopolistic commission directly through those transactions. The Court expressly rejected the argument that Illinois Brick turns on who sets the retail price. Although app developers set app prices, consumers were still direct purchasers.

Instead, the Court treated Illinois Brick as a bright-line rule that asks a simple question: from whom did the plaintiff purchase the product or service? Because iPhone users purchased apps directly from Apple, with no intermediary between buyer and seller, they had standing to sue.

That reasoning does not necessarily rescue the states’ claims against Live Nation and Ticketmaster. The question is not whether fans transact with Ticketmaster at checkout. They do. The question is whether fans purchased the product sold in the market allegedly restrained by the anticompetitive conduct.

The alleged restraint occurred in the venue-facing primary-ticketing market. Fans do not participate in that market. Venues purchase ticketing services from Ticketmaster. Fans purchase admission to live events. Those are distinct products sold in distinct markets.

Viewed this way, the Illinois Brick problem is not that venues stand between Ticketmaster and fans as traditional reseller intermediaries. It is that fans never purchased the allegedly restrained product in the first place. Their injury, while potentially sufficient under an expansive reading of McCready, flows downstream from a market in which they were never buyers.

Accordingly, fans fall outside the class of direct purchasers that Illinois Brick and UtiliCorp require for federal damages recovery under Section 4 of the Clayton Act. If that analysis is correct, states likewise cannot maintain a parens patriae action on their behalf under Section 4C.

The practical answer is that the states did not rely solely on federal law. They also asserted claims under state antitrust statutes, several of which contain Illinois Brick repealer provisions that permit indirect purchasers to recover damages under state law. The jury’s $1.72-per-ticket damages award therefore likely rests on those state-law theories rather than on federal Section 4C claims, which UtiliCorp appears to foreclose.

Liability Is Not Standing

Both cases turn on the same question: Can the plaintiff’s loss be tied directly to a competitive mechanism the antitrust laws protect?

In X Corp., that connection was missing from the pleadings. WFA and GARM allegedly coordinated buyers, but they did not control a supply-side chokepoint, and X’s lost revenue did not flow from a restraint on the competitive structure of any market. Antitrust-injury doctrine therefore ended the case at the pleading stage.

In Live Nation, the competitive mechanism exists in the venue-facing ticketing market. But the path from that upstream restraint to downstream consumer damages runs through antitrust-standing doctrine—specifically, whether fans who did not purchase in the restrained market may recover federal damages through the states’ parens patriae claims.

The lesson is the same in both cases: antitrust liability and antitrust recovery ask different questions. A plaintiff can identify real harm, describe anticompetitive conduct, and still fail. Harm to a competitor is not necessarily harm to competition. Downstream injury is not direct-purchaser standing.

Antitrust law does not compensate every casualty of bad conduct. It compensates injuries to competition—and only the plaintiffs close enough to prove them.

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The ruling is narrow. It concerns only Facebook Marketplace, and it turns largely on procedural rather than substantive grounds. But narrow rulings can still cast long shadows, and this one does. The court’s reasoning reaches beyond Marketplace to questions that will shape future DMA enforcement. 

This post argues that the decision’s greatest importance lies not in what it says about Facebook Marketplace, but in what it signals for the DMA’s future application. After outlining the case’s background, it explores three implications: for qualitative gatekeeper designations, for the DMA’s “gateway” logic, and for the wisdom of applying the DMA to challengers rather than incumbents. 

When Process Becomes Substance

The DMA applies only to “core platform services” designated as “gatekeepers.” A gatekeeper is a platform that exerts a significant impact on the European Union internal market, serves as an important gateway between businesses and end users, and enjoys an entrenched market position. The statute presumes these criteria are satisfied when a platform meets certain quantitative thresholds, such as turnover, market capitalization, and the number of business and end users. If those thresholds are not met, the European Commission must establish gatekeeper status through a qualitative assessment. 

The General Court’s judgment caps three years of DMA proceedings concerning Facebook Marketplace. In 2023, shortly after the DMA took effect, the Commission designated several Meta services—including Marketplace—as gatekeepers. 

A gatekeeper designation is not necessarily permanent, however. In 2024, Meta asked the Commission to reconsider Marketplace’s designation. It pointed to three changes designed to alter Marketplace’s role as a gateway between businesses and end users. Most importantly, Meta limited Marketplace to transactions between end users, excluding businesses from the platform. It also removed indications that businesses could use the service and stepped up enforcement of its terms. As a result, the number of users operating as businesses remained minimal and fell below the DMA’s quantitative thresholds. 

In light of those changes, the Commission repealed Marketplace’s gatekeeper designation in 2025. Meta nevertheless continued to seek annulment of the original designation because repeal alone did not restore it to the position it occupied before the Commission’s decision. In particular, Meta argued that the Commission should have considered the Marketplace changes during the original designation process, which might have led it not to designate the service at all. 

The General Court agreed. By annulling the decision, the court confirmed—with ex tunc effect—that the designation was unlawful because of the temporal scope of the evidence the Commission considered. Put simply, the judgment clarifies that a qualitative gatekeeper designation must be based on the facts and circumstances that exist at the time of designation. That differs from designations based on the DMA’s quantitative presumptions, which rely on data from the preceding three years. 

To borrow a phrase from James Venit, the judgment comes close to providing a “procedural answer to a substantive question.” Formally, the court annulled the Commission’s decision because it failed to provide adequate reasons and thereby prevented effective judicial review of the evidence presented by the parties. Even so, the ruling has potentially significant implications for the future direction of DMA enforcement. The next section highlights three of them. 

When Gatekeepers Aren’t Gateways

First, the judgment will shape the ongoing DMA investigations into cloud services. The European Commission is currently investigating whether certain cloud providers—especially Amazon Web Services and Microsoft Azure—should be designated as gatekeepers under the DMA. If those services fall short of the DMA’s quantitative thresholds, the Commission will have to rely on a qualitative assessment. 

That is where the General Court’s judgment matters. The court made clear that qualitative designations are not tethered to data from the preceding three years. They must reflect the facts as they stand at the moment of designation. In any future cloud designation, the Commission’s treatment of ongoing market developments will therefore face close judicial scrutiny. 

Second, the judgment supports the view that cloud services sit uneasily with the DMA’s regulatory logic. The DMA was designed for platforms that operate as “gateways” between businesses and end users. Marketplace’s gatekeeper designation was reversed precisely because Meta eliminated the possibility that businesses could operate there as business users. The Commission therefore concluded that “Marketplace should no longer be considered as an important gateway for business users to reach end users.” 

It is similarly difficult to describe cloud services as gateways in the DMA’s sense. They intermediate much like cable providers do: they provide infrastructure, but they do not typically connect business users to end users in the way the DMA contemplates. Read in this light, the General Court’s insistence that a gatekeeper must intermediate between businesses and end users sets a standard the Commission may struggle to satisfy for cloud. 

Third, and finally, comes the policy question: What did this judgment—and the Commission decisions that preceded it—actually achieve? 

In practice, Facebook Marketplace’s gatekeeper designation was reversed only after Meta removed the feature that made Marketplace a competitive presence on the business-user side. Once businesses could no longer operate on the platform, whatever competitive pressure Marketplace exerted on stronger marketplaces disappeared. The DMA proceeding thus likely made online marketplaces less contestable, not more. 

Unfortunately, this is not an isolated episode. The DMA tends to keep titans in quarantine. But the most effective competitive constraint on a large firm often comes from another firm of comparable scale. Yet DMA enforcement has once again taken aim at challengers rather than incumbents. TikTok was the first casualty in this campaign. Marketplace appears to be the latest. 

Similar outcomes can be avoided. The DMA is the law of the land, but the Commission retains meaningful administrative discretion in how it enforces the statute. It should wield that discretion with a clearer account of enforcement’s intended and unintended consequences. 

That does not require giving challengers to larger firms carte blanche. When the DMA is a poor fit as a matter of enforcement policy, competition law can fill the gap more surgically—just as the Commission did against Facebook Marketplace in 2024. 

The Cost of Keeping Titans in Quarantine

The real significance of Meta Platforms v. Commission may lie less in Facebook Marketplace than in what it signals for future DMA enforcement. By insisting that qualitative designations rest on the facts as they stand at the moment of designation, and that gatekeepers genuinely function as gateways between businesses and end users, the General Court has raised the bar for the cloud designations now on the horizon. 

The broader lesson concerns enforcement strategy. The DMA’s tendency to keep titans in quarantine risks blunting the very competitive pressures it was designed to protect, particularly when enforcement falls on challengers rather than incumbents. The law may be settled, but its application remains a matter of choice. As the Marketplace saga illustrates, today’s enforcement decision can become tomorrow’s lost competitive constraint.

The post The EU’s Facebook Marketplace Decision: The Gatekeeper That Wasn’t appeared first on Truth on the Market.

The White House’s new AI executive order is framed around innovation and security. Its clearest signal, however, is concern about frontier AI capabilities themselves: how they are benchmarked, who gets early access to them, and how their release should be coordinated with the federal government. That focus lands squarely in the middle of a growing debate over whether AI policy should target chips, models, or what Anthropic recently described as the “capability layer”—the environment where models are deployed, monitored, secured, and used.

Recent arguments within parts of the AI-policy community—most notably from Anthropic—have emphasized export controls, compute restrictions, and centralized governance as the primary tools for preserving U.S. leadership in frontier artificial intelligence. The concern is understandable. AI systems are likely to accelerate innovation across strategically important sectors, including semiconductors, cybersecurity, biotechnology, advanced manufacturing, and military systems. Maintaining American leadership in those areas is a legitimate national objective.

Much of the current debate, however, still views the strategic landscape primarily through the lens of chip denial and hardware restrictions. That perspective risks overstating the long-term importance of compute controls, while understating the significance of where AI systems actually meet the world: through cloud services, application programming interfaces (APIs), identity systems, monitoring tools, and security controls.

As models proliferate, open-weight ecosystems mature, and agentic orchestration techniques improve—that is, as systems become better at coordinating multiple tools and models to complete tasks—the core strategic challenge increasingly shifts. The question is less whether adversaries can ever obtain compute and more whether deployed systems can be hardened against misuse, monitored effectively, and integrated into resilient defensive architectures.

In practice, the emerging equilibrium looks far more like cybersecurity or fraud prevention than a traditional nonproliferation regime. Overindexing on the latter risks weakening both American national security and the ability of U.S. firms to remain at the technological frontier.

Put differently, current AI policy discussions too often blur the line between national security and incumbent protection. A sustainable strategy should preserve U.S. ecosystem leadership, focus governance on the places where models are actually accessed and abused, and encourage defensive open-source proliferation. Treating blunt hardware denial as the master key to AI security is unlikely to achieve any of those goals.

A Very Expensive Speed Bump

In a recent essay on AI competition with China, Anthropic argued that U.S. export controls on advanced AI chips have been highly effective. It suggests that Chinese firms have narrowed the gap primarily through talent, loopholes in export-control enforcement, and large-scale distillation attacks on frontier U.S. models. Distillation, in this context, means copying some of a model’s capabilities by systematically querying it and training another model on the results.

There is at least some evidence that export controls have imposed real friction on Chinese AI development, particularly by limiting access to leading-edge semiconductor manufacturing equipment and hyperscale computing resources. The controls have increased costs, reduced efficiency, and slowed progress at the frontier.

But slowing progress is not the same as stopping it. Substantial leakage, smuggling, stockpiling, and substitution remain. The restrictions have also encouraged “good enough” domestic alternatives, such as Huawei chips, that can leverage China’s abundant energy supplies to scale training workloads. In plain English: better chips do more work with less power, but weaker chips paired with enough electricity can still produce highly capable systems. 

A recent Federal Reserve Bank of New York staff report adds an important domestic-cost dimension. Affected U.S. suppliers complied with the controls by reducing sales to Chinese customers, but many struggled to replace those relationships through reshoring or friendshoring. Meanwhile, targeted Chinese firms developed alternative supplier networks. The report estimates that affected U.S. suppliers lost roughly $130 billion in market capitalization, underscoring that export controls can impose substantial costs on the same firms they are supposed to protect. 

The farther one moves from training frontier models from scratch, the less persuasive the hardware-denial framework becomes. At this point, enough advanced compute already exists around the world—and much of it is accessible remotely—that comprehensive denial is difficult to imagine. Even maintaining enough friction to preserve a meaningful gap across every relevant frontier appears increasingly implausible.

Perhaps export controls would have fundamentally altered the strategic landscape had they been implemented a decade earlier. But that counterfactual would also have changed the trajectory of American industry itself, making the ultimate outcome impossible to know. In any event, the world now contains a vast installed base of capable hardware, mature open-weight ecosystems, sophisticated distributed-training techniques, and increasingly commoditized model architectures. Those realities change the security problem.

Jeffrey Ding makes a related point. Anthropic’s short-horizon scenario depends on a contestable assumption about technological diffusion: general-purpose technologies often take years, if not decades, to translate into organizational and military advantage. That uncertainty matters because the longer the relevant timeline, the more significant the downsides of export controls become, including indigenous substitution, supply-chain reorientation, and permanent parallel technology stacks.

The more important question is no longer whether adversaries can access compute. It is whether the systems built on that compute can be hardened, monitored, defended, and instrumented effectively.

The Fight Has Moved Up the Stack

One revealing passage in Anthropic’s essay says that “[p]olicymakers have not tightened loopholes on the CCP’s access to compute.” But “access to compute” increasingly means far more than access to physical chips. It includes hosted AI services, distributed cloud infrastructure, stolen or synthetic identities used to evade know-your-customer (KYC) controls, and criminal proxy markets that can generate thousands of fraudulent accounts.

It also includes orchestration frameworks that combine smaller or older models into coordinated systems, as well as access to the outputs of frontier models through distillation attacks. In other words, the relevant threat is not always someone buying forbidden chips. Sometimes it is someone abusing a deployed model at scale.

Ironically, Anthropic’s own essay acknowledges this problem. It correctly identifies organized efforts to extract capabilities from hosted systems through mass account generation, structured querying, and automated harvesting.

But that observation undercuts the claim that hardware controls are the decisive battleground. If the principal threat vector is adversarial interaction with deployed systems, then the strategic center of gravity shifts upward from the chip layer to the capability layer. That is where the real contest increasingly lives.

This shift also exposes an application programming interface (API) blind spot. An API is the software doorway that lets outside users and applications interact with a model. Advanced capabilities can be reached globally through account arbitrage, distillation, synthetic identities, and evasion of customer-screening protocols. More rigorous end-use monitoring for APIs is technically possible, but commercially uncomfortable for high-growth labs. It requires limiting the same global access and revenue streams that make hosted frontier models so valuable.

That incentive helps explain why compute controls are often easier to champion politically than strict, auditable controls on model access. It is simpler to talk about denying chips than to explain how one will monitor millions of users, accounts, prompts, integrations, and downstream applications without crushing legitimate use.

Nonetheless, the emerging AI security architecture increasingly resembles cybersecurity, fraud prevention, anti-money-laundering systems, and counterintelligence operations more than traditional nonproliferation regimes. In this world, the key question is not simply who owns chips or trains model weights. It is who can access deployed systems, how they use them, and whether defenders can detect abuse in time.

That makes anomaly detection, threat telemetry, identity verification, and orchestration controls central. Unsurprisingly, this is where frontier firms are already pouring enormous effort.

The AI arms race is likely to resemble cybersecurity itself: a perpetual contest between attackers and defenders in which no side ever achieves total control. That does not make hardware irrelevant. But it does mean the decisive terrain is shifting away from pure hardware denial.

Dual Use Is Not a Blank Check

Another theme running through Anthropic’s essay is the claim that AI is a “dual-use technology.” That characterization is not wrong, per se, but it risks doing too much rhetorical work. Every sufficiently general technology is dual use. Electricity powers hospitals and weapons factories. Cryptography protects dissidents and criminal enterprises. The internet enables education, commerce, espionage, and fraud.

The existence of dual-use risks does not, by itself, justify highly centralized control. Nor does invoking an “arms race” automatically establish the case for expansive regulatory authority over general-purpose intelligence systems.

One danger in today’s policy debate is that legitimate security concerns become the rationale for an increasingly centralized, government-defined AI ecosystem. Such a regime would do more than limit misuse. It would shape which systems can be built, who may build them, which values become embedded in them, who may access them, and even which forms of machine cognition remain permissible.

Centralization also creates a straightforward market-structure problem. Licensing regimes, rigid compute thresholds, and data-center moratoria do not affect all market participants equally. They tend to favor firms that already possess capital, infrastructure, legal resources, and regulatory access, while raising barriers for startups, open-source communities, and downstream developers.

Even when framed as safety measures, these policies can function as ladder-pulling mechanisms, converting legitimate national-security concerns into incumbent protection. The risk is particularly acute in AI because the technologies, business models, and competitive landscape remain highly fluid.

History offers little reason to believe governments excel at managing rapidly evolving technological ecosystems through centralized control. More importantly, centralized institutions tend to outlive the emergencies that justified their creation. Temporary powers have a habit of becoming permanent ones.

Policy should also avoid treating China—or any other adversary—as a monolith. Private firms, state-owned enterprises, military-linked entities, and opportunistic commercial actors operate under different incentives and constraints. Policies that ignore those distinctions are more likely to block beneficial activity while missing the specific channels that pose genuine security risks.

The Best Defense Is More AI

One striking limitation in many AI-risk arguments is that they treat AI primarily as an accelerant for offensive capabilities. Anthropic notes that “[a]dvanced AI models will be able to compress R&D cycles in semiconductors, biotech, and advanced materials.” True enough. But AI will also improve defensive cybersecurity, malware analysis, anomaly detection, fraud prevention, vulnerability remediation, supply-chain auditing, biological-threat detection, infrastructure resilience, and autonomous defensive operations.

As models become more commoditized and the technology more diffuse, defensive capabilities will matter more. A dual-use framing that criminalizes—or tightly controls—the development of advanced capabilities risks leaving Americans at a serious disadvantage against emerging threats.

The same agentic systems that can accelerate attacks can also harden networks, monitor behavior, identify adversarial coordination, and defend users at machine speed. That matters because the likely long-run equilibrium is not a world in which dangerous capabilities disappear. It is one in which defensive capabilities proliferate alongside offensive ones.

The cybersecurity evidence also counsels against step-function panic. AI will improve vulnerability discovery, exploit adaptation, and triage, but those gains are more likely to accumulate as continuing pressure than to appear overnight as a clean break in the offense-defense balance. If frontier access is gated through regulatory delay or cartelized compliance costs, the defensive shortfall will fall hardest on smaller firms, open-source developers, and ordinary users who cannot buy bespoke security infrastructure.

This leads to a larger and still underdeveloped question: What does defensive AI look like at the individual level? Much of the current debate assumes security must come from centralized institutional control. Another possibility is emerging: the democratization of personalized defensive intelligence.

In practice, individuals may increasingly rely on persistent AI systems that function as anti-fraud and personal-security tools, monitoring the edges of their digital identities 24 hours a day. These systems would behave less like chatbots and more like always-on defensive infrastructure.

Cyberpunk fiction has a recurring concept of ICE and counter-ICE: intelligent defensive systems continuously contesting hostile intrusion attempts. The analogy feels less fictional by the day. As AI systems become commoditized, the strategic advantage may shift away from merely possessing powerful models and toward possessing trustworthy, aligned, defensive, and highly personalized systems.

In that world, raw model capability may not be the key competitive advantage. Public policy should therefore be careful with sweeping categories like “dual use,” which risk chilling beneficial development and deployment. The United States needs companies building trust systems that preserve individual autonomy while strengthening defensive architectures. Just as critically, those systems must not become entry points for future authoritarian control. That means continuing to develop these technologies in a distinctly American style: decentralized, private, and bottom-up.

A centralized regime that dictates which models can be built and deployed will not get us there. These questions are likely more important than whether an adversary possesses somewhat weaker chips.

Open source, then, is not merely a consumer convenience or a developer preference. It is a strategic lever. Broad domestic open-source availability commoditizes the model layer, prevents deployment from hardening around a few hosted APIs for regulatory reasons, gives emerging firms a distribution path against incumbent network effects, and keeps the global developer base oriented toward U.S.-aligned tools. If domestic open-source development is chilled, developers will not stop building; they will migrate to foreign open-source alternatives.

In short, open source is a strategic playbook, not merely a development norm. Suppressing domestic open-weight competition in the name of export controls would not eliminate open models. It would make foreign open ecosystems more attractive as the default base layer for global developers. 

The Future Is Defense, Not Denial

None of this means export controls—or analogous controls at the capability layer—are useless. There may be narrow domains where targeted restrictions remain sensible, particularly at the bleeding edge. Nor does it mean capability-layer governance will be easy. The better approach is a sliding scale: delay access to the most advanced hardware and model capabilities where delay genuinely matters, but avoid sweeping embargoes that permanently push global developers toward alternative technology stacks.

The capability layer is likely to remain a permanent adversarial battleground. The future is unlikely to resemble nuclear nonproliferation. It will look much more like cybersecurity: an ongoing contest between attackers and defenders in which neither side ever achieves complete control.

The appropriate response is not tighter chip controls alone, nor a government-administered mother-may-I regime for developing and deploying frontier AI systems. It is a policy framework that enables U.S. firms to build resilient systems, democratize defensive intelligence, and harden the places where AI is actually used. In practice, that means preserving reliance on U.S. platforms, governing real access vectors, and making defensive AI broadly available so that security scales alongside the threat.

It also means ensuring that individuals, firms, and institutions possess the tools they need to defend themselves in an increasingly AI-saturated world.

The central challenge of AI policy is not preventing powerful capabilities from existing. That ship has largely sailed. The challenge is ensuring that defensive capabilities diffuse at least as quickly as offensive ones.

If AI’s future looks more like cybersecurity than nonproliferation, then the goal should not be to monopolize intelligence. It should be to make the defense scale faster than the attack.

The post You Can’t Export-Control the Future: The Case for Defensive AI appeared first on Truth on the Market.

The Federal Court of São Paulo has now suspended the R$250,000, or about $50,000, daily fine that Brazil’s Administrative Council for Economic Defense (CADE) imposed on Meta in its WhatsApp AI-chatbots case, sending the parties into conciliation instead. The judicial decision remains under seal, but it marks the latest turn in a remarkable four-month saga. 

CADE opened an administrative inquiry and imposed an interim measure. A federal court initially suspended that measure, then reinstated it after a closer look. CADE then imposed a daily fine after concluding that Meta had not adequately complied. Now, that fine has been paused, too. 

The merits remain unresolved, making this a useful moment to ask what the dispute is really about—and where the harder questions lie. 

The short version is that CADE’s dominance theory rests on relatively firm ground. Its theory of harm, by contrast, depends on assumptions about AI markets that the available evidence is unlikely to support. In that sense, the court’s decision to pause the fine and require conciliation may create a useful off-ramp in an important antitrust dispute. 

How a WhatsApp Policy Change Became an International Incident

The case began when Meta amended its WhatsApp Business Solution Terms to bar third-party general-purpose AI providers from using the WhatsApp Business API as their primary functionality. Two AI-chatbot startups—Madrid-based Factoría Elcano, which operates Luzia, and Montevideo-based Brainlogic AI, which operates Zapia—filed a complaint with CADE’s General Superintendence (SG). They argued that the change amounted to abusive exclusionary conduct under Article 36 of Brazilian Competition Law (BCL) and sought an interim measure to preserve the status quo ante: free access to the API. 

Because Meta rolled out the policy globally, the dispute has not stayed within Brazil’s borders. The Italian Competition Authority (AGCM), which had already opened an investigation into Meta AI’s integration with WhatsApp, expanded its probe to cover the new Business Terms and ordered Meta to suspend them in Italy. 

The European Commission opened a parallel investigation under Article 102 of the Treaty on the Functioning of the European Union (TFEU), covering the European Economic Area (EEA) outside Italy. After Meta replaced the outright restriction with a paid-access policy of $0.06 per message, the commission issued a supplementary statement of objections, preliminarily finding that the new pricing produced the same exclusionary effect as the original ban. Meta announced last month that, “[a]s part of ongoing discussions with the European Commission,” general-purpose AI chatbots operating in the EEA would receive free access to the WhatsApp Business API for one month—effectively freezing the new terms to avoid a European Union interim measure. 

Other regulators are watching, too. Investigations are underway before the Common Market for Eastern and Southern Africa (COMESA) Competition Commission, whose director has publicly ruled out an interim injunction and reportedly said COMESA “will not be influenced by the European Commission’s recent statement of objections” challenging Meta’s chatbot fees. The Turkish Competition Authority is also investigating. Onyeka Aralu has a useful recent Truth on the Market piece on the COMESA proceeding. 

In Brazil, the dispute has unfolded across three parallel CADE proceedings. The first is the administrative inquiry, in which the SG is examining whether Meta’s conduct violates Article 36 of the BCL. In opening that inquiry, the SG also imposed a preventive measure under Article 84 of the BCL, suspending the new terms and preserving the prior arrangement, under which Meta did not charge third-party AI chatbots for API access. 

Meta appealed to CADE’s Tribunal—the second proceeding, known as the Recurso Voluntário—and also went to court. A federal court briefly suspended the measure, then reinstated it after concluding that the record contained enough indicia of anticompetitive conduct to make the measure proportionate and reasonable. With the interim measure restored, CADE’s Tribunal unanimously upheld it, following Commissioner Carlos Jacques’ opinion. 

The third proceeding concerns sanctions. Meta filed a compliance plan in March proposing to readmit AI chatbots to the API, but at a charge of $0.06 per message—the same policy it had introduced in Europe. The SG found that the practice could amount to a “constructive refusal to deal,” meaning access formally exists, but only on terms that make it practically unusable. It concluded that the interim measure required preserving the status quo ante of unlimited, free access and imposed a daily fine of R$250,000, or about $50,000, until Meta restored that arrangement. 

Meta appealed, but the Tribunal unanimously upheld the fine. Jacques’ opinion drew on Erik Hovenkamp’s work on “secondary refusals” and on the European Commission’s supplementary statement of objections, which the opinion framed as “international context as a reinforcement of rationality.” 

Six days later, the Federal Court of São Paulo suspended the fine and ordered Meta and CADE into conciliation. That opened a path toward settlement on terms reminiscent of the agreements recently reached with Google and Apple. 

The Easy Market and the Hard One 

CADE’s case rests on a two-market analysis. In the decision opening the administrative inquiry and imposing the interim measure, the SG declined to define the relevant markets precisely, leaving that question open because the investigation was still in its early stages. Jacques took the same approach in his opinion on Meta’s appeal, refraining from formally delineating the markets. 

Both decisions nonetheless drew broadly on the complainants’ proposed market definitions. The first is a Brazilian market for instant-messaging services, where WhatsApp allegedly holds an uncontestable dominant position and where the challenged conduct occurs. The second is a market for AI services and solutions, whose geographic scope remains open, where Meta AI competes with rivals, including the complainants. 

If CADE ultimately adopts those definitions, WhatsApp’s dominance in Brazilian instant messaging would be difficult to dispute. According to the complainants’ figures, WhatsApp has roughly 148 million users in Brazil, is installed on 99% of smartphones, and is used daily or nearly daily by 97% of users. Those numbers sit comfortably above the 20% rebuttable presumption of dominance established by Article 36, Section 2 of the BCL and would likely support characterizing WhatsApp as a virtual monopoly. 

The downstream market looks very different. There, the relevant market would be AI chatbots and assistants, where Meta competes against the complainants and other firms through Meta AI. The theory of harm combines offensive leveraging—using dominance in messaging to gain an advantage in AI assistants—with self-preferencing, by embedding Meta AI directly within WhatsApp. 

Jacques framed this theory through the lens of ecosystem orchestration, drawing on CADE’s recent Apple App Store case, for which I recommend Mario Zúñiga’s recent analysis. He also invoked the familiar “embrace, extend, extinguish” narrative associated with the 2001 decision in United States v. Microsoft Corp. 

The upstream side of the story is plausible enough. The harder questions arise downstream, in the AI market—a part of the case CADE has not yet meaningfully examined. 

The Bottleneck That May Not Be One

The theory of harm depends heavily on defining the AI-chatbot market narrowly—something like “general-purpose AI assistants distributed through consumer-messaging apps in Brazil.” Broaden the lens to AI assistants generally, which better reflects how users interact with these tools and how AI services are distributed, and the theory becomes much harder to sustain. 

The evidence points away from a simple foreclosure story. As Giuseppe Colangelo documents in his recent paper, “Don’t Catch Me (Too Early) If You Can,” OpenAI’s ChatGPT accounted for roughly 86% of the global AI-chatbot market from April 2024 to March 2025, before falling to about 65% by January 2026 amid increased competitive pressure from Google’s Gemini, which surpassed the 20% market-share threshold. Meta AI, by contrast, remained below 1% of global chatbot traffic. 

Dirk Auer makes a related point in his recent “Brussels’ AI Squeeze” post: Generative AI firms are aggressively expanding across distribution channels. Anthropic has Claude Cowork and Office plugins. Perplexity is available through WhatsApp, Telegram, and X, each with its own dedicated number. OpenAI offers a WhatsApp number that anyone can message. Microsoft distributes AI through Windows, Office, and GitHub. Google does the same through Search, Android, and Workspace. Even the complainants here, Luzia and Zapia, are available through proprietary apps and websites, though WhatsApp remains their main channel for Spanish- and Portuguese-speaking users. 

In a market where multichannel distribution is the norm, treating any single messaging app—even one as dominant as WhatsApp—as a foreclosable bottleneck is a stretch. So is the broader Big Tech-centric theory that this market will predictably tip because of strong network effects, decisive data-feedback loops, and winner-take-all dynamics inherited from earlier platform markets. The actual evolution of AI markets points in a messier—and more competitive—direction. 

Judge Amit Mehta made a similar point in United States v. Google, when he declined to impose a sweeping remedy that would have barred Google from self-preferencing Gemini in Chrome: 

The bar on self-preferencing also goes too far in that it would hamstring Google’s ability to compete. Take, for example, Plaintiffs’ proposal to prohibit Google from self-preferencing Gemini in Chrome. Such a restriction would set Google apart from its competitors. It is commonplace for companies in the GenAI space to leverage their own products to distribute their GenAI technologies. 

None of this means Meta cannot abuse its dominant position in messaging. It does mean that the theory of harm CADE has imported from the AGCM and the European Commission rests on assumptions about AI-market structure that the data do not support. The AI market into which Meta allegedly is leveraging its messaging dominance appears far more competitive than CADE’s current theory allows. 

The Price Sheriff Problem 

The Federal Court of São Paulo’s decision to pause the fine appears to turn on a tension running through Jacques’ opinion. 

The preventive measure required Meta to restore the status quo ante of access to the WhatsApp Business API. The SG and CADE’s Tribunal interpreted that obligation to mean AI chatbots could resume operating on WhatsApp under the same non-onerous conditions that existed when they were classified under the “Customer Service” category. 

Meta complied only in part. It allowed AI chatbots back onto the platform, but reclassified them under the paid “Marketing” category, which Brazilian small and medium-sized enterprises also use for notifications. The SG concluded that this failed to restore the status quo ante and amounted to the functional equivalent of a constructive refusal to deal. Jacques relied on Hovenkamp’s recent work on “secondary refusals” to reach a similar conclusion. CADE then imposed the daily fine. 

Yet Jacques’ opinion in the sanctions proceeding also emphasized that: 

In fact, it is not—and never has been—the role of the competition authority to determine what the fairest or most appropriate price for a specific market would be. CADE cannot be expected to dictate what price should be charged. Its institutional role is to identify guiding parameters to ensure that certain conduct does not have harmful effects on the market, such as raising barriers to entry or excluding competitors from the market. 

That principle is unobjectionable. The difficulty is that, in practice, the order appears to require exactly a price-of-zero outcome. 

The API category at issue was designed, according to Meta, for small and medium-sized enterprises’ messaging services. Under CADE’s interpretation of the interim measure, Meta may charge those firms for access through the Marketing category, but may not charge AI firms using the same infrastructure. As Meta frames the issue, the order prevents it from charging companies such as OpenAI (valued at roughly $850 billion) or Microsoft (valued at roughly $2.9 trillion), even though those firms account for about 98% of the contested API traffic. 

That tension appears to have helped motivate the court’s decision to order conciliation. CADE has sought to distance itself from the image of a “price sheriff,” even as it has become increasingly involved in disputes over platform pricing. The same issue surfaced in CADE’s recent settlement with Apple, where the authority effectively shaped the fee structure governing access to the iOS ecosystem. 

At the same time, CADE’s concern seems less about whether Meta may charge for access and more about Meta’s failure to restore the original arrangement. A similar nuance appeared in the Apple case, where CADE expressly recognized Apple’s right to receive reasonable compensation for granting developers access to its ecosystem. 

That leaves the real question: What is the proper baseline? Is it no tariff at all, or some commercially reasonable tariff—likely below the current Marketing rate, but above zero? The court’s conciliation order may provide both the space and the incentive to answer that question. 

Two Questions, One Case 

It remains unclear where this case ultimately will land, and it may be far from over. The administrative inquiry continues on the merits. The European Commission is moving forward in parallel, albeit with a somewhat more measured procedural posture. Meanwhile, the Federal Court of São Paulo’s conciliation order creates a path toward negotiated terms, rather than an endless cycle of fines and counter-fines. 

In the end, two questions are likely to determine the outcome. 

The first is how CADE defines the relevant AI market when it reaches the merits. A narrow market centered on AI assistants distributed through messaging apps points in one direction; a broader market for AI assistants generally points in another. 

The second is whether the emerging convergence among CADE, the AGCM, and the European Commission produces a coherent enforcement standard for AI distribution channels. Or whether, instead, it illustrates Auer’s warning—borrowing Robert Bork’s phrase—of a “policy at war with itself,” in which a platform is simultaneously constrained from monetizing through advertising, from sharing data across services, and from charging for API access. 

Brazil is an especially important testing ground for that question because WhatsApp occupies a uniquely central place in daily life. The answer may determine not only how platforms distribute AI, but whether competition authorities can develop a theory of AI-market competition that fits the market as it actually exists, rather than the one they expected to find.

The post Brazil, Bots, and the Price of Free appeared first on Truth on the Market.

The company’s reported initial public offering (IPO) has already drawn significant concern from institutional investors and corporate-governance observers. That concern is understandable. SpaceX reportedly seeks to raise as much as $75 billion at a valuation exceeding $2 trillion, potentially making it the largest IPO in history.

SpaceX is also no ordinary issuer. It is one of the most influential companies in the space industry, with billions of dollars in government contracts, considerable political influence, and a central role in both national-security and commercial-space infrastructure.

A May 13 letter from the New York State Comptroller, the New York City Comptroller, and the California Public Employees’ Retirement System (CalPERS) criticizes the reported governance package as “novel and extreme.” The letter highlights several concerns: perpetual super-voting shares, restrictions on removing Elon Musk, mandatory arbitration of shareholder claims, controlled-company status, Texas-law barriers to derivative litigation, and the concentration of the chief executive officer, chief technology officer, and chair roles in Musk, even as he simultaneously leads several other major companies.

Those objections are serious. But the SpaceX controversy is not merely about Musk, founder control, or the outer boundaries of shareholder rights. It also highlights a deeper shift in corporate law. Companies are no longer simply choosing where to incorporate. Increasingly, they are choosing among competing governance philosophies.

My forthcoming article, “New Corporate Geography,” argues that incorporation decisions increasingly reflect more than a preference for a particular chartering state. They also reflect a firm’s operational risks, litigation exposure, regulatory environment, investor base, and governance strategy. SpaceX offers a real-time example of that shift. The reported offering is not simply a test of whether investors will buy a founder-controlled company. It is a test of whether public markets will accept a governance structure built around Texas statutory ordering, federal securities disclosure, and investor choice, rather than the more familiar model of Delaware-style fiduciary review.

It’s Not Just SpaceX. It’s Texas vs. Delaware.

Much of the commentary on SpaceX assumes a Delaware baseline. That is unsurprising. Delaware has long supplied the dominant vocabulary of public-company governance. Its corporate law is flexible and contract-friendly, but it is also infused with equity—the old judicial tradition that courts may intervene when formally legal conduct is unfair or abusive.

That matters because corporate governance is not just about what a company’s charter says. In Delaware, private ordering operates against a backdrop of fiduciary duties, entire-fairness review, enhanced scrutiny, and the Court of Chancery’s power to police inequitable uses of corporate machinery. Put less lawyerly: Delaware lets companies write many of their own rules, but it keeps a judge nearby.

The familiar Delaware principle, stated in Schnell v. Chris-Craft, is that inequitable action does not become permissible simply because it is legally possible. Delaware is not anti-contract. But its contractarianism operates within a fiduciary culture that gives courts meaningful room to ask whether directors and controllers have used corporate rules unfairly.

Texas corporate law is moving in a different direction. Texas generally does not recognize a broad common-law duty of good faith and fair dealing in ordinary commercial contracts. Its recent corporate-law reforms emphasize statutory authorization, procedural predictability, and ex ante governance design—that is, rules chosen in advance, rather than reviewed later through open-ended equitable standards.

Senate Bill 29, enacted in 2025, created new governance tools for Texas corporations, including presumptions favoring directors and officers of public and electing corporations, heightened pleading requirements, internal-forum provisions, jury-trial waiver authority, narrower inspection rights, and derivative-action ownership thresholds of up to 3% of outstanding shares. Derivative suits are lawsuits shareholders bring on behalf of the corporation, often to police alleged fiduciary misconduct. Raising the ownership threshold can sharply limit who may bring them.

That distinction matters because the SpaceX debate is often framed as a shareholder-protection problem. That framing is not wrong, but it is incomplete. The reported SpaceX structure is better understood as a test of whether public markets will accept a disclosed governance arrangement that substitutes contractual and statutory ordering for many of the litigation rights and equitable protections traditionally associated with public-company investing.

Federal securities law reinforces the point. The public-offering regime is primarily disclosure-based, not merit-based. Regulation S-K requires companies to disclose material risk factors that make an investment speculative or risky. It generally does not prohibit investors from buying into a risky, founder-dominated, asymmetrical governance structure. The premise is not that investors will make wise choices. The premise is that they should receive material information before making them.

The Securities and Exchange Commission’s (SEC) recent position on mandatory arbitration points in the same direction. In September 2025, the SEC issued a policy statement clarifying that a mandatory-arbitration provision for federal securities claims would not, by itself, prevent acceleration of a registration statement’s effectiveness. In plain English: such a provision would not automatically block a company from going public. The SEC’s position does not decide whether every such provision is enforceable. But it reflects a disclosure-centered approach to public offerings: disclose the provision, disclose the consequences, and let investors decide whether to buy.

Texas law makes that disclosure-and-consent model more consequential. Under SB 29, eligible Texas corporations may adopt derivative-action ownership thresholds of up to 3% of outstanding shares. For a company valued in the trillions, that threshold would be functionally prohibitive for nearly all shareholders. The pension funds’ letter makes the point directly, warning that the threshold could require a shareholder to hold billions of dollars in stock before bringing a derivative claim.

That objection should not be dismissed. Derivative litigation remains one of the principal mechanisms through which shareholders police fiduciary misconduct. A threshold that only the largest institutions can satisfy meaningfully changes the shareholder-enforcement landscape.

But that change also illustrates the broader Texas model. Texas is not merely adjusting shareholder-litigation rights in isolation. It is building a governance framework that favors statutory clarity, advance ordering, and limits on litigation over open-ended equitable review.

What If Investors Say Yes?

This is where the SpaceX debate becomes part of the broader “DExit” conversation—the debate over whether companies are leaving, or should leave, Delaware for other states. Recent reincorporation fights are often framed as contests over court quality, doctrinal predictability, or judicial expertise. That framing is too narrow.

A firm with unusual investor demand, a founder closely identified with the company, national-security significance, and high expected growth may value a legal regime that reduces shareholder-litigation optionality. Whether investors should accept that tradeoff is a separate question. The point is that Texas gives companies a statutory vocabulary for making it explicit.

For many firms, Delaware’s model remains highly valuable. Delaware offers an experienced judiciary, a deep body of precedent, and well-developed doctrines governing conflicted transactions, board process, controller conduct, and fiduciary oversight. Those features reduce uncertainty in many contexts. They also lend legitimacy to public-company governance because investors understand that formal compliance may still be subject to equitable review.

For other firms—particularly founder-controlled companies with unusually strong investor demand—those same backstops may look less like investor protection and more like litigation uncertainty. Texas offers a different proposition: if the statute permits a governance structure, the governing documents adopt it, and investors are told what they are buying, the bargain should generally stand.

That does not mean investor choice is always meaningful. The pension funds’ strongest argument concerns index inclusion. If SpaceX becomes large enough to enter major equity indexes, passive funds and public pensions may hold its stock as a practical matter, even if they object to the governance terms. In a market increasingly dominated by index investing, consent is not always an individualized act of agreement. Sometimes it is a consequence of market structure.

That concern deserves serious attention. But it does not transform every restrictive governance provision into a legally illegitimate one. Instead, it points to the real policy question: Should public-company governance be constrained through substantive fairness rules, or through disclosure, pricing, and investor choice?

If the answer is substantive fairness, regulators, exchanges, index providers, and institutional investors should say so directly. They should identify which governance structures are too restrictive for public markets, even when investors are willing to buy them. That would move the system closer to merit regulation—the idea that regulators should judge whether an investment is acceptable—and further from the traditional disclosure-based model.

If the answer is disclosure and consent, then SpaceX may become the most prominent example yet of the new geography of corporate governance. In that geography, incorporation decisions are not merely technical choices about a chartering state. They are choices among competing legal infrastructures: equity and fiduciary review in one jurisdiction, statutory ordering and litigation filters in another, with federal securities disclosure layered across both.

What the Market Will Tolerate

The SpaceX IPO should not be read merely as a Musk story. It is a test of investor autonomy, shareholder enforcement, and the changing geography of corporate law. Public markets may reject the bargain. Investors may demand a discount. Index providers may resist inclusion. Regulators may eventually conclude that some governance terms go too far.

But if investors knowingly buy the stock, the offering will reveal something important about charter competition: incorporation is increasingly becoming a mechanism for sorting firms and investors across competing governance infrastructures.

In that emerging geography, governance rights are not always the only product being sold. Sometimes investors are buying exposure, upside, identity, and access.

The SpaceX IPO is ultimately a referendum not on Elon Musk, but on how much governance choice public markets are willing to tolerate.

The post SpaceX and the New Geography of Corporate Governance appeared first on Truth on the Market.

The consumer welfare standard asks whether business conduct harms the competitive process, typically by raising prices, reducing output, degrading quality, or slowing innovation. Neo-Brandeisianism, by contrast, is more skeptical of concentrated economic power as such. It worries not only about prices and output, but also about whether large firms wield too much political, social, or economic influence.

For all its political momentum, neo-Brandeisianism has had a much harder time reshaping antitrust law than reshaping antitrust rhetoric. Courts have largely continued to apply economically grounded standards focused on competitive effects, efficiency, innovation, and consumer welfare. As Lazar Radic and Nicolas Petit recently observed, neo-Brandeisianism has had far greater influence on the political conversation surrounding antitrust than on antitrust doctrine itself. 

Strategic rivalry between the United States and China has now added a new dimension to that debate. Innovation, technological leadership, and economic performance have become matters of national strategy, particularly in industries such as semiconductors, artificial intelligence, cloud computing, and advanced manufacturing. Ironically, that shift has strengthened the case for an antitrust framework centered on efficiency, innovation, and dynamic competition—the very considerations at the heart of the consumer welfare standard. 

When Antitrust Goes Geopolitical

This shift matters for antitrust policy. Critics have long argued that the consumer welfare standard is too narrow because it focuses on economic outcomes rather than broader social goals. Yet geopolitical competition has underscored the importance of precisely the factors that consumer-welfare analysis emphasizes: innovation, productivity growth, technological leadership, and efficient use of resources. These are no longer merely economic objectives. They are increasingly sources of national strength. 

That does not mean antitrust should become industrial policy in a wig and robe. Competition authorities should not promote national champions or sacrifice competition principles in pursuit of geopolitical objectives. Competition law and industrial policy serve different functions. Governments may legitimately pursue industrial-policy goals through subsidies, procurement policies, research funding, export controls, and investment incentives. Competition law, by contrast, should remain focused on preserving competition, market contestability—the ability of new rivals to challenge incumbents—and innovation. 

Even so, geopolitical realities reinforce the case for an antitrust framework that prioritizes efficiency and innovation, rather than treating scale itself with suspicion. The point is especially clear in artificial intelligence and semiconductor markets, which feature enormous fixed costs, significant economies of scale, intense global competition, and fast-moving technological ecosystems. In these sectors, large firms often play a critical role in financing research, building infrastructure, and commercializing new technologies. Indeed, some of the United States’ most important advantages in artificial intelligence depend on firms that would plainly qualify as “Big Tech.” 

Why Size Isn’t the Right Question

That reality creates an obvious tension for neo-Brandeisian approaches that treat bigness itself as suspect. If technological leadership depends partly on globally competitive firms operating at scale, policymakers may grow less willing to treat size as a problem in itself. That does not mean large firms get a free pass. Exclusionary conduct, anticompetitive acquisitions, and practices that suppress innovation remain legitimate antitrust concerns. 

The right question is whether particular conduct harms competition and innovation—not whether a firm has simply become large. That distinction matters especially in AI markets, where the most efficient business structures and technical architectures remain uncertain. Nobody yet knows exactly which models, platforms, chips, cloud systems, or organizational forms will prove most effective. Competition policy should therefore focus on preserving innovation rivalry, rather than trying to engineer market outcomes—or market structures—through highly prescriptive interventions. 

Recent survey evidence also suggests public attitudes toward artificial intelligence are more nuanced than often assumed. Although many Americans worry about artificial intelligence’s social consequences, RAND survey data indicate that a significant share of the public views U.S. leadership in artificial intelligence as strategically important and worries about China overtaking the United States in AI development. In other words, skepticism of large technology firms may increasingly coexist with the recognition that technological leadership is now part of geopolitical competition. 

The Consumer Welfare Standard Strikes Back 

To be sure, the political landscape remains fluid. Anti-bigness arguments continue to influence policy debates and are no longer confined to the neo-Brandeisian movement. Vice President J.D. Vance has expressed sympathy for concerns about excessive concentration, and some competition officials continue to advocate heightened scrutiny of large technology platforms based less on demonstrable competitive harms than on their size and economic importance. 

There are also reports that Sens. Amy Klobuchar (D-Minn.) and Chuck Grassley (R-Iowa) may soon reintroduce the American Innovation and Choice Online Act. Modeled in part on the European Union’s Digital Markets Act, the bill would impose a range of obligations on designated technology companies, limiting their ability to expand into adjacent markets, including, presumably, artificial intelligence. Among other things, it would restrict some forms of vertical integration—that is, combining complementary stages of production or distribution within the same company—and require covered platforms to share investments and capabilities through interoperability mandates, which require systems to work with rivals’ products or services. None of these provisions would require a showing of harm to consumers. The future direction of U.S. antitrust policy therefore remains contested. 

Even so, the broader trend is becoming harder to ignore. Geopolitical competition is pushing policymakers to focus on innovation, productivity, technological development, and dynamic efficiency. Across the Atlantic, Mario Draghi’s report on European competitiveness identified a lack of scale as a central weakness, while the European Commission’s draft merger guidelines increasingly treat scale and innovation as complementary competitive virtues. These developments sit more comfortably within the consumer welfare tradition than within approaches that regard economic scale as inherently suspect. 

The irony is difficult to miss. Geopolitical competition was supposed to make the consumer welfare standard look obsolete. Instead, it has highlighted the importance of the very factors the framework has emphasized all along: innovation, efficiency, and economic dynamism. In a world defined by technological rivalry, consumer welfare looks less like a narrow economic objective and more like a strategic necessity.

The post How China Accidentally Made Consumer Welfare Cool Again appeared first on Truth on the Market.

Brazil’s Google News inquiry belongs firmly in the latter category. On April 3, Brazil’s Administrative Council for Economic Defense (CADE) announced that its Tribunal had unanimously decided to send a seven-year-old administrative inquiry concerning Google’s use of journalistic content—whether for indexing information, displaying “snippets,” or, more recently, generating “AI Overviews”—back to the agency’s General Superintendence (SG/CADE). This time, the Tribunal instructed SG/CADE to open a formal administrative proceeding and conduct a deeper investigation. 

The shift followed the vote of acting President Diogo Thomson de Andrade (the “Thomson vote”) and a concurring opinion (voto-vista) by Commissioner Camila Cabral Pires-Alves (the “Pires-Alves vote”). The rapporteur, former Commissioner Gustavo Augusto Freitas de Lima, had originally voted to dismiss the matter, but later amended his vote to join the concurring opinion. 

A unanimous vote may sound decisive. In this case, it is almost the opposite. Rather than close a stale inquiry launched in 2019, whose original factual premises have changed beyond recognition, CADE chose to keep the matter alive while substituting a new theory of harm and, in some respects, a new set of facts. There are several reasons to view that move with concern and skepticism. 

A New Case in an Old File

A brief procedural recap is in order.

The inquiry traces back to another well-known Brazilian case: Google Scraping. CADE did not open the matter in 2019 through SG/CADE acting ex officio—that is, on its own initiative. Instead, the Tribunal itself launched the inquiry while adjudicating the Google Scraping proceeding. 

The trigger was Globo/G1’s 2019 response to a request for information, or RFI. Issued by Brazil’s largest media conglomerate, the RFI asked whether Google had used Globo’s content on its news platform. Globo responded: 

As for the period between 2011 and 2012, we know that G1 content appeared in search results, but we are unable to determine at this point [in 2019] how Google’s news platform worked at the time, nor what content was displayed. As for the subsequent period [2013 onwards], the answer is yes—the platform provided links, headlines, and photos of G1 content. However, we cannot say with certainty exactly when this content began to be displayed.

Globo’s central allegation was straightforward: Google News and Google Search displayed headlines and snippets derived from third-party news content without compensation. The claim was framed as both exclusionary and exploitative conduct. In antitrust terms, an exclusionary claim alleges that a firm harmed rivals or shut them out of the market; an exploitative claim alleges that a firm used its market power to impose unfair terms. In response, SG/CADE opened the inquiry through Order No. 4/2019. 

After four years of investigation, CADE’s Department of Economic Studies (DEE/CADE) issued Technical Note 24/2023, concluding that the evidence failed to support any of the proposed theories of competitive harm. The findings were unequivocal: no incentive to foreclose rivals, no essential-facility concerns, and—crucially—a positive net flow of traffic from Google to publishers, with “insufficient evidence that snippets led consumers to stop accessing the links” (Thomson vote, ¶36). SG/CADE reached the same conclusion in Technical Note 70/2024, recommending dismissal for lack of evidence of an antitrust violation—or, put differently, lack of competitive harm (Thomson vote, ¶63). 

Ordinarily, that would have ended the matter. 

Instead, on March 28, 2025, Commissioner Camila Cabral Pires-Alves issued Decision No. 9/2025, proposing that the Tribunal revisit the case through a procedural mechanism known as avocação. Under Brazilian competition-law procedure, a commissioner who disagrees with SG/CADE’s recommendation may ask the Tribunal to substitute its own judgment for that of the investigative authority. In practice, avocação allows the Tribunal to review and potentially overturn SG/CADE decisions, including dismissals in conduct cases and unconditional approvals in merger reviews. 

The Tribunal accepted Pires-Alves’ proposal and randomly assigned the case to Freitas de Lima as rapporteur (Thomson vote ¶¶66, 69). At CADE’s 249th Ordinary Judgment Session on June 11, 2025, Freitas de Lima voted to affirm SG/CADE’s recommendation of dismissal. 

His reasoning tracked the agency’s earlier findings. Snippets, he concluded, “generate additional traffic for the portals, effectively functioning as a form of free publicity” (Thomson vote ¶80). A leveraging theory failed because Google “does not operate directly” in the downstream market for news portals (Thomson vote ¶81). The practice was justified under the rule of reason because indexing is “a central element of the business model of search engines, facilitating users’ rapid access to information and generating gains in efficiency and utility” (Thomson vote ¶82). Finally, economic dependence by publishers, standing alone, does not constitute a competition-law violation (Thomson vote ¶86). 

By that point, DEE/CADE, SG/CADE, and the assigned rapporteur had all independently reached the same conclusion. 

The case nevertheless took another turn. 

At that same session, Acting President Diogo Thomson de Andrade filed a request for further consideration (Thomson vote ¶94) and launched what he described as a “supplementary investigation” (Section 2 of his vote). He issued additional decisions, sought further information from Google, sent RFIs to seven publisher associations (Thomson vote ¶99, Table 4), and explicitly invited “civil society in general to submit technical and factual contributions” (Thomson vote ¶101). 

The effort generated 10 additional submissions from Brazilian media outlets—including Zero Hora, A Gazeta, and Folha de S.Paulo—as well as advocacy organizations such as the Open Markets Institute’s Center for Journalism & Liberty, Article 19 Brazil, Reporters Without Borders, Sleeping Giants Brasil, and Foxglove (Thomson vote ¶102, Table 5). Many of these organizations are sophisticated advocacy groups with established positions on platform regulation. 

The timing matters. 

Google launched AI Overviews in Brazil only in August 2024 (Thomson vote ¶154), after SG/CADE had already prepared Technical Note 70/2024 and recommended dismissal. The original 2019 inquiry therefore could not have generated evidence concerning AI Overviews. In substantive terms, Thomson’s supplementary investigation looks less like a continuation of the original case than a new investigation into a new product feature, attached to an old file whose original premises—scraping, snippets, and headlines—had already failed on the evidence. 

Thomson is candid about the institutional rationale behind this approach. He writes that the supplementary investigation “was not guided solely by the aim of strengthening the evidence in the strict sense” (Thomson vote ¶111). Rather, it reflected “a broader institutional understanding of the need to increase the openness of competition policy to the involvement of civil society and of actors who, although not directly competing in the market under scrutiny, represent diffuse interests.” Later, he contrasts this approach with analysis driven primarily by technical specialists, arguing that doing otherwise would create “an artificial barrier to the expression of diverse perspectives” and risk “a kind of closure of the marketplace of ideas” (¶¶117-118). 

That rationale raises important concerns.

Treating SG/CADE’s dismissal recommendation as the starting point for a year-long, advocacy-driven supplementary investigation risks transforming the agency from a competition-law enforcer into a stakeholder-management forum. There is, of course, a place for broad debate about competition policy. Competition-law proceedings serve a narrower purpose. They assess specific harms under defined legal standards, using evidence-based tools intended to reduce error costs—the risk that an agency either condemns lawful conduct or misses genuinely harmful conduct. 

CADE should not hesitate to pursue cases when the evidence demonstrates harm to competition. But here, the evidence assembled by the agency’s own economic experts points in the opposite direction. Against that backdrop, the unusual duration and procedural trajectory of this case raise legitimate questions about the fairness and neutrality of the process. 

The Return of the Phantom Menace

The most consequential—and ambitious—move in the case is substantive. Thomson acknowledges that CADE’s technical bodies have thoroughly examined the traditional theories of harm. In his view, the case deserves a second life through a different lens: exploitative abuse.

As Thomson explains:   

Thus, the theories of harm already explored by the DEE/Cade – increased costs for rivals, leveraging of a dominant position, scraping of journalistic content that allows access to information without redirecting to publishers, and tying – as well as by the SG/Cade – predatory innovation and blocking of essential inputs, retention of traffic with a view to increasing advertising revenue, and self-preferencing in the event of traffic diversion or retention for one’s own benefit – and subsequently analysed in the opinion of the Reporting Commissioner, have, in my view, already received sufficient analytical treatment in the course of the proceedings and in previous decisions.

There remains, however, a need for a more detailed examination of the exploitative aspect of the conduct, even though this has been addressed in part in the aforementioned statements. For this reason, I shall focus here on the aspect of abuse of a dominant position that I consider relevant, specifically with regard to alleged exploitative abuse (or abuse of economic dependence), whether or not associated with elements of exclusionary abuse. (emphasis in original) (Thomson vote, ¶¶ 502-503).

Thomson reaches this conclusion through a particular reading of Article 36 of Brazil’s Competition Law (Law No. 12,529/2011). Having acknowledged that the conventional abuse theories failed on the evidence, he argues that Brazilian competition law nevertheless reaches the conduct through what both he and Pires-Alves call the “typological openness” of Article 36—that is, the statute’s non-exhaustive list of anticompetitive practices.

Pires-Alves expressly embraces this approach. “I concur with Commissioner Diogo [Thomson]: it seems more appropriate to frame the theory of harm, at the forefront, as exploitative” (Pires-Alves vote ¶10). She notes that “the trajectory of exploitative abuses in [Brazil] was marked more by institutional caution than by conceptual rejection” (Pires-Alves vote ¶11, citing Barbosa & Kastrup, 2021) and argues that Article 36’s typological openness permits scrutiny of “unilaterally imposed unfair conditions on dependent partners, including when the extraction of value does not manifest in the form of direct monetary price” (Pires-Alves vote ¶11).

Pires-Alves also acknowledges that the exclusionary case has largely failed. In her view, that failure does not doom the exploitative theory: 

The current absence of a stronger showing of exclusion does not neutralize the exploitative theory of harm. It is preferable to acknowledge that the issue admits a primarily exploitative reading, without ruling out possible exclusionary effects, and that the current state of the investigation does not yet point to a definitive conclusion in this regard. (Pires-Alves vote ¶14). 

That argument faces a significant obstacle: exploitative-abuse cases are competition law’s phantom menace. They are theoretically available in many jurisdictions, but rarely enforced in practice. 

European Union law permits exploitative-abuse claims under Article 102(a) of the Treaty on the Functioning of the European Union (TFEU). Yet the European Commission’s own enforcement guidance prioritizes exclusionary conduct, and exploitative-abuse cases remain uncommon. U.S. antitrust law has gone further. Since the U.S. Supreme Court’s 2004 decision in Verizon Communications Inc. v. Law Offices of Curtis V. Trinko, LLP, monopoly pricing itself has generally been treated as lawful absent anticompetitive conduct. As the Court explained: 

The mere possession of monopoly power, and the concomitant charging of monopoly prices, is not only not unlawful; it is an important element of the free-market system. The opportunity to charge monopoly prices—at least for a short period—is what attracts “business acumen” in the first place; it induces risk taking that produces innovation and economic growth. To safeguard the incentive to innovate, the possession of monopoly power will not be found unlawful unless it is accompanied by an element of anticompetitive conduct.

In Justice Antonin Scalia’s view—and, we would argue, the correct one—the prospect of earning monopoly profits is a feature, not a bug, of a competitive economy. The possibility of charging supracompetitive prices rewards risk-taking, investment, and innovation. Brazilian law reflects a similar principle. Article 36, §1 of the Brazilian Competition Law provides that “[a]chieving dominance in a market by natural process and by being the most efficient economic agent in relation to competitors does not characterize” an antitrust violation. Antitrust enforcement should therefore focus on supracompetitive prices achieved through anticompetitive conduct, not those resulting from superior efficiency, innovation, or business acumen. 

Even setting that debate aside, exploitative abuse is notoriously difficult to define and prove. Drawing a principled line between a “reasonable” price and an “excessive” one—or determining the “fair” allocation of economic benefits—is no easy task. As Massimo Motta and Alexandre de Streel observe, “the proof of an excessive price, or in other words the search for the competitive price, may be like a quest for the Holy Grail.” 

For that reason, even scholars who support the use of antitrust law against excessive pricing generally regard it as a remedy of last resort. Motta and De Streel argue: 

It is important that in those guidelines [referencing the European Commission’s Guidelines on Article 82, now Article 102 TFEU], the Commission commits itself to limit the use of its broad legal power and make explicit that excessive pricing actions should be an option of last resort for antitrust authorities, and that they should be used only when other routes fail. (emphasis added and footnotes omitted) (Motta & De Streel, p. 42). 

The Thomson-Pires-Alves approach points Brazilian law in the opposite direction. 

As Giuseppe Colangelo has explained, the “free-riding” narrative underlying many claims by newspapers and other media organizations against digital platforms “serves chiefly as a rhetorical tool, strategically used to legitimize a mandated transfer of revenue that policymakers appear ready to pursue even in the absence of clear evidence that platform-based business models have caused economic harm to press publishers.” 

Once that theory is accepted—and once Article 36’s “typological openness,” combined with notions of “structural dependence,” becomes a substitute for traditional dominance-and-foreclosure analysis—the doctrine is unlikely to stop with Google. Any platform with allegedly dependent counterparties, a category broad enough to encompass countless ordinary commercial relationships, could become a target for exploitative-abuse claims untethered from measurable competitive effects. 

As Craig Conrath has explained:

[I]t is necessary to distinguish between contract power and monopoly power, between post-contractual opportunism and abuse of dominant position. … Therefore, whenever there is a claim of monopolistic practice by a party in a contractual relationship with the person accused of a monopolistic practice, it is important not to ask the question, “Does this party now have any other choices?” This is a question of contract law. Instead, one should ask the relevant antimonopoly law question: When the contract was made, did this party have other choices? (Practical Handbook Of Antimonopoly Law Enforcement For An Economy In Transition, § 5, at 5 (1995)).

That distinction matters here. The mere fact that a business has become economically dependent on a successful platform does not transform a contractual dispute into an antitrust violation.

It is also worth remembering what platforms such as Google News and other content aggregators actually do. They collect, organize, and direct users to relevant information, reducing search costs—the time and effort consumers would otherwise spend finding news and other content on their own. That benefit should not vanish from the analysis simply because some publishers dislike the commercial terms of the relationship.

Dominance Without a Market

Even if one accepts the direction this case has taken, a threshold question remains: In what market, exactly, is Google supposed to be dominant for purposes of an AI Overviews investigation?

There are at least three possible answers, and the case looks weak under all of them.

One possibility is that AI Overviews are simply a new feature within Google Search. On that view, the relevant market remains general search, where Google’s share in Brazil has exceeded 90% for years, albeit with some recent erosion. If so, the AI-related concerns are merely an extension of the original snippets-and-scraping investigation.

The problem is that this framing ignores the competitive threat that generative artificial intelligence poses to Google itself. In the remedies phase of United States v. Google LLC, after finding that Google possessed monopoly power in general search, Judge Amit Mehta declined to impose structural remedies, such as divestiture of Chrome or Android. A key reason was the emergence of generative AI. Mehta concluded that AI had “changed the course of this case” and posed a meaningful threat to Google’s position. Against that backdrop, it is difficult to reconcile a theory that treats Google’s efforts to compete in AI as evidence of deepening monopoly power.

A second possibility is that AI Overviews compete in a distinct market for generative-AI assistants and conversational search tools. Users who seek information through ChatGPT, Claude, Perplexity, Grok, or Gemini are often making functionally similar choices.

In that market, Google is not dominant. It is a challenger. Gemini has gained traction, but it still trails ChatGPT and faces strong competition from Anthropic, Perplexity, xAI, and others. More importantly, the market itself remains highly dynamic. Google, Microsoft, OpenAI, Anthropic, Perplexity, and other firms are rapidly experimenting with how AI-driven search works, how sources are cited, how referral traffic is generated, and how these products are monetized.

Treating any particular implementation as the basis for an antitrust proceeding risks freezing a temporary design choice into law. In markets evolving this quickly, false positives are especially costly. Regulators may deter features that consumers value, publishers benefit from, and competitors do not challenge in practice.

Recent empirical evidence reinforces this point. In “The Emerging Market for Intelligence” (2025), Aydin Demirer, Andrey Fradkin, Steven Tadelis, and Yiming Peng document extraordinary turnover in generative-AI markets. Drawing on usage data from OpenRouter and Microsoft Azure, they find that the number of available large-language models grew from 253 to 651 in 2025 alone, while the number of model creators nearly doubled and the number of inference providers more than tripled. Intelligence-adjusted prices at the technological frontier fell by roughly 1,000x since 2023. Leadership shifted repeatedly among Anthropic, Google, and xAI models throughout the year, and “the top 10 models today accounted for just 20% of market share four months ago and did not even exist ten months ago.” Their conclusion is straightforward: “No single model dominates across use cases.” 

That is not what an entrenched market looks like.

A third possibility is a broader market definition encompassing search engines, AI assistants, social media, email, and other channels through which publishers reach readers. From the perspective of news organizations, the relevant question is not whether Google exists in isolation, but whether publishers have meaningful alternatives for distribution and audience acquisition.

The Thomson vote attempts to avoid this inquiry through a dependence-based framework:

In my view, the core of the case does not lie in precisely and exhaustively determining whether the relevant market should be defined, with mathematical precision, as general search, thematic news search, digital content distribution, or any other intermediate formulation. The core issue is to determine whether, regardless of the formal market definition adopted, publishers operate in a position of structural dependence on Google for purposes of discovery, distribution, monetization, and the management of their own economic risk.

This emphasis on dependence, rather than definitional formalism, is consistent with Padilla’s (2024) approach. If the test for exploitative abuse is to focus on the harm caused by dominance, it would make no sense to require, as an absolute prerequisite, an exhaustive mapping of all digital attention channels. The legally relevant point is another: whether the dominant firm is capable of imposing terms that the trading partner would not accept in a minimally competitive environment. (Thomson vote, ¶¶591-592)

That approach raises serious concerns.

For one thing, Brazilian law still requires dominance to establish abuse of dominance. Unlike jurisdictions such as Germany or Japan, Brazil has no standalone doctrine of abuse of economic dependence. More fundamentally, the reasoning risks becoming circular. The challenged contractual arrangement becomes evidence both of the firm’s alleged market power and of the harm that power supposedly produces. Competition law ordinarily requires separate proof of market power, improper conduct, and competitive harm. 

The Case for Closing the Case

Commissioner Gustavo Augusto Freitas de Lima’s approach would likely have produced the better outcome.

As noted above, Freitas de Lima—the case’s original rapporteur—initially voted to uphold SG/CADE’s recommendation of dismissal, relying heavily on the DEE’s technical analysis, which found no evidence of competitive harm arising from the challenged conduct. Following Thomson’s supplementary investigation, Freitas de Lima modified his position in one important respect. He agreed that AI Overviews might warrant scrutiny, but as a distinct investigation separate from—albeit related to—the original Google News matter. He explained:

§ 133. Accordingly, following the analysis conducted based on the submissions provided and taking into account the study prepared by the DEE, I find that there is no evidence of a material violation of the economic order with respect to the conduct originally investigated, which consisted of the collection, indexing, and display of journalistic content through snippets and related mechanisms in the search engine.

§ 134. I emphasize, however, that the use of artificial intelligence tools integrated into the search engine may imply a significant functional difference in the conduct, with potential competitive impacts distinct from those observed in the traditional search model. Considering the evidence gathered, and without delving into the merits of the matter, I believe there is sufficient evidence to justify the initiation of a new investigation to specifically address the innovations introduced by Google’s Artificial Intelligence Overview (AI Overview).

§ 135. In light of the foregoing, I vote to dismiss the present Administrative Inquiry regarding the original conduct, pursuant to Article 144, §3, I, of RICADE [CADE’s Internal Regulation] and Article 67, §2, I, of Law No. 12,1529/11. As for the conduct related to the use of artificial intelligence in the search ecosystem (AI Overview), I concur with the proposal contained in the dissenting opinion of Commissioner Diogo Thomson, under the terms outlined therein, understanding that the case should be returned to the SG for the opening of a new specific investigation.

If CADE genuinely believes that Google’s deployment of AI Overviews in Brazil raises competition concerns, then the procedurally sound response is the one Freitas de Lima outlined—not keeping a 2019 inquiry on procedural life support.

CADE should close the original inquiry. The factual premises that gave rise to it have been overtaken by technological change, and the evidentiary record assembled over seven years supports dismissal. Closing the case would not prevent future enforcement. It would simply require the agency to articulate a new theory of harm, define the relevant market, and build a new evidentiary record tailored to the conduct actually under scrutiny.

CADE should also resist the temptation to embrace a broad exploitative-abuse doctrine. Even in jurisdictions that recognize such claims, exploitative abuse remains a tool of last resort. It is difficult to define, difficult to prove, and even more difficult to remedy in a predictable and administrable way.

AI Overviews may or may not raise genuine competition concerns. That question deserves serious analysis. But if CADE wants to investigate the future of search, it should start a new case—not keep rewriting an old one.

The post Brazil’s Google News Case and the Art of Not Letting Go appeared first on Truth on the Market.

The European Union’s Digital Markets Act (DMA) is the clearest recent example. The DMA did not emerge because economists suddenly discovered universal truths about digital markets. It reflects a specific regulatory judgment: that the costs of underenforcement in digital markets outweigh the risks of overenforcement, and that values like fairness and contestability can justify departing from effects-based analysis. Whether those tradeoffs make equal sense for countries like India is a very different question.

India’s Draft Digital Competition Law, still in gestation, appears poised to follow the same path. Both regimes treat tying and bundling by dominant digital platforms as presumptively illegal. Both impose categorical ex ante prohibitions on practices economists have long recognized as frequently efficiency-enhancing.

In doing so, both misunderstand not only the economics of digital markets, but also the basic reality of how digital products are built. The laws’ treatment of tying and bundling make that failure especially vivid. They also make it especially consequential for a country like India, whose economy increasingly depends on technological innovation and is now being reshaped by artificial intelligence (AI). 

Brussels Is Not Bangalore

India’s Draft Digital Competition Law emerged, in large part, from the gravitational pull of the Brussels Effect. The two government reports that anchor the draft law—the Parliamentary Standing Committee’s 2022 report and the Ministry of Corporate Affairs’ 2024 follow-up—borrow directly from the DMA’s architecture. 

On tying and bundling, the first report effectively copy-pasted Article 5(8) of the DMA wholesale. The draft article provides: 

The Committee thus are, of the view that a SIDI should not force business users or end users to subscribe to, or register with, any further service as a condition for being able to use, access, sign up for registering with any of that platform’s core platform service. 

That language imposes an absolute bar on gatekeepers requiring users of one core platform service to subscribe to, register with, or use another core platform service as a condition of access. No exceptions. No weighing of effects. No room for the kind of case-by-case economic analysis that has characterized competition jurisprudence in both the EU and India for the better part of three decades. 

Before accepting this as a reasonable model, India should ask a question the drafters appear not to have considered: Was the DMA designed for India, or for Europe? 

The answer is plainly Europe—and perhaps not even all of Europe. The DMA’s per se approach to bundling reflects a deliberate regulatory tradeoff that makes sense, if it makes sense at all, only within the EU’s specific political economy. Of the top 100 technology companies in the EU by market capitalization, 74 are American. The DMA is, at least in part, an instrument of digital sovereignty: a mechanism to constrain the dominance of U.S. platforms and clear space for European tech firms. Whether that goal justifies the economic costs is a normative question Europeans must answer for themselves. It is emphatically not India’s problem to solve. 

At a more technical level, India’s draft law, like the DMA, wrongly assumes that all gatekeepers operate on similar business models and that digital markets are uniformly “winner-take-all.” The result is a one-size-fits-all set of 18 obligations that risks significant error costs—that is, the economic harm caused when regulators mistakenly condemn beneficial conduct or miss harmful conduct. 

India’s digital economy has its own competitive dynamics: fierce regional differentiation, strong multi-homing, and platform markets that look nothing like the winner-take-all archetypes animating Brussels’ regulatory imagination. In India’s taxi market, for example, Uber does not dominate everywhere. Goa Miles dominates in Goa, Namma Yatri in Bangalore, and Ola competes fiercely nationwide. 

Applied to India, DMA logic could sweep in many homegrown technology companies with regional strength and varied business models, categorizing them as gatekeepers despite their very different competitive realities. 

Case-by-Case Beats Copy-and-Paste

Here is what proponents of India’s Draft Digital Competition Law rarely acknowledge: India already has a sophisticated, effects-based framework for adjudicating tying and bundling cases. And it works. 

Under Section 4(2)(d) of the Competition Act, 2002, tying by a dominant firm is prohibited when three conditions are met: the tying and tied products are distinct; access to one product is conditioned on acceptance of the other; and that conditionality forecloses competition in the tied market. Critically, any finding of anticompetitive conduct also requires proof of an “Appreciable Adverse Effect on Competition.” That standard obliges competition authorities to weigh both pro- and anticompetitive effects ex post. 

Despite periodic suggestions that Section 4 functions as a quasi-per se prohibition, the Supreme Court settled the issue definitively in Competition Commission of India v. Schott Glass India Pvt. Ltd. (May 2025). The Court held unequivocally that a per se reading of Section 4 is wrong. Demonstrable market effects must be shown, and the analysis must account for both competitive harm and legitimate business justifications. 

The practical record shows the framework works as intended. When the Competition Commission of India (CCI) found Google liable for tying its Play Billing System to app distribution, it did so only after conducting a careful effects-based analysis showing concrete harms to innovation, developer autonomy, and ultimately consumer welfare. When similar claims were brought against Apple’s in-app payment rules, the same framework produced a similarly reasoned analysis. 

The CCI did not need a categorical prohibition to identify abusive conduct. It needed the right analytical tools, and it already had them. 

The real question, then, is not whether India needs a competition-law framework capable of handling digital tying. It already has one. The question is whether replacing careful case-by-case analysis with categorical prohibitions would improve that framework. 

It would not. 

Sometimes Integration Is the Product

Tying arrangements generate efficiencies across multiple dimensions. They can reduce production, distribution, and licensing costs—often substantially—when demand for bundled components is positively correlated. They can improve product quality by solving compatibility problems that arise when consumers combine components from different providers. They reduce consumer search costs, which become increasingly burdensome as products grow more technically complex. And they can solve the problem of double marginalization: when two monopolists sell complementary products independently, each adds its own monopoly markup; vertical integration through tying eliminates the duplicated markup and can reduce final prices for consumers. 

These are not abstract artifacts of the Chicago School. They are widely documented empirical regularities. 

In digital markets, these efficiencies do not weaken. They intensify. 

Software products typically involve high fixed costs and near-zero marginal costs, which magnifies the gains from cost reduction. Compatibility problems in complex technological products are also far more severe than in traditional manufacturing markets. A smartphone operating system, cloud platform, or AI ecosystem is not a kitchen mixer with interchangeable attachments.

The value of reducing consumer search costs is especially high in markets where many users lack the technical expertise to assemble and optimize products independently. That matters in India, where digital adoption has expanded rapidly, but digital literacy remains uneven. Much of India’s 1.4 billion-person population still lacks the technical knowledge to navigate highly modular digital ecosystems efficiently. 

Yet these efficiencies are largely absent from the DMA’s framework and from India’s draft law. That omission creates substantial error costs by treating potentially beneficial integration as presumptively suspect. 

There is also a category of efficiency unique to technological markets that antitrust doctrine has long struggled to describe clearly—and that categorical per se rules are structurally incapable of recognizing at all. It requires its own conceptual vocabulary. 

When 1 + 1 = 3

Systems theory uses the concept of emergence to describe a specific phenomenon: when components combine into a system, the system can acquire properties that are not present in—and cannot be predicted from—the individual parts. 

The classic examples are physical. Water is wet; hydrogen and oxygen, individually, are not. A murmuration of starlings produces fluid, coordinated movement that no individual bird directs. Ant colonies build and maintain complex infrastructure without any ant understanding the overall design. The emergent property—wetness, coordinated flight, colony architecture—belongs to the system as a whole. Break the system apart, and the property disappears. 

The same phenomenon appears in technology.

Technological integration, properly understood, occurs when two or more components are combined at a sufficiently deep technical level that the integrated system acquires a genuinely new capability—something neither component can perform independently, and something that cannot be recreated simply by running both side by side. 

That distinction matters. Bundling two apps onto a home screen is not technological integration. Nor is preinstalling a search engine alongside a browser in the relatively superficial sense addressed in the Google Android case. Technological integration, in the emergent sense, occurs when components are architected together so that their interaction produces a new system-level capability. 

The result is not merely Component A plus Component B. It is Product C—with distinct functionality, distinct user value, and often distinct consumer demand. 

Apple’s Face ID illustrates the point clearly. Three components work together: the TrueDepth infrared camera array, which captures detailed three-dimensional facial geometry; a neural-network recognition system, which processes and matches that data against a stored template in real time; and the Secure Enclave’s cryptographic architecture, which stores authentication data and executes verification without exposing sensitive information to the operating system.

Each component can be described independently. None, standing alone, performs secure biometric authentication.

The authentication capability emerges from their integration. Remove the Secure Enclave, and the system loses its trusted execution environment. Remove the neural network, and the camera loses its matching capability. The emergent system—secure, real-time, privacy-preserving biometric authentication—depends on the integration itself. Put simply, it does not survive unbundling. 

A second example comes from medical diagnostics. A medical-imaging tool integrated with a convolutional neural network trained on longitudinal patient data can forecast disease risk decades in advance. The imaging tool alone captures anatomical structure. The neural network alone classifies patterns. But the integrated system, trained on the relationship between imaging features and long-term clinical outcomes, produces predictions neither component could generate independently. The diagnostic capability emerges from the integration. 

Increasingly, AI-driven product design follows the same logic. When a large language model is integrated architecturally into a productivity suite—not as a chatbot bolted onto the side of an existing application, but as a capability woven into document editing, email composition, data analysis, and workflow automation—the result is a fundamentally new kind of tool. 

The product is no longer simply “word processor plus AI.” It becomes a qualitatively different productivity environment whose capabilities depend on deep technical co-design between the components. 

This is precisely why the standard “separate product test” in tying analysis—which asks whether the tied and tying products had independent pre-existing demand—is the wrong framework for evaluating emergent integrations. 

The test is inherently backward-looking. It asks whether the products existed separately before integration occurred. Emergence, by definition, creates something new. The integrated system could not have generated prior independent demand because it did not previously exist as an integrated system. Applying a backward-looking demand test to forward-looking innovation will systematically misclassify valuable integrations as presumptively suspect. 

Before the DMA, European courts in the Microsoft litigation gestured toward a more sensible framework when they held that technological integration must produce “superior technological efficiency” specifically dependent on the tie. That standard imperfectly captures the logic of emergence. It asks not whether products were historically separate, but whether integration produced something genuinely new and better. 

It is an imperfect test, but at least it asks the right question.

The DMA abandoned that approach entirely. India’s draft framework, with its backwards-looking “integral to the core service” exemption introduced in the 2024 report, risks making the same mistake.

Innovation, but Only If It Already Exists

The Ministry of Corporate Affairs’ 2024 report acknowledged, at least in principle, that a blanket prohibition would be too blunt. It proposed a narrow exemption for ties that are “integral to the provision of the Core Digital Service,” leaving the Competition Commission of India to specify, through future regulations, what qualifies.

That is better than nothing. It is not nearly good enough.

The amended draft provision provides:

15. Tying and Bundling: A SIDI shall not require or incentivize business users or end users of the identified Core Digital Service to use on or more of the Systemically Significant Digital Enterprise’s other products or services, or those of: (a) Related parties; or (b) Third parties with whom the Systemically Significant Digital Enterprise has arrangements for the manufacture and sale of provision of services Alongside the use of the identified Core Digital Service, unless the use of such products or services is integral to the provision of the Core Digital Service.

Explanation: For the purpose of this section, the Commission may specify, by regulations, the nature of products or services that may be considered ‘integral’ to the provision of a Core Digital Service.

The exemption is backward-looking by design. If a product feature was not already part of the core service, it may not count as “integral” to that service—no matter how transformative the integration may be. This freezes product definitions at the moment of designation and penalizes precisely the kind of forward-looking, emergent innovation that drives value in digital markets.

The rule effectively tells firms that technological improvement through integration is presumptively unlawful unless it mirrors the historical baseline. When innovation arises through emergence—where combining products A and B creates a new product C—a legal framework that tethers the value of C to the original provision of A risks pixelating emergent innovation, reducing a novel creation to its constituent inputs. 

The exemption also introduces a new and more opaque form of legal uncertainty. By delegating the definition of “integral” to future regulation, it replaces judicial standards with discretionary administrative determinations. Those determinations may be made without transparent market studies, meaningful appellate mechanisms, or stable limiting principles—and may be reclassified at any point.

That is not an improvement over the effects-based framework. In important respects, it is worse: less predictable, more susceptible to capture, and further removed from principled economic analysis. 

Crucially, the exemption makes no room for economic justification. It does not ask whether a tie reduces search costs, counters double marginalization, resolves compatibility problems, or produces superior technological efficiency. Those are precisely the factors Indian courts have long weighed in Section 4 cases. 

Under the draft law, they disappear entirely. 

The exemption is not a return to economic reasoning. It is a fig leaf on top of a prohibition. 

The AI Problem the DMA Cannot See

The inadequacy of both the DMA and India’s draft framework becomes clearest in frontier cases.

Consider, hypothetically, Microsoft’s integration of OpenAI’s model into Microsoft 365 through Copilot. Under the DMA, the threshold question is whether Microsoft’s AI offering qualifies as a “core platform service,” and whether Article 5(8) therefore applies at all. If AI-generated content tools do not qualify as core services, the analysis reverts to traditional Article 102 doctrine.

At that point, the familiar problems return. Does an iterative, self-learning AI assistant have independent demand apart from the productivity suite into which it is integrated? The answer is far from obvious. So, too, is whether the integration produces the kind of “superior technological efficiency” recognized in the pre-DMA Microsoft jurisprudence—the standard European courts developed before the DMA largely swept it aside.

Those are difficult questions. A per se rule simply refuses to ask them. 

Under India’s draft framework, the result is arguably worse. Microsoft Office would likely qualify as the core digital service. Copilot would be supplied through a commercial arrangement with a third party. On a literal reading of Section 15 of the draft law, the integration would therefore be unlawful unless Copilot were “integral to the provision” of Office.

But Office plainly functions without Copilot.

The rule would thus classify a genuine AI-driven productivity innovation as an anticompetitive tie in complete disregard of emergence. Not because the integration forecloses competition. Not because it harms consumers. But because the capability did not exist in the product before. 

That is not effects-based competition analysis. It is regulatory formalism masquerading as technological neutrality. 

Europe’s Regulatory Politics Are Not India’s Future

India should resist the DMA’s pull—not because tying is always benign, but because the economics of tying are always contextual. That is especially true in the age of AI. 

The right framework starts from the premise that tying warrants scrutiny, not automatic condemnation. A structured effects-based analysis should weigh demonstrated harms—leveraging, foreclosure, and price obfuscation—against the full range of pro-competitive efficiencies, including technological integration in the emergent sense described above. 

The European Commission’s pre-DMA approach—which asked whether an integration produced “superior” functionality genuinely dependent on the tie—offers a workable and economically coherent template. India’s Competition Act, updated to codify explicit exemptions for emergent technological integration and to clarify the role of multi-homing and switching costs in foreclosure analysis, would be far better equipped to handle AI-era tying cases than any per se rule imported from Brussels. 

Multi-homing matters here, especially in India. Users routinely rely on multiple platforms simultaneously across food delivery, payments, ride-hailing, messaging, and e-commerce. That reality substantially weakens the lock-in logic underlying many foreclosure theories. Strengthening data portability through frameworks like the Data Empowerment and Protection Architecture (DEPA) would weaken switching costs further, limiting the anti-competitive potential of even aggressive tying strategies. 

The goal of competition law, as the Indian Supreme Court reaffirmed in Competition Commission of India v. Schott Glass India Pvt. Ltd., is to protect competition through consumer welfare—not to protect competitors from competition. 

Protecting competition in India’s digital economy means preserving incentives to innovate through integration, not sacrificing them in pursuit of regulatory symmetry with the EU. 

India does not need a digital competition regime that mistakes integration for abuse, innovation for foreclosure, and product improvement for coercion. 

It certainly does not need to reinvent the License Raj for the AI era.

The post The Bundle of All Fears: India’s Risky War on Integration appeared first on Truth on the Market.

That is the central tension in the Commission’s April 16 preliminary findings under Article 8(2) of the Digital Markets Act (DMA), which specify how Alphabet must comply with Article 6(11)’s data-sharing obligations. The consultation closed May 1, and a final implementing act is due by July 27.

The proposed measures are detailed, and they reflect a serious effort to reconcile the DMA’s data-access mandate with the General Data Protection Regulation’s (GDPR) anonymization requirement. In particular, the Commission proposes a two-layer regime: a technical anonymization pipeline—attribute suppression, allowlisting, length thresholds, metadata generalization, and “mini-sessionization”—backed by contractual restrictions and recurring audits.

The problem is that the regime works only if both layers hold. And each layer depends heavily on trust in the other.

This post examines two questions the Commission has not adequately answered.

First, are the technical measures sufficient—on their own terms—to render the Search Dataset anonymous under European Union law? Put differently, do they ensure that re-identification “appears in reality to be insignificant,” under the Court of Justice of the European Union’s (CJEU) standard in Breyer, later reaffirmed in Single Resolution Board (SRB)?

Second, are the contractual and audit mechanisms robust enough to handle the realistic range of recipients? That includes recipients who are technically competent, commercially sophisticated, and not formally designated as hostile, but who may still behave adversarially in practice.

My short answer to both questions is no. More importantly, the two weaknesses reinforce each other.

The Commission has shifted meaningful risk-bearing work from the technical layer to the contractual layer, and from the contractual layer to private enforcement after the fact. If the audit process cannot be trusted, the anonymization process cannot be trusted. If the anonymization process cannot be trusted, the data was never lawful to share.

I will explain why both lines of defense are weaker than the consultation document suggests. In doing so, I draw on my earlier analyses of the coming  GDPR/DMA Article 6(11) conflict, my coverage of the 2024 and 2025 DMA compliance workshops, and my comparison of the EU DMA regime with Judge Amit Mehta’s user-side data-sharing remedy in U.S. v. Google. I also draw on the International Center for Law & Economics (ICLE) comments submitted to the Commission during the consultation by Geoffrey Manne, Dirk Auer, and Mario Zúñiga regarding Alphabet’s Article 6(11) obligations.

The Recital Can’t Save the Rule

Start with the legal standard. Article 6(11) of the DMA requires gatekeepers to provide access to “ranking, query, click and view data” on fair, reasonable, and nondiscriminatory (FRAND) terms. It then adds the crucial condition: “[a]ny such query, click and view data that constitutes personal data shall be anonymised.” Recital 61 further states that anonymization should occur “without substantially degrading the quality or usefulness of the data.”

The recital is doing more work in the Commission’s draft than it can bear.

As Peter Craddock has argued, and as Mark Leiser similarly argues in his consultation submission, the operative provision is unconditional: personal data “shall be anonymised.” The recital’s utility caveat operates within the anonymization requirement, not against it. If a given technique sufficiently anonymizes the data, the gatekeeper should prefer the version that preserves more utility. But if no available technique can adequately anonymize the data at a given utility level, the legal answer is to suppress the data—not to weaken the anonymization standard. The Commission’s Article 8(2) specification power does not extend to creating a softer, DMA-specific definition of “anonymisation.”

So what does anonymization require?

Under CJEU case law—most notably Breyer, and now SRB—the test is “relative.” The same dataset can be personal data for one entity and anonymous data for another, depending on the “means reasonably likely to be used” by the recipient, or by anyone else whose capabilities must realistically be considered.

Importantly, Breyer limits the analysis to lawful means of identification. The question is whether the risk of identification “appears in reality to be insignificant.” Opinion 05/2014 of the Article 29 Working Party supplies the operational framework: anonymization must prevent singling out, linkability, and inference.

The Commission’s two-layer approach—technical restrictions plus contractual controls—is, charitably read, an attempt to satisfy SRB by tightly constraining the recipient environment, so that re-identification tools available to recipients no longer count as “reasonably likely” means. The draft Joint Guidelines on the interplay between the DMA and the GDPR endorse this combined approach. (See paragraphs 180-181.)

The hard question—and the one the Preliminary Measures largely glide past—is whether that combination actually delivers what the DMA requires.

The Technical Pipeline’s Four Big Problems

To be fair, the five-step pipeline described in Section 3.1 of the Preliminary Measures is more sophisticated than Google’s earlier frequency-thresholding implementation. Third parties complained that Google’s original approach was so restrictive that it yielded little useful data. At the 2025 compliance workshop, DuckDuckGo and Seznam argued that 99% of distinct queries were excluded. 

As Alba Ribera Martínez explains, the Commission’s newer regime is more nuanced. It combines an entity-based allowlist—more than 50 signed-in users issuing queries containing a given entity over 13 months—with length-based suppression, metadata generalization, and “mini-sessionization.” That is more robust than Google’s original “30 globally signed-in users per exact query” filter, at least for queries that are lexically rare but semantically common.

Still, the pipeline has at least four structural weaknesses the Commission has not adequately addressed.

The detector layer is brittle at scale

The technical pipeline relies heavily on “personal data detectors” to identify names, addresses, and phone numbers before queries are split into entities. (See paragraph 22(a)(1) of the Preliminary Measures.) As Craddock explains, names are not a tractable detection problem at internet scale.

Capitalization conventions vary by user habit. A search for “james brown” may evade a capitalized-name detector. Naming conventions vary by culture: “Charles de Gaulle,” “LeBron James,” and “Sk?odowska-Curie” all behave differently. Meanwhile, countless ordinary words are also common surnames: “Cook,” “Smith,” “Rose,” “Bill.”

Across billions of search queries, even a low false-negative rate produces millions of records in which personal data slips through. Tightening the detectors creates the opposite problem: more false positives and more over-suppression of legitimate queries. Either way, the system degrades.

Notably, the Preliminary Measures contain no acceptable-error-rate metric, no public benchmark against which detector performance can be audited, and no contingency plan for inevitable misclassifications.

There is also a deeper problem, and this is where Leiser’s critique becomes especially powerful. Search queries routinely contain personal data about people other than the searcher: relatives, colleagues, public figures, complainants, alleged wrongdoers, former partners, and so on.

The Commission’s pipeline effectively assumes that the only relevant data subject is the user entering the search query. Article 6(11) does not support that assumption. The operative provision states that “any such query, click and view data that constitutes personal data shall be anonymised.” Under Article 4(1) of the GDPR, “personal data” is defined by reference to the person the data relates to—not the person who generated it.

To be sure, the “such data” referenced in Article 6(11) originates from end-user activity. But the phrase “constitutes personal data” does not limit the relevant data subject to the end user. Recital 61 opens by discussing “personal data of end users,” and the Commission appears to rely heavily on that wording. But the recital’s own anonymization test immediately drops the end-user qualifier, referring instead to whether the information relates to “an identified or identifiable natural person.”

In any event, a recital cannot narrow the operative provision, as Craddock notes. Reading Article 6(11) to authorize disclosure of identifiable third-party information—with no notice, no controller chain, and no remedy for the affected third party—would also sit uneasily with Article 52(1) of the Charter of Fundamental Rights, given the Article 7 privacy rights of those named individuals.

The mechanism is easy to see.

Imagine a user searches: “Jean Dupont infidelity divorce Brussels.” “Jean Dupont” is a placeholder for a moderately well-known professional whose name has appeared often enough in Google Search queries to land on the Commission’s allowlist. The detector recognizes the name. The entity clears the 50-user threshold. The remaining terms—“infidelity,” “divorce,” and “Brussels”—are common enough to survive filtering. The query length falls below the 95th-percentile threshold. The record flows into the export dataset.

Recipients then receive the full query text, along with country, language, device information, and an S2-cell-level location indicator. The searching user’s metadata may satisfy the Commission’s k=50 anonymity threshold. Jean Dupont’s data does not. The pipeline does not meaningfully treat him as a data subject at all.

The result is disclosure of identifiable information about Jean Dupont’s family life—and potentially his sexual conduct, depending on the inferences recipients draw—to entities with which he has no relationship, no contract, and no GDPR controller chain. Under the Commission’s design, he receives neither notice nor remedy.

Nor does the detector layer solve this. Even a perfect name detector would suppress third-party data only if the name itself were blocked, and only if suppression extended to the rest of the query string. The Commission’s pipeline does neither. If the name appears on the allowlist, the surrounding query text remains intact.

In fact, the allowlist may invite leakage. The more publicly salient a person is, the more likely his name appears on the allowlist, and the easier it becomes for any search mentioning him—by any user, for any reason—to flow into the daily export.

The metadata thresholds do not solve composition attacks

The Commission also requires that at least 50 signed-in users share the same combination of inferred language, location, and device type. (See paragraph 31.) This is essentially a k-anonymity guarantee. The problem is that k-anonymity has a familiar weakness: a group can be anonymous without being private.

If all 50 users in a cohort share the same sensitive characteristic, anonymity does little good. For certain query categories—a rare medical condition, for example, or a politically sensitive term within a small linguistic minority—the Commission’s k=50 threshold may still permit effective disclosure of the sensitive attribute across the entire cohort.

The Commission’s implicit answer seems to be that sensitive records can be filtered through entity and length thresholds. But that misses the point. Composition attacks are orthogonal to query rarity or query length.

The regime does not address active influence attacks 

The regime also appears vulnerable to active influence attacks by recipients themselves, a point Craddock and Leiser both emphasize.

Eligible recipients have lawful access to Google Search. They—or their employees, contractors, or paid panel users—can deliberately run searches designed to push particular entities across the 50-user threshold. Once a recipient has reason to believe a seeded query will appear in the Search Dataset, locating the resulting record becomes much easier.

None of this requires hacking, illicit databases, or breach of contract. It involves lawful conduct. That matters because Breyer asks whether identification methods are “reasonably likely to be used.” Lawful conduct that recipients can undertake unilaterally almost certainly qualifies.

The Commission’s contractual prohibition on “re-identification” or “sessionisation” does not solve this problem. (See paragraph 38(b).) Those restrictions matter only if the Commission later discovers the behavior and successfully enforces the rules after the fact.

The auxiliary-data problem is massive 

Finally, the linkage surface with auxiliary data is enormous. As the Chamber of Progress and others note in their consultation submissions, the dataset includes URLs, approximate timestamps, language, location, device information, access-point data, and increasingly detailed click and dwell-time signals.

Eligible recipients—by definition, rival search engines—already possess their own logs of user activity against many of the same URLs, during roughly the same time periods, and often involving overlapping users. Publishers and advertisers possess additional complementary data.

In other words, the DMA-protected dataset is intentionally designed to complement recipients’ own information. That is the entire competitive rationale behind Article 6(11).

Anonymization that might withstand attack by a recipient with no auxiliary data becomes far weaker when the attacker possesses structurally aligned datasets by design. Recital 26 of the GDPR explicitly directs regulators to consider identification means “reasonably likely to be used … by another person.” The Commission has not grappled seriously enough with that reality.

In short, the technical pipeline is more sophisticated than Google’s initial implementation. But it remains brittle along several dimensions the Preliminary Measures barely address. The Commission’s answer is to shift the remaining burden to contractual restrictions. That is where the second concern begins.

Trust Us, We Audited It

The Commission’s contractual measures, set out in Section 3.2 of the Preliminary Measures, are extensive on paper.

Recipients are prohibited from re-identification, data augmentation, record-level linkage with auxiliary datasets, sessionization beyond “mini-sessions,” and onward sharing. The regime also imposes purpose limits: recipients may use the Search Dataset only to optimize online search engine (OSE) services. (Paragraph 40.) There is a 13-month retention cap. (Paragraph 41.) The measures further require encryption at rest and in transit, least-privilege access controls, phishing-resistant multifactor authentication, restrictions on local workstation copying, and logging obligations with one-year retention periods. (Paragraphs 45-49.)

The compliance architecture culminates in a two-tier independent audit cycle under ISAE 3000 or an equivalent standard. Level 1 audits assess the design and suitability of controls; Level 2 audits assess operating effectiveness. (Paragraphs 52-70.)

At first glance, this looks like a serious compliance package. The problem is that the audit system is weaker than it appears. Three points matter in particular.

The audits mostly check boxes

The Level 1 and Level 2 reports focus on whether the recipient has documented controls and whether those controls appear to function as designed. (Paragraphs 56-57.)

Alphabet’s role is remarkably limited. It need only confirm that the report exists, is signed by a qualified practitioner, addresses the required assurance objectives, and satisfies the required format. (Paragraph 111.) The Preliminary Measures explicitly state that Alphabet “shall not reassess the substance or scope of the assurance conclusions.”

Nor does the Commission appear to conduct substantive review of the audits, beyond receiving notification that they occurred. The regime largely treats the existence of an audit as evidence of compliance, without meaningfully interrogating the quality of the audit or the rigor of its conclusions.

‘Independent’ auditors are not necessarily independent

The independence requirement is narrower than it sounds. The Preliminary Measures define auditor independence by reference to ISAE 3000 ethics standards. Those standards address the auditor’s independence from the audited entity itself. They do not address independence from outside commercial or strategic interests that may align with the audited entity.

That distinction matters. A formally qualified ISAE 3000 practitioner who is commercially eager for business, operating in a weak supervisory environment, technically outmatched by the auditee, or simply disinclined to push too hard against a paying client can still produce a clean report.

And that is before the harder cases: auditors who fully understand the weaknesses in the system, but nonetheless issue a reasonable-assurance opinion at the outer edge of what the standard tolerates.

The Commission’s framework assumes that “independent auditor” is a meaningful substitute for adversarial verification. Often, it is not.

The data move faster than the audit cycle

The timing mismatch is the final problem. After the initial Level 1 assessment, the audit cycle becomes annual. (Paragraphs 56-57.) The Search Dataset, by contrast, flows daily.

That means the system relies heavily on retrospective compliance review while disclosure occurs continuously and at scale. Even within an audit period, problematic behavior could persist for months before detection—assuming it is detected at all.

Put differently, the regime is built around after-the-fact accountability, not ex ante technical impossibility.

Those weaknesses become more serious once we stress-test the framework against the kind of recipient the Commission’s first assurance objective is supposed to screen out.

The Regime Is Built for Compliant Recipients

The Commission’s first assurance objective, set out in paragraph 67, comes closest to a substantive eligibility screen.

It excludes recipients that are directly or indirectly:

• sanctioned under EU restrictive measures;
• covered by sanctions that otherwise prohibit provision of the Search Dataset; 
• subject to action under the EU foreign direct investment screening regime  (Regulation 2019/452);
• designated as a “high-risk supplier” under Union law; or 
• identified by Union or member-state authorities as cybersecurity, public-security, or public-order threats. 

If a recipient is not on a sanctions list, has not been screened out under foreign-investment rules, and has not been formally designated a high-risk supplier, it clears the first assurance objective.

That sounds reassuring—until one asks what kinds of actors the framework actually excludes.

Consider a third-party “online search engine” incorporated in an EU member state, but beneficially owned through layered structures by actors aligned with an adversarial state. A Chinese or Russian intelligence-linked commercial proxy is the obvious example, although the same logic applies to organized criminal groups or sanctions-evasion networks.

Given the Commission’s expansive definition of “online search engine”—which now reaches AI chatbots with search functionality—setting up such a service is increasingly easy. The recipient applies, signs the license agreement, hires a formally qualified but commercially marginal ISAE 3000 auditor, obtains a Level 1 report, and begins receiving daily Search Dataset exports through the API.

What protects users—and anyone else named in search queries—in this scenario?

Very little.

The auditor verifies that, at the time of the report, the recipient is not formally sanctioned and has not been subject to an EU investment-screening decision. But the auditor need not pierce the corporate veil to identify ultimate beneficial ownership.

Nor is the auditor conducting an intelligence investigation. The recipient’s “credible and documented plans” for search-engine development, required under paragraph 68, are evaluated largely on the basis of the recipient’s own documents. Serious bad actors can produce credible paperwork.

The “change of control” procedure is similarly weak. (Paragraphs 117-120.) Recipients must notify Alphabet within 10 days of a public announcement, or 30 days before the change takes effect, whichever comes earlier. But the ownership transitions that matter most are precisely the ones designed not to become public.

Alphabet’s role remains largely ministerial. Under paragraph 111, it may verify only the formal completeness of the audit materials. It may not reassess the auditor’s conclusions. The Commission receives notice under paragraph 112, but there is no required forward-looking substantive review.

The contractual restrictions are equally fragile under real-world conditions. Yes, the recipient contract prohibits re-identification, augmentation, onward sharing, and related conduct. But contractual enforcement becomes largely aspirational once data has been exfiltrated, especially across borders.

Article 7 of China’s National Intelligence Law, for example, empowers Chinese entities to be compelled to cooperate with state intelligence work. Comparable authorities exist in other adversarial jurisdictions. If a state-linked recipient—or a state-linked subsidiary designed to receive the data—obtains the Search Dataset, the data will move. Any eventual contractual or regulatory penalties will fall on the EU shell entity long after the data has crossed the relevant border.

The expedited-termination mechanism does little to solve this. Paragraph 138 permits suspension only where there is “an urgent risk of serious and irreparable damage to the anonymisation of end users’ personal data.” That is a high evidentiary threshold. By the time the necessary evidence exists, the damage is likely done.

And termination does not rewind disclosure. The contract may require deletion of previously received data. (Paragraph 134.) A bad-faith recipient will simply ignore that obligation, especially where the EU lacks meaningful enforcement leverage.

The contrast with the U.S. District Court for the District of Columbia’s “Qualified Competitor” framework in U.S. v. Google is instructive. As I noted last September, the decree defines a Qualified Competitor as:

[A] Competitor who meets the Plaintiffs’ approved data security standards as recommended by the Technical Committee and agrees to regular data security and privacy audits by the Technical Committee, who makes a sufficient showing to the Plaintiffs, in consultation with the Technical Committee, of a plan to invest and compete in or with the GSE and/or Search Text Ads markets, and who does not pose a risk to the national security of the United States.

Several differences matter here.

First, the audits are conducted by the Technical Committee, not by auditors hired and paid by the recipient. That largely neutralizes the incentive problem on which the Commission’s framework depends.

Second, eligibility is substantively approved by the plaintiffs in consultation with the Technical Committee. It is not granted automatically based on the recipient’s own documents and a formally compliant audit report.

Third, recipient suitability is treated as a continuing supervisory question, overseen by a standing body throughout the life of the decree—not as a one-time compliance screen followed by annual reporting.

The national-security overlay is only the most obvious distinction.

To be clear, most actual recipients will not be hostile actors. Most will be legitimate search engines and AI assistants trying to build competing products. But that is not the relevant design question.

When the dataset may contain highly sensitive personal information, the regime cannot merely be safe for the median recipient. It must be resilient against the worst-case recipient, because the worst-case recipient is the one capable of imposing catastrophic privacy harms on EU users.

The Commission’s own first assurance objective implicitly recognizes this. Otherwise, there would be no sanctions-screening mechanism at all.

The problem is that the Commission has designed the screen around the wrong variable: formal designation status. The harder and more consequential questions—beneficial ownership, jurisdictional control, downstream state-compulsion risk, and operational alignment with adversarial actors—are left almost entirely to the recipient’s own auditor.

Anonymous by Trust Alone

The Commission has plainly moved beyond the crude frequency-thresholding regime that characterized Google’s early implementation, and the Preliminary Measures reflect a genuine effort to take privacy seriously.

But the regime still has two important weaknesses.

First, the technical layer leaves residual identification risks that, by the Commission’s own account, can only be reduced to an “insignificant” level through contractual restrictions. Second, the contractual layer depends heavily on private enforcement and an audit cycle that is formally rigorous but substantively thin. It also lacks anything resembling the continuing recipient-qualification framework the district court imposed in U.S. v. Google.

Together, the two layers produce an uncomfortable result: the legal status of the Search Dataset as “anonymous” depends on a chain of trust. That chain includes the recipient’s good faith, the auditor’s diligence, and Alphabet’s ability to police conduct it cannot directly observe. That is a fragile foundation for compliance with a statutory anonymization requirement.

Two changes would substantially improve the final implementing act.

First, the Commission should strengthen the technical layer, even at some cost to utility. The Preliminary Measures already contemplate multiple “samples” (A, B, and C), while the broader literature—particularly Leiser’s submission—offers a more graduated toolkit: aggregate-only access, controlled API access, clean-room environments, and regulator-supervised escrow arrangements. Differential-privacy budgets for aggregate signals, along with stronger detector-quality assurance backed by published false-negative metrics, are all feasible.

The Commission is right that Article 6(11) data should not be rendered useless. But Recital 61’s instruction that utility not be “substantially degraded” operates within the anonymization requirement, not above it. The recital cannot override the operative command that personal data “shall be anonymised,” and the final implementing act should say so clearly.

Second, the recipient-eligibility framework needs to grapple seriously with the risks the current proposal largely sidesteps—especially the incentive problem created when auditees choose and pay their own auditors, and the inability of formal-designation screening to detect undisclosed hostile ownership or jurisdictional control.

The Commission’s current framework does not do that. The precise mechanics of a more substantive merits-based review can be refined through the specification process. But the direction is already clear from the U.S. v. Google comparison.

At bottom, the Commission is attempting something difficult: forcing broad data sharing while insisting the shared data is no longer personal data. That is a legally and technically precarious balancing act.

And if the system works only so long as everyone behaves, it is probably not an anonymization regime. It is a trust regime.

The post The European Commission’s Search-Data Trust Fall appeared first on Truth on the Market.

Armen Alchian’s 1950 article, “Uncertainty, Evolution, and Economic Theory,” offers a powerful reply. Rather than defend the standard assumption, Alchian acknowledged the critics and suggested economists should stop describing firms as profit maximizers altogether. Under genuine uncertainty, firms cannot calculate profit-maximizing strategies in the way traditional models assume. Continuing to rely on that assumption, he argued, unnecessarily exposes economic theory to criticism.

Yet Alchian did not reject the predictive power of economic reasoning. Instead, he explained why profit-maximizing outcomes can emerge even when firms do not consciously maximize profits. Markets generate those outcomes through a process of variation, adaptation, and survival.

As Alchian famously observed, economists should focus less on firms’ intentions and more on the consequences of competition:

The economic counterparts of natural selection are imitation and innovation, the counterparts of heredity are the transmission of successful business practices.

In competitive markets, firms need not solve optimization problems for profit-maximizing behavior to emerge. It is enough that firms experiment—and that firms whose practices fail to generate sufficient profits eventually disappear.

Uncertainty and the Limits of Profit Maximization

Alchian emphasized a basic feature of economic life: uncertainty. Most economic models assume firms face risk—that is, situations in which the probabilities of various outcomes can be estimated and incorporated into decision-making. Under such conditions, firms could, in principle, calculate expected profits and choose strategies that maximize those expectations.

Real-world economic decisions rarely resemble this stylized environment. Firms introducing new products, entering unfamiliar markets, or responding to technological change often cannot reliably estimate the probabilities of future events. They do not know whether consumer preferences will shift, whether competitors will adopt new technologies, or whether regulatory environments will change.

Under genuine uncertainty, the information required to calculate profit-maximizing decisions simply does not exist. Firms cannot solve the optimization problems that economic models often attribute to them.

For Alchian, this observation carried an important methodological implication. If firms cannot calculate profit-maximizing strategies under uncertainty, then continuing to describe them as profit maximizers invites unnecessary criticism. Economists should instead focus on a different mechanism—one that does not require deliberate optimization. That mechanism is survival.

Survival of the Profitable

Markets operate as selection environments. In any competitive market, firms adopt a wide range of strategies. Some innovate; others imitate competitors. Some invest heavily in new technologies; others rely on established practices. Some pricing strategies succeed; others fail. Variation is inevitable.

Not all firms survive. Firms that consistently fail to generate sufficient profits eventually lose access to capital, fall behind competitors, or exit the market entirely. Firms that generate stronger profits persist and expand. Over time, this process produces a population of surviving firms whose behavior appears consistent with profit maximization.

That appearance, however, is misleading. Profit-maximizing behavior does not emerge because firms deliberately calculate optimal strategies. It emerges because firms that fail to approximate such behavior disappear.

This perspective also highlights something traditional models tend to obscure: widespread business failure. Many firms do not survive. They misjudge consumer demand, adopt inefficient production methods, or fail to adapt to changing competitive conditions. In a world characterized by uncertainty, such mistakes are inevitable.

Standard profit-maximization models offer little explanation for why so many firms fail. Alchian’s framework does. Firms operate with imperfect information and limited foresight. They make decisions based on guesses about the future, and some of those guesses inevitably prove wrong. Survival therefore depends not on perfect planning, but on the ability to adapt when predictions fail.

The firms we observe at any given moment are therefore not a random sample of all firms that attempted to compete. They are the survivors of a competitive filtering process. As Alchian put it, “Those who realize positive profits are the survivors; those who suffer losses disappear.” The appearance of profit maximization emerges from this process of elimination.

Business failures, however unfortunate, play an important role in this evolutionary process. When firms fail, their strategies provide information to competitors, investors, and entrepreneurs about what does not work under particular circumstances. Competitors observe those outcomes and adjust their own practices accordingly. In this way, markets learn collectively through the successes and failures of individual firms. The process is not merely eliminative. It is informational.

Adaptation Beats Perfect Foresight

If survival explains why profit-oriented behavior dominates at the population level, what does economic “rationality” look like inside firms? It does not look like solving equations. Instead, it looks like hard work, educated guesswork, and constant adjustment.

Alchian did not suggest business owners are indifferent to profits. On the contrary, the pursuit of profit remains the central objective of commercial activity. Business owners plainly want their firms to earn as much as possible. The difficulty is that, under conditions of uncertainty, no one knows in advance which decisions will produce the highest profits.

Alchian emphasized that economic success under uncertainty rarely results from perfect foresight. Firms operate by making conjectures about an unknowable future and adjusting when those conjectures prove mistaken. As he explained, “The greater the uncertainty of the environment, the greater the probability that actions will be taken which are not optimal.”

Managers and entrepreneurs therefore rely on intuition, experience, and experimentation. They try new pricing strategies, adjust production methods, explore new markets, and respond to feedback from customers and competitors. Some of these experiments succeed; others fail.

Consider a restaurant owner experimenting with menu items, pricing, and kitchen organization. She does not calculate the optimal menu mathematically. Instead, she observes which dishes sell, adjusts prices, revises recipes, and drops unpopular items. Over time, unsuccessful experiments disappear, while successful practices persist.

The same dynamic operates in large corporations. Firms test marketing campaigns, revise executive-compensation structures, restructure operations, and reallocate capital across divisions. Managers continually revise decisions in response to new information.

Alchian acknowledged that chance also plays an important role in this process. Some firms benefit from favorable circumstances, while others encounter unexpected setbacks. But chance does not imply pure randomness. What matters is how firms respond to these events. Firms that adapt to good or bad fortune are more likely to survive than firms that remain rigid in the face of changing conditions.

Profitability therefore emerges from a process of continual adjustment. Firms that repeatedly experiment, learn from mistakes, and revise unsuccessful strategies tend to move closer to profitable outcomes. Firms that fail to adapt eventually disappear.

Corporate Governance by Natural Selection

Alchian’s framework also sheds light on corporate governance. Governance debates often focus on whether boards are sufficiently independent, whether executive compensation properly aligns managers’ incentives with shareholder interests, or whether shareholder-voting mechanisms adequately discipline management. These discussions often assume governance arrangements must be carefully designed to achieve optimal results. But governance structures themselves evolve under competitive pressure.

Capital markets provide one important mechanism of selection. Firms that consistently underperform face higher costs of capital as investors demand greater returns to compensate for risk or shift investments toward more successful firms. Over time, this reallocates resources toward firms whose governance structures support profitable operations.

The “market for corporate control” provides an even more direct form of discipline. As Henry Manne famously argued, declining stock prices often signal managerial inefficiency. Those declines invite takeover bids by investors who believe they can operate the firm more effectively. When takeovers succeed, new owners frequently replace management, restructure operations, and adopt governance practices designed to improve performance. In this way, the possibility of acquisition encourages boards and executives to respond to competitive pressures before performance deteriorates too far.

Henry Butler and other scholars later emphasized the broader role financial markets play in this disciplinary process. Investors, analysts, and lenders continually evaluate corporate performance and governance practices. Firms that fail to meet expectations face declining share prices, restricted access to capital, and increased vulnerability to takeovers or activist intervention.

Executive labor markets reinforce these pressures. Managers associated with successful firms gain reputational advantages and expanded opportunities, while managers associated with persistent underperformance are less likely to retain leadership positions or secure future executive roles. Managerial practices that support firm survival therefore tend to spread through professional networks and imitation.

Corporate-governance arrangements such as independent directors, performance-based compensation, and enhanced disclosure requirements did not emerge all at once through centralized design. They developed gradually as firms, investors, and regulators observed which arrangements produced more reliable performance.

ESG and the Risks of Convergence

Alchian’s evolutionary perspective also offers a useful framework for thinking about contemporary debates over environmental, social, and governance (ESG) practices. Supporters argue ESG initiatives improve long-term corporate performance by strengthening reputation, retaining employees, or reducing regulatory risk. Critics worry such initiatives may distract firms from their core objective of generating profits.

In principle, Alchian’s framework suggests markets should eventually sort out these competing claims. Firms experiment with different strategies under conditions of uncertainty. Investors observe performance outcomes. Capital flows toward firms that demonstrate resilience and profitability, while less successful approaches gradually disappear. Over time, practices that support survival become more widespread.

This evolutionary process, however, depends on decentralized experimentation and effective selection mechanisms. When variation is suppressed or selection pressures weaken, the market’s ability to distinguish successful practices from unsuccessful ones may diminish.

Recent developments surrounding ESG illustrate this challenge. Large institutional investors, which collectively hold substantial stakes in many major corporations, have actively encouraged firms to adopt ESG policies. At the same time, regulatory initiatives increasingly require ESG disclosures and, in some cases, specific reporting frameworks. When governance practices spread primarily through coordinated pressure from large investors or through regulatory mandates, the process may begin to resemble centralized adoption rather than decentralized experimentation.

Under such conditions, the evolutionary mechanism Alchian described may operate less effectively. If many firms adopt similar practices simultaneously because of investor or regulatory pressure, markets have fewer opportunities to observe alternative approaches and compare their outcomes. Likewise, when regulatory frameworks constrain corporate responses, firms may have less freedom to adapt quickly to new information.

This does not mean ESG practices are necessarily inefficient. But it does complicate the evolutionary testing process that Alchian believed allowed markets to discover which business practices work best under conditions of uncertainty.

The Evolutionary Foundations of Law & Economics

Alchian’s evolutionary insight resonates with broader themes in law & economics. His framework complements Ronald Coase’s explanation of how transaction costs shape institutional structures and Harold Demsetz’s account of how property rights evolve in response to economic incentives. It also parallels Friedrich Hayek’s description of spontaneous order. Hayek emphasized that market institutions coordinate dispersed knowledge without centralized planning. Alchian showed that competitive selection produces profit-oriented behavior without requiring conscious optimization. In both accounts, order emerges from decentralized processes rather than deliberate design.

Alchian’s contribution therefore occupies an important place in the intellectual foundations of law & economics. He explained how competitive selection connects individual decision-making—often guided by guesswork, intuition, and imperfect information—to systematic market outcomes.

Why Alchian Still Matters

Seventy-five years after its publication, “Uncertainty, Evolution, and Economic Theory” remains a powerful reminder that markets do not require perfect rationality to function effectively.

Economic models often assume profit maximization, but Alchian showed such behavior can emerge even when decision-makers lack the information required to calculate optimal strategies. Firms survive not because they solve optimization problems, but because they adapt—and because firms that fail to adapt eventually disappear.

In a world characterized by uncertainty, prediction is difficult and mistakes are inevitable. Yet markets possess a remarkable capacity to learn from those mistakes. Firms experiment, adjust, and compete. Successful practices spread; unsuccessful ones fade away.

Profit maximization, in this sense, is not the product of perfect planning or flawless calculation. It is the outcome of competition, adaptation, and survival.

Markets do not reward omniscience. They reward firms that learn fast enough to stay alive.

Further Reading

• Armen A. Alchian, “Uncertainty, Evolution, and Economic Theory,” Journal of Political Economy, Vol. 58, No. 3 (1950)
• Friedrich A. Hayek, “The Use of Knowledge in Society,” American Economic Review, Vol. 35, No. 4 (1945)
• Ronald H. Coase, “The Nature of the Firm,” Economica, Vol. 4, No. 16 (1937)
• Milton Friedman, “The Methodology of Positive Economics,” in “Essays in Positive Economics,” University of Chicago Press (1953)
• Henry G. Manne, “Mergers and the Market for Corporate Control,” Journal of Political Economy, Vol. 73, No. 2 (1965)
• Armen A. Alchian and Harold Demsetz, “Production, Information Costs, and Economic Organization,” American Economic Review, Vol. 62, No. 5 (1972)
• Harold Demsetz, “Toward a Theory of Property Rights,” American Economic Review, Vol. 57, No. 2 (1967)
• Henry N. Butler and Larry E. Ribstein, “Opting Out of Fiduciary Duties: A Response to the Anti-Contractarians,” Washington Law Review, Vol. 65, No. 1 (1990)

The post ‘Uncertainty, Evolution, and Economic Theory,’ by Armen Alchian appeared first on Truth on the Market.

A little context. In 2006, Boeing and Lockheed Martin combined their launch divisions into a joint venture called United Launch Alliance (ULA). That’s what I mean when I say they “combined.” It wasn’t technically a merger, and the distinction matters once you get deep into antitrust doctrine. For simplicity, though, think of it as a merger to monopoly. That sounds especially bad in an industry responsible for launching national-security payloads.

By every static measure, this is the textbook nightmare.

Readers know I think textbook metrics are often a bad starting point. So I was intrigued by a recent paper from Ruibing Su, Chenyu Yang, and Andrew Sweeting arguing that the deal was probably a good idea after all.

Their key insight—missing from the standard model—is that every launch teaches engineers something. The team flying its 50th mission knows things the team flying its first mission does not. Before the “merger,” that knowledge accumulation was duplicated across two separate programs, with separate engineering teams, supply chains, and production systems. That creates a clear possibility for efficiencies in a very specific sense—not just cost cutting, but faster learning.

The question is whether those efficiencies were large enough to outweigh the standard monopoly problem.

Su, Yang, and Sweeting argue yes. Using a structural model packed with all the usual modern industrial-organization bells and whistles, they analyze the space-launch industry from 1985 to 2024. Their conclusion: the learning synergies were real and large enough to offset the harms from increased market power.

They also find that when the government committed to multiyear block buys—instead of shopping for launches one mission at a time—costs fell dramatically. Forward-looking procurement gave the supplier stronger incentives to invest in improving future performance, even without a direct competitor applying pressure.

That’s a serious empirical result for one industry. The harder question is what regulators were supposed to do with that possibility at the time.

‘Too Speculative’ Usually Means ‘Too Hard’

Every merger in a learning-intensive industry raises the same questions. Will the merged firm keep innovating, or grow complacent? What happens to entry when the incumbent has a decade of accumulated know-how and the challenger starts from scratch? Will the dominant firm use its position to lock up critical inputs and foreclose rivals?

In practice, regulators mostly punt. Merger review tends to focus on static effects—prices, concentration levels, market shares—while treating “dynamic efficiencies” as something vague and speculative floating out in the ether. That bias consistently cuts in one direction: against mergers in the industries where dynamic effects may matter most.

To be fair, studying dynamic efficiency is hard.

Economists can build full dynamic models that try to capture learning, investment, and long-run competition. But even the people building those models openly acknowledge the tradeoffs. Ariel Pakes and Ulrich Doraszelski, in their handbook chapter on “Applied Dynamic Analysis in IO,” caution that the framework “delivers very little in the way of analytic results of applied interest.” Steven Berry and Giovanni Compiani, surveying the same literature, are not much more optimistic: “[T]he attempt to add dynamics may create enough compromises that the result is not better than the static model.”

So, yes, if the only way to incorporate dynamics into merger review is through a five-year structural-modeling project, regulators will mostly keep ignoring them.

You Can’t Buy Experience

Let’s work through some alternatives. Instead of going full structural industrial organization, let’s think in simple price-theory terms. Strip away the details for a moment. There’s a relatively straightforward way to capture many of the important dynamics here.

The key idea is that output today affects costs tomorrow. A firm that produces more accumulates something valuable over time.

That’s a reduced-form way to capture several different phenomena. The rocket example relies on learning by doing, but it’s hardly unique. Retailers benefit from denser distribution networks. Airlines build advantages through route density. Manufacturers refine tooling and supplier relationships through sustained production volumes in ways smaller rivals struggle to match. In platform markets, the equivalent is an installed base.

The engineering details differ. Don’t let that distract from the economics. In each case, output today builds a productive stock that lowers future costs.

Notice that this differs from standard capital accumulation. There, investment and production are separate decisions that compete for scarce resources. Cash spent on a new machine is cash not spent making products. Time spent building the next semiconductor fabrication plant is time not spent operating the current one.

The productive stock here works differently. You can’t simply write a check for a year of launch experience or a denser airline route map. The only way to build those assets is by producing. Output and investment therefore do not compete with each other. They are the same decision. Producing more today is investing more today.

To circle back to mergers specifically, this framework does not explain every dynamic-efficiency claim. Some dynamics involve patent races, product repositioning, entry timing, demand-side network effects, or strategic investments chosen separately from output. Those may require different tools.

Most merger-efficiency claims, though, are more mundane. They involve scale, experience, density, know-how, or installed bases. Take T-Mobile and Sprint in 2019. The companies argued that combining their spectrum holdings and cell sites would allow them to build a higher-quality 5G network than either could alone. That’s fundamentally a network-density claim. The “stock” is network capacity, and producing more output helps build it.

Competition Falls. Production Might Rise.

A merger involving this kind of productive stock pulls in two directions at once.

Start with the familiar concern: less competition. After a merger, the combined firm no longer worries as much about losing customers to its closest rival because, in a sense, it owns the rival too. Before common control, if Boeing’s launch division raised prices, some customers would switch to Lockheed Martin. After common control, many of those “lost” sales stay inside the same organization. The merged firm recaptures business it used to lose.

That weakens the incentive to fight for the marginal customer. The predictable result is higher markups and less output.

You can think of this graphically. Common control rotates the marginal-benefit curve inward. Before the merger, winning a launch contract from your rival is a real gain. Afterward, winning business from another division of your own company is partly just stealing from yourself. The merged firm internalizes cross-product diversion, so the marginal benefit of expanding output falls. Quantity falls with it.

That’s the standard merger story.

But this framework introduces a competing force. If producing today builds productive capability for tomorrow, then every additional launch also generates experience, operational knowledge, and learning by doing. Those gains lower the effective marginal cost of future production.

Push that effect far enough, and the supply curve can actually slope downward. The firm still pays the immediate cost of the launch, but it is also effectively purchasing future cost reductions.

Under that logic, consolidation can increase the incentive to produce. A combined firm captures more of the returns from building productive capability, so producing today becomes more valuable. Larger production volumes accelerate learning. Greater internal coordination makes it more likely the resulting efficiencies will actually materialize, instead of being duplicated across separate organizations and supply chains.

Where the Textbook Graph Starts Misbehaving

To see both forces in a single picture, we need to think about the marginal cost of building this productive stock. How does learning change the firm’s marginal-cost curve? What’s the “price” of experience?

Physical capital has a rental rate—the amount you would pay each period to use it. Dale Jorgenson called this the “user cost of capital”: the implicit rental value of an asset the firm owns rather than leases. Experience and operating capability have a similar structure, even if nobody literally sends the firm a bill.

What’s the value of owning that stock? Each unit of output today adds to it and lowers future costs. The present value of those future savings is effectively the rental value of the stock. Producing one additional launch is therefore cheaper than the accounting cost suggests, because part of the expenditure is really purchasing future productivity improvements.

Call the normal accounting cost per launch the static marginal cost. Call the static cost minus this implicit “rental rebate” the dynamic marginal cost, net of rental. Static marginal cost is what shows up on the invoice. Dynamic marginal cost is what actually drives the firm’s production decision once it accounts for learning effects.

Pre-merger, in the top panel, the firm faces residual demand for its own product and a standard single-product marginal-revenue curve. Static marginal cost sits at 3. Dynamic marginal cost, net of the rental rebate, sits closer to 2. The firm produces where marginal revenue intersects dynamic marginal cost, and we can read off price and quantity from there.

Post-merger, two things change simultaneously.

First, marginal revenue rotates inward to “owned-product marginal revenue.” This is the standard diversion effect in picture form. If that were the only change, the merger would mechanically reduce output and raise prices.

Second, dynamic marginal cost rotates further downward to “post-merger dynamic marginal cost.” The combined firm captures more of the value from producing today. There is less duplication across engineering organizations. Higher combined output pushes the firm farther up the learning curve—or, equivalently, farther down the cost curve. More of the future cost reduction stays inside the firm instead of leaking to a competitor. The rental rebate grows larger, so the firm’s perceived marginal cost falls.

In the figure, the dynamic-marginal-cost shift dominates.

The picture also clarifies when the result flips. If the learning curve is relatively flat, dynamic marginal cost barely moves, and the marginal-revenue rotation wins. Quantity falls, prices rise…cats and dogs living together, mass hysteria.

In one sense, this is all almost tautological. If the force pushing quantity upward exceeds the force pushing it downward, output rises. Not exactly a profound insight. The value of the framework is that it helps us think systematically about when each force dominates.

That means thinking more carefully about how learning actually works. If experience spills over heavily to competitors, a merger does little to change how much of the rental rebate the firm captures. Dynamic marginal cost barely moves. The industry may already exhibit a downward-sloping cost structure even if no individual firm fully internalizes it. The outcome then depends on how steep the learning curve is, how private the learning remains, and how durable the resulting advantage proves to be. The theory helps organize the investigation.

It also pushes us to think about alternatives. A merger is one way to increase the returns from producing today, but it is hardly the only one.

Su, Yang, and Sweeting find that costs fell sharply when the government shifted from buying launches individually to committing to multiyear block purchases. That policy effectively guaranteed suppliers a stream of future orders. The mechanism is the same one illustrated in the figure. A larger committed order book increases the value of investing in learning today because the firm expects to produce the future launches that benefit from those improvements. The rental rebate gets larger. Dynamic marginal cost rotates downward.

Not Every Market Gets a SpaceX

The figure shows when a merger is most likely to generate genuine cost savings: when learning curves are steep, experience remains private, and the productive stock is durable.

We should be careful, though. Those same conditions also make entry harder. A new entrant starts with zero accumulated experience, while the merged firm sits on years of operational knowledge. If learning is steep, the cost gap will be large. If learning is private, entrants cannot easily catch up by poaching engineers. If the stock is durable, the advantage persists.

It is tempting to treat those entry barriers as the offsetting “cost” of the efficiency and simply net the two effects against each other. I’ve argued before that this gets the analysis backwards. Achieving scale is not an antitrust harm. Preventing rivals from achieving scale through better products or lower prices is not an antitrust harm either. That is what competition on the merits looks like.

The same logic applies to accumulated learning. If ULA’s experience made it harder for entrants to win contracts, that productive stock was doing exactly what productive stocks are supposed to do. The merger accelerated the accumulation of that stock by combining output. It did not do so by sabotaging competitors.

The actual antitrust harm must come from some separate exclusionary mechanism. The restraint is the problem, not the stock itself. Foreclosing key inputs. Locking up distribution. Raising rivals’ costs through means unrelated to the merged firm’s own productivity.

As it happens, ex post, entry turned out to be possible. SpaceX arrived and detonated the market structure so completely that much of this now feels almost quaint. Not every market gets a SpaceX, though.

Still, we have tools for thinking carefully about these problems. This kind of simplified framework does not replace a full structural model, but it gives you a way to reason through the question before building one—and a way to understand what the model is actually doing once you have.

Most importantly, it forces specificity. Which curves are shifting? What mechanism is moving them? Which effects are observable ex ante, and which only become visible after the fact?

If we can answer those questions clearly, we are already a long way toward understanding the market.

The post In Space, No One Can See Your HHI appeared first on Truth on the Market.

The carrier’s disappearance has transformed the JetBlue-Spirit merger litigation from an ordinary postmortem into a test case for how antitrust law should treat distressed challengers in concentrated network industries.

Protecting Competition, Minus the Competitor

The starting point is Judge William Young’s January 2024 decision enjoining JetBlue’s acquisition of Spirit under Section 7 of the Clayton Act after the U.S. Justice Department (DOJ) challenged the deal as anticompetitive. (The DOJ has jurisdiction over antitrust review of domestic airline mergers.)

In a Federalist Society discussion of the failed merger, I argued the case reflected a continuing skepticism in merger enforcement toward efficiencies, particularly claims that a merger could strengthen competition against larger incumbents. Events since then have sharpened a related critique: the court appears to have underweighted “out-of-market” efficiencies—benefits that would have accrued across a broader set of city-pair markets, even if travelers on certain overlapping routes lost the specific option of flying Spirit.

The critique is not that the route-level harms were imaginary. The government’s case followed a familiar Section 7 framework. Spirit was an ultra-low-cost carrier whose market entry often pushed fares downward. JetBlue, by contrast, operated as a higher-cost, higher-quality airline and planned to retrofit Spirit aircraft into the JetBlue product. In United States v. JetBlue Airways Corp., Judge Young concluded that, on certain routes, Spirit customers likely would face higher prices or fewer low-fare options if Spirit disappeared into JetBlue.

The objection, instead, is that the opinion treated those route-specific losses as dispositive, even though the merger’s broader competitive effect may have been to create a larger, more relevant JetBlue capable of challenging American, Delta, United, and Southwest—the “big four” U.S. airlines by market share. The court itself acknowledged that a stronger JetBlue would place competitive pressure on larger carriers and extend JetBlue’s higher-quality service to more customers. It nonetheless concluded those broader benefits could not justify the transaction if the merger substantially lessened competition in any relevant market.

What Happens When the Maverick Crashes?

Spirit’s subsequent collapse has made that reasoning look far less secure. After the ruling, Spirit entered bankruptcy, struggled to restructure, and ultimately ceased operations. The Associated Press reported that Spirit shut down after 34 years, ending the carrier most associated with rock-bottom base fares. Other accounts highlighted the fallout for passengers and the loss of roughly 17,000 jobs.

Christopher Gowen argued in a Bloomberg Law essay that Spirit’s demise should serve as a cautionary tale for merger review. In his telling, the government succeeded in preserving an independent Spirit in legal theory, but not in market reality.

That is an uncomfortable lesson for merger law. A court can block a merger to preserve a maverick competitor—a disruptive firm that constrains rivals through aggressive pricing or innovation. But if the maverick is too weak to survive on its own, the injunction may leave consumers with neither the original firm nor the stronger challenger the merger might have created.

The legal implications extend beyond airlines. Merger analysis depends on a realistic “counterfactual”—a prediction of what competition would look like if the deal does not occur. A static, route-by-route snapshot can overstate the competitive significance of a distressed carrier that is shrinking, financially unstable, or unable to secure aircraft, labor, capital, or airport access on workable terms.

To be sure, courts are understandably cautious here. The failing-firm defense—which permits an otherwise anticompetitive merger when the target company is effectively doomed—is notoriously difficult to establish. Judges do not want firms casually invoking financial distress to justify consolidation.

Still, there is middle ground between a formal failing-firm defense and treating a distressed company as though it were a durable, independent competitive force. In a network industry like air travel, a weakened-competitor analysis should ask whether the allegedly preserved competition is likely to survive long enough, and at sufficient scale, to constrain incumbents. JetBlue-Spirit may become the clearest recent example of what happens when courts answer that question too optimistically.

The Airlines Deregulated. The Airports Didn’t.

The post-Spirit U.S. airline industry presents a paradox. On paper, the market still includes plenty of names: American, Delta, United, Southwest, JetBlue, Alaska-Hawaiian, Frontier, Allegiant-Sun Country, Breeze, Avelo, and others. In practice, network effects, airport access, frequent-flyer programs, corporate contracts, and hub dominance give the largest carriers durable advantages.

The DOJ’s own recent account notes that American, Delta, Southwest, and United control roughly three-quarters of U.S. domestic markets, and that the largest airlines have absorbed dozens of rivals since deregulation. In a November 2025 speech, Deputy Assistant Attorney General Dina Kallay described airline competition as a core consumer issue and linked current concerns to the industry’s long history of consolidation.

Hub-and-spoke competition sits at the center of that market power. American, Delta, and United operate dense hub systems that let them offer business travelers frequent departures, extensive connections, lounges, loyalty perks, and corporate-contracting arrangements that point-to-point or leisure-focused airlines struggle to match. Those hubs also facilitate price discrimination. Competition may be fierce on major trunk routes, while passengers traveling to or from fortress hubs face fewer meaningful choices.

Southwest historically constrained that system through point-to-point service. More recently, though, Southwest has moved closer to the legacy-carrier model, relying more heavily on sophisticated revenue management, loyalty-program monetization, and ancillary-fee strategies. Spirit imposed a different kind of constraint. It was not a network-based rival to the legacy carriers, but an ultra-low-cost fare anchor. The so-called “Spirit effect” often disciplined prices even where Spirit’s market share remained modest.

Blocking the JetBlue-Spirit merger may have strengthened the Big Three’s position in two ways. First, it eliminated JetBlue’s most plausible path to rapid scale. Organic growth is slow in an industry constrained by aircraft shortages, engine problems, pilot supply, and limited airport access. Spirit’s Airbus fleet offered JetBlue a ready-made path to greater national relevance. Without Spirit, JetBlue remains an important but subscale competitor—strong in parts of the East Coast, but lacking the network depth of American, Delta, and United.

Second, Spirit’s liquidation fragmented its competitive assets. Aircraft, gates, slots, employees, and routes can all be redistributed. But the ultra-low-cost-carrier business model, network structure, brand identity, and pricing discipline do not automatically survive asset sales. The court’s hoped-for replacement through new market entry must now occur piecemeal, through selective route expansion and asset transfers, rather than through a functioning integrated airline.

A related point deserves emphasis: some of the most durable barriers to airline competition arise not from airline conduct alone, but from legal and regulatory limits on airport capacity. At the most congested airports, the Federal Aviation Administration (FAA) uses runway slots to ration scheduled operations. FAA slot-administration materials identify John F. Kennedy International Airport, LaGuardia Airport, and Reagan National Airport as slot-controlled airports, while describing schedule-facilitation processes at O’Hare International Airport, Los Angeles International Airport, Newark Liberty International Airport, and San Francisco International Airport.

Those restrictions may be justified by congestion, runway limits, air-traffic-control constraints, and delay externalities. They also turn airport access into a scarce asset. Scarcity predictably favors incumbents that already control slots, gates, terminal space, and commercially viable operating positions. New entry, therefore, is not simply a matter of an airline deciding to add service. It often requires access to a scarce bundle of slots, gates, takeoff and landing rights, ground facilities, and commercially usable flight times.

The deeper problem is that expanding airport capacity is legally and politically difficult. Major airport expansions, new runways, terminal projects, and new airport construction all trigger environmental review under the National Environmental Policy Act (NEPA) and related environmental statutes. FAA guidance states that environmental review must be completed before covered airport projects can begin. Local zoning rules, land-use controls, noise restrictions, community opposition, airport curfews, emissions review, surface-transportation bottlenecks, and funding constraints can further delay or block expansion.

The Government Accountability Office (GAO) has long recognized that airport-access barriers include physical constraints such as slots, gates, and noise restrictions. In key metropolitan areas, the competitive question is therefore not merely whether Frontier, Breeze, Allegiant-Sun Country, JetBlue, or Alaska-Hawaiian want to enter a market. The real question is whether they can obtain the airport access necessary to discipline incumbent hub carriers on commercially meaningful terms.

If You Can’t Merge, Collaborate

Recent industry developments illustrate both mitigation and risk. Rivals are already moving into some former Spirit markets. The Wall Street Journal reported that low-cost competitors are carving up Spirit’s former routes and airport slots, with Breeze, Frontier, and Allegiant seeking growth opportunities while larger airlines pursue valuable airport assets.

That redeployment will soften Spirit’s disappearance in some leisure markets and smaller airports. But selective entry is not the same thing as systemwide replacement. An airline that adds one or two former Spirit routes does not necessarily recreate Spirit’s low-cost network, pricing discipline, or willingness to stimulate demand through ultra-low base fares.

The same dynamic appears in new consolidation among smaller carriers. Earlier this month, Allegiant announced it had completed its acquisition of Sun Country, creating a larger leisure-focused airline serving nearly 175 cities with a combined fleet of 195 aircraft. The deal reduces the number of independent low-cost and leisure-focused airlines. At the same time, it may create a stronger scaled competitor in the very segment best positioned to backfill some of Spirit’s abandoned markets.

That is the recurring airline-antitrust tradeoff. A merger among smaller carriers may eliminate competition on certain routes while increasing the merged firm’s ability to challenge larger network airlines across many others. Whether courts and agencies should block or permit that tradeoff depends on the counterfactual—and on whether the acquired capacity would otherwise remain a viable competitive force.

The Alaska Airlines-Hawaiian Airlines integration, announced last month, reflects a more permissive regulatory approach. The airlines described their shared passenger-service system as “a significant integration milestone that provides guests flying with Alaska Airlines and Hawaiian Airlines a more seamless and consistent travel experience from booking to boarding across a growing global network.” The U.S. Department of Transportation (DOT) allowed the transaction to proceed after securing binding public-interest commitments. According to the department’s announcement, those commitments covered rewards programs, essential flight service, rural access, Honolulu hub access, family seating, and military benefits.

The contrast with JetBlue-Spirit is revealing. Alaska-Hawaiian involved fewer direct overlaps and less elimination of an ultra-low-cost fare constraint. It also rested on a clearer network-complementarity rationale—one regulators could condition and supervise without effectively destroying the transaction. Future airline mergers will likely be framed in similar terms: limited overlap, complementary networks, service preservation, loyalty-program protections, and concrete commitments designed to make claimed benefits verifiable.

Joint ventures and looser collaborations may become even more important than outright mergers. JetBlue and United’s “Blue Sky” collaboration, for example, links loyalty programs and customer benefits while emphasizing that the airlines will continue independently managing and pricing their routes, frequencies, and promotions.

Blue Sky, Red Flags

The BlueSky collaboration’s careful drafting is no accident. The American Airlines-JetBlue Northeast Alliance was enjoined because the DOJ and the courts viewed it as a de facto merger in the Northeast—a coordination arrangement that reduced the airlines’ incentives to compete independently on routes, schedules, and capacity. In her recent airline-competition speech, the Deputy Assistant Attorney General Kallay described the Northeast Alliance as a cautionary example and suggested that domestic “metal-neutral” arrangements—agreements that make airlines financially indifferent about which carrier actually transports a passenger—will face intense scrutiny.

The political and enforcement environment reinforces that caution. In July 2025, Sen. Richard Blumenthal (D-Conn.) sent a letter to the chief executives of United and JetBlue questioning whether the Blue Sky partnership resembles the failed Northeast Alliance. Blumenthal pointed to reciprocal loyalty-program features, airport-infrastructure implications, and potential effects on corporate accounts.

The letter itself carries no legal significance. Still, it reflects the concerns likely to shape future government oversight. Limited loyalty reciprocity, interline cooperation, or customer-facing benefits that stop short of coordinating capacity, schedules, or pricing may survive antitrust review. Deeper domestic joint ventures that pool revenue, allocate markets, coordinate frequencies, or create metal-neutrality are far more likely to draw DOJ challenge. The more a collaboration resembles a merger without Hart-Scott-Rodino review, the more skeptical courts are likely to become.

The DOJ and DOT have also made clear that airline competition remains an active enforcement priority. In October 2024, the agencies launched a broad public inquiry into the state of competition in air travel. The inquiry sought information on consolidation, exclusionary conduct, airport access, aircraft manufacturing, sales channels, pricing, rewards programs, and labor issues.

Dina Kallay’s 2025 remarks likewise emphasized airline competition, but with a notable twist: the need to address not only private restraints, but also public barriers to entry. That distinction matters. A serious airline-competition agenda cannot consist solely of blocking private mergers. It must also examine whether government-created scarcity—including slot rules, airport-access limits, infrastructure bottlenecks, and regulatory delays—helps entrench incumbent market power.

The Next Airline Deals Will Look Different

What comes next? The most likely near-term transactions involve acquisitions of Spirit assets: slots, gates, aircraft, airport facilities, route authorities, and personnel. Where those assets are scarce, the DOJ and DOT should generally prefer distributing them to non-dominant carriers. Sales of Spirit assets at LaGuardia, Newark, Fort Lauderdale-Hollywood International Airport, Orlando International Airport, or other constrained airports to American, Delta, or United would raise greater concerns than sales to Frontier, Allegiant-Sun Country, Breeze, JetBlue, or Alaska-Hawaiian.

The competitive value of those assets lies in enabling entry and expansion by challengers. Asset purchases are usually easier to clear than mergers. That changes, however, when the transaction strengthens fortress hubs or removes scarce airport access from smaller rivals.

A Frontier-led acquisition of Spirit remnants—or further consolidation among ultra-low-cost and leisure carriers—would present a more complicated case. Before Spirit’s collapse, a Frontier-Spirit merger would have been a straightforward horizontal merger between two closely competing ultra-low-cost carriers. In the wake of Spirit’s bankruptcy, a transaction involving scattered assets rather than an operating airline becomes easier to defend. Regulators would likely ask whether the buyer is preserving capacity that otherwise would disappear, whether meaningful overlaps remain, and whether the deal restores some degree of low-fare discipline. Courts may also view those arguments more sympathetically after JetBlue-Spirit, because the liquidation counterfactual is no longer hypothetical.

A future JetBlue merger would face skepticism, but not automatic condemnation. JetBlue’s strongest argument remains scale: without more aircraft, slots, and network breadth, it cannot seriously challenge the Big Three. Its biggest problem is its recent antitrust history. The Northeast Alliance was condemned as anticompetitive, while the Spirit merger was blocked because JetBlue planned to replace Spirit’s ultra-low-cost model with a higher-cost, higher-fare product.

Any future JetBlue deal would therefore need to look very different. JetBlue would likely need to show limited route overlap, preservation or expansion of low-fare capacity, no elimination of a maverick business model, and perhaps divestitures to low-cost entrants. It would also need to present a more concrete and measurable out-of-market efficiencies case than it did in the Spirit litigation.

A merger involving one of the Big Three legacy airlines would be the hardest case of all. Transportation Secretary Sean Duffy has suggested there may be “room for some mergers” in aviation, while emphasizing that deals would be reviewed case by case and larger airlines might need to divest assets to avoid excessive concentration. That reflects a plausible policy middle ground: openness to consolidation that creates stronger challengers, combined with skepticism toward deals that deepen hub dominance or transfer scarce airport access to incumbents.

A Big Three acquisition of a meaningful low-cost or regional competitor would almost certainly trigger DOJ opposition absent a strong failing-firm or failing-division justification, substantial divestitures, and enforceable service commitments.

The most likely equilibrium is selective consolidation paired with strict limits on coordination. The DOJ and DOT may tolerate mergers among smaller or complementary carriers when the result plausibly creates a stronger challenger, particularly if the parties accept conditions relating to routes, slots, loyalty programs, and service commitments. By contrast, regulators will likely resist transactions that hand scarce airport access to dominant incumbents or create metal-neutral domestic coordination.

Courts, meanwhile, will continue applying Section 7 market by market. But merging parties are likely to press harder on failing-firm, weakened-competitor, and out-of-market efficiencies arguments. The unresolved question is whether courts will adapt merger doctrine to account for an uncomfortable possibility: blocking a merger may sometimes accelerate the disappearance of the very competition antitrust law seeks to preserve.

When Antitrust Wins and Competition Loses

Spirit’s disappearance should not be read as proof that all airline mergers are beneficial. It should instead underscore how unusually dependent airline antitrust is on counterfactual assumptions. A static snapshot can mislead in an industry where aircraft shortages, fuel-price shocks, airport access constraints, loyalty ecosystems, and bankruptcy risk can rapidly reshape competition.

If the post-Spirit market ultimately leaves the Big Three more secure, the antitrust failure was not simply that JetBlue and Spirit were kept apart. It was that the legal system protected route-level competition without preserving the only carrier supplying much of that competitive pressure in the first place.

Over the longer term, antitrust enforcement alone cannot solve a scarcity problem created partly by public regulation. A more durable procompetitive agenda would pair merger and conduct enforcement with regulatory and legislative reforms that expand airport capacity, accelerate review of capacity-enhancing projects, rationalize slot-allocation rules, discourage slot hoarding, make divested slots and gates available to genuine entrants, and reduce unnecessary legal barriers to new airports and runways.

Future airline antitrust should therefore ask not only whether a deal eliminates a competitor, but whether blocking the deal realistically preserves competition—and whether public policy has made entry so difficult that incumbents remain protected even when merger enforcement formally “wins.”

In the end, Spirit’s collapse may become the uncomfortable reminder that antitrust can preserve competition on paper while watching it disappear at the gate.

The post The Case of the Vanishing Competitor appeared first on Truth on the Market.

Gov. Gavin Newsom signed Executive Order N-6-26 today, setting California’s workforce agencies in motion on directives involving research reviews, revisions to the state’s Worker Adjustment and Retraining Notification (WARN) Act, studies of new safety-net programs, a review of collective bargaining frameworks, an employment dashboard, and—near the end—a study of programs that would redirect artificial intelligence (AI) company revenues toward state-selected applications.

The animating concern is AI-driven labor disruption. Newsom’s order treats that disruption as sufficiently imminent to justify building new regulatory infrastructure across California’s workforce apparatus.

The empirical literature suggests that concern is running well ahead of the evidence. That includes a literature review published by the International Center for Law & Economics (ICLE) that I co-authored with Kristian Stout.

The Labor Panic Is Outrunning the Data

Generative AI has spread faster than any comparable technology. By late 2024, nearly 40% of U.S. adults ages 18-64 reported using AI tools—a pace that exceeded personal computers and the internet at comparable stages of adoption, according to a 2025 National Bureau of Economic Research (NBER) working paper by Alexander Bick, Adam Blandin, and David Deming.

The productivity gains are real and remarkably consistent across settings. Controlled studies show GitHub Copilot users complete coding tasks 55.8% faster. A randomized experiment by Shakked Noy and Whitney Zhang found ChatGPT reduced professional-writing completion times by 40% while improving quality scores by 18%. A Fortune 500 customer-support deployment produced a 15% average productivity gain, including a 36% gain for workers in the bottom skill quintile.

Those gains have not produced aggregate job destruction. The Budget Lab at Yale found no clear correlation between AI exposure and unemployment through August 2025. Jonathan Hartley, Filip Jolevski, Vitor Melo, and Brendan Moore found that, by December 2025, 35.9% of U.S. workers were using generative AI, with small positive wage effects and no statistically significant declines in job openings or aggregate employment in exposed occupations. Anders Humlum and Emilie Vestergaard linked survey-reported ChatGPT adoption to Danish administrative records across 11 occupations and found essentially no effects on earnings or hours through 2024.

Employment effects do appear, but in a narrower and more specific place: entry-level positions. Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen found that workers ages 22-25 in highly AI-exposed occupations experienced employment declines of roughly 16% relative to trend following ChatGPT’s release, while employment among senior workers held steady. Bouke Klein Teeselink found a similar pattern in UK data, with losses concentrated among junior roles, advertised salaries in exposed occupations declining, and average firm-level compensation rising as firms shed lower-paid entry positions.

Across these studies, the pattern is task reallocation and adjustment at the start of workers’ careers—not mass displacement. That distinction matters. WARN Act expansions, new safety-net programs, and revenue levies on AI companies are calibrated for broad labor-market disruption. The available evidence describes something much narrower.

Before New Mandates, Try Using the Old Ones

One of the executive order’s “whereas” clauses makes an admission that sits awkwardly with what follows. The order states that “California already has robust worker protection laws that apply to firms adopting emerging technologies,” and that existing programs like Work Sharing “are currently underutilized.”

Work Sharing allows employers to reduce employees’ hours instead of conducting layoffs, while unemployment insurance partially compensates workers for lost income. The program is designed for exactly the sort of gradual, partial labor adjustment that AI appears to be producing. If employers are not using it, the more likely explanation is lack of awareness and administrative friction—not a gap in the law.

Directive 3(b) sensibly responds to that diagnosis by calling for a plan to expand awareness of and enrollment in Work Sharing. That provision, though, sits alongside 13 others that would build new regulatory mechanisms atop programs the order itself acknowledges are already underused.

Before California imposes new mandates, it should answer a more basic question: Why are existing tools underutilized, and could better outreach address the problem without expanding the regulatory state?

Regulating AI Like a Factory Shutdown

Directive 2 instructs the Labor and Workforce Development Agency to recommend revisions to California’s WARN Act to make it “responsive to, and effectively provide[] early warning data on, emerging industry trends.”

California’s WARN Act addresses discrete mass-layoff events. If a firm closes a facility or lays off 50 or more workers, the law requires 60 days’ advance notice so employees have time to prepare.

The disruption described in the executive order is different in kind. The concern is not factory closures or sudden mass layoffs, but individual workers in AI-exposed occupations gradually losing hours or positions as firms reorganize tasks over time. Extending WARN-style notification requirements to that sort of reorganization could mean requiring advance notice whenever a firm reallocates work from employees to AI tools.

The compliance burden from such a regime would fall especially hard on smaller California employers. More fundamentally, the evidentiary case for expansion remains thin. The available data shows entry-level hiring reductions and task reallocation—not the plant closures and mass layoffs WARN was designed to address. Expanding the law to cover AI-driven labor adjustment would require rewriting the statute’s underlying logic.

That matters because California is home to 33 of the world’s top 50 private AI companies. The compliance costs from such a rewrite would fall directly on the industry the executive order’s own preamble identifies as a strategic asset.

The State’s New Idea: Tax AI, Then Pick Winners

Directive 9 asks the Government Operations Agency to recommend options, including “voluntary or mandatory programs that direct a portion of revenue generated by AI companies to support beneficial deployments of AI that otherwise would not be pursued based solely on market incentives.”

Economically, that amounts to a proposal to study a levy on AI-company revenues, with the proceeds directed toward state-selected applications. The proposal rests on two claims: first, that AI markets systematically underprovide socially beneficial uses; and second, that California officials can identify and fund those uses more effectively than private actors can.

The first claim requires identifying a concrete market failure—a situation in which the social benefits of an AI application genuinely exceed what any private buyer would pay for it. The executive order asserts such a failure without establishing one. If AI tools that improve government services, expand health care access, or address climate challenges are genuinely valuable, there are—and will be—actual buyers for them.

The Organisation for Economic Co-operation and Development’s (OECD) 2025 cross-country survey found that 31% of small and medium-sized enterprises had already adopted generative AI by 2024 without state direction, with most citing improved performance and reduced workloads. Government agencies are themselves buyers. The executive order’s own recitals note that California has already signed memoranda of understanding with NVIDIA, Adobe, Google, IBM, and Microsoft for AI-literacy programs. Those are voluntary market arrangements, not revenue levies.

The second claim runs into a more fundamental problem: information about which AI applications create the most value is dispersed across millions of firms, consumers, and developers. A mandatory revenue diversion would replace that distributed information with the preferences of the agencies allocating the funds. There is little reason to assume those preferences will reliably track actual social value.

The proposal also raises a straightforward competitive concern. If California requires AI companies to redirect revenues toward state-directed purposes, it creates incentives for those firms to incorporate or expand operations elsewhere. The executive order itself describes California’s AI dominance as a strategic asset. Strategic assets, though, are portable. Policies that structurally disadvantage AI firms operating in the state should clear a high evidentiary bar before adoption. This proposal does not.

Some Parts of the Order Actually Fit the Evidence

Several provisions in the executive order are better matched to the available evidence.

Directive 7 calls for an employment dashboard using unemployment-insurance data to track AI’s effects across sectors in real time. That is exactly the kind of infrastructure policymakers need before committing to larger interventions. One genuine constraint in the current debate is the lack of reliable, real-time labor-market data on AI’s effects. Building monitoring capacity now would allow future policy decisions to respond to actual disruption, rather than speculative forecasts.

Directive 5’s workforce-training review also addresses a documented productivity complement. Controlled experiments consistently find that AI’s productivity gains are largest when workers know how to evaluate AI outputs critically—when to rely on them and when to override them. Fabrizio Dell’Acqua and colleagues found that Boston Consulting Group consultants working within AI’s capability boundaries improved performance substantially, while consultants using AI for tasks just beyond those boundaries performed worse because they overrelied on plausible but incorrect outputs. Training workers to recognize that distinction has real value. California’s community college system, which serves more than 2.1 million people each year, along with university extension programs, is well-positioned to provide that training at scale.

Directive 11, which instructs the Governor’s Office of Business and Economic Development (GO-Biz) and the California Office of the Small Business Advocate (CalOSBA) to support small-business AI adoption, likewise targets a concrete barrier. The OECD found that 50% of non-adopting small businesses cited insufficient internal expertise as a reason for not adopting AI tools. Technical assistance and outreach can reduce that barrier at relatively low cost, without imposing additional compliance burdens on the firms receiving help.

Regulate the AI Economy You Have, Not the One You Fear

Executive Order N-6-26 contains provisions calibrated to two very different visions of AI’s labor-market effects.

The data-gathering and training provisions align with the adjustments the evidence actually documents: entry-level disruption, task reallocation, and a growing need for workers to build AI literacy. By contrast, the proposed WARN Act revisions and revenue-levy study are calibrated to a mass-displacement scenario the evidence does not yet support.

That distinction matters because the two categories carry very different cost profiles. Building an employment dashboard and expanding community-college AI training impose relatively low costs while generating useful information and human capital. WARN Act expansions and mandatory revenue diversions, by contrast, would impose real compliance costs on California employers today based on a disruption that may never arrive in the form the order anticipates.

The labor adjustment currently underway is narrower and more specific. AI appears to be compressing career ladders in some occupations, reducing demand for the discrete tasks that traditionally served as professional entry points, while leaving senior employment largely intact. That is a real problem for younger workers trying to build skills, experience, and credentials. Better on-ramps to midlevel work, expanded AI-literacy training, and redesigned career pathways address that problem directly. Mandatory notice requirements and revenue levies do not.

California also has a concrete interest in getting the calibration right. The state’s AI sector—which includes 33 of the world’s top 50 private AI companies—is producing the productivity gains the executive order seeks to distribute more broadly. Regulatory costs that push those firms toward lower-cost jurisdictions would not redistribute AI’s benefits to California workers. They would relocate them.

The provisions worth enacting now are the ones that build information and workforce capacity: the employment dashboard, the workforce-training review, and small-business outreach. The provisions worth deferring are those premised on speculative mass displacement. If disruption at that scale eventually materializes, the data infrastructure created by the order will reveal it, and California can respond with evidence in hand, rather than panic in search of a justification.

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Most of the current action has to do with consumer protection. That’s par for the course, really. Apart from issuing the ill-fated noncompete rule, since vacated—comments here if anyone wants a recap—FTC rulemaking has been almost wholly on the consumer-protection side for as long as there’s been FTC rulemaking and a consumer-protection side to the agency. “Unfair or deceptive acts and practices,” or the FTC’s UDAP authority, were added to Section 5 in 1938 via the Wheeler-Lea Amendments, so it’s been a while.

For one thing, there’s no controversy over whether Congress has granted the commission substantive rulemaking authority over consumer-protection matters. It has—both a general authority under Section 18 of the FTC Act to prescribe rules against specific acts or practices that violate Section 5’s UDAP prong, and authority under various statutes that charge the FTC with adopting and enforcing particular restrictions addressing specific issues or practices. These include, among others, the Fairness to Contact Lens Consumers Act, the Children’s Online Privacy Protection Act, and the Fair Credit Reporting Act, later amended by the Dodd-Frank Wall Street Reform and Consumer Protection Act, under which most—but not all—regulatory authority shifted to the Consumer Financial Protection Bureau.

But some competition-adjacent rulemaking remains under the Hart-Scott-Rodino Antitrust Improvements Act of 1976 (HSR Act).

Rulemaking under the HSR Act is not exactly competition rulemaking. It doesn’t directly regulate which mergers are lawful and which are not. Still, it regulates the process by which mergers are screened and imposes affirmative obligations on firms contemplating mergers and acquisitions—at least for transactions above the filing threshold. Hence, these are competition-adjacent rules, and HSR rulemaking is competition-ish.

HSR Rulemaking Gets a Do-Over

That brings us to a joint FTC/Department of Justice (DOJ) request for information (RFI) “on the effectiveness of the Hart-Scott-Rodino Antitrust Improvements Act (‘HSR Act’)’s premerger reporting requirements.” That’s about as preliminary as rulemaking gets, as it’s neither a notice of proposed rulemaking (NPRM) nor even an advance notice of proposed rulemaking (ANPRM)—a more preliminary stage required by statute for the FTC’s consumer-protection rulemaking under Section 18 of the FTC Act, but not a stage exclusive to Section 18 rulemaking or the FTC.

Well, it is and it isn’t all that preliminary. As the RFI recounts, the commission published an NPRM on revisions to the HSR reporting requirements and form in June 2023, followed by a final rule in November 2024, both under the energetic—if not so process-oriented, or consumer-welfare-oriented—leadership of former Chair Lina M. Khan. Side note: yes, it’s an FTC/DOJ rule, but the FTC takes the lead on HSR rule revisions, in consultation with DOJ. HSR NPRMs and final rules are published in the Federal Register by the FTC “with the concurrence of the Assistant Attorney General, Antitrust Division, Department of Justice.” As per Congress, this is the way.`

As I pointed out in another post, there was nothing odd, in the abstract, about updating the HSR reporting requirements—something the FTC, with DOJ concurrence, had done many times before. Indeed, there might have been a rough consensus across antitrust law and economics that some updates to the form and process were warranted.

At the same time, the 2023 NPRM shot for the moon, and then some. Some of the proposals were downright ludicrous. As I said at the time: 

In a nutshell, the proposed revisions are controversial because they promise to make pre-merger filing more cumbersome and, not incidentally, more costly, and it’s not at all clear what the payoff is likely to be. 

The International Center for Law & Economics’ (ICLE) formal comments on the NPRM’s overreaching motley provide more detail, and ICLE was hardly alone in its critique (see also Gus Hurwitz, Alden Abbott, the Global Antitrust Institute, Bilal Sayyed on behalf of TechFreedom, the Information Technology and Innovation Foundation, and the U.S. Chamber of Commerce.)

The final rule was at least somewhat responsive to comments and, no doubt, to haggling among the commissioners. The latter was noted in concurring statements by then-commissioners Melissa Holyoak and Andrew Ferguson, with Holyoak providing a useful summary table of deletions and other adjustments made to the initial proposal. To be fair, the final rule was considerably better than the NPRM.

Questions remained, however. Among them was a quick-and-dirty—and dubious—cost-benefit analysis. The U.S. Chamber of Commerce, among others, brought suit successfully, and on Feb. 12, 2026, Judge Jeremy D. Kernodle of the U.S. District Court for the Eastern District of Texas issued an order of vacatur. Kernodle found that the rule exceeded the FTC’s statutory authority because the commission had failed to show that “the Rule’s claimed benefits will ‘reasonably outweigh’ its significant and widespread costs,” and that the rule was arbitrary and capricious for “much the same reason.” The 5th U.S. Circuit Court of Appeals subsequently denied the FTC’s motion for a stay pending appeal. 

So now we’re back to the prior version of the rule, including the pre-2025 form and, not incidentally, the RFI. Inquiry seems an appropriate first step, and the RFI reasonably proposes various topics of interest and poses various reasonable questions. Both Ferguson and Holyoak had remarked on lingering doubts about certain provisions in the now-vacated final rule. Taking a beat and asking additional questions makes more sense than rushing to another NPRM.

The RFI notes that the updated HSR form was in use for more than a year—from the final rule’s publication to the order of vacatur. Further:

During that period, practitioners prepared, and the Agencies’ staff reviewed, over 3,000 filings for a wide range of transactions. Commission staff have also answered many questions from practitioners about complying with the Updated Form. Based on this experience, the Agencies’ staff and practitioners have obtained insights about what aspects of the Updated Form may be improved. 

My First Very Modest Proposal: Agencies share what they learned during that period. That might include, at least, informal observations from enforcement staff about which information was more—or less—useful. As I noted in November 2024:

…new requirements regarding submission of information on “non-horizontal” or supply relationships still seem excessive. Not because such information couldn’t be pertinent to a merger investigation, but because it is not typically useful to a preliminary screen and can be obtained in those cases where it’s more likely to be pertinent—likewise for required submissions about, e.g., private-equity acquisitions, “roll-up” strategies, and interlocking directorates.

What new information—if any—made a material difference, either in generating second requests or avoiding them? In decisions to file complaints? How often?

In addition, how does the agencies’ research staff—notably, staff in the FTC’s Bureau of Economics—analyze information gathered during that year, and previously? That information is not necessarily confined to matters at the enforcement margin. It could help show what new information is not just possibly useful, but likely to be most useful to the screening process.

The RFI sensibly asks about the time, labor, and financial costs associated with HSR notification requirements. That is no small matter, not least because the rule’s failure to show that its “claimed benefits will ‘reasonably outweigh’ its significant and widespread costs,” as required by the HSR Act itself, proved fatal to the rule. Gathering better information on compliance costs is a start, even if an open RFI has limits as a means of data gathering. Still, it’s hard to balance benefits and costs without a clear sense of the benefits. More focused reflection on the agencies’ learning up front might well lead to a more focused and productive RFI.

Getting this right may take a bit of time, but it will produce a more useful rule, and a more durable one, to the benefit of both competition and enforcement.

But mostly, it’s been about consumer-protection regulation.

A Premature Delivery

There’s also the recent ANPRM on unfair and deceptive fee practices in online food and grocery-delivery services. For the very, very short version, ICLE’s recently submitted comments recognize the potential for Section 5 violations and the need for vigorous enforcement of the FTC Act.

We also recognize that an ANPRM is a preliminary stage in rulemaking—one that doesn’t even require the FTC to propose specific regulatory requirements. At the same time, it is a distinct statutory requirement, and one of several process restrictions expressly imposed on such rulemaking in Section 18 through the Magnuson-Moss Warranty Act and related amendments to the FTC Act.

Based on those restrictions, among other things, we argue that the ANPRM is premature. The FTC has relatively limited enforcement experience in this area. There also remains room for continued federal and state law enforcement against harmful fee practices, new legislative initiatives at the state level, and wide variation among the practices and businesses that might be subject to such regulations.

No Room for a Sectorwide Rule

Similarly, Eric Fruits and I submitted comments on behalf of ICLE in response to the FTC’s ANPRM on “Unfair or Deceptive Rental Housing Fee Practices.” There, too, we noted real consumer-protection concerns, the potential for Section 5 violations, and enforcement efforts by the FTC and the states.

But we raised substantive concerns about the breadth of the inquiry and, again, both substantive and process concerns about the misfit between the agency’s enforcement experience and the requirements for Section 18 rulemaking. We concluded, in brief, that “the record does not justify a sector-wide Magnuson-Moss rule.”

At least one question seemed at odds with prior FTC policy positions on price and cost disclosures. For example, in comments addressing a New York State bill that would have regulated contractual relationships between health plans and pharmacy benefit managers, FTC staff noted that certain disclosure requirements were “analogous to requirements that firms reveal aspects of their cost structures to customers.” Staff recognized the importance of consumer access to truthful and non-misleading price information. At the same time, the comments noted that “[t]here is no theoretical or empirical reason to assume that consumers require sellers’ underlying cost information for markets to achieve competitive outcomes.”

Further, some cost disclosures might tend to undermine price competition, to consumers’ detriment. Indeed, the RFI itself cautions against submitting “competitively sensitive information, such as costs ….”

Rules mandating cost-reflective pricing do not merely regulate how firms present price information; they govern the relationship between firms’ costs and prices. In that regard, they can function as cost-of-service ratemaking—a form of price regulation historically applied to public utilities under distinct statutory authority.

There was an added wrinkle in the RFI’s consideration of “unfair” practices. Section 5(n) of the FTC Act expressly links the competition and consumer-protection missions charged to the FTC. Under Section 5(n), nothing is unfair under the FTC’s UDAP authority:

…unless the act or practice causes or is likely to cause substantial injury to consumers which is not reasonably avoidable by consumers themselves and not outweighed by countervailing benefits to consumers or to competition.

By statute, that restriction applies equally to Section 18 rulemaking that is supposed to address such unfair practices. In effect, the substantive and procedural restrictions of Sections 5(n) and 18 are bundled when it comes to unfairness regulation.

Click to Cancel Gets Cancelled

Building an appropriate record, and paying attention to process more generally, are no small matters. That should be clear from the order vacating the HSR amendments and, not incidentally, the commission’s 2025 loss in the 8th U.S. Circuit Court of Appeals in Custom Communications Inc. v. FTC. There, the 8th Circuit vacated the FTC’s 2024 Negative Option Rule, or “Click-to-Cancel” Rule, holding that the commission had failed to meet the procedural requirements for rulemaking under Section 22 of the FTC Act. Specifically, the FTC had failed to conduct the preliminary regulatory analysis the act requires for Section 18 rulemaking.

The 2024 rule had been adopted on a 3-2 party-line vote, with Andrew Ferguson and Melissa Holyoak voting no. As it happens, I joined a group of former enforcers in a TechFreedom letter on the rule, but I’d especially recommend dissents by sitting commissioners—Holyoak’s dissenting statement on the rule’s adoption and Christine Wilson’s 2023 dissenting statement on the preceding NPRM. As Wilson pointedly put it:

I might have supported a tailored rule to address the negative option marketing abuses prevalent in our law enforcement experience that consolidated various legal requirements. This proposal instead attempts an end-run around the Supreme Court’s decision in AMG to confer de novo redress and civil penalty authority on the Commission for Section 5 violations unrelated to deceptive or unfair negative option practices.

Holyoak would echo and expand on Wilson’s complaints a full year and a half later: 

I respectfully dissent, for three reasons. First, this rulemaking did not follow the FTC Act’s Section 18 requirements for rulemaking because: (1) the Rule is much broader than the “area of inquiry” proposed by the advance notice of proposed rulemaking (“ANPR”); (2) the Rule fails to define with specificity acts or practices that are unfair or deceptive, improperly generalizing from narrow industry-specific complaints and evidence to the entire American economy; and (3) the Rule fails to demonstrate that the unfair or deceptive acts or practices related to negative option billing are “prevalent.” Second, the Rule’s breadth incentivizes companies to avoid negative option features that honest businesses and consumers find valuable. Third, the Rule represents a missed opportunity to make useful amendments to the preexisting negative option rule within the scope of the Commission’s authority.

In brief, neither Wilson nor Holyoak suggested, as Gertrude Stein did of 1935 Oakland, that “[t]here is no there there.” Rather, they raised both substantive and procedural objections to the Khan majority’s rush to issue overbroad regulations.

Notice a pattern? Of course—it’s not as if I’ve been subtle in citing the comments my colleagues and I submitted in response to the food-delivery fee NPRM and, not incidentally, orders of vacatur issued by the U.S. District Court for the Eastern District of Texas for the HSR notice amendments, the 8th Circuit for Click-to-Cancel, and the U.S. District Court for the Northern District of Texas for the noncompete rule.

But Wait, There’s More

Fear not—or not so much—I’ve skipped over the 5th U.S. Circuit Court of Appeals’ January 2025 order vacating the CARS Rule; the 2025 final rule on “unfair or deceptive fees,” narrowed to cover fees in live-event ticketing and short-term lodging; various regulatory activities surrounding multilevel marketing; ongoing rule review—much of it business as usual—and more.

What we’ve covered is, in any case, rather a lot of rulemaking for an agency that has traditionally considered itself more of an enforcement agency than a regulator.  

Another Modest Proposal: Stop Cutting Corners

I’m hardly the first to note that scrupulous attention to process was not exactly the Khan commission’s forte. Neither was regulatory humility. Perhaps it’s no surprise that the FTC’s more recent regulatory setbacks in federal court can be traced to those shortcomings.

Back in March 2025, I had a post called “What Changes Might, and Should, a New FTC Majority Bring?” All of it was hopeful; some of it was right; and some of it was premature, if not just wrong. This March, I was fortunate to moderate an ICLE-sponsored panel discussion with Noah Phillips—a former FTC commissioner—and Chris Mufarrige—director of the FTC’s Bureau of Consumer Protection—on “The Competition and Consumer Protection Year in Review: A Panel on Enforcement Policy at the FTC.”

There, Phillips observed several salutary changes at the FTC in its first year under new leadership. One was the suspension of general hostility to mergers, which should not be confused with a diminution of enforcement vigor. Another was attention to process. A third, on the consumer-protection side, was a renewed focus on consumer welfare and, in enforcement, on anti-fraud efforts aimed at real consumer harm. All three have application to the current regulatory program. That brings us to..

My Second Modest Regulatory Proposal: The commission should rescind the revisions to the Section 18 rulemaking process that it adopted in July 2021. These were changes to internal process rules. if reminiscent of substantive rulemaking by the agency under the Biden administration in that they were adopted on a strict party-line vote. Commissioners Christine Wilson and Noah Phillips issued a joint dissenting statement.

As I wrote at the time, their dissenting statement is instructive. Also:

I suppose I also recommend the commission’s (majority) statement on the rule change, in which it said that the changes would “modernize the way it issues Trade Regulations rules under Section 18 of the FTC Act.” Wilson and Phillips pointed out that the changes diminished both the transparency of the rulemaking process and the opportunity for independent input, and eliminated the requirement of an expert staff report (and for Bureau of Economics review of a preliminary staff report), as we noted in ICLE’s comments on the commission’s Advance Notice of Proposed Rulemaking on Commercial Surveillance and Data Security.

The majority statement provides a bit of selective history:

In 1975, Congress passed the Magnuson-Moss Warranty—Federal Trade Commission Improvement Act laying out specific procedures for the promulgation of “Trade Regulation Rules” to protect consumers in a dynamic and changing economic landscape. Indeed, the Commission rightfully responded to this grant of authority by initiating more than a dozen rulemakings in the few months and years after its passage.

Strike the word “rightfully,” and it’s true enough, if incomplete. Congress did enact Magnuson-Moss in 1975. The act’s amendments to the FTC Act granted the FTC consumer-protection rulemaking authority and set out procedures for such rulemaking—that is, what is now Section 18 of the FTC Act. And the FTC did engage, enthusiastically, in rulemaking.

But as Wilson and Phillips pointed out, Congress “imposed significant procedural obligations on the Commission to cabin its discretion” in adopting such rules. And for good reason: the rulemaking flurry in the wake of Magnuson-Moss followed hot on the heels of what was widely regarded—not least by Congress—as excessive FTC rulemaking during the 1960s and early 1970s.

Tim Muris, a former FTC chairman, and Howard Beales, former director of the Bureau of Consumer Protection, have reflected on “the disastrous decade of the 1970s.” Maureen Ohlhausen, a former FTC commissioner and acting chair, observed that “[t]he backlash against this sweeping regulatory agenda was fierce. Ultimately, even the Washington Post criticized the Commission for being a ‘National Nanny.’” And here’s Muris again, for good measure.

It is well known that the flurry of late-1970s Magnuson-Moss rulemaking cheered by Khan, Rebecca Kelly Slaughter, and Alvaro Bedoya in amending the agency’s rules of process was widely condemned. Wilson and Phillips trod no new ground in observing that “[b]acklash from the agency’s sweeping regulatory efforts culminated in the Federal Trade Commission Improvements Act of 1980, which imposed additional procedural obligations on Section 18 rulemaking efforts.” Ohlhausen recalls that Congress even “demonstrated its disapproval of the FTC’s overreach by refusing to fund the agency, causing the Commission to close its doors for a brief time.” Here’s the Washington Post’s April 30, 1980, coverage of the shutdown.

As former FTC Chairman William Kovacic testified before Congress while a commissioner, there are good reasons to be cautious in rulemaking under Section 18, given the breadth of issues that may be swept under the FTC’s UDAP authority and its sectoral range, “reaching broadly across the economy, except for specific carve-outs.” These are also reasons to prefer acting on congressional mandates that identify “specific consumer protection issues” that call for regulation.

The lack of a more focused mandate and direction from Congress, reflected in legislation with relatively narrow tailoring, could result in the FTC undertaking initiatives that ultimately arouse Congressional ire and lead to damaging legislative intervention in the FTC’s work. This is precisely what occurred toward the end of the Carter administration. Ongoing Commission initiatives led Congress to turn against the Commission in 1979 and 1980, enacting significant legislative constraints (while individual members proposed even more significant cutbacks in Commission authority).

In brief, finally, the regulatory streamlining adopted under Lina Khan’s leadership was ill-advised. And overhasty attempts to promulgate rules as workarounds to the Supreme Court’s decision in AMG Capital are liable to founder in the courts, at least. As they have.

Rescinding those revisions would be consistent with the greater focus on process seen under current FTC leadership. And it need not require anything so involved as issuing new substantive—or “legislative”—regulations. 

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As I argued recently, “durable market power and demonstrable competitive harm remain elusive.” The market has not simply “tipped” toward Google, Meta, Amazon, Apple, or Microsoft. If anything, the most visible consumer-AI leader is OpenAI, which is reportedly preparing for an initial public offering.

Anthropic appears to have an edge in enterprise AI services, while Google and Microsoft benefit from distribution and infrastructure tied to their legacy businesses. Those advantages make them serious contenders, but hardly inevitable winners. On closer inspection, the AI ecosystem looks less like a market already captured by “Big Tech” than one defined by entry, rivalry, experimentation, and rapid technological change.

Despite those developments, a more pessimistic narrative continues to push for aggressive antitrust enforcement—and even direct state intervention—to “shape” AI markets. Advocates warn that AI could otherwise “supercharge ‘digital feudalism.’” That framing now runs through much of contemporary AI policy. The goal is no longer merely to police markets after anticompetitive conduct occurs, but to intervene ex ante—before the fact—to prevent a feared future of “private control” and “rent extraction.”

In his 2024 paper “The Case Against Preemptive Antitrust in the Generative Artificial Intelligence Ecosystem,” Jonathan Barnett argues that this turn toward enforcement in AI markets reflects a broader “preemptive approach” to antitrust. Under that approach, regulators presume certain practices by large technology firms are anticompetitive and place the burden on those firms to prove otherwise. Barnett’s warning is straightforward: in emerging markets, premature intervention risks suppressing “innocuous or efficient business practices” before regulators have enough evidence to assess their competitive effects.

That concern is especially acute in early-stage markets, where uncertainty is high and business practices that initially appear exclusionary may turn out to be competitively neutral—or affirmatively procompetitive. In AI markets, that includes product integration, minority investments, partnerships, licensing arrangements, and cloud-computing agreements that help firms assemble the complementary assets needed to compete.

Together with Dirk Auer, I have similarly warned that:

… overenforcement in the field of generative AI could engender the very harms that policymakers currently seek to avert. Indeed, preventing so-called “big tech” firms from competing in these markets (for example, by threatening competition intervention as soon as they build strategic relationships with AI startups) may thwart an important source of competition needed to keep today’s leading generative-AI firms in check.

This post examines three recent examples—from the European Union, Italy, and Brazil—that illustrate the common logic of this preemptive approach and suggest the warning is becoming increasingly urgent.

Emergency Remedies for Hypothetical Harms

One of the clearest examples is the European and Italian scrutiny of Meta’s integration of AI into WhatsApp.

The Italian Competition Authority (ICA) first opened an investigation in July 2025 into Meta’s decision to integrate Meta AI into WhatsApp. The ICA focused on the fact that Meta AI appeared prominently in the WhatsApp interface and was integrated into the search bar, while users allegedly had limited ability to remove or hide the feature.

The ICA later broadened the investigation to examine WhatsApp’s Business Solution Terms, which excluded Meta AI’s competitors from the platform beginning Oct. 15, 2025. Specifically, the terms barred rival AI-chatbot services from operating on WhatsApp. The ICA also opened proceedings to consider interim measures—that is, temporary remedies imposed before the underlying investigation has concluded.

The European Commission opened a parallel investigation, but moved faster and escalated further. After Meta updated its WhatsApp Business Solution Terms in October 2025 to exclude third-party general-purpose AI assistants effective Jan. 15, the Commission launched a formal Article 102 investigation into Meta’s restrictions on third-party AI assistants accessing WhatsApp. Article 102 of the Treaty on the Functioning of the European Union (TFEU) prohibits dominant firms from abusing their market position.

On Feb. 8, the Commission issued a Statement of Objections, a formal document setting out its preliminary view that Meta’s exclusion violated European Union antitrust rules. The Commission framed the conduct as an abuse of dominance in the “consumer communication applications market.”

Meta responded by restoring access for third-party AI assistants, albeit subject to a fee. The Commission remained unsatisfied. On April 14, it issued a Supplementary Statement of Objections arguing that the fee-based approach was “in effect equivalent to the previous access ban.” The Commission also notified Meta of its intention to impose interim measures requiring the company to restore third-party access under the pre-October 2025 terms, pending the outcome of the broader investigation.

Why call this preemptive? One could reasonably object that both the ICA and the Commission acted only after Meta restricted access to WhatsApp. The deeper problem is that both authorities appear to rely primarily on a theory of harm: that vertical integration—combining a platform with complementary services—creates incentives to disadvantage rivals.

But abuse-of-dominance cases require more than a plausible theory. They require evidence of dominance and evidence of harm to competition.

Even at this preliminary stage, the market definition looks implausibly narrow. As Dirk Auer has explained, consumers reach and use communications apps and AI assistants through a wide range of channels, often switching fluidly across platforms. Once those competitive dynamics are taken seriously, the relevant market likely becomes much broader, making it harder to sustain the claim that WhatsApp holds a dominant position.

The evidence of competitive harm also remains speculative. As Giuseppe Colangelo has warned:

It is uncertain that integrating Meta AI into WhatsApp would materially harm competition in AI assistants, especially given the success of rivals such as ChatGPT. ChatGPT achieved rapid adoption through cross-platform integration and partnerships enabling users to shop, book services, and perform other tasks within a single interface. By contrast, Meta AI’s market share remained minimal—about 0.2% during April 2024–March 202567 and below 1% in January 2026—and developer adoption was low.

The Commission’s choice of remedy makes the case look even more premature. Interim measures are an extraordinary tool in EU competition law. They are rarely used and traditionally require evidence of serious and irreparable harm before the main investigation concludes.

Yet the Commission seeks emergency intervention in a market where rival AI assistants—including ChatGPT, Claude, Gemini, Copilot, Perplexity, and Grok—have expanded rapidly through their own apps, browser integrations, operating-system partnerships, and enterprise channels. The Commission has not shown that exclusion from WhatsApp has actually foreclosed rivals from entering or expanding in the market.

Imposing emergency remedies based on asserted rather than demonstrated harms risks freezing the competitive process itself. It could even sideline Meta by stripping away one of the few advantages it plausibly holds in AI competition: distribution.

Brazil’s ‘Just in Case’ Merger Control

Brazil offers another example of this same preemptive impulse, this time in merger control.

In August 2024, Brazil’s competition authority, the Administrative Council for Economic Defense (CADE), opened proceedings into three major AI partnerships: Amazon/Anthropic, Microsoft/Mistral, and Google/Character AI. CADE said the goal was to “understand if these acquisitions, which would not require mandatory notifications, are to be investigated due to potential competitive harm.”

CADE’s own announcement acknowledged that opening proceedings did not necessarily mean the transactions were notifiable or raised competition concerns. The authority identified three possible outcomes: dismissal of the case, approval of the transaction, or initiation of formal merger-review proceedings to assess possible competitive harms.

Under Brazil’s merger rules, to be notifiable, one party to the transaction must exceed BRL 750 million (roughly $148 million) in turnover, while the other must exceed BRL 75 million (roughly $14.8 million). According to CADE’s March 31 technical notes for Amazon/Anthropic, Microsoft/Mistral, and Google/Character AI, the large technology firms each cleared the higher threshold. The AI startups did not.

In any mature merger regime, that should end the inquiry.

Instead, CADE’s General Superintendency referred each case to the Administrative Tribunal under the principle of in dubio pro societate. The authority argued broadly that “digital ecosystems present challenges for competition authorities” and that “the possibility of configuring associative contracts justifies, at least, the precautionary notification of certain contracts as acts of concentration.”

That reasoning stretches the principle beyond recognition. In dubio pro societate—roughly, “when in doubt, favor society”—is a controversial principle borrowed from criminal law. Whatever its proper scope there, it presupposes at least some meaningful doubt about harm. There should be conflicting evidence, or at least concrete indications of risk. In these cases, there appears to be neither.

To be sure, most merger-control systems include “call-in” powers, sometimes called “residual jurisdiction.” These mechanisms allow agencies to review transactions that fall below mandatory-notification thresholds. But such authority is meant to be exceptional, not routine.

The International Competition Network’s Recommended Practices for Merger Notification and Review Procedures emphasize precisely that point. They stress the limited nature of residual jurisdiction and note that, “[w]hen a jurisdiction maintains residual jurisdiction, it should take steps to address the desire of the parties to the transaction for certainty.”

That concern matters because merger thresholds exist for a reason: they provide legal certainty to firms contemplating transactions. Transplanting the controversial criminal-law principle of in dubio pro societate into merger control risks turning AI partnerships into inherently suspect arrangements under Brazilian competition law.

As Dirk Auer and I have argued elsewhere, the opposite is often true. Many of these partnerships may be essential mechanisms for AI challengers to scale. Developing advanced AI models is extraordinarily expensive. It requires computing power, specialized chips, engineering talent, distribution channels, and capital. Partnerships help firms assemble those complementary assets and compete against larger incumbents.

When Ex Ante Rules Age Overnight

The third example is the European Commission’s Digital Markets Act specification procedure concerning Alphabet’s data-sharing obligations under Article 6(11) of the Digital Markets Act (DMA). This is not traditional antitrust enforcement. It is explicit ex ante regulation.

The DMA imposes upfront duties on large digital “gatekeepers,” rather than waiting for a case-by-case finding of anticompetitive conduct. One could therefore argue that regulation is inherently preemptive and that this sort of intervention poses no special problem. But the case illustrates the broader concern running through this post: a pessimistic view of competition can produce flawed diagnoses and premature interventions in regulation as well as antitrust.

Even regulatory intervention requires threshold conditions. It is far from clear that this is an appropriate case for applying the DMA.

According to the Commission, Article 6(11) requires Alphabet to provide certain Google Search data to third-party online search engines on fair, reasonable, and non-discriminatory (FRAND) terms. In practice, FRAND rules are supposed to ensure access on terms that are not exclusionary, discriminatory, or opportunistically expensive. The Commission’s proposed specification would define how that obligation operates, including rules governing data access, anonymization, pricing, and eligibility.

Critically, the Commission’s preliminary measures would extend eligibility to “AI chatbots with online search engine functionalities,” even when search is merely one feature within a broader service.

My skepticism does not stem solely from the fact that Google would be compelled to share data. Data can represent a legitimate competitive advantage, although mandated access may be justified in some circumstances. The deeper concern is that data-sharing obligations can become tools for engineering competitive outcomes, rather than preserving the competitive process.

As Geoffrey Manne, Dirk Auer, and I argued in the International Center for Law & Economics’ (ICLE) recent submission to the consultation, the proposed measures “risk shifting Article 6(11) from a data-access obligation to a tool for delivering competitor success.” The effectiveness of the measures should be understood as creating opportunities to compete, not guaranteeing market-share gains for particular competitors.

More fundamentally, including AI chatbots within the scope of these obligations presupposes that Google can leverage dominance in search into dominance in AI-chatbot markets. That assumption looks increasingly detached from market realities.

The evidence points in the opposite direction: AI tools increasingly threaten Google’s position in search, rather than the reverse.

Google’s global search share fell below 90% for the first time since 2015 during the final quarter of 2024, a decline widely attributed to users shifting toward AI-native alternatives. Early in 2024, Gartner projected that traditional search-engine volume would decline 25% by 2026 because of AI chatbots—a forecast that, so far, appears remarkably prescient.

Meanwhile, Perplexity, which operates without privileged access to Google Search data, processed 780 million queries in May 2025, up 239% year over year, and reached a $20 billion valuation. ChatGPT now reportedly handles roughly 12% of Google’s daily search volume and has reached 900 million weekly users—again, without access to Google’s search data.

Perhaps most tellingly, during the remedies phase of United States v. Google, Apple executive Eddy Cue testified under oath that Google search volume on Safari had declined for the first time in more than two decades. Cue attributed the shift directly to users turning to AI tools for information discovery.

The U.S. court that conducted the most rigorous evidentiary review of Google’s search position reached much the same conclusion. Judge Amit Mehta found that “tens of millions of people use GenAI chatbots, like ChatGPT, Perplexity, and Claude, to gather information that they previously sought through internet search,” and that generative AI represents “a nascent competitive threat” to Google’s dominance in search.

That assessment mattered. It informed Mehta’s decision not to impose structural remedies on Google, with the court noting that competition in AI “is plentiful.” After a full adversarial proceeding examining precisely these market dynamics, the court declined to treat Google’s search dominance as the key to AI competition. If anything, the evidence suggested the opposite: AI competition is emerging as a threat to Google.

The Commission’s specification procedure moves in the other direction. It extends search-data-sharing obligations into a market where Google appears to be under competitive pressure, not successfully leveraging dominance.

That is the broader problem with ex ante AI regulation—and, more generally, with regulatory regimes like the DMA. They can quickly become moving targets. The DMA was designed to avoid the perceived slowness of traditional antitrust enforcement. But AI markets expose the limits of that ambition. When technology and consumer behavior evolve rapidly, ex ante rules risk hardening assumptions that may already be obsolete.

Don’t Freeze the Race at the Starting Line

Taken together, these cases reveal a common pattern.

First, agencies are importing legacy-platform theories into AI markets. Meta’s integration of AI into WhatsApp is framed through familiar tying and leveraging theories. The DMA’s Article 6(11) specification procedure extends data-access concepts developed for search into AI-chatbot services with search functionality. CADE’s scrutiny of AI partnerships draws heavily on “killer acquisition” and nascent-competition theories. (On that point, I strongly recommend Selçuk Ünekbas’s excellent post.)

Second, agencies are intervening before AI markets have stabilized. Interim measures against Meta, DMA specification proceedings, and Brazil’s procedural referrals all reflect a fear that waiting for proof may mean acting too late. From a public-choice perspective, that anxiety is understandable. Agencies face strong incentives to intervene early in politically salient markets. Antitrust law, however, has traditionally imposed safeguards precisely because those incentives exist.

Third, the evidentiary center of gravity is shifting away from demonstrated harm and toward generalized risk management. The recurring vocabulary is telling: firms “may exclude,” “may foreclose,” create “future bottlenecks,” undermine “contestability,” or threaten “effective access.” None of those concepts is meaningless. But they become dangerous when they displace disciplined analysis of market power, foreclosure, efficiencies, and consumer welfare.

That is precisely why Barnett’s error-cost framework matters here. The error-cost approach asks which mistakes are more likely and more harmful: false positives, where lawful conduct is wrongly condemned, or false negatives, where harmful conduct is wrongly allowed. In early-stage markets, false positives can be especially costly. Premature interventions can suppress efficient or experimental business practices, freeze product design, discourage investment, and transform access obligations into competitor subsidies.

Ironically, many of these interventions are defended in the name of protecting dynamic competition. But dynamic competition requires experimentation. Firms must be free to pursue integration, partnerships, open- and closed-source strategies, application programming interfaces (APIs), vertical specialization, and distribution through existing services. If enforcers treat each of those strategies as presumptively suspect whenever a large platform employs them, they risk narrowing the very pathways through which AI competition may emerge.

None of this means agencies should ignore AI. The technology will reshape products and services across the economy. Competition enforcement in this space matters. But it should remain disciplined.

First, authorities should distinguish between power in legacy digital markets and power in AI markets. A firm that dominates messaging, search, e-commerce, mobile operating systems, or cloud infrastructure does not automatically dominate AI assistants, foundation models, or agentic services—AI tools designed to perform tasks on a user’s behalf. Leverage theories require evidence of leverage, not merely evidence of size.

Second, agencies should require actual evidence of foreclosure. Product integration, default placement, and preferential design may matter, but they are not inherently anticompetitive. The relevant question is whether rivals are truly denied access to efficient distribution channels or competitively necessary inputs.

Third, partnerships should be evaluated not only as potential threats, but also as potential engines of competition. AI development requires compute, chips, data, talent, capital, and distribution. Partnerships can help firms assemble those complementary assets, allowing startups to scale and incumbents to challenge current leaders. Scrutiny may be appropriate where there is exclusivity, control, reduced rivalry, or problematic information sharing. Partnership alone, however, should not become a red flag.

Antitrust should remain focused on consumer welfare and innovation, not on preserving existing market structures. If AI changes how users search, communicate, shop, write, and code, competition law should not reflexively protect yesterday’s distribution of traffic, bargaining power, or rents.

The risk is no longer that regulators will arrive too late to AI markets. It is that they will arrive too early, declare the race over, and start handing out medals before the runners have cleared the first turn.

The post Don’t Freeze the AI Race at the Starting Line appeared first on Truth on the Market.

That anxiety is understandable. But preserving the old delivery system is not the same thing as preserving journalism.

The News Business Is Evolving. Washington Wants It Taxidermied.

In “The Future and Its Enemies,” Virginia Postrel framed the central political conflict as one between dynamism and stasism. Dynamism treats the future as fundamentally unknowable. The best outcomes emerge through experimentation, competition, and individual choice. Stasists, by contrast, see change as a threat and prefer a predictable, stable, orderly world managed by institutions and experts. Postrel further divided stasists into two camps: technocrats, who believe change should occur only under expert supervision toward some rationally designed end, and reactionaries, who long to restore a perceived “golden age.” 

Modern debates about journalism policy often manage to combine both instincts at once.

The rise of the internet disrupted the traditional business models of television broadcasters, forcing firms to adapt to changing consumer preferences. Broadcasters consolidated. Large station groups acquired local outlets. Newsrooms shrank. Cue the predictable policy panic. Commentators and lawmakers increasingly argued that government should protect local broadcast television from competitive pressures in order to preserve local journalism.

Indeed, it is a technocratic program in service of a reactionary goal: preserving an older media structure because the alternative feels unfamiliar, chaotic, or uncomfortable.

The problem is that stasism consistently overestimates what regulators can know about fast-moving markets. Policymakers may look at the closure of a local broadcast newsroom and conclude that local news itself is disappearing. But the decline of one legacy distribution model does not necessarily mean consumers receive less local reporting overall. It may simply mean audiences—and advertisers—have moved elsewhere.

Viewed through a dynamist lens, the news industry is not collapsing so much as mutating, sometimes awkwardly and noisily, into something new. New business models, distribution strategies, and products continue to emerge to meet consumer demand in formats audiences increasingly prefer. Some experiments will fail. Many already have. That is what experimentation looks like outside a PowerPoint presentation.

Regulators cannot reliably predict which models will succeed because no central planner possesses enough information to forecast the future of a rapidly evolving information market. Markets exist precisely because knowledge is dispersed. Consumers, entrepreneurs, advertisers, journalists, and platforms all respond to localized information and changing incentives in ways no regulator can fully aggregate in advance. Protecting incumbent broadcasters merely because policymakers cannot imagine what comes next risks preventing those alternatives from emerging at all.

Much of the current debate surrounding “local news” nonetheless treats the decline of traditional broadcasting as either sudden or temporary. It is neither. Consumer migration toward digital distribution began more than two decades ago, back when people still willingly used Internet Explorer. Policymakers should therefore resist framing the issue around preserving legacy institutional arrangements or reacting to anecdotal examples of newsroom decline featured in the very outlets under discussion.

The relevant question is not whether a particular historical market structure survives indefinitely. Very few do. The relevant question is whether consumers continue to receive access to information, civic reporting, and local accountability through evolving competitive mechanisms.

More to the point, local news has not disappeared. It has increasingly shifted to digital markets. That transition will inevitably involve instability, failed business models, and periods of genuine disruption. But that process is also how sustainable digital media ecosystems develop. If regulators decide intervention is necessary, they should focus on identifiable consumer harms, rather than attempting to engineer the “correct” structure of the media market from Washington.

Local News: Not Quite Dead Yet

Pew Research Center has extensively documented the long-term shift in how Americans consume news, particularly local television news. In 2024, only 32% of Americans identified television—including local cable channels—as their primary source of local news. In 2018, that figure was 41%. The trajectory is not subtle.

Radio, by contrast, has remained surprisingly stable. Roughly 9% of Americans prefer it as their primary local-news source, and that figure has held relatively steady over time. That suggests at least some baseline audience for traditional broadcast formats may persist. But “persist” is not the same thing as “dominate.”

Ratings data reinforce the broader trend. In 2016, affiliates of ABC, CBS, Fox, and NBC averaged just under 4 million viewers during primetime news programming and just under 2.5 million viewers during midday broadcasts. By 2022, those figures had fallen to roughly 2.2 million and 1.8 million viewers, respectively. Consumers are not abandoning news altogether. They are abandoning scheduled television viewing.

That shift has obvious consequences for the economics of local broadcasting. Over-the-air advertising revenue has steadily declined for years. In 2004, local television advertising revenue reached $22.4 billion. Today, it sits at roughly $15.5 billion—a 56% decline after adjusting for inflation.

Retransmission fees paid by multichannel video programming distributors (MVPDs), meanwhile, continue to rise. But those fees can be misleading as a measure of the value consumers place on local news itself. Retransmission-consent rules, many of which date back to a very different media environment, distort the market. In practice, MVPDs largely pay for access to high-value national programming, especially live sports and marquee network content such as Sunday Night Football. Because local broadcasters operate as network affiliates, national networks can effectively require stations to pass a substantial share of retransmission revenue upstream.

At first glance, those numbers paint a fairly bleak picture for local broadcasters. Curiously, though, declining broadcaster stability has not necessarily translated into less local news programming. Quite the opposite. Over the past 20 years, the average number of local TV news hours broadcast per weekday increased from just under 4 hours to more than 6.5 hours.

That does not mean local journalism is flourishing. Newsrooms around the country continue to face layoffs, budget cuts, and consolidation pressures. But it does suggest broadcasters still view local news as a valuable way to differentiate themselves in an increasingly fragmented media market. The quantity of local broadcast news available to consumers has not declined nearly as dramatically as the broader rhetoric surrounding “news deserts” might imply.

There is, of course, an important caveat. Local broadcasters—particularly those owned by large national groups—increasingly blend national content into local broadcasts. Syndicated stories and nationally produced segments allow station groups to spread production costs across multiple markets while preserving the appearance of robust local coverage. In effect, broadcasters can maintain the volume of news programming while reducing the cost of producing genuinely local reporting.

The News Survived the Medium

Importantly, declining viewership and shrinking advertising revenue for local broadcast stations do not necessarily mean the overall supply of local news has declined. In many respects, the opposite appears true. Consumers have not stopped consuming local news. They have simply migrated to digital platforms where news is cheaper, faster, and more convenient to access.

Local broadcasters themselves increasingly acknowledge this reality. Many stations now maintain substantial digital operations that no longer function merely as sidecar websites for broadcast content. Digital publishing increasingly operates alongside television production as part of an integrated newsroom strategy designed to avoid duplicative work, expand audience reach, and attract younger consumers who regard appointment television the way earlier generations regarded carrier pigeons.

These digital platforms also create new advertising opportunities while funneling some viewers back toward traditional broadcasts. Consumers benefit from additional engagement opportunities with local outlets and from receiving news in formats they actually use, rather than formats policymakers nostalgically wish they still used.

Newspapers have undergone a similar transition. Faced with declining print revenue and distribution costs that increasingly resemble a hostage situation, many local papers shifted aggressively toward digital subscriptions and online advertising. Some of those efforts are working. Top-performing news organizations have experienced 77% growth in digital subscriber volume, while 83% of local media companies forecast digital advertising revenue to either increase or remain stable in 2025.

The Minneapolis Star Tribune offers a particularly instructive example. After emerging from bankruptcy in 2009, the paper reorganized as a for-profit public benefit corporation and focused heavily on “utility reporting”—coverage that readers find directly useful in their daily lives, from local politics and schools to weather and community events. That strategy helped the paper surpass 100,000 digital-only subscriptions.

Many successful digital models also increasingly rely on bundling. News subscriptions now frequently include games, recipes, podcasts, or other lifestyle features that encourage consumers to engage multiple times throughout the day. Critics sometimes dismiss these features as distractions from “real journalism.” Consumers, meanwhile, appear to like receiving products they actually use.

The News Desert Might Have Wi-Fi

To be sure, the decline of traditional broadcasters and newspapers can leave some communities with fewer legacy-news options. That phenomenon has given rise to the now-common term “news deserts”—geographic areas with little or no access to professional local journalism. The problem stems from pressures facing both broadcasters and local newspapers. In 2024 alone, 127 newspapers ceased operations, and more than 200 U.S. counties reportedly lacked a single locally based news outlet.

That sounds alarming because, in many cases, it is. Local reporting serves important civic functions. Somebody needs to sit through city council meetings, track school-board disputes, and notice when the county comptroller suddenly starts billing taxpayers for suspiciously frequent “consulting retreats” in Boca Raton.

Still, the existence of a market gap also creates a market opportunity.

In many communities, hyperlocal digital outlets have emerged to fill at least part of the vacuum. Hyperlocal journalism focuses on highly specific geographic communities—sometimes a single town, neighborhood, or suburban corridor—and covers issues larger regional or national outlets often ignore. These publications emphasize intensely local concerns: zoning fights, municipal budgets, high-school sports, local business openings, school-board politics, and the like.

Historically, hyperlocal journalism struggled because small audiences rarely generated enough revenue to offset the high fixed costs of print production and distribution. Running a newspaper turns out to be expensive when one must physically manufacture and deliver thousands of copies every day.

Digital infrastructure changes that equation considerably. Online distribution dramatically lowers overhead costs, allowing smaller publications to survive with narrower audiences. Digital distribution also expands the potential readership beyond the immediate community itself. Former residents, local alumni, nearby commuters, and people with niche regional interests can all consume the same content without ever touching a print edition. Ironically, the internet may make certain forms of intensely local journalism more economically viable than they were during the supposed golden age of local newspapers.

Walter Cronkite Never Had a Substack

Relatedly, many Americans now receive news primarily through social media platforms rather than traditional news outlets. Approximately 53% of U.S. adults access news through social media—a larger share than those who primarily rely on television or news websites and apps. Much of that shift comes from younger consumers. Adults under 30 increasingly treat social media as their default information environment, while older Americans remain far more attached to legacy media.

Traditional news organizations still operate on these platforms, of course. But social media has also lowered the barriers to entry for entirely new kinds of news providers. So-called “news influencers” now serve as a primary news source for roughly 37% of Generation Z and nearly 20% of Americans overall.

Part of their appeal is stylistic. These creators often adopt a conversational, peer-to-peer tone that feels more authentic—or at least less corporate—than traditional broadcast journalism. They also operate outside many of the institutional constraints that govern legacy newsrooms. Without layers of editors, producers, standards departments, and legal review, influencers can respond more quickly, speak more casually, and tailor content specifically for platform algorithms and audience engagement.

Just as importantly, many of these creators are building business models designed for the internet, rather than awkwardly retrofitted onto it. Revenue from platform partnerships, sponsorships, direct subscriptions, and services such as Substack can allow smaller creators to monetize niche audiences more effectively than some legacy outlets still trying to preserve advertising models built for the Clinton administration.

This shift also reflects a broader change in how audiences evaluate credibility. Traditional media institutions historically acted as information gatekeepers, applying editorial standards intended to filter errors, verify claims, and limit the spread of misinformation. That role had real benefits. It also created growing public suspicion that institutional media selectively filtered or framed information in ways audiences perceived as incomplete, biased, or overly curated.

As a result, many consumers increasingly place trust not in institutions, but in social relationships and perceived authenticity. In practice, people often evaluate information based less on the original source than on who shared it. A news story posted by an unfamiliar influencer may carry little weight on its own. The same story shared by a trusted friend, family member, or online personality can suddenly appear far more credible. Economists sometimes describe this as distributed trust. Everyone else describes it as “my cousin saw it on TikTok.” 

People Are Informed-ish

The data increasingly point in one direction: consumers are moving toward digital sources for information and news. The stasist response—that policymakers should use technocratic tools to protect broadcasters from competition—rests on an assumption that now appears increasingly implausible. Namely, that without traditional broadcast newsrooms, citizens would simply stop consuming news and become less informed altogether.

Consumers plainly still want information. They are just consuming it differently.

That does not mean the transition to digital news comes without tradeoffs. It does mean policymakers should focus on identifying concrete harms rather than trying to preserve legacy broadcasting models for their own sake. Protecting incumbent broadcasters from competition will not reverse underlying shifts in consumer behavior any more than subsidizing fax machines would revive long-distance correspondence.

One genuine concern is that digital news consumption often encourages passivity. Consumers still want to stay informed, but many no longer want to spend 10 minutes reading a fully developed article or sitting through an evening broadcast. Historically, newspapers and scheduled broadcasts required a more intentional form of engagement, even if headlines and cable-news tickers always rewarded some degree of superficial consumption. Today, algorithmic feeds, AI-generated summaries, short-form videos, and audio briefings allow users to absorb a constant stream of information with remarkably little effort.

The result is a strange informational equilibrium in which people know a little about nearly everything and not very much about any particular thing.

A second concern involves monetization. Passive consumption weakens business models that rely on subscriptions or sustained audience engagement. If consumers feel sufficiently informed after reading an AI summary or skimming several posts on social media, the incentive to pay for deeper reporting declines. That dynamic creates a classic free-rider problem: consumers continue benefiting from original reporting while increasingly bypassing the outlets that actually bear the cost of producing it.

The internet, unfortunately, remains extremely good at distributing information and somewhat less good at convincing people to pay for it.

A third concern involves misinformation and sensationalism. Digital platforms dramatically lower barriers to entry for publishers, commentators, influencers, and outright cranks. If revenue depends primarily on advertising and engagement metrics, publishers face strong incentives to maximize clicks, shares, outrage, and virality. Sometimes rigorous reporting generates engagement. Sometimes “This Celebrity Lost 20 Pounds With One Weird Breakfast Food” performs better.

None of this is entirely new. Sensationalism predates the internet by quite a bit. But digital distribution increases both the scale and speed at which misleading or false information can spread.

Even so, nostalgia for an idealized era of local broadcasting will not solve these problems. Consumers who prefer passive news consumption are unlikely to abandon algorithmic feeds in favor of sitting through a 30-minute local newscast simply because regulators wish they would. Likewise, misinformation would persist even in a world with more local broadcasters and newspapers. False information is a function of human behavior and incentives, not merely the distribution technology used to deliver it.

These challenges may warrant policy responses. But they will not be solved by artificially propping up legacy media models that consumers increasingly choose to leave behind. 

The News Keeps Surviving Its Obituaries

The conflict between dynamists and stasists runs through nearly every modern debate about journalism and media policy. The decline of legacy broadcast newsrooms understandably creates anxiety about the future of the profession. Local television stations and newspapers long occupied a central role in American civic life, and watching those institutions shrink can feel unsettling.

But institutional change does not necessarily mean the public becomes uninformed. Nor does it mean journalism disappears altogether. Consumers still demand news, information, commentary, and accountability reporting. What has changed is the technology used to deliver it, the business models that support it, and the ways audiences choose to consume it.

The stasist argument for protecting broadcasters at the expense of competing technologies rests on a flawed premise: that legacy media institutions are synonymous with journalism itself. They are not. Legacy outlets are already adapting to digital distribution, while entirely new forms of news production continue to emerge. Some business models will succeed. Others will fail spectacularly and probably launch a podcast on the way down. That process is not evidence of market failure. It is what experimentation looks like.

Policymakers should resist the temptation to freeze the media landscape in place simply because the future feels uncertain. A dynamist approach recognizes that innovation, competition, and consumer choice—not regulatory nostalgia—offer the best chance of producing sustainable forms of journalism over time.

The printing press disrupted scribes. Radio disrupted newspapers. Television disrupted radio. The internet disrupted everything. The news survived every time.

The post The Future of News and Its Frenemies appeared first on Truth on the Market.

Unsurprisingly, proponents of the Credit Card Competition Act, the Illinois Interchange Fee Prohibition Act, and a growing slate of state-level proposals have seized on the paper as fresh evidence that credit card rewards programs redistribute wealth upward. But the claim rests on some heroic assumptions about where people shop, what they buy, how merchants set prices, and whether cash is actually cheaper than cards. This post takes a more skeptical view.

The Reverse Robin Hood Story Hits a Speed Bump

The paper’s authors—Mark Egan, Gregor Matvos, Amit Seru, Lulu Wang, and Vincent Yao—deserve credit for building ambitious models and applying sophisticated econometric techniques to a pair of unusually rich datasets. In doing so, they avoid some of the more obvious methodological flaws that plagued the two Federal Reserve Bank of Boston studies and the Federal Reserve Bank of Philadelphia study we criticized previously.

Those earlier papers advanced the familiar “reverse Robin Hood” claim: poorer consumers subsidize wealthier consumers through credit card rewards programs. As Egan et al. correctly observe, however, “[r]edistribution in the payment system requires consumers who use different payment methods to shop at the same stores.”

That is a surprisingly important point. Because the earlier studies lacked transaction-level data, they could not establish that cash-paying consumers and credit-card users were actually shopping at the same merchants. Having failed to clear that basic evidentiary hurdle, they could not demonstrate the redistribution they purported to measure.

Egan et al. at least partially solve that problem by combining firm-level card-settlement data from Fiserv—a merchant acquirer that processes card transactions—with establishment-level data from Clover, Fiserv’s point-of-sale platform. The Clover data includes card, cash, and check transactions for roughly 800,000 merchants. Using that data, the authors show that, by transaction value, credit-card users generally do not shop at the same stores as cash users.

Specifically, the authors note that, in the Clover dataset covering 2019 to 2022:

On average, cash accounts for around 11% of transaction dollars (dollar-weighted across merchants; Figure 2b). However, this masks substantial heterogeneity. For approximately two thirds of merchant-year observations, cash accounts for less than 2% of transactions. In contrast, for the one-third of merchants for which cash represents at least 2% of sales, it accounts for an average of 30% of their transactions—and for merchants in the 90th percentile, over 80%, highlighting the concentration of cash usage among a subset of merchants.

The implication is straightforward:

For the two thirds of firms where cash use is almost nonexistent, there is little scope for redistribution between cash and card users. Similarly, at firms where cash is the predominant payment method, redistribution is limited. Redistribution inherently requires a mixed payment environment, so dispersion in payment composition directly constrains the scope for cross-subsidization.

In other words, the “reverse Robin Hood” narrative about massive transfers from poorer cash users to wealthier credit-card users appears, at least initially, to stumble at the first hurdle.

The picture becomes more complicated, however—both for cash transactions, to which we will return later, and for the 89% of sales conducted through debit and credit cards. On that front, Egan et al. observe:

The distribution of credit card sales is bimodal, with peaks at approximately 25% and 70%. This bimodality suggests that although credit cards account for 53% of card transactions on average, merchants tend to fall into two distinct groups: those where credit accounts for around 25% of card transactions and those where it accounts for about 75%. This pattern has implications for cross-subsidization. Debit cards carry lower interchange fees and rewards relative to credit cards. Just as cash transactions can subsidize credit card rewards, debit card transactions also potentially play a subsidizing role. The bimodal nature of the distribution suggests that variation in card payment mix is significant at the merchant level—again limiting the potential for cross-subsidization, as many merchants are dominated by a single payment type.

That finding suggests there may be some opportunity for cross-subsidization from debit-card users to credit-card users, particularly in sectors where debit and cash usage remain relatively high, such as grocery stores, gas stations, and restaurants. 

Even there, though, the data point toward substantial sorting among customers. The figure below shows large peaks around 20% credit-card usage, followed by a long taper. Put differently, a relatively small subset of merchants accounts for a disproportionately large share of cash and debit transactions. As the authors explain: “This dispersion is important, as it implies that even within a given sector, consumers with different payment preferences tend to shop at different merchants, reinforcing the role of sorting in shaping redistribution.”

That further narrows the scope for meaningful within-store cross-subsidization. The aggregate figures reported by Egan et al. make the redistribution story appear more sweeping than the underlying merchant-level patterns likely support.

There is another complication the paper is unable to address: variation in what consumers purchase within the same store. Cross-subsidization is more likely if consumers using different payment methods purchase goods with similar margins and pricing structures.

The available evidence suggests that higher-income and more educated consumers systematically buy different products than lower-income consumers, particularly in grocery stores. Studies consistently find that wealthier consumers tend to purchase foods marketed as healthier or more premium, for example.

Grocery stores often earn higher gross margins on premium packaged foods, private-label “wellness” products, and other differentiated items than they do on commodity staples or national-brand basics. To the extent those higher-margin products are disproportionately purchased by credit-card users, the economics may run in precisely the opposite direction from the “reverse Robin Hood” story. Credit-card users could, in effect, be subsidizing lower-margin shoppers, including debit-card users. 

More fundamentally, this illustrates a broader flaw in the “reverse Robin Hood” hypothesis. The theory isolates one component of merchant costs—interchange fees—while ignoring the larger pricing ecosystem in which merchants operate. What holds for grocery stores likely applies across many retail sectors. If wealthier consumers disproportionately buy premium goods and services, merchants can recover higher interchange costs through higher margins on those purchases, eliminating any meaningful cross-subsidy.

Indeed, many businesses operate on precisely that kind of variegated-margin model. Mainstream consumers provide scale and volume, while a smaller number of premium customers generate most of the profits. Airlines are the classic example: premium cabins effectively subsidize economy seating. Grocery stores and other retailers often operate in much the same way.

The Load-Bearing Assumption

This cuts directly to the question of pass-through. Egan et al. assume that merchants pass 100% of interchange costs through to consumers in the form of higher prices, although they include a sensitivity analysis that lowers the figure to 70%. Previous studies, by contrast—including those Ben Sperry and I reviewed here—generally find pass-through rates closer to 30%.

That difference is of crucial significance because the assumed pass-through rate underpins Egan et al.’s result; it is the load-bearing assumption. The authors are explicit: “in our baseline implementation of the sufficient statistic approach, we assume full pass-through of interchange fees to prices.” Without that assumption, the headline $30 billion redistribution estimate cannot be generated.

Their own robustness exercise underscores the point. When pass-through falls from 100% to a still-generous 70%, the estimated transfer to credit-card users shrinks by roughly one-third, to about $20 billion. The authors do not explore lower pass-through rates. Instead, they argue that the evidence supports something close to full pass-through. As discussed below, that claim is highly contestable.

The most sophisticated attempt to measure interchange pass-through directly remains Vladimir Mukharlyamov and Natasha Sarin’s study of the Durbin Amendment, which imposed price controls on debit-card interchange fees for banks with more than $10 billion in assets—so-called “covered banks.” The Durbin Amendment capped debit interchange at 0.05% plus fixed fees of $0.21 and $0.01 for fraud prevention.

Mukharlyamov and Sarin examined gasoline prices in areas with relatively high concentrations of covered banks and compared them to areas with fewer covered banks between 2009 and 2013—that is, two years before and two years after the Durbin caps took effect. They found evidence consistent with pass-through of roughly 28%, but the results were not statistically significant because gas prices were highly volatile during the period studied.

Egan et al. pursue a less direct strategy. Rather than examining prices themselves, they analyze spending patterns at restaurants. Specifically, they compare average spending per customer in ZIP codes with differing concentrations of covered-bank customers. In other words, they infer price effects indirectly through changes in demand.

One wrinkle is that the Durbin Amendment affected restaurant interchange fees in a non-linear fashion. Restaurants with small average ticket sizes sometimes saw interchange fees increase, because covered banks could apply the maximum fixed fee to all transactions. By contrast, restaurants with larger ticket sizes generally saw interchange fees fall because the capped percentage fee became a smaller share of the overall transaction.

Accounting for those effects, Egan et al. estimate that a one-percentage-point increase in interchange fees reduced restaurant sales by roughly 6.9%, which they interpret as implying approximately a 1.15% increase in average ticket size.

There is a problem, though: the paper’s pass-through framework operates at far too high a level of aggregation to capture how many merchants actually price goods and services.

Retailers and restaurants rarely apply a uniform markup across all products. Staples, entry-level products, and “traffic-driving” items are often sold at razor-thin margins—or even at a loss—to attract customers into the store. Premium goods, add-ons, discretionary purchases, and branded products typically carry much higher margins.

As a result, even if overall ticket sizes reflect some response to payment-acceptance costs, the incidence of those costs may differ dramatically depending on what consumers actually buy. If lower-income consumers disproportionately purchase staples, while higher-income credit-card users disproportionately buy premium products, desserts, alcohol, branded goods, or other high-margin items, then the effective incidence of interchange costs may be far less regressive than Egan et al. assume.

As already noted, the effect could even run in the opposite direction. Premium-product purchasers might bear more of the relevant costs, producing a “Robin Hood” effect rather than a “reverse Robin Hood” effect. This is a form of within-merchant sorting that the paper’s merchant-level framework cannot adequately capture.

The problem becomes especially acute in the paper’s restaurant analysis. Egan et al. do not observe menu prices or item-level purchases. Instead, they use average transaction size as a proxy for price.

But average ticket size can change for many reasons unrelated to price pass-through. Party size can change. Consumers may order more alcohol or desserts. More customers may dine in rather than order takeout. Tips may rise. The customer mix may shift. Payment-method composition may change. Most importantly for present purposes, credit-card users may simply purchase different products than debit-card users.

A rise or fall in average ticket size therefore cannot establish, by itself, that merchants adjusted menu prices one-for-one with interchange costs. It may instead reflect changes in purchasing composition or customer behavior. That is a significant limitation because the restaurant analysis serves as the paper’s principal evidence for broader claims about systemwide pass-through.

The paper also gives insufficient weight to the benefits merchants receive from accepting credit cards. Egan et al. largely treat interchange as a pure cost imposed on merchants and passed through to consumers. But credit cards also create substantial merchant-side benefits.

Most importantly, credit cards relax liquidity constraints. A cash user can spend only the money in her wallet. A debit-card user can spend only the funds available in her bank account. A credit-card user, by contrast, can spend against an available credit line. That can increase ticket size, facilitate larger purchases, and support higher-margin sales.

Interchange revenue also helps fund the bundle of features that makes credit cards attractive to consumers: fraud protection, zero-liability policies, chargeback systems, interest-free grace periods, rewards programs, purchase protection, travel insurance, and rental-car coverage.

Those features are not merely transfers to cardholders, as Egan et al. often imply. They also generate benefits for merchants who accept credit cards. The features encourage consumers to use credit cards, which may increase merchant profits. Analyses of payment systems have long recognized merchant benefits such as ticket lift, faster checkout, lower cash-handling costs, and reduced theft or skimming risk.

A real-world example illustrates the point. In 2018, Atlanta’s Mercedes-Benz Stadium became the first major U.S. sports venue to operate entirely cash-free. The following year, per-capita food and beverage sales rose 16%, wait times fell by 20 to 30 seconds, and operating costs declined by more than $350,000.

Similar patterns appear in grocery stores, quick-service restaurants, transit systems, and sports venues more broadly. Remove cash from the equation, and merchants often see higher revenue per customer. Yet Egan et al. treat interchange solely as a cost. They never seriously consider the possibility that merchants receive offsetting revenue benefits.

These considerations are especially important in the context of the Durbin Amendment, which capped debit-card interchange fees for covered banks, but left credit-card interchange untouched. Covered banks therefore lost much of their ability to fund debit-card rewards programs, free checking, and other debit-linked benefits.

Many banks responded exactly as one would expect: they eliminated debit-card rewards while preserving credit-card rewards. Consumers consequently had stronger incentives to shift spending from regulated debit cards to credit cards.

The available evidence suggests that is precisely what occurred. Federal Reserve survey data show that debit-card use declined and credit-card use rose significantly between 2010 and 2012, immediately following Durbin’s enactment, as shown in the figure below.

Source: 2019 Survey of Consumer Payment Choice

To the extent customers of covered banks shifted disproportionately from debit to credit, restaurants in ZIP codes with greater exposure to covered-bank customers would have experienced more than merely lower regulated-debit costs. They also may have experienced increases in credit-card usage, resulting in larger ticket sizes and other changes in purchasing behavior, including shifts toward higher-margin products.

If increased credit-card use generated ticket lift or encouraged purchases of premium goods, then some—or potentially much—of the observed revenue increase could reflect payment-method substitution rather than pass-through of lower debit interchange costs.

The upshot is that the restaurant evidence does not cleanly isolate pass-through. Instead, it may conflate several different mechanisms: the observed changes in debit acceptance costs plus unobserved shifts in payment-method use (including migration away from cash, which is unobservable because Egan et al do not have Clover data for the relevant period), ticket lift due to higher levels of credit card use in locations with greater proportions of covered banks, product-level pricing differences, and customer-composition effects.

Egan et al. assume fixed consumer payment preferences and model interchange primarily as a merchant cost passed through in a uniform way to retail prices. But if payment methods influence what consumers buy, how much they spend, and which products carry the highest margins, then the incidence of interchange becomes substantially more complicated than the paper allows.

At a minimum, average restaurant ticket sizes should not be treated as dispositive evidence of systemwide one-for-one pass-through. A convincing analysis would require item-level prices or baskets, total sales including cash transactions, and direct evidence regarding debit-to-credit substitution among customers of covered banks.

Cash Isn’t Free

Finally, let’s return to the question of cash.

Egan et al. explain that their analysis attempts to calculate transfers among different categories of payment users—and, by extension, among income groups—by comparing the current payment system to a hypothetical world with “zero interchange.” The paper correctly notes that merchants pay acquirers a merchant discount rate (MDR), which covers not only interchange fees remitted to issuing banks, but also other payment-processing costs.

The implicit assumption is that, absent interchange fees, merchants would simply pay this lower baseline MDR on all transactions. For the roughly two-thirds of merchants whose transactions are almost entirely electronic, that assumption is reasonably plausible.

For the remaining third of merchants—those where cash accounts for more than 2% of payments—it is anything but plausible. In many cases, it is simply wrong.

The reason is straightforward: cash may avoid interchange fees, but handling cash is expensive.

The most comprehensive recent study of cash-handling costs was conducted by IHL in 2018. The study identified nine major categories of cash-related expense:

1. Start/rebuild drawer costs associated with opening and resetting cash drawers; 
2. Closing-drawer reconciliation and counting; 
3. Cash pickups during shifts;
4. Change-order management;
5. Audits and discrepancy resolution;
6. Preparing and coordinating deposits;
7. Cash-in-transit and bank-deposit costs;
8. Bank fees and related charges; and
9. Cash shrink, including theft, fraud, and unexplained losses.

Once those costs are fully accounted for, IHL estimated that cash handling costs merchants between 4% and 15% of transaction value.

By comparison, the only directly comparable costs for electronic payments are cashier time during payment acceptance and certain bank-service fees that supplement the merchant discount rate. In most cases, the all-in cost of cash transactions equals or exceeds the cost of debit or credit transactions.

Earlier work points in the same direction. Daniel D. Garcia-Swartz, Robert W. Hahn, and Anne Layne-Farrar found that, for typical grocery-store purchases, cash transactions cost more than either credit or debit transactions. Even for transactions as small as $12, cash cost roughly the same as credit and more than debit.

More recent research reaches similar conclusions. The Federal Reserve Bank of San Francisco’s 2019 paper “Cash Me If You Can” concluded that “cashless operations help businesses save on cash handling costs, reduce exposure to theft, and increase the speed of transactions.”

Our own review reached the same bottom line: once all parties to the transaction are considered, electronic payments are generally less costly than cash for most transactions—not the other way around.

That makes Egan et al.’s suggestion that cash users subsidize credit- and debit-card users difficult to sustain. If anything, the economics more likely run in the opposite direction.

The paper’s accounting problem extends beyond merchant costs. Egan et al. also omit—or treat merely as transfers—a long list of benefits funded through interchange revenue.

Those benefits include the fraud-liability protections established under Regulation Z, which caps consumer liability for unauthorized credit-card transactions at $50 and, in practice, usually at zero. They include chargeback and dispute-resolution systems that function as a form of purchase insurance unavailable to cash users or users of direct bank-transfer systems.

They also include the interest-free grace period attached to credit cards. Consumers who pay their balances in full each month effectively receive a short-term, unsecured, zero-interest loan. That is not a trivial convenience. For many lower-income households managing uneven cash flow, it is a genuinely valuable financial tool—and one available regardless of income, FICO score, or financial sophistication.

Credit cards also provide emergency access to liquidity. An available credit line can function as a substitute for precautionary savings, which matters most for households with limited liquid assets.

Then there is the infrastructure itself. Interchange revenue helps fund tokenization, EMV chip technology, contactless payments, 3D Secure authentication, real-time fraud detection, and the broader payment-network innovations consumers now take for granted.

For the very populations the “reverse Robin Hood” narrative purports to protect, these are not incidental benefits. They are first-order features of the system.

A redistribution analysis that ignores them is not merely incomplete. It is measuring the wrong thing altogether.

The Reverse Robin Hood Story Has It Backwards

Putting all this together, the “reverse Robin Hood” story largely falls apart. There may be some degree of cross-subsidization from debit-card users to credit-card users, driven in significant part by the distortions introduced by the Durbin Amendment. But the much larger effect likely runs in the opposite direction: from electronic-payment users to cash users.

Because cash transactions are disproportionately associated with lower-income consumers, that dynamic looks a lot more like Robin Hood than reverse Robin Hood. Wealthier consumers who use premium credit cards help fund a payments ecosystem that lowers costs and expands functionality for everyone else.

So how do Egan et al. arrive at such a different conclusion?

Part of the answer appears on the opening page of the paper itself. The authors argue that: “Existing debates often frame interchange as either a ‘tax’ on merchants or a mechanism to fund consumer rewards. Both views overlook that interchange fees primarily redistribute consumption across consumers …” (p.1).

But that framing omits a crucial fourth possibility—one the authors themselves acknowledge later in the paper:

Fee variation across sectors reflects Visa’s and Mastercard’s attempts to balance merchant acceptance versus issuer incentives, trading off participation on both sides of the platform.

That is not some secondary observation buried in the weeds. It is the core economic logic of payment-card networks.

Ever since William Baxter’s seminal 1983 work, economists have understood payment systems as classic two-sided markets. Jean-Charles Rochet and Jean Tirole later formalized the insight. Payment networks must attract both merchants and consumers simultaneously. Interchange fees function as the balancing mechanism that keeps both sides participating. 

The goal is not redistribution for its own sake. The goal is maximizing the value of the platform across both sides of the market.

Merchants receive faster checkout, higher throughput, lower cash-handling costs, fraud protection, guaranteed payment, and larger ticket sizes. Consumers receive convenience, security, recordkeeping, zero-liability protection, access to credit, and—in the case of premium credit cards—rewards and ancillary benefits that make card use more attractive.

Interchange is the mechanism that funds that ecosystem. It supports issuer investment, fraud prevention, credit risk management, and the incentives that encourage consumers to adopt electronic payments in the first place.

That pricing structure has played a central role in the remarkable migration away from paper-based payments over the past two decades. Cash and checks have steadily given way to cards because electronic payments became faster, safer, more convenient, and often less costly for both sides of the transaction.

Federal Reserve data show the trend continuing. Cards accounted for roughly 66% of transactions in 2025, up from 45% in 2009. Cash usage, meanwhile, fell from roughly 30% to just 13%. 

Premium credit cards have been an important driver of that transition. Higher-value cards use interchange revenue to fund rewards, insurance, fraud protections, and related benefits that induce consumers to participate more heavily in electronic-payment systems. Greater consumer demand, in turn, increases the value of card acceptance to merchants, encouraging adoption of innovations such as contactless payments and increasingly cashless retail environments.

That dynamic remains important because cash still retains advantages for some consumers and some transactions. As the Federal Reserve recently observed:

Despite a small decline from last year, the consistent use of cash year-over-year shows there are certain transactions where people want or need to use cash. This is likely due to several unique characteristics of cash payments that other payment options have yet to fully replicate, such as anonymity, widespread acceptance, instant settlement and reliability as a secondary payment instrument.

In other words, the migration away from cash is incomplete. Premium credit—and potentially more competitive debit products—still plays an important role in encouraging consumers to adopt electronic payments and helping merchants move away from expensive cash-handling systems.

Indeed, one implication of this analysis is that policymakers should seriously reconsider the Durbin Amendment itself, or at minimum relax the Federal Reserve’s interchange caps on covered banks. Doing so would allow debit cards to compete more effectively against both credit cards and cash.

That could benefit lower-income consumers directly through expanded checking-account access and richer debit-card rewards, both of which would promote financial inclusion and reduce reliance on cash. Merchants would benefit as well if more consumers shifted back toward debit, which generally carries lower acceptance costs than credit.

The irony, then, is that the policies sold as fixes for “reverse Robin Hood” redistribution may well undermine one of the most successful financial-inclusion and payments-modernization systems ever created. Sometimes the supposed subsidy story turns out to be the reverse.

The post Robin Hood Carries a Credit Card appeared first on Truth on the Market.

On May 27, after repeated delays, the European Commission is expected to unveil the Cloud and AI Development Act (CAIDA), the centerpiece of its broader “Tech Sovereignty” package. In a new International Center for Law & Economics (ICLE) issue brief published today, I argue that the stricter versions of CAIDA favored by some stakeholders would impose most of their costs on European users, businesses, and public institutions. The package’s implied objective—legal immunity from non-European Union legal systems accessing EU data—is also unlikely to be achievable in practice.

The empirical backbone of the brief comes from SemiAnalysis’ research on the artificial-intelligence infrastructure market. Their numbers, more than the political messaging surrounding the package, make the clearest case against a categorical version of CAIDA.

This post puts those numbers front and center, while pointing readers to the full brief for the legal and policy analysis that follows from them.

The Market Did Not Wait for Europe

Three market realities all point to the same uncomfortable conclusion. None is something the EU can plausibly change fast enough to matter during this regulatory cycle.

Sovereignty Is Not a Compute Cluster

First, Europe does not host the top tier of rentable artificial-intelligence compute infrastructure. SemiAnalysis’ April 2026 “ClusterMAX 2.1” ranking evaluates graphics-processing-unit (GPU) cloud providers on the operational metrics that actually matter for frontier-AI development: how reliably a cluster performs useful work, and how quickly customers can deploy large-scale training jobs.

Across the entire Platinum-through-Silver range—the only tiers where serious frontier-model work happens consistently—the EU accounts for just three providers: Scaleway (France), Gcore (Luxembourg), and Nebius. Nebius, moreover, exists in its current form only because of the 2024 corporate split from Yandex, the Russian technology company.

GPU cloud providers in each tier of SemiAnalysis ClusterMAX 2.1 (April 2026), grouped by country of headquarters. The EU band (highlighted) contains one Gold-tier provider (Nebius, the post-Yandex Dutch entity), one Silver-tier provider in France (Scaleway) and one in Luxembourg (GCORE), and the rest in “Not Recommended.” Country-of-origin classification mine, not SemiAnalysis’s.

Cross-reference those rankings with the Cloud Sovereignty Framework procurement the European Commission completed last month: €180 million over six years, evaluated under the Commission’s Security and Eligibility Assurance Levels (SEAL) framework for legal and operational sovereignty. Only one of the four winning “sovereign” providers ranks in ClusterMAX’s top three tiers.

To be fair, SEAL and ClusterMAX are measuring different things. That is precisely the problem. A provider can score highly on legal sovereignty while performing poorly on the operational metrics that determine whether advanced AI systems can actually be trained and deployed effectively.

The Bottleneck Is a Cleanroom, Not a White Paper

Second, the semiconductor and memory supply chains are already effectively locked in. SemiAnalysis’ “Great AI Silicon Shortage” analysis finds that nearly every major AI-accelerator family has converged on Taiwan Semiconductor Manufacturing Co.’s (TSMC) N3 manufacturing process. AI demand is projected to consume 86% of N3 wafer output by 2027, with effective utilization exceeding 100% in the second half of 2026.

The bottleneck is not money. It is cleanroom capacity, which takes years to build.

The memory market tells a similar story through a different mechanism. SemiAnalysis describes a “once-in-four-decades” high-bandwidth-memory (HBM) supercycle, dominated by just three suppliers worldwide: Samsung, SK Hynix, and Micron. Customers are already signing long-term agreements backed by prepayments simply to secure future allocation.

None of these constraints responds, on any meaningful timeline, to directives from Brussels or the capitals of EU member states. Industrial policy cannot conjure advanced semiconductor fabs out of thin air—at least, not before this regulatory cycle ends.

You Are Not Outbidding Anthropic

Third, the rental market is already sold out, and frontier-AI customers are not about to be outbid. SemiAnalysis’ “Great GPU Shortage” analysis reports that on-demand GPU rental capacity is exhausted across both Nvidia’s Hopper and Blackwell architectures. Capacity scheduled to come online through August and September 2026 is already fully booked.

Prices reflect that scarcity. The H100 one-year contract-price index rose from $1.70 per GPU-hour in October 2025 to $2.35 by March 2026—a roughly 40% increase in just five months for what is now effectively a previous-generation chip.

Meanwhile, Hopper contracts originally due to expire this year are being renewed at the same rates customers agreed to two or three years ago, with terms extended through 2028.

Why are buyers willing to commit at that scale? Because the economics of frontier models have detached from the rest of the market. SemiAnalysis reports that Anthropic’s annualized revenue grew from roughly $9 billion at the end of 2025 to more than $44 billion by spring 2026. During the same period, inference gross margins rose from below 40% to above 70%.

A European entrant into this market—“sovereign” or otherwise—does not arrive as a market-maker. It arrives as a price-taker.

The Price of Sovereignty Is Paid by Users

If those three facts hold, then a version of CAIDA that pushes European users away from non-EU compute providers and application-programming interfaces (APIs) would not create meaningful European capability fast enough to matter during this regulatory cycle. It would, however, raise costs and reduce the quality of the AI systems European users can actually deploy.

Those costs vary by workload, which is worth unpacking separately.

SemiAnalysis’ “Cluster Total Cost of Ownership” methodology estimates that a Silver-tier cluster carries roughly 15% higher total cost of ownership than a Gold-tier cluster for a representative large-language-model (LLM) pretraining workload, even assuming identical GPU-hour pricing.

For any European lab trying to compete at the frontier, that translates into a research-velocity penalty measured in months of engineering time.

Inference workloads—the process by which trained AI models generate outputs for users—look somewhat different. There, the same methodology places the equal-priced Gold-versus-Silver gap below 1%. As the brief explains in greater detail, frontier-model training and frontier-model access through APIs bear sovereignty-related costs differently.

For European businesses and public institutions using Claude, GPT-5, or Gemini through an API, the binding sovereignty constraint is not where a request physically lands. It is whether users retain legal access to the API at all. That is the layer at which most European users actually encounter frontier AI.

The broader problem, developed at length in the brief, is that the categorical approach does not even deliver the legal immunity it implicitly promises.

The “immunity from non-EU law” standard embedded in the European Cybersecurity Certification Scheme for Cloud Services (EUCS) High+ framework assumes that EU headquarters and EU-based data processing sufficiently shield data from the reach of foreign legal systems. Canada’s King v. OVHcloud case is the live counterexample.

In September 2024, the Ontario Court of Justice issued a production order requiring OVHcloud to disclose subscriber data stored on servers in France, the United Kingdom, and Australia. The appeal remains pending.

That the most prominent extraterritorial production order of the past 18 months targeted Europe’s flagship sovereign-cloud provider, involving EU-hosted data, should weigh more heavily in this debate than it has so far.

Digital Sovereignty Is Not Autarky

At the EU level, CAIDA should take a risk-based rather than categorical approach, while preserving member-state subsidiarity for genuinely stricter public-administration requirements, instead of turning them into a single-market default. The genuinely narrow category of residual extraterritorial-risk concerns can already be addressed through Article 9 of the General Data Protection Regulation (GDPR), tailored national-security exceptions, and the proportionality principles that govern public-sector procurement more broadly.

The “build” side of the agenda—where European policymakers actually have leverage—looks very different. It runs through corporate-law reform, financial-single-market integration, and faster, harmonized permitting for data centers and electric-grid expansion.

The European Commission’s proposed “EU Inc.” framework belongs in that conversation, although its current drafting risks dilution through excessive deference to member-state legal autonomy—the same pattern I have criticized in earlier work.

The Commission’s own Joint Research Centre captured the core point with unusual bluntness for a JRC paper: “digital sovereignty cannot be equated with autarky.”

I will return to the package, the Council negotiations, and the EUCS High+ debate as the implementing acts come into view. For now, the key point is simpler than much of the rhetoric surrounding “AI sovereignty” suggests.

Europe’s binding constraints are silicon, capital, power generation, and its own hesitation to enact the corporate-law reforms its technology sector has requested for years—not jurisdiction.

A categorical CAIDA would not change those constraints. It would mostly change who pays for them.

The post You Can’t Regulate a GPU Into Existence appeared first on Truth on the Market.

That is the modern First Amendment problem. Censorship no longer arrives only as an outright ban. More often, it comes dressed as “oversight,” “public interest,” or “compliance”—all perfectly respectable words, right up until they become tools of political pressure.

Recent actions by the Federal Communications Commission (FCC) against Disney-owned ABC have sparked a high-stakes legal fight. This is not merely a technical dispute over “equal time” rules or corporate diversity policies. It points to a troubling pattern of censorship by proxy: government officials leaning on private actors to suppress or reshape speech the government disfavors.

By pushing back, ABC is defending more than its own editorial independence. It is helping hold the line for the “marketplace of ideas.”

The FCC Doesn’t Need a Muzzle When It Has a Licensing Bureau

The core issue is “jawboning.” As I explain in my recent International Center for Law & Economics (ICLE) white paper, “Censorship-by-Proxy: Jawboning in the Marketplace of Ideas,” jawboning occurs when government officials use informal pressure—threats of regulation, litigation, investigations, or license reviews—to coerce private actors into suppressing speech. 

Because the First Amendment bars the government from directly banning protected speech, officials sometimes target intermediaries—the “proxies”—that host, distribute, or amplify it. If the government can threaten a network’s multimillion-dollar broadcast licenses, for example, it does not need to pass a law to get its way. The network may “voluntarily” self-censor to avoid financial ruin.

Shortly after taking office in January 2025, President Donald Trump issued executive orders aimed at ending federal censorship and the weaponization of government. Even so, the subsequent conduct of both the FCC and the Federal Trade Commission (FTC) suggests the underlying tensions remain. Both agencies wield substantial discretionary authority over firms operating in speech-adjacent markets. Recent FCC enforcement and oversight activity—carried out under the agency’s sprawling “public interest” standard—shows how that discretion can still raise familiar First Amendment concerns, even when framed as advancing pro-speech goals. 

The most prominent FCC example discussed in the white paper is the commission’s order requiring ABC to seek early license renewals for its eight owned-and-operated stations. Those licenses were not scheduled for review until 2028-31. The FCC accelerated the process after public calls from the White House to fire Jimmy Kimmel over a controversial late-night monologue.

The FCC says the review concerns Disney’s diversity, equity, and inclusion (DEI) practices and alleged “unlawful discrimination.” The timing, though, has led observers to ask whether the licensing process is being used to influence the network’s editorial decisions.

ABC to the FCC: See You in Court

Recent reporting suggests ABC has decided not to capitulate. In a sharpy worded filing led by veteran Supreme Court litigator Paul Clement, the network argues that the FCC’s actions “threaten to upend decades of settled law and practice and chill critical protected speech.” 

The dispute centers on The View. The FCC is investigating whether the show violated “equal time” rules by featuring a political candidate without offering equivalent airtime to rivals. Those rules generally require broadcasters that give airtime to one legally qualified candidate to offer comparable access to opposing candidates. They also include important exceptions, including for bona fide news programming.

ABC’s response has two parts. First, it argues that The View is, indeed, a “bona fide news program” that has long qualified for exemptions allowing news-oriented programs to cover political figures without hosting every opposing candidate. Second, ABC argues that the equal-time rule itself violates the First Amendment.

The first argument rests on longstanding FCC practice. The commission has treated The View as exempt from equal-time requirements since 2002. The second argument is far more consequential. ABC contends the rule rests on outdated constitutional assumptions that no longer fit the modern media market.

Specifically, the filing argues that the equal-time rule traces back to the FCC’s broad “public interest” authority over broadcasters, which the Supreme Court upheld in Red Lion Broadcasting Co. v. FCC (1969) and related cases. Those decisions relied heavily on the so-called “scarcity rationale”—the idea that broadcast spectrum was so limited that government regulators could exercise greater control over broadcasters than over newspapers or other speakers. 

That logic made more sense in the 1960s and 1970s, when most Americans received news and entertainment from a handful of over-the-air television networks. Today’s media environment looks nothing like that world. As I explain in my ICLE white paper:

The factual assumptions underlying those decisions no longer reflect modern media markets. … Cable television, satellite providers, streaming services, internet-video platforms, podcasts, and social media now compete aggressively for audience attention. Even traditional broadcast content is increasingly consumed through cable, satellite, or internet distribution rather than over-the-air antennas. 

The modern media ecosystem therefore bears little resemblance to the world in which Red Lion and [FCC v. Pacifica Foundation (1978)] were decided. Treating ABC differently from CNN or Netflix because ABC content can theoretically still be accessed through “rabbit ears” increasingly makes little practical or constitutional sense.

ABC makes much the same point, arguing that “Red Lion’s factual predicates no longer obtain…. These justifications were dubious then… But they are unsupportable now in an age of information ubiquity.”

The constitutional problems do not stop there. Even deciding what qualifies as “newsworthy” programming—and therefore merits exemption from equal-time requirements—requires the FCC to make content-based judgments about speech. Outside the broadcast context, courts would almost certainly treat that kind of government line-drawing with deep skepticism.

As ABC put it:

Given the inherent value-laden judgments that the FCC must make when determining whether a particular program is newsworthy, the risk that the FCC will use its authority to suppress disfavored content and viewpoints is overwhelming.

Preserving this scope of FCC authority leaves open the possibility that agency discretion will burden protected speech. That is precisely the sort of censorship and governmental weaponization Trump’s executive orders were supposed to prevent.

The FCC May Have Finally Picked the Wrong Fight

ABC’s pushback makes a court fight increasingly likely over how far the FCC’s “public interest” authority extends into editorial content. If that happens, the case could become a vehicle for the Supreme Court to revisit Red Lion itself.

There are already signs the justices may be open to doing so. Both Justice Clarence Thomas and the late Justice Ruth Bader Ginsburg suggested, from opposite ends of the ideological spectrum, that the Court should reconsider Red Lion. An FCC overreach case involving The View or Jimmy Kimmel could provide the opportunity.

That would likely be a good thing. The First Amendment rests on the premise that editors—not regulators—decide what counts as newsworthy speech. If the FCC can decide which programs qualify as “news” based on whether regulators approve of the host’s politics, then the “public interest” standard starts looking less like neutral oversight and more like a mechanism for state-influenced media control.

The practical consequences matter, too. If every controversial monologue, interview, or joke can trigger license scrutiny, broadcasters will inevitably become more cautious. That is the classic “chilling effect” in First Amendment law: speakers self-censor because the risks of speaking freely become too high. Over time, only the safest and most government-friendly content survives.

By forcing the FCC to defend its actions in court, ABC is demanding something fundamental: that administrative agencies remain bound by constitutional limits. Put differently, ABC is insisting that the government cannot use sprawling regulatory discretion to accomplish indirectly what the First Amendment forbids it from doing directly.

Keep the Government Out of the Writers’ Room

The marketplace of ideas works only when decentralized private actors—broadcasters, platforms, publishers, and creators—remain free to exercise their own editorial judgment. Once the government starts using its regulatory teeth to shape those decisions, the marketplace stops functioning as a marketplace and starts looking more like managed speech.

ABC’s refusal to yield to the FCC is therefore about far more than Jimmy Kimmel or The View. Whether you find either program insightful, obnoxious, funny, or unbearable is beside the point. What matters is that government officials should not be able to jawbone media companies into silence by dangling licensing threats and regulatory scrutiny over their heads.

That principle cuts to the core of the First Amendment. A free society cannot preserve open debate if agencies can pressure speakers indirectly whenever direct censorship would be unconstitutional.

In this fight, ABC is not merely defending a pair of television programs. It is defending the idea that the government does not get to decide who may speak, what counts as “news,” or which viewpoints are too risky to broadcast. And if the FCC’s tactics ultimately lead the courts to bury Red Lion once and for all, that may be the funniest punchline Jimmy Kimmel never had to write.

The post The FCC Wants Final Cut appeared first on Truth on the Market.

That is the frame for a deceptively basic question: How should consumer-financial law foster a stronger competitive environment?

The question arises from the Consumer Financial Protection Bureau (CFPB) Taskforce’s report on federal consumer-financial law, published in January 2021. Chapter 8 of the report’s first volume emphasized a point too often lost in consumer-protection debates: competition is not separate from consumer protection. In many markets, competition is consumer protection.

When consumers can choose among rival providers, switch accounts, transfer their data, compare prices, and obtain credit from lawful new entrants, they are less vulnerable to poor service, excessive pricing, and exclusion. When law fragments markets, raises fixed compliance costs, or shields incumbents from competition, consumers often end up with fewer choices, not better protection.

The Taskforce drew on an earlier warning from the National Commission on Consumer Finance, which argued in 1972 that “fractionalized” legislation and regulation should be reviewed to ensure both free entry by firms and fair treatment of consumers. That warning has aged unfortunately well.

Developments since 2021—in law, regulation, litigation, and scholarship—do not support a simple call for more regulation. They point instead toward better regulation: rules that reinforce competitive markets rather than displacing them. The most promising reforms are structural and market-reinforcing: clearer allocations of federal and state authority, greater legal certainty for fintech-bank partnerships, workable consumer-data portability, improved credit-information systems, and more disciplined agency analysis of competitive effects.

That emphasis also echoes the CFPB Taskforce report. As Chapter 6 of that report explained:

Market-reinforcing regulation refers to regulatory action designed to “promote competition and consumer choice so that consumers can find those products that they think are best for themselves and their families.”

Market-reinforcing regulation is consistent with the disclosure-based regulatory strategy of the past several decades that is designed to help markets function and to satisfy consumer demand more effectively by enabling consumers to shop more easily among competing product providers. It also includes vigorous prosecution of fraud, deception, and other unlawful practices that undermine consumer choice.

That description remains as accurate as ever.

When Consumer Protection Is Incumbent Protection

Consumer finance does not suffer from a lack of government involvement. It is already governed by a dense web of federal and state regulation: the Truth in Lending Act (TILA), Equal Credit Opportunity Act (ECOA), Fair Credit Reporting Act (FCRA), Electronic Fund Transfer Act (FCRA), Fair Debt Collection Practices Act (FDCPA), Real Estate Settlement Procedures Act (RESPA), Home Mortgage Disclosure Act (HMDA), Gramm-Leach-Bliley Act (GLBA), state licensing regimes, state usury laws, prudential supervision, CFPB supervision, Federal Trade Commission (FTC) enforcement, state attorneys general, and private litigation.

The larger problem is not too little regulation. It is poorly targeted regulation. Some legal rules make markets work better. They deter fraud, require accurate disclosures, prohibit discrimination, keep payment systems reliable, and make contractual promises enforceable. Those rules can strengthen competition by giving consumers confidence to participate in markets.

Other rules can do the opposite. They raise entry costs, impose vague or open-ended liability, restrict truthful information, create state-by-state fragmentation, and substitute administrative price-setting for consumer choice. In practice, consumer protection can become incumbent protection.

So the question is not whether consumer finance should be regulated. It plainly already is. The better question is which rules increase consumer choice, market entry, accuracy, and accountability—and which ones suppress them.

New Financial Products, Old Legal Boxes

Since 2021, consumer finance has become more digital, more data-driven, and more dependent on partnerships. Consumers may experience a product as a single app or transaction. Behind the scenes, though, that product may involve a chartered bank, fintech platform, payment processor, data aggregator, servicer, cloud provider, program manager, and investor.

That layered structure can produce real benefits. It can lower distribution costs, reach consumers underserved by branch-based banking, create products better tailored to household cash-flow patterns, and intensify competition against large incumbent firms. It also creates legal uncertainty: Who is the lender? Who is responsible for disclosures? Who bears liability for fraud? Which state laws apply? Which federal regulator has supervisory authority?

Traditional legal categories do not map neatly onto modern market structures. Regulators that force every new financial product into an old legal box risk smothering useful innovation. Regulators that do nothing risk inviting evasion and consumer harm. The better approach is product-neutral regulation: similar consumer risks should receive similar legal treatment. But regulators should not assume that every new financial product is simply an old one wearing a hoodie.

When ‘Security Concerns’ Mean ‘Please Don’t Leave’

Consumer-authorized data portability may be the most promising competition reform in consumer finance. Data create switching costs. A consumer’s transaction history, income flows, recurring payments, balance patterns, and repayment behavior can help a rival provider offer a better account, loan, payment tool, budgeting service, or underwriting decision. Without meaningful data portability, incumbents can effectively hold consumers hostage. The consumer may “own” the relationship in theory, but the incumbent controls the data in practice.

That is why the CFPB’s Section 1033 open-banking rulemaking addresses a real competition problem. Open banking simply means that consumers can access their financial data and authorize third parties to use it securely. The hard part is implementation.

A workable open-banking framework should do five things. First, it should begin with the consumer’s own right to access usable, machine-readable data. Second, third-party access should depend on clear, revocable consumer authorization. Third, liability rules should be straightforward. If a consumer authorizes a third party and that third party misuses the data, the data provider should not face open-ended liability absent its own fault. At the same time, data providers should not be allowed to block access through conveniently vague “security concerns.”

Fourth, technical standards should evolve through private standard-setting. Government should define the relevant outcomes—security, authentication, revocation, availability, and auditability—without freezing a particular technology stack into law. Fifth, privacy rules should remain simple and enforceable. Data should be used only for the consumer-requested service, retained only as long as necessary, and deleted after authorization is revoked, subject to legitimate legal obligations.

If designed well, open banking can improve switching, refinancing, budgeting, income verification, small-business lending, and consumer choice. If designed poorly, it can become yet another compliance maze that only the largest firms can afford to navigate.

Bank-Fintech Partnerships Need Rules, Not Guesswork

Bank-fintech partnerships are now central to competition in consumer finance. These arrangements can help smaller banks reach new customers and allow fintech firms to offer products nationwide. They may also expand credit access to consumers underserved by traditional banks. The partnerships nonetheless raise a legitimate concern: some may exist primarily to evade state usury or licensing laws while leaving the bank as little more than a nominal participant.

The answer is neither blanket hostility toward bank-fintech partnerships nor blanket immunity for them. What is needed instead is a clear federal “true lender” framework. In plain English, true-lender rules determine who legally counts as the lender when a bank and a fintech work together. That matters because banks and nonbanks may face different rules, including different state interest-rate limits and licensing requirements.

A bank should qualify as the lender when it meaningfully controls underwriting, approval, funding, compliance, and the loan’s key economic terms—and when it retains genuine responsibility and economic exposure. By contrast, a fintech should be treated as the lender when the bank merely rents out its charter while the fintech controls the real economics of the transaction. If the bank is basically a regulatory mascot, courts and regulators should say so plainly.

The key point is that the governing rule should be clear, federal, and knowable in advance. The current regime of state-by-state litigation and after-the-fact multi-factor balancing tests creates pervasive uncertainty. That uncertainty favors large incumbents and well-capitalized firms that can absorb legal risk. Smaller entrants often cannot. In that sense, legal uncertainty is not just a legal problem. It is a barrier to entry.

Not Every Unpopular Fee Is a Market Failure

Another major development since 2021 has been the regulatory campaign against so-called “junk fees,” including credit-card late fees and overdraft charges. Some consumer concerns are real. Fees may be poorly disclosed. Consumers may underestimate their frequency or magnitude. Financial institutions may manipulate transaction-posting order to maximize overdrafts. Repeated overdraft or representment fees—fees charged when a previously rejected payment is submitted again—can surprise consumers and create real hardship.

Still, the mere fact that a fee is unpopular does not establish market failure. The relevant policy question is whether the fee is deceptive, coercive, unavoidable, unrelated to cost or risk, or insulated from competition by switching costs. If so, targeted intervention may be appropriate.

Broad price caps, by contrast, can produce unintended consequences. If overdraft credit becomes unavailable, some consumers may instead face declined transactions, late rent payments, payday loans, pawn transactions, or informal borrowing from friends and family. If late fees are capped below their cost or deterrence value, issuers may respond through higher interest rates, annual fees, tighter underwriting, or reduced access for higher-risk consumers.

That does not mean all fees are justified. It means reform should focus first on competition and transparency: real-time alerts, clearer opt-in rules, limits on manipulative practices, easier account switching, low-cost account competition, and targeted enforcement against deception. Consumer protection should not become a backdoor system of price administration absent strong evidence that price controls will improve consumer welfare after accounting for reduced access and substitution effects.

You Can’t Price Risk With a Blindfold On

Credit reporting is another area where competition and consumer protection intersect. Accurate credit information can expand access to credit and lower borrowing costs. Inaccurate information can wrongly exclude consumers from the market or force them to pay more than their actual risk justifies. The policy goal should be a credit-information system that is accurate, contestable, and competitive.

Recent debates over medical-debt reporting illustrate the tension. Medical debt can be noisy data. It may reflect insurance disputes, billing errors, or administrative confusion more than genuine creditworthiness. Those are legitimate concerns. At the same time, broad suppression of truthful information can reduce predictive accuracy, create cross-subsidies among borrowers, and raise costs for consumers who would otherwise benefit from more accurate risk differentiation.

The better approach is not politically popular deletion of disfavored data categories. It is to improve the underlying system: better accuracy standards, stronger dispute-resolution procedures, clearer furnishing requirements, improved identity matching, and more rigorous model validation. The key questions should be straightforward: Is the information accurate? Is it predictive? Was it fairly obtained? Can consumers meaningfully contest it? If the answer to those questions is no, policymakers should fix those problems directly.

Competitive credit markets depend on good information. Restricting information may feel consumer-friendly in the short run, but it can reduce credit availability over time.

A National App Shouldn’t Need 50 Different Licenses

The CFPB Taskforce’s warning about “fractionalized” regulation is especially relevant today. Digital finance is national. Consumers use the same financial apps across state lines. Fintech products scale nationally. Bank-fintech partnerships routinely operate across multiple jurisdictions. Yet many nonbank providers must navigate a maze of state-by-state licensing regimes, disclosure mandates, interest-rate limits, and enforcement risks.

Some state experimentation is valuable. States can identify local abuses, enforce anti-fraud laws, and serve as regulatory laboratories. But fragmentation can also become a form of economic protectionism. The costs of complying with 50 overlapping regulatory systems do not fall evenly. Large incumbent firms can absorb them. Smaller entrants often cannot. The result may be fewer competitors rather than safer markets.

That is why some form of federal chartering or passporting deserves renewed attention. A federal charter or passport would allow a qualified firm to operate nationally under federal standards, rather than securing separate permission in every state. Congress should consider creating an optional federal consumer-finance charter or passport for nonbank lenders, payment firms, and data intermediaries that satisfy robust federal standards.

Such a charter should not function as a regulatory loophole. Firms should still meet meaningful requirements for capital, compliance systems, cybersecurity, fair-lending controls, complaint handling, examination, and resolution planning calibrated to their risk profile. In exchange, qualifying firms should receive genuine nationwide operating authority, along with preemption of duplicative or inconsistent state rules that materially obstruct market entry.

After all, if consumers can use the same app in all 50 states, it is not obvious why the provider should need 50 different permission slips to operate it.

A well-designed federal framework could preserve consumer protection while reducing artificial barriers to competition.

Regulatory Whiplash Favors Incumbents

Recent Supreme Court decisions will also shape the future of consumer-finance policy. In CFPB v. Community Financial Services Association of America (2024), the Court upheld the CFPB’s funding structure, eliminating one existential challenge to the Bureau’s continued operation. At the same time, the Court overruled Chevron deference in Loper Bright Enterprises v. Raimondo (2024). Under the old Chevron framework, courts often deferred to an agency’s “reasonable” interpretation of an ambiguous statute. After Loper Bright, agencies can no longer rely as heavily on ambiguity to expand their authority. Courts will exercise independent judgment over statutory meaning.

For consumer finance, that shift should encourage a more disciplined regulatory environment. Rules that rest on clear statutory text, substantial evidence, and careful attention to reliance interests are likely to prove more durable. Rules that stretch older statutes into new markets through aggressive analogy are likely to face greater judicial skepticism.

That matters for competition because regulatory whiplash is itself anti-competitive. Large firms can usually survive prolonged uncertainty. Smaller firms often cannot. When a rule is adopted, stayed, litigated, revised, vacated, and revived, the practical beneficiary is frequently the incumbent firm with the deepest legal budget and the highest tolerance for uncertainty.

Post-Loper Bright, regulators should become more careful, not less active. The CFPB and other agencies should rely on clearer statutory authority, adopt narrower and more targeted rules, and support those rules with stronger economic analysis.

Every Consumer Rule Has a Competition Effect

One useful institutional reform would be to create a more serious competition-impact analysis function within the CFPB. The Bureau already has statutory objectives that include promoting access, encouraging innovation, and reducing unwarranted regulatory burdens. In practice, though, those goals are often overshadowed by enforcement and rulemaking priorities.

The CFPB’s existing competition office could play a more meaningful role by reviewing major rules and enforcement initiatives for their effects on market entry, small providers, product availability, switching costs, and consumer access. Before adopting a major rule, the Bureau should ask a few basic questions: Does this rule increase fixed compliance costs? Does it favor large incumbents over smaller entrants? Does it reduce access for higher-risk consumers? Does it restrict truthful information? Does it create vague or unpredictable liability? Does it impede interstate entry? Are there less restrictive alternatives?

That kind of analysis would not weaken consumer protection. It would recognize an often-overlooked reality: competition effects are consumer-protection effects. Put differently, if a regulation leaves consumers with fewer choices, fewer entrants, and higher switching costs, the Bureau should at least ask whether it is solving one problem by quietly creating another.

How to Regulate Consumer Finance Without Smothering It

The reform agenda follows from that basic principle. Congress should create an optional federal consumer-finance charter or passport, allowing qualified nonbank firms to operate nationally under strong federal standards without navigating duplicative state barriers.

Congress or the banking agencies should establish a clear federal framework for true-lender and valid-when-made rules. The valid-when-made doctrine generally means that a loan valid when made by the original lender does not become invalid merely because it is later sold or assigned.

Responsible bank-fintech partnerships should receive legal certainty. Sham arrangements should not.

Open banking should be implemented as secure competition infrastructure: consumer access rights, authorized data sharing, clear liability rules, revocation mechanisms, data minimization, and flexible technical standards that can evolve over time.

Regulators should favor targeted conduct rules over broad price caps. The focus should be deception, manipulation, surprise, and lack of meaningful consumer choice—not simply the existence of unpopular fees.

Disclosure rules should be modernized. Consumers need timely, salient, digital disclosures about total cost, repayment timing, default consequences, cancellation rights, and dispute rights. They do not need another stack of unread legal forms that everyone scrolls past at the speed of light.

Credit-reporting reform should focus on accuracy, dispute rights, furnishing standards, and competition among data providers—not broad suppression of truthful information absent careful evidence that doing so improves consumer welfare.

The CFPB should conduct meaningful competition-impact analysis for major rules and enforcement initiatives.

Finally, policymakers should preserve state experimentation while limiting protectionist fragmentation that materially obstructs federally supervised national competition.

Two objections are likely. The first is that market-oriented reform may leave consumers exposed. That objection misses the point. This agenda is not laissez-faire. It supports strong enforcement against fraud, deception, discrimination, data misuse, and coercive surprise. It supports meaningful dispute rights, privacy protections, and fair-lending controls. What it rejects is the assumption that more prescriptive regulation necessarily produces greater consumer welfare.

Consumers are harmed not only by bad products. They are also harmed by missing products: credit they cannot obtain, accounts they cannot open, data they cannot move, and competitors that never enter the market.

The second objection is that federal preemption or chartering could weaken state consumer protection. That concern is legitimate. Any federal charter should come with strong baseline obligations and genuine supervision. But state fragmentation can also undermine consumer welfare. If overlapping compliance regimes prevent firms from entering the market, consumers may end up with fewer choices and higher prices.

A national market needs at least some national rules. The real challenge is distinguishing legitimate state consumer protection from rules that operate mainly as entry barriers with a consumer-protection label attached.

The Best Consumer Protection May Be the Exit Door

The central lesson of the CFPB Taskforce report remains compelling: consumer protection and competition should be understood together, not treated as opposing goals.

A healthy consumer-finance market is one in which consumers can compare products, switch providers, authorize data sharing, access lawful credit, challenge errors, and choose among meaningful rivals. It is also a market in which firms know the rules before they enter.

Since 2021, the need for that framework has become even clearer. Digital finance, open banking, fintech partnerships, payment apps, buy now, pay later (BNPL), and alternative data have made consumer finance more dynamic and potentially more competitive. At the same time, legal uncertainty and regulatory fragmentation have grown.

The best path forward is neither endless new regulation nor deregulation for its own sake. It is market-reinforcing regulation: clear rules against fraud and abuse, strong information rights, secure data portability, workable federal pathways for responsible entry, and disciplined attention to competitive effects.

If policymakers want stronger consumer protection, they should not underestimate the protective force of competition itself.

Consumers are often safest not when regulators eliminate every risk, but when they can walk away from firms that treat them badly.

The post The Exit Door Theory of Consumer Finance appeared first on Truth on the Market.

Meta has launched Incognito Chat with Meta AI, a way to talk to an artificial intelligence assistant on WhatsApp and the Meta AI app under privacy guarantees that, as Mark Zuckerberg puts it, are “similar to how end-to-end encryption means no one can read your conversations, even Meta or WhatsApp.” The system runs server-side AI inference—meaning the AI processes requests on remote servers rather than on your device—inside a hardware-isolated environment known as a trusted execution environment (TEE). In principle, that means even Meta cannot inspect users’ chats. Meta pairs that architecture with stateless processing, anonymous routing, and at least partially verifiable transparency measures.

Two design tradeoffs deserve attention because, in my view, Meta made the right call on both. First, the architecture makes personalized advertising harder. Second, it constrains trust-and-safety systems that depend on human review of flagged conversations. Both choices will draw criticism—from inside the company and from outside advocates of broader monitoring powers. Both are worth defending before the inevitable pressure campaign begins.

I have argued previously that hardware-backed confidential computing offers the most credible path to combining frontier-grade AI capabilities with meaningful data security. I have also argued that voluntary technical self-restraint by AI providers strengthens the case for legal protections for AI conversations. Incognito Chat is the first major consumer AI product that, at least on its face, appears to satisfy both halves of that argument.

My initial reaction, then, is simple: more of this, please—and from every provider.

Privacy by Hardware, Not by Pinky Promise

The privacy claim matters only if you understand, at least roughly, how the system works. End-to-end encryption protects a message while it travels between two endpoints. But the computer running the AI model is itself an endpoint. To generate a response, it has to see the message in plaintext. Without additional engineering, “AI in WhatsApp” would simply mean your messages arrive on Meta’s servers in readable form.

The trick is to make the AI endpoint itself something Meta cannot inspect.

That is the role of the TEE. In the architecture Meta describes in its Private Processing white paper, the AI workload runs inside an AMD confidential virtual machine, paired with NVIDIA confidential-computing graphics processing units (GPUs). The hardware encrypts the virtual machine’s memory and prevents inspection by the host operating system, the hypervisor (the software layer that manages virtual machines), system administrators, or Meta employees.

The key guarantees include:

• The confidential virtual machine uses attested code. Before a user’s device sends data, it cryptographically verifies that the server is running the specific software image Meta publicly committed to—and nothing else.
• Processing is stateless. The system decrypts a request, processes it, and discards it. No persistent log or long-term key-value cache of conversations remains.
• Routing is oblivious. Requests pass through a third-party relay operated by Fastly, which hides the user’s IP address from Meta. Anonymous credentials further prevent Meta from linking requests to identified users.
• Critical software artifacts are committed to an append-only transparency log hosted by Cloudflare. In plain English: Meta cannot silently swap in different code without leaving a public trace.

None of this is perfect. Meta acknowledges, for example, that NVLink connections between GPUs are not yet encrypted on the deployed hardware and that GPU memory itself remains unencrypted. AMD also does not guarantee protection against certain side-channel attacks—attacks that infer information indirectly through timing, power use, or similar signals—within its threat model. Several of Meta’s verifiable-transparency commitments, particularly third-party researcher access to auditable artifacts, also remain only partially fulfilled.

I flagged that transparency gap in my earlier piece, and I am flagging it again here.

Still, the architecture is fundamentally right. More importantly, the remaining risks are the right kinds of risks: vulnerabilities that would require sophisticated, large-scale attacks, rather than a routine subpoena or an employee with access to the right internal database.

For users, the practical upshot is straightforward: no one—not even Meta—should be able to read these conversations. At least for now, Incognito Chat is text-only, conversations are not saved by default, and messages disappear unless users choose otherwise.

Confidentiality That Survives a Subpoena

Users ask ChatGPT, Claude, and Meta AI about their lab results, medications, mental health, relationships, finances, and potential legal exposure. Treating that as merely “the user’s responsibility” misses the point.

First, this is exactly the sort of information least suited to leakage. Much of it is inherently sensitive. Much of it would also qualify under the European Union’s General Data Protection Regulation (GDPR), as a “special category” of personal data under Article 9, which triggers heightened legal protections. And a sufficiently large breach—or a sufficiently aggressive data-preservation order, like the one issued against OpenAI in The New York Times litigation—could expose millions of intimate, decontextualized fragments of conversations between people and AI systems.

Second, the costs of weak privacy are real. A person who does not feel safe asking an AI assistant about a troubling symptom, a possible medication interaction, or a personal crisis may simply stay silent. That forecloses a category of benefits—better access to information, better opportunities for expression—that European fundamental-rights doctrine treats as carrying substantial weight.

This connects directly to the argument I previously made for something like “AI privilege.” OpenAI CEO Sam Altman has discussed extending professional-secrecy-style protections to conversations with AI, and I am broadly sympathetic to that direction. But the strongest version of that argument requires a credible technical foundation.

Providers cannot merely promise discretion. They need to show they could not betray user confidences even if they wanted to—or even if governments compelled them to try.

Meta’s Incognito Chat is the first meaningful demonstration of that proposition in a mass-market messaging context. It establishes a technical baseline that legal protections can plausibly map onto. Just as importantly, it creates a benchmark that regulators and courts can reasonably expect privacy-by-design AI deployments to approach, rather than dismiss as exotic or impractical.

If we want courts to treat AI conversations as something closer to private reflection than ordinary cloud storage, the industry has to make those conversations look, technically, more like private reflection.

This is what that looks like.

The Tradeoffs Are the Point

Two tradeoffs here deserve special attention because they impose real costs on Meta. I expect both to generate pushback—from inside the company, from regulators, and from the broader trust-and-safety ecosystem.

Even so, both are reasons to give Meta credit.

First, a genuinely confidential AI architecture constrains behavioral advertising. If Meta cannot read users’ conversations, it also cannot freely mine those conversations for ad targeting or model training. That limitation cuts directly against the incentives of the modern ad-tech stack.

Second, meaningful confidentiality limits moderation systems that depend on human review of flagged conversations. Once content leaves the TEE for inspection, the “no one—not even Meta—can read your conversations” promise starts to weaken in ways that are hard to cabin and harder to explain.

Those are not incidental side effects. They are the architecture working as intended.

The Ad-Tech People Are Not Going to Love This

Inference inside a TEE, combined with stateless processing and no exfiltration channel, means Meta cannot read what users ask Meta AI. It also means Meta cannot use those conversations to train ad-targeting systems or feed their contents into real-time advertising auctions.

For a company whose business still depends heavily on behavioral advertising, that is not a minor concession. It is exactly the sort of concession that, in my experience, tends to generate substantial internal friction long before a product ever ships.

At the same time, this is not—and should not be—a permanent ban on monetization.

One could imagine architectures that allow ads to flow into the TEE while tightly constraining what flows back out. For example, ad creatives could enter the confidential environment and match against conversation content there, while only deterministic, predefined, aggregated, and otherwise constrained signals—say, bucketed impression counts—leave the system. The attested code would need to enforce those limits, publish them to the transparency log, and ideally combine them with differential-privacy-style noise. That kind of noise makes individual users harder to identify in aggregate data and helps prevent the output channel from becoming a covert way to smuggle conversation data back out.

There are real design challenges here. Click-through measurement, for example, gets complicated once a user action leaves the TEE boundary. There is also plenty of room for the adtech industry to overreach, as it reliably does when presented with a new data source.

Still, Meta has adopted the right starting position: no targeting based on conversation content unless and until the company builds a privacy-preserving mechanism that is technically constrained, cryptographically attested, and transparently auditable.

That is effectively the inverse of the familiar real-time-bidding (RTB) model, in which companies broadcast user data first and draft rules about its use later.

The Safety Team Hits a Cryptographic Boundary

Meta’s framing in the Reuters briefing is notable for what it does not say. A company representative told reporters that “the AI will also have built-in safety guardrails, refusing to answer problematic questions or steering conversations in different directions.” Read carefully, that describes model-level guardrails—refusals trained or prompted into the model operating inside the TEE—and little else.

There is no mention of human review. No mention of flagging “problematic” conversations for downstream moderation. No mention of a separate classifier system reporting user prompts outside the confidential environment.

That omission matters.

Meta’s white paper emphasizes a principle of “non-observability.” Under that design, classification can occur inside the TEE, but even the size or timing of traffic between an internal classifier and the orchestration system should not reveal anything about the classifier’s output to the outside world.

To see why this is significant, compare Incognito Chat with the standard architecture used by major web-based AI chatbots. In a typical ChatGPT, Claude, or Gemini deployment, user prompts are processed in cleartext on the provider’s servers, alongside a trust-and-safety stack that usually includes:

• classifier models that score prompts and outputs for categories such as self-harm, child sexual-abuse material, weapons, or fraud;
• rule-based denials and topic blocklists;
• refusal behaviors trained through reinforcement learning from human feedback (RLHF);
• and, critically, escalation paths that route some conversations to human reviewers and, in narrow circumstances, to law enforcement or other authorities.

OpenAI recently made that structure explicit when discussing planned client-side encryption features. The company described “fully automated systems to detect safety issues,” with human review reserved for “serious misuse and critical risks—such as threats to someone’s life, plans to harm others, or cybersecurity threats.”

That escalation layer is precisely what Meta does not appear to replicate inside Incognito Chat. And for good reason. The moment flagged content leaves the TEE for outside review, the no-access promise starts to unravel.

I think Meta made the right call here.

Model-level guardrails are imperfect. Motivated bad actors can sometimes circumvent them, just as they can circumvent classifier systems. But the virtue of the model-level approach is its conceptual honesty: a refusal generated inside the TEE creates nothing that exits the TEE.

Once a flagging-and-review mechanism gets bolted onto this architecture, users immediately face two questions they cannot answer for themselves: What kinds of content trigger a flag? And what happens to flagged content after it crosses the boundary of the confidential environment?

Whatever enters a human-review queue necessarily exists in cleartext outside the TEE. At that point, it falls back into precisely the threat model the TEE was designed to neutralize: subpoenas, broad preservation orders, compelled disclosure, external breaches, and insider misuse.

The promise quietly shifts from “no one—not even Meta—can read your conversations” to something much weaker: “no one can read most of your conversations, although we cannot tell you in advance which ones we might read.”

If that position eventually becomes politically or regulatorily untenable—say, because regulators conclude that model-level safety alone is insufficient for a consumer product operating at WhatsApp’s scale—there is a possible fallback. It is also, in my view, a substantially worse design.

Meta could run a second large language model (LLM) safety classifier inside the TEE that monitors conversations and exfiltrates only narrowly defined categories of flagged content for human review. Whether such a system could remain meaningfully attestable is itself debatable. Even assuming it could, two major problems would remain.

First, motivated actors would still try to game the classifier, just as they already game model-level guardrails. Red-team exercises demonstrate this constantly.

Second, using an LLM as the gatekeeper makes the user-uncertainty problem nearly impossible to solve. There is no stable rulebook Meta could realistically publish because the classifier itself is probabilistic. Its outputs can shift based on wording, surrounding context, or silent model updates.

An ordinary user thinking out loud about a worrying symptom or a medication interaction would have no reliable way to know, ex ante, whether the system might classify the conversation as “self-harm adjacent” or “drug-related” and route it for review. Nor would the user know whether those messages could wind up in a queue subject to discovery demands, data breaches, or insider access.

At that point, the privacy promise effectively collapses into: “Trust us to behave reasonably about what we exfiltrate.”

Refreshingly, Incognito Chat appears to be trying very hard not to make that promise.

The Privacy Baseline Just Moved

The first thing I hope is that Meta closes the remaining verifiable-transparency gaps quickly. That means publishing the binaries corresponding to entries in the Cloudflare transparency log, broadening third-party researcher access to auditable artifacts, and shortening coordinated-disclosure timelines where possible. The white paper says most of the right things. Now the product needs to finish implementing them.

Second, I hope the other major AI providers—OpenAI, Anthropic, Google, and the cloud platforms hosting them—converge on some version of this architecture. OpenAI’s recent client-side-encryption announcement is a promising signal. But as I argued at the time, the implementation details will determine whether the result amounts to genuinely comparable confidentiality or something softer. The key questions are where safety detection occurs—on-device, inside a TEE, or somewhere else entirely—and how transparently the system exposes those choices.

Anthropic, meanwhile, has a particularly strong incentive to move in this direction. A company that has built much of its public identity around AI safety is well-positioned to show that “safety” and “the provider can read every conversation” are not synonymous.

Third, I hope EU policymakers treat Incognito Chat as a benchmark rather than a regulatory inconvenience. There is a real risk that TEE-based systems will strike some regulators as frustrating precisely because they limit visibility and control. I have written previously (here and here) about the tendency to treat strong privacy architectures as obstacles rather than achievements.

That gets the analysis backward. This is what a fundamental-rights-respecting AI architecture actually looks like. Policymakers should encourage other providers to match it—and in some contexts, perhaps require them to.

Finally, and most speculatively, I hope we begin developing a serious legal vocabulary for AI privilege that takes technical architecture seriously.

Legal privilege has always depended on a combination of professional obligation and practical limitation. A lawyer cannot disclose information the lawyer never possessed. Confidential computing creates an analogous structure for AI systems: a provider commitment to privacy, a technical inability to access user conversations, and an external audit trail through verifiable transparency mechanisms.

If society wants AI systems deployed at scale for genuinely sensitive use cases—health, mental health, legal advice, financial guidance—then the law arguably should reward providers that adopt architectures like this, rather than punish them for the resulting blind spots.

The alternative is an AI ecosystem built around the assumption that every intimate conversation must remain readable by someone, somewhere, just in case. That is not a safety model. It is a surveillance model.

The post The Blind Spot Is the Point: Meta’s Incognito Chat and the Future of Private AI appeared first on Truth on the Market.

In Brussels, Article 6(7) of the Digital Markets Act (DMA) has begun to suffer a similar fate. The DMA is the European Union’s flagship law for regulating large digital platforms, which it calls “gatekeepers.” Article 6(7) is supposed to require those gatekeepers to make certain hardware and software features interoperable—that is, usable by rival services—while preserving their ability to protect security, privacy, and system integrity.

Increasingly, though, the provision gets invoked with great solemnity in every new specification proceeding the European Commission opens, while its content keeps shifting to mean whatever the Commission needs in a given case. The trajectory of enforcement—from Apple’s iOS connected-devices proceedings last year to the current Google Android artificial-intelligence (AI) proceedings—suggests the Commission increasingly treats Article 6(7) less like a legal text with internal structure and more like a slogan: interoperability now, for everyone, on whatever terms Brussels prefers.

That is unfortunate, because Article 6(7) is not a blank check. It has two distinct halves. It requires gatekeepers to provide “effective interoperability with, and access for the purposes of interoperability to, the same hardware and software features” enjoyed by their own services. But it also expressly permits integrity measures that are “strictly necessary and proportionate.” Recital 50 of the DMA confirms that such safeguards are a legitimate part of implementation, not a loophole to be sheepishly apologized for after the fact.

Properly read, Article 6(7) establishes a balancing test, not a maximalist openness mandate. “Effective” does not mean “identical,” and “identical” does not mean “unlimited.”

The Commission’s enforcement to date has steadily read the second half out of existence. The result is weaker security, less inter-platform competition, and a regulatory tool that increasingly puts a thumb on the scale in the rapidly unfolding generative-AI race—often to the detriment of European consumers.

The Curious Disappearance of ‘Proportionate’

The first real-world stress test came in late 2024 and early 2025, when the European Commission opened specification proceedings against Apple. Those proceedings concerned iOS interoperability with connected devices and the process through which third-party developers can request access to iOS features. 

The features at issue were relatively well-defined: near-field communication (NFC) pairing, Bluetooth, Wi-Fi accessory configuration, AirDrop, AirPlay, notification forwarding, and related functions. In ordinary English, this was about how well non-Apple devices—headphones, watches, fitness trackers, speakers, and the like—could work with the iPhone.

What made the case interesting—as the International Center for Law & Economics (ICLE) pointed out in its January 2025 comments—was that these features were already meaningfully open. Third-party headphones, fitness trackers, and smartwatches already worked on the iPhone. Pairing was generally reliable. Battery information was accessible. First-party apps from Sony, Bose, JBL, and Fitbit extended the experience further.

What Apple’s own devices received on top of that was mostly a smoother pairing process and a handful of display and design refinements. Those are precisely the sort of product-differentiation features Apple plausibly needs to compete against the broader Android ecosystem in the first place.

Forcing parity at the level of system access—in pursuit of parity at the level of the pairing animation—was a textbook example of a measure that is not “necessary and proportionate.” The marginal gains for contestability, or the ability of rivals to compete, were trivial. The risks were not.

The July 2024 CrowdStrike incident, which knocked airlines, hospitals, banks, and other critical systems offline for hours, illustrated the problem vividly. Investigations part of the issue to a 2009 European Union agreement requiring Microsoft to grant third-party security software the same kernel-level access as Microsoft’s own tools. The kernel is the core layer of an operating system—the part with near-total control over the device. Giving outside software that kind of access is not like letting someone borrow a charging cable. It is more like handing over the master key to the building and hoping they do not trip over the wiring.

Apple stopped giving third parties that level of access to macOS in 2020. Apple devices were not affected by the CrowdStrike outage.

The Commission’s March 2025 specification decisions nonetheless went well beyond what was necessary to achieve effective interoperability. The Commission’s June 2025 noncompliance decision against Meta then revealed why. There, the Commission expressly disclaimed any obligation to weigh the economic consequences of its enforcement choices on either the gatekeeper or third parties.

That posture has now bled into the Commission’s interpretation of Article 6(7)’s integrity exception. If it need not consider tradeoffs, then “strictly necessary and proportionate” collapses into little more than “necessary in the Commission’s view.”

From Pairing Animations to Ambient Surveillance

If the iOS case was the rehearsal, the Android AI case is the main event. On April 27, the European Commission adopted preliminary findings outlining the measures it proposes Alphabet should implement under Article 6(7) for AI-facing features in Google Android. 

The measures listed in the annex would require Google to provide third parties access, on terms equally effective to those enjoyed by Google’s own services, to:

• continuous background access to a device’s core ambient sensors, including the microphone, camera, screen, speakers, accelerometer, and GPS;
• centralized, concurrent access to data shared by other apps through on-device databases like AppSearch, including data shared by Google’s own first-party apps;
• custom always-on wake-word detection—the “Hey Google” or “Alexa”-style listening function—running on the device’s digital signal processor, including while the device is in battery-saver mode;
• the ability to take agentic control of other apps through screen automation, including observing screen content, imitating user inputs, and executing multi-step transactions in a background virtual window;
• system-privileged access to AICore, Gemini Nano, and the underlying neural-processing-unit (NPU), graphics-processing-unit (GPU), and random-access-memory (RAM) resources currently reserved for Google’s own on-device AI models; and
• expanded background-execution privileges equivalent to those enjoyed by Google’s own apps.

Some translation is useful here. “Ambient sensors” are the parts of a phone that can see, hear, locate, and measure the world around it. A “wake word” is the phrase that activates a voice assistant. “Agentic control” means software that does not merely answer a question but can take actions for the user, such as opening apps, reading screens, clicking buttons, and completing transactions. NPUs and GPUs are specialized chips that help run AI models efficiently on the device, rather than sending everything to the cloud.

Put differently: this is not just about whether a third-party smartwatch gets the pretty pairing animation. It is about whether third-party AI assistants should receive deep, persistent access to the phone’s sensors, app data, computing resources, and user interface.

That is a qualitatively different proposition. As Mikolaj Barczentewicz put it in his April 2026 post, this is “opening Pandora’s interface.” These features run continuously, span the device’s app-data layer, and grant programmatic control over other applications. In the wrong hands, they enable mass surveillance, credential theft, and unauthorized transactions at scale.

Article 6(7) expressly recognizes those risks. It permits integrity measures that are “strictly necessary and proportionate.” But the Commission’s annex operationalizes that exception in a way that, as a practical matter, largely closes it off.

Section 5.3 requires any integrity measure to rest on “objective and verifiable evidence showing the existence and magnitude of the integrity risk,” apply symmetrically to Google’s own services, and remain capable of independent verification by parties other than Alphabet itself.

Each criterion sounds reasonable in isolation. Together, they make the most natural response to genuinely novel risks—declining to expose a sensitive feature until the threat landscape becomes better understood—effectively unavailable. By definition, evidence of harm cannot be “objective and verifiable” ex ante. Conservative assumptions about attacker behavior, which underpin modern operating-system security architecture, are not “objective evidence” under this framework.

A standard that treats the absence of demonstrated past harm as evidence that a restriction is unjustified would not have produced many of the security practices the European Union now takes for granted.

The symmetry requirement is equally perverse. Google typically distinguishes between its own first-party services and user-installed third-party apps across multiple dimensions of trust, including code-signing provenance, internal review processes, contractual liability with device manufacturers, and the ability to revoke access quickly when problems emerge. Code-signing, in simple terms, verifies where software came from and whether it has been tampered with. These distinctions are not decorative. They are part of how modern platforms keep devices from becoming very expensive malware terrariums.

The annex insists those trust differences are irrelevant: any restriction applied to a third-party app must also apply to Google’s own services.

That leaves Google with a binary choice. Either it extends sensitive capabilities to every third party on equal terms, or it strips those capabilities from its own services entirely. The first option may be unsafe. The second leaves European users with a worse product. Neither option is what users actually want.

Contestability Cuts Both Ways

There is another problem with the Android AI case, and it flips the usual DMA narrative on its head.

In the AI-assistant market itself, Google is the challenger, not the incumbent. Recent StatCounter data puts ChatGPT at roughly 70% of European Union AI-chatbot usage. Anthropic’s Claude has reportedly been adopted by eight of the Fortune 10 and was operating at a $14 billion annualized revenue run rate as of February 2026. Google’s Gemini, despite the company’s enormous investment in its integrated AI stack, arguably trails both.

Google’s competitive strategy depends precisely on that integrated stack: chips (Tensor and TPU), cloud infrastructure (Google Cloud), foundation models (Gemini), platform integration (Search, Maps, Calendar, Gmail, YouTube, and Photos), and distribution channels (Android, Chrome, and Play). Foundation models are the large AI systems trained on vast amounts of data that power tools like chatbots, coding assistants, and image generators. Google’s bet is that it can combine those models with the services people already use and the devices they already carry.

Deep Android integration—wake-word reservation, on-device database access, preferential NPU, GPU, and RAM allocation, along with structured App Functions—is one of the few ways Google can translate that stack into a differentiated user experience capable of competing with OpenAI’s and Anthropic’s dedicated assistants. App Functions are structured ways for apps to expose actions—say, booking a ride, sending a message, or editing a photo—so an assistant can perform those tasks reliably rather than simply guessing where to tap.

To some observers, Google leveraging its ecosystem to compete more effectively in AI may sound like precisely the scenario the DMA was designed to prevent. But in a world where Google is playing catch-up to OpenAI and Anthropic, that integration may be the difference between having two major AI competitors and three.

That is difficult to square with the DMA’s contestability rationale. Proponents often invoke contestability as a tool for disciplining entrenched big-tech incumbents. But contestability cuts both ways. If promoting competition is genuinely the law’s goal, it should matter just as much when enforcement reduces rivalry as when it increases it.

The Android proceedings are not unique in this respect. The same structural pattern is emerging in the Meta/WhatsApp matter, where enforcement framed as protecting AI competition on a dominant European consumer platform has the practical effect of requiring the platform owner to grant equivalent system access to the very firms that already lead the AI-assistant market.

It is also worth questioning the empirical premise underlying the Android case: that Android currently functions as an “important gateway” for AI services. The Commission’s preliminary findings largely take that proposition for granted, but the available evidence is shakier. AI services today are still consumed disproportionately on desktop devices, not smartphones.

In October 2025, Gemini reportedly recorded roughly 813 million monthly desktop sessions, compared to 369 million mobile sessions—a desktop-to-mobile ratio of more than two-to-one. Usage patterns across other AI services appear similarly lopsided. Mobile may become more important over time. It does not yet appear to be the indispensable distribution channel these proceedings assume. 

The broader lesson is uncomfortable, but important. Mandating openness to increase rivalry within Android can simultaneously weaken rivalry between AI firms. The DMA’s twin goals—contestability and fairness—can pull in opposite directions. Right now, the Commission appears to be privileging the wrong side of that tradeoff.

What Six-Seven Actually Means

Article 6(7) has the potential to become a powerful tool for promoting contestability in digital markets. But realizing that potential requires the European Commission to take seriously both halves of the provision: openness and integrity. It also requires the Commission to evaluate enforcement decisions based on their effects in the markets that ultimately matter to European consumers.

Concretely, that means doing three things the Commission has largely skipped so far.

First, reconsider the gateway premise. Article 3 of the DMA conditions gatekeeper obligations on a service functioning as an “important gateway for business users to reach end users.” In plainer terms, the DMA’s special obligations are supposed to attach to services that businesses really need to reach customers. If a particular class of business users—in this case, AI-service providers—primarily reaches users through other channels, the case for mandating risky mobile access becomes weaker, not stronger. The Commission’s limited enforcement resources could likely be deployed more effectively elsewhere.

Second, account for cross-market effects. When the platform owner is the trailing competitor in the upstream market a remedy will reshape, the “fairness” gains from mandated equal access may be outweighed by reduced competition among the firms the platform is trying to catch. That is not an argument for abandoning Article 6(7). It is an argument for applying it cautiously, with the recognition that competition policy often affects multiple markets at once.

Third, take integrity seriously. “Strictly necessary and proportionate” cannot mean regulators demand proof of harm before the harm occurs. No modern security architecture operates that way. Many of the security practices the European Union now treats as commonplace were built precisely on precautionary assumptions about how systems might fail or be exploited. Time-limited restrictions on the most sensitive features should remain available to gatekeepers without requiring an ex ante showing that the threat has already materialized.

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When European settlers arrived in North America, they brought royal charters granting them ownership of vast tracts of land. Almost immediately, they encountered numerous Indian tribes that could credibly claim control over those same lands. Faced with these conflicting claims, both parties had to decide whether to press their claims or abandon them. If they pressed, they then had to decide whether to exchange the land peacefully or fight it out.

Contrary to the popular narrative, Indian-white relations were not violent from first contact. History is complex, but the general pattern is that Indian-white relations began fairly peacefully and worsened over time. At first, Indian tribes often appeared inclined to drop ownership claims in the face of settler intrusion. When Indians did press a claim, the typical result was peaceful negotiation and exchange. Anderson and McChesney show that, from the Founding era to about 1830, treaties between Indians and whites were frequent, while battles were few and far between. As the American frontier moved farther west, the dominant mode of settling disputes shifted to warfare.

Why did Indian-white relations move from the peaceful interactions symbolized by the first Thanksgiving to the Massacre at Wounded Knee? Or, as Anderson and McChesney put it: “If both sides prefer settlements to violence, what caused the increasing resort to warfare between Indians and whites?” More broadly, under what conditions might Europeans have settled North America more peacefully?

From Courtroom to Continent

Like early land disputes between Indians and whites, most modern legal disputes settle long before they reach court. William Landes, in his 1971 article, “An Economic Analysis of the Courts,” developed a formal model to explain why. In Landes’ model, the decision to settle or litigate turns on the stakes of the dispute, the relative costs of settlement and litigation, and each party’s estimate of its odds of winning. Landes, Richard Posner, and others used the model to explore how settlement rates might be affected by the bail system, court delay, prejudgment interest, pretrial discovery, and rules governing who pays trial costs.

Anderson and McChesney’s key insight is that the decision to trade or raid mirrors the modern decision to settle or go to trial. The Landes model, in other words, is a general model of dispute resolution. All disputes—not just modern legal ones—are resolved according to the relative costs and benefits of each available method.

Where Peace Has a Price

When European settlers occupied Indian territory, the tribe had to decide whether to assert its claim to the land and its resources. An Indian tribe would assert its claim if the marginal benefit of the land—the value of one additional unit—exceeded the marginal cost of asserting that claim. That might mean making a credible commitment to retake the land by force, if necessary.

On first contact with European settlers, Indian-held land was abundant and the marginal value of land to Indians was relatively low. We should therefore expect Indians to allow whites to settle fairly unimpeded. Indians would assert a claim only when the marginal benefit of the land exceeded the marginal cost of asserting and enforcing it.

The logic worked symmetrically. Whites would expand their territorial control only so long as the marginal benefit of acquiring an additional strip of land exceeded the marginal cost of claiming and controlling it. Between those two points lies what Anderson and McChesney call the “zone of controversy.” That is where all disputes between Indians and whites occur.

Within the zone of controversy, Indians and whites had to decide how to resolve individual disputes. A peaceful settlement required both parties to walk away with a surplus they would not have gained from fighting. Indians would choose peaceful negotiation if the costs of fighting, plus the value of reclaimed land, exceeded the costs of negotiation. Whites would go to the negotiating table if the costs of negotiation were lower than the costs of fighting, plus the losses they expected from fighting.

Thus, all else equal, as the cost of negotiation rises, so does the likelihood of violence. And as the cost of violence falls, violence becomes more likely.

Uncertainty about the outcome of conflict adds another wrinkle. If one party is substantially more optimistic than the other about its chances of victory, fighting becomes more likely—even when the expected costs of fighting exceed the costs of negotiation.

This point is about information asymmetry, not merely imperfect information. If faulty information about military capacity causes whites and Indians to hold incorrect but identical expectations about their chances in battle, peace can still prevail if fighting costs more than negotiation.

When the Frontier Moved, So Did the Math

As white settlers moved farther west, the costs of negotiation rose, the costs of fighting fell, and information asymmetries widened. All of this meant more warfare.

Unlike the sedentary agricultural tribes in the east, Indian tribes west of the Mississippi were nomadic, like the buffalo they hunted. On the vast commons of the Great Plains, Indian tribes did not have neatly defined territorial ownership claims. The ownership rights they did have came from capture and possession.

The nomadic western tribes also lacked the more centralized political structures common among eastern tribes. Western tribal leaders did not fully control their members, and individuals routinely ignored treaties signed by their chiefs. Negotiation is difficult when both the party across the table and the object of negotiation are unclear. The western Indians’ nomadic lifestyle also made it harder to communicate with the tribe and “size up” its strength.

Over time, the party we have simply been calling “whites” was no longer an individual settlement community or local militia, but the growing federal government and the U.S. Army. Treaties signed by politicians in Washington were difficult to enforce locally when a citizen or rogue government employee violated their terms. The existence of a standing army—staffed by officers and bureaucrats whose careers benefited from fighting—also lowered the cost of violence.

It should not be too surprising, then, that whites shifted toward taking what they wanted by force.

Better Lawyers Than Molotovs

Stepping back, Anderson and McChesney implicitly teach us a valuable lesson about the importance of well-functioning legal institutions. Like it or not, violence is part of the human condition. If I find myself in a legal dispute with my neighbor, I must decide whether to negotiate and settle, go to trial and litigate, or throw a Molotov cocktail through his window.

Litigation might sometimes feel like combat. But compared with the alternatives, it is another way to resolve disputes peacefully. A capable legal system—one that upholds the rule of law and delivers fairly predictable judgments—lowers the costs of negotiation and raises the costs of fighting. That, in turn, reduces violence.

Further Reading

• Terry L. Anderson & Fred S. McChesney, “Raid or Trade? An Economic Model of Indian-White Relations,” The Journal of Law and Economics, Vol. 37, No. 1 (April 1994)
  
• William Landes, “An Economic Analysis of the Courts,” The Journal of Law and Economics, Vol. 14, No. 1 (April 1971)
• Robert D. Cooter & Daniel L. Rubinfeld, “Economic Analysis of Legal Disputes and Their Resolution,” Journal of Economic Literature, Vol. 27, No. 3 (September 1989)
• John Umbeck, “Might Makes Rights: A Theory of the Formation and Initial Distribution of Property Rights,” Economic Inquiry, Vol. 19, No. 1 (January 1981)
  
• Richard Posner, “An Economic Approach to Legal Procedure and Judicial Administration,”  The Journal of Legal Studies, Vol. 2, No. 2 (June 1973)
  
• George L. Priest & Benjamin Klein, “The Selection of Disputes for Litigation,” The Journal of Legal Studies, Vol. 13, No. 1 (January 1984)

The post ‘Raid or Trade? An Economic Model of Indian-White Relations’ by Terry L. Anderson & Fred S. McChesney appeared first on Truth on the Market.

Plenty of major AI figures seem to agree. Elon Musk says AI will “replace all jobs.” Anthropic CEO Dario Amodei regularly warns about mass job loss, framing AI as “a general labor substitute.” OpenAI investors talk openly about AI replacing “80% of all jobs by 2030.” These are influential people, not random bloggers. Still, they are not necessarily a representative sample of the world’s most careful economists.

And the fear itself is hardly new. Economist Wassily Leontief—best known for developing input-output analysis, a way of mapping how industries depend on one another—raised similar concerns in the early 1980s. If AI really were a perfect substitute for human labor, the logic would be straightforward. Any cost advantage would eventually drive firms toward 100% AI labor. You do not need a long essay to prove that result.

The problem is that the phrase “AI will eventually be a perfect substitute” does almost all the analytical work. That assumption hides a great deal: differences across tasks, industries, and workers; the many margins along which firms adjust; and the messy heterogeneity that makes the real economy more than a toy model.

How substitutable is AI today? What would need to happen for that substitutability to rise meaningfully? What other conditions would also need to hold? Even the historical analogy—“tractors could do what horses did, therefore horses became obsolete”—compresses several distinct steps into one neat sentence. “AI can do what humans do, therefore humans become obsolete” hides even more.

So let’s unpack those steps.

(This post draws on a new working paper that walks through the math and economics in detail. Really, though, it is mostly basic accounting.)

Before We All Become Horses

For those unfamiliar with the history of horses in the United States, the horse population actually rose for decades alongside industrialization. It increased from 4.3 million in 1840 to 27.3 million in 1920. The collapse came later, as tractors and motor vehicles displaced horses in agriculture and transportation. The number of farm horses and mules then fell to roughly 3 million by 1960.

Horses, in effect, had one main economic role, and that role disappeared. Humans are different. So before jumping from “AI can do tasks” to “humans become obsolete,” we should define carefully what that outcome would actually mean.

To keep things simple, suppose demand for human labor falls to zero. Not “low.” Zero. What would that require?

It would mean that no dollar spent anywhere in the economy passes through human labor at any point in the supply chain. Not the person who made the product. Not the person who shipped it. Not the person who designed it, marketed it, maintained it, or cleaned the building where it was assembled. Zero human labor embodied in final expenditure. That is the benchmark. That is what “humans become horses” would mean, stated precisely.

This is the input-output framework the aforementioned Wassily Leontief built his career on. The idea is straightforward: trace any final purchase backward through its supply chain and add up all the labor that contributed to it, both directly and indirectly. A cup of coffee includes the labor of the barista, but also the roaster, the truck driver, the coffee farmer, and the workers who built the truck. “Embodied labor” means all of it.

For labor demand truly to collapse, every one of those links would need to disappear across every good and service consumers buy. That is a much stronger claim than “AI can do some jobs.” The economy is not a single production function. It is a sprawling network of activities. When AI makes one activity cheaper, consumers do not simply buy more of the same thing forever. They redirect spending elsewhere.

Every dollar lands somewhere. Some spending flows into highly labor-intensive activities, such as restaurants, therapy, or home repair. Other spending flows into activities that require very little labor, such as cloud storage, automated checkout systems, or streaming subscriptions. So the relevant question is not merely: “Can AI do my job?” It is: “When AI makes some things cheaper, where does the saved money go next?”

Aggregate labor demand depends on at least three things: total spending in the economy, the share of spending that goes toward labor-intensive activities, and the amount of labor embodied in each activity. For labor demand to fall to zero, AI cannot merely displace workers in a few sectors. Every dollar of spending, wherever it ultimately lands, must shed all embodied human labor. The “humans become horses” story therefore requires three separate margins to collapse simultaneously.

A useful starting point is the simple observation that firms do not want labor per se. A restaurant does not want waiters because it enjoys employing waiters. It wants orders taken, customers reassured, mistakes fixed, and meals delivered. Labor demand is therefore “derived demand”—firms demand workers because workers help produce something else consumers value.

When AI can perform those underlying tasks more cheaply, two things happen at once. First, firms substitute AI for workers, reducing labor demand per unit of output. Second, lower production costs reduce prices, output expands, and that expansion tends to pull labor demand back upward. Whether total labor demand rises or falls depends on which force dominates.

Economists call this the Hicks-Marshall decomposition of derived demand into substitution effects and scale effects. The terminology sounds forbidding, but the intuition is simple: cheaper production reduces the need for workers in one sense, while expanding the market for output in another. That tension will organize the rest of the discussion.

When a dollar gets saved, where does it go? Into new tasks? New jobs? New industries? The money has to end up somewhere.

Your Job Is Not a Checklist

The case that AI can automate many tasks is not speculative anymore. This is obviously true to some extent, and it has been true for years.

Even early large language models (LLMs) showed substantial potential to affect workplace tasks. One widely cited paper by Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock estimated that roughly 80% of the U.S. workforce could see at least 10% of their job tasks affected by LLMs. When paired with complementary software tools, 86% of occupations crossed that 10% exposure threshold.

Since then, the empirical literature has grown rapidly, and the task-level evidence is hard to dismiss. In a large customer-support study, access to generative AI increased the number of issues resolved per hour by roughly 15%. In an experiment involving professional writing tasks, ChatGPT reduced average completion time by 40% while increasing measured output quality by 18%. In a controlled GitHub Copilot study, software developers completed coding tasks 55.8% faster. Those are not rounding errors.

But they are effects on tasks, not necessarily on jobs. That distinction matters. When a task gets automated, the saved dollar does not disappear into the void. Firms and workers often redirect it toward new activities within the same occupation: more client management, more review and verification, more coordination, more judgment calls, more customization.

Just as there is no fixed amount of demand in the economy, there is no fixed bundle of tasks that permanently defines a job. Jobs evolve. They absorb new responsibilities, shed old ones, and reorganize around whatever remains scarce and valuable.

The O-Ring Problem

There is a familiar ritual in AI discourse. Someone posts a demo. The demo performs a task associated with a particular job. People immediately conclude that the job is doomed.

Sometimes they are right. But that inference skips about 15 intermediate steps.

What does it actually cost to deploy the system once error rates are included? Do customers trust it? Can firms reorganize workflows around it? Does management even know how to integrate it effectively? A chatbot demo can appear overnight. A hospital cannot reorganize clinical liability around AI overnight.

That distinction matters because firms are not simply collections of isolated tasks. They are organizations. In many cases, the result will not be pure replacement, but rather a human-AI team producing output together. Economists call this complementarity: two inputs become more valuable when used jointly than separately.

But complementarity is not free. A human-AI pair that produces only marginally more value than the AI alone will not justify paying a full human wage. The human worker must contribute something the AI cannot reproduce cheaply or reliably.

That matters especially in high-stakes settings where errors are extraordinarily costly. Surgery, aviation, structural engineering, fiduciary advice, and many legal services all fall into this category. In these fields, the cost of failure can easily dwarf the savings from cheaper production.

That could eventually change. It probably will change in some areas over time. But it is not likely to change quickly.

This is essentially the “O-ring” logic from economics, named after the tiny rubber seal whose failure destroyed the Space Shuttle Challenger. When the value of the entire system collapses because one component fails, buyers do not focus primarily on sticker price. They focus on the expected cost of a system that actually works.

In those environments, human-supervised production can remain economically efficient even if AI itself becomes extremely cheap.

Horses Had Nowhere Else to Go

Suppose substitution effects really do dominate within most jobs. The saved dollar then escapes the workplace entirely. Where does it go next?

Most standard economic models collapse the economy into a single “final good,” which makes that question disappear by assumption. Real economies do not work that way. They contain many sectors, and every dollar eventually lands somewhere.

Start with software, which serves as a useful microcosm. Software-intensive industries have already undergone decades of automation through digital tools. If automation were going to drive human labor out of a sector entirely, this is where you would expect to see it first. The chart below groups industries according to how much software they purchase relative to value added: low, medium, and high software intensity. The result is striking.

The most software-intensive industries do not merely retain human labor. They actually devote a larger share of income to labor compensation—about 67%—than the least software-intensive industries, which devote roughly 55%. In other words, the industries that automated the most heavily also remained highly labor-intensive.

The same pattern appears in employment projections. The Bureau of Labor Statistics (BLS) projects total U.S. employment to increase by 5.2 million jobs between 2024 and 2034. Employment for software developers—a profession directly exposed to AI tools—is projected to grow 17.9%. BLS could ultimately prove wrong. Forecasting always carries uncertainty. Still, the evidence so far points strongly toward scale effects dominating in software-intensive industries. Automation reduced costs, output expanded, and labor demand remained robust.

Software may be an extreme case, but versions of this pattern appear across the broader economy and over much longer periods. Take the shift from goods to services. In 1929, most consumer spending went toward physical goods. Today, roughly two-thirds of consumer spending flows toward services. As manufacturing became dramatically more efficient, consumers did not respond by purchasing infinite refrigerators and toasters. Instead, spending shifted toward health care, education, restaurants, entertainment, travel, and personal services.

That is the “saved dollar” in action at the economy-wide level. Goods became cheaper. The substitution effect largely won within goods-producing industries. Employment growth in manufacturing did not continue indefinitely. But the freed-up purchasing power migrated elsewhere, and the scale effect emerged across sectors instead.

From a macroeconomic perspective, output expanded overall. Consumers simply redirected spending toward new categories of consumption. But migration alone does not help workers unless the destination sectors still contain substantial human labor. Did they?

Again, the answer appears to be yes.

Services consistently devote a larger share of value added to employee compensation than goods-producing industries do. Spending did not merely migrate. It migrated toward sectors where more of each dollar ends up in someone’s paycheck.

So yes, one could argue that this still resembles the horse story in one respect. The relative importance of goods production declined as productivity increased. The point, though, is that large, diverse economies contain adjustment margins that horses never had. There are escape valves.

Comparative advantage keeps reappearing. When automation makes some activities extremely cheap, spending tends to shift toward the activities that remain relatively expensive. And the activities that remain expensive are often the ones that are hardest to automate. Those are precisely the areas where humans continue to hold a comparative advantage—that is, where human labor remains relatively more productive or valuable than machine substitutes. The saved dollar therefore tends to drift toward areas where humans are still worth paying.

That is not technological optimism. It is simply the logic of comparative advantage.

James Bessen documents this dynamic sector by sector. In early textile manufacturing, power looms sharply reduced labor required per yard of cloth. But cloth became so much cheaper that demand exploded, and total textile employment increased for decades. Similar patterns appeared in steel and automobile production. Eventually, demand saturated. Prices stopped falling rapidly enough to offset labor-saving automation, and employment in those sectors declined.

The key question for AI, then, is not whether automation can destroy jobs. Of course it can. The real question is: Which sectors are in which phase? Where might AI-generated savings flow today?

Health care already accounts for roughly 18% of U.S. GDP, and that share continues to rise. Elder care will likely expand further as populations age. Personalized services, human-intensive care work, and new categories of consumption may absorb growing shares of spending.

Joel Mokyr, Chris Vickers, and Nicolas Ziebarth make this historical argument well in a Journal of Economic Perspectives article. Across prior waves of technological change, new tasks emerged, comparative advantage persisted, and entirely new categories of work appeared that earlier generations could not have anticipated.

Horses had no equivalent adjustment path. They did not move into elder care.

Will Humans Become a Luxury Good?

The saved dollar migrated toward human-intensive sectors last time. The strongest argument for why this time could be different comes from economist Philip Trammell’s paper, “Is Labor a Luxury in the Long Run?”

His answer is: probably not. Even if richer consumers initially spend more on human-intensive goods and services—live music, handmade products, personal care, bespoke experiences—four long-run forces may steadily erode that demand.

1. AI-generated variety keeps expanding. New AI-produced goods compete for every dollar that might otherwise land on a human-made product or service.
2. Human experiences carry opportunity costs. Time spent at a live concert is time not spent consuming some potentially superior AI-generated alternative.
3. Labor competes with other scarce goods for consumers’ willingness-to-pay premiums. Beachfront property, status goods, intellectual property, and research-intensive products may all absorb spending that might otherwise flow toward human labor.
4. Capital goods become cheaper over time. If investment opportunities continue expanding, the share of economic activity devoted to capital accumulation could grow indefinitely.

Trammell’s Coca-Cola analogy captures the intuition cleanly. Original Coke once held roughly 50% of the soda market. Then came Diet Coke, Cherry Coke, Pepsi Max, energy drinks, flavored sparkling water, and endless other varieties. Even with enormous brand loyalty and supply constraints, Coke’s market share fell below 20%.

The implication for AI is straightforward. Even if consumers initially prefer human-made goods, that preference may weaken as AI continuously generates new substitutes and varieties. Human labor does not need to become worthless. Its share can erode through dilution.

That is a serious argument, and I take it seriously. Still, notice what the argument requires. It is not enough for AI-generated variety merely to expand. That will almost certainly happen. The stronger claim is that AI-generated substitutes must expand broadly and rapidly enough to pull spending away from every human-intensive category simultaneously.

The real question is not whether AI competes with some human-produced goods. Of course it will. The question is whether any human-intensive islands survive. Does anyone still spend money on something with a person inside it?

The arithmetic quickly becomes more demanding than many “humans become horses” narratives imply. Suppose AI eventually captures 85% of economic activity. Software, accounting, logistics, medicine, law, management, and much of media production become almost fully automated. Human labor largely disappears from those sectors.

Now suppose the remaining 15% of spending flows toward activities that still contain at least 30% human labor: elder care, live entertainment, skilled trades, therapy, surgery, in-person education, luxury craftsmanship, status goods, and other relational or trust-intensive services.

The aggregate labor share would still equal at least:

S ? 0.15 × 0.30 = 0.045

That leaves labor with at least a 4.5% share of economic output. That may not sound comforting, but remember what this calculation is doing. It is merely establishing a lower bound under extremely aggressive automation assumptions. It is not utopia. It is not full employment. But it is also not zero. And a falling labor share does not necessarily imply falling labor demand if total output grows rapidly enough.

Alex Imas offers another reason to doubt the “humans disappear” story. As AI drives down the cost of commodities, real incomes rise. Historically, richer consumers tend to shift spending toward what Imas calls “relational goods”—goods and services whose value depends partly on human connection, scarcity, or social meaning.

That idea connects to a large economics literature on structural change. Over time, economies tend to shift from agriculture to manufacturing to services as incomes rise. The key debate is why. Do consumers simply buy more of whatever becomes cheaper? Or do rising incomes fundamentally change what people want?

Diego Comin, Danial Lashkari, and Marti Mestieri decompose those effects and conclude that income effects account for more than 75% of the long-run shift toward services. That distinction matters enormously here. If structural change were driven mainly by falling prices, then AI-generated abundance might pull spending overwhelmingly toward AI-produced goods. But if structural change is driven mainly by rising incomes and evolving preferences, then richer consumers may continue demanding more human-intensive experiences and services. Historically, that is exactly what has happened.

Experimental evidence points the same way. In one set of experiments, subjects learned that other people would be excluded from purchasing an otherwise identical product. Willingness to pay roughly doubled. The exclusivity itself created value.

Importantly, the exclusivity premium was stronger for human-made goods than AI-generated ones. Human-created artwork gained roughly 44% in value from exclusivity, compared with about 21% for AI-generated artwork. AI-made goods feel infinitely replicable. Human-made goods feel scarce, even when they technically are not. People value what other people cannot easily obtain. That impulse does not disappear as societies grow wealthier. If anything, it intensifies.

Perhaps AI-generated variety eventually overwhelms even those preferences. Maybe. Still, the structural-change evidence consistently suggests that income effects dominate price effects by roughly three to one. When basic goods become cheaper, humans do not announce that they are finally satisfied and stop developing new wants. They invent new forms of distinction, identity, taste, and status competition. The open question is where those new desires land. So far, the evidence points toward humans retaining an important role.

One final clarification matters here, because popular AI discussions often conflate two distinct claims. A falling labor share is not the same thing as falling labor demand. Labor’s share of national income can decline even while total employment and total wages continue rising, provided the overall economy grows fast enough. In that world, AI appears to “take over” a larger share of production while human workers still earn more in absolute terms because the economic pie itself expands dramatically.

That may well describe the phase we are currently entering. We already observe the basic pattern. Higher-income households consume more services, and service sectors remain relatively labor-intensive. Could that eventually reverse? Of course. But at the moment, this is the evidence we actually have.

The Horse Story Ends Here

Walking through all these layers—from tasks, where we are only beginning to see meaningful substitution, up through firms, sectors, and the macroeconomy—leaves me fairly skeptical of the “humans become horses” outcome. I know I have concealed that conclusion masterfully until now.

AI will absolutely perform many tasks. It will reorganize jobs, sometimes painfully. Some sectors may lose most of their human labor. Spending will often chase automation and lower prices. All of that can happen without driving human labor demand to zero. Because at every stage of the process, there is still a saved dollar looking for somewhere to land. And the same question keeps reappearing: Where does it go next?

For the horse outcome to occur, that saved dollar must eventually fail to find any activity with meaningful human labor embodied in it. Not some activities. All activities.

That is a very specific future. It is logically possible. But it requires substitution to dominate simultaneously across tasks, firms, sectors, and final consumption patterns, with no surviving human-intensive islands anywhere in the economy. The evidence we currently have—structural change, revealed preferences, comparative advantage, and experimental results—keeps pointing the other way.

Horses lost because the economy stopped needing horsepower. Humans are not just horsepower.

The post Why Humans Are (Probably) Not Headed for the Glue Factory appeared first on Truth on the Market.

Sharkey treats that mismatch as a design problem, not a terminological one. Her proposal separates two functions long bundled together under the “punitive” label: (i) punishment, which she calls “anti-social penalties,” and (ii) compensation for social harms that ordinary compensatory damages do not reach, which she terms “societal damages.”

The point is not to deny punitive damages’ deterrent role. Rather, Sharkey argues that once we concede punitive awards often respond to harms beyond those suffered by the named plaintiff, courts should stop laundering that broader social objective through a plaintiff windfall.

Methodologically, the paper is both doctrinal and institutional. It reads constitutional punitive-damages doctrine as a constraint and then asks what remedial architectures—split recovery, compensation funds, or aggregation substitutes—might better align punitive damages with their asserted social function.

Everybody’s Harm, One Plaintiff’s Check

The paper’s organizing claim is that legal actors already behave as if punitive damages incorporate a social-harm component. Sharkey opens with State Farm v. Campbell, where plaintiffs’ counsel urged jurors to punish a nationwide course of conduct, and the Utah Supreme Court defended a large punitive award by invoking harms to other insureds and to “society in general.” The system plainly reaches for a justification broader than the individual plaintiff.

Yet the system then does something inconsistent with that logic: it pays the money to the plaintiff. Sharkey’s move is to argue that courts and legislatures should make explicit what the doctrine already assumes implicitly—that part of the award serves a compensatory function for harms extending beyond the named plaintiff.

Once that premise becomes explicit, the next question becomes unavoidable: where should the “societal” portion of the award go?

Deterrence Is Only Half the Design Problem

The standard law & economics defense of punitive damages is the under-detection multiplier: when wrongdoing is difficult to detect or prove, compensatory damages alone will underdeter, so the remedy should be scaled up to bring expected liability closer to expected harm. Sharkey engages this view sympathetically, but presses on what it abstracts away: distribution.

The law & economics approach starts from the under-detection and under-deterrence problem and treats remedy design—especially who receives the money—as an input into deterrence, not merely a distributional afterthought. If the “extra” dollars exist to internalize social costs, paying them to a randomly positioned plaintiff may seem like a crude but tolerable shortcut.

Sharkey argues that shortcut carries consequences economic models often treat as secondary, but institutional design cannot. Recipient choice affects legitimacy, settlement leverage, litigation incentives, and the relationship between private enforcement and public objectives. Her societal-damages framing makes those consequences central, rather than incidental.

Iowa: Keep the Plaintiff, Split the Windfall

Iowa’s split-recovery approach requires a special finding about whether the defendant’s conduct was “directed specifically” at the claimant. If it was not, then—after specified costs and fees—no more than 25% of punitive damages may go to the claimant, with the remainder paid into a civil reparations trust fund. (Iowa Code § 668A.1).

The rule matters because it operationalizes the moral and economic intuition Sharkey wants doctrine to acknowledge. When the wrong is not specifically targeted—when it instead appears part of a broader pattern affecting others—the argument that the plaintiff should capture the entire punitive award becomes less persuasive. Iowa makes that intuition concrete through a binary interrogatory and a simple routing rule.

The predictable objection is incentive-based: reducing the plaintiff’s upside may weaken private enforcement. Sharkey’s response is not that incentives are irrelevant, but that they are variables to be engineered. The institutional challenge is to balance deterrence, administrability, and legitimacy.

The Ohio Supreme Court Creates a Public Fund

Sharkey also emphasizes that some courts have improvised “societal” routing even without statutory authorization. In Dardinger v. Anthem, the Ohio Supreme Court stated: “The plaintiff remains a party, but the de facto party is our society …” (Dardinger, 98 Ohio St. 3d 77 (2002)). The court then tried to implement that logic by remitting the punitive award and directing a substantial portion—after attorneys’ fees—to a court-created cancer-research fund.

One can object to the specifics. Courts are not natural fund administrators, and ad hoc earmarks invite arbitrariness. Even so, the episode is instructive for Sharkey’s purposes: once courts explicitly frame the wrong as social, they become pulled toward social distribution. The doctrinal vocabulary is already doing the work; the remedial architecture simply lags behind.

Catastrophic AI Risk Is Not Built for One-Plaintiff Remedies

Advanced artificial-intelligence risk—especially catastrophic misuse or large-scale misalignment, where an AI system pursues goals in ways its designers did not intend—creates a familiar enforcement problem for tort law. The harms that matter most may be practically noncompensable because they exceed feasible insurance limits, overwhelm defendant solvency, or occur in states of the world where adjudication is unavailable.

If expected social harm is driven by low-probability but catastrophic “tail” outcomes, insurance markets often fail to internalize those risks reliably. Diversification breaks down when risks are highly correlated; model uncertainty makes pricing difficult; and coverage is capped, excluded, or implicitly dependent on public backstops.

In that setting, two failures compound. First, relying on private insurance to price externalities becomes structurally unreliable. Second, relying on a plaintiff-windfall model to finance deterrence and redress becomes unstable. It routes public-harm money through sporadic, high-variance litigation outcomes while creating no durable institutional capacity for monitoring, remediation, or compensation in diffuse-harm cases.

Sharkey’s split also sharpens how punitive damages should be used—if at all—in frontier AI cases. If part of an award is meant to function as a deterrent sanction, it cannot be structured in a way that predictably allows the defendant to neutralize it through indemnification or contractual pass-through. A “punitive” price signal that can be shifted is not, in any robust sense, punitive.

By contrast, the portion of an award better understood as “societal damages” should be treated as a routing problem. If the justification is harm to nonparties or diffuse social costs, the remedy should not be delivered as a plaintiff windfall. Instead, it should go to institutions capable of converting those funds into public goods that reduce under-detection and manage diffuse harms, such as risk measurement, independent auditing capacity, and incident-response infrastructure.

The point is not that such institutions solve AI-governance problems. It is that once punitive damages are justified in social-cost terms, allocation becomes part of the mechanism, rather than an afterthought.

Conclusion

Sharkey’s contribution is to treat remedies as governance instruments with institutional form. Punitive damages are often defended as tools for internalizing externalities when compensatory damages underdeter. The societal-damages reframing asks a further question: if the function is partly social, why should the form remain purely private?

For correlated, partially uninsurable AI risk, that question is not academic. If courts continue to describe society as the relevant beneficiary of punitive awards, Sharkey’s framework forces a follow-on demand: build a remedial architecture that reflects that claim, and then defend it—doctrinally, constitutionally, and institutionally—on its own terms.

Further Reading

• Catherine M. Sharkey, “Punitive Damages as Societal Damages,” Yale Law Journal, Vol. 113 (November 2003).
• Catherine M. Sharkey, “The Future of Classwide Punitive Damages,” University of Michigan Journal of Law Reform, Vol. 46 (2013).
• A. Mitchell Polinsky & Steven Shavell, “Punitive Damages: An Economic Analysis,” Harvard Law Review, Vol. 111, No. 4 (February 1998).
• Gabriel Weil, “Closing the AI Accountability Gap: Strict Liability and Punitive Damages for Advanced Artificial Intelligence,” Oregon Law Review (forthcoming 2027)
• Gabriel Weil, “Overcoming Judgement-Proofness: The Law & Economics of Insuring and Mitigating AI Risk” (working paper, February 2026)

The post ‘Punitive Damages as Societal Damages,’ by Catherine M. Sharkey appeared first on Truth on the Market.

But the harder constitutional question is whether courts can do all that through broad, after-the-fact liability standards without telling platforms what the law actually requires. The legal world is currently watching a high-stakes collision between two foundational principles: the government’s power to protect children from allegedly “addictive” technology, and the constitutional requirement that laws give regulated parties fair notice of what conduct is illegal. As the Supreme Court  has explained, “we insist that laws give the person of ordinary intelligence a reasonable opportunity to know what is prohibited, so that he may act accordingly.”

That tension is now coming to a head. Appeals are likely in both New Mexico v. Meta and K.G.M. v. Meta, even as federal courts continue issuing rulings in the NetChoice litigation challenging state social-media laws, including in Arkansas. Together, these cases are exposing a basic fault line in modern tech regulation: Can courts impose liability on social-media platforms under broad “negligence” or “unfair trade practices” theories, or are those standards simply too vague to satisfy constitutional due-process requirements? 

The answer appears to turn largely on how courts characterize core platform features, such as algorithmic recommendations, infinite scroll, disappearing messages, autoplay, and push notifications. Put simply, are these features protected First Amendment activity, or merely product design?

That distinction matters enormously. If courts treat these features as protected speech or editorial discretion, then laws targeting them face constitutional scrutiny. Courts must ask whether the law restricts more speech than necessary, and whether the law—or its application—is too vague for regulated parties to know when they are crossing the line.

If, by contrast, courts treat these features as mere conduct, then the First Amendment largely drops out of the analysis. Courts instead presume that businesses have adequate notice of how broad negligence or consumer-protection laws apply to platform design choices.

Scrolling Into Tort Law

Before the jury trials in New Mexico and Los Angeles, courts largely treated social-media features as conduct, rather than speech, and allowed what amounted to product-liability theories to proceed. In denying Meta’s motion for summary judgment, the California court framed the issue this way:

[T]he allegedly addictive features of Defendants’ platforms (such as endless scroll) cannot be analogized to how a publisher chooses to make a compilation of information, but rather are based on harm allegedly caused by design features that affect how Plaintiffs interact with the platforms regardless of the nature of the third-party content viewed.

That framing matters because it shifts the case away from the First Amendment and into ordinary tort law. Under California negligence law—as in most jurisdictions—a plaintiff must show that the defendant: 1) owed a duty of care, 2) breached that duty, 3) proximately caused harm, and 4) caused legally cognizable damages. In ordinary commercial relationships, businesses generally must act reasonably under the circumstances to avoid foreseeable harm.

The theory against Meta therefore sounds familiar, at least at a high level. A car manufacturer may be negligent if it designs defective brakes. Likewise, plaintiffs argue, Meta may be negligent if it designs platform features that foreseeably contribute to harms like compulsive use, self-harm, or other mental-health injuries among minors.

From this perspective, the common-law “reasonableness” standard is not impermissibly vague because American courts apply it every day. Businesses are expected to behave reasonably, even when the standard itself cannot be reduced to a precise checklist.

The same basic logic applies to unfair-trade-practices claims. To prove “unfairness” under laws like New Mexico’s, the state typically must show that a defendant’s conduct: 1) caused substantial consumer injury, 2) inflicted harms consumers could not reasonably avoid, and 3) produced harms not outweighed by countervailing benefits to consumers or competition.

Again, the analogy is straightforward. If a company places an unreasonably dangerous product into the marketplace, that may qualify as an unfair trade practice. Plaintiffs argue that Meta’s allegedly addictive design features similarly create unavoidable and substantial harms for users.

Courts traditionally give regulators and plaintiffs considerable leeway when these standards apply to conduct rather than speech. The argument, in other words, is that negligence and unfairness doctrines provide sufficient notice to businesses, even if they operate through flexible, case-by-case standards.

That distinction also shapes vagueness analysis. Courts generally apply less demanding scrutiny to civil laws regulating economic conduct than to laws burdening speech. Even courts that have pushed back on the Federal Trade Commission’s (FTC) use of Section 5 unfairness authority have still recognized that principle. In FTC v. Wyndham, for example, a federal court explained that fair-notice standards are “especially lax for civil statutes that regulate economic activities.” Under that framework, a law fails for vagueness only if it is “so vague as to be no rule or standard at all.” 

The Constitution Hates Vibes-Based Liability

The analysis changes once courts conclude that speech—or at least editorial discretion—is involved. When laws burden First Amendment-protected activity, courts demand far more clarity about what conduct is prohibited.

As the Supreme Court explained in Vill. of Hoffman Ests. v. Flipside, Hoffman Ests., Inc.: 

[P]erhaps the most important factor affecting the clarity that the Constitution demands of a law is whether it threatens to inhibit the exercise of constitutionally protected rights. If, for example, the law interferes with the right of free speech or of association, a more stringent vagueness test should apply.

That heightened scrutiny reflects a familiar concern in First Amendment law: vague rules chill speech. Faced with uncertain liability, speakers and publishers often self-censor rather than risk litigation.

Two recent federal decisions striking down Arkansas social-media laws at the request of NetChoice turned heavily on that principle. In both cases, the U.S. District Court for the Western District of Arkansas concluded that heightened vagueness scrutiny applied because the laws targeted platform features intertwined with speech and editorial judgment. 

That makes sense. Social-media platforms necessarily organize, rank, filter, and recommend enormous amounts of user-generated content. The moment a law tells a platform how it may present speech, First Amendment questions arise.

The Arkansas court captured the problem vividly:

In a world where billions of pieces of content are posted on social media every day, social media would be functionally useless as a “vast democratic forum[ ]” if platforms were not allowed to use any algorithm—any system—for selecting and ordering content to display to users… If a social media platform was a library, banning algorithms would be roughly equivalent to requiring books be placed on shelves at random. Such a prohibition would burden users’ (or library patrons’) First Amendment rights by making it significantly more difficult to access speech a user wishes to receive, so a state probably could not constitutionally ban algorithms for the organization of speech (on social media or elsewhere) altogether…

[The law] does not prohibit all algorithms…[but it] forces covered services to restrict as to all users—minor or adult—anything that could have a forbidden effect (here, addiction) on any users—again, minor or adult.

That concern drove the court’s treatment of Arkansas Act 901, which imposed a negligence-style duty on platforms that use a “design algorithm, or feature” the platform:

[K]nows, or should know through the exercise of reasonable care, causes a user to: (1) Purchase a controlled substance; (2) Develop an eating disorder; (3) Commit or attempt to commit suicide; or (4) Develop or sustain an addiction to the social media platform.

The court concluded the law was likely “unconstitutionally vague” because it “fails to specify a standard of conduct to which platforms can conform.” Liability, the court warned, depended on “the sensitivities of some unspecified user” and on what a judge or jury later decided the platform “should have known” about those sensitivities.

In other words, ordinary negligence standards may work tolerably well for defective brakes or slippery floors. They become far murkier when applied to speech-ranking systems used by billions of people with wildly different psychological responses and preferences.

The court found Arkansas Act 900 even more problematic. Unlike Act 901’s negligence framework, Act 900 effectively imposed strict liability. It required platforms to “ensure” they:

[Do] not engage in practices to evoke any addiction or compulsive behaviors in an Arkansas user who is a minor, including without limitation through notifications, recommended content, artificial sense of accomplishment, or engagement with online bots that appear human.

The court concluded that “liability under Act 900 is even more uncertain than under Act 901.” The law extended beyond addiction to the platform itself, and imposed liability even where a company “could not have known through the exercise of reasonable care” that a feature would affect a particular child in a particular way.

That, ultimately, was the constitutional problem. The court found the statute likely void for vagueness because “[b]usinesses of ordinary intelligence cannot reliably determine what compliance requires.”

The Feed Is the Speech

The Supreme Court has already made clear that curating and presenting speech is itself protected First Amendment activity. Courts should follow the speech-based approach adopted by the federal court in Arkansas, not the conduct-based approach used by the state courts in New Mexico and Los Angeles.

In NetChoice v. Moody, the Court explained that “expressive activity includes presenting a curated compilation of speech originally created by others.” That principle applies directly to products like Instagram Feed. “[D]eciding on the third-party speech that will be included in or excluded from a compilation—and then organizing and presenting the included items—is expressive activity of its own.” The platform’s expressive product stems from Meta’s “choices about whether—and, if so, how—to convey posts.”

That last point matters. The First Amendment protects not only a platform’s decisions about what speech to display, but also how to present it.

As the International Center for Law & Economics (ICLE) argued in its amicus brief before the Massachusetts Supreme Judicial Court in litigation challenging Meta under the Commonwealth’s unfairness authority: 

[I]t is clear the First Amendment protects the right of newspapers to choose not only the content it will print, but “the decisions made as to limitations on the size” of the paper. Miami Herald Publishing Co. v. Tornillo, 418 U.S. 241, 258 (1974). Just as a government couldn’t tell a newspaper to expand its size, it couldn’t tell them to use smaller font, or to reduce its margins. In other words, how a newspaper presents its content is as much part of its “editorial control and judgment” as the content itself.

It is much the same here. The Commonwealth alleges that notifications, alerts, infinite scroll, autoplay, and ephemeral content are mere conduct rather than protected editorial choices. But this simply can’t be the case.

That does not mean social-media platforms are immune from regulation. It does mean that lawsuits targeting how platforms organize and present speech—whether styled as negligence claims or consumer-protection “unfairness” actions—must satisfy the more demanding vagueness standards courts apply when speech is at stake.

Anything less would create enormous pressure for platforms to over-censor. If avoiding liability becomes the overriding imperative, companies will inevitably restrict more user speech, suppress more borderline content, and redesign their services to be blander and less engaging in order to avoid allegations of “addictive” features.

That may make platforms less interesting. It would also make online speech less free.

Protecting minors online is a legitimate and important goal. So is giving parents better tools to supervise their children’s online activity. But education, parental controls, and user-empowerment tools are far more likely to survive constitutional scrutiny—and probably more likely to work—than vague liability regimes that effectively ask courts and juries to decide when an app has become “too engaging.”

The Constitution does not require the internet to be pleasant. It does require the government to speak clearly before punishing how platforms present speech.

The post Addicted to Vagueness: Lawmakers Can’t Regulate Social Media by Vibes appeared first on Truth on the Market.

That question has triggered the sort of debate that is easy to politicize and much harder to analyze carefully.

Within hours of Spirit ceasing operations, critics of the Biden administration’s antitrust policy cast the episode as a simple morality play: regulators blocked JetBlue’s acquisition of Spirit in the name of competition, Spirit then failed, therefore antitrust killed Spirit.

Defenders of the merger challenge answered with an equally tidy narrative. Spirit, they argued, was already in deep trouble—hamstrung by Pratt & Whitney engine groundings, mounting debt, soft demand, and rising fuel costs. In the words of Sen. Elizabeth Warren (D-Mass.): “Spiking fuel prices from Trump’s war was the nail in the coffin for twice-bankrupted Spirit airline.” On that account, antitrust policy had little to do with the carrier’s demise. 

Both stories contain some truth. Neither is fully satisfying.

It would be too easy—and almost certainly wrong—to say the U.S. Justice Department (DOJ) killed Spirit. The airline’s collapse was overdetermined. A Pratt & Whitney engine-inspection crisis grounded large portions of its fleet. The Big Four legacy carriers steadily cannibalized Spirit’s niche with their own basic-economy offerings. Fuel prices surged in the wake of the Iran war. Management’s attempt to reposition Spirit as a more premium carrier also failed to gain traction.

Still, it would be equally wrong to treat the merger challenge as irrelevant. The DOJ and the court should have taken more seriously the possibility that Spirit’s independence was, at best, precarious. That points to the more important question: whether merger doctrine, as currently applied, can adequately handle cases where the target firm is visibly distressed and where the realistic alternative to acquisition is not continued independent competition, but gradual attrition, restructuring, or outright exit from the market.

Scale or Die Trying

Critics of the Spirit-JetBlue transaction often frame it as JetBlue opportunistically swooping in on a vulnerable target. That framing misses what the deal actually represented. Both airlines came to the table because each had independently concluded that scale was becoming a survival imperative in an industry dominated by four major carriers.

For Spirit, the problem was deteriorating standalone economics. For JetBlue, the problem was different but related: its East Coast-focused, point-to-point network lacked the breadth needed to compete with the Big Four carriers for the high-value corporate and connecting traffic that drives airline profitability. 

Spirit itself had already acknowledged this reality before JetBlue entered the picture. In February 2022, Frontier and Spirit announced a stock-and-cash merger they described as creating “America’s most competitive ultra-low fare airline.” The companies promised roughly $1 billion in annual consumer savings and pitched the deal as a way to create a credible fifth-carrier rival to American, Delta, United, and Southwest.

That point matters. Spirit’s board was not defending independence as the optimal strategy. To the contrary, management was already telling the market that remaining a standalone carrier in the post-pandemic airline industry was an increasingly risky bet.

JetBlue entered the bidding in April 2022 with an unsolicited all-cash offer at a substantial premium, which it repeatedly sweetened over the following months. JetBlue argued that combining its product with Spirit’s fleet and lower-cost structure would create a “national low-fare challenger” capable of exerting competitive pressure on the Big Four, especially at a time when organic growth had stalled and aircraft were difficult to obtain on the open market. 

Spirit’s board initially rejected JetBlue’s offer—not because it preferred independence, but because it believed the transaction faced “substantial regulatory hurdles,” particularly while JetBlue’s Northeast Alliance with American Airlines remained in place. After Spirit terminated its merger agreement with Frontier on July 27, 2022, it signed the JetBlue deal the very next day.

That sequence matters, both legally and rhetorically. Spirit’s directors understood the antitrust risk, weighed it against JetBlue’s higher offer, and concluded the risk-adjusted value to shareholders favored JetBlue. They were also making that judgment against a backdrop of increasingly weak fundamentals.

The Pratt & Whitney geared-turbofan inspection crisis—which would eventually ground roughly one-quarter of Spirit’s neo fleet, on average, through 2024 and continue disrupting operations through 2026—was already emerging. Ultra-low-cost-carrier, or ULCC, margins were under pressure. Consolidation was the strategy management chose, and by mid-2022, JetBlue was the only realistic consolidation partner left standing. 

The mirror image of Spirit’s predicament is also worth emphasizing, because it highlights how poorly the conventional framing fits the underlying economics of the airline industry.

JetBlue’s interest in Spirit was not simple opportunism. Like Spirit’s interest in Frontier, it reflected a structural disadvantage. For much of the past decade, JetBlue has searched for ways to add the network depth that legacy carriers use to attract and retain lucrative corporate and connecting passengers.

Its 2020 Northeast Alliance with American was one attempt. Its bid for Spirit was another. Its May 2025 “Blue Sky” partnership with United Airlines—which combined reciprocal loyalty benefits, interline arrangements, and John F. Kennedy International Airport slot coordination, and took effect in October 2025—was a third. 

Different legal mechanisms, same commercial problem. JetBlue has long operated as a point-to-point carrier with a strong consumer brand, but a network too thin to compete effectively for the traffic that actually pays the bills. The Biden administration’s DOJ challenged the first two arrangements, and succeeded in blocking the second. The third moved forward under a Trump administration U.S. Department of Transportation (DOT) review without enforcement objection.

JetBlue’s repeated return to the same problem—using whatever legal instrument happens to remain available—suggests the underlying scale disadvantage is real. Blocking any single transaction does not make that structural problem disappear.

The Law Said Spirit Was Fine Until It Wasn’t

The DOJ filed suit on March 7, 2023, joined by Massachusetts, New York, and the District of Columbia. Four additional states joined later that month. The complaint portrayed Spirit as the nation’s largest and fastest-growing ULCC and credited it with generating a “Spirit Effect”—the tendency of Spirit’s entry into a market to drive down fares across competing airlines. 

The government argued the merger would eliminate roughly half of all ULCC capacity in the United States. It also emphasized that JetBlue planned to retrofit Spirit’s densely configured aircraft into JetBlue’s lower-density seating layout, reducing the number of available seats and, in the DOJ’s view, raising prices for the most price-sensitive travelers.

The DOT publicly endorsed the lawsuit the same day. That was notable in its own right. For decades, DOT had largely avoided intervening at the Hart-Scott-Rodino merger-review stage. Its support for the case reflected how fully President Joe Biden’s Executive Order 14036 and its “whole-of-government” competition agenda had reshaped federal antitrust enforcement. 

Politically, the center of gravity inside the Biden administration had already shifted firmly against the deal. That shift accelerated after the DOJ’s May 2023 victory against the JetBlue-American Northeast Alliance, which the agency appears to have viewed as evidence that JetBlue was no longer a disruptive outsider, but an emerging consolidator in its own right. Sen. Amy Klobuchar (D-Minn.) applauded the Spirit lawsuit. Sen. Warren had already urged DOT, in September 2022, to use its full authority to oppose the transaction. 

Judge William G. Young, a Ronald Reagan appointee sitting in the U.S. District Court for the District of Massachusetts, blocked the merger on Jan. 16, 2024, in a 109-page  opinion. The opinion is more nuanced than some critics admit. Judge Young expressly acknowledged that a combined JetBlue-Spirit carrier “would likely place stronger competitive pressure on the larger airlines”—precisely the out-of-market competitive benefit JetBlue had emphasized throughout the case. 

But the court ultimately concluded that those broader competitive benefits could not offset the loss of Spirit’s particular business model for highly price-sensitive travelers:

Although the Defendant Airlines provide ample evidence at the rebuttal stage that the anticompetitive harms of the proposed acquisition will be offset, both by new entries into the harmed markets and potential pro-competitive benefits, this evidence fails to establish that the proposed merger would not substantially lessen competition in at least some of the relevant markets.

Judge Young also addressed Spirit’s deteriorating financial condition directly and rejected the failing-firm defense:

Although Spirit is struggling, its executives testified that the airline had a long-term plan to return to profitability.

And:

JetBlue is also far from the only available purchaser, should Spirit find itself in dire need.

That last move is the legal hinge of the case, and it deserves closer attention. The defendants did not aggressively pursue a failing-firm defense at trial. Instead, they framed the transaction as affirmatively procompetitive. The court, meanwhile, analyzed Spirit’s financial distress within the narrow doctrinal framework established by Citizen Publishing Co. v. United States and its progeny. 

Within those doctrinal boundaries, Judge Young arguably reached a defensible conclusion based on the record before him. The harder question is whether those boundaries are themselves too cramped for analyzing mergers involving visibly distressed firms operating in industries already under severe structural pressure.

The Counterfactual Crashed Too

The chronology after Judge Young’s ruling is brutal.

JetBlue and Spirit terminated the merger on March 4, 2024. Spirit collected a $69 million termination fee and publicly recommitted to a standalone strategy. In May 2024, the airline eliminated change and cancellation fees. Two months later, it unveiled its “Go Big” and “Go Comfy” fare bundles—an attempt to move away from the bare-bones ULCC model and toward something resembling JetBlue Lite. 

In June 2024, Spirit CEO Ted Christie told shareholders at the company’s annual meeting that Spirit was not considering Chapter 11 bankruptcy. By Nov. 18, 2024, Spirit had filed a prearranged Chapter 11 petition anyway. 

The airline emerged from bankruptcy on March 12, 2025, after converting roughly $795 million in funded debt into equity and securing a $350 million equity infusion, and having rejected a renewed merger proposal from Frontier Airlines the prior month. By August 2025, Spirit had filed for bankruptcy again. On May 2, 2026, it ceased operations entirely after a last-ditch effort to secure Trump administration support reportedly collapsed because bondholders—including Citadel and Ares, according to published accounts—refused to approve the plan. 

The important point, viewed retrospectively, is not that Spirit’s May 2026 liquidation was specifically foreseeable. It was not. The more important point is that Spirit’s fragility—and the real possibility that an independent Spirit would shrink, restructure, or fail outright—became part of the public conversation almost immediately after Judge Young’s 2024 ruling.

Bloomberg Law captured the issue with striking economy on Jan. 17, 2024, less than 24 hours after Judge Young’s opinion, in a piece headlined: “Keeping Spirit Cheap for Flyers Threatens to Kill It Altogether.”

That same day, Brett Snyder’s Cranky Flier—arguably the most consistently informed publication in the airline trade press—warned that the ruling “isn’t great news for Spirit” and walked readers through plausible restructuring scenarios. Snyder also reproduced a same-day TD Cowen note from analyst Helane Becker stating that “a more likely scenario is a Chapter 11 filing, followed by a liquidation.” 

In other words, within 24 hours of the ruling, informed industry observers were already openly modeling not just bankruptcy, but liquidation.

The financial record confirms this was not merely hindsight bias. Spirit’s 2023 Form 10-K reported a net loss of $447.5 million and disclosed that Pratt & Whitney engine groundings were expected to sideline an average of roughly 25 aircraft throughout 2024, with continuing effects through 2026. By the time Spirit’s 2024 10-K disclosed a $1.23 billion net loss and included explicit going-concern warnings, the airline was already in Chapter 11.

But the trajectory had been visible much earlier—in Securities and Exchange Commission (SEC) filings, in trade reporting, and, if one reads the trial record carefully, in Spirit’s own pre-deal board materials (here and here).

None of this means the merger should automatically have been approved. Diana Moss’ commentary, William McGee’s ProMarket defense of Judge Young’s opinion, and Brad Shrago’s empirical research on the “Spirit Effect” all support the view that Spirit’s business model materially lowered fares for budget-conscious travelers and that those benefits would not necessarily survive absorption into JetBlue’s different product strategy. 

But those analyses shared an important assumption: they treated standalone Spirit as a stable competitive counterfactual, implicitly assuming the airline could continue offering something like its 2019-era fare structure indefinitely. The post-pandemic record suggests that assumption was already untenable.

Fuel shocks, labor-cost increases, and the Pratt & Whitney engine crisis forced fares upward across the entire ULCC segment, merger or no merger. The relevant question, therefore, was not whether Spirit’s low fares would survive the merger. It was whether Spirit’s low fares were likely to survive at all. By late 2023, the evidence increasingly suggested the answer was no.

What looks especially problematic in retrospect is the durability assumption embedded in the government’s theory of harm. The DOJ’s complaint treated Spirit as a persistent, forward-looking competitive constraint while largely sidestepping contrary evidence contained in Spirit’s own SEC disclosures. Judge Young’s opinion was more candid. It acknowledged cumulative losses approaching $2 billion since 2020, as well as the uniquely severe impact of the Pratt & Whitney geared-turbofan groundings.

Still, once the court concluded the strict failing-firm defense did not apply, most of that evidence effectively dropped out of the core competitive analysis. The “Spirit Effect” was treated less as a fragile market phenomenon than as a permanent feature of the airline industry.

That binary doctrinal framework—either a legally cognizable failing firm or merely background noise—fit awkwardly with the facts. What the case really called for was a probability-weighted assessment of competitive durability, not an all-or-nothing inquiry into whether Spirit had already crossed the formal threshold of failure.

The Theory Survived. Spirit Didn’t.

Three lessons stand out from the Spirit saga.

The first is that the failing-firm doctrine, as currently formulated, is too binary to do much useful work in industries defined by high fixed costs, capital-intensive operations, and large exogenous shocks. The doctrine asks whether a company faces imminent failure and lacks any alternative purchaser. If the answer is no, courts largely proceed as though the firm will survive indefinitely in its existing competitive form.

That framework collapses what is really a continuous question into a crude yes-or-no proxy. The relevant inquiry is not simply whether the firm is technically “failing,” but what the likely distribution of outcomes looks like over the next five or 10 years. Will the company remain an aggressive competitive force? Shrink? Restructure? Drift into irrelevance? Exit entirely?

Spirit was not a failing firm in the strict Citizen Publishing sense in January 2024. But it was plainly a fragile firm whose continued role as the marginal fare-disciplining force in low-end airline markets depended on contingencies largely outside its control. A doctrine that cannot account for that fragility will systematically overweight static structural harms while underweighting dynamic competitive risk.

The second lesson concerns out-of-market efficiencies in mergers involving differentiated firms. Judge Young’s opinion is unusually candid in acknowledging that a combined JetBlue-Spirit carrier would likely have imposed stronger competitive pressure on the Big Four legacy airlines. That benefit would have accrued to a broader population of travelers than Spirit’s traditional ULCC customer base.

Still, the opinion treated those broader competitive gains as legally subordinate to the loss of direct competition for highly price-sensitive flyers. As Daniel Gilman, Brian Albrecht, and Geoffrey Manne argued in a Truth on the Market post shortly after the ruling, the “any-market” logic associated with United States v. Philadelphia National Bank and United States v. Topco Associates tends to operate selectively rather than consistently. But where courts do apply it, localized competitive harm controls the analysis regardless of the magnitude of broader market benefits. 

Gilman later observed that Judge Young effectively recognized that the merger would likely be procompetitive on net at the national level—and blocked it anyway. Whatever one thinks of that conclusion doctrinally, it raises an uncomfortable policy question. Blocking a merger to preserve surplus for the very lowest-fare travelers may not be welfare-enhancing if the likely long-run result is both weaker competition against the Big Four and the eventual disappearance of the ULCC altogether. 

The third lesson is institutional humility. The Biden administration treated the JetBlue-American Airlines Northeast Alliance challenge and the JetBlue-Spirit merger challenge as components of a broader effort to re-discipline the airline industry by preventing further consolidation among smaller carriers.

But airlines are not an industry where firms can easily scale organically through grit and good intentions. Network depth matters. Aircraft-acquisition timing matters. Gate access matters. Frequent-flyer ecosystems matter. Scale itself is often a competitive asset.

In that environment, forcing smaller carriers to remain small in the name of preserving competition can, paradoxically, produce less competition over time—especially when the ULCC business model is already under pressure from legacy carriers copying its core pricing strategies.

None of this proves the JetBlue-Spirit merger should have sailed through unchanged. The government’s concerns about harm to budget-conscious travelers were plausible. The Northeast Alliance litigation weakened JetBlue’s claim to outsider status. And the defendants never fully developed the sort of failing-firm record that might have forced the court into a more dynamic analysis.

But the post-ruling history does suggest the framework applied in the case was too static and too confident about the durability of standalone ULCC competition.

The enforcers were right to ask whether the merger would eliminate a disruptive competitor. They were wrong not to ask, with equal seriousness, what would happen if that competitor was forced to go it alone.

Two-and-a-half years later, we have the answer. Spirit is gone. The “Spirit Effect” is gone with it. And many of the travelers the lawsuit purported to protect are now buying tickets from the same Big Four airlines the challenge was supposed to constrain.

The post Nonstop to Nowhere: Spirit, JetBlue, and the Limits of Merger Doctrine appeared first on Truth on the Market.

It’s an eloquent piece. Lindsey is a serious thinker, and I’ve long admired him and his work. But here his prescription would pull the movement in the wrong direction. The intellectual tradition he draws on also carries a troubling recent track record in policy.

(Lindsey refers throughout the essay to his recent book, “The Permanent Problem” (2026), which I haven’t yet read. What follows comes with an obvious caveat: the book may address some of these arguments, and I may be judging his views based on a necessarily truncated version of them.)

Liberalism Isn’t Morally Empty; It’s Morally Humble

Lindsey describes the progress movement as a “shriveled liberal faith”—“bloodless and technocratic” and allergic to “making substantive moral judgments about how people choose to live their lives.” He calls for “value rationality”: a framework that not only expands human capabilities but also takes a stand on what people ought to do with them. We should “lift up and improve the ends that we try to achieve,” he writes, and increase “the number of individuals living excellent, admirable, rewarding lives.”

That critique brushes aside too much of liberalism’s intellectual history. The classical liberal tradition that animates much of the progress movement is not morally vacant. It rests on one of the most demanding moral commitments in political thought: human flourishing requires decentralized, plural conceptions of the good life, and no authority—no matter how wise or well-intentioned—knows enough to dictate the right ends for others.

Friedrich Hayek made this point with characteristic depth in “The Constitution of Liberty” (1960). Lindsey invokes Wilhelm von Humboldt’s line—“the absolute and essential importance of human development in its richest diversity”—as evidence that liberalism once embraced a more substantive moral vision. John Stuart Mill placed that line at the head of “On Liberty.” And Hayek quotes it, too, giving it the final word in “The Constitution of Liberty” before his famous postscript, “Why I Am Not a Conservative.”

But both Mill and Hayek deploy the line to reach a very different conclusion. The emphasis falls on “human development in its richest diversity.” For Hayek, that insight cuts against the kind of moral project Lindsey advances. We need liberty because we cannot know in advance which ways of living—which experiments, values, and pursuits—will foster human flourishing. Progress, on this account, is evolutionary. Individuals try different paths. Most fail. Some succeed in ways no planner could have predicted.

James Buchanan, the Nobel laureate in economics (public choice), put the point starkly:

To lay down any “social” purpose, even as a target, is to contradict the principle of liberalism… that leaves each participant free to pursue whatever it is that remains feasible within the limits of the legal-institutional parameters.

This isn’t a narrow, technical claim about markets. It’s the core commitment of the liberal tradition. A progress movement that adopts “value rationality”—that takes positions on which ends people should pursue—would not deepen liberalism’s moral content. It would undercut liberalism’s defining insight.

None of this makes moral values unimportant. Like Lindsey, Hayek recognized that shared norms help sustain a functioning society. As he wrote in “The Constitution of Liberty”:

We understand one another and get along with one another, are able to act successfully on our plans, because, most of the time, members of our civilization conform to unconscious patterns of conduct, show a regularity in their actions that is not the result of commands or coercion, often not even of any conscious adherence to known rules, but of firmly established habits and traditions. The general observance of these conventions is a necessary condition of the orderliness of the world in which we live, of our being able to find our way in it, though we do not know their significance and may not even be consciously aware of their existence (p. 123).

But Hayek also stressed that trying to impose a preferred set of such rules is a mistake, in part because

they require knowledge which exceeds the capacity of the individual human mind and [because], in the attempt to comply with them, most men would become less useful members of society than they are while they pursue their own aims within the limits set by the rules of law and morals (p. 127).

The most relevant passage concerns the flexibility of voluntary norms:

There is an advantage in obedience to such rules not being coerced, not only because coercion as such is bad, but because it is, in fact, often desirable that rules should be observed only in most instances and that the individual should be able to transgress them when it seems to him worthwhile to incur the odium which this will cause. It is also important that the strength of the social pressure and of the force of habit which insures their observance is variable. It is this flexibility of voluntary rules which in the field of morals makes gradual evolution and spontaneous growth possible, which allows further experience to lead to modifications and improvements. Such an evolution is possible only with rules which are neither coercive nor deliberately imposed—rules which, though observing them is regarded as merit and though they will be observed by the majority, can be broken by individuals who feel that they have strong enough reasons to brave the censure of their fellows. Unlike any deliberately imposed coercive rules, which can be changed only discontinuously and for all at the same time, rules of this kind allow for gradual and experimental change. The existence of individuals and groups simultaneously observing partially different rules provides the opportunity for the selection of the more effective ones (pp. 123–24).

A progress movement that precommits to any single vision of the good life would shrink the diversity Humboldt celebrated and Hayek treated as the engine of progress. The movement’s allegedly “thin” framework—remove barriers to innovation, grow the pie, let people choose—is not an absence of moral commitment. It is the moral commitment: the conviction that human flourishing emerges from freedom and experimentation, not from imposing any particular vision of the admirable life.

When ‘Human Flourishing’ Becomes Policy Power

This might sound like an abstract philosophical dispute. It isn’t. We don’t have to guess what happens when policymakers swap measurable welfare standards for vague appeals to “human flourishing.” We can watch it unfold—most clearly in antitrust, the policy domain most closely tied to economic growth and consumer welfare.

Shortly after taking up his post as a newly confirmed Commissioner of the Federal Trade Commission (FTC) in May of 2025, Mark Meador published a broadside expounding his moral vision of antitrust, entitled “Antitrust Policy for the Conservative.” The essay defines conservatism as “the political, religious, and cultural project in the West of pursuing the just ordering of society that best facilitates human flourishing.” It “prioritized tradition and custom over newness for newness’s sake, and beauty and virtue over cold, calculated efficiency.” Antitrust should “reaffirm[] these first principles,” Meador writes, “to provide clarity and certainty around what antitrust law is and how the law should be enforced.”

In Meador’s framework, antitrust does more than protect competition. It advances a moralized vision of the economy. The promotion of free markets “is a legal and moral choice—not just an economic choice.” Innovation is no longer presumptively beneficial (“not all that is innovative is good”). Large firms are presumptively suspect (“big is bad”). And economics itself takes a back seat, because “[a]n obsessive preoccupation with efficiency is… incompatible with a humane way of living.”

The resulting approach to antitrust would entail a meaningful shift. As Gus Hurwitz and I have noted before:

This view contradicts the past century’s worth of experience and learning. It would require jettisoning the crown jewel of modern antitrust law—the consumer welfare standard—and returning antitrust to an earlier era in which inefficient firms were protected from the burdens of competition at the expense of consumers. And doing so would put industrial regulation in the hands of would-be central planners, shielded from any politically accountable oversight.

The parallels to Lindsey’s call for “value rationality” are hard to miss. Both seek to move beyond “purely materialist” measures. Both draw on classical and theological traditions. Both treat welfare maximization as morally thin. And both run headlong into the same critical question: whose vision of human flourishing governs?

As Corbin Barthold has observed, Meador’s antitrust is not neutral inquiry. It is “national conservatism, if not flat-out Christian nationalism” dressed up in antitrust clothing. Meador describes human beings as “embodied souls seeking communion with their fellow man and their Creator.” He declares short-form video “bad for the soul.” He speaks, Barthold writes, “with complete confidence in his own superior vision for the tech industry”: “The man is, apparently, a prophet.”

More troubling still, Barthold argues that Meador’s concerns are not really about products, but about people:

People shouldn’t like short-form video. The government, Meador seemed to suggest, must protect them from themselves. You might say that Meador wants to replace the consumer-welfare standard, under which the FTC protects markets that work to give people what they want, with a moral-welfare standard, under which the FTC pushes markets to give people what they are supposed to want—as determined by Mark Meador.

This mindset has already shaped policy.

In February 2025—just before Meador joined the agency, but under Chairman Andrew Ferguson, who shares his worldview—the FTC issued a Request for Public Comment Regarding Technology Platform Censorship. The agency framed it as an effort to “understand how technology platforms deny or degrade users’ access to services based on the content of their speech or affiliations, and how this conduct may have violated the law.”

The political subtext was obvious. Denial and degradation were assumed. The “speech or affiliations” in question were “conservative” ones. The question was not whether this assumed conduct violated the law, but “how.” As Dan Gilman and Ben Sperry observed:

It took little imagination to wonder whether an inquiry into “censorship” and how “technology platform” conduct “may have violated the law” may have skewed submissions to the agency and, perhaps, biased the inquiry itself. Encouraging input from “[t]ech platform users who have been banned, shadow banned, demonetized, or otherwise censored” (but no others) did not seem a neutral solicitation of public comment on the potential costs and benefits of platform conduct. Indeed, some of the Commission’s commentary seemed ominous. The FTC’s press release stated that “[c]ensorship by technology platforms is not just un-American, it is potentially illegal.”

The true intention may have been discernible (if obscured), but the legal authority was absent, as it must be: The First Amendment protects private speech from government interference. Yet here is an agency signaling it may coerce private platforms to carry speech the government favors—despite lacking statutory authority, and likely constitutional authority, to police the political content of private speech.

Later in 2025, after Meador had joined the FTC, the agency announced its Omnicom–IPG merger settlement. It allowed two large advertising firms to merge—but only if the combined company agreed not to base advertising decisions on a publisher’s political or ideological views. Again, the subtext was clear. As Barthold writes:

[t]he settlement is a transparent assault on advertising firms’ First Amendment right to boycott publishers on grounds of social or ideological principle. It is also a nakedly political effort to redirect advertising dollars toward right-wing outlets.

The agency has also waded into gender medicine. It held an all-day conference titled “The Dangers of ‘Gender-Affirming Care’ for Minors” and launched a public inquiry. As Barthold notes, “[t]he FTC is not a medical regulator; it has no expertise in this area. But transgender issues are at the center of the culture war, so the agency could not resist weighing in, thumb firmly on the scale for the political right.”

This is the risk of any effort to “thicken” the progress movement’s moral commitments. Lindsey’s vision of the good life is thoughtful and humane. Meador’s is, at best, contestable. But the institutional framework Lindsey proposes offers no way to distinguish between them.

Once policy turns on moral judgments about flourishing rather than economic criteria, those judgments track political power, not philosophical reflection. As Hayek warned in “The Road to Serfdom” (1944):

Once you admit that the individual is merely a means to serve the ends of the higher entity called society or the nation, most of those features of totalitarian regimes which horrify us follow of necessity (p. 209).

And despite Meador’s claims to be implementing a “conservative” antitrust, there’s arguably nothing at all conservative about it. As James Cooper and Thom Lambert have written:

In short, the FTC’s crusade against “big tech censorship of conservatives” risks converting antitrust and consumer protection law into tools for ideological regulation. That transformation would be bad policy, bad law, and a troubling departure from the principles of limited government and private ordering that conservatives themselves have long championed.

It is striking that these arguments have gained traction in the Age of Trump. Classical liberals (and even some conservatives) have long warned that empowering government to pursue moral ends also empowers it to decide which ends to pursue. You may not like the answer when your side loses.

Meador’s approach—however dressed up—amounts to politicized enforcement. In principle, it differs little from the more overtly partisan uses of state power seen elsewhere in the Trump era. It is hard to believe that advocates of a “less narrowly economic” policy framework—including Lindsey—had this in mind.

With respect to antitrust, Lazar Radic and I develop this critique at greater length in “Competition and Competition Law in the Classical Liberal Tradition” (forthcoming in the “Routledge Handbook of Classical Liberalism” (2026)). The central mistake behind projects like Meador’s, neo-Brandeisian antitrust, and the European Digital Markets Act is a conflation of standards and goals. Standards assess whether market processes work. Goals prescribe outcomes. Classical liberal systems rely on the former and resist the latter. As we put it there:

A classically liberal antitrust law does not contort market processes to achieve the government’s preferred policy outcomes. It sets out a general framework and employs standards to gauge market processes but does not impose “goals.”… Attaching any end goal to this inquiry vitiates its outcome and misconstrues the law’s purpose.

Once you import a “thick” moral vision—“value rationality”—antitrust becomes unavoidably discretionary:

[I]ll-defined, value-pluralistic antitrust law inevitably bestows enormous discretion on enforcers who, in weighing incommensurate goals and values, are ultimately required to act as regimenters and social planners…. [H]ow does one assess the welfare effects of conduct adjusting for all the possible facets of human well-being across different groups, all of which are presumably deserving of (varying degrees of) special protection?

A progress movement that takes positions on what counts as an admirable life—what ends matter, how to balance work and leisure—opens the door to an almost limitless range of moralized interventions. Each new commitment becomes a lever for rent-seeking and rent extraction. Interests aligned with the favored vision will push for subsidies and protections. Those that are not will face pressure and penalties.

A thinner, growth-focused agenda resists capture precisely because it offers less to grab onto. A thicker, value-laden one is an open invitation.

If Growth Is the Culprit, the Evidence Didn’t Get the Memo

At this point a sympathetic reader might object: fine—but what about Lindsey’s strongest claim? Growth, he argues, has decoupled from well-being for large segments of the population. Social atomization is rising. The progress movement shrugs.

Fair enough. Lindsey is pointing to something real.

Deaths from suicide, drug overdose, and alcohol abuse among working-age Americans—what Anne Case and Nobel laureate Angus Deaton called “deaths of despair”—more than tripled between 1992 and 2017. Loneliness isn’t imagined. The U.S. Surgeon General’s 2023 advisory found that roughly half of American adults reported feeling lonely even before the pandemic. Marriage rates have fallen. Community institutions have weakened. Life expectancy gaps by education have widened.

These are serious problems. Ignoring them would be willful blindness.

But the implied causal story—that a myopic focus on economic growth and the market order caused these pathologies, and that fixing them requires reorientating the progress movement’s goals—doesn’t hold up.

Start with deaths of despair. Christopher Ruhm’s paper, “Deaths of Despair or Drug Problems?,” finds that changes in economic conditions explain less than one-tenth of the rise in drug mortality between 1999 and 2015. The main driver was not economic “despair,” but changes in the drug supply—overprescribed opioids, followed by a surge of cheap heroin and fentanyl.

The pattern fits. The epidemic hit hard in relatively strong economies like Massachusetts. It didn’t recede as economic conditions improved. And it didn’t appear in other developed countries facing similar patterns of economic stagnation.

Case and Deaton themselves reject the simple story. “Like Ruhm, we directly contradict the idea that deaths are related to economic conditions from 1999 to 2015,” they write. Mortality rose steadily through the Great Recession, largely unaffected by the downturn. European countries with comparable economic distress saw no similar spike.

Their broader argument—that long-term institutional decline damaged working-class communities—is more plausible. But it’s also harder to test and far less clearly connected to any growth-versus-values reframing of policy.

The loneliness epidemic poses a similar problem. Its suspected causes are all over the map: technology, urbanization, the erosion of civic life documented by Robert Putnam, changing family structures, and the COVID-19 pandemic, among others. But the actual directional arrow of causation remains murky. Does growth produce atomization? Or do both reflect deeper, independent shifts?

The global data complicate the story. Loneliness rates are higher in low-income countries—about 24% versus roughly 11% in high-income countries, according to the World Health Organization’s 2025 Commission on Social Connection. If material progress drove disconnection, you’d expect the opposite.

Even the standard narrative of economic stagnation looks shakier when you examine living standards, rather than income. Bruce Meyer and James Sullivan find that while income inequality rose about 25% since the early 1960s, what they term “consumption inequality” rose only about 7%. Consumption—because it reflects taxes, transfers, borrowing, and assets that living-standard measures miss—better captures what people actually experience.

Meanwhile, Bruce Sacerdote finds that real consumption for families at the 25th percentile rose 164% between 1960 and 2015. That’s not a story of broad material stagnation.

None of this denies that social fragmentation, status anxiety, and weakening institutions are real problems. But the diagnosis matters enormously for the prescription.

If the drivers of these pathologies are primarily “supply-side” defects including drug-policy failures, housing constraints, bad urban design, and the disruption that comes with rapid social change—as the evidence suggests—then the response looks familiar: targeted, institutional reform. Remove barriers. Expand opportunity. Let people build the lives they want.

Indeed, as Paddy Maher argued recently in The Argument, financial distress is one of the most significant predictors of loneliness. If loneliness is your target, the best thing you can do may be to promote abundance, not to try to “fix” the morality of the market:

To be lonely is to be in need, and the loneliest group in society is young people who are about to miss rent. The new political focus on affordability is perfectly timed for anyone who wants to make a dent in loneliness and suicides; helping more people maintain economic security is one of the best levers we have.

From Policy to Lifestyle Policing

To see how quickly “value rationality” turns political, consider Lindsey’s own wish list. He writes:

We need to recover the physical fitness that once came naturally from an active life; we need to reclaim our attention spans and cognitive acuity from screen-addled brain rot; we need to revitalize neighborhoods and communities withered by atomization; and we need to re-center marriage and parenthood as cornerstones of normal adulthood.

Each of these claims carries normative baggage. Each depends on empirical assumptions that are far less settled than the essay’s confident tone suggests.

Take “screen-addled brain rot.” The research on screen time is, by most accounts, a mess—“conceptual and methodological mayhem,” as one comprehensive review puts it. That meta-survey finds little support for the standard narrative. There is no “undeniable evidence” that social media is broadly toxic. At most, “time spent on social media does not have a strong effect on the well-being of its users.”

The idea of widespread “social media addiction” is similarly shaky. Experts disagree on whether it exists, how to define it, and how to measure it. Once you account for the main drivers of well-being, social media use appears to be a negligible factor.

Much of the literature is correlational, which makes causal claims precarious. Do screens harm children? Or do children who are already struggling gravitate toward screens? Emily Oster puts it bluntly: “All of the studies we see of screen time are deeply flawed.” Different screen habits track different family environments, and those underlying differences, not the screens themselves, likely explain the observed effects.

None of this proves screens are harmless. But “screen-addled brain rot” reads more like a moral panic than settled science—the kind of contested claim a progress movement should hesitate to enshrine.

The call to “re-center marriage and parenthood as cornerstones of normal adulthood” raises similar issues. Stable two-parent families may benefit children, though even that literature is contested (see, among others, here and here). But declining marriage also reflects expanded choice, particularly for women.

Sociologist Andrew Cherlin describes marriage as having undergone “deinstitutionalization.” It is no longer a mandatory social arrangement, but one option among many. Whether or not that framing is complete, attitudes have clearly shifted. Many feminists and sociologists interpret declining marriage rates as a sign of greater autonomy, not social decay. Others—such as the Institute for Family Studies—see costs.

The point is not that marriage doesn’t matter. It’s that “re-centering” it as a policy goal reflects one contested moral vision among many. In practice, it would alienate large constituencies. Even among conservatives who share Lindsey’s instincts, there is little agreement about what government should actually do to raise marriage rates.

A growth-focused agenda sidesteps these fights. It expands people’s options and lets them decide what kind of lives to build.

When Moralism Gets in the Way of Living

Perhaps the strongest objection to this broader project comes not from libertarians, but from thinkers who share much of Lindsey’s worldview.

The essay takes aim at amoral “techno-capitalists” for believing that “[a] brighter future thus consists of better and more absorbing paid employment, not increased leisure to be filled with non-commercial pursuits.” But philosophers like Michael Sandel—the furthest thing from a free-market fundamentalist—offer a different moral vision. He emphasizes “the dignity of work,” arguing that “[w]ork is not only a way of making a living; it is also a way of contributing to the common good and earning social recognition and esteem for doing so.”

So which moral vision should prevail?

That question is not hypothetical. The debate over artificial intelligence is testing it in real time, and the evidence thus far cuts against the less-work ideal. A Stanford Institute for Human-Centered AI survey of 1,500 workers found that 94% prefer AI that augments human labor rather than replaces it. Workers want tools that free them up for higher-value tasks—not tools that eliminate work altogether. A Harvard Business School study finds similar resistance to full automation, even when AI can outperform humans at lower cost.

Many people value work itself, not just the income it produces.

Daron Acemoglu, a Nobel laureate and no market fundamentalist, makes the point plainly in his exchange with Sandel: “I completely agree that the market is not a perfect anchor. But I worry that the alternative would simply be what intellectual elites value.”

He offers an example that gets at the heart of the problem:

Let me give you an example that has long bothered me. Opera is often treated as high art and is heavily subsidized, even though it is largely consumed by the well-educated and the wealthy. Meanwhile, heavy metal, which came out of working-class pubs, is not. That is a judgment made by intellectual elites, and it translates into policy.

So, I’m always afraid that if we give intellectual elites too much power to decide, we’re going to end up with a lot of situations like this. You might like opera, but many people like heavy metal.

Timothée Chalamet may have put it more bluntly, but the point stands.

Acemoglu is echoing a much older warning emanating from the liberal tradition. Adam Smith captured it in “The Theory of Moral Sentiments” (1759) with his famous critique of “the man of system”:

The man of system… seems to imagine that he can arrange the different members of a great society with as much ease as the hand arranges the different pieces upon a chess-board; he does not consider that…, in the great chess-board of human society, every single piece has a principle of motion of its own, altogether different from that which the legislature might choose to impress upon it (Part VI, Section II, Chapter II).

Thomas Sowell later described “the anointed”—elites whose vision of progress resists empirical challenge. Frédéric Bastiat warned of “manipulators of society” who would reshape human life by force.

The critique is consistent across centuries. When elites define “human flourishing” and try to impose it through policy, people bristle. They have their own preferences, values, and plans. Lindsey’s vision—more stable families, stronger communities, healthier lives—is appealing. But it is still just one man’s vision. Building a political movement around it risks turning that movement into yet another group of “men of system,” rearranging the chess board.

This is the “who decides?” problem in its clearest form. And it connects directly to the Meador example. Once you build institutions that impose substantive moral visions, those institutions will be run by elites—and they will reflect elite preferences. Maybe that yields Lindsey’s humane vision. Maybe it yields something else: religious revivalism, nationalist priorities, social-justice maximalism, or whatever else happens to dominate politically. Meador’s “human flourishing” antitrust offers a preview.

You can see the dynamic even in smaller, more personal contexts.

Consider Lindsey’s call to recover “the physical fitness that once came naturally from an active life.” We are in the middle of a revolution in treating obesity and diabetes. GLP-1 receptor agonists like Ozempic and Mounjaro may be the most significant advance in metabolic medicine in decades.

And yet, a moralistic stigma persists. Users are often seen as taking an “easy way out.” Many people—including physicians—still frame weight loss as a test of personal discipline and view pharmaceutical help as “cheating.” As former Secretary of Health and Human Services Alex Azar has noted, there is little stigma around taking statins for cholesterol, but GLP-1s carry a moral taint.

I’ve seen this firsthand. My own doctor initially resisted prescribing a GLP-1 medication for my diabetes—not because of medical concerns, but because he believed there was something more virtuous about managing it through diet and exercise alone.

That is “value rationality” in practice. And it’s not benign.

I told him that I had spent decades failing at the “virtuous” path. I wasn’t going to suddenly succeed. The real choice wasn’t between doing it the “right” way or the “easy” way. It was between taking the medication or accepting a shorter life. That ultimately persuaded him.

There’s no virtue in dying of a preventable metabolic disease because you lacked the time, willpower, genetic luck, or simply the desire to meet an idealized standard. The virtue is in living. If a drug makes that possible, that is progress—full stop.

The nostalgia for fitness “that once came naturally” is not just historically naïve—the lives that produced it were often harsh and short. When translated into policy or medical practice, that kind of sentimentality can delay the adoption of life-saving innovation.

Apollo Shows the Problem with ‘Thick’ Progress

Lindsey points to the Apollo program—and the public’s eventual indifference to it—as evidence that technophiles are “mutants” who need a broader vision to win support. The descriptive point is fair. Most people aren’t technophiles. The progress movement has to show how its agenda improves ordinary lives.

But a “thick” agenda doesn’t solve that problem. It makes it worse.

Apollo is the perfect case study. It was driven by exactly the kind of value rationality Lindsey favors: a substantive vision of what was admirable and inspiring, tied up with national prestige and civilizational ambition. The result was extraordinary—and unsustainable. The public eventually saw it for what it was: a significant government expense, disconnected from everyday concerns.

As Boom Supersonic founder Blake Scholl put it, Apollo “pursued glory without regard to cost or practicality…. Glory is a dangerous goal, and when it is pursued without regard to pragmatic utility, much damage is done.”

A thinner progress agenda does a better job of motivating support because it delivers tangible gains. It focuses on investments in science and technology that generate broadly useful knowledge and lower costs across the board. Housing reform that lets people build. Energy policy that makes power cheaper. Regulatory reform that gets new medicines to patients faster.

These improvements don’t require anyone to buy into a particular vision of the good life. They expand options and let people decide for themselves.

Jason Crawford makes this point directly. The progress movement, he argues, should offer “a moral defense of material progress”—not chase prestige projects, but understand how progress happens and how to accelerate it:

What technology and wealth can do is empower us to pursue the goals we have chosen. When we view material progress this way, it is something much grander than a generator of comfort and leisure: it is the great liberator allowing us to pursue rich, full lives.

Crawford’s focus is practical. Remove institutional barriers. Enable discovery and diffusion. Not because of abstract ideology, but because that’s what actually improves lives. As he notes,

more lives have been saved and suffering relieved by efforts to pursue general growth and progress than direct charitable efforts…. A root-cause analysis on most human suffering, if it went deep enough, would blame government and cultures that don’t foster science, invention, industry, and business.

The unglamorous work of clearing those obstacles is what delivers results. Not moonshots chosen for their symbolic appeal. Not moralized agendas reflecting the tastes of whoever holds power.

Dystopia Is What Happens When Someone Decides for You

Lindsey invokes “Wall-E” and “Brave New World” to argue that a materialist theory of progress “can’t distinguish between eutopia and dystopia”—and is therefore “worse than useless”:

Both of these dystopias feature impressive technological advances—but advances twisted into the service of human degradation. Humanity’s collective capacities may have expanded, but individuals’ opportunities to develop and exercise their own personal capacities have been crushed.

But this misses the mark. No one in the progress movement is arguing for a world of passive consumption and automated complacency. The case for material progress is about expanding choice sets—not dictating how people use them.

Jason Crawford, for example, advocates “techno-humanism,” the view that “science, technology, and industry are good—because they promote human life, well-being, and agency.” That is a moral claim. But it differs sharply from Lindsey’s “value rationality.” Crawford does not prescribe a particular vision of the good life. He defends a framework in which people can pursue their own:

A better guide to well-being and human progress is whether people can achieve their goals and fulfill their values. The good life is one of constantly discovering, pursuing, achieving, and maintaining values.

Now look more closely at Lindsey’s dystopias. What do they actually have in common? Not too much technology, but too much control. In both worlds, a central authority has decided what the good life should be—total leisure in “Wall-E”; pharmacological contentment in “Brave New World”—and imposed it across society.

In other words, they depict exactly the kind of “thick,” substantive vision of flourishing Lindsey wants to import into policy.

The lesson is not that we need more “value rationality.” It’s that we should be wary of anyone who claims to know, in advance, what ends other people ought to pursue.

The Moral Case for Keeping Progress ‘Thin’

Lindsey worries that a vision of progress grounded in material advance alone is too thin. That concern is understandable. But his alternative—a progress movement that takes substantive positions on how people should live—is worse. It invites political capture, fractures coalitions, and accelerates the kind of moralized policymaking already distorting key domains.

The social problems he highlights—atomization, weakening institutions, the erosion of shared meaning—are real. He is right to push the progress movement (and everyone else) to take them seriously. But the most effective response is not to prescribe better lives. It is to remove the barriers that prevent people from building the lives they want: build more housing, reform drug policy, fix permitting, expand access to medical innovation. These reforms improve lives without requiring anyone to sign on to someone else’s vision of the admirable.

Adam Smith, as reported by Dugald Stewart, captured the core insight in 1755:

Little else is requisite to carry a state to the highest degree of opulence from the lowest barbarism, but peace, easy taxes, and a tolerable administration of justice; all the rest being brought about by the natural course of things. All governments which thwart this natural course, which force things into another channel or which endeavour to arrest the progress of society at a particular point, are unnatural, and to support themselves are obliged to be oppressive and tyrannical.

It would be a mistake to read Smith as a mere instrumentalist about wealth. As Jacob Viner observed, Smith’s defense of laissez faire also rested on moral grounds: it preserved the “natural system of liberty” to which individuals have a right. He likely would have resisted extensive state direction even if it increased aggregate wealth.

Hayek was explicit in “The Road to Serfdom” that liberalism rests on individualism and personal liberty:

Economic liberalism… regards competition as superior not only because it is in most circumstances the most efficient method known but even more because it is the only method by which our activities can be adjusted to each other without coercive or arbitrary intervention of authority (p. 110).

Milton Friedman, when pressed in the final episode of Free to Choose (1980) on whether he would trade freedom for prosperity, chose the “human and ethical and moral values” of the former.

This is the answer to the charge that a growth-focused agenda is “bloodless and technocratic.” The classical liberal case has always been consequentialist and moral—a double helix running from Smith through Mill, Hayek, Friedman, and others.

The progress movement inherits this tradition. Its agenda is not thin because it lacks moral content. It is thin because its moral content—growth, diversity, self-direction—requires restraint. A plurality of ends is not a problem to solve; it is the engine of progress.

Grow the pie. Let people decide for themselves what to do with their slice.

The post Grow the Pie, Skip the Sermon appeared first on Truth on the Market.

That is the question now before the California Supreme Court, which heard oral argument yesterday in Gilead Tenofovir Cases (S283862). The case asks whether California law recognizes a previously unheard-of “duty to commercialize a safer alternative drug.” Under the First Appellate District Court of Appeal’s rule, that duty arises whenever a pharmaceutical manufacturer allegedly “knows” that another formulation in its pipeline is at least as effective, and safer, than the product currently on the market. 

The International Center for Law & Economics (ICLE) filed an amicus brief in the case urging reversal. The brief argues that the appellate court’s rule effectively eliminates the longstanding product-defect requirement, rests on a misreading of  Brown v. Superior Court, and—most interestingly from a law & economics perspective—would invite courts and juries to second-guess R&D decisions that the patent system and U.S. Food and Drug Administration already heavily structure. 

Oral argument, of course, is no oracle of case disposition. Even so, two lines of questioning stood out to me, and both, I think, vindicated concerns we raised in the brief.

The Duty That Couldn’t Be Defined

The most revealing stretch of argument came when the justices repeatedly asked plaintiffs to explain, with any precision, what their proposed duty would actually require pharmaceutical manufacturers to do. The court never got an answer it seemed able to work with.

Plaintiffs argued that manufacturers owe “a duty to act reasonably in making commercialization decisions about an allegedly safer and at least equally effective alternative drug.” But when the justices pressed them on what that meant in practice, plaintiffs largely circled back to the same formulation: manufacturers must “act reasonably.”

The bench kept returning to the obvious follow-up: reasonable according to what standard?

At one point, the court tested the theory with a hypothetical. Could a jury find a pharmaceutical company acted unreasonably by pursuing one research path over another—perhaps backing a less promising candidate that ultimately failed—based on how it allocated scarce R&D resources? Plaintiffs said yes.

That answer immediately raised another concern: hindsight bias. Once a lawsuit is filed years later, after more data emerges and one research path succeeds while another does not, what exactly is the legal rule supposed to tell drug developers ex ante? What is the operating instruction?

Notably, defense counsel faced no parallel interrogation. The court’s questioning there focused mostly on how Gilead’s position fit within existing California law.

The exchange tracked almost perfectly with what our brief predicted. Plaintiffs’ theory is, as we argued, “a recharacterization of a traditional products liability claim, attempting to achieve the same result while avoiding the required showing of defect.”

California has long required plaintiffs alleging injury from a product to prove that the product was defective for a reason. A free-floating “duty to act reasonably,” untethered from any product defect, gives juries little meaningful guidance and leaves manufacturers with no workable way to structure their conduct in advance.

The justices’ discomfort echoed concerns the California Supreme Court itself expressed in Brown v. Superior Court. There, the court warned:

Perhaps a drug might be made safer if it was withheld from the market until scientific skill and knowledge advanced to the point at which additional dangerous side effects would be revealed. But in most cases such a delay … would not serve the public welfare.

The court also raised a closely related question: Why didn’t plaintiffs plead a design-defect claim in the first place?

The answer is straightforward. Plaintiffs have repeatedly conceded—including again at oral argument—that the drug at the center of the case is not defective and should not be removed from the market.

But that concession is the whole problem.

Without a defective product, plaintiffs are left arguing for what our brief describes as “an unprecedented expansion of tort liability” that “would expose manufacturers to potentially unlimited liability for products that are reasonably safe and defect-free.”

The justice who asked the question almost certainly understood that. In many ways, the question itself was the point.

A Jury Is Not a Drug-Development Committee

The second revealing exchange came from a different line of questioning, one aimed squarely at institutional competence. The court asked plaintiffs how their proposed duty fit alongside the patent and regulatory regimes that already govern pharmaceutical development.

Plaintiffs responded largely by criticizing the pharmaceutical industry’s allocation of resources between R&D and marketing. In a sense, that response went directly to one of the core concerns in our amicus brief: a rule like the one adopted in Gilead would effectively give plaintiffs’ attorneys and juries a veto over enormously complex, expensive R&D decisions.

Under the court’s framing, though, the problem ran deeper. The patent system already reflects a deliberate legislative bargain about innovation incentives. Congress decided how long manufacturers receive exclusivity for new inventions. Overlaying that framework with a tort duty requiring companies to commercialize drugs on some judicially determined timetable would replace an explicit statutory scheme with ad hoc, case-by-case litigation judgments.

The court appeared deeply uneasy with that prospect. At one point, the justices described the proposition, in so many words, as “uncomfortable.”

As our amicus brief explained, plaintiffs’ theory “would set an unrealistic and dangerous standard of perfection for drug development.” More fundamentally, though, the theory largely ignores the patent system that already shapes the timing of pharmaceutical commercialization.

That matters because patent terms run from the filing date, not from the date a drug reaches the market. In practical terms, sitting indefinitely on a superior product is not some masterstroke of profit maximization; it is usually economically irrational. Every year spent delaying commercialization is a year of patent exclusivity burned off the clock.

In other words, plaintiffs’ suppression theory depends on a view of the patent system that does not fit how pharmaceutical markets actually work.

As we noted in the brief, if Gilead truly believed Tenofovir alafenamide (TAF) was both safer and equally effective, the company generally would have had every incentive to bring it to market sooner, not later.

A Tough Day for an Expansive Tort Theory

Of course, oral argument is famously unreliable tea-leaf reading. The justice grilling you at the lectern may ultimately write the opinion in your favor; the justice tossing softballs may end up ruling against you.

Still, the substance of the questioning mattered. Plaintiffs repeatedly struggled to explain what “reasonable” commercialization behavior would actually require in practice. At the same time, the court repeatedly drifted toward concerns about institutional competence and the role of legislatures, regulators, and the patent system in structuring pharmaceutical innovation.

That combination feels significant.

If I had to guess, reversal now seems more likely than not. The most plausible paths are the two the defense laid out at argument: either the court reaffirms the defect requirement as the exclusive framework for product-based negligence claims, or it recognizes some broader duty in principle before concluding—under the familiar Rowland framework discussed extensively at argument—that this particular theory goes too far.

Either way, the argument suggested a court searching less for a way to expand tort law than for a limiting principle to stop it from expanding indefinitely.

And that may tell us all we need to know.

The post With Gilead’s Reasonableness Standard, Side Effects May Vary appeared first on Truth on the Market.

A growing share of scholars, advocates, and policymakers now rejects much of conventional antitrust doctrine and method. They argue that this framework enabled the rise of purported digital monopolies in platform markets and contributed to a broader, purported decline in competition.

Among the favorite targets is the “error-cost” principle: the view that the costs of antitrust overenforcement typically exceed the costs of underenforcement, in part because market forces tend, over time, to compete away inefficient practices. If that proposition is correct, regulators and courts should hesitate before condemning business practices as antitrust violations without compelling evidence of anticompetitive harm. Much of federal antitrust case law reflects this prudential approach.

Critics of the error-cost principle have offered little by way of hard numbers to show the competitive harms supposedly caused by caution in antitrust enforcement and adjudication. Nor have they seriously grappled with the countervailing costs of overenforcement. Even so, competition agencies in the United States, European Union, and United Kingdom have made significant interventions and pursued far-reaching remedies on this theory, brushing aside concerns about false diagnoses of anticompetitive maladies.

In the antitrust equivalent of Silicon Valley’s “move fast and break things,” agencies have been especially eager to block mergers based on inherently conjectural theories of potential future harm. Debates over whether such challenges are premature rarely yield clear answers at the time, precisely because they require crystal-ball predictions about future market trajectories. But now that critics of conventional antitrust have led several major competition agencies around the world, we have some accumulated evidence to inform the policy debate.

That evidence casts doubt on the merger-review strategy of “challenge first, ask questions later,” and on the assumption that false-positive error costs can be ignored or heavily discounted. It now appears that several recent merger challenges by U.S., EU, and U.K. regulators destroyed significant economic value—often accompanied by substantial losses in employment and related business activity—without any clear improvement in competitive conditions. In at least one case, those conditions worsened.

JetBlue-Spirit: The Maverick That Got Grounded

Let’s start with Spirit Airlines’ recent shutdown, which came just a few years after JetBlue’s failed 2022 attempt to acquire the low-cost carrier. The U.S. Justice Department (DOJ) and seven states challenged the transaction in court, arguing that it would eliminate a “maverick” carrier that disciplined pricing by larger airlines.

Concentration metrics offered only limited support for that theory. As the district court observed, Spirit accounted for about 4% of the U.S. passenger-airline market, and the combined firm would have represented only about 10%. That was far smaller than the market shares held by each of the four largest airlines—United, American, Delta, and Southwest—which together accounted for about 80% of the domestic market. The JetBlue-Spirit combination would, however, have produced high concentration levels on certain routes, particularly those connecting some Northeast cities with destinations in Florida and the Caribbean.

JetBlue argued that the combined entity would still face pricing pressure from the four largest airlines, as well as from smaller carriers such as Alaska and Frontier, in relevant geographic markets. It also argued that, for a smaller low-cost airline like Spirit, which tended to operate on thin margins, consolidation into a larger entity could provide the scale needed to improve economic viability and operational continuity for consumers.  At the same time, acquiring Spirit would enable JetBlue to compete more vigorously against the legacy carriers.

To address concerns about competition on routes with high overlap between acquirer and target, JetBlue committed in the acquisition agreement to divest assets—slots and routes—to secure regulatory approval, “up to a material adverse effect” on the combined airline. As the district court acknowledged, JetBlue also agreed to asset divestitures in the Boston, New York, and Fort Lauderdale markets and, at the time of the litigation, had entered into divestiture agreements with several low-cost airlines.

Despite these concessions, the district court insisted that the merger would do “violence to the core principle of antitrust law to protect the United States’ markets . . . from anticompetitive harm.” Recent developments suggest the decision helped produce something closer to the opposite result.

Several economic factors contributed to Spirit’s demise. But its complete exit from the market raises serious questions about whether the DOJ’s challenge and the court’s decision harmed the very consumers antitrust law is supposed to protect. Blocking the JetBlue acquisition most likely contributed, at least in some measure, to Spirit’s inability to weather volatility in the air-travel market. The result was value destruction for shareholders, lost jobs for Spirit’s workers—about 15,000 layoffs at the time of the airline’s shutdown—and lost business for the airline’s contractors and suppliers.

In the JetBlue acquisition, Spirit shareholders would have received $3.8 billion in consideration. Today, Spirit shares are largely worthless, as the company enters bankruptcy. It is impossible to know precisely how much blocking the merger contributed to Spirit’s demise. But the realized outcome is plainly worse for consumers than the alternative remedy on the table: a merger with asset divestitures to competitors.

That targeted intervention would have produced a combined entity better positioned to harness the scale efficiencies and financial stability of a larger carrier, while using divestitures to mitigate potential competitive harms on specific routes.

Illumina-Grail: The Cancer-Test Merger Regulators Diagnosed Too Early

Let’s turn to Illumina/Grail, which involved Illumina’s reacquisition of Grail, an emerging firm it had spun out a few years earlier. Illumina is the world’s leading producer of DNA-sequencing equipment.

Grail had developed a pioneering multicancer early detection, or MCED, test with the potential to save lives by identifying cancers earlier, enabling earlier treatment and improving the odds of success. After the approximately $8 billion deal was announced in September 2020, regulators in the United States and European Union challenged the transaction. Their theory was that Illumina, as the leading DNA-sequencing platform, would have incentives to foreclose access to that platform for Grail’s competitors in the nascent MCED-test market.

Keep in mind: Grail’s MCED test was not yet commercially available, and the MCED-test market was still in its earliest stages.

Illumina sought to allay regulators’ concerns about the acquisition’s effect on this emerging market. First, Illumina argued that it would have no incentive to foreclose other MCED-test providers, because doing so would likely produce revenue losses exceeding any prospective gains from excluding rival test developers. Second, Illumina publicly issued a binding “open offer” to maintain access for all other test providers on fair, reasonable, and nondiscriminatory terms for 12 years.

As I have shown elsewhere, platform providers often make this type of commitment voluntarily to encourage adoption of new technology—such as MCED tests—and to seed the market by assuring users that access will remain available on comparable terms going forward.

The agencies were unconvinced. In September 2022, the Federal Trade Commission (FTC) lost its challenge before an administrative law judge. The commission reversed that decision, and Illumina appealed to federal court.

In the EU, the European Commission accepted “referrals” from national competition authorities to review the transaction, even though Grail had no EU revenue and the deal therefore did not meet EU or national reporting thresholds. When challenged in court, the commission’s assertion of jurisdiction was initially upheld. On appeal, the European Court of Justice annulled it in September 2024.

By then, Illumina had already elected to terminate the transaction and divest Grail. The company faced the Commission’s decision to block the deal and impose a €432 million gun-jumping fine for closing the acquisition before securing EU merger approval, ongoing FTC litigation, and intense shareholder pressure amid mounting regulatory costs and delays. In June 2024, Grail was divested as an independent public company.

This two-front international regulatory challenge coincided with a precipitous decline in Illumina’s stock price. Illumina’s stock fell by approximately 52% between the deal’s announcement on Sept. 21, 2020, and its termination on Dec. 17, 2023, using the closing price on Dec. 18, 2023. That compares with an approximately 11% gain in the iShares U.S. Medical Device ETF (IHI) over the same period.

This is not to say regulatory challenges were mostly responsible for this exceptional decline in Illumina shareholder value. But they likely accounted for some material part of it, especially given Illumina’s dramatic underperformance relative to the broader medical-device and equipment sector.

In retrospect—which admittedly benefits from hindsight—the regulators’ foreclosure theory has lost even more force. During the Illumina/Grail litigation, the FTC asserted that Illumina controlled approximately 90% of the global market for high-throughput next-generation DNA-sequencing systems, leaving MCED-test developers with no comparable sequencing platform as a viable alternative.

That market share was critical to the agencies’ theory that Illumina would have an incentive to forfeit sequencing-platform revenue by excluding MCED-test developers, thereby capturing supracompetitive gains from Grail’s product. Today, Illumina faces increasing competition across segments of the next-generation DNA-sequencing market from BGI/MGI, Thermo Fisher, PacBio, Oxford Nanopore, Element Biosciences, and others.

At the same time, Grail faces growing competition from multiple MCED tests, although none—including Grail—have yet secured Food and Drug Administration (FDA) approval. For 2025, Grail reported revenue of $147 million and a net loss of $408 million—hardly the stuff of a dominant provider.

None of this could have been known with certainty at the time of the Illumina/Grail transaction. But the FTC specifically dismissed the possibility that new competitors to Illumina’s platform would emerge, stating that Illumina had failed to show that “new entry of an MCED test that does not rely on Illumina’s [] platform would be timely, likely, or sufficient to offset the anticompetitive effects” of the acquisition.

Subsequent developments suggest the FTC understated both the pace of technological change in the sequencing-platform market and the pace of adoption and clinical success in the testing market. About five years after the FTC challenged the Illumina/Grail deal, adoption of MCED tests has proceeded more slowly than expected, reflecting in part continued uncertainty about their clinical utility. Given the market’s uncertain trajectory, any sequencing platform would seem to have little incentive to limit access for MCED-test developers.

The Illumina/Grail litigation and its aftermath suggest that regulatory caution is especially warranted when a market remains in its infancy. That is precisely the opposite of the view now fashionable among some regulators, who seek to act preemptively as a kind of social planner steering the market’s future course.

At a minimum, this still-developing market does not appear to present the level of anticompetitive risk needed to justify the investment of significant taxpayer resources by two major competition agencies—especially in exchange for little, if any, apparent improvement in competitive conditions.

Amazon-iRobot: Antitrust Vacuums Up the Roomba Deal

In August 2022, Amazon announced an agreement to acquire iRobot, producer of the Roomba robotic vacuum and a pioneer in the home-robotics market, for approximately $1.7 billion. The transaction drew scrutiny from the FTC, the European Commission, and the UK Competition and Markets Authority (CMA).

Regulators relied on a foreclosure theory of anticompetitive harm. They focused on Amazon’s purported incentive to favor Roomba after the acquisition and disadvantage rival robot-vacuum manufacturers on Amazon’s platform. In broad strokes, the concern was that Amazon could leverage its position in online marketplace services to restrict competition in the vertical market for robot vacuums, hampering rival suppliers’ ability to compete on a level playing field.

That theory depended on two key assumptions: first, that Amazon could charge supracompetitive prices for Roomba; and second, that the additional revenue from Roomba sales would exceed the losses from depressing sales of rival robot-vacuum products.

Both assumptions were hard to square with the competitive reality of the robot-vacuum market, even at the time of the proposed acquisition. The market included multiple competitors, including larger and more established firms such as Dyson, SharkNinja, and Ecovacs, as well as lower-cost entrants. Those firms also had several channels to reach consumers, including direct-to-consumer websites, other online marketplaces such as Walmart, big-box retailers such as Best Buy, Target, and Costco, and brand-owned stores and showrooms.

Given those alternative distribution channels, and consumers’ resulting ability to vote with their feet—or their digital equivalent—Amazon likely had limited ability or incentive to disadvantage rivals. It would more likely do best by maximizing sales of robot vacuums across the market. Regulators also seemed to place little weight on the efficiencies that could come from integrating iRobot into Amazon’s product-development and distribution infrastructure, especially given iRobot’s precarious financial condition at the time of the acquisition.

The CMA eventually withdrew its objections. The FTC and European Commission persisted, and in January 2024, Amazon abandoned the transaction in light of regulatory scrutiny.

Here, the evidence more clearly indicates that regulators’ challenge played a significant role in iRobot’s post-termination financial decline. Immediately after the acquisition fell apart, iRobot laid off about one-third of its workforce. In the ensuing months, it continued to face serious financial distress—due in part to the very competition the agencies had downplayed.

The company filed for bankruptcy in December 2025, wiping out shareholders’ investment. iRobot was then acquired by a China-based manufacturer in January 2026, raising concerns about the security of users’ home-mapping data.

Qualcomm-Autotalks: Regulators Pump the Brakes on V2X

Let’s end the review with Qualcomm’s proposed acquisition of Autotalks. Qualcomm is a leading chip developer focused on the mobile-communications device market. Autotalks is a fabless semiconductor startup that designs pioneering vehicle-to-everything, or V2X, chipsets for the automotive market.

The technology is compatible with both leading V2X standards and enables cars to communicate with other vehicles, road infrastructure, pedestrians, and wireless networks. That, in turn, can improve road safety and support advanced driver-assistance and autonomous-driving systems. The acquisition appears to have been part of Qualcomm’s strategy to expand beyond telecommunications and deploy its connectivity solutions in the automotive sector.  For Autotalks, it was a mechanism to realize the value on the innovative technology that its founders and employees had been developing since 2008—a classic startup story.

Qualcomm announced the proposed acquisition in May 2023. The deal would have paid $350 million to $400 million to Autotalks’ founders, employees, and investors. Despite that relatively small deal value, the transaction attracted close scrutiny from the FTC, European Commission, and CMA. The agencies expressed concern that Qualcomm’s acquisition of a leading independent supplier of V2X chips could help it secure a dominant position in the automotive-connectivity market.

As in the other merger challenges, regulators seemed to give little weight to the synergies that could come from combining a small startup’s innovation with a larger company’s scale economies, infrastructure, and institutional expertise. As I have documented elsewhere, significant evidence shows that platform-startup acquisitions—a regular feature of healthy tech ecosystems—typically accelerate diffusion of the target’s technology through new products and services for businesses and consumers.

Here, the transaction likely would have promoted the build-out of connectivity solutions for assisted-driving and self-driving technologies, while encouraging adoption by original-equipment manufacturers  and Tier 1 suppliers and broader market growth.

Regulators’ opposition to the Qualcomm-Autotalks acquisition rested on the implied premise that Autotalks could efficiently develop and diffuse its technology as an independent firm.  Yet Autotalks had been attempting to do just that for about a decade since its first “design win” in 2016.  If the premise was unfounded, the merger challenge risked suppressing the startup’s technology or slowing market adoption—the sort of cure that leaves the patient wondering why they came in.

Qualcomm terminated the deal in March 2024 amid regulatory scrutiny. It ultimately completed the acquisition in June 2025 at a substantially reduced price—reportedly less than $100 million—and under terms that rendered the startup employees’ stock options worthless.

That steep decline in consideration implies a more than two-thirds reduction in deal value relative to the original transaction. The reduced valuation suggests Autotalks continued to struggle to deploy the technology independently after the initial deal was withdrawn. It also likely reflects the company’s limited exit options under the cloud of regulatory scrutiny.

The transaction eventually closed. But the regulatory delay may have slowed deployment of V2X technology in the automotive market and effectively shifted economic value from a startup to the larger acquirer.

When Error Costs Come Due

A significant share of antitrust scholars, advocates, and policymakers now argues that the error-cost principle—specifically, its emphasis on avoiding overenforcement relative to underenforcement—helped produce purported increases in concentration and entrenchment in tech and other markets. They would therefore discard it.

The practical result is a green light for regulators to act with little concern for the costs of getting it wrong, reinforced by a policy audience that sometimes seems to welcome intervention—especially against the largest firms—for its own sake.

Even without the benefit of hindsight, the transactions discussed here rested on tenuous factual grounds for inferring a material competitive threat. In two cases—JetBlue-Spirit and Illumina/Grail—regulators rejected significant concessions from acquirers that likely would have preserved the transactions’ efficiencies while safeguarding against anticompetitive side effects.

This rush to intervene reflects too little attention to the error costs of unwarranted action, especially in cases involving nascent markets, as in Illumina/Grail; financially precarious targets, as in Amazon-iRobot and, to a lesser extent, JetBlue-Spirit; or small acquisitions, as in Qualcomm-Autotalks. Those costs seem to have been overlooked by regulators as they challenged transactions based on largely conjectural theories of anticompetitive harm.

The interventions discussed here did little to enhance competition. In JetBlue-Spirit, intervention likely reduced it. They also contributed to significant losses in shareholder value and employment, reduced startup valuations, and delayed technological deployment.

Some scholars, advocates, and policymakers may place little weight on the costs of erroneous intervention. These merger challenges show why that confidence is misplaced. Error costs are not an abstraction; they are borne by the consumers, entrepreneurs, and workers that regulators purport to protect.

The post False Positives, Real Casualties: The High Price of Populist Antitrust appeared first on Truth on the Market.

That is the problem now facing the Federal Communications Commission’s (FCC) Lifeline program, which was designed to ensure that low-income Americans can connect to the communications networks modern life depends on. As the program has expanded to cover new services, the Universal Service Fund (USF) contribution factor—essentially a tax on consumer phone bills—has climbed to 37%. That makes every dollar lost to fraud more than an accounting error. It is a charge passed on to the consumers funding the system.

Reasonable people can debate the merits of the existing USF structure, and of broadband-subsidy programs more generally. But if regulators want to continue supporting broadband deployment and adoption, those programs should maximize benefits while minimizing costs.

USF costs have risen largely because support has expanded to broadband. But waste, fraud, and abuse still drive costs higher without producing any offsetting public benefit. Most notably, the FCC Office of Inspector General (OIG) found that providers in states that opted out of federal verification systems collected nearly $5 million in reimbursements tied to more than 116,000 deceased individuals over just five years. Roughly 40% of those payments went to individuals who may have died before they ever enrolled.

Eliminating waste, fraud, and abuse matters. But doing so also carries costs. Compliance burdens that are too heavy, eligibility rules that are too blunt, or verification requirements that are too cumbersome can prompt eligible households to abandon enrollment. They can also push providers out of thin-margin markets rather than absorb mounting administrative overhead.

Fortunately, the FCC could take two immediate steps to address this specific fraud with minimal burden on consumers.

First, the FCC should require all states to use federal verification systems, ensuring that up-to-date mortality information is used to verify claims. Second, it should require a secondary consent mechanism to confirm that the individual subscribed to a Lifeline-supported service is actually eligible and receiving that service.

Taken together, these reforms would directly target one of the Lifeline program’s most significant fraud vectors without undermining participation in the program. 

The Fine Line Between Oversight and Overkill

Fraud reduction and program access are not competing values. Still, policies designed to reduce fraud can also make it harder for legitimate recipients to access the program. Section 254 of the Communications Act requires the FCC to promote affordable access to quality communications services. That means every proposed reform should be judged not only by how much fraud it eliminates, but also by the burdens it imposes on eligible households and the providers that keep the program running.

Eliminating waste, fraud, and abuse has obvious value. Every dollar paid to a deceased subscriber, a fictitious enrollee, or a provider that never delivered service is a dollar that did not reach a qualifying family trying to afford a phone bill. With the USF contribution factor now at 37%, the surcharge has become one of the largest hidden taxes on American telecommunications consumers. Improper payments therefore impose a direct and ongoing financial cost on the very people subsidizing the system.

Reducing waste, fraud, and abuse also stretches existing program dollars further, lowers the contribution burden on ratepayers, and helps restore the legitimacy that any subsidy program needs to maintain public and political support.

At the same time, anti-fraud reforms are not costless. Compliance burdens that appear modest on paper can become decisive in thin-margin markets. When the FCC layers on enhanced compliance plans, mandatory audits, expanded financial disclosures, and duplicative reporting obligations, providers must decide whether participation in Lifeline still makes economic sense.

When the answer is no, providers scale back operations, exit high-cost markets, or leave the program altogether. The subscribers they serve then lose access. Overly rigid rules can produce the same result through a different mechanism, deterring eligible households from enrolling or remaining enrolled.

The goal of Lifeline reform should be a program that is harder to defraud and easier to use—not one that replaces a fraud problem with an access problem of equal or greater magnitude.

Fraud Thrives in Fragmented Systems

Earlier this year, the FCC OIG released findings that exposed a structural fraud problem in the Lifeline program. The report found that providers in states that opted out of the National Lifeline Accountability Database (NLAD) collected nearly $5 million in reimbursements tied to more than 116,000 deceased individuals over a five-year period. Roughly 40,000 of those enrollments appear to have occurred after the subscriber had already died. 

The OIG also uncovered widespread duplicate claims, including cases in which the same individuals were enrolled simultaneously in multiple states. Taken together, the findings describe a verification system with gaps large enough to sustain systematic abuse at scale, year after year, without detection.

The opt-out framework was intended to give states flexibility. In practice, the OIG’s findings suggest that flexibility came at a substantial cost. State-run systems lack the direct, real-time connections to federal mortality and identity databases that the National Verifier uses. States can also adopt rules that conflict with federal policy.

California offers a particularly striking example. The state eliminated requirements that applicants submit Social Security numbers, making it impossible for the Universal Service Administrative Co. (USAC) and the FCC to reliably verify Lifeline enrollment and reimbursement claims. These state-by-state variations have also produced uneven outcomes, creating inequities among participants depending on where they live. 

The most straightforward solution is to eliminate the opt-out framework entirely and require all remaining states to transition to the National Verifier on a clear and reasonable timeline. That reform would directly address the vulnerabilities identified by the OIG while imposing minimal burdens on legitimate participants.

Federal verification systems already perform functions that state systems have demonstrably failed to perform. The National Verifier runs automated death checks against authoritative federal mortality data, applies uniform identity-verification standards nationwide, and conducts real-time or near-real-time eligibility checks that do not depend on varying state policies or administrative capacity.

Centralizing verification would also reduce compliance burdens on providers. Right now, providers operating in multiple states must navigate different verification systems, documentation requirements, and reimbursement processes depending on the jurisdiction they serve. A single federal framework would replace that patchwork with a more predictable and administrable system. 

One More Click, Much Less Fraud

In addition to creating a single verification system, regulators should consider adopting a secondary-consent requirement. Under such a rule, any Lifeline enrollment or benefit transfer would require affirmative confirmation from the actual subscriber before completion.

The mechanics are straightforward. When an enrollment or transfer is initiated, the system sends a confirmation message to the applicant’s contact information on file. The transaction proceeds only if the individual responds affirmatively. The reform would not impose new documentation requirements or tighten eligibility standards. It would simply confirm that the person named on the application is the same person who actually wants the service.

That reform would target two common forms of enrollment fraud.

First, when fraudsters fabricate contact information, nobody can respond to the confirmation request, and the enrollment fails automatically. Second, when someone uses a real person’s information without that individual’s knowledge or consent, the legitimate subscriber receives an unexpected notification and can reject the transaction. What otherwise would have become a completed fraud instead becomes a detectable red flag that triggers scrutiny rather than reimbursement.

A secondary-consent mechanism would also help curb fraud in benefit transfers. Under the current NLAD framework, a provider can initiate a transfer of a subscriber’s benefit to a new carrier based largely on the provider’s own certification of consent. Applying secondary verification to transfers would reduce the ability of fraudulent actors to use benefit transfers as a workaround for existing verification checks. A subscriber who did not authorize a transfer would receive notice before it occurs and could block it.

As with eliminating state opt-out verification systems, the burden on legitimate users would be negligible. In most cases, the reform would require nothing more than a single tap or reply—the sort of action smartphone users perform dozens of times each day in other contexts. It would be far less onerous than the annual recertifications, officer certifications, and documentation requirements the program already imposes on both subscribers and providers.

Paired with the opt-out reforms discussed above, a secondary-consent requirement would directly target one of the Lifeline program’s remaining major fraud vectors without undermining participation. 

Dead Subscribers Make Bad Public Policy

The Lifeline program exists to ensure that low-income Americans can access the communications networks modern economic and civic life increasingly requires. If federal regulators intend to preserve the program, they must also ensure that it operates efficiently and does not impose costs that outweigh its benefits.

Every dollar lost to a deceased subscriber, a fictitious enrollee, or an unauthorized transfer is a dollar that never reached a qualifying household. It is also a cost shifted onto the ratepayers funding the system and another blow to the public legitimacy that keeps the program politically sustainable.

The reforms outlined above directly target the structural weaknesses identified in the OIG’s findings. Just as importantly, they do so without raising eligibility barriers, increasing documentation burdens, or imposing compliance costs that could drive providers out of thin-margin markets.

The broader lesson for Lifeline modernization is that the FCC does not have to choose between program integrity and program access. That tradeoff becomes unavoidable only when regulators ignore who bears the costs of reform and whether those costs are proportional to the expected gains.

A rigorous cost-benefit framework—one that weighs the harms of fraud and overregulation with equal seriousness—can produce targeted, high-impact, low-burden reforms. Done properly, Lifeline reform can create a program that is harder to defraud, easier to administer, and more reliably available to the households that depend on it most.

Because a subsidy program that cannot distinguish between a living subscriber and a dead one is not merely inefficient—it is unsustainable.

The post The Lifeline Program’s Afterlife Problem appeared first on Truth on the Market.

That trend creates a real risk of confusion. Antitrust is not supposed to function as an all-purpose ministry of industrial policy. At the same time, antitrust cannot pretend modern competition is captured by a static snapshot of domestic market shares. In innovation-driven global markets, firms compete through scale, capabilities, investment, and technological change—not just price levels in narrowly defined markets.

Policymakers increasingly invoke this sense of global “competitiveness” in debates over merger control, innovation, resilience, scale, and global rivalry. The term is often frustratingly imprecise. At its worst, it becomes a polite euphemism for protectionism: approve this merger, excuse this restraint, or subsidize this favored firm because it is “ours.” That version of competitiveness has no legitimate place in antitrust analysis. It substitutes national favoritism for competition on the merits.

There is, though, a more coherent—and more defensible—understanding of competitiveness. Properly defined, competitiveness refers to firms’ ability to succeed on the merits by operating efficiently, innovating, and serving customers effectively. Understood that way, competitiveness is not a rival to consumer welfare. It is one of the primary mechanisms through which consumer welfare emerges over time.

That distinction matters. The competitiveness concern worth taking seriously is not whether a domestic firm becomes larger, more politically influential, or better insulated from rivals. The relevant question is whether antitrust analysis accurately accounts for productive efficiencies, dynamic capabilities, innovation incentives, global competitive constraints, and the long-run process through which firms create value for consumers. That distinction should frame the current debate.

The ‘National Champion’ Temptation

There is nothing new about claims that antitrust enforcement may undermine national competitiveness. In the United States, merging parties and public officials have long argued that firms need greater scale to compete against larger domestic or foreign rivals. Courts have given those arguments a mixed reception, but the underlying legal principle has remained relatively stable: a merger that substantially lessens competition in a relevant market is not redeemed merely because it produces a larger or more prominent firm.

The canonical example is United States v. Philadelphia National Bank, where the Supreme Court rejected the argument that a merger was necessary to compete more effectively against larger banks. The decision gave rise to the now-familiar structural presumption, which continues to loom large over merger analysis. Critics have increasingly argued that the presumption encourages undue reliance on concentration metrics and industry structure, rather than evidence of actual competitive effects.

The problem is not that market structure is irrelevant. It is that structure is not destiny. A presumption that may have offered administrative convenience in 1963 fits poorly in industries shaped by rapid innovation, multisided platforms, intellectual property, global supply chains, and dynamic entry.

When “competitiveness” is invoked merely to wave away anticompetitive concentration, antitrust should reject it. But when the term is used to challenge overly mechanical reliance on market shares and to demand a fuller account of rivalry over time, antitrust should pay attention.

The European experience illustrates both the appeal and the danger of competitiveness rhetoric. The European Commission’s 2019 decision blocking the Siemens/Alstom Franco-German rail merger triggered calls for a more permissive policy toward “European champions.” That reaction raised legitimate concerns that competitiveness could become a backdoor around merger law. A large European firm shielded from European rivals may become less—not more—capable of competing globally. Protecting firms at home can dull the very competitive pressures that make them formidable abroad.

At the same time, Europe’s more recent debate has become notably more nuanced. The Draghi Report, the European Commission’s competitiveness agenda, and the Commission’s 2026 draft merger-guidelines process all reflect growing interest in scale, innovation, investment, resilience, and dynamic effects. That marks a meaningful shift, but not necessarily a retreat from competition. The critical question is whether those considerations enter merger analysis through disciplined economic reasoning or through ad hoc political exceptions.

Competition Is More Than Market Share

Competitiveness should mean the ability to win in the marketplace by offering better products, lower costs, higher quality, faster innovation, greater reliability, or more effective commercialization. Properly understood, that definition fits comfortably within a procompetitive consumer-welfare framework. Consumers benefit not only from lower prices today, but also from the new products, improved services, more resilient supply chains, and cost reductions that innovation generates over time.

This is where David Teece’s framework of dynamic competition becomes especially important. Teece argues that antitrust analysis has too often privileged static models centered on price, output, and concentration, while undervaluing innovation, entrepreneurship, and firm capabilities. In a 2025 Antitrust Law Journal article, he emphasizes that firms frequently compete for markets, across future product spaces, and against “unseen” potential rivals—not merely against visible incumbents in narrowly defined present-day markets.

That insight is not a license to abandon antitrust. It is an argument for doing antitrust better. A firm with a large market share is not necessarily insulated from competition if technological change, customer heterogeneity, and entrepreneurial entry constrain its behavior. By the same token, a firm with only a modest current share may possess assets or capabilities that make it a significant future competitive constraint. Antitrust analysis that ignores those dynamics risks condemning conduct that promotes innovation while approving conduct that suppresses it.

Teece’s argument also highlights the limits of merger analysis built primarily around concentration metrics. Dynamic competition depends on capabilities: R&D assets, engineering talent, data, manufacturing expertise, complementary technologies, commercialization capacity, and organizational adaptability. Current market shares do not always capture those realities.

In innovation-driven markets, the key question is often not “how many firms sell similar products today?” but “which firms possess the capabilities, incentives, and opportunities to constrain one another tomorrow?”

That is the competitiveness question antitrust should be asking.

Innovation Cuts Both Ways

The recent enthusiasm for “innovation competition” in merger enforcement has often become strikingly one-sided. Agencies increasingly argue that mergers may reduce innovation incentives, eliminate nascent rivals, or dampen future product development. Sometimes those concerns are well-founded. But an antitrust policy that recognizes only innovation harms while discounting innovation benefits is systematically biased against welfare-enhancing transactions.

In a 2024 Journal of Business & Technology Law article co-authored with Daniel Spulber, I argued that merger policy should not presume mergers either diminish or enhance innovation competition. The relevant inquiry is transaction-specific: how does a particular deal affect innovation incentives, investment, commercialization, and consumer welfare? The article also warns that presumptions of innovation harm may themselves suppress innovation by chilling transactions that provide innovators with capital, complementary assets, or viable exit opportunities.

That concern is not theoretical. Innovation is not magic. It requires financing, appropriability, complementary assets, managerial expertise, and viable routes to market. In some cases, a startup acquisition may eliminate a potential competitor. In others, it may encourage entrepreneurship by making entry more attractive in the first place. An acquisition may allow an invention to be scaled, manufactured, integrated, distributed, or commercialized far more effectively than the startup could achieve on its own.

The same logic applies to both horizontal and vertical mergers. A horizontal merger may reduce rivalry along some dimensions, but it may also combine complementary R&D programs, eliminate duplicative fixed costs, generate scale economies, or support more ambitious innovation investments. A vertical merger may raise foreclosure concerns, but it also may solve coordination problems, reduce double marginalization, protect relationship-specific investments, or improve the commercialization of emerging technologies.

The appropriate policy response is not blanket permissiveness. It is analytical symmetry. If agencies may advance innovation-based theories of harm, merging parties must be permitted to present innovation-based theories of benefit. Those benefits should not be discounted merely because they are dynamic, difficult to quantify, or likely to emerge over a longer horizon than short-run price effects.

Not Every Synergy Slide Is Fiction

A competitiveness-informed antitrust policy must take efficiencies seriously. That does not mean accepting every consultant slide labeled “synergy.” It means recognizing that efficiencies are often the reason firms compete more effectively and consumers benefit.

The current U.S. approach remains too cramped. The 2023 Merger Guidelines from the U.S. Justice Department (DOJ) and Federal Trade Commission (FTC) acknowledge innovation as a dimension of competition, but they place far greater weight on structural presumptions and theories of harm than on efficiencies, innovation benefits, or dynamic competitive gains. That imbalance carries real costs. It risks treating scale, scope, and integration as inherently suspicious simply because they make firms larger.

A better framework would distinguish procompetitive scale from market power. Scale can be harmful when it entrenches dominance, raises entry barriers, or facilitates exclusionary conduct. But scale also can be indispensable in high-fixed-cost, R&D-intensive, capital-intensive, and globally competitive industries. Semiconductor fabrication, next-generation wireless networks, pharmaceuticals, cloud infrastructure, aerospace, and advanced manufacturing all require enormous sunk investments and involve uncertain returns. In those settings, mergers that achieve efficient scale or combine complementary capabilities may strengthen competition rather than weaken it.

The European Commission’s 2026 draft merger-guidelines discussion appears to move in that direction. It treats scale, innovation, investment, and resilience as potentially procompetitive factors, while insisting those claimed benefits be distinguished from mere exercises of market power. That is the correct framework. The challenge is maintaining analytical discipline. Claimed efficiencies should be merger-specific, verifiable, and likely to benefit consumers. At the same time, agencies should not impose evidentiary burdens so demanding that dynamic efficiencies become effectively impossible to prove.

The same principle applies to out-of-market and long-term efficiencies. Traditional merger analysis is understandably reluctant to balance harms in one market against benefits in another. Antitrust should not casually sacrifice one group of consumers to benefit another. But rigid market-by-market analysis can break down in interconnected innovation ecosystems, multisided platforms, global supply chains, and international markets. A merger may generate dynamic benefits that do not map neatly onto the narrowly defined market in which short-run price effects are alleged.

That does not require embracing an unconstrained total-welfare standard. It simply requires recognizing that consumer welfare is forward-looking. Consumers are harmed not only by higher prices today, but also when antitrust blocks transactions that would have produced better products, faster innovation, more resilient supply chains, or lower future costs.

Administrability Is Not Accuracy

A dynamic approach to antitrust must also take error costs seriously. False negatives matter. Anticompetitive mergers and exclusionary conduct can distort markets and harm consumers. But in innovation-driven industries, false positives—condemning or deterring procompetitive conduct—are often even more damaging.

The reason is straightforward. Antitrust intervention can block or chill experimentation, integration, and investment whose benefits are uncertain, but potentially enormous. The costs of an erroneous prohibition are often invisible because the innovation never occurs, the product never launches, the startup never receives funding, or the efficiency is never realized. By contrast, many false negatives can be corrected over time through entry, technological change, follow-on enforcement, private litigation, or ordinary market adjustment. Not always, of course. But often enough that antitrust policy should avoid excessive confidence in static predictions about dynamic markets.

That point matters especially in merger review. Agencies must evaluate transactions quickly, under substantial uncertainty, and before the full effects of a merger can possibly be known. Those institutional realities naturally encourage administrable presumptions. But administrability is not the same thing as accuracy. A framework that systematically discounts efficiencies and dynamic competition will deter transactions at the margin. Firms will abandon deals, restructure investments, or avoid collaborations that could have improved consumer welfare.

A properly framed competitiveness lens should therefore make agencies more humble, not more passive. It should encourage better questions. Is the market genuinely domestic, or do global rivals meaningfully constrain behavior? Do current market shares tell the relevant story, or do capabilities, assets, and innovation pipelines matter more? Are claimed efficiencies speculative, or are they grounded in identifiable business realities? Would the merger expand output, accelerate innovation, or improve commercialization? Are there remedies capable of preserving competition while allowing efficiencies to proceed?

Those are not loopholes. They are what serious antitrust analysis looks like.

Remedies Are Not Theater

A competitiveness-sensitive approach also has implications for remedies. When a transaction threatens competitive harm but also promises meaningful efficiencies, agencies should ask whether a remedy can preserve rivalry without destroying the deal’s procompetitive rationale.

Structural remedies will often remain the cleanest option. But reflexive hostility toward behavioral or enabling remedies can be counterproductive. Access commitments, licensing arrangements, firewalls, interoperability requirements, or investment obligations may sometimes preserve competition while allowing the benefits of scale or integration to proceed. The United Kingdom’s recent merger-remedies reforms reflect this more pragmatic approach. Those reforms show greater openness to behavioral remedies at Phase 1 and distinguish between enabling remedies and more intrusive controlling remedies.

The United Kingdom Competition and Markets Authority’s (CMA) clearance of the Vodafone/Three merger is illustrative. The CMA initially raised concerns about a four-to-three mobile-network merger, but ultimately accepted commitments tied to substantial 5G investment. That does not mean every telecommunications merger deserves approval. It does suggest agencies can sometimes protect consumers more effectively by securing rivalry-enhancing investment than by blocking a transaction outright.

At the same time, remedies cannot become theatrical substitutes for competition. Investment commitments, price caps, and access obligations can prove difficult to monitor and enforce. They also may distort incentives or push antitrust agencies into the role of sector regulators. A remedy that merely papers over an anticompetitive merger solves little. But a remedy that preserves meaningful competitive constraints while allowing merger-specific innovation or scale efficiencies to proceed can remain fully consistent with consumer welfare.

The Qualcomm litigation offers a related lesson. Remedies affecting intellectual property can have profound consequences for innovation incentives, appropriability, and global technological leadership. A remedy compelling broad licensing of valuable technology may appear to expand access in the short run while weakening the incentives that made the technology possible in the first place. The 9th U.S. Circuit Court of Appeals’ rejection of the FTC’s theory in FTC v. Qualcomm avoided a remedy that could have undermined both innovation and competitiveness.

Antitrust Should Not Run on Vibes

The greatest danger in adding “competitiveness” to the antitrust vocabulary is not that agencies will discuss scale, innovation, or resilience. They should. The real danger is that competitiveness becomes a euphemism for unreviewable discretion.

An agency could define markets broadly to obscure competitive harm. It could accept weak efficiency claims because the merging firms are politically favored. It could approve a domestic “champion” while saying little about the resulting consumer tradeoffs. Or it could block a procompetitive transaction based on speculative innovation harms that no one can meaningfully test.

That is why transparency matters. If competitiveness refers to efficiency, innovation, investment, resilience, and dynamic rivalry, agencies should say so explicitly. They should explain how those considerations enter the analysis, identify the evidence necessary to establish them, and distinguish consumer-benefiting competitiveness from simple producer favoritism. And when agencies are making difficult tradeoffs, they should admit it.

A transparent competitiveness framework should rest on six principles:

• Competitiveness must remain tied to competition on the merits. Nationality alone should not qualify as a cognizable antitrust benefit. A firm does not deserve favorable treatment simply because it is domestic.
• Agencies should explicitly account for dynamic competition. In some markets, innovation pipelines, technological capabilities, potential entry, appropriability, and commercialization assets matter more than current market shares.
• Efficiencies should receive symmetrical treatment. If agencies are willing to credit speculative innovation harms, they also should be willing to credit well-supported innovation benefits.
• Evidentiary burdens should remain realistic. Dynamic efficiencies are often harder to quantify than short-run price effects, but that does not make them imaginary.
• Agencies should evaluate remedies pragmatically. The objective is to preserve competition and consumer welfare—not to maximize agency leverage or reflexively punish scale.
• Agencies must remain alert to public-choice risks. Competitiveness rhetoric can easily be captured by incumbents seeking protection from competition rather than competition itself.

Competitiveness, on Antitrust Terms

The best response to competitiveness concerns is not to weaken antitrust. It is to restore antitrust to its proper economic foundation.

A consumer-welfare framework is not limited to short-run price effects. Properly understood, it encompasses quality, innovation, variety, output, resilience, and long-run productivity growth. It asks whether challenged conduct harms the competitive process and thereby harms consumers. It does not condemn size for its own sake. It does not assume concentration is inherently harmful. It does not relegate efficiencies to an afterthought. And it does not mistake competitors’ complaints for harm to competition.

That framework is fully capable of incorporating competitiveness—provided competitiveness is defined correctly. Efficient firms are competitive. Innovative firms are competitive. Firms that use scale to lower costs, improve quality, or develop new products are competitive. Firms that succeed globally because they serve customers better are competitive. Antitrust should not obstruct those outcomes.

By contrast, firms seeking protection from rivalry are not competitive in any meaningful sense. Firms that merge to gain pricing power, suppress innovation, exclude rivals, or divide markets are not advancing competitiveness. They are reducing it.

The policy challenge is distinguishing between the two.

A dynamic, evidence-based antitrust policy would be more skeptical of structural shortcuts, more attentive to innovation and firm capabilities, more receptive to efficiencies, more pragmatic about remedies, and more cautious about false positives. It still would condemn anticompetitive mergers and exclusionary conduct. But it would not allow antitrust to become a barrier to the competitive process it is supposed to protect.

Competitiveness therefore belongs in antitrust analysis—but only on antitrust terms. Properly understood, it is not an industrial-policy escape hatch from consumer welfare. It is a reminder that consumer welfare emerges from firms competing, innovating, investing, and improving over time.

The post Competitiveness Without the Cronyism appeared first on Truth on the Market.

A recent post by Dean Ball, a senior fellow at the Foundation for American Innovation, tees up that familiar tension. On one hand, there is a strong case against broad, ex ante regulation of AI systems. On the other, highly capable systems may pose risks that are hard to dismiss—particularly in areas like cyber operations or biosecurity. The question is how to reconcile those instincts without defaulting to heavy-handed control. 

That framing is useful. It also leaves something important underspecified.

The debate still tends to treat “AI” as a single object of governance, rather than a layered system in which different interventions operate in very different ways. Knowing Dean Ball personally, I doubt he intended that simplification. But his post could leave that impression. It is worth unpacking why the distinction matters.

Once those layers collapse, any risk can justify sweeping oversight. Once they are separated, many proposed interventions look far less precise than their advocates suggest.

Intermediaries, Incentives, and Illusions of Independence

One proposal gaining traction in this space is the idea of independent verification organizations (IVOs). The basic structure is straightforward: governments set outcome-based safety goals, and licensed third parties verify whether AI systems meet them. Firms that opt in receive certification benefits, including reputational gains and, in some cases, legal safe harbors. 

At a high level, this moves beyond traditional command-and-control regulation. It aims to shape incentives rather than dictate design choices, while leveraging technical expertise outside the state. That has real appeal. In simulated environments, verification appears to push firms toward engaging underlying problems, rather than defaulting to defensive posturing.

Once you move from concept to implementation, though, the structure becomes clearer. Independent verifiers need licenses. They need access to proprietary systems. They need authority to compel information disclosure and, ultimately, to suspend or revoke certifications. Without those features, the framework does not work as intended.

In other words, what starts as a market-based mechanism depends on a fairly robust enforcement apparatus. The system does not replace state oversight so much as reorganize it around a new set of intermediaries. If the underlying problem is that legal categories fail to track capability, building a system that depends on those categories being precise enough to audit may not solve the problem—it may just relocate it.

That raises familiar institutional concerns. Verifiers are paid by the firms they assess. They operate in a setting where maintaining relationships can conflict with enforcing standards. The parallels to earlier verification regimes—most notably the credit-rating ecosystem—are hard to miss. Ensuring independence in that context is not a theoretical challenge. Relatedly, system-level interventions risk shaping competitive outcomes in ways only loosely tied to technical merit, with downstream effects across the broader ecosystem.

More fundamentally, the IVO model operates at a level that does not map well onto the risk profile or structural characteristics of AI systems. It evaluates systems as a whole, monitors deployments, and triggers recertification when systems change. That pulls policy toward continuous oversight of model behavior. It also blurs the line between public policy and private contracting. That boundary matters, particularly in an international context, where firms’ credibility as independent actors underpins their global position.

If the concern is misuse of specific capabilities—as it arguably should be—this is an indirect and often inefficient approach.

Ball’s recent comments on EconTalk underscore the tension. He flagged the mismatch between legal categories and technical capability, as well as the risks of state leverage over private firms. Those concerns complicate proposals that rely on formal verification regimes backed by enforcement authority. These frameworks aim to solve real problems, but they depend on precisely the kind of definitional clarity and institutional stability that remain unsettled. 

Mind the Gap Between Law and Capability

A more useful starting point is to ask where risk actually attaches.

In most of the scenarios driving concern, the issue is not the existence of a model. It is the availability of particular capabilities, and the conditions under which those capabilities can be accessed and deployed. Ball acknowledged this on EconTalk, noting that legal categories like “surveillance” increasingly fail to track real-world capability once AI scales analysis across commercially available data. That gap between law and capability is exactly where misuse risk emerges. 

That framing points to a different set of policy levers—ones focused on capability and access, rather than general system-level oversight.

For example, policymakers could develop more structured approaches to monitoring access to the most sensitive capabilities. Mechanisms that allow providers to detect anomalous use patterns or identify high-risk actors may play a role. Something analogous to know-your-customer (KYC) frameworks for certain high-end access points is at least worth considering, even if it raises its own concerns.

The point is not that this approach is obviously correct. It is that it targets the margin where misuse actually occurs.

The Best Defense Is More AI

There is also a broader dynamic that tends to get lost in these discussions: the same capabilities that create risk are also the tools needed to manage it. Defensive uses of AI are not peripheral. They are central to any plausible equilibrium.

Managing misuse will depend on the widespread deployment of defensive systems that can detect, interpret, and respond to anomalous behavior in real time. Those capabilities will need to spread across firms and, over time, to the local and individual level. Security in this environment will not be centralized. It will depend on the diffusion of capability across the ecosystem.

What changes is not just capability, but scale. Systems that once required scarce human attention can now operate across entire populations. As Ball put it, “AI [creates] ‘millions and tens of millions of analysts.’” That dynamic does not just increase risk; it raises the stakes for how widely defensive capabilities are deployed.

There is an intuitive way to think about this, drawn from early conceptions of networked computing in William Gibson’s cyberpunk novels. Users relied on persistent, individualized defensive systems operating at the edge. However stylized, that intuition points in the right direction. Security scales through distribution, not centralized control.

Frameworks that concentrate evaluation and oversight in a small set of licensed intermediaries risk slowing that diffusion. They may improve certain forms of accountability, but they also push the system toward centralization at precisely the moment when distributed defensive capacity matters most.

A simpler approach may prove more effective: establish clear rules of the road around specific forms of misuse, make those rules legible and enforceable, and then allow firms to operate within those constraints. That structure creates strong incentives to develop both offensive and defensive capabilities in response to changing conditions.

This approach does less from an institutional-design perspective. It does not try to build a comprehensive governance layer over AI systems. It does, however, align more closely with how these systems evolve in practice. It preserves flexibility, supports experimentation, and allows defensive capabilities to emerge and scale more organically.

When Oversight Becomes the Risk

None of this eliminates the tension Ball identifies. The need to manage real risks coexists with a well-founded concern about overbroad intervention. That tension is not going away.

The question is how to navigate it. The answer, in my view, is to be precise about where intervention occurs, resist the pull toward general system-level oversight, and avoid constraining the development of defensive capabilities in the name of safety.

If policy is going to shape this space, it should do so at the margins where it is most effective, while preserving the broader ecosystem that drives both innovation and resilience.

Get the margins right, and the system has room to adapt. Get them wrong, and oversight becomes the risk.

The post AI Risk and the Very Large Hammer appeared first on Truth on the Market.

As a description of how value gets created at the technological frontier in 2026, it is badly outdated.

Watch Jensen Huang’s recent conversation with Dwarkesh Patel and the gap becomes obvious. Asked whether NVIDIA risks commoditization, Huang does not just answer the question—he reframes it. On its face, the concern is reasonable. NVIDIA “sends a GDS2 file to TSMC,” which fabricates the dies, packages them with high-bandwidth memory (HBM) from SK Hynix and Micron, and hands them off to a Taiwan-based original design manufacturer (ODM). Meanwhile, NVIDIA “fundamentally makes software that other people manufacture.” 

The jargon is worth unpacking because it is the point. A GDS2 file is the final design file used to manufacture a chip. Taiwan Semiconductor Manufacturing Co. (TSMC) fabricates the chip dies. HBM is the fast memory stacked close to advanced processors. An ODM builds products for another company to sell under its own brand.

In other words, the “product” is not a thing NVIDIA simply makes and ships. It is a coordinated chain of design, fabrication, packaging, memory, systems, software, and deployment.

Huang’s response shifts the lens. NVIDIA’s job, he says, is to turn “electrons to tokens”—to do “as much as necessary, as little as possible.” Whatever the firm does not need to do, it pushes to partners. Whatever it does do, it structures so that everyone else can coordinate around it.

That’s the business.

NVIDIA’s moat is not the graphics processing unit (GPU). It is the coordination layer that makes hundreds of upstream and downstream actors rational in betting on its platform. That list runs long: TSMC, ASML, SK Hynix, Lumentum, Coherent, hyperscalers—the giant cloud providers that buy and operate massive computing infrastructure—artificial-intelligence (AI) labs, framework communities, application developers, financiers, and even, as Huang half-jokes, the plumbers and electricians wiring data-center buildouts.

In short, ecosystem orchestration—not discrete product innovation—is the dominant value-creation pattern in the AI economy.

That reality does not map cleanly onto the categories regulators reach for: single-product monopoly, classic two-sided “matching” platforms, or vertical foreclosure. It also cuts against policymakers’ instincts about where innovation comes from. If the underlying economic phenomenon is ecosystem coordination, analysis that fixates on discrete—and often poorly defined—“products” will miss the mark.

Get the framework wrong, and policy will not just misfire. It will penalize the coordination that drives growth.

The costs do not stay contained. Broad export controls aimed at slowing Chinese competition will also hit U.S. firms and erode the durability of American technological leadership. That tradeoff remains underweighted in a policy debate still anchored to a chip-centric view of the industry.

The Five-Layer Cake and the Real Business Beneath It

Huang’s mental model is that AI is “a five-layer cake.” Energy sits at the bottom—along with capital and other supply-chain inputs—followed by chips, systems and networking, the model layer, and, at the top, applications. NVIDIA’s job is not to own each layer. It is to make them mutually rational. 

That framing matters for competition policy, regulation, trade policy, and national-security policy in chips and AI. Huang’s interview surfaces four mechanisms that do the work.

Start with upstream–downstream coordination as market-making. Suppliers—Micron on memory, TSMC on logic and packaging, and the silicon-photonics ecosystem on networking—invest at scale because Huang has aligned them around a roadmap they trust. Downstream demand from hyperscalers and AI-first firms makes those forecasts credible.

NVIDIA’s GPU Technology Conference (GTC), on this view, is not just a conference. It is a full-scope market-making event—“the entire universe of AI all in one place”—where “the downstream could see the upstream, the upstream could see the downstream.” The coordination function is the product. 

Next is installed-base gravity. Even hyperscalers that can write their own kernels—the small pieces of code that tell processors how to perform specific computations—still want their software to run across the largest possible installed base: clouds, on-premises systems, robots, old GPUs, and new GPUs alike.

Compute Unified Device Architecture (CUDA), NVIDIA’s proprietary software platform and programming model, derives its value not just from kernel performance, but from portability across hundreds of millions of GPUs in varied environments. That is a standard platform network-effect story: the platform becomes more valuable as more users, developers, and tools build around it. The relevant question is not “whose chip is faster?” It is “whose installed base is bigger?”

A third mechanism: complementors as demand multipliers. Complementors are firms or developers whose products make another platform more useful. Much of the AI debate assumes “AI eats software”—agents replace tools, and the industry compresses. Huang argues the opposite. Agents will use Excel, Synopsys, Cadence, compilers, and design tools. That means more instances of those tools, not fewer.

Today, the number of users of Synopsys’ design compiler is capped by the number of human engineers. If agents can drive those tools, that cap moves up by orders of magnitude. AI does not displace complementors; it multiplies them. The ecosystem expands.

The same logic extends to labor markets. When AI complements tools rather than replaces them, demand rises for both the tools and the workers who use them. That aligns with early empirical evidence on AI’s labor effects and cuts against the zero-sum displacement story (see here and here). 

Finally, roadmap credibility becomes part of the product. Blackwell, Rubin, Rubin Ultra, and Feynman—NVIDIA’s successive GPU architectures and system platforms, announced on a predictable cadence to signal future performance and capability—anchor expectations across the ecosystem. Customers planning $100 billion AI factories need a supplier they can plan around. Predictability is a feature. “I can completely depend on them,” Huang says of TSMC. A credible roadmap makes upstream and downstream coordination self-reinforcing.

Put it together and NVIDIA’s market power, if that is the right term, is not “we make the best GPU.” It is “we make it rational for adjacent actors to coordinate around us.” That is a stronger—and more demanding—claim than CUDA lock-in or GPU path dependence.

In that sense, NVIDIA looks less like a traditional manufacturer and more like an ecosystem orchestrator. It resembles what Michael Cusumano, Annabelle Gawer, and David Yoffie describe as “platform leadership,” and what Marco Iansiti and Roy Levien call a “keystone.” It also tracks Alden Abbott’s observation that “firms that look like rivals in one dimension may act as complementors in another, and a static antitrust lens can easily misread value-creating coordination as competitive harm.”

This framing carries implications.

First, a two-sided-market analysis fixated on price differences across sides will miss the action. The relevant unit is the coordination surface connecting many specialized actors. Conduct that looks like self-preferencing or vertical foreclosure in a single-product frame often functions, in an ecosystem frame, as the set of hooks that makes complementor investment rational. Blanket bans on self-preferencing risk depressing integration and chilling that investment.

The depth of the moat depends as much on complementor switching costs as on user switching costs—and both may be lower than they first appear. 

More broadly, the regulatory, trade, industrial-policy, and national-security stakes do not reduce to any single layer of the stack. If the relevant economic unit is the ecosystem, policy that targets component shares, static market structure, or bilateral firm conduct risks aiming at the wrong object.

Antitrust can mistake coordination for exclusion. Trade policy can weaken the installed base, developer mindshare, and complementor density that sustain U.S. advantage. Industrial policy can overfocus on fabs—semiconductor fabrication plants—while underinvesting in standards, software, and downstream adoption. National-security policy can restrict hardware flows while conceding the broader ecosystem through which technological leadership reproduces itself.

The throughline is simple: Winning the chip layer is not the same as winning the system. The same holds for policy that tries to control it.

Models Compete, Ecosystems Compound

The pattern shows up clearly in a current example: Anthropic’s strategy around Claude is, at bottom, an ecosystem play.

Claude Code turns the model into a substrate that third parties extend through hooks, Model Context Protocol (MCP) servers, slash commands, and plugins. Cowork applies the same logic to non-developer knowledge work, offering a desktop layer where third-party plugins, skills, and connectors compose Claude into task-specific workflows—legal review, marketing analytics, financial modeling, and more.

MCP functions as an application programming interface (API) standard that lets this ecosystem scale without Anthropic building every integration itself.

The economics are straightforward. Each plugin, skill, and connector represents a specialized investment by an outside firm. Each one makes Claude more valuable for a particular use case. That raises users’ willingness to pay, which drives API usage, which, in turn, justifies more complementor investment.

That feedback loop—not any single feature—is what makes the position durable.

Against Claude’s recent gains, OpenAI is now visibly playing catch-up on this dimension. Not on raw model quality—where leadership shifts quarter to quarter—but on the complementor stack: custom GPTs, an apps platform, Operator, the ChatGPT enterprise tier, and Codex’s developer ergonomics.

Codex targets developers directly. At the same time, its streamlined modes suggest a push toward non-coding use cases—much as Cowork does.

This is exactly what Jensen Huang means when he warns that “ecosystems are hard to replace. It costs an enormous amount of time and energy.” In pure technological terms, OpenAI is not far behind Anthropic. But that is not the margin that decides the outcome.

The edge goes to whoever builds the largest installed base and the deepest set of third-party integrations that make users’ lives easier.

Competition, in other words, does not hinge on the model alone. It plays out across the ecosystem—and it is both intense and multidimensional.

A National Symbol Isn’t an Ecosystem

The clearest live case for the ecosystem framing—and the one with the highest policy stakes—is China’s own DeepSeek.

DeepSeek V4 launched April 23, 2026. In its technical report, the team concedes the model sits “3 to 6 months behind” the closed-source frontier. Outside observers suspect the gap is wider. CEO Liang Wenfeng framed the company’s mission around “hardcore research,” not products. He criticized Chinese tech for “freeriding on Moore’s Law” and positioned DeepSeek as a contributor to foundational innovation rather than an applications company. 

It was an earnest—and widely admired—vision.

It was also costly.

DeepSeek lost core talent to Tencent, ByteDance, Xiaomi, and DeepRoute.ai across large language models (LLMs), agents, optical character recognition (OCR), and multimodal systems, which process more than one kind of input, such as text and images. It missed China’s consumer-AI inflection point, as ByteDance’s Doubao became the most-downloaded chatbot, Alibaba’s Afu broke through in health, and firms like MiniMax and Z.ai moved toward public markets.

Operationally, it stumbled. A major training-run failure in mid-2025 hit as the firm migrated from NVIDIA to Huawei Ascend. By April 2026, DeepSeek had opened to external financing, no longer able to self-fund frontier-scale training. A Qwen employee, quoted in 36Kr, captured the shift: “the golden age of nonprofit AI development is over.”

In an ecosystem-driven economy, “failure to develop a vision of commercialization” is another way of saying “failure to build a compelling ecosystem.” Commercialization is not just revenue. It is how a frontier lab secures distribution, capital, complementor relationships, and the feedback loops that fund the next training run—and the next architectural bet.

DeepSeek did not need to become a marketplace. It needed to become an orchestrator: a substrate around which Chinese hyperscalers, application developers, chip vendors (Huawei Ascend, Cambricon, Biren), and end users could coordinate.

That role did not stay vacant. ByteDance (Doubao), Alibaba (Qwen), and increasingly closed-source Chinese labs filled it. DeepSeek became a national symbol without an industrial ecosystem.

The episode also underscores a broader point: Multiple ecosystems operate simultaneously at different layers of the stack. NVIDIA is one orchestration layer. So are firms higher up the stack. Each integrates across layers; none operates in isolation.

Huang flags this dynamic in both directions. Forcing U.S. firms out of China, he warns, risks backfiring: “if we are forced to leave China, it would be a policy mistake … it [will] accelerate their chip industry and force all of their AI ecosystem to focus on their internal architectures.”

The ecosystem argument cuts both ways. The failure of a Chinese lab to commercialize creates an opening for U.S.-aligned ecosystems. Policy that closes that window does so at a cost.

Closing the Window on Ourselves

Competition policy already understands ecosystem dynamics. Trade and national-security policymakers need to catch up.

U.S. policy on advanced-AI export controls has, by design, tried to shut the window through which American ecosystems might win. The logic is intuitive: restrict the flow of frontier accelerators—and the extreme ultraviolet (EUV) equipment used to produce them—to the People’s Republic of China (PRC), widen the capability gap, and give U.S. labs time to reach dangerous thresholds first and harden systems before adversaries catch up.

Run that logic through an ecosystem lens—and against the empirical record Huang describes—and two problems emerge. Neither is the one the policy is meant to solve.

First, the regime struggles on its own terms. Huang’s central move is to dissolve the chip-bottleneck premise. At the lowest layers of the stack, energy and chips function as partial substitutes. Where power is abundant, less-advanced chips can still deliver effective compute at scale.

The PRC has built massive excess generation capacity—“data centers that are sitting completely empty, fully powered… ghost cities… ghost data centers”—and is willing to absorb environmental, fiscal, and political costs U.S. operators will not. Under those conditions, “seven-nanometer chips are essentially Hopper… today’s models are largely trained on Hopper.” Huawei has shipped millions of accelerators, and its Ascend roadmap—now including FP4 support on the 950 family—is narrowing the per-chip performance gap export controls were meant to lock in. (FP4 refers to a very low-precision number format that can make AI computation cheaper and faster when models can tolerate the tradeoff.)

AI, as Huang puts it, is “a parallel computing problem.” More lower-end chips, paired with cheap power, can substitute for fewer high-end ones.

A second point follows. Frontier capability now turns more on algorithmic and architectural advances than on transistor scaling. Moore’s Law contributes roughly 25% annually; computer-science gains deliver order-of-magnitude improvements per generation. The International Center for Law & Economics (ICLE) has made a similar point about the limits of static performance metrics.

Roughly half of the world’s AI researchers are Chinese. The key levers—Mixture-of-Experts architectures, attention variants, low-precision numerics, and distributed training—are not export-controllable. Mixture-of-Experts models route different tasks to specialized parts of a model, making training and inference more efficient. Distributed training spreads computation across many chips or machines.

DeepSeek V4 provides a clean proof of concept. Its kernels no longer rely purely on CUDA, instead targeting a portable domain-specific language called TileLang. (A domain-specific language is a programming language designed for a narrow technical purpose.) Its post-training and inference pipelines use MXFP4 precision, another low-precision format, to run across heterogeneous chips—that is, different types of chips used together. Its kernel already runs on Huawei Ascend supernodes.

On this margin, the export regime is accelerating, not slowing, the indigenization of the Chinese stack.

The Mythos example sharpens the calibration problem. Huang notes that Mythos was trained on “fairly mundane capacity and a fairly mundane amount of it.” The cyber-offensive capabilities that motivate chip controls are not tightly gated by marginal access to frontier accelerators.

If the concern is models like Mythos, the relevant lever is the capability layer, not the chip layer. That means restrictions on releasing models with materially dual-use capabilities—models useful for both civilian and harmful purposes—combined with controls on remote access, something akin to know-your-customer (KYC) rules for APIs.

Anthropic’s decision to withhold Mythos from all users—not just Chinese ones—until vulnerabilities were patched offers a real-world example of capability-layer governance. ICLE’s comments to the National Telecommunications and Information Administration (NTIA) on dual-use foundation models and misuse risk sketch out the doctrinal framework. A regime that overweights chip controls while underinvesting in capability controls is mismatched to its own threat model. 

The second problem is more structural: The regime cedes the ecosystem layer to PRC-centered stacks.

Ecosystem analysis makes this visible in a way component-level analysis cannot. Durable advantage in AI comes from orchestrating capital, complementors, developers, standards, and customers around a credible roadmap. Export controls that wall U.S. firms out of the world’s second-largest computing market undercut that ecosystem across all of those margins.

Huang is explicit: “50% of the AI developers are in China. We don’t, we shouldn’t, the United States should not give that up.” And: “China is the largest contributor to open source software in the world… the largest contributor to open models in the world… today, it’s built on the American tech stack, NVIDIA.”

The question is not whether China reaches the frontier. It is which stack the global developer base optimizes for along the way.

If U.S. policy forces Chinese developers to build on domestic stacks—because they lack access to U.S. alternatives—those optimizations will propagate outward. As Chinese platforms mature, they diffuse into the Global South, the Middle East, Southeast Asia, and parts of Europe. Huang puts the endgame plainly: “AI models around the world… run best on not American hardware.”

That is the ecosystem-fragmentation cost the current regime imposes on its own intended beneficiaries.

The takeaway is not to abandon controls. It is to target the right layer.

An ecosystem-aware export-control regime would shift weight from chip-layer to capability-layer interventions. It would treat developer mindshare and framework standards as first-order policy concerns. It would recalibrate the Committee on Foreign Investment in the United States (CFIUS), the Bureau of Industry and Security (BIS), and outbound-investment regimes to reflect how ecosystems actually form—through partnerships, equity-and-compute deals, and standards bodies—not just through component flows.

This is not a call to “open the doors.” It is what follows from taking both the empirical and theoretical records seriously.

Coordination Eats the Widget

The garage-and-widget story always understated the roles of standards, capital markets, supplier coordination, and complementor mobilization. In an AI economy built on five-layer stacks, it is no longer even a useful approximation. The firms that lift the system are the ones that make it rational for everyone else to coordinate around them—and that means absorbing coordination costs the rest of the ecosystem would otherwise bear.

For competition policy, the question is not “is this firm dominant?” It is “is the ecosystem contestable, and does the orchestrator face enough discipline to keep it honest?”

For U.S.–China strategy, DeepSeek does not show that China cannot reach the frontier. It shows that reaching the frontier is not enough. The real contest runs through developer mindshare, capital flows, complementor density, and standards. Policies that concede those layers in the name of locking down the chip layer will miss their target—and, as Huang warns, risk achieving the opposite.

The firms that matter are no longer those with the cleverest widget. They are the ones that make every adjacent actor’s bet rational—“as much as necessary, as little as possible.”

That is the game now.

The post Stack Wars: The Garage Myth Meets the Five-Layer Cake appeared first on Truth on the Market.

The draft TTGs introduced a dedicated section on LNGs and, more notably, offered a soft antitrust safe harbor. In practice, qualifying LNGs would have avoided a full case-by-case assessment if they satisfied a defined set of conditions.

That approach did not come out of nowhere. A few months earlier, the European Commission signaled its position in an informal guidance letter issued jointly with the German competition authority, addressing the creation of the Automotive Licensing Negotiation Group.

That episode sets the stage for this post. It begins by situating LNGs within the broader SEP debate. It then examines the competition-law risks they raise, the limits of analogizing them to patent pools, and their uneasy fit with the Huawei framework.

Finally, it turns to the final TTGs. While the Commission dropped the proposed safe harbor, it kept a dedicated section on LNGs—raising the obvious question: was the intervention worth it?

LNGs: Technical Fix or SEP Policy Redux?

The Commission’s approach drew sharp criticism. Many viewed LNGs not as a technical clarification, but as the latest move in the longrunning SEP wars. On that account, the proposal looked less like guidance and more like a backdoor revival of debates that followed the collapse of the highly contested EU SEP Regulation.

As argued in a previous paper, LNGs resemble a consolation prize for the automotive sector. Firms in that sector had strongly backed the withdrawn Regulation. LNGs would deliver part of what that proposal promised: greater collective leverage for implementers in negotiations with SEP holders.

The logic tracks the earlier proposal. Both rest on the view that SEP licensing suffers from hold-up. The remedy, in turn, shifts bargaining power from SEP holders to licensees—ostensibly to rein in what critics characterize as excessive licensing prices.

When ‘Collective Bargaining’ Starts to Look Like a Cartel

Granting favorable antitrust treatment to LNGs is no neutral move. Collective buying arrangements raise familiar competition-law concerns. They can facilitate exchanges of commercially sensitive information and enable coordination on core competitive parameters, including price and output.

U.S. antitrust authorities have framed the issue more bluntly. In recent remarks, the U.S. Justice Department (DOJ) singled out the Commission’s comfort letter for the Automotive LNG as a regulatory intervention that appears to permit collusive licensing negotiation groups—conduct it characterized as buyer-cartel behavior under U.S. antitrust law.

The concern extends beyond any single dispute. By offering regulatory comfort to coordinated bargaining, such interventions risk spillover effects. They can anchor expectations and encourage firms to seek similar carveouts, rather than comply with antitrust law.

Reverse Patent Pools? Not So Fast

The proposed LNG section in the revised TTGs rests on a simple claim: the logic of technology pools extends to other forms of aggregation. On this view, LNGs deliver similar procompetitive benefits. They act as a one-stop shop, lower transaction costs, and function as a kind of reverse patent pool—counterbalancing the alleged market power of SEP holders or existing pools.

That analogy does not hold. Pools and LNGs aggregate fundamentally different things.

Patent pools combine complements. Each patent covers a distinct slice of a standard, and licensees need all of them to implement the technology. By bundling these inputs, pools address the familiar Cournot-complements and patent-thicket problems. The result is lower aggregate royalties and easier access to interlocking technologies. That market failure—and its fix—explains why antitrust law generally treats pools favorably.

LNGs do the opposite. They aggregate substitutes: competing implementers, each capable of negotiating its own license, band together to coordinate purchasing.

Pooling patents and pooling licensees are not mirror-image exercises. The former integrates inputs that are useless on their own. The latter consolidates buyer-side bargaining power among firms that would otherwise compete for the same inputs. The competitive effects run in opposite directions.

Pools mitigate double marginalization. LNGs risk creating monopsony power and providing cover for collusion among downstream rivals. The safeguards that discipline pools—limits to essential, complementary patents; fair, reasonable, and non-discriminatory (FRAND) commitments; and nondiscrimination duties toward licensees—have no real counterpart on the buyer side. The draft Guidelines’ procedural constraints—open access, limits on information exchange, and bans on collective boycotts—do not replicate them.

From Hold-Up to Group Hold-Out

To the extent LNGs rest on hold-up theory, they risk overlooking the mirror-image problem of hold-out. Worse, they may enable it—collectively. The risk is especially acute where LNG members can walk away after joint negotiations and pursue bilateral talks with the SEP holder. In that scenario, firms can free-ride on the group process, then delay—or refuse—to conclude individual licenses.

One obvious fix would have been to bind members to the outcome of joint negotiations. At a minimum, LNGs could require members to conclude licenses within a defined period after negotiations end—though even that would be a second-best solution compared to a world without LNGs.

The Commission did not go that route. It raised no objection to the Automotive LNG’s operating rules, which do not require members to accept the negotiated agreement. If a member rejects the outcome, the parties revert to the status quo ante, as if the LNG never existed, leaving the SEP holder and that firm to negotiate bilaterally. The final TTGs take the same approach. They impose no requirement that LNG members be bound by the result of collective negotiations.

That choice creates tension with the Huawei framework, which remains the central reference point for SEP licensing in the European Union. The framework aims to balance hold-up and hold-out through the willing-licensee test. How that test applies when negotiations proceed collectively—yet individual implementers remain free to reject the outcome and go it alone—remains an open question.

No Safe Harbor, Same Drift

In response to the criticism, the Commission ultimately dropped the draft safe harbor for LNGs. The final TTGs still include a dedicated section, though. It maps out potential pro- and anticompetitive effects, draws a line between genuine LNGs and buyer cartels, and identifies the factors relevant under Article 101.

The final text also engages—at least briefly—with Huawei. Paragraph 322 acknowledges that LNGs may facilitate coordinated delay during negotiations. It adds that implementers pursuing such a strategy may struggle to rely on Article 102 of the Treaty on the Functioning of the European Union (TFEU) as a defense against an injunction sought by an SEP holder that has committed to license on FRAND terms.

That is an improvement over the draft safe harbor. It does not, however, resolve the underlying problem. By giving LNGs a bespoke framework, the Guidelines normalize buyer-side coordination in SEP licensing. The debate shifts from whether such coordination should be allowed to how it should be designed.

The Commission’s safeguards—transparency, limits on information exchange, and case-by-case effects analysis—do not fully address the core concern. LNGs can aggregate implementers’ bargaining power while leaving them free to reject the outcome, prolong disputes, and push royalties downward.

The contrast with the United Kingdom is telling. In its draft Guidelines under consultation, the UK Competition and Markets Authority (CMA) declined to offer LNG-specific guidance. It opted instead for a case-by-case approach grounded in the facts and economic context of each arrangement. 

The safe harbor is gone. The detour remains. Alas, in these matters, detours have an unfortunate habit of becoming the road.

The post Reverse Patent Pools and Other TTBER Tall Tales appeared first on Truth on the Market.

The draft guidelines span more than 100 pages and raise a host of issues. This post zeroes in on one that should give competition lawyers pause: the quiet politicization of competition law through soft-law instruments that sidestep—and ultimately erode—the coherence of established frameworks.

On the surface, the document updates the European Union’s merger-review framework. Read more closely, and a different project emerges: a systematic effort to advance industrial policy through competition law, dressed up as technical refinement.

What follows breaks down how the guidelines do this, the mechanisms they deploy, and why that trajectory should concern you.

Nothing New Here (Except What’s New)

It is true, as some have argued, that much of the document codifies existing case law. Its treatment of market shares and Herfindahl-Hirschman Index (HHI) thresholds, the counterfactual framework, the failing-firm doctrine, and much of the foreclosure analysis tracks established European Commission practice and court-endorsed principles. Guidelines that do this perform their proper function, i.e., they codify rather than create new law.

The problem lies in what’s actually new. Those elements appear to do something quite different.

The most consequential expansion concerns “parameters of competition.” Paragraph 20 states that: 

Non-price competition can encompass a variety of parameters to the extent they are relevant for the competitive process in the markets at hand, including output and quality in various aspects, also covering choice (which may include media and cultural diversity), capacity, investment, innovation , privacy, sustainability, resilience (including security of supply). A merger can have an impact on one or multiple parameters of competition, and that impact is subject to an overall assessment. Many non-price parameters are not readily subject to quantification. Some of them, among other things, may also reflect the objectives of EU policies recognised by the Treaties.

In addition: 

The Commission enjoys a margin of discretion in weighing such price and non-price parameters of competition in the balancing exercise in order to establish an overall assessment of the merger’s effects on businesses and consumers.

It is true that, in prior Commission decisions cited in the guidelines—including Ball/Rexam, Tata Steel/thyssenkrupp (later upheld by the General Court and the Court of Justice of the European Union (CJEU) on conventional horizontal-effects grounds), and Norsk Hydro/Alumetal—security of supply and sustainability appeared as factual elements within a standard competitive analysis. Customers valued reliable local production as a product characteristic; sustainability credentials shaped purchasing decisions. But the alleged harm was always reduced to familiar concerns, most often higher prices, fewer suppliers, or reduced output. These concepts fed into a standard significant impediment to effective competition (SIEC) assessment. They did not operate as independent grounds for intervention.

On a first reading, the draft guidelines appear to construct something qualitatively different. They place resilience and sustainability on par with price, quality, and innovation.

There are several other signals that corroborate this reading. Paragraph 55 defines market power to include the ability to reduce resilience or sustainability “below competitive levels,” elevating these concepts to the foundation of the analytical framework, not merely contextual factors.

Paragraph 23 lists harm to resilience and sustainability as bases for what reads like a standalone theory of harm. Paragraph 299 treats them as verifiable efficiencies capable of supporting approval. And Paragraph 342 subjects them to open-ended balancing at the Commission’s discretion, where they may be weighed against e.g., price, output, innovation, or quality.

In the Commission’s own words:

In its prospective analysis of concentrations, the Commission disposes of a margin of discretion and weighs up different, sometimes incommensurable price and nonprice parameters of competition.

The result is a doctrinal shift. Resilience and sustainability can now independently tip a merger decision toward prohibition or approval, without being translated into price or output effects. An alternative reading is that traditional competition concerns automatically dovetail with resilience and sustainability so that what is good for competition is good for resilience and sustainability. But  if these references are merely obiter dicta, then why include them at all?

If my reading holds, the implications are significant. A merger could, in principle, be blocked because it reduces supply diversity—for example, by concentrating sourcing in a non-European supplier. Or it could be approved because it increases that diversity, e.g., by combining complementary European assets.

That is not simple codification. It is the construction of a new, standalone merger-control variable from concepts that prior case law treated as factual context. The Commission goes just far enough to claim it is codifying existing law, while taking an additional step that, so far as I can tell, no court has endorsed.

Innovation Shield for Some, Entrenchment for Others

We’ve covered the legal-interpretive move that lets the draft guidelines shift the law while claiming continuity. The shift runs in one direction: toward greater discretion. By expanding the universe of potential harms, the guidelines create a larger menu of interventions—and more room for selective, asymmetrical enforcement.

Consider the growing list of competition parameters: innovation competition, dynamic foreclosure, entrenchment, portfolio effects, resilience, sustainability, competitiveness. Each can operate as a standalone theory of harm or combine with others. Together, they give the European Commission enough analytical flexibility to reach almost any outcome while maintaining a veneer of neutrality.

The asymmetry becomes clear in the treatment of startup acquisitions.

The guidelines introduce an “innovation shield”—a safe harbor under which acquisitions of small, innovative firms presumptively do not raise competition concerns, provided market-share thresholds are met and enough independent R&D competitors remain. That is a genuine improvement. It reflects sound law & economics: credible exit options sustain upstream venture-capital investment, and overenforcement in innovation markets is costly.

But Paragraph 192 immediately narrows the shield. It excludes acquisitions by “the largest firm in the relevant market or a gatekeeper,” as defined by the Digital Markets Act. In practice, that means that the largest and most successful tech firms get no shield. They instead  face a distinct theory of harm—entrenchment—with no equivalent safe harbor and a clear tilt toward false positives (i.e., making acquisitions harder). 

The entrenchment theory (Paragraphs 252-256) sets a low bar. The Commission need only show that the acquirer is dominant in a “core market,” that the target operates in a “related” market—not necessarily horizontal or vertical—and that the target is “important to effectively compete.” There is no market-share floor or structural constraint. If the markets are “related” and the asset is “important,” entrenchment becomes plausible.

In digital ecosystems, that standard sweeps broadly. It can capture almost any acquisition by a large platform. And if the target were not “important,” why acquire it in the first place? Smaller acquirers, by contrast, can invoke the innovation shield for the same transaction.

That asymmetry creates a basic economic problem: scale is secular. The efficiencies from acquisition—cost reduction, integrated R&D, capital deployment, access to complementary pipelines—do not depend on the acquirer’s nationality, size, or regulatory status under the DMA.

Mario Draghi’s report identified Europe’s lack of scale as a central competitiveness problem. The guidelines seem to translate that into a different directive: permit scale when it benefits European firms, restrict it otherwise. 

At points, the text says as much. Paragraph 13 states:

Mergers increasing the competitiveness of European industry, particularly in global markets, improving the conditions for growth and raising the standard of living in the Union are welcomed.

Other provisions reinforce the theme. Paragraph 15 outlines when scale-enhancing mergers may advance European competitiveness, but the categories are so broad and open-ended that they could justify almost any outcome in the name of “European competitiveness.”

It is far from clear that the Draghi report supports this reading. One could just as easily conclude the opposite. When a large U.S. technology firm acquires a European startup and integrates it into a global pipeline, that still generates scale. It still benefits European consumers. It still signals to venture-capital investors that exit options remain viable.

The counterfactual also does not hold up. Blocking a foreign acquisition does not mean a European buyer steps in or that the startup grows into a continental champion. Often, neither happens. The startup may fail to find a buyer at all, or it may settle for one with fewer resources and weaker integration capacity. In the worst case, it exits the market.

The guidelines’ asymmetrical treatment of acquirers does not guarantee more European scale. It risks the opposite: fewer viable exits, weaker venture-capital incentives, and a less dynamic European innovation ecosystem—the very outcome the Draghi agenda aims to avoid.

Industrial Policy in a White Glove

The expansion of competition parameters raises concerns that go beyond policy disagreement. As argued above, the guidelines equip the European Commission with a toolkit broad enough to approve European consolidation and block foreign acquisitions of European assets—while invoking the same analytical framework in both cases.

That, it seems, is how the Commission has read the Draghi report’s mandate.

The guidelines operationalize that approach. Theories layer thickly enough that almost any outcome becomes defensible. Resilience can sink a foreign deal; competitiveness can save a European one. The result is industrial policy in a “white glove” of technical neutrality—one that systematically advantages European acquirers while placing procedural and analytical hurdles in the path of foreign firms. Those firms appear less as contributors to European scale than as threats to European strategic autonomy.

Whether that is good policy is a fair question. I don’t think it is.

The harder question is whether it is legal. Again, I have my doubts.

The European Union Merger Regulation (EUMR) sets SIEC as a clear test. The CJEU has long drawn a line between competition analysis and broader public-interest considerations, reserving the latter for narrow, reviewable interventions—most notably, Article 21 of the EUMR, which allows Member States to invoke legitimate interests.

By recasting resilience and competitiveness as “dimensions of competition,” the Commission appears to fold policy goals it lacks authority to pursue under Article 2 of the EUMR into the competition analysis itself.

Several legal constraints come into view.

Article 2 of the EUMR limits the merger test to competition. Article 119 of the Treaty on the Functioning of the European Union (TFEU) commits the union to an open-market economy with free competition—a principle not easily satisfied by redefining competition to include its policy substitutes. Articles 63–66 of the TFEU guarantee the free movement of capital between Member States and third countries; restricting foreign acquisitions requires a clear legal basis that competition law does not obviously provide. And the Meroni doctrine bars EU institutions from expanding their mandate through interpretive discretion—though courts have read that constraint narrowly.

These are not trivial issues.

The Commission had other tools. Article 21 of the EUMR allows Member States to block mergers on legitimate-interest grounds, subject to proportionality and judicial review. Perhaps that route seemed too decentralized, too uneven, or too limited.

Instead, the Commission expanded the definition of competition and produced guidelines that—according to one senior antitrust partner quoted in Politico—are “a bit like the Bible: everybody can read anything into it.”

That is not a fringe critique. The Commission effectively acknowledges the point—and appears to embrace it. Paragraph 342 states that “the Commission disposes of a margin of discretion and weighs up different, sometimes incommensurable price and non-price parameters of competition.”

When parameters are incommensurable and discretion at the balancing stage is unconstrained, the guidelines do more than guide. They function as a delegation of power—one that allows the Commission to reach its preferred outcome, shielded from meaningful judicial review and justified by an ever-expanding set of “parameters” that stretch competition analysis beyond its legal and conceptual limits.

A Compromise That Doesn’t Cohere

There is a version of these guidelines that would deserve straightforward praise: one that genuinely codifies established case law, sharpens the efficiency framework, introduces a meaningful innovation safe harbor, and preserves the boundary between competition and other policy goals.

The draft does some of that. The theory-of-benefit framework improves the analysis. The innovation shield—scope problems aside—points in the right direction. The distinction between direct and dynamic efficiencies reflects modern industrial-organization thinking.

But the genuinely new elements pull the document elsewhere.

Observers of the Brussels policy scene will not be surprised. According to Politico, the guidelines reflect a compromise between European Commission President Ursula von der Leyen’s industrial-policy agenda and Executive Vice President Teresa Ribera’s defense of competition-law orthodoxy. The result tries to serve both—and commits fully to neither.

Where von der Leyen’s priorities prevail, the guidelines expand competition parameters, introduce asymmetrical theories of harm, and preserve a margin of discretion that makes the framework difficult to review. Where Ribera’s influence shows, the text gestures toward legal certainty, codification, and continuity with established case law.

Those two strands do not cohere. And when a framework embeds irreconcilable objectives, the side that preserves discretion usually wins in practice. Here, that is the industrial-policy side.

If the Commission wants to pursue industrial policy—steering which firms scale in Europe, limiting foreign acquisitions of European assets, favoring European consolidation—it has tools to do so. Article 21 of the EUMR. Sector-specific legislation. Foreign-investment screening with democratic accountability.

Redefining competition until it does all that work is not one of them.

Whether the courts will accept that move remains an open question. The harder truth is that the challenge is already written into the text.

The post Merger Guidelines for the Industrial Policy Curious appeared first on Truth on the Market.

Late last year, the Brazilian government submitted the bill to the Chamber of Deputies as part of its “Digital Brazil Agenda.” The proposal borrows from Europe’s Digital Markets Act (DMA), but it is not a straight copy. Its structure more closely resembles the United Kingdom’s Digital Markets, Competition and Consumers Act (DMCC).

Unlike the DMA, the bill would not impose a fixed list of obligations as soon as a company is designated. Instead, it creates a second-stage process in which the Administrative Council for Economic Defense (CADE) would study the designated firm’s markets and then decide which firm-specific duties to impose.

That may sound more restrained. It is still a major shift in Brazilian competition policy.

The bill would amend the Brazilian Competition Law (BCL) to create a new Digital Markets Superintendency (SMD) within CADE. It would empower CADE to designate firms as having “systemic relevance in digital markets” for up to 10 years. It would then allow the agency to impose tailor-made “special obligations” drawn from an open-ended statutory menu.

I have previously written here at Truth on the Market about several problems with this proposal. The bill risks quietly pushing aside the consumer-welfare standard and replacing it with vague goals like “the protection of the competitive process” and “the promotion of freedom of choice.”

Lazar Radic and I have also examined the institutional risks of creating the new SMD. That office would duplicate much of the work of CADE’s existing General Superintendence (SG), rather than building digital-market expertise inside CADE’s current investigative body.

The scale of this proposed overhaul deserved a more comprehensive look. To that end, Geoffrey Manne, Dirk Auer, and I recently published an International Center for Law & Economics (ICLE) white paper, “Digital Overreach: A Premature Turn to Ex Ante Regulation in Brazil.” Policymakers, legal practitioners, and academics should consult the full paper for a detailed economic and institutional assessment of the proposed regime.

This post highlights several of the paper’s central claims. Bill 4,675/2025 raises serious institutional concerns, and it may be unnecessary, given Brazil’s existing antitrust toolkit.

Europe’s early experience also offers a warning. Importing a DMA-style model could bring meaningful tradeoffs, including higher compliance and operational costs, more user friction, and further strain on Brazil’s already notorious “Custo Brasil”—the regulatory and structural cost of doing business in the country.

With that in mind, here are several points the Brazilian Congress should consider before enacting an ex ante regime like Bill 4,675/2025.

Ex Post, Not Exaggerated: Brazil’s Antitrust Tools Already Deliver

The premise behind Bill 4,675/2025—like other ex ante regimes—is familiar: traditional, ex post antitrust enforcement moves too slowly and cannot keep up with digital markets.

Brazil’s recent experience does not clearly support that claim.

In December 2025, CADE reached two landmark settlements—Termos de Compromisso de Cessação (TCCs)—with Apple and Google. TCCs are settlement agreements in which companies commit to stop or change conduct under CADE supervision.

In the Apple settlement, CADE required the company to permit third-party app stores, enable alternative payment processors, and remove anti-steering restrictions on iOS. For a deeper dive, see my Truth on the Market post with Mario Zúñiga, “Apple in Brazil: Ex Post Antitrust Meets Ex Ante Ambitions.”

CADE’s settlement with Google followed a similar path. It prohibited Google from conditioning Play Store licensing on the preinstallation or preferential placement of proprietary apps like Google Search and Chrome.

These outcomes mirror the kinds of structural remedies Bill 4,675/2025 would impose. The key difference is how CADE got there. Traditional antitrust enforcement allowed the agency to tailor remedies to the facts, security concerns, and competitive dynamics of each case, while keeping consumer welfare as the guiding principle for unilateral-conduct enforcement.

That flexibility matters. In the Apple case, privacy and security concerns led CADE to drop a sideloading requirement that had appeared in an earlier interim measure. A rigid ex ante regime would have a harder time making that kind of security-sensitive adjustment.

CADE has continued to move aggressively. In January 2026, it opened an administrative inquiry—alongside an interim measure—against Meta over new WhatsApp terms and the integration of AI chatbot tools.

The Administrative Tribunal upheld the interim measure in March 2026, citing irreparable harm and a likelihood of success on the merits. CADE has since sanctioned Meta for breaching that order. ICLE will publish a more detailed analysis of this case in the coming weeks.

CADE has plainly stepped up digital-market enforcement in recent months. But the number of settled or fully adjudicated cases remains limited. That makes it hard to justify a sweeping legislative overhaul aimed at market failures that have not yet proven beyond the reach of ex post enforcement.

Put differently, the bill tries to solve problems Brazil’s existing framework may already be able to handle. CADE’s recent actions suggest as much. If speed is the concern, the better response is to give CADE more resources and streamline its procedures—not to build a parallel regulatory bureaucracy.

Europe’s DMA: More Clicks, Less Competition

Supporters of the Brazilian bill often point to the DMA as a model. As noted, however, the bill’s architecture more closely tracks the UK’s DMCC because it uses a two-step process.

Even so, Europe’s early experience with ex ante regulation gives Brazil good reason to pause. Early evidence suggests that some DMA obligations create user friction and compliance burdens, while doing little to make markets more contestable. 

The consumer-welfare evidence makes the point concrete. Louis-Daniel Pape and Michelangelo Rossi studied the DMA’s restrictions on self-preferencing in the context of Google Maps. By forcing Google to remove the Maps tab from EU search pages, the DMA increased mapping-related queries by 21%. Rival mapping services, however, saw virtually no traffic gains. Users simply had to take more steps to reach the same destination. More clicks, same map, no competitive upside.

Other evidence points in the same direction. Chiara Farronato, Andrey Fradkin, and Alexander MacKay examined the effects of restrictions on Amazon’s private-label products, a common target of self-preferencing rules. They found that such a ban reduced consumer surplus by 5.5%, largely because consumers lost product variety and cost advantages.

Consumers appear to feel these changes. A European Center for International Political Economy (ECIPE) survey found that 39% of EU users reported more cumbersome online experiences after the DMA took effect. At the same time, 80% had no idea the law existed.

The effects extend beyond user experience. Ex ante rules can operate as an “innovation tax,” raising the cost and uncertainty of launching new products. Apple has delayed the European rollout of features such as AirPods live translation, iPhone mirroring, and Apple Maps enhancements, citing DMA compliance uncertainty. Meta postponed the launch of Threads in the EU, and Google delayed certain AI features.

Mario Draghi, former president of the European Central Bank, underscored the broader stakes in his widely cited 2024 report. He linked Europe’s persistent productivity gap with the United States in large part to the tech sector, and identified “inconsistent and restrictive regulations” as a major barrier to scaling innovative firms.

Brazil wants to attract technology investment and raise labor productivity. That makes the lesson straightforward: policymakers should think carefully before importing rules that may dampen innovation and growth.

More Rules, Same Bottlenecks

If the DMA has proved difficult to implement in a well-resourced, highly institutionalized setting like the European Union, transplanting a similar model to Brazil would likely create even greater risks.

Brazil ranks 78th out of 143 countries on the 2025 World Justice Project Rule of Law Index, and 124th on the World Bank’s 2020 Doing Business ranking. In that environment, broad, discretionary regulatory regimes invite strategic behavior and rent-seeking. In simpler terms: rivals may try to use the regulator to hobble competitors, rather than to help consumers.

Brazil also contends with the well-known “Custo Brasil”—the thicket of structural and regulatory inefficiencies that raises the cost of doing business. Those inefficiencies cost an estimated R$1.7 trillion annually, or roughly 19.5% of GDP in 2022. Adding a complex, compliance-heavy ex ante digital regime would likely make those burdens worse, not better.

Capacity constraints raise a related concern. Even in Europe, the European Commission has struggled to staff its DMA enforcement unit, filling only 19 of 80 planned positions. The shortfall shows how hard it is to recruit the technical expertise these regimes require.

CADE faces an even steeper challenge. It would have to absorb major new responsibilities through internal reallocations, with no additional funding. That leaves Brazil with an uncomfortable tradeoff: either the new regime remains under-resourced, or CADE diverts resources from its core antitrust mission.

Beyond the False Choice: Fix the Framework, Don’t Import It

The push for Bill 4,675/2025 rests on what amounts to an “imaginary antitrust consensus.” It also frames the choice too narrowly, as if Brazil must either import a DMA-style regime or leave digital markets untouched.

That is a false binary.

Before making a structural shift of this magnitude, policymakers should consider narrower, more coherent options. Our white paper lays out a sequence of steps Congress could take short of adopting a sweeping ex ante regime.

The first—and best—option is to rely on Brazil’s existing ex post framework. Recent cases show that CADE can address alleged digital-market concerns through tailored, case-specific remedies, including negotiated outcomes like the Apple and Google TCCs.

If lawmakers still decide more intervention is necessary, they should favor targeted measures over economy-wide rules. Japan’s Mobile Software Competition Act offers one useful example. It focuses on smartphone software ecosystems, accounts for cybersecurity, and avoids broad mandates that risk degrading the user experience or undermining the benefits of integrated systems.

If Congress proceeds with Bill 4,675/2025, it should at least add meaningful guardrails to reduce error costs and improve institutional coherence.

Start with the standard. The bill should restore consumer welfare as the lodestar, tying both its objectives and enforcement to demonstrable consumer harm. Without that anchor, enforcement could drift toward protecting less-efficient rivals or chasing vague notions of “the competitive process.”

The bill also needs an explicit efficiencies defense. As drafted, its omission sits uneasily with Article 36 of the Brazilian Competition Law. Designated firms should be able to show that challenged conduct—such as self-preferencing or vertical integration—creates net benefits for users, including quality improvements, lower prices, better security, or reduced search costs. Remedies should reflect those benefits.

The bill also needs tighter designation criteria. Designation should rest on established antitrust principles, including evidence of durable market power and a plausible theory of competitive harm—not proxies like turnover, scale, or cross-market presence.

Institutional design matters, too. Congress should house any digital-markets function within CADE’s General Superintendence, likely as a specialized unit. It should not create a parallel bureaucracy that risks duplication, fragmentation, and internal conflict.

Finally, legislation of this scope should not move forward without a rigorous regulatory impact assessment (RIA). An RIA is a structured analysis of a proposal’s likely benefits, costs, enforcement demands, and market effects. The Brazilian Economic Freedom Act already embraces this logic in Article 5 for major regulatory measures. Congress should hold itself to the same standard here.

The European Commission conducted an impact assessment before adopting the DMA. Brazil has offered no comparable, publicly grounded analysis for Bill 4,675/2025—covering expected benefits, compliance costs, enforcement capacity, and likely effects on consumers, business users, and innovation.

Mind the ‘Brussels Defect’

Brazil need not choose between complacency and comprehensive ex ante regulation. It already has a sophisticated competition authority. It already has legal tools capable of addressing digital conduct. And it has ample reason for caution before adding more discretionary oversight to an economy that suffers from too much regulatory friction, not too little.

Before creating a new digital-markets regime, Brazil should first show that the existing one is failing. So far, the evidence points the other way.

A better path remains available. Brazil can reinforce existing competition-law tools, exercise regulatory humility, and resist the urge to import “Brussels Effect” rules that increasingly resemble a “Brussels Defect.” That approach would better serve Brazil’s 212 million citizens.

No overhaul required—just a skilled and steady hand.

The post Before Brazil Scrubs In: The Case Against Digital-Market Surgery appeared first on Truth on the Market.

A recent Economist essay by a former competition lawyer offers a clean example.  Writing in the magazine’s By Invitation section, Marie Potel-Saville argues that digital platforms engage in “cognitive exploitation” through infinite scroll, dark patterns, and dopaminergic feedback loops—practices that, in her view, erode the conditions necessary for functioning markets. Her proposed fix would flip the burden of proof, forcing platforms to show they are “not predatory by design” before deployment. The piece is polished and clearly motivated by real concern. It also neatly illustrates the problem it sets out to diagnose. 

When Analogies Do All the Work—and None of the Proof

This style of argument usually rests on a familiar rhetorical move: analogize the conduct at issue to a well-established legal category, then let that category do the normative heavy lifting. The analogy makes intervention feel legally natural, even when it does not hold up. Neo-Brandeisians often reach for infrastructure comparisons—treating e-commerce platforms like Amazon as if they were railways, telephone lines, or electricity grids. The comparison sounds intuitive. It is also deeply flawed.

In the Economist essay, the chosen analogy comes from securities regulation. When a trader manipulates stock prices, the argument goes, the law treats it as structural harm to the market: a corrupted price no longer conveys reliable information. Cognitive exploitation, on this view, works the same way. If platforms “manufacture” the preferences of billions of users, then consumer signals lose their informational value.

It is a tidy parallel. It is also technically unsound—and the problem runs deeper than a loose comparison.

Securities law can identify manipulation because it has an objective benchmark: the unmanipulated price. Courts and regulators can specify a counterfactual and test against it. “Manufactured preferences” offer no such anchor. There is no observable, measurable “authentic preference” that a regulator can point to or that a court can adjudicate against. What would an unmanipulated preference for social media consumption look like? How would anyone identify it? The analogy does not say, because it cannot. Without a workable counterfactual, the framework collapses at the threshold.

This flaw shows up across the genre. The Digital Markets Act (DMA) starts from a similar premise: that platform “gatekeeper” power mirrors traditional bottleneck monopoly, and so justifies ex ante obligations without proof of harm in individual cases. The German Federal Cartel Office (Bundeskartellamt) takes a comparable tack in its Amazon decision, treating price-prominence effects as if they were exclusionary conduct—without establishing the competitive harm that analogy requires. In each instance, the analogy carries the argument where the evidence does not. 

Deception Isn’t the Same as Desire

The deeper problem is a conflation that runs through the Economist essay—and much of the broader literature. It treats dark patterns and engagement optimization as the same phenomenon when, in analytically relevant respects, they are opposites.

Dark patterns work against user preferences. Hidden unsubscribe buttons, fake urgency timers, deliberately obscured cancellation flows—each steers users toward outcomes they did not choose and would not endorse if the interface were honest. The Federal Trade Commission’s (FTC) case against Amazon, which resulted in a $2.5 billion settlement in September 2025, targets this kind of conduct. It focuses on interface design—specifically, the “Iliad Flow,” a four-page, six-click cancellation labyrinth that impedes a decision the user is actively trying to make. The multistate lawsuit against Meta Platforms Inc. (Meta) by 42 attorneys general raises related issues, particularly around disclosure and consent for minors. These cases may rise or fall on the merits, and the legal standards remain unsettled (here and here). But they at least point to a coherent target: conduct that allegedly deceives users about what they are getting. 

Engagement optimization moves in the opposite direction. A recommendation algorithm that learns, in real time, what a user watches, clicks, and dwells on—and then delivers more of it—matches preferences with a level of precision no earlier market mechanism could achieve. Users do not stay on social media because they have been tricked. They return, often for hours, across demographics and income levels, despite abundant alternatives, because the product delivers what they demonstrably want.

Lumping these phenomena together under the label “cognitive exploitation,” as the Economist essay does, lets a defensible claim about deception do the work for a much more contestable claim about efficient engagement. That move is rhetorical, not analytical. It posits a structural unfairness across the entire platform and hands regulators a mandate far broader than the specific harms could justify.

There is a further irony—one that warrants its own treatment. The platforms drawing the most scrutiny often have the strongest internal incentives to limit manipulative design. Facebook and Amazon depend on sustained user engagement and trust; dark patterns that alienate users undermine both advertising and subscription models. The more aggressive dark-pattern ecosystem—fake close buttons, deceptive download prompts, cookie-consent walls designed to exhaust rather than inform—thrives in the long tail of the web, among publishers and ad networks with no ongoing relationship with users and no reputational stake. If the goal is to curb deceptive design at scale, current enforcement priorities look almost exactly backward.

From Choice to Paternalism

Once you separate the two concepts, the engagement-optimization claim runs into a problem its proponents rarely face head-on: revealed preferences.

Users stay. They return. What critics call manufactured compulsion looks, from a standard welfare perspective, like a product doing its job—matching supply to demand with a level of efficiency earlier markets could not reach. To reframe that as exploitation, you have to argue that revealed preferences count for less than some other set of preferences that should replace them. Proponents rarely say what those substitute preferences are, let alone defend why they should override observed behavior.

What they are reaching for—without quite saying so—is the distinction between first- and second-order preferences, i.e., between what people want in the moment and what they would endorse on reflection. That is a serious idea with a substantial literature, from Harry Frankfurt’s work on second-order volitions to Cass Sunstein and Richard Thaler’s ”nudge” framework, which at least states its normative commitments openly. But this is political philosophy, not competition or consumer-protection law. It requires answers to hard questions: Which preferences count? Who decides? On what basis should a regulator’s view of reflective endorsement displace what consumers actually do? Dressing the argument in market-failure language does not answer those questions; it sidesteps them. 

This is the same move I have identified elsewhere as a hallmark of anti-consumer-welfare antitrust: substituting regulatory preferences for consumer preferences, justified by an abstract appeal to what consumers would want if they wanted differently. Whether the vehicle is neo-Brandeisian antitrust, the DMA’s choice-architecture mandates, or “cognitive exploitation” theory, the logic is the same. Consumers generate outcomes critics dislike—platform dominance, high engagement, preferences for convenience and integration—and the framework steps in to override them, with “market failure” standing in for what is, at bottom, a dispute about values.

A Rule Without Edges

Arguments like this share a second structural flaw: they lack any limiting principle.

The Economist essay’s test—design that engineers behavior users might not endorse on reflection—does not describe anything unique to digital platforms. Loyalty programs create switching costs. Scarcity cues create urgency. Aspirational advertising creates desire. Subscription defaults exploit inertia. Loss-leader pricing skews comparison. If dopaminergic feedback loops are disqualifying, why not manufactured brand attachment? Why treat the sensory engineering of a restaurant, mall, or store as categorically different from the algorithmic engineering of a social media feed? The line never appears, because the framework offers no principled way to draw one.

This is not just an academic concern. Courts and regulators need administrable standards—rules with enough substance to produce predictable outcomes and to constrain enforcement discretion. “Predatory by design” offers neither. Who determines compliance? Against what baseline? Using what method? The vagueness hands regulators sweeping discretion without guidance on how to use it, creating what Brian Albrecht and Erik Hovenkamp have described as a wishing well “into which one may peer and find nearly anything he wishes.” Far from checking platform power, a standard like this injects uncertainty that hits smaller firms hardest while giving regulators a tool they can wield selectively against disfavored companies.

The Startup Filter Disguised as Safety

The proposed remedy adds a structural problem to the analytical one. Precautionary, pre-deployment review—i.e., requiring platforms to prove they are not harmful before launch—is a standard typically reserved for irreversible, catastrophic risks: pharmaceuticals, nuclear plants, medical devices. Extending that framework to consumer software would impose compliance costs that only established incumbents can bear. A startup building a new social app cannot finance the legal and regulatory apparatus needed to clear a “not predatory by design” standard before it even reaches users. Meta can. Google can

The predictable result is higher barriers to entry—high enough to keep out the very challengers such rules claim to protect. The policy would entrench the incumbents it purports to discipline. We have seen this dynamic before. The General Data Protection Regulation (GDPR), for example, has imposed compliance burdens that fall hardest on smaller firms, often foreclosing the competitors most likely to disrupt larger platforms. 

The progressive framing should not obscure the bottom line. This is incumbency protection by another name.

When Getting What You Want Is the Harm

There is a more candid version of this argument—and it is worth stating directly.

The concern is not that markets are failing. It is that they are working—efficiently allocating attention to content that humans, given their cognitive architecture, tend to overconsume. On this view, the problem is not deception. Users are getting exactly what they want. The problem is that what they want may be bad for them in ways they do not fully appreciate. That concern has familiar roots. It echoes long-running debates over sugar, gambling, alcohol, and cheap calories—markets where the interaction of human preferences and efficient supply produces outcomes critics find troubling. None has been “solved” by regulating product design.

That is a legitimate debate. But it is not a competition or consumer-protection argument. It is about the limits of preference satisfaction as a measure of welfare, about paternalism and autonomy, and about whether—and when—second-order preferences or “human flourishing” (as defined by regulators) should override first-order ones. Those are questions of political philosophy and political economy. Labeling them “market failure” does not make them so. It just obscures the normative premises doing the work. 

If the claim is that human wants can conflict with human flourishing—and that platforms efficiently satisfying those wants create a problem—then the argument should say so, and defend it on those terms. That would at least let readers evaluate whether the proposed remedy—regulatory override of consumer choice, implemented through discretionary design standards and precautionary, pre-deployment review—is proportionate to the concern, and whether it fits within the legal tradition invoked to justify it. Or, for that matter, within a liberal polity at all.

Call the Problem by Its Name

The preference-substitution problem in platform regulation is not, at bottom, about platforms. It is about analytical honesty. Conflating deceptive design with efficient engagement, leaning on analogies that collapse on inspection, skipping any limiting principle, ignoring entry effects—these are features of a style of argument, not defects in any single proposal.

Where dark patterns actually exist—where design works against what users are trying to do—existing consumer-protection frameworks can respond, if applied with care. The Amazon and Meta lawsuits show as much, whatever their ultimate merits. That is no small point.

What those frameworks cannot do—and what no legal regime can do without stating its premises—is condemn preference satisfaction at scale because regulators dislike the preferences being satisfied. Every proposal in this space ultimately turns on a simple question, and most try not to answer it: are digital markets failing—or succeeding too well?

The post The Hidden Premise: Smuggling Paternalism Through the Back Door appeared first on Truth on the Market.

As AI spreads, it has sharpened an old question in a new setting: How should competition policy preserve room for innovation while also addressing the anticompetitive risks new technologies can create? The pace of AI development raises an even more pointed concern. Can recently adopted ex ante rules—rules meant to prevent competitive harm before it occurs—remain “future-proof” in digital markets? The DMA did not anticipate AI’s rapid rise. It may age faster than expected.

That concern looks more urgent with the emergence of assistive and agentic AI. These tools could reshape core digital intermediation functions, including web browsing, online search, and e-commerce. Put less grandly: They may change how users find, choose, and buy things online. AI assistants and agents increasingly act as standalone interfaces, allowing users to access third-party goods, services, and content without leaving a conversational environment. That shift could reconfigure where—and how—competitive power concentrates.

This backdrop highlights a familiar tension. Competition law moves slowly, but it adapts. Its open-ended standards can evolve with changing market realities. Sector-specific regulation works differently. It can target problems quickly and directly, but it often locks in assumptions about how markets operate. When technology shifts, those assumptions can break.

The DMA illustrates the tradeoff. Despite its recent adoption, the regime already risks rapid aging. Lawmakers designed it for a digital ecosystem that AI-driven intermediation may soon transform.

Against that backdrop, the European Commission asked stakeholders whether the DMA can address AI-powered services as they roll out. It also considered whether to revise the list of core platform services—the categories of digital services covered by the DMA—and the obligations attached to them. 

New AI entrants sit at the center of that inquiry. When existing gatekeepers integrate AI into their ecosystems, the DMA may capture those services. The regulation does not clearly reach new, standalone AI operators.

The report tempers expectations. So far, the Commission has taken a cautious line. It views the DMA as fit for purpose and does not propose amendments. In the Commission’s view, the regulation has proved adaptable enough to keep pace with developments like AI. Even so, its analysis focuses mainly on how existing gatekeepers deploy AI within designated core platform services.

That leaves a mixed picture. The Commission is, at once, both right and wrong.

Where the Commission Gets It Mostly Right

AI services may fall within the DMA through two main paths. First, an AI provider might itself offer a core platform service and meet the thresholds for gatekeeper designation. Second, an existing gatekeeper might embed AI features into an already designated core platform service, bringing those features within the DMA’s obligations.

On the second path, the Commission is largely right. This scenario does not pose especially novel problems for applying the DMA, as written, to AI-related markets. The familiar competition concerns remain. Gatekeepers benefit from structural advantages, including privileged access to cloud infrastructure and data. They also can integrate AI assistants and agents directly into their core platform services. In principle, the regulation’s existing provisions can address those risks.

The Commission’s recent specification proceeding against Google illustrates the point. The Commission seeks to ensure that Google gives third-party AI-service providers access to the same Android operating-system features and functionalities available to its own services. It also aims to require Google to grant third-party online search-engine operators—including AI chatbot providers that offer search functionalities—access to anonymized ranking, query, click, and view data on fair, reasonable, and non-discriminatory (FRAND) terms. At the same time, the Commission is examining whether Google’s integration of AI Overviews into Google Search complies with the DMA, particularly the ban on self-preferencing in Article 6(5). 

The harder question concerns new AI entrants. The DMA may not reach them at all. Standalone AI applications do not fit neatly within any of the core platform service categories listed in the regulation. Unless those services can be folded into existing categories—such as online search engines, web browsers, or virtual assistants—providers cannot be designated as gatekeepers based solely on their AI offerings. The result may be a split regulatory landscape, where large incumbent platforms and new AI entrants face different rules.

That gap matters most for AI assistants and agents. These systems could reshape competitive dynamics and market structures in fundamental ways. They may become new gateways—ones the DMA did not anticipate—and place AI providers in a role functionally similar to gatekeepers.

When the Model Doesn’t Fit the Models

As we argue in a forthcoming paper, agentic AI should prompt a rethink of the DMA’s overall architecture.

The regulation reflects a different technological and economic moment. Lawmakers designed it around the structural features of earlier digital markets—features that helped identify both gatekeepers and core platform services. In that sense, the DMA embodies a competition-policy framework rooted in a Big Tech-centered view of digital competition. But AI may not follow the same path. Foundation models—the large AI systems that power many downstream applications—differ in important ways from the platforms that shaped that earlier paradigm.

Some familiar concerns remain. Access to key upstream inputs—computing power, data, and highly skilled labor—still matters. So do the incentives of large incumbent digital firms to fold AI into their existing products and services. Those dynamics can reinforce existing advantages.

But the downstream picture looks different. Competition in AI markets appears active and crowded. New entrants continue to emerge. Many have achieved commercial success and attracted significant investment. Barriers to entry, at least for developers, may be lower than initially feared. And the range of downstream applications suggests that foundation models will not produce uniform winner-takes-all outcomes across sectors.

The DMA, however, targets a specific problem: the gatekeeping power of vertically integrated firms that operate in a dual role. These firms control access to an important platform while also competing on that platform. Much of the DMA focuses on limiting leveraging strategies, especially self-preferencing. That logic fits traditional digital platforms. It fits less comfortably with the structure and behavior of many new AI entrants.

The Clock Keeps Ticking as Brussels Buys Time

In principle, the logic behind the DMA supports extending it to new AI players. The regulation rests on a simple premise: Traditional antitrust enforcement often arrives too late in digital markets, after positions have hardened and become difficult to dislodge. That concern could apply with equal force to AI, given its potential to produce new gatekeepers. Unless European policymakers intend to abandon the strategy that has defined the Brussels approach to digital markets, they will need to rely on the DMA as the primary framework governing competition in the AI era.

But that instinct runs into a real risk: acting too soon. The AI sector remains in flux. Technologies, business models, and market structures have not yet settled. Extending ex ante regulation to foundation models and AI services at this stage could constrain innovation by limiting developers’ ability to experiment with new features, design choices, and platform architectures.

It would also force policymakers to predict not just the pace, but the direction of technological change in fast-moving and uncertain markets. That is a tall order. Misjudging where market power will emerge—or how it will operate—could carry real costs for innovation, competition, and consumer welfare.

The harder question, then, is one of timing. Intervene too late, and the DMA risks irrelevance. Intervene too early, and it risks distortion.

That is what makes the first review of the DMA more than a routine stocktaking exercise. It surfaces a deeper dilemma for a regime that remains, in many respects, still young. The issue is not just whether specific provisions work as intended. It is whether AI forces policymakers to reopen the legislative framework itself.

For now, the Commission has chosen to buy time. It is sticking with its Big Tech-centered approach and continuing to monitor how AI develops. That may be prudent in the short term.

But the underlying dilemma will not wait.

The post The DMA’s AI Dilemma: Too Soon, Too Late, or Both? appeared first on Truth on the Market.