In 2017, eight researchers across Google’s Brain and Research divisions released a paper titled “Attention Is All You Need.” What followed is now familiar: a technological inflection point, rapid diffusion, and an explosion of competitors building on the same core idea. Less appreciated is the mechanism behind it. This is not just a story about a breakthrough. It is a story about why that breakthrough did not—and perhaps could not—remain proprietary.

That dynamic is the focus of this post. The norms that govern AI research—what I will call the “scientific ethos”—systematically undermine any single firm’s ability to hoard knowledge for long. The transformer is the clearest example.

The Transformer Leaves the Building

The 2017 paper introduced the transformer, a neural-network architecture that replaced sequential processing with self-attention. Self-attention allows a model to interpret a sentence by weighing how each word relates to every other word in the same sentence. Instead of processing tokens one at a time, the model can evaluate the entire sequence simultaneously, focusing on the most relevant relationships to determine meaning. That shift—from step-by-step processing to parallel attention—proved decisive.

Within a few years, the paper became one of the most-cited in computer science. More importantly, it supplied the architectural backbone for nearly every major AI system in use today—from OpenAI’s GPT series (the “T” in ChatGPT stands for “transformer”) to Google’s Gemini and Anthropic’s Claude.

It is no exaggeration to say that, without this paper, the generative AI boom would look very different—or would have arrived years later. Some might argue that an equivalent breakthrough would have emerged eventually, or that competitors could have reverse-engineered Google’s models. Neither claim holds up. The transformer was not a visible product that others could disassemble. It was a counterintuitive insight: dispense with recurrence and convolutions in favor of self-attention. Without its publication, outsiders would have had no practical way to understand why Google’s models kept improving.

As Geoffrey Hinton—himself a former Google researcher and widely regarded as the godfather of modern AI—told Wired: “Without transformers I don’t think we’d be here now.” Other architectures, such as state space models or recurring neural networks, may now rival transformers. But they arrived years later—after the paper’s open publication enabled new entrants that otherwise would not have existed.

That raises a basic question: why did Google publish the paper at all? The company could have treated the research as a trade secret and secured a multi-year competitive advantage. Part of the answer lies in the “scientific ethos”—the shared norms among AI researchers. The field prizes competition and individual achievement, but also transparency and the idea that knowledge should be universal and research pursued for its own sake.

Google’s broader culture reinforces that ethos. The company offers many products, such as Gmail and Maps, for free in their basic versions and often shares research openly. It recently released its Gemma model under a relatively permissive license. Even so, Google did not face an obvious incentive to publish the transformer research.

Employee incentives cut the other way. Google struck an implicit bargain with the researchers it recruited. As the Acquired podcast’s episode on Google’s AI era recounts, the company built its mid-2010s leadership by hiring nearly every major figure in the field: Geoffrey Hinton, Ilya Sutskever, Demis Hassabis, Dario Amodei, and many others. Most came from academia and kept joint appointments. Hinton retained his university position while at Google; Yann LeCun did the same at New York University while working with Meta.

These researchers joined Google for its unmatched compute and data—but on the understanding that they would continue to operate as scientists. They would pursue fundamental research, present at conferences, and publish their findings. Google likely could not have assembled the team that invented the transformer without offering that freedom.

Even so, all eight authors eventually left Google. Some sought greater academic freedom; others pursued commercial opportunities. Six went on to found companies that collectively raised more than $1.3 billion and produced multiple unicorns. Noam Shazeer co-founded Character.AI, which reached a $1 billion valuation in under two years before he returned to Google in August 2024 in a $2.7 billion deal. Aidan Gomez co-founded Cohere, now a leading enterprise AI firm. Ashish Vaswani and Niki Parmar left together in 2021, co-founded Adept AI, and later Essential AI. Llion Jones co-founded Sakana AI in Tokyo, explicitly pursuing a non-transformer approach. Jakob Uszkoreit co-founded a biotech startup applying AI to drug design. ?ukasz Kaiser did not found a startup but joined OpenAI in 2021, where he worked on GPT-4 and the o1 and o3 reasoning models.

In short, a single openly published paper seeded an entire ecosystem of competitors to the company that produced it. Google gave away the blueprint for what may be the world’s most valuable technology—and its own researchers walked out the door to build on it.

That deeply embedded scientific ethos among leading researchers offers reason for optimism about competition in AI, even as firms scale and commercial pressures intensify.

It’s Hard to NDA a Scientist

The AI research community grew out of academia, and it still carries that culture. Its norms look less like a Silicon Valley product team and more like a university department. Researchers present at conferences, share code, and publish. The Partnership on AI captures the point: openness is a “fundamental scientific value” in artificial intelligence and machine learning (AI/ML). Indeed, one of the field’s signal achievements is its shift toward open-access platforms like arXiv—“going against significant pressure to publish in traditional closed academic journals.”

That choice matters. The AI/ML community embraced openness even as the academic establishment pushed in the opposite direction. Researchers routinely post preprints before peer review, share code on GitHub, and present work at conferences such as NeurIPS and ICML, where the culture rewards novelty and reproducibility over secrecy. Firms such as Google, Meta, and OpenAI reinforced these norms by releasing landmark research and normalizing preprints across both academic and policy contexts.

These norms shape what researchers want. And those preferences, in turn, shape competition. The people building frontier AI systems are not interchangeable employees executing top-down strategy. They are scientists with strong views about how their work should be conducted, shared, and recognized. When firms disregard those views, researchers leave.

The pattern repeats. After Google’s DeepMind merged with Google Brain and shifted toward productization, some researchers began to exit. They had been hired to recreate an academic environment inside a company. As one former DeepMind researcher told Sifted: 

We hired a lot of really good, really senior engineers, researchers who we basically asked to replicate an academic setting within industry, which was unique at the time…  It’s no longer just an academic setting and rightfully so, in my view. But if you came from that [academic] perspective, you go, ‘This isn’t great—what we were hired to do is no longer the priority. 

The departures followed. Sixteen former DeepMind employees launched ventures in a single 12-month period, up from seven the year before. David Silver, a central figure behind AlphaGo and AlphaZero, left in early 2026 to found Ineffable Intelligence, arguing that the field must move beyond the large-language-model paradigm.

The same dynamic appears across the industry. Half of xAI’s founding team left within 30 months, citing disagreements over research direction and strategy. In 2025, more than 20 leading researchers exited OpenAI, Google, and Meta to join Periodic Labs, which focuses on AI-driven scientific discovery, rather than scaling language models. That same year, DeepMind alumni launched Reflection AI—which raised $2 billion at an $8 billion valuation—along with Generalist AI in robotics and a range of other ventures.

The throughline is clear. As one cross-industry analysis puts it:

Compensation alone does not retain elite AI talent. The researchers and engineers who define the frontier of AI capability are motivated by a mix of intellectual challenge, research freedom, mission alignment, and peer quality. When any of these factors degrade, the financial incentives to stay become insufficient, because every other major lab is willing to match or exceed the compensation package.

The Knowledge Won’t Stay Put

At its core, this is a story about knowledge diffusion. The scientific ethos creates powerful counterweights to any single firm’s incentive to bottle up AI capabilities for long. It operates through several reinforcing channels.

Start with talent mobility. Unlike trade secrets embedded in manufacturing processes or proprietary datasets, AI’s most valuable knowledge sits with people. And those people are often scientists who value publication, autonomy, and intellectual freedom. They move—to competitors, startups, and ventures of their own. Each departure carries institutional knowledge and often seeds a new rival. The transformer paper is the extreme case, but the broader pattern is everywhere. As the Acquired podcast documents in its episode on “Google, the AI Company,” nearly every major AI lab today traces key talent back to Google circa 2014—including OpenAI (Ilya Sutskever), Anthropic (Dario Amodei), and Microsoft’s AI division (Mustafa Suleyman, via DeepMind).

Next, open publication. When researchers publish their methods, anyone with the technical capability can build on them. The Stanford AI Index 2025 underscores the scale: from 2013 to 2023, AI publications nearly tripled, from roughly 102,000 to more than 242,000. AI’s share of all computer-science publications rose from 21.6% to 41.8% over the same period. The field is expanding both absolutely and relatively. Industry now produces nearly 90% of notable AI models, but academia remains the leading source of highly cited research. That division of labor matters. Firms build and scale systems, but foundational insights still flow through open academic channels.

The competitive implications follow. As Stanford Human-Centered Artificial Intelligence (HAI) researchers note:

When research is shared openly, innovation accelerates, duplication is minimized, and ideas build upon one another. In AI research, these shared open-source tools, datasets, libraries, and benchmarks have enabled progress that emerged from one lab and spread globally—from students, to startups, to large industry deployments.

Then there are open-source and open-weight models, which lower barriers to entry. Google’s recent release of  Gemma 4—its most capable open-weight model family to date—illustrates the shift. The company released it under the Apache 2.0 license, one of the most permissive open-source licenses available. Earlier Gemma versions used a more restrictive custom license that limited certain uses and reserved Google’s right to terminate access. Gemma 4 allows anyone to download, modify, fine-tune, and deploy the model weights commercially, without fees or special permission.

These dynamics can reinforce one another. Researchers want to share their work, and firms accommodate that preference to attract and retain talent. Some firms release models, which draws more researchers. Meta’s decision to open-source its Llama models, for example, attracted top talent from Google DeepMind and OpenAI. Many open-source contributors later joined Meta’s AI teams. Openness begets talent, which begets more openness.

None of this eliminates countervailing incentives. As AI grows more commercially valuable—and as computing costs rise—firms will sometimes prefer secrecy. The relevant question for competition, though, is not whether any single firm remains fully open. It is whether the industry’s overall dynamics allow knowledge to diffuse in ways that enable entry and rivalry. On that score, the evidence points in a favorable direction.

Why the Moat Won’t Hold

For competition scholars and enforcers, the scientific ethos in AI belongs in the analysis. It does not eliminate the risk of anticompetitive conduct. Firms can still engage in exclusionary practices. The recent wave of acquihires, for example, has raised concerns about merger-control evasion and “further consolidating the Big Tech industry.” Dirk Auer and Onyeka Aralu, however, argue those concerns may be overstated. Acquihires also facilitate labor mobility, and the scientific ethos helps drive that movement.

This is only one piece of the competitive puzzle. Other factors point in the same direction. As I discussed in prior posts, the cost of compute continues to fall, and data remains accessible and replicable.  If those conditions persist—and they show every sign of doing so—new entrants will keep emerging, competitive gaps will narrow, and the “inevitable concentration” narrative will keep colliding with a more dynamic reality.

For now, the evidence points one way. The people building frontier AI systems are, at their core, scientists. They want to publish. They want recognition. They want to work on meaningful problems with the freedom to pursue them. When firms constrain those preferences, researchers leave—taking knowledge with them and, more often than not, building the next competitor.

You can’t bottle that.

The post AI’s Scientific Ethos and the Moat That Wouldn’t Hold appeared first on Truth on the Market.

Americans have real reasons to be frustrated about high health care costs, and large conglomerates rank somewhere between unpopular and reviled—roughly where Microsoft’s “Clippy” sat in the 1990s. So it’s no surprise that Sens. Elizabeth Warren (D-Mass.) and Josh Hawley (R-Mo.) have teamed up to introduce S. 3822, the Break Up Big Medicine Act (BMBA). The bipartisan bill targets vertical integration—firms operating at multiple levels of the supply chain—in health care.

If enacted, the BMBA would bar parent companies of prescription-drug or medical-device wholesalers from owning or controlling health care providers or management services organizations (MSOs). It would also prohibit parent companies from owning or controlling both insurers or pharmacy benefit managers (PBMs) and care providers.

The bill defines “providers” broadly. It sweeps in physician practices and hospitals, along with pharmacies, urgent- and emergency-care providers, and ambulatory-surgery centers. MSOs include entities that support providers with “payroll, human resources, employment screening, payer contracting, billing and collection, coding, information technology … patient scheduling, property or equipment leasing, and administrative or business services,” as well as other nonclinical functions.

The BMBA never defines “control.” That omission matters. It could extend the prohibition beyond full ownership to cover negative controls, minority equity stakes with governance rights, or other governance arrangements.

The compliance timeline is tight. Covered entities would have just one year to divest prohibited assets. That is a tall order. Untangling conglomerates, splitting data systems, reallocating contracts, and rebuilding independent management structures carry significant operational risk. The bill also opens the door to enforcement by the Federal Trade Commission (FTC), U.S. Justice Department (DOJ), and Department of Health and Human Services (HHS), as well as state attorneys general and private plaintiffs. It authorizes the FTC and DOJ to challenge future mergers and acquisitions, and it imposes penalties that include forced divestiture and profit disgorgement.

States have already experimented with similar “break up” efforts. Arkansas, for example, is locked in a constitutional fight over a law that bars PBMs from owning or controlling pharmacy chains. Even supporters of stricter PBM regulation have criticized the measure for risking pharmacy closures, creating “pharmacy deserts,” and limiting access to medicines in rural areas. Other states—including Massachusetts, California, and Oregon—have taken a different tack, imposing structural limits on health care ownership primarily through expanded merger review.

Supporters of the BMBA argue that separating ownership will boost competition. In their view, integration creates conflicts of interest that allow firms to leverage supply chains, exclude rivals, limit patient choice, and raise prices. But the economic evidence offers a more complicated picture. Vertical integration can create harmful conflicts in some cases. In others, it can align incentives, reduce costs, and improve how firms meet patient needs. Blanket bans risk missing that distinction—and may produce the opposite of their intended effect.

Vertical Integration: More Competition, Not Less

Entry by a competitor operating at multiple levels of the supply chain usually benefits consumers. It tends to intensify competition by creating more effective players in both upstream and downstream markets. That pattern shows up clearly in the empirical literature. A recent meta-analysis by Daniel Kulick, Gregory Price, and Justin Pierce (October 2025) finds many instances of procompetitive effects and some neutral ones, but only a handful of anticompetitive outcomes. Studies using the most reliable methodologies overwhelmingly identify procompetitive effects and no net anticompetitive harm.

Vertical integration can also cut costs by improving efficiency. Firms avoid the time and expense of repeatedly negotiating contracts at each stage of production and distribution. They can also eliminate “double marginalization” —the need to layer profit margins at multiple stages. Those efficiencies reduce business costs and can translate into lower prices for consumers. Like horizontal integration, vertical integration can generate complementary synergies that improve value. But unlike horizontal mergers, vertical integration does not reduce the number of competitors at any level of the supply chain. That distinction explains why antitrust enforcers have historically treated vertical mergers as posing less risk to competition and consumers than horizontal ones.

Consider Amazon. The company operates an e-commerce marketplace while also competing on that platform with its Amazon Basics house brand. That dual role lets Amazon leverage scale economies, tap into detailed knowledge of consumer demand, and apply expertise across a wide range of products. The result: new offerings, lower shipping costs through bulk orders, and lower production costs through large-scale manufacturing.

When Amazon introduces its own version of a product, competitors face pressure to cut costs, improve quality, offer better deals, or differentiate. Amazon can—and often does—use its control of the marketplace to promote its own products. But that strategy has limits. Steering consumers toward inferior products would erode Amazon’s value proposition. In some cases, steering may also reduce shipping overhead by increasing order volumes.

Research suggests that a marketplace promotes its house-brand products when it expects greater returns from direct sales than from commissions on third-party sellers. That dynamic may explain why Amazon competes directly in only about 8% of products sold on its U.S. marketplace. A 2023 study found that removing Amazon’s house brands would reduce consumer surplus by nearly 4%.

Other examples point in the same direction. Google’s 2015 entry into the photography app market with Google Photos on Android increased user attention in ways that benefited independent developers. Instagram also became more valuable to users after its its integration with Facebook.

Regulation Built This Mess—Integration Helps Survive It

In targeting rising concentration and vertical integration in health care, Sens. Warren and Hawley take aim at a symptom, not the disease. Health care ranks among the most heavily regulated sectors in the economy. The rulebook keeps growing: strict privacy and data-protection standards, extensive administrative and recordkeeping requirements, professional-licensure regimes, and much more. Some of these rules serve essential purposes, given the stakes. Others address real concerns but sweep too broadly, imposing costs that exceed their benefits. Still others do little more than entrench incumbents with the resources and lobbying power to shape the regulatory landscape.

Consider “certificate of need ” (CON) laws. Incumbent hospital systems often lobby for these rules, which require providers to obtain state approval before building new facilities or purchasing equipment. The effect is predictable: they shield well-connected incumbents from competition, while driving up costs and limiting access to care. Independent pharmacists’ support for laws that would block insurers from operating pharmacy chains reflects the same dynamic.

Some regulators go further and actively restrict competition. State dental boards offer a telling example. These boards—typically staffed by practicing dentists—control licensure and often act to exclude would-be competitors. The result is higher prices for patients. The Cato Institute reports that, between 2003 and 2015, the North Carolina State Board of Dental Examiners sent more than 45 cease-and-desist letters to entrepreneurs offering routine teeth-whitening services. It even pressed shopping-mall operators to evict them. The U.S. Supreme Court ultimately found that the board acted under a clear conflict of interest and engaged in illegal, anticompetitive conduct. Yet states did not respond by curbing regulatory capture. Michigan, Georgia, and North Dakota have since increased the number of board seats held by conflicted members. Eight states go further, requiring governors to select board members from lists supplied by dental associations. That is not competition policy—it is cartelization under color of law. And unlike private firms, such boards often enjoy immunity from antitrust liability. If policymakers want to dismantle monopolies that harm patients, this is an obvious place to start.

Layer onto that a fragmented regulatory landscape. States—and sometimes localities—impose their own rules on everything from CON laws to insurance-coverage mandates. The result is a patchwork that drives up compliance costs and makes interstate competition exceedingly difficult. It is hard to square that outcome with any coherent vision of a national market.

This regulatory thicket helps explain consolidation and integration. Compliance carries high fixed costs. Larger firms can spread those costs across greater output, giving them a structural advantage over smaller rivals. Firms also have strong incentives to enter new markets by acquiring upstream or downstream entities. Vertical integration, in turn, can ease compliance by pooling resources, expertise, and institutional knowledge. In heavily regulated markets, it can intensify—rather than dampen—competitive pressure.

The benefits extend beyond compliance. Vertical integration can harmonize governance, align incentives, and facilitate technology upgrades and data sharing. Payer-provider integration, for example, combines claims data with clinical data. That improves predictive modeling, allowing earlier identification of high-risk patients and more proactive care in risk-based or capitated arrangements. Consolidating data flows can also strengthen data security by reducing the number of transfer points—and thus the number of potential targets for hackers.

Integration also supports more effective use of AI-enabled tools. When systems across providers, insurers, PBMs, and pharmacies can communicate seamlessly, firms can allocate resources more efficiently based on patient need. Larger, integrated entities can also diffuse technological and organizational innovations more quickly across the supply chain, extending those gains to more patients.

The BMBA would put a chill on these dynamics. By constraining integration, it risks raising costs, slowing innovation, and ultimately reducing access to care—the very outcomes its sponsors say they want to avoid.

Use Antitrust as a Scalpel, Not a Sledgehammer

None of this suggests that vertical integration or mergers are always benign. They are not. Firms with market power at any level of the supply chain can use integration to raise rivals’ costs, misuse commercially sensitive information, or foreclose competitors. Those strategies can reduce output, limit innovation and choice, and push prices higher.

Contracting practices can cut both ways, too. Steering arrangements, “most favored nation” (MFN) clauses, and similar tools can produce procompetitive or anticompetitive effects, depending on how firms use them. Take steering. When health plans steer patients to affiliated PBMs or pharmacy chains, they may lower costs by aggregating demand. But the same arrangements can also exclude independent pharmacies, weaken competition, and keep prices elevated.

This is precisely why antitrust law exists. The Sherman Act prohibits unreasonable restraints of trade and exclusionary conduct that creates or entrenches monopoly power. Courts apply the “rule of reason” to weigh procompetitive benefits against anticompetitive harms before condemning a practice. That framework matters. It allows enforcers to police abuse without outlawing integration outright. And it works on the front end, too: Section 7 of the Clayton Act already empowers agencies to block mergers likely to harm competition, including in health care markets.

Targeted enforcement preserves what works. When agencies stop firms from abusing market power, they leave intact the efficiencies that benefit consumers. That approach reflects a long-standing principle of U.S. antitrust law: tailor remedies to the harm, rather than punishing firms simply for achieving scale through innovation, business acumen, or even luck.

Blanket bans take the opposite approach. They eliminate entire categories of business arrangements—such as common ownership of insurers and providers—without regard to their actual effects. In trying to prevent potential harms, they also wipe out real efficiencies. The result is likely higher costs, reduced access, and worse outcomes.

Herbert Hovenkamp, one of the country’s leading antitrust scholars, has criticized structural breakups on these grounds. Courts have a mixed record with such remedies, and often a poor one. Even when violations occur, Hovenkamp argues, structural separation should remain a last resort—used only when narrower remedies, like injunctions, cannot address the competitive harm.

Targeted relief may demand more from enforcers. Monitoring compliance is not costless. But it avoids unnecessary collateral damage. In health care, for example, a court could enjoin a steering arrangement that lacks procompetitive justification, while preserving integration that lowers costs. Likewise, if a vertically integrated wholesaler raises rivals’ costs by restricting access to a critical drug, a court can require it to supply competitors on reasonable commercial terms.

Structural breakups, like those mandated by the BMBA, offer no such precision. They discard the benefits along with the risks—leaving consumers worse off in the process.

A Cure Worse Than the Disease

Bills like the BMBA trade on populist appeal. They offer a simple “solution” that treats efficiency-generating arrangements as nails to be hammered. But bans on cross-supply-chain ownership punish firms for responding to administrative complexity, fragmented contracting, and duplicative overhead.

They also sidestep the hard questions. Does the firm even have market power? Is it using that power to harm competition? Do the efficiencies outweigh the risks? Is a breakup necessary—or would it fail to solve the problem, or even make things worse? These are not bureaucratic hurdles. They are the core of sound antitrust analysis. Ignore them, and enforcement risks undermining its own goals.

Policymakers who want lower costs and less consolidation in health care should start elsewhere. Remove the regulatory barriers that entrench incumbents and discourage entry. Clear the path for competition, and much of the pressure to integrate will dissipate on its own.

The post Bad Medicine: Why Breaking Up Big Health Care Could Make It Worse appeared first on Truth on the Market.

Recent commentary suggests U.S. artificial intelligence (AI) policy may be coalescing around a federal framework. A widely discussed Tech Policy Press piece argues that a short “term sheet” emerging from negotiations between the White House and industry could reshape American AI policy. An Axios report, meanwhile, highlights how Anthropic imposed constraints on its latest model before release. Taken together, these developments point to two distinct—and too often conflated—mechanisms of governance: political coordination and market discipline. Washington policy debates fixate on the former. The latter already shapes behavior across the industry.

That distinction matters. A political “term sheet” can influence expectations, shape investment decisions, spur compliance planning, and create focal points for firms trying to anticipate the regulatory landscape. It can affect how boards, general counsel, venture investors, enterprise customers, and journalists define “responsible AI.” In that limited sense, the strongest version of the term-sheet argument holds: nonbinding political coordination can produce real economic effects before Congress enacts a statute or an agency promulgates a rule.

But that concession does not answer the harder question—whether those effects are beneficial. The issue is not whether a term sheet shapes expectations. It is how it shapes them, and whether it improves or distorts the market process through which information about AI safety, reliability, and value emerges. Skepticism is warranted. In a fast-moving industry defined by dispersed knowledge, entrepreneurial experimentation, and radical uncertainty, a politically generated focal point can do more than reduce uncertainty at the margin. It can create the wrong kind of certainty—and in the AI context, that may prove worse than having less of it.

Term Sheets Don’t Scale to Washington

Anyone who has negotiated a complex commercial deal knows a term sheet is not a solution. It is, at best, a preliminary framework—one that sketches broad principles while deferring the hardest questions. That is not a criticism. In private bargaining, term sheets serve a useful role because the parties engage directly in a process of discovery and adjustment. They test proposed terms against cost, risk, information asymmetry, financing constraints, and incentives. The final agreement emerges through feedback-rich iteration, with each party bearing the consequences of its own errors.

That analogy weakens when it moves from private ordering to political governance. In that setting, the “term sheet” no longer marks a step in reciprocal adjustment among parties disciplined by profit and loss. It becomes a signal from a political actor with different incentives—one insulated from ordinary mechanisms of correction. In game-theoretic terms, this signaling often resembles cheap talk, rather than credible commitment. That risk increases in an election year, under divided government, amid federal-state conflict, and in a technological domain evolving faster than the political process can track.

None of this denies that such signaling affects expectations. The problem is directional. It can move expectations the wrong way. Roger Koppl’s “Big Player” theory helps explain why. When a powerful actor enters a market and can shift outcomes without profit-and-loss discipline, participants reorient. They look less to consumer preferences and more to the anticipated actions of the powerful actor. That shift matters. It changes what firms try to learn from—and what they try to optimize against. In this context, the White House becomes not just another source of information, but a privileged source of noise.

The issue runs deeper than incomplete guidance. Political signaling can generate artificial herding, rather than genuine coordination. In a market process, coordination emerges from decentralized experimentation—firms test plans, correct errors, and respond to price signals, customer demand, and competitive pressure. What looks like coordination is the emergent product of adaptation. A politically salient term sheet does something different. It encourages firms to cluster around what is legible to Washington. It pushes them to align with politically visible definitions of “safety,” “accountability,” or “responsible deployment”—not because those standards have proven superior, but because they have become focal in a political game.

That distinction matters even more in AI, where no one yet knows the optimal mix of safety constraints, openness, auditability, latency, accuracy, model autonomy, or domain-specific risk controls. In a functioning market, firms can experiment with different combinations, and customers can reward or reject them. But when a Big Player supplies a politically privileged focal point, the ecology of plans shifts. Firms invest less in discovering what users value or which safety practices work best, and more in anticipating what regulators, staffers, and aligned commentators will bless. That is not discovery. It is mimicry under political uncertainty.

Pointing the Market the Wrong Way

That problem intensifies given AI’s underlying characteristics. The relevant knowledge is not centralized, stable, or even fully articulable. It is dispersed across model developers, downstream integrators, enterprise customers, open-source communities, safety researchers, and users. Much of it remains tacit. Much of it emerges only through deployment and use. AI is not a static object that policymakers can govern from a fixed blueprint. It more closely resembles what Ludwig Lachmann described as a kaleidic world—defined by constant change, shifting expectations, and a future that resists any stationary forecast.

That distinction matters. Political coordination invites observers to mistake temporary conceptual order for durable institutional knowledge. A federal term sheet may project stability, and firms and investors may welcome it. But in a kaleidic environment, that stability often proves illusory. It channels capital toward structures optimized for today’s political vocabulary—even as the technological frontier moves on. In Austrian terms, that dynamic points toward malinvestment.

Capital is not homogeneous. It is time-structured, complementary, and oriented toward expected future states of the world. When political signaling distorts expectations, it distorts investment. Firms build compliance systems, safety teams, documentation regimes, and product architectures around what appears politically prudent. Some of that investment may prove useful. Some will not. More important, it can reallocate resources away from entrepreneurial experimentation and toward politically induced conformity. The cost is not just wasted compliance spending. It is a market process that drifts away from discovering superior alternatives.

Israel Kirzner’s work sharpens the point. The entrepreneurial market process depends on alertness to previously unseen opportunities. Competition does not simply select among known options; it discovers what the options are. Regulation—or even preliminary quasi-regulation—can narrow that discovery process by constraining entrepreneurial vision. Once a politically endorsed conception of “safe AI” becomes focal, it reduces incentives to search for better or different approaches. The result is not just compliance. It is the foreclosure of imagination.

This is why the “term sheet” framing understates the risk. It treats the problem as one of incomplete follow-through—as if a preliminary framework simply points in a useful direction. In AI, pointing in a politically approved direction may itself distort the process. It encourages firms to build toward current consensus, rather than test competing approaches that users, enterprises, or downstream markets might validate. In a domain where discovery drives progress, central focal points can obstruct, rather than assist.

Compliance as a Competitive Weapon

Public choice theory sharpens the concern. Political coordination in high-value sectors rarely amounts to neutral problem-solving. It creates opportunities for incumbents to shape rules in ways that entrench their position. Gordon Tullock’s work on rent seeking remains essential here. Once regulatory standards carry economic weight, firms have incentives to compete over their design. That competition may be rational for individual firms, but it can impose real social costs.

This does not require bad faith. The problem is structural. Once “safety” language becomes a vehicle for barrier creation, the institutional environment shifts. Terms like “responsible deployment,” “model evaluation,” “frontier capabilities,” “compute thresholds,” and “red-team requirements” can be framed as public-interest measures while aligning more closely with the internal capacities of established firms than with those of smaller rivals. That is classic public-choice dynamics: private advantage pursued through public-facing justification.

AI amplifies the risk. Many proposed standards are complex, resource-intensive, and legible primarily to insiders. That makes them well suited to raising rivals’ costs. Large firms can absorb compliance staff, documentation burdens, staged evaluation protocols, and structured reporting. Smaller firms, open-source communities, and new entrants often cannot. A politically salient term sheet may do more than coordinate expectations—it can tilt the competitive landscape toward those best positioned to translate political language into operational compliance.

None of this implies that term-sheet proponents consciously seek rents. But it does counsel caution. Analysts should resist treating preliminary political alignment as a neutral public good. Once a political focal point emerges, it shifts the margin of competition. Firms no longer compete only on product quality, customer trust, model performance, and contractual reliability. They also compete on their ability to shape—or adapt to—the emerging regulatory vocabulary. That is a different game, and often a negative-sum one.

The Governance Washington Keeps Overlooking

If political coordination carries more risk than its defenders suggest, what is the alternative? Not a romantic claim that markets are perfect or that harms internalize automatically. The better answer is more grounded: market discipline already operates through institutional mechanisms that are more adaptive, information-rich, and corrigible than political precoordination.

Anthropic’s decision to impose constraints on its model before release offers a concrete example. No statute required it. No regulator ordered it. The company appears to have acted based on expectations about customers, reputational risk, and the long-term consequences of deploying capabilities likely to trigger backlash. That is not law. But it is governance—rooted in anticipated responses from users, enterprise customers, partners, investors, employees, and the broader market.

To make that claim persuasive, the mechanisms matter.

Reputational Capital

In AI markets, trust is not ornamental; it is an input into adoption. Firms perceived as reckless, unreliable, or cavalier about model risk can lose customer confidence, enterprise contracts, developer integrations, and talent. Reputation functions as a bond posted to the market. Firms protect it because it conditions future revenue.

Enterprise Procurement

Many of the most economically significant uses of AI occur through integration into enterprise workflows, software stacks, and decision-support systems—not casual consumer use. Enterprise customers care about hallucination rates, privacy protections, audit trails, uptime, support, indemnification, and predictable performance. They do not need Congress to tell them to care. They already do. Providers face pressure to self-regulate to win and retain those customers.

Contractual Governance

Downstream deployers increasingly allocate risk through contracts, service-level agreements, and integration requirements. AI firms must negotiate around these constraints to embed their products in production systems. In Coasean terms, transaction costs remain, but bargaining, reputation, and repeat dealings can partially internalize relevant externalities. This is not the frictionless Coase theorem of textbooks. It is a practical point: when parties can identify, price, and allocate risk, decentralized governance can emerge without prior regulatory design.

Switching Behavior

In many AI applications, switching costs—while real—are lower than in legacy industries marked by deep consumer lock-in. Users can compare outputs across models. Enterprises can multi-home. Developers can benchmark APIs. The market is not frictionless, but providers face a credible threat of substitution. If a model behaves in ways customers find unsafe, biased, unstable, or unusable, alternatives exist. That creates pressure to improve.

Capital-Market Discipline

Investors care about regulatory risk, but they also watch for reputational failures, botched launches, litigation exposure, product withdrawals, and fragile business models. A firm that deploys irresponsibly may lose customers and face tighter access to capital. Market governance operates not just at the point of sale, but across the financing ecosystem.

Evolution Beats Edict

Market signals are noisy. Users do not perceive risk perfectly. Some harms emerge only over time or remain diffuse. Information asymmetries persist. Feedback loops can lag, and fads can skew judgment. Those concerns are real, and any serious defense of market discipline should acknowledge them. But that concession does not resolve the issue in favor of political direction. The relevant comparison is not imperfect markets versus perfect regulation. It is imperfect markets versus imperfect politics.

Steven Horwitz’s work clarifies the point. From an Austrian perspective, noisy price signals do not refute the market process—they are integral to it. Disequilibrium signals do not provide omniscience. They highlight where knowledge is missing and where entrepreneurial correction can occur. In AI, the same logic applies. Public criticism, customer hesitation, developer complaints, benchmark failures, enterprise demands, and product defections do not pinpoint the ideal safety frontier. But they generate information and incentives to improve.

This is why the market account is better understood as evolutionary, rather than static. Richard Nelson and Sidney Winter’s framework is instructive. Firms operate through routines, experiment under uncertainty, and face selection pressures. In AI, firms try different combinations of safety and capability. Some impose tighter guardrails; others emphasize transparency, enterprise trust, or openness. Some overreach and pull back. These variations are tested against market responses. Firms whose governance choices diverge from user and customer demands face reputational and financial consequences. Firms that better match the evolving environment survive and scale.

The process is not instantaneous. It involves trial, error, and loss. That is precisely what makes it adaptive. A politically coordinated framework, by contrast, tends to convert provisional judgment into uniform standard. It suppresses variation before selection can do its work. The result is often slower learning, not better governance.

Friedrich Hayek’s insight into competition as a discovery procedure ties this together. We do not know ex ante the optimal balance between openness and safety, speed and interpretability, or general-purpose deployment and domain-specific constraint. Those margins must be discovered. Competition allows firms to test different combinations, and allows customers, developers, and enterprises to sort among them. What appears from Washington as a need for ex ante alignment may, from within the market, amount to premature foreclosure of discovery.

Better Late Corrections Than Early Mistakes

A brief Coasean clarification helps frame the issue. Critics often assume that any defense of markets rests on the fiction of zero transaction costs and fully internalized externalities. That is not the claim. The question is not whether transaction costs exist. It is whether AI governance problems are more likely to be addressed through decentralized adaptation or centralized precommitment.

In many AI settings, transaction costs for decentralized governance are falling. APIs make benchmarking and substitution easier. Enterprise contracting creates repeat relationships and structured risk allocation. Public visibility accelerates reputational sanctions. Open technical communities surface flaws quickly. None of this eliminates harm. But it does show that the market’s capacity to generate governance is not fixed—it improves as the technology diffuses.

Political governance carries its own transaction costs: legislative delay, bureaucratic rigidity, information bottlenecks, path dependence, and capture. These costs often draw less attention than product failures or public controversies, but they matter. In AI, they may matter more. A system that corrects late but can correct continuously may outperform one that coordinates early but locks in error.

Too Much Order, Too Soon

The strongest defense of the “term sheet” view is straightforward: even incomplete political coordination can shape expectations, encourage caution, and nudge markets toward socially desirable norms. That argument is not frivolous. But it is incomplete. It treats coordination as the central institutional problem. In AI, the deeper problem is discovery.

Political term sheets can influence investment, compliance, and norms. The question is whether that influence improves discovery or distorts it. Framed that way, the risks come into focus. Big Player signaling can induce herding rather than experimentation. In a kaleidic industry, it can drive malinvestment by projecting false stability. Through public-choice dynamics, it can invite rent seeking and raise barriers to entry. And by making current political understandings focal, it can dampen incentives to discover better approaches to safety, trust, and governance.

Market discipline, by contrast, is neither utopian nor passive. It operates through reputation, procurement, contract, switching, financing, and evolutionary selection. Its signals are noisy, but that is how adaptation works under uncertainty. It allows firms to test competing governance models and lets users, enterprises, and downstream markets sort among them. In Hayekian terms, it is a discovery process. In Coasean terms, it enables decentralized governance where transaction costs permit. In Kirznerian terms, it preserves the entrepreneurial alertness through which better solutions emerge. And in public-choice terms, it avoids mistaking political focal points for neutral reflections of social knowledge.

The danger in the current policy debate is not just that Washington may act too slowly or too clumsily. It is that observers may mistake politically induced alignment for genuine order. A term sheet may shape American AI policy. The harder question is whether it should. In a sector defined by uncertainty, experimentation, and rapid adaptation, the real risk is not too little coordination—but too much, too soon.

The post Too Much Order, Too Soon: The Case Against AI Term Sheets appeared first on Truth on the Market.

On April 15, the European Commission sent Meta a Supplementary Statement of Objections. It signaled its intent to order the company to restore third-party AI assistants’ access to WhatsApp under the terms that applied before Meta’s Oct. 15, 2025 policy change. The move—an interim-measures procedure—marks the latest front in an increasingly strained effort to police competition in generative AI.

The case makes little sense from a consumer-welfare standpoint. It reads less like a coherent theory of harm and more like an attempt to “do something” about generative AI, with the analysis shaped to fit that goal. The Commission proposes to deploy one of its most far-reaching—and rarely used—enforcement tools to protect competitors in a market that, by almost any reasonable metric, remains intensely competitive.

The result is what Robert Bork famously called a “policy at war with itself.”

Squeezing the Balloon: Constrain Monetization, Then Regulate What’s Left

To understand what the European Commission now seeks to unwind, start with the commercial target of its intervention: the WhatsApp Business Platform (formerly the WhatsApp Business API).

WhatsApp has remained free to end users since before Meta acquired it in 2014. Meta has largely honored its commitment not to monetize the consumer-facing app through conventional advertising. That leaves the company to recoup a $19 billion acquisition elsewhere. Historically, it has relied on two channels: cross-service monetization—using WhatsApp data to improve ad targeting on Facebook and Instagram—and the Business Platform, where firms pay a per-conversation fee to communicate with customers at scale.

In Europe, the Digital Markets Act (DMA) has sharply curtailed the first path. Article 5(2) bars a designated gatekeeper from combining personal data across core platform services, or cross-using such data, absent specific user consent. In April 2025, the Commission fined €200 million for noncompliance, finding its “Consent or Pay” model failed to offer a valid free-and-equivalent alternative. The joint-monetization strategy Meta pursued when it integrated WhatsApp into its advertising stack has narrowed dramatically. As a result, the Business Platform now carries more of the burden of monetizing WhatsApp.

Against that backdrop, Meta updated WhatsApp’s terms to exclude third-party general-purpose AI assistants from the Business Platform. That change triggered a regulatory back-and-forth with the Commission. On Feb. 9, the Commission issued a Statement of Objections arguing the amendment constituted an abuse of dominance. Meta responded by reversing course. It brought third-party AI assistants back into the Business Platform under the same paid-access structure that applies to other users. The Commission now contends this revised framework “seems to have the same effect” as the original ban.

The Commission’s theory appears straightforward: WhatsApp functions as a critical distribution channel for general-purpose AI assistants that compete with Meta’s offerings. Meta’s conduct therefore forecloses competition in the adjacent AI-assistant market. Because those markets evolve quickly, the Commission argues interim measures are necessary to prevent “serious and irreparable harm.”

That theory sits uneasily with the underlying business reality. The Commission has singled out one of the few remaining avenues through which Meta can meaningfully monetize WhatsApp. Having constrained cross-service monetization through the DMA, it now seeks to dictate the terms of the paid API that has become Meta’s primary WhatsApp revenue stream. In effect, it would require free—or near-free—access for one category of business user.

Nothing in the DMA required Meta to preserve the pre-October 2025 terms for third-party AI assistants. Extending the same per-conversation pricing that applies to other heavy API users looks like a rational response to the monetization squeeze created by the Commission’s own interventions.

The choice of procedural tool also merits scrutiny. Until recently, the Commission reserved interim measures for manifestly serious cases. Its own procedural manual notes that Broadcom (October 2019) marked the first use of Article 8(1) of Regulation 1/2003 in nearly two decades.

Using this exceptional instrument on a theory that depends on contested market definitions and speculative foreclosure claims in a nascent market should raise eyebrows.

If Everything Is Everywhere, Nothing Is Essential

Public information about the Commission’s analysis remains thin, but its contours are easy enough to sketch. For the theory to hold, the Commission likely needs an unusually narrow relevant market—something like “general-purpose AI assistants distributed through consumer messaging apps.” Broaden the lens to “general-purpose AI assistants,” which better reflects how users actually switch between services, and the theory of harm starts to unravel.

The wider generative-AI space ranks among the most hotly contested in recent memory. Since ChatGPT’s public launch, AI firms have attracted enormous venture capital inflows. The result: a crowded field of serious players—Anthropic, Google, Microsoft, Meta, Mistral, Perplexity, xAI, DeepSeek, and others—whose relative positions shift constantly. Recent Similarweb data, for example, show ChatGPT’s chatbot-traffic share falling from about 87% at the start of 2025 to roughly 64% a year later, as Google’s Gemini nearly quadrupled. That is not what an entrenched bottleneck looks like.

A narrow market definition also clashes with observable competitive behavior. Generative-AI firms aggressively expand across distribution channels. Anthropic has rolled out Claude Cowork and Office integrations. Perplexity distributes through WhatsApp, Telegram, and X via dedicated numbers. Google has embedded Gemini across Search, Android, Gmail, and Workspace. Microsoft has integrated Copilot into Windows, Office, and GitHub. OpenAI, for its part, made ChatGPT available inside WhatsApp by publishing a phone number.

In a market where firms pursue multi-channel distribution as a matter of course, it is hard to treat any single entry point—here, a messaging app—as an indispensable “essential facility” for AI assistants.

Needing the Platform Is Not Being Entitled to It

Even if WhatsApp access matters, it is hard to see who suffers foreclosure in any meaningful competitive sense. The major AI labs already reach users through their own apps, websites, browsers, smartphone operating systems, productivity software, and APIs that power thousands of third-party services.

ChatGPT counts hundreds of millions of weekly users and installs easily across platforms. Claude runs across the web, apps, and a growing set of enterprise integrations. Google’s preinstalled distribution on some Android devices dwarfs anything WhatsApp could offer. Against that backdrop, the claim that losing one distribution channel would “seriously and irreparably” harm competition in AI assistants stretches credulity.

The more plausible losers are smaller firms that rely heavily on messaging-app distribution—companies like Luzia, which targets Spanish- and Portuguese-speaking users primarily through WhatsApp. That is a real business risk for those firms. It is not, however, a sound basis for European competition policy, much less for invoking interim measures.

In a fast-moving industry, freezing today’s market structure can do more harm than good. Platform success often reflects adaptation to user demand, not exclusionary gatekeeping. Efforts to “open up” platforms by fiat risk short-circuiting the experimentation that drives progress.

The Foundem saga offers a cautionary tale. Foundem, a small UK search site, saw its rankings decline and spent years blaming Google’s algorithm—a narrative the Commission ultimately endorsed in its 2017 Google Shopping decision. A simpler explanation is that Foundem mistook needing a platform for being entitled to placement on it. It confused its own needs with consumer preferences. The Meta-WhatsApp theory repeats that mistake, treating firms that benefit from WhatsApp access as entitled to it on their own terms.

Compete—Just Not Too Effectively

Beyond the immediate effects, the deeper problem lies in the signal this case—and others like it—sends. Firms compete in AI in part by finding new ways to deliver functionality, often through deep integration into widely used products. Think of Anthropic’s Claude launching Cowork and building an Excel plugin. Current EU competition policy discourages that kind of integration.

Under the DMA, Google has effectively pulled back from embedding AI-generated answers into Search in ways that would most clearly benefit European users, because such moves risk being labeled self-preferencing under Article 6(5). The Meta case sends the same message from the opposite direction: integrate your own AI into your own distribution channels—even ones third parties have used—and you may face interim measures. The practical effect is to force Web-2.0 incumbents to compete in AI with one arm tied behind their backs.

That is an odd stance for competition policy. Meta Platforms Inc. is among the firms best positioned to challenge OpenAI, Anthropic, and Google in generative AI. Its open-weight Llama models and its recent acquisition of Manus underscore that potential. Penalizing Meta for integrating its own assistant into WhatsApp—while also constraining Google’s integration of Gemini into Search—reduces, rather than increases, competitive pressure on current AI leaders.

The intervention also rests on a broader assumption: that incumbents will inevitably dominate AI absent regulatory constraints. The evidence cuts the other way. Despite their Web-2.0 scale, Google, Meta, and Microsoft have yet to convert that advantage into dominant AI positions. Instead, startups—OpenAI in consumer use and Anthropic in enterprise—currently lead.

A Policy at War With Itself—Now With AI

Robert Bork’s 1978 diagnosis of American antitrust was that it had become “a policy at war with itself”—a body of doctrine that undermined the consumer-welfare goals it claimed to serve. The same critique now fits competition policy in AI.

In the name of addressing speculative and unlikely foreclosure risks, policymakers are creating concrete harms. They deter integration that would benefit consumers, blunt the competitive pressure Web-2.0 incumbents could bring to bear on AI leaders, and replace market evolution with regulatory micromanagement.

It is especially troubling that the European Commission has chosen this case to normalize interim measures as a routine enforcement tool. Those measures should remain reserved for clear-cut, urgent harms—not used to lock in the design of an emerging market on the basis of a novel and contested theory.

Sound competition policy protects the competitive process, not particular competitors. It centers consumer welfare, relies on evidence, and respects the limits of regulatory foresight in fast-moving markets. The Commission’s Meta-WhatsApp case misses on all three.

Bork saw the danger clearly. Europe is now proving him right.

The post Brussels’ AI Squeeze: Regulating What It Leaves Standing appeared first on Truth on the Market.

In March 2026, the Italian Competition Authority (AGCM) launched an “IC59 fact-finding inquiry” into quantum, citing concerns that ongoing developments could shape long-term competition. Drawing on lessons from artificial intelligence (AI) and cloud markets, the AGCM flagged familiar risks: lock-in, technological preemption, and barriers to knowledge and entry.

The move reflects a broader shift in European competition policy following the Digital Markets Act (DMA). Regulators now focus less on prices and market share, and more on how markets are designed and governed—especially where control over key inputs may entrench gatekeepers and confer durable advantages.

That shift carries real risks for emerging technologies like quantum computing. The AGCM casts its inquiry as a “timely reconnaissance” of a nascent market—exploratory, not enforcement-driven. Even so, treating the quantum ecosystem as if it were already a mature market risks overstating what we can know about its trajectory. The technology remains too uncertain for reliable market analysis. Any risk assessment necessarily rests, at least in part, on incomplete and uncertain information, raising the prospect of unintended consequences for technological development.

Competition law rests on familiar assumptions: markets generate observable signals, and analysts can use those signals to assess competitive dynamics and identify harm. Authorities evaluate conduct—pricing, access restrictions, exclusionary agreements—against established benchmarks grounded in current market conditions and evidence. In quantum computing, however, market structures remain too underdeveloped to support meaningful competition analysis.

Premature Market Definition Is the Root of Many Errors

What exists today in quantum computing is not a “market” in any meaningful competition-law sense. It is a hybrid ecosystem of large technology firms, startups, and publicly funded research programs—shaped less by competitive equilibrium than by financing patterns, experimentation, and deep technological uncertainty.

When signals of efficiency, substitution, and consumer preference remain incomplete or unstable, the inferences drawn from them become fragile. That fragility increases the risk of mistaking technological development for anticompetitive conduct. Authorities may read experimentation as exclusion, partnerships as foreclosure, and vertical integration—often necessary in hardware-intensive technologies—as evidence of dominance. Apply competition law too early, and you risk injecting regulatory uncertainty that chills the very investment needed to move the technology forward.

The AGCM’s inquiry hints at a deeper problem: an analytical framework that does not quite fit. That mismatch risks producing flawed regulation, confused enforcement, and slower market development. Competition authorities often face the temptation to treat emerging technologies as if they already constitute a single, unified market. Quantum computing does not. It is far from clear that it ever will.

A better way to understand quantum computing is as a stack of loosely connected layers: hardware, software, algorithms, and domain-specific applications. Each layer evolves at its own pace and faces distinct constraints—from physics and engineering to mathematics and economic viability. Progress is uneven and interdependent. Advances in one layer do not automatically unlock gains in others, which helps explain the slow pace of commercialization. In that sense, quantum computing functions more like enabling infrastructure than a discrete market. It does not fit neatly into the tidy categories regulators prefer.

The same ambiguity appears in AI. Generative AI, in particular, is not a single, unified technology, but a loose collection of models, techniques, and systems built on an evolving stack. These components operate as inputs in broader pipelines, supporting a wide range of downstream applications, business models, and user-facing functions. What matters is not the structure alone, but how these systems are integrated, orchestrated, and deployed.. 

Both quantum computing and generative AI are better understood as assemblages of loosely connected technological layers, not monolithic systems. Their differences lie in use and combination, not in the existence of a stack. Over time, quantum computing will likely integrate into existing computational pipelines, potentially alongside AI systems that orchestrate workflows and decision-making. That is not a novel development—it is another instance of technological stacks interacting and co-evolving. In the near term, neither technology is likely to consolidate into a single, unified system.

Quantum technologies also share underlying scientific principles while serving distinct applications. Innovation in one domain will likely spill over into others. Approaches that seem suboptimal in one context may prove highly valuable in another. That reality undermines the idea of a single, well-defined future “quantum market” and risks obscuring important cross-domain complementarities—both within quantum technologies and across adjacent fields. Early intervention, then, risks foreclosing design choices that could ultimately prove welfare-maximizing.

Seeing Monopoly in a Pre-Market

A limited understanding of how the quantum-computing stack evolves can distort assessments of market concentration and power. More troubling, those assessments often get projected onto a market that does not yet meaningfully exist.

The Italian Competition Authority (AGCM) already flags the concentration of activity among a small number of well-resourced actors and draws an analogy to artificial intelligence (AI). That comparison does not hold. AI is already deployed commercially—albeit not at its most advanced stages—while quantum computing remains largely pre-commercial and research-intensive. What looks like concentration instead reflects technological constraints: high costs, uncertain development paths, and limited commercial returns. The sector is expanding, but from a very small base. Opening formal inquiries at this stage risks getting ahead of both the technology and its market implications.

Why Concentration Misleads

Even in AI, concentration is more complicated than it first appears. High development costs favor larger firms, but regulation reinforces that dynamic. As compliance costs rise, the ability to meet regulatory requirements increasingly determines which firms can scale. The result is a market structure with a concentrated core and a broader layer of smaller, innovative firms—often linked back to the core through partnerships, licensing, or acquisitions.

Seen in that light, concentration alone is a weak signal of market power. In quantum computing, it is weaker still. Apparent concentration reflects technical complexity, specialized expertise, collaboration, and high-risk investment—not clear evidence of anticompetitive conduct. Commercial maturity and technological capability are not moving in tandem. That does not eliminate the possibility of future competition concerns, but it does weaken the case for early intervention. On this basis, the AGCM’s concerns about barriers to entry may be overstated.

Competition in this setting turns less on firm count than on whether the ecosystem sustains continuous innovation. Treat concentration as a problem, and you risk misreading how competition actually works in an emerging field. Capital clusters around a small number of players not because they control a market, but because they can absorb the costs and uncertainty of long-term development.

Architecture Matters: Interoperability Over Control

The structure of the quantum ecosystem further weakens any inference that concentration will translate into durable market power. Quantum technologies are not developing as vertically integrated silos. Instead, they rely on interoperable, hybrid architectures that combine classical and quantum resources.

Initiatives such as the Munich Quantum Software Stack and platforms like NVIDIA’s CUDA-Q reflect a deliberate move toward hardware abstraction, allowing developers to write code that runs across multiple quantum back ends—from simulators to physical processors. Open-source tools—including the Guppy programming language and the Selene emulator released by Quantinuum—push in the same direction. Where access to hardware remains limited, innovation shifts to the software layer by widening participation.

This separation between software and hardware lowers switching costs and limits the extent to which control over a specific quantum processor can create user lock-in. Even where hardware capabilities remain concentrated, access increasingly runs through cloud-based interfaces and standardized development environments. Cloud platforms act less like gatekeepers and more like integrators of heterogeneous back ends, allowing users to interact with multiple quantum technologies through a single interface.

The quantum cloud does not map neatly onto traditional antitrust frameworks. Several major providers—notably Amazon Web Services (AWS) and Microsoft—act as aggregators, competing to offer broad access to diverse quantum processors. Others, such as IBM and Google, remain more vertically integrated. Platforms like Scaleway’s QaaS unify multiple European quantum modalities under a single cloud-native interface, allowing developers to switch between GPU clusters and quantum hardware without changing their development environment. Vendor-independent middleware, including Q-AIM and Qunicorn, enables translation across different quantum-circuit and result formats, reducing friction when running workloads across competing clouds. The value of the quantum cloud lies in integration, not exclusion.

No Gatekeepers (Yet)

As platforms aggregate more processors, they become more useful—not because switching is costly, but because they reduce the complexity of accessing multiple systems. That dynamic can be pro-competitive. Platform power is constrained by users’ ability to multi-home, by rival platforms offering similar integrations, and by open-source middleware that continues to lower switching costs.

These dynamics extend to system architecture. Research efforts, including middleware developed at the KTH Royal Institute of Technology, aim to decouple applications from underlying hardware, allowing programs to run across platforms with minimal adjustment. IBM’s quantum architecture incorporates vendor-agnostic layers that abstract hardware-specific features, while platforms such as Quantum Rings provide cloud-based access to multiple processors.

Taken together, these developments point to an ecosystem in which interoperability is not incidental, but engineered. By lowering switching costs and enabling multi-homing, they limit any single firm’s ability to convert control over hardware or infrastructure into market power. While still evolving, these dynamics weaken the link between concentration and control that conventional indicators assume. The modular structure reduces the scope for any one firm to internalize the entire value chain and instead promotes interdependence across layers. Emerging open-source approaches reinforce this trend by lowering coordination costs, facilitating benchmarking, and expanding the pool of developers—further complicating efforts to define clear market boundaries.

The AGCM itself acknowledges this structure, noting that the sector “features both global big tech companies providing cloud-based services and small to medium-sized players, often still start-ups, focused on developing specialised technologies and services.” This division of labor is not incidental; it is structurally significant. If control over quantum hardware were enough to secure dominance, one would expect tighter vertical integration and the marginalization of independent actors. The continued presence of specialized firms across multiple layers instead suggests that control remains distributed and that complementarities—not consolidation—are shaping the sector’s development.

This also cuts against concerns about gatekeeping, often framed through a DMA-style logic. For example, Pasqal’s neutral-atom quantum processing units (QPUs) are already accessible across five major cloud platforms, including Azure Quantum, Google Marketplace, OVHcloud, and Scaleway. This kind of multi-platform distribution places quantum hardware “where users already work,” weakening the idea that access will be tightly controlled by a single provider.

At the same time, emerging approaches may further reduce the need for direct hardware ownership. Equal1, for instance, aims to develop quantum processors using existing semiconductor fabrication infrastructure, bringing semiconductor-scale manufacturing to quantum computing. This model challenges the assumption that access requires bespoke, vertically integrated infrastructure controlled by a few dominant players. Instead, it distributes capabilities across different layers of the stack. The result is a fluid and uncertain ecosystem—one that weakens claims that current patterns of concentration will translate into durable market power.

Jumping the Gun on Quantum

None of this dismisses the Italian AGCM’s underlying concern. Early design choices can shape long-term market outcomes. The question is not whether to intervene early, but whether competition-law tools fit a setting where their core assumptions—clear boundaries, stable roles, and identifiable dominance—have yet to take hold.

Intervene too soon, and you risk forcing an ill-suited framework onto a still-forming ecosystem. That framework tends to privilege market structure—counting firms, measuring concentration, and inferring that structure determines conduct, and in turn, performance. But market structure is itself an output of the competitive process. In quantum computing, that process remains in flux across multiple sectors.

A more fitting response may lie in anticipatory governance, rather than extending competition-law reasoning into terrain where its assumptions do not yet hold. Given quantum computing’s enabling nature, concerns around access, control, and conduct may already fall—at least in part—within existing sector-specific frameworks. They do not automatically warrant a competition-law response.

Competition law works best ex post: when markets have formed, conduct is observable, and harm can be demonstrated. Apply it too early, and speculation substitutes for evidence. Worse, you risk slowing markets before they have a chance to emerge.

The post Schrödinger’s Quantum Market: Regulating What May or May Not Exist appeared first on Truth on the Market.

Benjamin Klein’s 1993 article, “Market Power in Antitrust: Economic Analysis after Kodak,” alls this a category error. Hold-up is real; Klein helped define it in “Vertical Integration, Appropriable Rents, and the Competitive Contracting Process” (1978), co-authored with Robert Crawford and Armen Alchian.

But hold-up is not market power. The Court took the framework Klein helped build and pressed it into service for a task it was never meant to perform.

That misstep carries a broader lesson for law & economics. The Court in Kodak relied on sound economic concepts—hold-up, switching costs, lock-in—but aimed them at the wrong legal question. Law & economics demands more than importing good economics into legal disputes. It requires matching the right economic concept to the right legal question. Klein’s contribution lies in doing exactly that—and in understanding both sides well enough to know the difference.

Mistaking Hold-Up for Market Power

Independent service organizations (ISOs) had long repaired Kodak copiers—often at lower cost and higher quality than Kodak. In the mid-1980s, Kodak restricted access to replacement parts, effectively tying parts and service and pushing ISOs out. The legal question: could Kodak, which lacked market power in the equipment market, still wield market power in the aftermarket for parts and service?

The Court said yes—in principle. A firm without power in the foremarket might still exercise power in the aftermarket if buyers face high switching costs, significant information costs, and the kind of lock-in that makes single-brand aftermarkets viable. The dissent, led by Justice Antonin Scalia, took a different view. In a competitive equipment market, prices should adjust to reflect any aftermarket exploitation, protecting buyers ex ante at the point of purchase.

Klein argues that both sides missed the mark—but the majority’s mistake runs deeper. The dissent leaned on a model of perfect competition: fully informed buyers, zero switching costs. That model doesn’t describe real markets, and Klein agrees the Court was right to reject it.

The majority, though, made a different error. It identified a plausible hold-up story and treated it as evidence of antitrust market power.

That move collapses an important distinction. Hold-up appears everywhere: landlords and tenants, employers and employees, manufacturers and dealers. If hold-up equals market power, then routine contract disputes become antitrust cases. That can’t be right.

Klein’s core point turns on timing. As he puts it, “the seller’s market power must be determined at the point in time when the tie-in contract was agreed to, not at the time when a ‘hold-up’ is occurring” (p. 58). The majority focused on whether Kodak could exploit buyers after they made equipment-specific investments. That shows hold-up potential, but says nothing about whether Kodak had market power when buyers entered the arrangement.

Buyers need not predict precise aftermarket prices to protect themselves. They only need to recognize that switching costs exist and that hold-up is possible. From there, they can rely on reputation or contractual safeguards. Hold-up may redistribute surplus from locked-in customers, but it does not restrict output in the equipment market.

Why Kodak’s Tie Looks Like Metering, Not Muscle

If hold-up doesn’t explain Kodak’s tie, what does? Klein points to discriminatory marketing. The record is murky. Kodak claimed the parts tie dated to 1975, but ISOs may have purchased parts freely before the 1985 policy shift. Did customers anticipate the restriction? The evidence is unclear. Even so, Klein argues price discrimination fits the facts better.

The idea traces to Aaron Director and Edward H. Levi’s “Law and the Future: Trade Regulation” (1956). That paper displaced the old “leverage” theory of tying with a metering explanation. IBM tied punch cards to its machines not to extend monopoly power, but to meter usage it could not directly observe. Klein applies that logic to Kodak, with a more tailored mechanism.

Start with two customer segments: self-service buyers, who maintain their own equipment, and purchased-service buyers, who rely on Kodak. Within the purchased-service group, the tie allows further discrimination based on type and urgency of demand. Customers who value fast on-site repair and single-vendor accountability pay more through bundled service pricing. Kodak sets equipment and parts prices with the self-service segment in mind. Service pricing then acts as a residual, extracting more from purchased-service buyers.

The self-service exception does the real work. Why not simply overprice parts and meter that way? Because self-service customers would respond by substituting toward more labor-intensive servicing to avoid high parts prices, distorting the parts–labor margin. By tying service and exempting self-servicers, Kodak avoids that distortion.

This arrangement also reflects customer preference. Purchased-service buyers value bundled accountability. The self-service carveout suggests Kodak could not force unwilling buyers into the bundle. Customers chose among aftermarket options when they bought the equipment. The fact that many chose Kodak’s bundle suggests the arrangement created mutual gains.

The deeper point: price discrimination neither requires nor implies antitrust market power. Most firms face downward-sloping demand curves and can price above marginal cost. Economists often treat that as evidence of market power. Klein rejects that move. A firm’s ability to price above marginal cost given its own demand curve—what he calls “individual pricing discretion”—is not the same as antitrust market power.

Consider branded grocery products. Lester Telser estimated brand-level demand elasticities for orange juice, coffee, beer, and similar goods, finding most fall between 2.5 and 5. That implies markups of 25% to 67% over marginal cost. Later work finds similar results for cereals. By the Lerner Index, these firms appear to have substantial market power. Yet they hold small market shares, face elastic supply from rivals, and operate in markets with free entry. No one treats them as monopolists.

Klein labels this phenomenon “individual pricing discretion.” It is widespread, competitive, and typically benign. Antitrust market power is something else: the ability to raise market-wide prices by restricting output. That distinction matters. It separates conduct that warrants antitrust scrutiny from conduct that reflects ordinary competition.

As Klein puts it:

One would not want to refer to the pervasive examples of price discrimination in the real world as implying that “market power” or “monopoly power” in any relevant economic or policy sense also is pervasive. Instead, all it means is that most firms in the marketplace possess some “individual pricing discretion.”

Why the Lerner Index Leads Courts Astray

What, then, should courts measure? Klein’s answer may look like a detour into measurement theory. It isn’t. If the Lerner Index points courts in the wrong direction, the entire framework for identifying market power needs a reset.

Klein engages directly with William Landes and Richard Posner’s “Market Power in Antitrust Cases” (1981). He doesn’t dispute the math. He disputes the target. Landes and Posner use market share as a proxy for a firm’s own elasticity of demand. Klein argues market share should capture something else entirely: a firm’s ability and incentive to restrict market-wide output.

That distinction does real work. A differentiated product has two components. One is firm-specific: brand loyalty, product features, and other factors that produce a downward-sloping demand curve and allow pricing above marginal cost. The other is market-wide: the firm’s place in a broader market where output interacts with rivals.

A firm can enjoy substantial pricing discretion over its own product and still lack any ability to move the broader market. A branded grocery company may face relatively inelastic demand from loyal customers. But if its market share is small and rival supply is elastic, it cannot raise market-wide prices. That firm has individual pricing discretion. It does not have market power in any sense antitrust law should recognize.

The same logic applies beyond antitrust. Klein illustrates the point with Reuben Kessel’s study of physician pricing. Kessel observed that doctors charged wealthier patients more and inferred that the American Medical Association enforced a cartel. Klein rejects that inference. Doctors price-discriminate because patients face switching costs—familiarity, continuity of care, reluctance to start over—that give each physician some pricing discretion over existing patients. But that says nothing about whether the profession can restrict output and raise market-wide prices.

The Court in Kodak was right to focus on lock-in and switching costs. It erred in treating a firm’s ability to exploit those features as antitrust market power.

From Kodak to Epic: Getting the Question Right

Klein’s distinction has started to surface in doctrine. In Epic Games v. Apple (2023), the 9th U.S. Circuit Court of Appeals articulated a four-part test for single-brand aftermarkets. Plaintiffs must show that aftermarket restrictions were not generally known at the time of purchase, that significant information costs prevented accurate life-cycle pricing, that meaningful switching costs exist, and that standard market-definition principles support the proposed market. If buyers knowingly accepted the terms, courts have no competition problem to solve. The Epic test stays within Kodak’s framework, but it gives operational form to Klein’s insight. The key question is not whether lock-in exists, but whether buyers understood it ex ante.

The framework, however, remains contested. In Surgical Instrument Service Co. v. Intuitive Surgical (No. 25-1372), the Federal Trade Commission (FTC) argued as amicus before the 9th Circuit that when a defendant already has market power in the foremarket, courts need not require proof of the Kodak lock-in factors to define the aftermarket. Klein signed an International Center for Law & Economics (ICLE) amicus brief opposing that position.

The FTC’s approach would apply the Kodak factors only when the foremarket is competitive—and dispense with them when the defendant already has foremarket power. Klein would see that as a familiar mistake: treating lock-in as a substitute for proof of antitrust market power. Retaining customers is not the same as raising market-wide prices.

When Theory Meets the Wrong Legal Question

I find the conceptual distinction persuasive. The empirical questions prove tougher. Klein’s price-discrimination account and the hold-up story both predict higher aftermarket prices for locked-in buyers. They diverge on why Kodak did it: to exploit locked-in customers, or to price efficiently across segments. The data alone rarely settles that dispute.

Klein writes in the Director-Levi tradition, which treats tying as efficient marketing more often than exclusion. Michael Whinston showed in 1990 that bundling can exclude rivals under certain conditions, particularly where firms can commit and deter entry. But Whinston’s model starts with foremarket power. Without it, there are no monopoly profits to protect through tying. On Klein’s account, Kodak lacked that power. Even when such theories apply, courts should still demand evidence of actual market-wide effects.

The real value of Klein’s paper is not its bottom-line conclusion. It is the lesson in translation. The Court in Kodak relied on sound economics but answered the wrong legal question. Klein understood both disciplines well enough to spot the mismatch.

That turns out to be the hard part. Economists often assume the challenge lies in getting the theory right. Klein shows the harder task is knowing which theory fits which legal question. The economics of hold-up was sound. The translation wasn’t.

Further Reading

• Benjamin Klein, “Market Power in Antitrust: Economic Analysis after Kodak,” Supreme Court Economic Review, Vol. 3 (1993)
• Benjamin Klein, Robert G. Crawford & Armen A. Alchian, “Vertical Integration, Appropriable Rents, and the Competitive Contracting Process,” Journal of Law and Economics, Vol. 21 (1978)
• William M. Landes & Richard A. Posner, “Market Power in Antitrust Cases,” Harvard Law Review, Vol. 94 (1981)
• Aaron Director & Edward H. Levi, “Law and the Future: Trade Regulation,” Northwestern University Law Review, Vol. 51 (1956)
• Benjamin Klein & Keith B. Leffler, “The Role of Market Forces in Assuring Contractual Performance,” Journal of Political Economy, Vol. 89 (1981)
• Michael D. Whinston, “Tying, Foreclosure, and Exclusion,” American Economic Review, Vol. 80 (1990)

The post ‘Market Power in Antitrust: Economic Analysis after Kodak,’ by Benjamin Klein appeared first on Truth on the Market.

Competition policy has its own fixation on “washing.” In this context, “washing” describes efforts by undertakings to invoke public policy goals—such as sustainability, privacy, or sovereignty—to justify anticompetitive conduct. The terminology has proliferated. “Greenwashing” is now standard; “privacy-washing” has gained traction; and “sovereignty-washing” has entered the lexicon. Many commentators now treat these risks as widespread and in need of urgent attention.

That concern is overstated. European Union competition law leaves little room for undertakings to defend otherwise anticompetitive conduct by invoking public policy objectives. The risk of successful “washing” strategies remains low under Article 101 of the Treaty on the Functioning of the European Union (TFEU), and almost nonexistent under Article 102 TFEU.

No Gentle Cycle for Greenwashing Claims

The risk of “washing” appears highest in the context of environmental protection and Article 101 TFEU. Recent scholarship frames sustainability as either a “sword” or a “shield” in antitrust proceedings. It operates as a “sword” when firms engage in conduct that risks environmental harm—for example, an agreement not to develop advanced emissions technology. It operates as a “shield” when otherwise anticompetitive conduct purportedly delivers environmental benefits—for example, an agreement to reduce coal output. The “shield” scenario tends to raise the specter of greenwashing: firms cloaking restrictive agreements in sustainability language to sidestep Article 101 TFEU.

But how realistic is that risk? The European Commission’s Guidelines on Horizontal Cooperation Agreements set out a detailed framework for assessing sustainability agreements. The Guidelines elaborate the four cumulative conditions in Article 101(3) TFEU and require, among other things, that claimed sustainability benefits be substantiated, verifiable, and passed on to consumers in a way that offsets the competitive harm. This is no rubber stamp. A framework this demanding leaves little room for undertakings that aim to engage in greenwashing.

The risk shrinks further outside the sustainability context. Claims that privacy or sovereignty concerns justify an otherwise anticompetitive agreement must clear the same Article 101(3) hurdle. The four cumulative criteria track consumer welfare and competitive effects. They leave little space for creative arguments about broader public policy gains. A firm attempting to “privacy-wash” or “sovereignty-wash” an agreement would face the same evidentiary and analytical burdens. If anything, it would confront an even less hospitable doctrinal landscape than in sustainability, where the European Commission has at least offered tailored guidance. In short, the barriers to a successful “washing” strategy under Article 101(3) remain substantial.

No Detergent Strong Enough: Article 102’s Hard Line

If “washing” risks remain limited under Article 101 TFEU, they are vanishingly small under Article 102 TFEU. The Court of Justice of the European Union’s case law makes the point clear. A dominant undertaking can, in principle, justify anticompetitive unilateral conduct in two ways: efficiency defenses and objective justifications.

In theory, a dominant firm may show that its conduct generates efficiencies that outweigh the competitive harm. In practice, that path is steep. The burdens are not symmetrical. An enforcer may rely on a theory of harm grounded in potential anticompetitive effects, but the firm must prove efficiencies in terms of “their actual existence and extent.” To date, no efficiency defense raised by a dominant undertaking has succeeded under Article 102.

Objective justifications fare no better. The Court of Justice has stressed that safeguarding public policy objectives is not the role of dominant firms—that task belongs to public authorities. In cases such as Tetra Pak II, Romanian Power Exchange/OPCOM, and Baltic Rail, firms invoked justifications based on consumer protection, combating tax evasion, and public safety. None succeeded. As with efficiency defenses, no case has turned on a successful objective justification under Article 102.

For a dominant firm contemplating a “washing” strategy, the doctrine offers no real foothold.

No Spin Cycle: Antitrust Isn’t a Washing Machine

I do not suggest that “washing” strategies will never appear. They can be tempting, and some undertakings will try them. In adjacent domains—such as sustainable finance or corporate social responsibility—they may even be widespread. Even in competition law, a few attempts may succeed in a narrow sense by escaping enforcement through de-prioritization. But that is a far cry from showing that firms routinely pervert public policy objectives to restrict competition. The doctrinal structure of EU competition law stands in the way.

Competition authorities, for their part, remain deeply skeptical of public policy justifications for anticompetitive conduct. One can debate whether that skepticism always strikes the right balance. An overly rigid approach carries risks of its own—I have argued elsewhere that a more open stance may be warranted in some cases. Still, claims of systematic green-, privacy-, sovereignty-, or other forms of “washing” lack doctrinal support.

Antitrust is not about to go on a spin cycle.

The post Rinse, Repeat, Reject: ‘Washing’ Claims in Antitrust appeared first on Truth on the Market.

The GitHub issue filed last week against Anthropic’s Claude Code product carried a blunt title: “Claude Code is unusable for complex engineering tasks with the Feb updates.” The author—Stella Laurenzo, an AMD senior AI director—laid out a detailed account of technical decline.

According to Laurenzo, months of session-log data show that from January to March, median “thinking” output fell roughly 70%. The model began bailing out or asking permission to continue about 10 times per day—up from zero before early March. Self-contradictions in its reasoning tripled. API requests spiked, suggesting users had to retry repeatedly to get usable results.

Most striking, performance appeared to degrade during peak GPU-load hours and recover late at night. That pattern offers circumstantial—but suggestive—evidence that quality was being throttled as a function of server demand, rather than any deliberate design improvement.

The issue went viral. Within about 20 minutes of reading it, three of us found ourselves in a lively disagreement about how to understand it through a law & economics lens.

Same Model, Less Thinking

To see why this matters, start with the commercial arrangement. Users subscribe to Claude’s premium tiers—marketed as “Pro” and “Max”—and pay substantial monthly fees, up to $200, for access to the most capable model. The value proposition is simple: you are paying for the system’s best reasoning. Product pages highlight superior performance on complex tasks, extended “thinking” capabilities, and the ability to handle professional-grade engineering and analytical work.

The AMD engineer’s log analysis suggests that, sometime after January, Anthropic quietly reduced the computational resources allocated per query. The model did not become “dumber” in the sense that its weights changed. Instead, it appears to have had less time—and fewer resources—to think through each problem.

A rough analogy: you hire a brilliant consultant to service a client, then limit them to 30 seconds per question instead of an hour—without telling the client the terms have changed.

That possibility raises a cluster of legal and economic questions. Reasonable people, as it turns out, disagree about them quite sharply.

When Optimization Looks Like Deception

The first view among us—call it the consumer-protection hawk position, advanced by Eric Fruits—is that this could present a straightforward deception case under Section 5 of the Federal Trade Commission Act (FTC Act) and its state-law analogs.

The argument runs like this: Anthropic marketed a product with defined capabilities. Users subscribed based on those representations. The company then degraded the product without disclosure. Whether the change was operationally justified or economically rational does not matter. What matters is the gap between what Anthropic promised and what it delivered.

On this view, the log data looks like a smoking gun. If median thinking depth fell 70% and retry rates spiked, then the March product differed materially from what users bought in January—and, from a legal standpoint, no one told them. The Federal Trade Commission’s (FTC) deception standard asks whether a representation or omission is likely to mislead a reasonable consumer under the circumstances. A reasonable consumer paying premium prices for an AI reasoning engine would expect roughly consistent performance—or, at minimum, notice if that performance changed. Ideally, users would also understand, ex ante, how reasoning capability might vary across tasks.

Put differently: Would a user subscribe if they knew reasoning would be throttled when demand peaked?

This position also draws support from European consumer-protection law, which may offer an even more hospitable framework. The European Union’s (EU) Unfair Commercial Practices Directive and Digital Content Directive impose affirmative obligations on providers of digital services to maintain service quality as it existed at the time of contract, absent explicit agreement otherwise. Under that standard, a measurable decline in quality can itself constitute a breach, even without an affirmative misrepresentation.

There is also an economic-waste argument. If degraded outputs force users to re-query to get adequate results, the effective cost of the service rises, even if the subscription price does not. The AMD engineer’s data showed API requests increasing by a factor of 80 from February to March. That is not just friction. For developers on metered billing, it is direct financial harm: more tokens, worse results.

Fruits acknowledges, however, that even a strong unfair or deceptive acts or practices (UDAP) theory raises serious error-cost concerns. As Jonathan Barnett has argued, aggressive Section 5 enforcement that discounts false positives can chill legitimate business conduct. In a fast-moving AI market, the risk of locking in rigid resource-allocation practices is not hypothetical. 

And if the remedy is disclosure—requiring firms to tell users how compute gets allocated—the track record is bleak. As Omri Ben-Shahar has shown, mandated disclosure regimes routinely fail to inform consumers, improve decisions, or change firm behavior. A rule requiring AI companies to publish “thinking-token” budgets would likely join that long list of well-intentioned failures.

For Fruits, then, the existence of a plausible UDAP theory may say more about the breadth of the FTC’s authority than about AI firms’ conduct. If the resource-allocation behavior is reasonable (and it likely is), if disclosure does not change consumer behavior, and if enforcement does not change firm behavior, what exactly does the claim accomplish? The ease with which one can frame ordinary business optimization as deception may itself suggest that Section 5’s UDAP prong has grown too capacious for its own good.

When ‘Worse’ Depends on the Question

The second position, from Kristian Stout, concedes that the consumer-protection theory has some bite, but argues it is much harder to prove than Eric Fruits suggests. The problem is “quality.” In the context of a large language model, quality is not fixed or easily measured. It is highly context-dependent.

Take a simple example. Ask Claude who the first president of the United States was. That query requires essentially no extended reasoning. A model running at full capacity and one running at reduced capacity will produce the same answer. For most routine queries, the difference is likely invisible. Degradation shows up only on complex, multi-step reasoning tasks—the very tasks a smaller subset of users (albeit the highest-paying ones) tends to perform.

That distinction matters for the legal analysis. A deception claim requires evidence that actual consumers were misled about something material. It is not enough to show that internal resource allocation changed. You need to show that real users, in real usage patterns, experienced a meaningful decline in output quality. The relevant question is not whether the model could reason at a lower level, but whether—across the distribution of actual queries—it did produce materially worse results.

An analogy helps. If Albert Einstein offers tutoring services and then spends less time preparing for each session, that is actionable only if the tutoring quality declines. If he had been over-preparing for basic calculus sessions, cutting prep time to a still-adequate level is not deception; it is efficiency. The legal question is whether students got Bill Nye when they were promised Einstein, not whether Einstein spent fewer hours in the library.

There is a useful parallel to an earlier debate. When critics argued that internet service providers (ISPs) used data caps to exploit consumers, Geoffrey Manne and Ian Adams responded that usage-based billing is a standard, efficient practice. It aligns costs with usage and prevents light users from subsidizing heavy ones. The same logic carries over to AI compute allocation. 

A flat-rate subscription that delivers maximum “thinking” tokens to every query, regardless of complexity, resembles an all-you-can-eat buffet. It sounds generous, but it forces everyone to pay a price set by the heaviest users and reduces the firm’s incentive to invest in capacity for marginal, high-complexity queries. As Manne and Adams noted, even Obama-era Federal Communications Commission (FCC) leadership recognized that banning tiered pricing “would force lighter end users of the network to subsidize heavier end users.” Swap in “simple queries” for “light users” and “complex engineering tasks” for “heavy users,” and you have the AI compute-allocation debate in miniature.

None of this forecloses a claim. The AMD engineer’s data suggests that, for her use case—complex engineering work—the degradation was both severe and measurable. But any viable legal theory must grapple with what Anthropic actually represented, to whom, and whether the alleged degradation was material in the context of how those users actually used the product.

Let the Market Sort It Out

The third position, advanced by Dirk Auer, pushes back further. On this view, there is no real problem here, or at least not a legal one.

Firms providing AI services face genuine resource-allocation constraints. GPU compute is expensive and finite. Managing how those resources get distributed across queries is not optional; it is essential. A company that allocates maximum compute to every query, regardless of complexity, would either go bankrupt or charge prices that exclude most users. As Manne and Adams put it in the broadband context, usage-based allocation “is, and has always been, a basic business decision—as it is for every other company that uses it (which is to say: virtually all companies).”

The market-rationalist argument follows naturally. Consumers are not well-positioned to judge how much “thinking” a given query requires. Most users have no idea how much compute their question should consume, and they should not need to. What they care about is output quality. If a firm can deliver satisfactory results while using fewer resources on simpler queries, that is a Pareto improvement: the firm cuts costs it can reinvest, and consumers still get what they need.

On this view, competition—not regulation—provides the relevant discipline. If Claude’s quality degrades enough that users notice and care, they will switch to alternatives: GPT, Gemini, or whatever comes next. The AMD engineer’s viral GitHub issue is itself evidence that this feedback loop works. A sophisticated user identified a problem, publicized it, and put the company under pressure to respond. That is the market doing its job.

This cautionary stance draws support from the uneven track record of consumer-protection regulation in digital markets. The European Union offers a prominent example. Its Digital Markets Act (DMA) promised more competition and better services. In practice, it has often delivered the opposite: degraded user experience, higher costs, and reduced functionality, all in the name of consumer protection. Platforms have stripped features, imposed consent walls, and raised prices.

Importing a similar quality-maintenance mandate into the AI context risks the same result. Regulators would dictate resource-allocation decisions that firms are better positioned to make, while consumers bear the costs of the resulting inefficiencies.

From Fixed Promises to Flexible Performance

What stands out in this disagreement is how quickly three people working within the same law & economics tradition reached sharply different conclusions. That divergence reflects genuinely novel facts. We lack well-developed legal frameworks for deciding when a dynamically allocated computational service has been “degraded” as opposed to “optimized.” This is not a static product with fixed specifications. It is a real-time service, where quality emerges from resource-allocation decisions that can vary by time of day, server load, and query complexity.

The answer likely turns on facts we do not yet have. If Anthropic’s internal documents show that it knowingly reduced quality below represented levels to cut costs, the hawk position gains force. If the changes reflect genuine optimization—preserving output quality for most use cases while reducing waste—the market-rationalist view looks stronger. If the truth falls in between—quality held steady for most users but slipped for power users paying the most and relying on the product the hardest—then we land in the messy middle where consumer-protection law usually operates.

One point seems clear. As AI services embed more deeply in professional workflows, and as the gap between what a model could do and the resources it is given becomes a tunable parameter, these disputes will recur. The FTC, the European Commission, and their counterparts will need to grapple with what “quality” means when capability is a dial, not a fixed attribute—and when the provider’s hand never leaves the knob.

The post Turning Down the Thinking: A Law & Economics Trilogue on AI Throttling appeared first on Truth on the Market.

Against that backdrop, a recent French Senate proposal would add a striking procedural innovation. It creates a presumption that AI systems used protected works whenever there is a plausible indication of such use. In practice, that shifts the burden of proof in civil cases. Plaintiffs would no longer need to show their works were used in training or deployment. AI providers would have to prove they were not.

At first glance, the idea has intuitive appeal. It responds to a well-known problem in AI litigation: information asymmetry. Model developers control the key facts—training data, model architecture, and deployment. Rightsholders and other outsiders often lack visibility into whether and how their works were used. Seen this way, the proposal aims to rebalance evidentiary burdens in light of technological change. Done carefully, that approach could benefit both creators and AI developers.

The details matter. The French proposal collapses distinct categories of evidence into a single trigger for burden shifting. That choice carries significant consequences for how the rule would operate in practice.

Looks Like, Therefore It Is? Not So Fast

There is a meaningful distinction in this context between two types of evidence: inputs and processes, on one hand, and outputs, on the other.

The first category includes documentation about training datasets, internal communications about data ingestion, and technical records related to model development. When a plaintiff can point to this kind of evidence—and when it is reliable—the presumption functions as a fairly conventional procedural tool. It encourages disclosure from the party best positioned to provide it. Courts already move in this direction in other complex cases where one side controls the key information. Seen in that light, the presumption looks less like a departure from established practice and more like a formalization of it.

The second category presents greater difficulties. The proposed French statute allows the presumption to arise from indications tied to the “result generated” by the AI system. That language invites arguments based on output resemblance, stylistic similarity, or probabilistic inference. These forms of evidence differ in kind from evidence about known—or highly likely—training inputs. They are indirect, often ambiguous, and in most cases consistent with lawful behavior.

Modern machine learning systems are designed to capture statistical regularities across large corpora. As a result, they can generate outputs that resemble existing works without memorizing or relying on any specific protected work. That feature is not unique to artificial systems. Human creators operate in much the same way. Authors, musicians, and artists routinely internalize patterns, conventions, and stylistic elements from prior works. In some cases, they build entire careers around recognizable forms of influence. 

Copyright law has long accommodated this reality. It distinguishes between protected expression and the unprotected ideas, styles, and building blocks that circulate through creative fields. Much of what appears “original” already reflects layers of prior influence embedded in derivative or transformative works. Models trained on large corpora may reproduce patterns that reflect this accumulated structure, rather than any particular protected work. Treating similarity as evidence of use risks collapsing that distinction and attributing to AI systems a form of copying that the law has historically declined to infer in analogous human contexts.

Similarity, in this sense, is not a reliable proxy for use. Treating it as such conflates two distinct questions: whether an output resembles a protected work, and whether that work was actually used in developing the system.

When ‘Close Enough’ Is Enough to Sue

From a procedural standpoint, the shift matters because the presumption does more than shape the ultimate finding of liability. It lowers the bar for bringing—and sustaining—litigation. If output similarity can trigger burden shifting, plaintiffs can proceed on relatively weak signals. Once triggered, the defendant must disprove use—a costly and sometimes elusive task. Proving a negative, especially in complex technical systems, is no simple matter.

This dynamic raises familiar law & economics concerns about error costs and litigation incentives. Lowering plaintiffs’ evidentiary threshold increases the risk of false positives. Some claims will move forward even when no actionable use occurred. At the same time, defendants bear the cost of rebuttal, including extensive discovery and technical analysis. Expected litigation costs rise, regardless of the merits.

Those asymmetries shape behavior. Even when an AI provider has strong arguments, the cost and uncertainty of litigation may push toward settlement. Over time, that pressure can produce a de facto licensing regime—not because liability is clear, but because it is expensive to fight. The presumption then operates less as a tool for resolving disputes and more as a mechanism for reallocating bargaining power and rents.

The rule also risks over-deterrence. If output similarity alone can trigger meaningful exposure, developers may avoid training data or capabilities that produce socially valuable outputs. The risk is most acute where expressive works naturally share structures or styles. The line between legitimate generalization and actionable use blurs. The safest path may be to scale back development in ways that reduce legal risk, but also constrain innovation.

Finally, the proposal’s domestic, one-off nature raises concerns about legal certainty. As more countries adopt their own approaches to AI training, regulatory fragmentation is becoming a serious risk—if it is not already here.

Fixing Asymmetry, or Just Moving the Goalposts?

Despite sustained efforts to reconcile competing interests, policymakers still lack a clear approach to the relationship between copyright and generative AI. Nor have they settled on a workable solution that both streamlines access to data for AI developers and secures fair remuneration for rightsholders. As one of us has previously noted, current proposals focus more on the “why” and “how” of compensation than on the “when.”

The French proposal follows that pattern. Its core aim—ensuring remuneration in all circumstances—carries intuitive appeal. But that same objective risks producing significant unintended effects.

None of this undercuts the underlying concern about information asymmetry. It is real, and it sits at the center of AI-related copyright disputes. The harder question is whether this mechanism addresses that problem with sufficient precision.

A more tailored approach would draw clearer lines. It would distinguish between indicia causally connected to training or deployment and those that merely suggest similarity. For instance, the presumption could apply only when plaintiffs identify specific evidence about datasets, ingestion processes, or internal decision-making. Courts could also require a more particularized showing before allowing output-based arguments to trigger burden shifting. Constraints like these would address asymmetry without inviting opportunistic claims.

As drafted, however, the proposal treats all plausible indications as equivalent. That choice creates a wide gateway to burden shifting—one likely to be used most in cases where the underlying inference is weakest. Resemblance, in effect, can stand in for evidence of use, with significant procedural consequences.

More broadly, the proposal reflects a familiar pattern in AI governance. Faced with hard doctrinal questions, policymakers are turning to tools that reshape incentives rather than resolve first principles. That strategy can work when it is carefully calibrated. When it relies on imprecise proxies, it does not clarify the law. It just moves the fight.

The post C’est Presumé: France’s AI Copyright Shortcut appeared first on Truth on the Market.

Two years into the Competition and Markets Authority’s (CMA) enforcement of the Digital Markets, Competition and Consumers Act (DMCC), a similar ambiguity hangs over the United Kingdom’s flagship digital-competition regime. Plenty deserves praise. Plenty invites criticism. More unsettling, the balance between the two may turn less on the statute itself than on who stands at the regulatory saloon door.

On balance, this story looks more encouraging than the one unfolding across the Channel under the European Union’s Digital Markets Act (DMA). The CMA has largely resisted the European Commission’s instinct to fire every barrel at once. At times, it has declined to fire at all. It has also shown a real—if uneven—sensitivity to the costs of intervening in markets that may already function tolerably well.

But those same features expose the regime’s fragility. The DMCC’s better outcomes reflect well-exercised regulatory discretion. And discretion can evaporate with a change in government.

With that caveat in mind, let’s survey the terrain.

The Good: Restraint, for Once

The most important DMCC decision of the past two years is one the CMA did not make. After a lengthy market investigation, it declined to designate Amazon Web Services (AWS) as having Strategic Market Status (SMS) in cloud services.

That outcome was anything but inevitable. The CMA’s provisional findings had described UK cloud infrastructure as a “two-horse race” dominated by AWS and Microsoft, and floated SMS designation for both firms as a remedy. Designation would have triggered the DMCC’s full ex ante toolkit: bespoke conduct requirements, ongoing oversight, and the prospect of pro-competition interventions reshaping how these firms operate in the United Kingdom.

As International Center for Law & Economics (ICLE) scholars argued in comments on the provisional findings, that approach would have been a mistake. Cloud computing remains in the relatively early innings of replacing on-premises IT, which still accounts for the lion’s share of global IT spending. Within cloud, market shares have shifted. Microsoft and Google have made significant gains in recent years, while AWS’s share has held steady or declined.

Competition has not stood still. New entrants and adjacent competitors—Oracle, IBM, Alibaba, Huawei, OVHcloud, and DigitalOcean—continue to vie for customers. The pace of innovation, including in custom silicon, looks far more like a race to innovate than the kind of ossified gatekeeping SMS designation targets. Designating AWS in such a market would risk freezing a snapshot of competition just as the picture changes. The CMA’s March 2026 announcement suggests it reached much the same conclusion, at least for AWS.

That does not mean everything is perfect. Through engagement with Microsoft and Amazon, the CMA secured material commitments on cloud egress fees and interoperability—the two issues most plausibly raising competitive concerns—without resorting to SMS designation. Reasonable observers can disagree about the necessity of those remedies. From an error-cost perspective, however, it is encouraging that the CMA used the least-intrusive tool available. In other words, the CMA appears to deploy SMS designation more cautiously than its European counterparts.

Along similar lines, the CMA did open a separate SMS investigation into Microsoft’s business-software ecosystem, citing concerns that licensing practices spill over into cloud competition. Whether designation here would make sense remains an open question. The broader point is clearer: the CMA’s approach contrasts sharply with the European Commission’s tendency to designate first and ask questions later.

More generally, the CMA’s “4Ps” framework—pace, predictability, proportionality, and process—introduced in late 2024 and formalized in early 2025, has guided a measure of regulatory self-discipline. Combined with the May 2025 Strategic Steer, which placed economic growth at the center of the CMA’s mandate and directed it to focus on harms with “clear and direct UK impact,” the regime has, at least so far, avoided the worst pathologies of compliance-by-checklist that characterize life under the DMA.

The Bad: When Restraint Gives Way to Overreach

If the cloud decision offers the headline good news, the proposed conduct requirements emerging from the Apple, Google mobile, and Google search  SMS designations serve as a reminder: even a measured regulator can talk itself into interventions whose costs may outrun their benefits.

Start with mobile ecosystems. The CMA’s proposed designations cast Apple and Google as a “stable duopoly” with limited competitive constraint. That framing undersells the rivalry between iOS and Android. User churn between the platforms hovers around 20%. Each ecosystem leapfrogs the other on features and security. New users continue to flow into both. These are not the hallmarks of an ossified market.

They point instead to two distinct business models competing head-to-head. Apple offers an integrated, curated system. Android offers a more open, customizable alternative. They compete on quality, price point, and user experience. The remedies the CMA appears to contemplate—mandated interoperability with rival browsers and wallets, choice screens, and restrictions on revenue-sharing with device makers—risk forcing convergence between two deliberately different products.

As ICLE scholars have argued, much of what the CMA labels anticompetitive “self-preferencing” or “lock-in” functions as the connective tissue that delivers security, privacy, and reliability. Consumers value—and pay for—those features. Mandated interoperability carries real, demonstrated costs. The July 2024 CrowdStrike/Microsoft outage, which grounded airlines and disrupted hospitals for hours, was at least partly the product of such mandates. These risks are not hypothetical.

The concerns extend to search. The Publisher Conduct Requirement proposed under Google’s general-search designation would impose granular obligations: opt-out controls for AI training and grounding, plus transparency, reporting, and attribution rules. Those obligations would not apply to OpenAI, Anthropic, Meta, or other generative-AI firms relying on similar public web content. The result is regulatory asymmetry that burdens one firm in a fast-moving market—while tackling issues that sound more in copyright than in competition policy.

The deeper problem lies in the CMA’s analytical drift. At points, it moves from “competition policy protects competition” to “competition policy protects competitors.” Conduct that disadvantages rivals—integrated features, direct answers in search, preinstallation of native apps—often benefits users by lowering search costs and improving relevance.

The empirical record under the DMA should give pause. Banning Google’s vertical-search self-preferencing in the European Union reportedly reduced clicks to hotel websites by 17.6% and direct bookings by as much as 36%, diverting traffic to intermediaries—including Booking.com, itself a designated gatekeeper. Choice screens have produced minimal shifts in search-market share while adding friction for users.

If the CMA imports these mistakes wholesale, it will squander the goodwill its more measured approach in cloud has earned.

The Ugly: It Depends Who’s Holding the Gun

There is a more troubling dimension to this otherwise mixed assessment of the CMA’s performance. The CMA has, on balance, enforced the DMCC more sensibly than the European Commission has enforced the DMA. But the statute does not compel that outcome. The CMA, under the current government’s Strategic Steer and its leadership, has chosen to exercise its discretion in a more measured way.

That choice has a political backstory. In January 2025, then-CMA Chair Marcus Bokkerink stepped down, warning in his farewell remarks that “investors put a price on the risk of political intervention, unpredictability and inconsistency.” His successor, former Amazon UK head Doug Gurr—who recused himself from the cloud designation decision for obvious reasons—arrived alongside an explicit ministerial expectation that regulators would “supercharge the economy.”

The CMA’s “4Ps” framework, the Strategic Steer, the new growth duty, and merger-review reforms all reflect a deliberate political shift toward pro-growth, pro-investment enforcement. Whatever one thinks of that shift, the CMA’s current restraint flows in no small part from who occupies 10 Downing Street.

That should give pause for two reasons. First, it points to internal tension within the CMA. Staff and case teams often propose conduct requirements that look every bit as ambitious as Brussels’ DMA playbook. The board and political leadership have, so far, pulled in a more cautious direction. The mobile and search cases suggest where the agency might land if that balance shifts.

Second, and more fundamentally, the DMCC itself grants sweeping regulatory power. Its architecture—designation, conduct requirements, pro-competition interventions, and fines of up to 10% of global turnover—rivals the DMA in scope. A future government could redirect the regime toward far more interventionist enforcement using the same statutory tools. The statute offers little in the way of a firebreak.

The current consultation on “Refining Our Competition Regime” underscores the risk. Proposals to abolish the independent panel system, expand algorithmic-investigation powers across the broader economy, and create a single-phase market-review tool would extend DMCC-style ex ante features into areas where the case for them remains unproven. Reforms that concentrate decision-making and weaken structural safeguards do not constrain discretion. They amplify it.

Calm in the Street, Tension in the Holster

Reasonable people can disagree about the merits of ex ante digital-competition regulation. But once these regimes exist, how regulators wield them matters enormously. On that score, the CMA deserves real credit. Its first two years enforcing the DMCC have been more proportionate, more targeted, and more open to engagement than the European Commission’s parallel efforts under the DMA.

The decision not to designate Amazon Web Services (AWS), the negotiated commitments on cloud egress fees and interoperability, and the broader discipline imposed by the “4Ps” framework all count as meaningful achievements.

Still, the warning signs are hard to miss. The overbroad mobile designations, the asymmetric publisher conduct requirement, and the pull to replicate the DMA’s missteps on self-preferencing and choice screens all point in the same direction. Expansive enforcement has a gravity of its own.

And the uncomfortable truth is this: what separates the United Kingdom from Brussels right now is not the law on the books, but the discretion of those enforcing it. That is an achievement. It is also a thin reed.

In Leone’s film, the dust settles, the music fades, and no one walks away entirely clean. Two years into DMCC enforcement, the same ambiguity lingers. The sheriff has kept his powder dry—for now. The question is who shows up for the next draw.

The post A Fistful of Discretion: The UK’s DMCC After Two Years appeared first on Truth on the Market.

The consumer welfare standard (CWS) often draws criticism as narrow or inattentive to broader concerns. That familiar critique rests on a basic misunderstanding of what the standard is designed to do.

At bottom, the disagreement concerns what should trigger antitrust intervention. Under the CWS, intervention is warranted when market power distorts consumer demand—when firms restrict output and raise prices, blocking transactions that would otherwise occur. The issue is not what consumers choose, but whether firms have constrained or manipulated those choices.

Many contemporary critiques start from a different premise. They would justify intervention even when consumers freely select among available options. On this view, market outcomes—the dominance of certain platforms, levels of concentration, particular business models—count as problematic not because demand is distorted, but because they reflect preferences that regulators or scholars dislike. Neo-Brandeisian theorists have pushed to replace the CWS with a new legal standard, arguing that the existing framework errs in “orienting antitrust toward material rather than political ends” and should instead serve as a tool for “improving democratic self-government.”

That shift creates a paradox. Proponents frame their approach as populist and democratic , urging that antitrust operate “not solely as part of corporate law, but also as part of political law” in service of the “public interest.” Yet it requires sidelining the preferences of the very consumers it claims to protect.

In practice, this approach replaces the public’s revealed preferences with an abstract vision of what regulators think the “Public Interest” should be. That vision often runs in the opposite direction—correcting, rather than enabling, consumer choices. We call this “anti-consumer welfare antitrust”: anti-consumer in substance, while functioning as a welfare check for laggard competitors, politically salient groups, and other rent-seekers.

The result is predictable. Real consumers end up footing the bill for regulators’ paternalism and for the academic theories that encourage it.

The Anti-Consumer Turn in Antitrust

This critique reflects what might be called the preference-substitution fallacy: the idea that antitrust should not merely correct distortions in market outcomes, but should steer those outcomes toward arrangements regulators or scholars deem superior.

The German Bundeskartellamt’s recent Amazon decision illustrates the point. The agency did not sanction Amazon for harming consumers by showing less relevant or more expensive products. Quite the opposite. Amazon had relegated more expensive products to secondary pages or delisted them altogether.

That enforcement posture carries an implicit premise: consumers should choose higher-priced products because regulators prefer a different distribution of rents among sellers—one that favors less-efficient firms for political, social, or ideological reasons. In that framework, antitrust does not protect the consumer so much as it overrides her.

The distinction is straightforward. Under the CWS, intervention targets distortions in what consumers would otherwise buy. Under an anti-CWS approach, intervention targets the fact that consumers buy what they do. Antitrust shifts from protecting consumer choice to substituting it.

Your Preferences Are Not on Trial

The point comes into sharp focus when we consider what antitrust law actually measures. The framework is deliberately agnostic about the social value of any given product. It does not ask whether consumers should prefer lower-priced alcohol or tobacco, or whether their privacy preferences are sufficiently developed. The inquiry remains constant: are firms restricting output such that prices rise above competitive levels?

Whether the product is bread or bourbon, milk or cigarettes, the analysis does not change. When firms collude or exercise market power to restrict output and raise prices, consumers suffer because transactions that would have occurred at lower prices no longer happen. Cartels involving alcohol or tobacco face condemnation on the same grounds as cartels involving staple goods. The nature of the product does not drive the analysis; the effect on output and prices does.

Antitrust protects the process through which consumers express preferences—and the metrics used to evaluate outcomes: prices, output, and, where appropriate, innovation. It does not police the content of those preferences. That neutrality, however, is under increasing strain. As Francisco Marcos observes, recent enforcement trends have shifted toward structural and noneconomic considerations, departing from traditional consumer-welfare analysis.

Too Popular for Comfort

Once the framework comes into view, the trajectory of many contemporary policy proposals becomes clearer—and more troubling.

Digital markets present the paternalist regulator’s nightmare. Consumers have real choices, and they have voted with their clicks—consistently and at scale. They prefer Amazon to local retailers, Google Search to privacy-focused alternatives, and Apple’s closed, curated ecosystem to the open smorgasbord regulators would impose. They choose convenience and low prices over whatever else regulators think they should value. This is not market failure. It is market success—and that, it turns out, is the problem.

The Digital Markets Act (DMA) reflects this logic. Through mandatory choice screens, limits on self-preferencing, and data-sharing mandates, it aims to reshape patterns of use rather than correct output restrictions or price increases. The goal is to reengineer outcomes regulators dislike—perceived dependence on a handful of large U.S. platforms—even when those outcomes reflect consumer preferences.

Apple, for example, must now allow alternative app-distribution channels and payment systems, despite evidence that many users prefer the integrated App Store. Users who wanted a more open ecosystem already had Android. Google must implement search-choice screens on Android in the European Union—not to remedy a demonstrable restriction on output, but to redirect consumer choice. In effect, consumers themselves become the problem: they have produced what regulators view as an unacceptable outcome—the success of a few firms—without any clear sense of what the “right” number of firms should be.

A similar dynamic appears outside the European Union. In South Africa, the Competition Commission required Google to modify its search results to promote local firms through a dedicated carousel, explicitly prioritizing certain outcomes over revealed user preferences. Authorities have also imposed measures that tilt competition toward local players, including free advertising and training. The premise remains the same: consumers are choosing “wrongly”—favoring foreign-owned products when they should prefer domestic alternatives. 

In each case, the concern is not that firms restrict output or raise prices. It is that consumers keep choosing certain products, platforms, and business models—and that those choices deliver the “wrong” results.

Philistines at the Checkout

The CWS is often defined broadly to include not just price and output, but also quality, service, and innovation. Even so, its focus remains on access to these options—not on judging the preferences themselves. The CWS does not assume consumers choose “correctly” in any normative sense. It takes their preferences as given and asks whether market power has impaired their ability to act on them.

Contemporary proposals increasingly invert that logic. They treat certain outcomes as problematic precisely because they reflect what consumers choose. Intervention rests less on constrained choice than on the “wrong” results—too few firms, too many American or foreign ones, or the “wrong” privacy-insensitive platforms prevailing. Choice screens, for example, purport to expand options. But those options already existed. Consumers could choose a different browser or a more “open” operating system. The real objective is not to create choice in the abstract, but to steer how it is exercised—away from so-called gatekeepers and toward preferred alternatives, often local or homegrown.

Proponents often cast these interventions as populist and democratic. Margrethe Vestager, introducing the DMA, warned that gatekeeper power “threatens our freedoms, our opportunities, even our democracy.”  The underlying premise runs in the opposite direction. This tradition has deep roots. Justice Louis Brandeis himself argued that consumers needed protection from their own tendencies, describing them as “servile, self-indulgent, indolent, ignorant.”

Once intervention rests on the view that consumers choose the wrong products or services, it assumes their preferences cannot guide market outcomes. Consumers become unreliable—philistines who cannot be trusted to organize markets. Policy no longer follows the consumer; it corrects him.

This tension surfaces clearly in “curse of bigness” arguments. Proposals that treat firm size as inherently suspect often proceed without evidence that large firms restrict output or raise prices. They also discount evidence that scale can produce efficiencies, higher wages, and innovation. By treating size as a proxy for harm, these approaches conflate market structure with market performance. They replace consumer outcomes with a normative preference for smaller or more locally distributed firms. That sits uneasily with any serious claim of populism. Neo-Brandeisian and recent populist approaches converge on a shared skepticism of observed market outcomes: consumers overvalue price, convenience, and integration, and undervalue the factors regulators seek to promote.

When Antitrust Turns on the Consumer

An antitrust framework centered on output and prices imposes a real constraint. It requires evidence that firms are restricting output, raising prices, or otherwise harming consumers. That keeps enforcement tethered to identifiable harm and observable effects.

Relax that constraint, and the basis for intervention shifts. Market structure, firm size, or patterns of consumer use can become triggers for enforcement—without any neutral benchmark. As Brian Albrecht and Eric Hovenkamp observe, a framework that tries to weigh competition against democracy, inequality, or firm size inevitably “collapses into subjective speculation”—a wishing well “into which one may peer and find nearly anything he wishes.”

The shift also changes what antitrust scrutinizes. The focus moves from conduct that distorts competitive outcomes to the outcomes themselves. When consumers consistently favor certain firms or business models, their preferences no longer guide analysis—they become the problem.

Antitrust, recast this way, does not protect consumers. It protects regulators and scholars—and their preferred vision of how markets should look—from the consumer’s inconvenient, and often uncouth, choices.

In the end, the question is simple: should antitrust correct markets, or correct consumers?

The post The Nanny State Goes Shopping appeared first on Truth on the Market.

In January, the European Commission opened proceedings to define Google’s duties under the DMA for Android. The focus: how much access third-party AI services should get to features like hotword detection, on-screen content, and audio-output monitoring—capabilities Google currently reserves for its own AI assistants. The Commission has six months to issue a specification decision, and its announcement already signals where it may land.

This marks the first time the Commission has applied Article 6(7) DMA—the interoperability obligation for operating systems—to AI-assistant features. It has already deployed the same provision against Apple. In March 2025, it issued two specification decisions requiring Apple to open iOS connectivity features—near field communication (NFC), Wi-Fi, Bluetooth pairing, and notification forwarding—to third-party devices. In doing so, the Commission developed a narrow “integrity” doctrine that sharply limits when gatekeepers may restrict interoperability on security and privacy grounds.

The key question is whether that doctrine can hold when applied to the more sensitive system access that AI services demand. I argue that the Commission should offer a more robust, explicit account of Article 6(7) for AI-facing features—one that advances the DMA’s aims while accommodating security controls. Otherwise, the DMA risks an awkward outcome: interoperability for an AI assistant’s sensory inputs—what appears on a screen or plays through a device’s speakers—would face a weaker legal safety valve than something like sideloading an app.

The legal basis for the Android proceedings lies in Article 6(7) DMA, which requires gatekeepers to:

… allow providers of services and providers of hardware, free of charge, effective interoperability with, and access for the purposes of interoperability to, the same hardware and software features accessed or controlled via the operating system … as are available to services or hardware provided by the gatekeeper.

Article 6(7) also reaches “hardware or software features” not formally part of the operating system if they are “available to, or used by” the gatekeeper in providing services “together with, or in support of” the operating system.

Article 6(7) in Practice: Apple as Test Case

Article 6(7) DMA is the same provision the European Commission has already enforced—aggressively—against Apple. In March 2025, the Commission adopted two specification decisions under Article 8(2) DMA, directing Apple to implement concrete interoperability measures for iOS and iPadOS.

The first—the “Connected Devices Decision” (DMA.100203)—targeted nine connectivity features Apple had reserved for its own ecosystem, including peer-to-peer Wi-Fi, NFC access in Reader/Writer Mode, background execution for Bluetooth companion apps, automatic Wi-Fi connection, and proximity-triggered pairing. The second—the “Process Decision” (DMA.100204)—required Apple to build a structured, transparent system for handling third-party interoperability requests.

Apple’s experience under Article 6(7) offers a useful preview. It shows both how broadly the Commission reads the interoperability obligation and why its approach has proven controversial.

On the procedural side, Apple created a dedicated engineering team to develop interoperability solutions for new iOS features. It also built a formal request system through its Feedback Assistant. Developers can submit interoperability requests that move through three phases: eligibility assessment, project planning, and development and release. They can also submit technical reference queries seeking documentation on how iOS enables specific features.

Apple publishes an Interoperability Request Tracker and Technical Reference Summaries so developers can follow progress and timelines. For disputes, it set up a two-tier process: an internal Interoperability Request Review Board (IRRB) for initial appeals, followed by external, nonbinding conciliation led by independent experts.

On the technical side, Apple has rolled out several interoperability measures. These include support for Wi-Fi Aware 4.0 for high-bandwidth, peer-to-peer connections; a new NFC API that lets iOS apps use the NFC Controller in Reader/Writer Mode without restrictions on payment-related Application Identifiers; and a Wi-Fi Infrastructure framework that shares network metadata with third-party connected devices. Notably, Apple initially disabled Wi-Fi sync between iPhone and Apple Watch in Europe rather than extend the feature to rivals, before later implementing the framework. 

Other features remain in beta. These include proximity-triggered pairing—enabling AirPods-like, one-tap setup for third-party accessories—and an Accessory Notifications framework to forward iOS notifications.

Even as it complies, Apple is contesting the Commission’s approach. The company has appealed both the Connected Devices Decision (Case T-354/25) and the Process Decision (Case T-359/25) to the EU General Court. It has also challenged aspects of its gatekeeper designation as they relate to Article 6(7).

Apple’s DMA compliance reports underscore that tension. The most recent report describes itself as a “factual record,” not a statement of Apple’s position on the “validity, scope and proper application” of the DMA. It emphasizes that all measures are implemented “without prejudice to Apple’s legal position.” Apple also argues that its preexisting developer programs already go “well beyond the scope of effective interoperability as required by Art. 6(7) DMA.”

The compliance burden has produced real tradeoffs. Apple attributes EU-specific delays for features like AirPods live translation and iPhone Mirroring to DMA interoperability requirements, noting that compliance has consumed “hundreds of thousands of hours” of engineering time.

Apple has also raised security concerns. Discussing its new NFC API, the company notes in its compliance report that it was built “[n]otwithstanding the security concerns Apple has repeatedly highlighted.” It makes similar arguments about alternative browser engines—required under Article 5(7) DMA, which bars gatekeepers from mandating their own browser engine—warning that browser engines “are one of the most common attack vectors for bad actors” and are “constantly exposed to untrusted and potentially malicious content.”

In the International Center for Law & Economics’ (ICLE) response to the Commission’s consultation on the proposed connected-devices measures, Geoffrey Manne, Dirk Auer, and Mario Zúñiga raised parallel concerns. They warned that mandated NFC access could enable skimming attacks and unauthorized transactions; that background-execution requirements create battery-drain and data-collection risks; and that third-party developers may lack the resources to match platform-level security protections. They proposed a risk-based, tiered approach—restricting access to sensitive features while allowing lower-risk interoperability—that the Commission’s narrow integrity framework does not easily accommodate.

These objections point to a real tension in the DMA’s interoperability mandate. Opening platform features inevitably trades off against the gatekeeper’s ability to secure its ecosystem. Article 6(7) recognizes that tension by allowing “strictly necessary and proportionate measures” to protect operating-system integrity. But drawing the line between legitimate security concerns and strategic gatekeeping is the hard part—and one the Commission and courts will have to resolve, first for Apple, and soon for Google.

Android AI as the Next Interoperability Frontier

The opening decision—formally, the Android AI specification proceedings (DMA.100220)—is only a procedural step. It initiates proceedings under Article 20(1) DMA “with a view to the possible adoption of an implementing act” under Article 8(2). The Commission now has six months to issue a final specification.

For now, the decision does not mandate specific measures or resolve the scope of “integrity” under Article 6(7). But its reasoning already signals the Commission’s priorities—and, more importantly, the fact pattern it has chosen to test its interoperability doctrine.

That investigation centers on AI services. Google offers a range of AI capabilities on Android—Gemini, Google Assistant (together, “Google AI Assistants”), Gemini Nano (an on-device AI model), AICore (a system service that manages on-device models), and Android System Intelligence. These services benefit from privileged access to features controlled through the operating system.

The Commission highlights several of those features: hotword detection (voice-triggered activation, as in “Hey Google”); Circle to Search (selecting on-screen content to query); access to on-screen content; access to audio output (what media is playing); and tools that integrate AI services with other apps on the device. In the Commission’s view, third-party AI providers cannot access these capabilities on equal terms.

Google responds that the Android Open Source Project (AOSP) already delivers full interoperability. Because the code is open source, third parties can access and interoperate with the operating system just as Google’s own apps do. Google also points to its developer portal for interoperability requests.

The Commission is not convinced. It notes that many of the relevant features remain available only to apps preinstalled by original equipment manufacturers (OEMs) under Google’s Compatibility Definition Document (CDD) requirements. That leaves user-installed, third-party AI apps effectively locked out.

These proceedings are not about permitting sideloading (Article 6(4)) or allowing alternative app-store listings. Instead, the Commission targets deeper system-level access: voice invocation, screen-content reading, audio monitoring, and inter-app communication channels. These sit at the boundary between the operating system and AI services.

And the Commission is pursuing this under Article 6(7), whose only safety valve allows “strictly necessary and proportionate measures” to protect “the integrity of the operating system.” The provision, notably, does not explicitly mention security.

Testing the Boundaries of ‘Integrity’

This is where the Android AI proceedings start to look like a real stress test of the Commission’s approach to Article 6(7). To be clear, the opening decision does not adopt a definitive reading of “integrity.” It largely restates the Article 6(7)(b) exception and suggests that further guidance may be needed on “exact interoperability solutions,” their technical and contractual design, and the modalities of access.

The more important point is the Commission’s choice of case. It has picked a scenario in which simply reusing its Apple-focused interpretation would be especially exposed.

In the Apple specification decisions, the Commission embraced a narrow view of integrity. It held that “integrity has a distinct meaning from users’ privacy and security” (DMA.100204, recital 82), that “some privacy and security aspects fall outside the scope of integrity” (recital 87), and that integrity “does not allow gatekeepers to impose their own model of security and privacy on third-party services” (recital 87).

It also imposed what amounts to a ceiling rule: an integrity measure “cannot be considered strictly necessary and proportionate if it seeks to achieve a higher level of integrity than the one that Apple requires or accepts in relation to its own services or hardware” (recital 91). And it dismissed the argument that, in some cases, third parties are less trustworthy than the gatekeeper, reasoning that “whether a gatekeeper trusts a third party is a subjective assessment exclusively within the gatekeeper’s control” (recital 93).

That framework was designed for a particular set of features: NFC access, peer-to-peer Wi-Fi, Bluetooth companion apps, notification forwarding.

Now consider what is at stake in the Android AI proceedings: hotword detection (always-on microphone access), access to on-screen content (potentially everything a user sees), access to audio output (capturing what a user hears), and tools that integrate AI services across apps on the device.

At that level of access, the line between “platform integrity” and broader security and privacy concerns starts to collapse.

The Structural Gap Between Articles 6(4) and 6(7)

Article 6(4) DMA—covering app sideloading and alternative app stores—includes two distinct safety valves. The first allows measures to protect “the integrity of the hardware or operating system.” The second, in a separate subparagraph, permits “measures and settings other than default settings, enabling end users to effectively protect security in relation to third-party software applications or software application stores.”

Article 6(7), by contrast, explicitly includes only the integrity valve.

The Commission leans on this alleged asymmetry to construct its legal theory. In the Apple Process Decision, it notes that while Article 6(7) measures are limited to integrity, this “does not exclude” separate Article 6(4) measures that enable end users to protect security in relation to third-party apps. The result is a layered model: Article 6(7) governs feature-access restrictions; Article 6(4) governs app-level security settings; and Article 8(1) aligns the entire framework with the General Data Protection Regulation (GDPR), the ePrivacy Directive, cybersecurity rules, and product-safety law.

That layered answer does not fully resolve the problem—and the Android AI case exposes the gap. Article 6(4) is an app- and app-store provision. Article 6(7), however, expressly covers “providers of hardware and services,” not just app developers.

For the AI features at issue here, the main risk may arise from the interoperability interface itself: a third-party AI assistant with access to on-screen content, audio output, and inter-app integration. That risk may not come from—and be manageable by imposing conditions on—a downloadable app in the Article 6(4) sense.

Treating Article 6(4) as the security backstop for Article 6(7) is therefore underinclusive. It leaves a gap where the most sensitive access patterns sit. The Commission risks giving Article 6(7) a narrower legal safety valve than Article 6(4)—in a context that is at least as sensitive.

AI Interoperability and the Privacy Blind Spot

The Android AI proceedings opening decision makes clear that Article 6(7) interoperability is not privacy-neutral. In the AI-assistant context, the features at issue involve access to some of the most sensitive data streams on a device. Yet, on the Commission’s reading, the integrity exception does not appear to accommodate privacy-protective measures—unless they can be recast as narrow, anti-tamper safeguards.

Android brings that tension into focus. Google relies on secure environments like the Private Compute Core to process AI tasks locally, isolating sensitive contextual data—what users see, hear, and say—from extraction. Apple and Meta have converged on similar architectures: Trusted Execution Environments (TEEs) that provide hardware-level isolation, stateless computation, and cryptographic verification. Apple’s Private Cloud Compute and Meta’s WhatsApp Private Processing reflect the same design logic.

As I have argued, this infrastructure can—and should—be opened to third-party developers. But access should run through the security architecture, not around it. A narrow reading of integrity that forces gatekeepers to bypass their own TEE protections would undermine the very infrastructure that makes safe AI interoperability possible.

The gap shows up elsewhere. The draft joint European Commission–European Data Protection Board (EDPB) guidelines  on the interplay between the DMA and the GDPR largely sidestep Article 6(7). They treat the provision as if it raises no serious privacy or data-protection issues.

As I argued in ICLE’s response to that consultation, that omission matters. Article 6(7) inherently raises questions about compliance with the GDPR and the ePrivacy Directive—questions the draft guidelines leave unanswered.

The Android AI case makes the problem hard to ignore. If interoperability means giving third parties access to on-screen content, audio output, and hotword detection, the claim that Article 6(7) is data-protection-neutral does not hold.

From Standard Protocols to Uncharted Interfaces

Even if someone accepts the Commission’s narrow integrity doctrine in the context of Apple’s tightly controlled stack, it is much harder to transplant it unchanged into Android’s multi-OEM ecosystem. The Commission acknowledges as much in the Android AI proceedings opening decision: Android is largely deployed on third-party OEM devices, OEM customization produces variation across implementations, and those differences affect how Article 6(7) operates in practice.

In Android’s world, features vary by OEM and by device. A specification decision that mandates uniform access to AI features across this fragmented landscape will likely require a more nuanced integrity framework than the Apple decisions offered.

There is also a difference in kind. The Apple cases involved features—peer-to-peer Wi-Fi, NFC, Bluetooth connectivity—that map onto well-established, industry-standard protocols with known risk profiles.

The Android AI case does not. There are no obvious cross-platform standards comparable to Wi-Fi Aware, NFC, or Bluetooth for third-party access to hotword detection, screen-content reading, audio-output capture, or AI-to-app integration. The Commission is not asking Google to implement an existing protocol. It is asking Google to design new interoperability interfaces for capabilities at the frontier of platform engineering.

That shift raises the stakes. The security and privacy implications of these interfaces remain uncertain, even to specialists. The specification task becomes harder. The evidentiary burden on the gatekeeper becomes more difficult to meet—how does one demonstrate the “existence and magnitude” of risks for interfaces that are still emerging or do not yet exist? And the need for a workable integrity framework becomes more urgent.

Reading Article 6(7) in Light of Alphabet

The Court of Justice of the European Union’s recent judgment in Alphabet and Others (C-233/23, 25 February 2025) points in the same direction—though it requires careful handling. The case arose under Article 102 TFEU, not the DMA, and addressed Google’s refusal to make Android Auto interoperable with a third-party EV-charging app.

The Court held that a refusal to grant interoperability may be justified “where to grant such interoperability … would compromise the integrity or security of the platform concerned.” That is not a direct gloss on Article 6(7) DMA. But it does undercut claims that platform security is legally irrelevant to interoperability analysis under EU law.

At a minimum, the judgment supports a reading of “integrity” that remains tied to security where the two are functionally inseparable—rather than one that treats security as largely out of bounds once the analysis runs through Article 6(7).

Toward a Workable Test for AI Interoperability

The Android AI proceedings opening decision does not settle the scope of “integrity,” but it moves the debate into a far more security-sensitive setting. If the Commission carries over its Apple-based reading unchanged, it risks treating Article 6(7) as offering a thinner legal safety valve than Article 6(4)—in a context involving hotword detection, screen content, and audio output, where evidence-based security controls matter most.

The Commission’s own Apple decisions recognize the constraint. Compliance measures must align with the GDPR, the ePrivacy Directive, cybersecurity rules, and product-safety law. They also emphasize that Article 6(7) must be applied consistently with proportionality and the EU Charter of Fundamental Rights.

The final specification will have to make those commitments concrete. That means articulating a clearer, more predictable test for acceptable AI-access controls than the current Apple line provides.

The post Opening Pandora’s Interface: AI Assistants and the DMA appeared first on Truth on the Market.

Last November, the European Commission opened three market investigations  into whether Amazon Web Services and Microsoft Azure qualify as gatekeepers under the DMA—even as it acknowledged that no cloud service provider meets the act’s own quantitative thresholds. That admission deserves more attention than it has received. It is not a procedural footnote. It is a quiet confession that the framework does not fit.

The DMA was always a blunt instrument—an agglomeration of highly heterogeneous companies and products shoehorned into a single, static regulatory regime, united by little more than size, American origin, and political unpopularity in Brussels. No category in the law’s sprawling list of core platform services (CPS) better exposes that incoherence than cloud computing. 

Cloud is not a platform. It is not a gateway. It does not connect millions of consumers to businesses. It is B2B infrastructure. Yet the Commission now presses ahead with bespoke investigations to sidestep the logic—and the thresholds—its own regulation imposes. Understanding why, and what it reveals, goes to the core of what is wrong with the DMA.

Gatekeepers Without Gates

The DMA targets so-called “gatekeepers”—firms that intermediate between end users and business users, like an e-commerce platform connecting buyers and sellers. So what “gate” do cloud services control? None, in any meaningful sense.

Cloud computing is infrastructure. When a bank runs fraud detection, a startup hosts an app, or a manufacturer monitors factory equipment in real time, they rent computing power, storage, and software instead of owning physical hardware. Providers stack and bundle services because integration creates real efficiency gains. The whole is worth more than the sum of its parts, and customers understand that.

This differs fundamentally from the services the DMA was designed to regulate. A social network, a search engine, an app store—for all their differences—act as intermediaries in the relevant sense. They sit between large numbers of consumers and the businesses trying to reach them. The DMA’s unifying theory holds that these services generate network effects that inevitably produce the lock-in dynamics the act aims to address.

That theory is more contested than the DMA’s drafters acknowledged. Identifying network effects in a market does not, by itself, establish that consumers are locked in or that intervention is warranted. Evidence of durable, competitively significant lock-in in digital markets remains weaker than regulators often assume. But even if one accepts the DMA’s premises at face value, cloud does not fit.

Cloud providers do not have 45 million monthly active end users in the European Union because they do not have end users in any meaningful sense. They have corporate clients running workloads. The intermediation logic—whatever its merit elsewhere—does not apply here.

Cloud is not a platform in the sense of online intermediation. That distinction explains why the Commission now investigates companies that, under the DMA’s own logic, should not be investigated at all.

Regulate First, Justify Later

The Commission’s current position was predictable. The DMA’s causality runs backward: gatekeepers exist because the DMA says they do, not the other way around. The Commission identified targets first, then reverse-engineered the justification.

Cloud made the list largely because policymakers deemed it “critical” infrastructure, not because anyone seriously asked whether its B2B model fit a consumer-platform framework. Strip away the technical language, and the logic becomes clear: cloud matters to the European Union’s digital economy; Europe depends heavily on U.S. providers (here and here); therefore, cloud belongs under the DMA. That move gives the Commission ongoing supervisory authority—and, not incidentally, the power to levy significant fines. Whether the framework actually fit cloud appears to have been a secondary concern, if it was a concern at all.

This dynamic reflects the DMA more broadly. It is a political project to discipline large technology firms through a powerful ex ante tool—the spiritual successor to the abandoned “New Competition Tool”—while sidestepping the burden of proof and procedural safeguards that typically constrain enforcement. The focus on American firms is not incidental. As the Draghi report makes clear, Europe’s digital economy lags the United States and China, and the DMA partly channels that frustration. The fact that European cloud providers lost roughly half their market share to U.S. rivals between 2017 and 2022—just as the DMA was proposed and adopted—underscores the industrial-policy undercurrent.

There is a cost to grouping economically distinct phenomena under a single, politically constructed framework. The structure starts to buckle when applied to services it was never designed to regulate. That is exactly what we see here: a regulation that classifies cloud as a core platform service, sets designation criteria cloud providers cannot readily meet, and then forces the Commission to open bespoke investigations to get around thresholds that never contemplated cloud in the first place.

Designation may follow. The Commission has the tools to make it happen. But the contortions required to get there amount to their own indictment of the framework.

A Tipping Point That Never Tips

The market data makes cloud’s inclusion in the DMA even harder to square with the statute’s own logic. Start with the text. Recital 26 explains that the DMA targets markets “prone to tipping”—where a firm’s advantage “could become unassailable” and evolve into something entrenched and durable in the near future. The point is to intervene before tipping occurs, to prevent irreversible lock-in. The implication follows: if a market shows no signs of tipping, the legal and conceptual basis for designation falls away.

The European Union cloud infrastructure market shows no such signs. According to  Synergy Research Group, Amazon Web Services, Microsoft Azure, and Google Cloud together account for roughly 70% of the market. European providers hold about 15%, with a long tail of smaller U.S. and Asian competitors making up the rest. Within the Big Three, no single firm approaches the kind of dominance the tipping theory requires. Estimates place AWS at about 32%, Microsoft’s Azure at 23%, and Google Cloud at 10% (here or here). 

Those figures fall well short of traditional dominance thresholds under Article 102 of the Treaty on the Functioning of the European Union (TFEU). The European Court of Justice has repeatedly indicated that a firm with less than 40% market share is unlikely to be dominant. Market shares here also shift over time. AWS has steadily lost ground since 2021, while Azure and Google Cloud have gained. Google, in particular, has grown through specialized AI- and data-analytics workloads. These are not the dynamics of a market tipping toward a single, unassailable incumbent. They reflect active, ongoing rivalry.

The Commission thus appears to invoke a pre-tipping rationale in a market that has not tipped, shows no trajectory toward tipping, and lacks the structural features that typically produce tipping. That is not precaution. It is the application of the DMA to a market it was not designed to reach, justified by a theory the market’s own data does not support.

Regulating the Same Thing, Three Times Over

Even if one accepts that cloud markets raise legitimate competition concerns—interoperability, switching costs, contractual imbalances—the European Union already has tools to address them. 

Article 102 TFEU prohibits abuses of a dominant position under an effects-based standard: show actual harm, craft a proportionate remedy, and proceed accordingly. Beyond that the Data Act—fully applicable from September 2025—mandates interoperability among cloud providers, bars unfair contractual terms, and phases out egress fees by January 2027. These are targeted, cloud-specific rules that pursue the same objectives the Commission now seeks under the DMA, including through Articles 5(7) and 6 on tying and interoperability.

The result is three overlapping frameworks governing the same market. That is no accident. It reflects a broader problem the European Union only half acknowledges: it regulates not just too many things, but the same thing too many times.

The costs follow predictably—higher compliance burdens, legal uncertainty, and increased litigation. Those burdens fall hardest on smaller European firms, the very companies the regulation claims to support. As Mario Draghi’s 2024 competitiveness report warns, layering redundant frameworks on top of existing tools creates a structural drag on productivity. It is difficult to see what the DMA adds here beyond duplication—and the risk of contradiction.

When ‘Gatekeeper’ Means Whatever You Need It To

The cloud case offers a window into something more fundamental. The DMA’s core aims—rent redistribution from platforms to business users, support for European rivals, and reassertion of regulatory authority over successful private firms—are, at bottom, political. The “gatekeeper” label maps onto no coherent set of technical or economic characteristics. Cloud, voice assistants, and social media appear side by side as core platform services, despite being structurally unrelated. They are bound together by contested theories of tipping, vertical integration, and network effects—and by moralized notions of “fairness”—treated as axiomatic and largely insulated from contrary evidence.

Cloud makes that mismatch unusually clear. The fit is plainly poor. The thresholds are plainly unmet. The tipping logic does not apply. To proceed, the Commission must stretch the framework beyond its design limits—and acknowledge that it is doing so. That candor may be uncomfortable, but it is also revealing.

Article 53 of the DMA requires a review of the regulation by May 3, 2026, including whether the list of core platform services remains appropriate. The AWS and Azure investigations are expected to conclude around November 2026. The review will come first, at least formally creating space to ask whether cloud belongs in the framework at all.

Whether the Commission takes that opportunity is another question. Regulators rarely volunteer to limit their own reach. But the question will have to be asked. Cloud is where the answer proves hardest to defend—and hardest to avoid.

The post Cloudy Logic: The DMA’s Search for a Gatekeeper appeared first on Truth on the Market.

In a previous commentary, I explored the antitrust implications of a potential acquisition of Warner Bros. Discovery (WBD). That uncertainty is now resolved. On Feb. 27, Paramount Skydance Corp. agreed to acquire WBD for roughly $110 billion in enterprise value—$31 per share, all cash.

The merger has already drawn concern from politicians and regulators (see here and here). But antitrust analysis does not turn on headline valuations or deal size. The relevant question is whether the transaction is likely to harm competition in a meaningful way. On that score, the evidence points in the opposite direction.

Fewer Studios, Still Plenty of Competition

Today’s video-content market looks nothing like the distribution-constrained environment that shaped earlier merger reviews. Consumers access content through subscription video-on-demand, ad-supported streaming, traditional cable TV, theatrical releases, and an ever-growing universe of user-generated and short-form platforms.

The competitive set has expanded accordingly. It now includes not only legacy studios but also deep-pocketed technology firms such as Netflix, Amazon, Apple, and Alphabet. These firms operate at global scale and often subsidize content through unrelated revenue streams.

In that environment, a snapshot of market concentration says little. What matters is whether the merged firm could raise prices, reduce output, or slow innovation without losing customers to rivals. Nothing in the available evidence suggests it could.

Yes, the deal combines two content libraries and reduces the number of independent studios. But antitrust law has never treated a reduction in firm count as dispositive. Market structure marks the starting point—not the conclusion.

The relevant question is whether the merger increases the risk of coordinated or unilateral effects. Given strong remaining competitors, differentiated content, and low switching costs, that risk appears limited.

No Foreclosure, No Follow-Up

Unlike some high-profile media mergers, this deal lacks a meaningful vertical component. Paramount does not control a broadband network or a dominant cable distribution platform.

That matters. Without control over key distribution channels, the merged firm cannot plausibly foreclose rivals or raise their input costs by withholding “must-have” content. Courts have repeatedly required a fact-specific showing of both ability and incentive to foreclose. Those elements are missing here, which makes vertical theories of harm largely inapposite.

The deal’s procedural posture reinforces that view. Paramount certified compliance under the Hart-Scott-Rodino Act on Feb. 9, and the statutory waiting period expired without a second request or enforcement challenge.

That outcome does not amount to formal approval, and agencies retain authority to challenge consummated transactions. But it does suggest that enforcers did not identify issues serious enough to warrant deeper scrutiny. It is therefore unsurprising that Federal Communications Commission (FCC) Chairman Brendan Carr expects the merger to clear relatively quickly.

Theory Meets the Cost Curve

Producing high-quality film and television content keeps getting more expensive. Talent costs are rising, production has grown more complex, and global distribution demands more investment. At the same time, revenues face pressure from piracy, audience fragmentation, and competition from both traditional and digital platforms.

Under those conditions, scale can be a feature, not a bug. By combining operations and content libraries, Paramount and WBD may reduce duplicative overhead, improve distribution, and invest more efficiently in new content. Antitrust law credits these efficiencies when they are merger-specific and likely to benefit consumers.

Courts have long emphasized that antitrust analysis must reflect real-world market conditions, not theoretical models. That principle carries particular weight in fast-moving industries like media and entertainment.

As Kristian Stout and Ben Sperry of the International Center for Law & Economics (ICLE) explain:

Video competition today is a cross-platform contest for viewer attention. . . . Policymakers should resist artificially narrow market definitions that exclude realistic substitutes. The central question is whether the evidence supports a likely risk of consumer harm, considered alongside merger-specific efficiencies. Sound review starts with how people actually watch video and what constrains firms in practice, not with legacy categories that no longer describe how the market works.

Much Ado About Not Much

The Paramount-WBD transaction combines two firms operating under significant competitive pressure in a dynamic market. It does not present a clear mechanism for anticompetitive harm, nor does it appear likely to enable the exercise of market power. At the same time, it offers plausible efficiencies that could strengthen competition against larger, well-capitalized rivals.

Ongoing monitoring makes sense as the industry evolves. But based on current evidence, the case for intervention looks weak. An ex ante effects-based approach—focused on likely outcomes rather than structural assumptions—fits the facts here.

The post The Paramount Question Isn’t Paramount appeared first on Truth on the Market.

In January, I published a short post—“Rewriting Antitrust, California Style”—that touched on the inner workings (machinations?) of the California Law Review Commission (CLRC). I flagged concerns about the staff’s recommendations on single-firm conduct. I was hardly alone. The International Center for Law & Economics (ICLE) submitted comments, as did Bilal Sayyed and Tech Freedom; Joe Coniglio and the Information Technology & Innovation Foundation; Daniel Francis of New York University School of Law; and Herbert Hovenkamp, who offered a characteristically concise note on cross-market effects.

My own comments were unusually (for me) brief. I raised a high-level concern about efforts to distance California from federal antitrust law and promised more as the CLRC process unfolded. Time flies. In the interim, two—count ’em, two—antitrust bills have landed before the California Legislature.

The first is California Assembly Bill 1776, laboriously titled in search of an acronym: the Competition and Opportunity in Markets for a Prosperous, Equitable and Transparent Economy (COMPETE) Act. It is a direct descendant of the CLRC recommendations. The Assembly Judiciary Committee advanced the bill April 7 on a strict party-line vote and re-referred it to the Appropriations Committee. Jonathan Nuechterlein—a former Federal Trade Commission (FTC) general counsel—recently analyzed the bill, its prospects, and its defects.

The second is California Senate Bill 1074, the Blocking Anticompetitive Self-Preferencing by Entrenched Dominant Platforms—the “BASED” Act. The acronym tries a bit too hard—and lands a bit obscure. State Sen. Scott Wiener (D-San Francisco) introduced the bill, reportedly with enthusiastic backing (if not drafting) from Y Combinator and Economic Security California—a misnomer, but never mind.

Both bills appear to be based on faith in unconstrained judicial intervention and a general rejection of established antitrust principles–or, at least, a rejection of U.S. antitrust. The BASED Act in particular seems to embody a “neo-Brandeisian” animus towards large tech firms and a rejection of consumer welfare. Neither the facts of federal enforcement experience nor decades of economic learning seem to have made much of an impact.  

Spoiler alert for the impatient reader who, on the one hand, reads my posts here at Truth on the Market but, on the other, cannot quite predict my reaction: I do not care for either bill. Taken together, they look like a recipe for disaster.

The COMPETE Act’s Anything-Goes Approach

AB 1776 is an odd piece of drafting, but its intent to break from established federal antitrust law is expressly stated and runs throughout the bill.

The statement of purpose opens with a sweeping declaration. The bill aims to promote and protect:

…free and fair competition, which is fundamental to a healthy marketplace that protects all trade participants, including workers and consumers, and to an environment that is conducive to the preservation of our democratic, political, and social institutions.

In other words, it protects everyone and everything—from democratic, political, and social institutions (or at least an “environment” conducive to them), to consumers, workers, and “all trade participants.” So much for the Supreme Court’s oft-repeated line, dating to Brown Shoe, that antitrust law protects “competition, not competitors.” Tradeoffs? What tradeoffs?

The rest of the bill—starting with the remainder of the purpose statement and continuing through the substantive provisions—both recites and rejects core federal antitrust principles. A proposed Section 16730 of California’s Business & Professions Code reminds us that “[t]he California Supreme Court has determined that the Cartwright Act is ‘broader in range and deeper in reach’ than the Sherman Anti-Trust Act.” It adds that courts “may consider federal case law as persuasive authority to the extent they find it consistent with California law”—but that federal precedent “is not binding on California state courts.”

That theme—citing and discarding established principles of federal antitrust law—runs throughout.

Take Ohio v. American Express Co. There, the Supreme Court held that courts must consider both sides of a two-sided platform when assessing competitive effects, at least with certain two-sided platforms. Drawing on work by Richard Schmalensee and David Evans, and by Benjamin Klein, Andres Lerner, Kevin Murphy and Lacey Plache, the Court explained that “[e]valuating both sides of a two-sided transaction platform is also necessary to accurately assess competition.” And accurately assessing competition, and the question of whether or not it had been impaired, was the task at hand, not formalistic category distinctions. That holding has sparked debate over how broadly to read Amex: Does it apply only to “transaction platforms”? Does simultaneity matter, and if so, why?

Hovenkamp captures the stakes succinctly: 

Antitrust rarely does cross-market balancing [because] it is hard.  Preventing it even when it isn’t is dumb: like doing cost-benefit analysis by looking only at costs. would you condemn the iPhone camera by looking only at the impact on camera makers, ignoring users of iPhone?

Wherever one lands on Amex, AB 1776 sidesteps the debate. The bill describes—and then repudiates—the Court’s holding (without citation). Harm on one side of a multisided platform “may constitute evidence of a violation,” but liability does not require harm on more than one side, nor any weighing of harms against benefits across sides.

The pattern repeats across doctrines.

Refusals to deal? Under federal law, Aspen Skiing Co. v. Aspen Highlands Skiing Corp. sets a demanding standard for the imposition of a duty to deal—a departure from the default rule that firms should be free to choose their trading partners. That was a sufficient departure from a sufficiently general presumption that the Supreme Court later described it as “at or near the outer boundary of §2 liability.”

Yet AB 1776 treats factors like terminating a prior course of dealing, differential treatment of rivals, or conduct that “makes no economic sense” as neither necessary nor sufficient for a court to impose a duty to deal. They may be evidence of anticompetitive conduct, but they are not required to make a case for court-ordered (and supervised) dealing.

Predatory pricing fares no better. Federal law requires below-cost pricing, likely exclusion, and a plausible path to recoupment. AB 1776 requires none of these. What does it require? The bill leaves that to the discretion of California courts.

Nuechterlein underscores the problem:

Although reasonable people can disagree about the doctrinal details, some such limiting principles are needed for sound competition policy. Then-Judge Stephen Breyer put it best when he observed in 1983 that “the consequence of a mistake here is not simply to force a firm to forego legitimate business activity it wishes to pursue; rather, it is to penalize a procompetitive price cut, perhaps the most desirable activity (from an antitrust perspective) that can take place in a concentrated industry where prices typically exceed costs.”

And the bill does not just relax federal standards—it abandons them:

[It] would impose state-level liability for price cuts that easily comply with the federal standard, and it would impose no alternative limiting principles instead. Without such principles, California would leave it to the subjective intuitions of California judges or juries about whether price cuts are excessive or unfair to rivals. Unlucky defendants in such cases would have to pay treble damages for the rivals’ lost business. And to avoid that fate, well-counseled companies would pull their punches and err on the side of charging consumers more—precisely the anticompetitive outcome that Breyer warned about.

All this follows a familiar script. The bill declares that the Cartwright Act is “broader in range and deeper in reach” than the Sherman Act. It distances California law from U.S. Supreme Court precedent. It cites—then disclaims—market-share and market-power thresholds recognized under Section 2 of the Sherman Act. They may serve as evidence, but they are not required, and the act specifies no substitutes (never mind for monopoly power). 

In short, the COMPETE Act does not merely articulate a clear, plaintiff-friendly standard to replace federal law. Instead, it rejects a range of established limits on liability for unilateral (and, at times, joint) conduct, and invites California courts and juries to go further, all in service of “all trade participants.”

And so much for the consumer welfare standard.

The BASED Act’s Billion-Dollar Gatekeeping

Here, by the way, are some brief but useful comments on SB 1074—by my ICLE colleagues Geoffrey Manne, Ian Adams, Dirk Auer, and Eric Fruits. SB 1074 addresses what might seem a narrower domain: “self-preferencing” by large online platforms. It is arguably narrower than the COMPETE Act, in that it applies only to “self-preferencing” and to “covered providers”—firms that operate large digital platforms. Narrower, perhaps, but not at all narrow. 

“Large digital platforms” are defined in two parts. First, a platform must provide “a digital interface that allows business users or sellers to connect with consumers or other business users.” Second, it must meet a scale requirement: “The platform, at any time in the last 24 months, had an average of 100,000,000 or more monthly active users in the United States.”

That is a large number. Even so, “at any time” means that a short-term spike in usage could sweep more firms into the scope of the bill than one might expect. And notably, the statute keys this threshold to nationwide users, not California users.

Second, the bill limits its reach to “covered providers,” defined as:

…a person or entity operating a platform that meets either of the following conditions: “(A) At any point prior to January 1, 2030, it is owned, controlled, or operated by an entity or person that in the last 24 months has had an average of one trillion dollars ($1,000,000,000,000) or greater in market capitalization. [or] (B) At any point on or after January 1, 2030, it is owned, controlled, or operated by an entity or person that in the last 24 months has had an average of one trillion dollars ($1,000,000,000,000) or greater in market capitalization or private valuation.”

Again, the bill sets a high threshold—this time using an average over a 24-month period, rather than a momentary spike. But it does not require that the platform itself (as opposed to the parent firm) meet that threshold. Nor does it require that the firm hold assets in California at or above that level, or that the line of business in question generates any particular income or has any particular valuation.

Consider Walmart. It is headquartered in Arkansas and operates more stores in Texas than in California. Still, it appears to qualify as a “covered provider,” with a market capitalization (as of April 8) exceeding $1 trillion and a platform—walmart.com—with more than 100 million monthly users. On the bill’s terms, its conduct would be subject to the stipulated prohibitions, whether or not Walmart holds market power in any relevant market.

One small but telling detail: the statute fixes a dollar threshold with no adjustment for inflation. That may sound like nitpicking, but it matters. Try an inflation calculator over a decade or so—especially using late-1970s rates of 9%, 13.3%, 12.5%, and 8.9%. Today’s $1 trillion threshold may not mean the same thing for long.

More fundamentally, as the ICLE comments note, “[t]hese criteria target a small number of firms without grounding in neutral legal principles. This approach subjects identical conduct to different legal standards based solely on firm size and invites strategic behavior.” To that, one might add: without any apparent grounding in the economic literature. The thresholds look either arbitrary or reverse-engineered from the market capitalizations of particular firms.

Even the “progressive” and ambitious CLRC (recall the COMPETE Act from two minutes ago) reached a different conclusion after a multi-year review:

It found that exclusionary conduct can arise in any industry and advised that any reform should apply across sectors. It also declined to recommend abuse-of-dominance provisions, citing concerns about vague and arbitrary thresholds. (internal citations omitted)

When ‘Self-Preferencing’ Means Whatever You Need It To

The focus on “self-preferencing” might sound narrow. It is not. The BASED Act prohibits—“but is not limited to”—a laundry list of conduct that is not generally harmful to competition or consumers. The seven categories of prohibited conduct in proposed Section 16851(a) sweep across a range of vertical integration practices—both within firms and in agreements with third parties—that may often be procompetitive or benign.

The bill offers an affirmative defense, but it comes at a price. Defendants must show that the conduct was both (a) “narrowly tailored, non-pretextual, and reasonably necessary to achieve a procompetitive purpose” and (b) that the “procompetitive benefits and actual effects clearly outweigh the competitive harms in the same market.” That is a demanding standard. For a broad and loosely defined set of conduct—much of it frequently procompetitive—the bill effectively presumes illegality. Defendants may rebut that presumption, but only under a burden more stringent than anything in federal antitrust law or current California law. Meanwhile, plaintiffs—including rivals—could survive motions to dismiss without satisfying established pleading standards for harm to competition or consumers.

Add private rights of action and treble damages, and the incentives become obvious. Expect suits across the spectrum—from meritorious, to questionable, to spurious.

The litigation risk does not stem only from the breadth of these prohibitions, but also from their ambiguity. Once we move beyond clear, limiting cases, the boundaries become hard to discern. For example, one might have a casual or intuitive sense that a firm might unduly favor its own products or services in search results or rankings. If so, an unanalyzed prohibition against the manipulation of “the order of search results or rankings to favor the products or services of the covered provider” might seem straightforward. 

But straightforward compared to what? Why should a firm not promote its own products, so long as it is not materially deceptive and does not violate the Sherman Act in doing so? Even setting that aside, what does “manipulation” mean in practice for a firm that constantly designs, tests, and refines ranking algorithms or models? What is the neutral baseline against which “manipulation” becomes unlawful?

The same sorts of questions arise elsewhere. The bill would prohibit using “transaction data or nonpublic proprietary data collected from a third-party seller to market or develop the products of the covered provider.” Why should such use be categorically unlawful, regardless of the nature of the data or the conditions under which it was obtained?

In addition to the seven categories of prohibited “self-preferencing” under proposed Section 16851(a), the bill adds four additional categories of “independence or interoperability” obligations under proposed Section 16851(b). These are framed as prohibitions, but they operate as affirmative technical and managerial mandates, constraining how platforms collect data, and how they design and use data structures and computational systems.

Consider data portability. The bill would prohibit conduct that “[r]estrict[s] a business user or consumer from obtaining a copy of their data in a useful and portable format.” In limited contexts, that may be sensible. Stated at this level of generality, it is anything but. What counts as “useful”? To what extent would a covered person be liable to make such data useful? What counts as “their data”? Outside simple cases—such as clearly defined “personally identifiable information” (PII), “sensitive PII,” or standardized business inputs—the answers quickly become unclear. For complex data structures, making data “useful” to a particular user may be burdensome or even infeasible.

Similarly, the bill bars platforms from “[r]estrict[ing] a consumer from voluntarily providing data through a covered platform to a third party.” That may sound innocuous. It is not. What data? In what format? To which third parties? And with what implications for privacy and data security?

The bill also imposes interoperability obligations for artificial intelligence (AI), defined in strikingly broad terms, and under what may be the haziest definition of AI I have seen (and I’ve been reading such things since the 1970s): 

“Artificial intelligence” means an engineered or machine-based system that varies in its level of autonomy and that can, for explicit or implicit objectives, infer from the input it receives how to generate outputs that can influence physical or virtual environments.

That definition sweeps in a vast range of systems. The bill attempts a safe harbor: the AI provisions do not apply if the platform “consistently applies a neutral methodology” to both its own and third-party content, and any differences arise solely from that methodology. But the burden of proof rests with the platform. And the bill offers no clear definition of “neutral methodology,” nor any explanation of why a platform should ignore its own commercial interests when designing its systems.

The Economics the BASED Act Forgot

It has long been understood that vertical restraints—and vertical integration more generally—can be anticompetitive under particular facts and circumstances. That much is clear from the Vertical Merger Guidelines jointly adopted by the FTC and the U.S. Justice Department (DOJ) in June 2020 (if only to be summarily withdrawn by agency leadership under the Biden administration), and even from the deeply flawed and controversial Merger Guidelines adopted on partisan lines in December 2023.

Even so, federal courts—and many state courts—have moved away from per se condemnation of vertical integration. That shift reflects decades of enforcement experience and economic learning.

The BASED Act moves in the opposite direction. It does not merely relax standards of proof for certain types of conduct associated with competitive harm. It reverses them. It turns decades of judicial and enforcement experience on their head—and disregards a substantial body of economic literature in the process.

As the ICLE comments put it:

SB 1074 rests on the premise that self-preferencing and vertical integration are inherently anticompetitive. The empirical literature does not support that premise.

Indeed, the empirical literature points the other way. Francine Lafontaine—a former director of the FTC’s Bureau of Economics—and Margaret Slade conducted a comprehensive meta-analysis and found that profit-maximizing vertical integration typically benefits consumers:

In spite of the lack of unified theory, overall a fairly clear empirical picture emerges. The data appear to be telling us that efficiency considerations overwhelm anticompetitive motives in most contexts. Furthermore, even when we limit attention to natural monopolies or tight oligopolies, the evidence of anticompetitive harm is not strong.

Similarly, James Cooper, Luke Froeb, Daniel O’Brien, and Michael Vita conclude:

Because the welfare effects of vertical practices are theoretically ambiguous, optimal decisions depend heavily on prior beliefs, which should be guided by empirical evidence. Empirically, vertical restraints appear to reduce price and/or increase output. Thus, absent a good natural experiment to evaluate a particular restraint’s effect, an optimal policy places a heavy burden on plaintiffs to show that a restraint is anticompetitive.

(FWIW, all four authors are former FTC economists, including three deputy directors and Froeb, who served as director of the FTC’s Bureau of Economics and as chief economist of the DOJ Antitrust Division.)

Work focused on digital platforms reinforces the point. See, e.g., Zhuoxin Li and Ashish Agarwal on Facebook’s acquisition and integration of Instagram and Jens Foerderer and coauthors on platform entry and innovation. 

None of this suggests that vertical integration is always benign. It is not. Nor has anyone seriously argued that it should be per se lawful. The point is more modest—and more important: such conduct does not generally harm competition or consumers. The weight of the economic literature—and not just federal precedent—cuts strongly against the presumption of illegality at the core of the BASED Act.

SB 1074 would instead impose a de facto “guilty-until-proven-innocent” regime. That approach will generate false positives—many of them—condemning conduct that is procompetitive and pro-consumer. Mounting an affirmative defense will be costly and uncertain. At the same time, the presumption of illegality will encourage less efficient rivals to bring opportunistic suits.

Add treble damages and private rights of action, and the effect becomes predictable: heightened litigation risk around ordinary business conduct, including innovation and product improvement. The rational response for covered providers will be caution—excessive caution. That is a recipe for less competition, not more, particularly in dynamic markets where innovation depends on precisely the kinds of integration the bill would deter.

Two Bills, One Big Mistake

Either bill—the COMPETE Act or the BASED Act—would substantially expand antitrust liability for competitively ambiguous and, in many cases, procompetitive conduct. Each rejects long-established substantive and procedural standards of federal antitrust law. More than that, each turns its back on the Supreme Court’s oft-repeated observation that antitrust law protects “competition, not competitors.” (Brown Shoe initially, and, e.g., in many various subsequent cases.) One need not believe federal antitrust law is static—or optimal in all respects—to question the rationale for these alternatives.

Some competitors will benefit. That much seems clear. California courts will likely see heavier dockets. But reasons to expect gains in innovation, competition, or consumer welfare are thin—and often exceedingly so. Meanwhile, the costs will not stay within California. The bills’ interstate reach, through spillover effects, is not just likely—it is effectively baked in.

That prospect has prompted questions about federal preemption and constitutional limits. Alden Abbott notes precedent that affords states latitude to depart from federal antitrust standards, but suggests the COMPETE Act may push far enough to invite dormant Commerce Clause scrutiny. Eric Stock similarly explores limits on state antitrust autonomy in proposals from both California and New York. A Commerce Clause challenge may face long odds, and Jonathan Nuechterlein expresses skepticism about its viability, at least as to the COMPETE Act. Still, he identifies a core tension with federal policy:

…what can keep individual states from unilaterally knocking down the limiting principles that federal antitrust law has erected precisely to promote federal competition objectives?

At what point might Congress step in? It is not clear. The tension, however, is.

Whatever courts or Congress may do, the bills themselves are overbroad and poorly justified. Taken separately—or together—they would upend antitrust as we know it. If enacted, they are far more likely to do harm than good. Their reach also undercuts any claim that they function as contained “experiments” in state policy.

Whatever else they are, these bills do not reflect sober, incremental, evidence-based reform. Once more, and with feeling, California legislators should just say no.

The post California Dreamin’ or an Antitrust Nightmare? appeared first on Truth on the Market.

The recently released 2026 National Trade Estimate Report on Foreign Trade Barriers (NTE) catalogs foreign barriers to U.S. exports, foreign direct investment, and electronic commerce, country by country. It is a long document, and it invites a superficial reading as a grab bag of discrete complaints.

That reading misses the point. Some barriers frustrate but remain manageable. Others matter more. They reshape entire markets, steering outcomes away from competition on the merits. Those are the barriers the United States should prioritize. They impose the greatest economic costs and offer the greatest gains if negotiated away.

This is where Shanker Singham’s framework for anticompetitive market distortions (ACMDs) proves useful. The problem is not simply that governments block imports at the border. It is that they skew domestic markets through favoritism, discriminatory regulation, weak property-rights protections, or state-backed commercial privilege. In Singham’s Growth Commission white paper, ACMDs fall into three pillars: domestic competition, international competition, and property rights.

This post applies Singham’s framework to five major jurisdictions—China, the European Union, India, Indonesia, and Mexico—where ACMDs documented in the NTE impose significant economic harm.

Three Ways to Tilt a Market

The domestic-competition pillar asks a basic question: do firms compete on the merits, or do governments tilt the field through favoritism, incumbent protection, or directed allocation of capital and demand?

The international-competition pillar asks whether foreign firms can compete on reasonably equal terms. Localization rules, discriminatory standards, procurement preferences, and similar measures often push them to the sidelines.

The property-rights pillar asks whether firms can rely on secure legal protection for intellectual property, data, contracts, and investment-backed expectations.

This framework helps distinguish ordinary trade frictions from true market-rigging. A tariff can impose costs without reshaping the competitive order. By contrast, rules that channel procurement to politically favored firms, force technology transfer, or grant regulatory privileges to state-owned enterprises operate differently. They decide winners before competition even begins.

Viewed through this lens, the NTE reveals a clear hierarchy across the five jurisdictions at issue: China, the European Union, India, Indonesia, and Mexico.

China presents the most extensive and systematic ACMD problem. Mexico may pose the most urgent near-term negotiating challenge, because its distortions directly affect North American integration. India and Indonesia both exhibit significant issues with localization, standards, and state intervention.

The European Union differs in form. Many of its measures take the shape of facially neutral regulations. Still, certain digital and standards rules function like ACMDs. They burden foreign firms asymmetrically and, in some cases, weaken property-rights security.

China: When the Whole System Tilts the Field

China remains the easiest case analytically. The NTE’s China chapter describes an economy where industrial planning, state ownership, procurement favoritism, standards policy, and administrative discretion work in concert to advantage domestic firms and sideline foreign rivals. This is not marginal protectionism. It is a competitive system organized around state preference.

The report highlights extensive industrial plans aimed at securing dominance in targeted sectors, continued reinforcement of state-owned enterprises, procurement and tendering practices that favor Chinese-owned or Chinese-controlled firms, and standards processes that limit meaningful foreign participation.

Each of these maps directly onto the ACMD framework. They distort domestic competition by shielding favored firms from rivalry. They distort international competition by denying outsiders equal access. And in some cases, they threaten property rights by weakening the security of technology, know-how, and contract expectations.

China’s scale amplifies the problem. The NTE reports that China remained the third-largest U.S. goods export market in 2025, with $106.3 billion in exports and $414.7 billion in total bilateral goods trade. In a market that large, discriminatory procurement, subsidy-linked overcapacity, and standards favoritism do more than shave off export sales. They shape global supply chains and undercut U.S. firms in third-country markets.

If the United States aims to target the most consequential structural barriers to export growth, China remains the central long-run focus. The challenge is obvious. China’s ACMDs are not discrete policies. They sit at the core of a state-led competition model. That makes them difficult to negotiate away incrementally—and too consequential to ignore.

Mexico: Close to Home, Closer to the Problem

Mexico presents a different picture. It is not a state-capitalist system in the Chinese mold. But in one critical sector—energy—the NTE’s Mexico chapter identifies a serious ACMD problem. Recent constitutional and statutory reforms aim to restore the primacy of state-owned electricity and hydrocarbons firms, Comisión Federal de Electricidad (CFE) and Petróleos Mexicanos (PEMEX), while limiting private participation.

That shift matters. Under an ACMD analysis, Mexico’s energy policy plainly distorts domestic competition. The government is not just regulating the market; it is structuring it to favor its own firms. When the legal framework guarantees CFE a dominant position, prefers it in mixed-investment arrangements, and imposes regulatory burdens on private actors that do not apply in the same way to PEMEX, the state is tilting outcomes by design.

This helps explain why Mexico may warrant the highest short-run negotiating priority. The NTE reports that Mexico was the largest U.S. goods export market in 2025, with $338 billion in exports and $872.8 billion in total bilateral goods trade. Unlike China, these distortions operate within the North American production system and inside the institutional framework of the United States-Mexico-Canada Agreement (USMCA). That makes the economic costs more immediate and the legal tools for negotiation and enforcement more concrete.

The NTE also flags new procurement preferences in the medical-products sector. These rules award extra points to firms that invest in Mexico’s domestic production chain. The scale is smaller than in energy, but the logic is the same. Government demand is being used to privilege local investment and production over best-value competition.

India: Standards as Gatekeepers, Not Just Safeguards

India’s ACMD profile is more mixed, but still substantial. The NTE’s India chapter highlights an extensive set of Quality Control Orders (QCOs) that make Indian standards mandatory across a wide range of products and inputs.

Standards can serve legitimate public goals. But when regulators roll them out opaquely, without consistent notice, and without alignment to international norms, they can operate as market-screening tools. They favor firms already embedded in the domestic compliance system.

That is why India’s QCO regime is more than a technical-barriers issue. It can distort international competition by raising entry costs for foreign producers—especially where certification, testing, and licensing processes are slow, discretionary, or nontransparent.

India’s pharmaceutical and agricultural policies reinforce the ACMD diagnosis. The NTE reports that the Ayushman Bharat program, which covers more than 700 million people, does not reimburse patented medicines. That sharply limits the practical reach of innovative drug suppliers in one of the world’s largest health care markets.

Agriculture shows a similar pattern. Public stockholding and support-price policies distort domestic production and can spill into global markets.

For U.S. negotiators, India is not just a tariff story. It is a behind-the-border competitiveness problem. The goal should not be limited to trimming tariff lines. It should focus on more open standards governance, more neutral reimbursement and procurement policies, and less trade-distorting state support.

Indonesia: Local Content, Global Consequences

Indonesia may be the cleanest textbook case after China. The NTE’s Indonesia chapter details local-content requirements across sectors such as telecommunications, oil and gas, and electricity infrastructure. These are classic ACMDs. They do not merely raise import costs. They require firms to source, hire, or manufacture locally in ways that displace competition on the merits.

Indonesia also relies on export restrictions and domestic-market obligations to advantage preferred downstream users. The NTE notes export prohibitions or restrictions on certain raw materials, along with energy-sector rules that require a share of production to be sold domestically at discounted prices. This goes beyond a conventional trade barrier. It is a direct intervention in input markets that shifts rents toward favored domestic interests.

Indonesia’s market is smaller for the United States than China, the European Union, India, or Mexico. But the distortions are not trivial. When a government leans heavily on localization and domestic-market mandates, even a mid-sized market becomes a high-value target for structural reform.

Recent U.S.-Indonesia trade understandings, as described in the NTE, may deliver incremental improvements. They do not alter the underlying diagnosis.

The EU: Neutral Rules, Uneven Effects

The European Union stands apart from the rest. Many of its barriers do not look like classic cronyism or state capitalism. They emerge from large regulatory programs, often justified in terms of privacy, safety, fairness, or environmental protection. But an ACMD analysis does not end with stated intent. It asks how those rules operate in practice.

That lens brings the EU’s digital regulations into focus. The NTE’s EU discussion notes that the Digital Markets Act’s thresholds capture predominantly U.S. firms, while the Digital Services Act, Artificial Intelligence Act, and Data Act impose extensive obligations affecting trade secrets, training data, proprietary business information, and cross-border data use. The issue is not that the EU regulates digital markets. It is whether those rules systematically burden a concentrated group of foreign firms and weaken the security of legally protected assets.

The EU’s product and sustainability rules raise similar concerns. The NTE criticizes the timing and quality of EU notifications and argues that some measures rely too heavily on EU-specific standards or due-diligence requirements that do not track risk well. When access to a trillion-dollar market depends on navigating uniquely European compliance systems—systems foreign firms have limited ability to shape or challenge—those measures can function as ACMDs, even if they arrive dressed as neutral regulation.

Still, the EU calls for a targeted, not polemical, response. The Office of the United States Trade Representative’s (USTR) EU trade summary underscores the scale of the transatlantic relationship. That makes prioritization essential. The focus should remain on the rules that matter most—digital regulation, standards alignment, and protection of trade secrets—rather than treating the EU model as equivalent to China’s.

From Tariff Tallies to Market Reality

One virtue of the ACMD framework is that it moves the analysis beyond static lost-export estimates. The NTE acknowledges the limits of those estimates. They rest on contestable assumptions and do not aggregate cleanly across sectors. Singham’s three-pillar approach offers a broader lens: how much a given distortion suppresses domestic rivalry, blocks foreign contestability, or weakens property-rights security.

In practice, U.S. analysts could score major barriers along several dimensions—by pillar, by sectoral breadth, and by the degree of favoritism toward domestic or state-backed firms. They could then link those scores to the Growth Commission framework, which predicts that reducing ACMDs should boost productivity and GDP per capita by strengthening market contestability and the returns to investment and innovation.

This exercise would not produce a single, definitive number. But it would do something more useful. It would help negotiators separate the visible barriers from the ones that actually matter.

Picking the Right Fights in Trade Policy

If the United States wants to prioritize trade negotiations intelligently, the 2026 NTE points to a clear ordering. China remains the largest systemic ACMD challenge. Mexico may present the most urgent practical case, as energy favoritism cuts directly into North American integration. India warrants sustained attention for standards, reimbursement, and subsidy distortions. Indonesia remains a textbook localization problem. And the European Union calls for a narrower—but still consequential—strategy focused on digital regulation and standards. The approach to other jurisdictions should likewise turn on the nature of their ACMDs, as documented in the NTE.

That is the payoff of the ACMD lens. The most consequential barriers are rarely the most visible tariffs. They are the behind-the-border rules that determine, ex ante, who can compete and on what terms.

U.S. negotiators should also consider how to create leverage. One option is to use tariffs to press trading partners to reduce their ACMDs (see here, here, and here). Another is to lead by example. The United States can point to its own efforts to reduce ACMDs by dismantling anticompetitive regulations, as outlined in President Donald Trump’s April 2025 Executive Order on Reducing Anti-Competitive Regulatory Barriers.

A mutually agreed reduction in ACMDs—paired with tariff cuts—offers a genuine win-win. It would not just expand trade flows. It would generate dynamic gains in productivity, investment, and growth across the global economy.

The post The Barriers Behind the Border appeared first on Truth on the Market.

Others share that concern. U.S. Sens. Elizabeth Warren (D-Mass.) and Richard Blumenthal (D-Conn.) recently urged the antitrust agencies to investigate so-called “reverse acquihires,” which they argue evade scrutiny and “risk further consolidating the Big Tech industry.”

Skepticism about regulatory arbitrage makes sense. But in the case of acquihires—and related license-and-hire agreements, where firms license a target’s technology while hiring its staff—the concern looks misplaced. Section 7 of the Clayton Act and Section 1 of the Sherman Act target conduct with meaningful competitive effects. Acquihires rarely clear that bar. They center on talent transfers, which makes their structural impact too fleeting for Section 7 and their competitive harms too speculative for Section 1. As a result, most fall outside enforcement.

That gap reflects design, not defect. When agencies face novel conduct, uncertain harms, and resource-intensive enforcement, the better course often is restraint. Acquihires can serve legitimate ends: enabling entrepreneurial exit, preserving labor mobility, and protecting professional reputations in the tech ecosystem. Treating them as presumptively suspect risks destroying real economic value. It also invites agencies to spend scarce resources on cases that current law is unlikely to support.

This piece proceeds from that premise. Acquihires differ from transactions typically policed by antitrust law because they involve the transfer of talent. That distinction carries important implications, especially in startup ecosystems where such deals play a central role. For now, there is little reason to rethink a doctrine that largely treats them as benign.

The ‘Reverse Acquihire’ Panic Meets Reality

Calls for heightened antitrust scrutiny rest on what critics describe as a new evasion tactic: the “reverse acquihire.” 

The theory is straightforward. Big Tech firms may “acquir[e] control of a company’s key assets without acquiring the company itself.” They can do so by licensing a target’s technology while hiring most of its workforce—leaving behind an empty shell. These deals can also be structured to stay below HSR thresholds or to fall outside the statute’s definition of acquiring “voting securities” or “assets.” Firms might rely on talent hires, licensing arrangements, equity transfers, or staged consideration to get there. In short, critics argue that companies design these transactions to exploit gaps in the HSR Act.

The Nvidia/Groq transaction often serves as the leading example. On Dec. 24, 2025, Groq announced a non-exclusive licensing agreement—reportedly worth $20 billion—with Nvidia for its inference technology. The deal also included the migration of Groq founder Jonathan Ross, President Sunny Madra, and other team members to Nvidia to help scale the licensed technology. Even so, Groq told investors and enforcers it would remain independent and continue operating GroqCloud.

Critics, including Sens. Warren and Blumenthal, labeled the deal a reverse acquihire. In their telling, Groq became a vehicle for licensing its proprietary GPU technology, while its commercially meaningful operations shifted to Nvidia. As they put it:

[B]y licensing its technology and hiring its most important employees, NVIDIA has effectively acquired Groq in all but name.

That framing overstates the case. The Nvidia/Groq deal is a poor poster child for expanding antitrust scrutiny. Even if the parties had notified the transaction, there is little reason to think enforcers would have found it anticompetitive on the merits.

As International Center for Law & Economics (ICLE) scholars have argued elsewhere, AI partnerships often provide critical funding for cash-hungry startups whose technology might otherwise stall—especially in a sector where outright mergers can attract scrutiny. The Groq deal fits that pattern. By late 2025, the company reportedly faced layoffs and attrition including the departure of its former chief architect. Its first-generation LPU lagged Nvidia’s current products, and analysts questioned whether its roadmap could close the gap. Against that backdrop, casting the deal as the elimination of Nvidia’s “most formidable rival” requires a heroic counterfactual.

The broader market cuts the same way. AI inference—distinct from training—remains highly competitive. Nvidia and Groq compete with established firms like Google, Amazon Web Services, AMD, Qualcomm, and Intel, alongside specialized startups such as Cerebras, SambaNova, and Graphcore. That competition has driven steep price declines. Inference costs reportedly fell fell by two orders of magnitude between 2023 and 2025—hardly a market tipping toward monopoly.

Some analysts reached a similar conclusion. Rather than chilling competition, the Nvidia/Groq transaction appears to have validated the market for specialized inference chips, boosting valuations and strategic interest in rival firms.

None of this means the deal raised no legitimate questions. But its escape from HSR review does not indict current merger-control doctrine. On any plausible substantive analysis, the transaction likely would have cleared.

You Can’t Buy People—Only Convince Them to Leave Together

Expanding merger-notification rules to capture acquihires runs headlong into a basic reality: talent acquisitions do not actually “acquire” talent.

Commentators often describe acquihires as transactions centered on “acquisition of the talent” of the target firm. But labor is not a commodity—even under antitrust law. Section 6 of the Clayton Act makes that explicit.

The same principle runs through the common law. Employment contracts are unassignable absent the employee’s consent. As Lord Atkin put it in the UK labor-law case Nokes v. Doncaster Amalgamated Collieries Ltd [1940] AC 1014, this “right of choice” marks the line between “a servant and a serf.”

That right extends to personal-service contracts, which courts will not specifically enforce. In the United States, attempts to compel performance raise serious concerns under the Thirteenth Amendment’s prohibition on involuntary servitude. One cannot sell oneself into slavery.

The Restatement Second of Contracts reinforces the point. Section 367 provides that “a promise to render personal service will not be specifically enforced,” and that courts will not enjoin a worker from taking other employment if doing so would effectively force continued service or leave the employee without a reasonable livelihood.

So what, exactly, do firms obtain in an acquihire?

Not human capital, strictly speaking. Instead, firms structure deals that encourage employees to make a coordinated move. The transaction aligns incentives across a complementary team, turning what would otherwise be independent labor-market choices into a predictable, collective outcome.

That distinction matters. If firms do not—and cannot—acquire employees, then the key question becomes what they are acquiring instead. The answer may determine which statutory framework, if any, properly applies.

Section 7 and the Problem of ‘Buying’ What You Can’t Own

Section 7 of the Clayton Act prohibits acquisitions where the effect “may be substantially to lessen competition, or to tend to create a monopoly.” It covers acquisitions of stock or share capital and—after the Celler-Kefauver amendment—assets. At first glance, it looks like a natural tool for challenging acquihires. On closer inspection, the fit is awkward.

Start with the threshold problem: employees are not acquired. Human capital is inalienable. Workers can leave, regroup, or re-enter the market, subject only to narrow—and often unenforceable—constraints. That makes the competitive effects of acquiring a team inherently transient and speculative. Merger doctrine, by contrast, targets structural and durable shifts in competition.

What, then, is actually transferred? Typically, some mix of intellectual-property licenses, equity stakes, and contractual arrangements that coordinate employee movement. Whether any of these qualifies as an “asset” under §7 is not obvious. Courts generally require that the acquired assets carry independent competitive significance—productive capacity that can be deployed against rivals.

A licensing arrangement like the one in Nvidia/Groq may satisfy that test for the underlying technology. But where the deal’s value lies primarily in a coordinated team departure, the asset theory strains. Human capital does not transfer by assignment, and agreements that align employee incentives sit several steps removed from the productive assets §7 was designed to reach.

The second problem is analytical. Section 7 turns on market definition and structure—whether a transaction meaningfully increases concentration in a properly defined market. Acquihires do not slot neatly into that framework. The alleged harm is rarely horizontal consolidation. Instead, it is the neutralization of a nascent rival by stripping away the human capital that drives innovation.

That theory is not foreclosed. Courts have read §7 to reach potential competition and nascent threats. But it forces courts to make difficult predictions about innovation trajectories—precisely where merger doctrine’s structural presumptions offer the least guidance. The earlier the target, the more speculative the counterfactual—and the weaker the case.

A third complication: timing. Acquihires often occur at the earliest stages of a firm’s life, before it has a defined market presence, meaningful revenue, or a clear competitive footprint. The 2023 Merger Guidelines acknowledge the challenge of assessing harm to innovation competition but do not resolve it. Showing a probable substantial lessening of competition in a market the target might have entered—or disrupted—requires constructing a counterfactual that is, by definition, uncertain.

None of this makes §7 irrelevant. It applies when an acquihire crosses the line from a labor-coordination device to a functional acquisition of a competitive entity. The key question is whether the target holds assets that independently constrain competition—and whether the transaction plausibly eliminates that constraint.

The Nvidia/Groq deal illustrates the point. If Groq’s technology is licensed on terms that make Nvidia the effective commercial beneficiary, one could argue the transaction functions as an asset acquisition in licensing form. Reports that roughly 80% of Groq’s engineering team departed reinforce that view. As Noah Bean notes, without the engineers who can implement and iterate on the technology, the license’s value to third parties may drop sharply.

That said, the loss of personnel should carry limited independent weight. It provides context, not the answer. The dispositive question is whether Nvidia’s rivals retain commercially meaningful access to the relevant intellectual property. If they do, the deal looks like a “license plus a hire.” If they do not, the licensing structure may instead operate as a de facto asset transfer.

In short, §7 reaches acquihires that extinguish a firm’s competitive significance—not those that simply hire its employees or license its technology without crossing that line.

Section 1: It’s Not the Hiring—It’s the (Alleged) Killing

Where Section 7 strains, Section 1 of the Sherman Act offers a cleaner doctrinal fit. Section 1 prohibits contracts, combinations, and conspiracies in restraint of trade. It does not turn on the acquisition of stock or assets, nor does it focus on durable shifts in market structure. It targets coordination—agreements that distort competitive outcomes regardless of whether anything formally changes hands. That makes it a more plausible tool for transactions defined less by what they acquire than by what they may suppress.

The threshold requirement is an agreement, and acquihires will usually satisfy it. These deals are not spontaneous migrations of talent. Firms structure them through interfirm arrangements—asset purchases, equity transfers, licensing agreements—designed to move a team together.

The harder question is whether the agreement unreasonably restrains trade. Under the rule of reason, courts typically apply a four-step framework. The plaintiff must first show anticompetitive effects in a relevant market. The burden then shifts to the defendant to offer procompetitive justifications. The plaintiff must respond by identifying a less-restrictive alternative or showing the restraint is not reasonably necessary. Finally, courts balance the effects to determine whether the conduct is, on net, procompetitive or anticompetitive.

In the acquihire context, the most plausible theory of harm is not labor-market coordination as such. It is the elimination of a nascent competitive threat—the suppression of a product, service, or line of innovation that might otherwise have matured into a meaningful constraint.

That theory fits Section 1 more comfortably than Section 7. It directs attention to coordinated suppression of emerging competition, rather than to the acquisition of presently durable productive assets.

Incentive structures within acquihires may inform that inquiry—but only at the margins. Group-transition bonuses, synchronized offers, and retention packages tied to collective participation can turn independent employment decisions into a coordinated outcome. That coordination alone does not violate the law. Firms routinely structure incentives, and employees remain free to decline.

What matters is the link between coordination and competitive harm. Coordinated hiring becomes relevant only if it ties to the suppression of competition in a product or innovation market. Without that link, acquihires reduce to ordinary hiring decisions—conduct antitrust law generally leaves alone. Section 1 scrutiny becomes plausible only where an agreement aims to eliminate a genuine line of rivalry.

That limiting principle tracks the FTC’s concern. The agency does not object to talent mobility as such. It worries about the loss of competitive threats embodied in small, specialized teams. Properly applied, Section 1 can reach that harm—if it materializes.

Where the target’s product has already failed, or the team did not drive any meaningful competitive constraint, acquihires should draw no scrutiny. These transactions do not suppress competition; they redirect talent. Antitrust law should not prop up failed ventures to preserve them as hypothetical rivals.

Before You Ban It, Ask What It’s Doing

Calls to expand antitrust doctrine to capture acquihires—including license-and-hire agreements and so-called reverse acquihires—might carry more weight if these transactions were pure regulatory arbitrage with no redeeming value. They are not.

As Frank Easterbrook famously observed, “Wisdom lags behind the market.” That insight fits here. Acquihires serve several functions that antitrust debates tend to overlook.

Start with the basics of the startup ecosystem. Many ventures are built, financed, and staffed with a clear endgame: a successful exit. Acquisition by a larger firm often provides the most viable path. Founders, investors, and employees routinely plan around that outcome. If regulators narrow that exit channel—including through acquihires—the predictable result is less investment and less innovation.

Acquihires also carry social and reputational value. When a startup is acquihired, its engineers and core team can credibly claim they helped build something worth buying. That narrative carries “significant cultural cachet.” It offers a nonpecuniary payoff that softens the downside of startup risk, encourages entrepreneurial entry, and helps sustain the industry’s talent pipeline. Disrupt that mechanism, and the ecosystem’s incentive structure starts to fray.

They may also facilitate labor mobility. John Coyle and Gregg Polsky, drawing on interviews with Silicon Valley participants, find that highly sought-after engineers often prefer to move via acquihire rather than defect individually. The reason is practical. An acquihire can reduce the risk of informal sanctions—lost investor support for future ventures or social ostracism within tight-knit networks.

Put differently, acquihires do real work. Stretching antitrust doctrine to reach them requires strong evidence of systematic anticompetitive harm. As discussed above, that evidence is thin.

What’s Next?

The instinct to scrutinize acquihires is not misguided. When a transaction strips a firm of its competitive capacity under the guise of a talent deal, antitrust law has a role to play. In that sense, Chair Ferguson’s view that substance should prevail over form reflects a sound principle.

The problem is that most deals do not cross that line. Section 7 of the Clayton Act targets transactions whose competitive significance lies in the acquisition of durable productive assets. That rarely describes deals centered on coordinating the movement of human capital. Section 1 of the Sherman Act, for its part, reaches only agreements that unreasonably restrain trade—a standard most acquihires will not meet.

Regulators thus face a choice.

One option is legal reform—reshaping the statutory framework to better capture these transactions. But that path cuts both ways. Overbroad rules risk deterring beneficial deals, and there is little evidence that competitively significant transactions systematically evade review under current law.

The alternative is to work within existing doctrine, accepting that its limits may reflect design rather than failure. That approach calls for restraint. Agencies should focus on transactions that genuinely eliminate competitive constraints, not those that simply reallocate talent. Antitrust law should not become a vehicle for broader political or ideological agendas.

Either path demands a clear-eyed understanding of what acquihires are—not just what they resemble. Getting that judgment right matters. The costs of error will fall on the innovation ecosystem, entrepreneurial incentives, and ultimately, consumers.

The post Acquihires and Antitrust: When Buying the Team Isn’t Buying the Company appeared first on Truth on the Market.

On its face, the case is narrow. It asks whether a generic drug manufacturer can face liability for inducing patent infringement based on how it markets a product approved under a so-called “skinny label.” The dispute turns on whether Hikma’s conduct plausibly encouraged physicians to prescribe its generic drug for a patented use.

But the Court’s decision to grant certiorari hints at something broader. It reflects a persistent belief that regulators and courts can engineer complex pharmaceutical markets to deliver lower prices—chiefly by speeding generic entry.

That belief misses the mark. Worse, it risks undermining the innovation that produces new therapies in the first place.

Hatch-Waxman’s Strategic Reality

Since 1984, U.S. drug policy has been shaped by the Hatch-Waxman Act—a statute meant to balance innovation and competition. The premise was simple: allow generics to enter quickly after patent expiration to drive down prices, while granting additional patent terms and data exclusivities to preserve incentives for innovation.

That balance has proved elusive.

Instead of a streamlined path to competition, Hatch-Waxman has produced a dense, highly strategic legal environment. Brand manufacturers engage in lifecycle management, including building portfolios of follow-on patents. Generic firms exploit regulatory mechanisms such as the 180-day market exclusivity period. Both sides lean heavily on litigation and settlement strategies—most notably so-called “pay-for-delay” agreements—to shape when products reach the market.

The result is not a tidy story about delayed competition. It is a complex, litigation-heavy ecosystem that often looks nothing like what Congress envisioned.

Many observers call this a policy failure. A better reading sees something more basic: the predictable outcome of trying to design, ex ante, the optimal structure of a dynamic, uncertain market.

The Fatal Conceit, Now in Pill Form

Economist Friedrich A. Hayek warned against what he called the “fatal conceit”—the belief that policymakers can design complex social systems that depend on knowledge they do not possess.

Pharmaceutical innovation is exactly that kind of system.

Drug development is uncertain, decentralized, and constantly adapting. Firms invest billions without knowing which research paths will succeed, which therapies will prove effective, or how future discoveries will reshape the competitive landscape. Some of the most valuable advances—including new uses for existing drugs—emerge only after years of follow-on research.

No legislature can see that far ahead. No statute can pre-calibrate the right balance between innovation and competition.

Yet Hatch-Waxman tries to do just that.

The statute assumes Congress can specify when generic entry should occur and how much protection innovators need to sustain investment. In a system defined by dispersed, evolving knowledge, that assumption collapses. Firms adapt. They arbitrage incentives. Legal rules become just another input into strategic behavior.

The rise of so-called “patent thickets,” pay-for-delay settlements, and prolonged litigation timelines are not aberrations. They are the predictable result of imposing a designed framework on a system that resists design.

Skinny Labels, Fat Problems

Hikma v. Amarin makes sense only against this backdrop.

Amarin’s drug, Vascepa, was first approved for a narrow use. The company later invested heavily to identify a new cardiovascular indication—one that significantly reduces the risk of heart attack and stroke. That second use, now the drug’s primary clinical value, reflects exactly the kind of post-approval innovation the patent system is meant to reward.

Hikma plans to market a generic version using a “skinny label” that omits the patented indication. Amarin argues that Hikma’s broader communications and marketing still encourage physicians to prescribe the drug for that protected use.

The legal question is whether that conduct can support liability for induced infringement.

Petitioners ask the Court to adopt a more rigid rule—one that would effectively shield generic manufacturers from liability so long as their labeling formally excludes the patented use.

That rule may look clean on paper. But it repeats the same mistake embedded in Hatch-Waxman: the idea that formal legal categories can stand in for context-specific analysis of real-world behavior.

Inducement doctrine does not work that way. It is inherently fact-driven. It asks what firms actually do—not just what they say in regulatory filings. Replace that inquiry with a bright-line safe harbor, and the system does not get simpler. It gets distorted.

You Can’t Generic Your Way to Innovation

At bottom, Hikma rests on a familiar policy instinct: faster generic entry is the main fix for high drug prices.

That instinct is incomplete.

Generic competition lowers prices—but only after innovation has done its work. It does not produce the next generation of therapies. Weaken the mechanisms that let innovators capture the value of their discoveries, and you risk cutting the investment that makes future competition possible.

A more durable path to lower prices runs through innovation, not just generics. It means more drugs competing within therapeutic classes—and entirely new classes that reshape treatment options.

That kind of competition expands patient choice and puts downward pressure on prices over time.

But it depends on a legal framework that reliably protects returns to risky, uncertain research—especially in areas like post-approval innovation, where the benefits often emerge only after initial approval.

The Court Passes on the Real Issue

If the Supreme Court wants to address the structural drivers of pharmaceutical innovation, it does not have to look far.

For years, litigants have asked the Court to clarify its fractured, unpredictable patent subject matter eligibility (SME) doctrine under § 101. Those petitions go to the core of the innovation economy.

The Court has repeatedly declined to take them.

That choice carries consequences. SME doctrine now creates acute uncertainty for diagnostic technologies—an area central to modern medicine. Many cutting-edge therapies, especially in oncology, rely on companion diagnostics to identify which patients will benefit. When patent protection for those diagnostics is unclear or unavailable, investment predictably falls.

Clarifying—and where appropriate, expanding—patent eligibility would do far more to promote innovation, and ultimately competition, than continued tinkering with the timing of generic entry.

Instead, the Court has opted to focus on a narrow dispute over induced infringement under Hatch-Waxman.

The Blueprint Fallacy

The deeper problem is not doctrinal. It is conceptual.

Efforts to manage drug pricing through ever more intricate legal and regulatory schemes reflect an overconfidence in policymakers’ ability to design outcomes in systems they cannot fully understand. The premise is that the right mix of rules can deliver both lower prices and optimal innovation.

Hayek warned against that kind of confidence.

Markets are not machines to be engineered. They are discovery processes, shaped by dispersed knowledge and constant adaptation. Legal frameworks can support those processes. They cannot substitute for them.

Hatch-Waxman—and the policy mindset behind it—tries to do exactly that.

A Missed Opportunity

The Supreme Court’s docket is limited. Every case it takes signals what it thinks matters.

By choosing Hikma v. Amarin, the Court has opted to address a narrow question that tracks prevailing concerns about drug pricing—while leaving unresolved deeper uncertainties in patent law that shape innovation across the entire economy.

That choice is revealing.

The real problem is not that generics enter too slowly. It is that the legal framework governing innovation has grown unstable, complex, and increasingly detached from how innovation actually happens.

Fixing that problem will take more than incremental doctrinal tweaks. It requires recognizing the limits of centralized design—and recommitting to the principles that let decentralized innovation work.

The post The Fatal Conceit of Cheap Drugs appeared first on Truth on the Market.

Panic over doomscrolling, brainrot, gambling, pornography, online predators, and minors’ interactions with AI chatbots has fueled a familiar policy response: calls to age-gate the internet, social media, and apps.

The ASAA fits squarely in that trend. The bill has cleared the U.S. House Energy and Commerce Committee and now heads to the full House for consideration. It mirrors several state-level proposals. The ASAA would require Google Play and Apple’s App Store to verify the age of all users using “commercially reasonable methods,” and would bar minors unless they have parental consent.

Some of this will sound uncontroversial. Lawmakers have long required age verification to buy cigarettes or alcohol, or to enter casinos and strip clubs. More than 20 states now impose similar requirements on pornographic websites. Few object, in principle, to reasonable safeguards that protect minors from harmful or inappropriate content.

The ASAA goes much further.

It would expose all app-store users to new privacy and security risks, while saddling even developers of age-appropriate content with compliance burdens. The likely result: less competition and less innovation in the app economy. At the same time, the bill would do little to empower parents or meaningfully improve protections for children.

Put simply, the costs outweigh the benefits.

From Parental Choice to One-Size-Fits-All Mandates

As a baseline, society assigns parents primary responsibility for shielding children from harmful or inappropriate content. That principle hasn’t changed since earlier debates about excessive television use. Monitoring a child’s online activity can be difficult, and kids often push back—arguing that limits on messaging or social media will cut them off from their peers.

Even so, parents already have tools that offer more control than television ever did.

Apple, for example, gives parents a robust set of controls at the device level. On an iPhone, they can limit screen time, restrict website access, require approval for app downloads, block explicit content, and confine messaging to approved contacts. Parents can set these controls during device setup and adjust them at any time. Google offers similar functionality. Beyond that, a growing ecosystem of standalone parental-control tools exists, alongside controls built into apps, social media platforms, video games, internet service providers, and websites.

These tools already deliver as much—or more—control than the ASAA. The key difference lies in who decides. Existing tools rely on parental opt in. The ASAA would replace that flexible, choice-based system with a one-size-fits-all regime that requires parental approval at every stage.

It would also come with real costs. The ASAA would force app stores and developers to collect and process sensitive data on both minors and adults. That shift would raise compliance costs and expand the attack surface for hackers and identity thieves—putting all users at greater risk.

The Hidden Costs of ‘Just Verify Age’

The ASAA would require app stores to sort every user into one of four age brackets—“young child” (under 13), “child” (13-15), “teenager” (16-17), or “adult” (18+)—using a method “reasonably designed to ensure accuracy.” Get it wrong by even a year, and firms face enforcement by the Federal Trade Commission (FTC), along with steep financial penalties.

Proponents argue that tools like biometrics, blockchain, and AI make age verification both accurate and privacy-preserving. Experts dispute that claim. It also overlooks the litigation risk firms face when those systems fail. Others suggest payment systems like Apple Pay as a workaround. That, too, falls short. Not everyone uses these services. Even those who do must still submit sensitive documents to verify their age.

The result: millions of Americans would face a choice between handing over personal data or losing access to apps altogether. That includes the roughly 19% of Americans who lack a credit card.

Compliance would force app stores to collect, process, and store large volumes of sensitive data. That shift would divert resources away from product development and meaningful security, privacy, and anti-fraud measures. It would also create centralized troves of personal data—prime targets for hackers and foreign adversaries.

These risks are not hypothetical. As Sarah Forland and Prem Trivedi of the Open Technology Institute note:

In July 2025, hackers exposed 13,000 selfies and photo IDs used to verify account holders from the Tea Dating Advice app. In October, Discord found that 70,000 users may have had their government-ID photos exposed; they were submitted as part of the platform’s age-gating process.

App developers would feel the impact even more acutely. Large firms can spread fixed compliance costs across massive user bases. Smaller developers cannot. More than 90% of apps on Apple’s App Store came from small businesses as of 2022.

The ASAA does not require developers to collect age-verification data directly. But it does require app stores to pass along age-category “flags” for developers to store and process—whether they want them or not. Many developers lack the infrastructure to handle that data securely, especially if their apps serve general audiences and were never designed with age segmentation in mind.

The burden grows for apps with younger users. If a user falls under 13, developers must comply with the Children’s Online Privacy Protection Act (COPPA). That means building compliance systems, modifying algorithms, and collecting identifying information linking children to consenting parents—or excluding younger users altogether.

That outcome would sweep in even low-risk apps—calculators, fitness trackers, weather tools. It would not matter whether parents approve of their children using them.

Noncompliance carries legal, financial, and reputational risk. Compliance carries its own costs. Trusted Future (2025) estimates that a similar Texas law could impose up to $80,000 in compliance costs per small business. Evidence from the European Union’s General Data Protection Regulation (GDPR) points in the same direction: disproportionate burdens on small firms, coupled with high demand for scarce—and expensive—compliance professionals.

The ASAA would also shrink the app economy’s user base. Friction from age verification—time, paperwork, and data disclosure—will deter users. Fewer users mean less data. Less data means weaker products. Courts in antitrust cases have recognized that limited access to user data can prevent firms from reaching the “minimum efficient scale” needed to compete, even with superior algorithms.

Some compliance burdens may make sense for apps aimed at children or those hosting age-inappropriate content. The ASAA does not draw those lines. It applies across the board—even to low-risk apps.

That breadth raises constitutional concerns. A federal judge, reviewing a similar Texas law, likened it to requiring “every bookstore to verify the age of every customer at the door and, for minors, [requiring] parental consent before the child or teen could enter and again when they try to purchase a book.” The court blocked the law as an “exceedingly overbroad” restriction on protected speech that failed the “least restrictive means” test.

The ASAA would likely face similar First Amendment challenges. In the meantime, businesses would still bear the costs—possibly for years—while litigation runs its course.

All of this, despite existing tools that already give parents meaningful control. A narrower, better-targeted law could address genuine risks to children without imposing sweeping costs or raising serious constitutional problems.

A Bad Bargain for Everyone

Every law involves tradeoffs. Minimum-wage laws, for example, can reduce employment, business formation, and overall economic activity, while raising prices for goods and services. Many jurisdictions accept those costs in exchange for a guaranteed wage floor.

The ASAA presents a far less compelling bargain.

Its costs—heightened data-security risks, reduced innovation, and heavier compliance burdens—would fall on parents, children, app stores, and developers alike. The promised benefits, by contrast, remain thin. The bill would not meaningfully strengthen parental control or materially improve protections for children.

That is not a tradeoff worth making.

The post A Cure Worse Than the Scroll appeared first on Truth on the Market.

No one seriously disputes that naked price-fixing and horizontal market-division schemes remain unlawful. But not every agreement among rivals amounts to a cartel. In innovation-driven sectors, collaboration often reduces risk, combines complementary assets, and enables new products and productive capacity that would not emerge nearly as quickly through atomized action. Law & economics scholars have long recognized this point, and it should anchor any new guidance.

AI provides a particularly useful test case. Building and deploying advanced systems requires vast, specialized inputs: semiconductors, cloud capacity, engineering talent, model-evaluation tools, cybersecurity safeguards, privacy-preserving techniques, land, electricity, transmission access, cooling systems, and sometimes shared technical standards. In that environment, antitrust overdeterrence can be as harmful as underenforcement. Guidance that treats coordination with reflexive suspicion will raise costs, slow deployment, and weaken dynamic competition. Sensible safe harbors and administrable rule-of-reason principles, by contrast, can promote innovation without giving cover to true cartel conduct.

That is the core point. Updated competitor-collaboration guidance should make clear—early and often—that collaboration aimed at expanding innovation, infrastructure, interoperability, privacy, and safety usually promotes competition. The law should target agreements that suppress rivalry, not those that make rivalry more effective.

The Danger of Seeing Cartels Everywhere

Law & economics has long emphasized error costs. In the collaboration context, false positives can be especially damaging, because many benefits of cooperation are dynamic and difficult to observe in real time. When the government deters a joint research project, a shared testing environment, or a standards effort, the harm rarely appears as a visible price increase or a smoking gun. It shows up later—as a product never built, a safety protocol that never matured, or a facility that came online months or years too late.

That concern carries particular weight in AI markets, which remain unsettled. Product boundaries are fluid. Entry occurs across multiple layers of the stack, from chips and cloud services to model development, fine tuning, and application-layer deployment. Firms that look like rivals in one dimension may act as complementors in another. A static antitrust lens can easily misread value-creating coordination as competitive harm. Speculative theories should not outweigh real-world evidence, and policy should not discourage the productive use of new technologies merely because they are new. Those principles should guide any serious competitor-collaboration inquiry.

The FTC and DOJ should also avoid vague guidance that recites familiar warnings about spillovers and information exchange. Businesses already understand that naked collusion is illegal. What they need is assurance that enforcers will not second guess procompetitive collaboration simply because independent action seems preferable in theory—even when it would be slower, costlier, and less effective in practice.

A broader institutional point reinforces this caution. Antitrust enforcers are not central planners. They lack the information needed to determine, ex ante, which combinations of technical assets, engineering teams, compute resources, and physical infrastructure are necessary to bring new technologies to market. Guidance should reflect that reality. It should target clear anticompetitive abuse without assuming regulators can outperform decentralized business judgments about how to innovate. Government efforts to “perfect” markets—especially during periods of rapid technological change—risk falling prey to the Nirvana Fallacy and making matters worse.

The Case for Letting Firms Build Together

The strongest case for new safe harbors lies in R&D. Research joint ventures can internalize spillovers, spread risk, reduce duplicative fixed costs, and combine specialized know-how that no single firm possesses in sufficient measure. Those benefits grow in AI and other nascent technologies, where research is expensive, failure rates are high, and the social value of success can far exceed what any one participant can capture.

The FTC and DOJ should say so clearly. Bona fide R&D collaborations should ordinarily receive rule-of-reason treatment and, in appropriate circumstances, safe-harbor protection. A well-designed safe harbor would cover collaborations devoted to research, testing, validation, benchmarking, or precommercial engineering, so long as participants remain free to compete independently downstream and any restraints are reasonably related to the venture’s legitimate objectives.

This would not break new ground. Congress recognized the value of collaborative research in the National Cooperative Research and Production Act, which reduces litigation risk for qualifying ventures and encourages innovation. The agencies should build on that foundation, not treat modern AI collaboration as uniquely suspect.

None of this means every AI partnership is benign. A joint venture can still mask collusion if its restrictions exceed what the project requires, if it facilitates price or wage coordination, or if it excludes firms for reasons unrelated to technical needs. That risk does not justify blanket skepticism. Sound guidance should distinguish sham collaboration from the real thing, not discourage both.

The agencies should also acknowledge a point often overlooked: some cooperation lowers barriers to entry. Shared safety tools, open benchmarking methods, and collaborative validation environments can make it easier for smaller firms to participate. Coordination upstream can intensify competition downstream. That dynamic deserves more weight in agency rhetoric.

The same logic extends beyond core model development. AI progress depends on complementary R&D in privacy-enhancing tools, model-evaluation systems, cybersecurity testing, provenance and auditability methods, and domain-specific applications in health care, manufacturing, and logistics. Firms often have strong reasons to collaborate in these adjacent areas because the resulting knowledge is nonrival, diffuses quickly, or must operate within shared technical architectures. Antitrust guidance should treat this kind of precommercial experimentation as part of the innovation process—not as a suspicious deviation from it.

Infrastructure Isn’t Collusion

America’s AI future will depend as much on physical infrastructure as on software. Advanced models require massive data centers, reliable energy, transmission access, networking, cooling, and developable land. These are not marginal inputs. They are essential complements to innovation.

Antitrust law should not criminalize efficiency in this setting. Joint purchasing, pooled site development, shared infrastructure projects, and coordinated efforts to secure power or interconnection can reduce transaction costs and solve real bottlenecks. They can also make otherwise uneconomic projects viable. When firms work together to assemble sites, finance improvements, or secure long-lead infrastructure inputs, the likely effect is not reduced output, but expanded capacity.

The FTC and DOJ should reflect that reality. They should adopt a safe harbor—or at least a strong presumption of legality—for collaborations aimed at bringing new AI-related infrastructure online. The key questions are straightforward: Does the arrangement expand productive capacity? Does it avoid spillovers into downstream price or customer coordination? Is any exclusivity reasonably tied to investment incentives rather than strategic foreclosure?

These questions have real-world stakes. Building AI data centers at speed may require coordination on land acquisition, transmission upgrades, specialized construction inputs, backup generation, and regional power contracting. A legal regime that treats these efforts with suspicion will slow deployment and may push investment elsewhere. Clear guardrails, by contrast, can accelerate domestic buildout while preserving the ability to prosecute naked buyer cartels or exclusionary conduct.

Competition policy should complement—not undermine—efforts to promote innovation and economic growth. Competitor-collaboration guidance offers a concrete opportunity to do so. Antitrust should not stand in the way of rapid, lawful, output-expanding AI infrastructure development.

The underlying economics reinforce the point. Data-center projects often involve indivisible investments, long lead times, and sequential approvals. Delay is costly. So is fragmentation. If firms cannot coordinate to aggregate demand, secure infrastructure financing, or share development risk, some projects will not proceed on commercially viable timelines. Guidance that ignores these constraints risks functioning as an unintended anti-investment policy.

Don’t Fear the Standard-Setters

The FTC and DOJ should also say more about standards. Properly structured standards efforts typically lower transaction costs, improve compatibility, reduce uncertainty, and open markets to new entry. That is not a loophole in antitrust law—it is why standard-setting organizations have long received rule-of-reason treatment.

AI presents a strong case for this approach. Firms may need to coordinate on testing protocols, incident-reporting formats, red-team practices, provenance tools, security baselines, and interoperability interfaces. These forms of coordination can improve market performance by reducing lock-in, enhancing comparability, and helping users and complementors connect across systems.

The agencies should also make clear that cooperation aimed at preventing dangerous model outputs, malicious misuse, or national-security harms is ordinarily legitimate. Collaboration around abuse indicators, secure deployment practices, or shared technical baselines can help internalize externalities that no single firm can address alone. Antitrust should not penalize firms for reducing serious risks—so long as those efforts do not serve as a pretext to exclude disruptive rivals.

Administrability matters here. Guidance should emphasize practical safeguards: objective technical criteria, transparent procedures, reasonable access rules, and limits on sharing competitively sensitive information unrelated to the standards effort. Where those features are present, enforcers should say plainly that the activity is unlikely to raise serious concern.

Clarity would do more than protect incumbents. Interoperability and standards can lower switching costs and make it easier for new firms to plug into larger ecosystems. In other words, coordination can expand the field of competition. That point deserves a central place in the agencies’ analysis.

The same logic applies to safety-testing consortia and secure model-evaluation arrangements. When firms jointly develop testing suites, secure sandboxes, or reporting templates that improve confidence in model performance, users can compare offerings more effectively and deploy them with lower risk. Those are classic competitive benefits. The agencies should say so directly, rather than leaving firms to infer legality from silence.

When Sharing Data Protects Competition

Modern antitrust debates too often treat information sharing as inherently suspicious. It is not. Whether information exchange is harmful depends on what firms share, why they share it, and the market effects that follow. In AI and digital markets, some forms of information sharing can improve privacy, security, and competition at the same time.

Consider privacy-enhancing technologies. Collaborative work on privacy-preserving machine learning, federated systems, secure multiparty computation, and related tools can help firms extract insights from data while reducing exposure of sensitive information. Joint benchmarking and best-practice development can also build trust and accelerate adoption. The National Institute of Standards and Technology’s (NIST) work on trustworthy AI and privacy frameworks underscores how shared methods and technical validation can improve outcomes.

The same logic applies to data portability. Common principles and technical tools for secure data transfer can reduce switching costs, facilitate multi-homing, and weaken lock-in. The FTC’s earlier work on data portability recognized these competitive benefits when efforts are designed with privacy and security in mind. Updated guidance should reinforce that lesson

A sensible safe harbor here would cover collaborations aimed at benchmarking privacy tools, developing trusted privacy standards, sharing privacy-protective best practices, and creating secure portability mechanisms. The usual guardrails would apply: firms should avoid unnecessary exchanges of current pricing, output, or customer-specific strategic information; use aggregation, anonymization, clean rooms, or independent administrators where appropriate; and ensure the collaboration is genuinely tied to privacy, portability, or security objectives.

Those caveats should not swallow the rule. The better rule is straightforward: collaboration focused on privacy and portability often promotes competition by lowering switching costs, building trust, and enabling rivalry on dimensions beyond price.

This is another area where antitrust should look forward, not backward. If firms fear that discussions of technical best practices will later be recast as improper information exchange, they will underinvest in shared solutions to privacy and security problems. That chilling effect would harm consumers and likely entrench larger incumbents that can address these issues internally. Smaller and midsized firms often depend on shared learning and common tools to compete effectively.

Competition Policy Meets Geopolitics

None of this means antitrust should morph into industrial policy. It does mean the FTC and DOJ should account for the institutional context in which AI competition unfolds. Advanced AI now intersects with national security, supply-chain resilience, and global technological rivalry. Guidance that needlessly discourages lawful collaboration in strategic sectors will have consequences that extend beyond antitrust doctrine.

That point has limits. It should not excuse genuine anticompetitive conduct. Still, sound economics recognizes that domestic infrastructure buildout, shared safety efforts, and efficient collaborative R&D can generate public benefits that firms cannot fully capture alone. When collaboration expands U.S. productive capacity, improves secure deployment, or accelerates innovation amid foreign competition, the case for avoiding false positives grows stronger.

The agencies need not announce a sweeping geopolitical program. They need only make clear that antitrust law accommodates lawful coordination that accelerates American innovation. That message would matter. It would signal that enforcers can distinguish between cartels that choke off competition and collaborations that strengthen it.

Clarity Beats Cautionary Boilerplate

If the FTC and DOJ want this guidance to matter, they should move beyond abstraction. They should offer concrete examples drawn from AI development, infrastructure buildout, standards, privacy engineering, and portability tools. Businesses need practical signals, not reminders that some collaborations fall under the rule of reason.

At a minimum, the guidance should state that the agencies ordinarily will not challenge bona fide AI R&D joint ventures, collaborative efforts to build or procure data-center-related infrastructure, standards-development activity focused on interoperability, testing, or safety, and information-sharing arrangements reasonably necessary to develop privacy-enhancing technologies or secure portability tools.

The agencies should also identify clear red flags that remove a collaboration from any safe harbor: naked agreements on price or output, labor-market collusion, restrictions unrelated to the project’s aims, exclusionary access rules without technical justification, or exchanges of competitively sensitive information that are not necessary to the venture. That kind of line drawing would do far more work than high-level warnings about vigilance.

Guidance should also include examples of permissible safeguards. Firms should know that firewalls, clean teams, independent administrators, time-lagged or aggregated data, and objective access criteria weigh in their favor. If the agencies want compliance, they should reward well-structured compliance architecture.

The bottom line is straightforward. In fast-moving technology markets, collaboration often forms part of the competitive process. When firms coordinate to innovate, build capacity, improve interoperability, protect privacy, or reduce safety risks, they often make markets more effective and more contestable. Antitrust should leave room for those gains.

The Right Guidance at the Right Time

The competitor-collaboration inquiry presents the FTC and DOJ with a choice. They can issue a document that restates familiar cautions, preserves uncertainty, and invites overdeterrence. Or they can produce modern guidance that addresses the institutional realities of AI-era competition.

The better course is clear. Safe harbors for R&D collaboration, practical assurances for infrastructure buildout, clarity on standards and interoperability, and protection for privacy- and portability-related information sharing would strengthen dynamic competition. None of this would legalize cartels. It would reduce the risk that antitrust policy—through vagueness and overbreadth—slows the innovation and market expansion it is meant to protect.

Markets work best when firms can experiment, invest, and cooperate to create value, subject to clear limits on conduct that actually suppresses rivalry. Updated competitor-collaboration guidance should reflect that principle. If it does, the AI economy will be better for it.

The post Rethinking Competitor Collaboration in the AI Era appeared first on Truth on the Market.

AI is undeniably impressive. It can already handle a wide range of professional tasks. Large language models draft legal memos, build financial models, and generate the sort of analysis that fills the early years of many careers. The technical capability is real.

But “can perform the tasks” does not mean “will eliminate the jobs.” Between those two claims lies an enormous institutional chasm—one the AI hype cycle, which conveniently serves both fundraising and regulatory agendas, tends to gloss over.

Someone Still Gets Sued

Predictions like Amodei’s run into a basic problem: someone has to be liable when things go wrong. When an AI-drafted contract contains an error that costs a client millions, who gets sued? When an AI-generated financial analysis leads to a bad investment, who faces the shareholders’ lawsuit? Not the AI—and not, in any practical sense, the AI developer. The information asymmetries involved in anticipating every downstream use of a general-purpose model are impossible to manage. Liability will flow, as it always has, to the human professionals who apply these tools.

That reality does more than preserve a thin accountability layer. It demands real judgment. A pilot doesn’t just confirm that the autopilot is engaged; he needs thousands of hours of flight time to recognize when the system is subtly off and intervene before it matters. An attending physician doesn’t simply initial an AI-assisted radiology read; she needs years of training to understand the clinical context the model never sees. The accountant signing off on AI-prepared tax filings must know the tax code well enough to catch the confident-sounding hallucination that triggers an audit.

Professional expertise is not a formality layered on top of automation. It is the prerequisite for using automation responsibly. The entire structure of professional licensing, fiduciary duty, and malpractice law reflects a simple reality: complex economies require people who can be held accountable for informed judgment. AI changes the tools. It does not change that institutional requirement, or eliminate the need for professionals who actually know what they are doing.

If anything, liability cuts the other way. These professions will invest more—not less—in training people to work effectively with AI. That means entry-level professionals who understand their domain well enough to supervise AI output, not a world in which they have been “wiped out.”

The Economy Isn’t in a Hurry

There’s a deeper reason to doubt dramatic displacement claims. Economists have wrestled with it for decades. In 1987, Robert Solow observed that “you can see the computer age everywhere but in the productivity statistics.” The IT revolution was supposed to supercharge output. Instead, productivity growth slowed—from about 3% annually in the 1960s to roughly 1% in the 1980s. Economists now call this the Solow Paradox. Despite a brief surge in the late 1990s, the broader patterns has held through successive waves of automation.

Why? The leading explanation comes from Erik Brynjolfsson, Daniel Rock, and Chad Syverson’s “Productivity J-Curve.” New technologies require costly, time-consuming complements: reorganized workflows, retrained workers, redesigned processes. Those investments depress measured productivity before gains materialize. That account explains a lot. 

But a less polite explanation sits in plain view. A growing body of organizational-behavior research—beginning with Yili Hong Lim’s work on “cyberloafing“—shows that workers routinely repurpose workplace technology for personal use, and firms largely tolerate it. When email replaced memos, people didn’t produce more memos-worth of work. They spent some of the saved time in meetings, in workplace socializing, or in the ambient task-switching that looks like busyness but rarely shows up in productivity data. Management, broadly speaking, let it happen. Organizations are not pure productivity-maximizing machines; they are social institutions with a revealed tolerance for slack.

That insight connects to David Graeber’s thesis about “bullshit jobs.” Graeber relied more on anecdote than systematic evidence, and he pushed the argument too far. But he identified something real. A nontrivial share of modern employment exists not because it maximizes output, but because organizations and societies value other things—structure, social belonging, status hierarchies. How many corporate roles created in the past two decades reflect those preferences more than a drive to squeeze out every last unit of output?

If that sounds abstract, consider a canonical case. When ATMs spread in the 1980s, forecasts predicted the end of bank tellers. Instead, teller employment grew as ATMs proliferated. Lower per-branch costs led banks to open more branches. Tellers shifted from counting cash to selling financial products and managing customer relationships. The jobs changed; they didn’t disappear. Daron Acemoglu and Pascual Restrepo formalize this dynamic in their “Automation and New Tasks” framework: automation displaces labor from existing tasks, but it also creates new ones where human comparative advantage reasserts itself. The net effect on employment depends on institutions, not just technical capability.

The Apocalypse Has a Business Model

None of this means the transition will be painless, or that liability rules or regulatory requirements will—or should—freeze the workforce in place. AI will have real distributional effects, and those effects warrant serious policy attention. But “serious policy attention” is a far cry from “50% of entry-level professionals will be wiped out.”

It’s also worth asking who benefits from the apocalyptic framing. AI companies do. Existential predictions help justify extraordinary valuations. They also invite regulatory frameworks that tend to favor well-capitalized incumbents who can absorb compliance costs. The hype cycle serves financial and political interests at the same time. The loudest voices warning about the storm are often the ones selling the umbrellas.

The more likely outcome is messier—and more human. AI will automate tasks, reshape roles, and demand new skills. Some jobs will disappear; others will emerge. Organizations will convert some efficiency gains into slack, leisure, and the persistent preference for working with other people.

And when something goes wrong, someone will still be responsible. That means someone will still need to know enough to deserve that responsibility. This is not a world in which entry-level professionals vanish. It is a world in which they use better tools and do different work. History suggests that’s exactly how these transitions play out.

The post The Hype Cycle Meets Malpractice Law: Why the Jobs Persist appeared first on Truth on the Market.

Fuel prices are climbing sharply across Europe following military escalation in the Middle East and disrupted shipping through the Strait of Hormuz. The political demand for “something to be done” can be nearly irresistible.

In Germany, several major political groups have answered that demand with the “Kraftstoffmaßnahmenpaket,” or “Fuel Market Intervention Package.” Lawmakers filed the draft legislation March 17, and the lower house of the Bundestag has already approved it. The government is targeting April 1 for entry into force.

The speed alone should raise concern.

The bill’s explanatory memorandum admits, with unusual candor: “No substantive contributions from third parties were considered in drafting.” Policymakers considered no alternative approaches. The process ran from filing to final vote in nine days. There was no consultation. No meaningful impact assessment. The only estimate—a back-of-the-envelope calculation—suggests compliance costs (“Erfüllungsaufwand”) “should not exceed” €200,000 for the State.

As for costs to businesses and citizens, the bill claims the “draft law will not create any compliance costs.” That assertion is plainly wrong to anyone familiar with price controls or similar interventions. For legislation that restructures a core instrument of German competition enforcement, the lack of scrutiny is hard to defend.

Crisis conditions create political urgency. But, as Roberta Romano suggests, “legislating in the immediate aftermath of a public scandal or crisis is a formula for poor public policymaking.”

The procedural critique, though valid, is not the most interesting one.

The more consequential problem lies in what the Kraftstoffmaßnahmenpaket does to the law—specifically, to Section 32f of the Act Against Restraints of Competition (ARC), the market-investigation tool introduced through the 11th ARC Amendment in 2023.

The bill frames its changes as temporary responses to a fuel crisis. In fact, they are not limited to the fuel sector. They apply across all markets. And what the bill describes as a procedural simplification turns out, on closer inspection, to be something quite different: a substantive expansion of state authority over lawful commercial conduct, one that stretches well beyond what competition policy can coherently justify.

Antitrust Without the Filters

To understand what has changed, start with what Section 32f already did.

In 2023, Germany introduced what its architects called a “fourth pillar” of competition policy, loosely modeled on the UK Competition and Markets Authority’s market-investigation regime. Section 32f allows the Federal Cartel Office (FCO) to impose behavioral and structural remedies—up to and including forced divestiture—following a sector inquiry, even where no competition-law infringement has been established.

A firm need not have broken any law. It need only operate in a market where the FCO finds a “significant and continuing disruption of competition.” That framework already marked a sharp departure from traditional competition-law enforcement.

The conventional model relies on two filters that discipline regulatory intervention. The first is market power. Competition law targets dominant firms, because only firms with substantial market power can restrict output, raise prices durably, or exclude rivals in ways that reduce consumer welfare. As William Landes and Richard Posner explained in their canonical 1981 analysis, antitrust should focus on the ability to sustain a profitable price increase above the competitive level—not on firm size or market structure in isolation.

A firm in a competitive market that raises prices simply loses customers. Remedies aimed at firms without meaningful market power do not improve consumer welfare. They distort it, penalizing success and discouraging efficient conduct.

The second filter is competitive harm. Even where market power exists, enforcement is justified only when the exercise of that power harms competition—typically through exclusionary conduct or coordination among competitors. This requirement ensures that enforcement targets genuine problems, rather than conduct that merely appears objectionable from the outside.

Section 32f, as enacted in 2023, weakened both filters.

It replaced the market-power requirement with the broader concept of a “significant and continuing disruption of competition,” defined through non-exhaustive, open-textured indicators. It replaced the harm requirement with a structural diagnosis: the FCO identifies a market it considers distorted and imposes remedies on firms it concludes have contributed to that distortion.

Even so, one limiting principle remained. The addressee of a remedy order had to have “materially contributed, through its conduct, to the competition disruption in its specific manifestation.” This requirement—former § 32f(3) sentences 2 through 5—created a link between the firm’s behavior and the identified problem.

It was no substitute for the traditional market-power and harm filters. But it served a similar function: it constrained the FCO’s authority to firms with some causal connection to the market failure it sought to address.

The Kraftstoffmaßnahmenpaket eliminates that constraint entirely.

Proportionality Is Not a Filter

The explanatory memorandum justifies the deletion in two sentences.

First, it claims the conduct-nexus requirement “is not considered conducive to the objectives of a structural protection instrument” like Section 32f. Second, it asserts that firms remain protected by “the high requirements for the existence of a competition disruption and the selection of possible remedy measures.” In other words, proportionality review is supposed to substitute for the conduct filter.

That reasoning does not withstand scrutiny.

Competition law’s traditional requirements—market power and evidence of harm to competition—are not bureaucratic hurdles or “mere technicalities.” They are filters in the precise sense that Judge Frank Easterbrook described in his canonical 1984 article, “The Limits of Antitrust”: sequential screens designed to identify cases

…in which the risk of loss to consumers and the economy is sufficiently small that there is no need of extended inquiry and significant risk that inquiry would lead to wrongful condemnation or to the deterrence of competitive activity as firms try to steer clear of the danger zone. 

These filters do more than protect individual defendants. They calibrate the system. Properly applied, they deter anticompetitive conduct without chilling procompetitive behavior by firms operating near that “danger zone.”

To be clear, even with such filters, competition law generates error costs. But removing them compounds those costs. A proportionality review of individual remedy orders—however careful—operates downstream of the problem. It may limit the severity of a given intervention. It cannot restore the ex ante certainty that threshold filters provide to firms deciding how to behave before any investigation begins.

As discussed above, Germany had already weakened those filters. The bill now removes them altogether.

If enacted, the FCO could impose significant behavioral or structural obligations on a firm simply because it operates in a market the authority has identified as distorted. It would not matter whether the firm contributed to that distortion. It would not matter whether the firm holds a dominant or otherwise meaningful market position. It would not matter whether its conduct has harmed consumers or competitors.

Proportionality would constrain how far the remedy goes—but not who gets targeted. That shift matters.

Competition law has long rested on a simple principle: intervention is justified when a firm’s conduct causes, facilitates, or threatens competitive harm, and when the firm possesses sufficient market power to make that harm durable. Both elements are necessary. Market power without harmful conduct does not justify intervention. Harmful conduct without market power is a problem markets can often correct on their own.

Only their combination warrants the extraordinary step of ordering a firm to change how it runs its business—who it supplies, how it prices, or which assets it may retain. Remove those filters, and enforcement becomes discretionary in ways that make error costs difficult to estimate and legal certainty effectively impossible. 

Firms will no longer be able to assess risk by examining their own conduct and market position. Instead, they must guess which markets the FCO might investigate and whether their industry has become politically salient at a given moment. That kind of discretion invites abuse. It also creates incentives to lobby—and to capture—the regulator. 

As Herbert Hovenkamp recently observed, “[a]ntitrust law’s requirement of competitive harm is also important as a means of limiting legislative capture by specific interest groups.” 

The German government does not deny the breadth of this change. The bill’s own summary states that the amendment to Section 32f will apply to competition enforcement “across all sectors.”

The crisis supplied the political opening. The change itself is structural—and permanent.

A Price Control by Another Name

The bill’s most visible feature is the “Fuel Price Adjustment Rule” (“Kraftstoffpreisanpassungsgesetz”). It limits petrol stations to one price increase per day, at noon. Violations carry fines of up to €100,000.

The government frames this as a transparency and consumer-protection measure, modeled on Austria’s 2009 regime. A more accurate description is simpler: it is an indirect price control, with the same familiar downsides as the direct kind.

At first glance, a rule that restricts “only” when and how often prices may rise can seem modest—certainly less intrusive than a price cap. In practice, it interferes with the same core mechanism: the real-time signaling function of prices.

During a supply shock, price increases do three things at once. As Ben Sperry explains, they ration scarce supply to its highest-valued uses, signal producers and distributors to increase output, and attract substitution around bottlenecks. A rule that allows prices to fall at any time, but permits increases only once per day—at a fixed hour—disrupts all three functions.

The result is not just higher prices or lower output. It is misallocation.

As Brian Albrecht, Alex Tabarrok, and Mark Whitmeyer show, the costs of price controls extend beyond the standard Harberger triangle. They arise from all-or-nothing allocations that emerge when prices can no longer adjust continuously. Without that constant feedback, supply does not flow to where it is most needed.

The 1970s gasoline crisis offers a familiar example. Policymakers at the time also invoked the “rocket-and-feather effect.” The result was not a modest 9% reduction in supply per market, but a breakdown: severe shortages in some states, surpluses in others. Price controls prevented tanker shipments from moving to their highest-value destinations.

The Austrian experience—the model this bill invokes—points in the same direction, though the government does not acknowledge it.

In a 2014 study, Martin Obradovits modeled a two-period duopoly with consumer search and showed why this type of rule can backfire. When firms can raise prices only once per day, they build a risk premium into that single adjustment to hedge against cost increases they cannot pass through later. The noon price rises strategically. It does not fall.

Austria’s own ÖAMTC traffic club reached a similar conclusion when commenting on a recent tightening of the regime to three permitted price increases per week: “We see no potential for immediate and sustainable relief in this proposal.”

German industry groups have been just as direct. In a joint statement, they note that more than half of pump prices consists of taxes and duties. “If you want to reduce fuel prices permanently,” they argue, “you have to talk about government price components—not about interfering in competition.”

Stacking the Deck

The bill’s third component—the new § 29a ARC—reverses the burden of pleading and proof in abuse-of-dominance cases in the upstream fuel market.

Under this provision, suppliers with a dominant position or relative market power must demonstrate, in proceedings before the FCO, that their prices are cost-justified and—where costs significantly exceed market norms—that those costs are reasonable. The FCO retains the initial burden of showing that prices unreasonably exceed costs. Once it meets that threshold, the evidentiary burden shifts to the firm.

Viewed in isolation, this measure is more limited than the changes to Section 32f. It applies to a defined sector and only to firms already found to possess market power. Nor is it entirely novel. Section 29 ARC already contains a similar rule for the energy sector, and burden-shifting frameworks are not uncommon in comparative competition law.

The concern lies in how this provision interacts with the weakened Section 32f standard.

Taken together, the changes substantially expand the FCO’s toolkit. The authority can initiate a sector inquiry, identify a “significant and continuing disruption of competition,” and impose remedies on any undertaking in the sector—without establishing that the firm contributed to the disruption. At the same time, it can pursue pricing investigations against dominant firms under a reversed burden of proof.

The result is not just a broader set of tools, but a system in which the constraints that once structured their use have largely fallen away. 

A Crisis Is a Terrible Thing to Waste

Germany’s “Fuel Market Intervention Package” would make a near-perfect case study for a Public Choice class: a genuine crisis used to justify a regulatory response that goes well beyond what the problem requires.

Germany’s fuel market may well raise competition concerns. The FCO identified several in its February 2025 sector inquiry and proposed targeted remedies. This bill does not build on that analysis.

Instead, it imports a contested retail price-increase cap that Austria’s own traffic club has deemed ineffective. It introduces a burden-of-proof reversal that, while problematic, at least remains sector-specific and rooted in an existing framework.

And then there is the central change.

The deletion of the conduct-nexus requirement from Section 32f—presented as procedural housekeeping—is nothing of the sort. It is a permanent, economy-wide expansion of the FCO’s discretionary authority over firms engaged in lawful, rational conduct, including firms that have not been found to possess significant market power. The oil crisis supplies the pretext. The change itself is structural.

Competition law derives both its legitimacy and its effectiveness from analytical discipline. It requires findings of market power because, without them, there is no basis to infer durable harm. It requires evidence of competitive harm because, without it, intervention risks punishing efficiency, rather than protecting the competitive process. And it requires a causal connection between a firm’s conduct and the harm being addressed—not as a procedural nicety, but as the line between a genuine competition remedy and a populist intervention.

Remove those constraints, and competition law does not become more flexible. It becomes a vehicle for politically responsive intervention, rather than rule-bound enforcement.

The post Crisis Opportunism: Germany’s Turn to Antitrust Without Limits appeared first on Truth on the Market.

The Court’s unanimous reversal of the billion-dollar copyright verdict against Cox Communications has drawn predictable headlines. Some commentators cast it as a reprieve for “mere conduit” internet service providers (ISPs) from overzealous copyright enforcement. At a doctrinal level, that’s at least directionally right: an ISP that provides undifferentiated internet access does not incur secondary copyright liability simply because some subscribers use that connection to pirate content.

But the decision’s real significance likely lies elsewhere. Its importance extends beyond the immediate holding to a more consequential question: how courts calibrate legal protections for online platforms and their management of user-generated content.

That shift deserves closer attention. It signals a growing judicial willingness to revisit longstanding immunities. Cox fits into an emerging line of cases—including Twitter v. Taamneh and Gonzalez v. Google—that increasingly render statutory safe harbors decorative by narrowing the scope of background secondary liability. The implications reach directly to issues we, along with Kristian Stout, explored in “Who Moderates the Moderators?: A Law & Economics Approach to Holding Online Platforms Accountable Without Destroying the Internet.”

The Court Draws the Line at Intent

Cox Communications serves roughly 6 million subscribers. Over about two years, it received more than 163,000 infringement notices identifying subscriber IP addresses linked to piracy. It terminated just 32 accounts. (For context, during that same period, Cox terminated “hundreds of thousands of subscribers for nonpayment.”)

Justice Clarence Thomas, writing for seven justices (with Justices Sonia Sotomayor and Ketanji Brown Jackson concurring separately), held that contributory copyright liability turns on intent. Plaintiffs must show either inducement or the provision of a service tailored to infringement. Mere knowledge that users infringe does not suffice:

The provider of a service is contributorily liable for the user’s infringement only if it intended that the provided service be used for infringement. The intent required for contributory liability can be shown only if the party . . . induces infringement [by] actively encourag[ing] infringement through specific acts. . . . [or] if [a service] is “not capable of ‘substantial’ or ‘commercially significant’ noninfringing uses.” (citing Grokster and Sony) (Slip op. at 7).

This Court has repeatedly made clear that mere knowledge that a service will be used to infringe is insufficient to establish the required intent to infringe. (Slip op. at 8).

Given the scale of lawful uses for internet access—and the absence of any evidence that Cox encouraged piracy—the majority treated the case as straightforward.

As a matter of copyright law, the holding is significant but relatively narrow. The Recording Industry Association of America (RIAA) called the decision “disappointing,” while emphasizing its limited scope: it applies only to contributory-infringement claims against defendants that do not themselves copy, host, distribute, or publish infringing material. Services that host or distribute infringing content remain exposed under Grokster’s inducement theory, vicarious liability (not at issue here), and the Digital Millennium Copyright Act’s (DMCA) notice-and-takedown regime under § 512(c).  And in practice, the labels’ primary enforcement targets are not conduit ISPs, but platforms and services that more directly facilitate infringement.

A Safe Harbor With No Storm

Section 512(i)(1)(A) of the DMCA offers ISPs a deal: implement a policy to terminate repeat infringers “in appropriate circumstances,” and receive immunity from secondary copyright liability. Congress structured this as a bargain—conditional immunity in exchange for demonstrably reasonable behavior.

Cox effectively wipes out one side of that bargain. If ISPs face no secondary liability to begin with—because internet access is a general-purpose service with substantial noninfringing uses—then the safe harbor has nothing to shield. It becomes a phantom defense to a nonexistent claim—a shield without a sword. 

Justice Sotomayor makes the point directly in her concurrence. Cox’s own counsel agreed at oral argument: under the majority’s rule, the safe harbor “will not do anything at all” going forward: 

The majority’s decision thus permits ISPs to sell an internet connection to every single infringer who wants one without fear of liability and without lifting a finger to prevent infringement. It also means that Cox is free to abandon its current policy of responding to copyright infringement. As Cox’s counsel conceded at oral argument, under the rule the majority adopts today, the safe harbor provision will not “d[o] anything at all” going forward. Congress did not enact the safe harbor just so that this Court could eviscerate it. (Slip op., Sotomayor, J., concurring in judgment, at 7). 

Once the out-of-harbor state carries no cost, the harbor stops functioning as a harbor. It instead operates as a subsidy for indifference.

The majority is remarkably unbothered by this. Justice Thomas dispatches the argument  in two brisk paragraphs, emphasizing that the DMCA creates defenses—not liability—and that failing to qualify for the safe harbor does not count against a defendant who can show its conduct is not infringing.

Finally, Sony argues that the DMCA safe harbor would have no effect if Internet service providers are not liable for providing Internet service to known infringers. . . . Sony argues that Congress must have enacted the DMCA on the presumption that Internet service providers could be held liable in cases such as these.

Sony overreads the DMCA. Sony does not contend that the DMCA expressly imposes liability for Internet service providers who serve known infringers. It does not. The DMCA merely creates new defenses from liability for such providers. And, the DMCA made clear that failure to comply with the safe-harbor rules “shall not bear adversely upon . . . a defense by the service provider that the service provider’s conduct is not infringing.” (Slip op. at 10).

From Carrots to Blank Checks

Cox spotlights a familiar problem: an exclusive focus on intent risks foreclosing liability when an intermediary sits in the best position to mitigate harm—what law & economics calls the least-cost avoider. 

We have seen this before in modern Section 230 jurisprudence. In “Who Moderates the Moderators?,” we argued that early internet law worked because it relied on conditioned immunity. Whether under Section 512 of the DMCA or the original conception of Section 230, immunity operated as a carrot—encouraging socially beneficial behavior, calibrated to costs and benefits: 

The animating principle behind Section 230 was always to protect platforms from legal liability for their own efforts to deter undesirable online content… The relevant question attending Section 230 reforms that encourage platforms to engage in more moderation is not whether this will deter some legal/harmless content (it will), but whether the marginal increase in the amount of legal/harmless content deterred is warranted. (p. 35-36)

But courts have increasingly treated Section 230 as an absolute shield, untethered from a platform’s capacity to mitigate harm. That move removes the stick of potential liability. Cox does something similar to the DMCA safe harbor: it converts conditional immunity into unconditional immunity by eliminating the underlying threat that gives the condition its force.

The Court’s focus on specific intent—rather than knowledge—tracks directly from its 2023 ruling in Twitter v. Taamneh. There, the Court held that platforms do not aid and abet terrorism simply by offering a generally available service, even if they know terrorists use it. 

The statutes differ—Taamneh arose under the Antiterrorism Act, Cox under copyright law—but the logic converges. In both, the Court permits intermediaries to take no meaningful action despite specific, actionable knowledge of harm, even where they might be uniquely positioned to reduce it. That result sits uneasily with the law & economics of intermediary liability. As we explained:

[T]he law has long wrestled with how to frame the legal duties owed by a service provider to its customers and the public, while also policing the bad acts of third parties. (p. 104)

[T]he common law has developed several standards of care for intermediaries in situations where the intermediary either otherwise prevents or reduces the direct enforcement of the law, or else where the intermediary is the least-cost avoider of harm, such that imposing upon it a duty of care results in the efficient level of precautions and activity to mitigate harm. (p. 106).

Raising the liability bar to specific intent or active inducement carries a predictable consequence: it effectively grants intermediaries a judicially conferred right to ignore particularized knowledge of ongoing harm on their services. That sits in tension with the standard economic rationale for imposing duties on actors who can prevent harm at relatively low cost.

To be sure, Taamneh reached a defensible result. The Court held that Twitter was not liable for aiding and abetting terrorism based on algorithmic recommendations alone. It also left the door open to  liability where a platform “consciously and selectively” promotes terrorist content. As one of us noted at the time:

[T]his language could suggest that, as long as the algorithms are essentially “neutral tools” (to use the language of Roommates.com and its progeny), social-media platforms are immune for third-party speech that they incidentally promote. But if they design their algorithmic recommendations in such a way that suggests the platforms “consciously and selectively” promote illegal content, then they could lose immunity.

Perhaps algorithmic recommendations are, in fact, suitably neutral tools to merit immunity under Section 230 and to avoid liability under aiding-and-abetting statutes. The deeper problem lies not in the outcome, but in the reasoning. The Court sidesteps two central questions: whether an intermediary can monitor and control harms on its platform, and whether imposing liability would generate excessive collateral censorship. 

We made this point before the Court decided Taamneh:

Taamneh presents a complex question of intermediary liability generally that goes beyond the bounds of a (relatively) simpler Section 230 analysis. . . . [I]ntermediary liability generally cannot be predicated on the mere existence of harmful or illegal content on an online platform that could, conceivably, have been prevented by some action by the platform or other intermediary.

The specific statute may impose other limits (like the “knowing” requirement in the Antiterrorism Act), but intermediary liability makes sense only when a particular intermediary defendant is positioned to control (and thus remedy) the bad conduct in question, and when imposing liability would cause the intermediary to act in such a way that the benefits of its conduct in deterring harm outweigh the costs of impeding its normal functioning as an intermediary. (emphasis added).

In other words, one might conclude—as we did—that Twitter had the technical capacity to monitor and control harmful content, but that imposing liability would generate excessive costs in the form of collateral censorship. The Court, however, largely bypassed that inquiry. In doing so, it risks collapsing the distinction between a dumb pipe and a sophisticated recommendation engine, making an intermediary’s ability to monitor and control increasingly irrelevant to the liability analysis.

Cox follows the same path. The majority centers intent and dismisses knowledge as insufficient. From a law & economics perspective, that is incomplete. Knowledge alone may not justify liability, but it may still identify an intermediary as the least-cost avoider—especially where monitoring costs are low and direct enforcement against end users is impractical.

The record in Cox underscores the point. Rightsholders sent Cox infringement notices tied to specific IP addresses, and Cox terminated at least some accounts. That suggests a meaningful—if imperfect—capacity to monitor and control user behavior. Given the difficulty of pursuing individual end users, that capacity matters.

The remaining question, then, is the one the Court largely leaves untouched: whether imposing contributory liability on Cox would generate costs that outweigh the benefits of reducing infringement.

The Unlikely Ally Who Takes Incentives Seriously

There is an irony here worth pausing over. The opinion in Cox that most closely tracks a law & economics approach to intermediary incentives—the one that takes seriously how conditional immunity shapes behavior—comes not from Justice Thomas or Justice Neil Gorsuch, but from Justice Sotomayor.

Her concurrence (joined by Justice Brown Jackson) reaches the same bottom line—Cox prevails—but through a framework that preserves the possibility of fault-based secondary liability grounded in common-law aiding and abetting. Drawing on Taamneh and Smith & Wesson Brands v. Estados Unidos Mexicanos, she articulates a standard requiring conscious participation in wrongdoing: an affirmative act coupled with intent to help the misconduct succeed.

Cox still wins under that test. Plaintiffs could not show that Cox intended to aid specific acts of infringement. A notice ties infringement to an IP address, not to a particular individual in a household, coffee shop, or dormitory. Without more granular knowledge—and without evidence of “pervasive, systemic, and culpable assistance”—plaintiffs established, at most, indifference:

Without proof that Cox knew more about individual instances of infringement, and without evidence of “pervasive, systemic, and culpable assistance” needed to support a more generalized theory of liability, plaintiffs have at most shown that Cox was “indifferent” to infringement conducted via the connections it sells. Mere indifference, however, is not enough for aiding and abetting liability to attach. (Slip op., Sotomayor, J., concurring in judgment, at 12).

What matters most, though, is not the outcome. It is the structure of the analysis. Sotomayor preserves what the majority discards: the incentive framework Congress built into the DMCA safe harbor.

Her reasoning closely tracks Reinier Kraakman’s gatekeeper-liability model. Conditional immunity works only if the condition has teeth. As she puts it:

The majority’s new rule completely upends that balance and consigns the safe harbor provision to obsolescence. . . . After today, ISPs no longer face any realistic probability of secondary liability for copyright infringement, regardless of whether they take steps to address infringement on their networks and regardless of what they know about their users’ activity. Slip op., Sotomayor, J., concurring in judgment, at 9)

Under the majority’s rule, an ISP faces no liability even if a customer walks in and announces he needs a new provider because the last one cut him off after years of piracy. The safe harbor’s logic—behave reasonably and receive protection—depends on the inverse: unreasonable conduct must carry risk. By foreclosing knowledge-based secondary liability for general-purpose service providers, the majority eliminates that risk—and with it, the incentive the safe harbor was designed to create.

It is, to put it mildly, an unusual moment. The most rigorous economic analysis of intermediary incentives in a Supreme Court copyright opinion comes from the Court’s left flank, not from the justices more typically associated with economic reasoning. But Sotomayor takes the core insight seriously: conditional immunity aligns private incentives with social welfare, and stripping away the condition while preserving the immunity breaks the mechanism entirely.

Not All Intermediaries Are Created Equal

To be fair, several distinctions caution against reading too much from Cox into the Section 230 context.

Cox concerns a conduit provider—an entity offering undifferentiated internet access. Cox does not host, curate, recommend, or monetize specific content. Content platforms operate differently. They maintain account-level data, deploy content-matching algorithms, exercise editorial judgment through recommendation engines, and receive particularized complaints tied to identifiable users.

That difference matters. As we have argued, intermediary liability makes economic sense only when the intermediary can monitor and control the relevant conduct, and when the benefits of deterrence outweigh the costs of disrupting the service. Cox may fail that test. A platform that hosts and curates content—and knows exactly who uploaded a defamatory post—may not. The informational gap that defeated liability in Cox (and in Taamneh and Smith & Wesson) narrows considerably for platforms with granular user-level data.

Our proposed approach to Section 230 reflects that distinction. In “Who Moderates the Moderators?,” we argue for conditioning immunity on a platform’s own conduct—specifically, whether it exercised reasonable care. That is not a derivative claim based on a user’s underlying offense. It is a straightforward negligence standard, closer to premise-liability doctrine than to the contributory-infringement theory at issue in Cox.

To avoid death-by-10,000-duck-bites litigation, we also propose procedural safeguards: heightened pleading standards, a “certified answer” mechanism allowing platforms that follow industry-developed best practices to secure early dismissal, and agency oversight (with a sunset) to guide the development of those standards.

Notably, the Motion Picture Association’s (MPA) amicus brief in Cox sketches a similar framework: graduated responses, proportionate measures, and conditional protection. In substance, the MPA argued for liability when ISPs fail to take reasonable, proportionate steps in response to known infringement—a duty-of-care standard. The Court rejected that premise by eliminating the underlying liability. But the framework itself remains analytically sound and readily generalizable beyond copyright.

The Genie, the Bottle, and What Comes Next

We should not overstate the holding. Cox may be a defensible application of existing secondary-liability principles to a passive conduit provider on these facts. From a social-welfare perspective, one could reasonably worry about a regime that imposes billions in liability on an ISP because a small fraction of its 6 million subscribers downloaded music. Still, Justice Sotomayor is right to flag the majority’s categorical rejection of knowledge-based theories.

The more interesting question is what comes next. The RIAA has already urged “policymakers” to “look closely at the impact of this ruling”—a thinly veiled call for legislative intervention. The MPA’s amicus brief, meanwhile, outlined a familiar framework: conditioned immunity, proportionate responses, and duties of care.

Cox may also complicate any lingering faith in the common-law process to calibrate intermediary liability in digital markets. The Court has now struggled with this problem across multiple domains: Cox in copyright, Taamneh and Gonzalez v. Google under the Antiterrorism Act, and a long line of Section 230 cases in the platform-liability context. The pattern is hard to miss. Courts preserve the formal structure of liability while hollowing out its practical effect.

Perhaps the genie does not go back in the bottle without legislative action. Unless policymakers restore the conditioned nature of safe harbors, we may continue to operate in a system where the machinery of intermediary accountability remains formally intact but functionally inert. (Of course, recent legislative proposals suggest that any statutory fix could easily make things worse.)

At a minimum, the logic of Cox unsettles our first reform principle:

First and foremost, we believe that Section 230(c)(1)’s intermediary-liability protections for illegal or tortious conduct by third parties can and should be conditioned on taking reasonable steps to curb such conduct, subject to procedural constraints that will prevent a tide of unmeritorious litigation. (p. 106).

And, more concretely:

[O]nline platforms should not face liability for communication torts arising out of user-generated content unless they fail to remove content they knew or should have known was defamatory. . . . Once it has such knowledge, however, it should have an obligation to make reasonable efforts to remove and prevent republication of the defamatory material. This is an extension of the common law rule for offline distributors of tortious content, keyed (again) to the relevant distinctions between offline and online intermediaries cutting both for and against heightened liability. (p. 110).

After Cox, it is no longer clear that this approach remains viable. That is unfortunate.

The post Decorative Safe Harbors: The Judicial Hollowing-Out of Intermediary Accountability appeared first on Truth on the Market.

For years, digital platforms have relied on two core protections: the First Amendment and Section 230. Together, they let companies host, organize, and moderate speech without facing crushing liability. In a 48-hour span, that foundation took a hit.

A New Mexico jury delivered a $375 million verdict tied to child safety claims against Meta.  A California jury found Meta and Google (YouTube) liable for allegedly addictive design features. Meanwhile, Missouri v. Biden—the case over government pressure on social media—ended in a settlement that promises restraint but sets little precedent.

Taken together, these developments push platforms toward tighter speech controls. They also showcase a rising legal strategy: recasting editorial decisions as defective-product design.

At the same time, the Missouri v. Biden settlement leaves largely intact the government’s ability to pressure platforms behind the scenes. Little now prevents government officials—or even private litigants—from pushing social media companies to significantly reduce speech without any formal legislative or regulatory action.

If Speech Is a Product, Everything Is a Defect

The juries in Santa Fe and Los Angeles reached a similar conclusion. They treated Meta’s and Google’s platforms not as forums for speech, but as engineered products capable of causing harm. That shift reframes how courts evaluate platform behavior—and raises a threshold question: when does it make sense to treat online speech platforms as products? 

From a law & economics perspective, intermediary liability can make sense when the intermediary is the least-cost avoider of harm. Meta, for example, may be well-positioned to protect minors if it can monitor and control harms at relatively low cost. Even then, the analysis doesn’t end there. Courts must still ask whether imposing liability produces greater social costs than it prevents. In the online-speech context, that means weighing accountability against the risk of collateral censorship, as platforms restrict speech to avoid liability.

Juries can and should resolve questions of fact. Judges, by contrast, must get the law right. Here, that means asking how consumer-protection or product-liability theories apply in light of the First Amendment and Section 230. It is not clear the courts in New Mexico and California did so.

In New Mexico, the court sidestepped Section 230 by focusing on Meta’s conduct rather than the effects of applying consumer-protection law to user-generated content. The case turned on allegations that the company failed to prevent child exploitation and misled users about safety. The jury found those actions violated the state’s Unfair Practices Act.

In California, jurors were instructed to ignore content altogether. They focused instead on features such as infinite scroll and autoplay. By isolating design from content, the court treated the algorithm as a product mechanism, rather than an editorial tool.

That distinction doesn’t hold. Decisions about how to present content—what to prioritize and how to display it—are editorial judgments. Courts have long recognized that the First Amendment and Section 230 protect not just what is said, but how it is presented.

A newspaper chooses placement, headlines, and story order to capture attention. Platforms do the same with feeds, notifications, and recommendations. Labeling those choices “addictive design” does not strip them of their expressive character; it attempts to sidestep the First Amendment. Section 230 likewise protects not just the conveyance of information, but its display. Section 230(f)(4) expressly contemplates tools that “filter, screen, allow, or disallow content… pick, choose, analyze, or digest content… or transmit, receive, display, forward, cache, search, subset, organize, reorganize, or translate content.”

The International Center for Law & Economics (ICLE) has warned courts against this sleight of hand in its amicus brief to the Supreme Court of Massachusetts on similar claims. When plaintiffs repackage speech decisions as product defects, they invite courts to regulate expression through consumer-protection or tort law—weakening constitutional protections without confronting them directly.

The logic extends beyond presentation. Claims that platforms acted negligently by failing to verify users’ ages clash with federal rulings striking down age-verification mandates. Courts should not allow common-law claims to impose what statutes cannot.

Jawboning Without Consequences

While the Meta verdicts target design, Missouri v. Biden centers on government influence. The case alleged that federal officials pressured platforms to suppress disfavored views on COVID-19 and elections. The U.S. Supreme Court never reached the merits, instead finding the plaintiffs lacked standing. Even so, the settlement signals that the government’s pressure campaign crossed a line—despite the absence of a definitive judicial ruling.

That acknowledgment matters. Government threats can distort the marketplace of ideas and produce government failure worse than the market failure they purport to fix. Even well-intentioned efforts can turn coercive when backed by regulatory authority.

The settlement, however, is narrow. Only the parties can enforce it, and only against the federal agencies originally sued. Missouri and Louisiana may act only when pressure affects their own official speech—not the speech of their citizens. The agreement creates no precedent for others, including those facing pressure from different federal agencies or from state and local officials.

The broader dynamic remains intact. Future administrations can continue to test the limits of informal pressure. If the jury verdicts stand, they introduce another background regulatory threat—one that state officials can use to push for changes without public scrutiny. Platforms, wary of investigations and the risk of ruinous consumer-protection suits, may comply. Private litigants, too, can wield the threat of litigation to force changes in platform behavior.

Large incumbents like Meta and Google may absorb these costs—paying judgments and scaling up content-moderation teams. Smaller platforms, and potential entrants, likely cannot. The result could be perverse: a legal regime that entrenches dominant firms by making it harder for competitors to survive.

The ‘Safe’ Internet Is a Smaller Internet

The result is a new equilibrium: liability-driven curation.

Platforms now face pressure from every direction. Moderate too little, and they risk consumer-protection suits. Build engaging features, and they invite claims of addiction. Resist government pressure, and they may trigger investigations or regulatory backlash.

Under those conditions, the safe path narrows quickly. Companies will remove more content, limit reach, and steer clear of controversy. They will favor caution over innovation. They will design systems to minimize legal exposure, not to maximize user engagement or expressive diversity.

That shift will not yield a healthier public square. It will produce a quieter one—less dynamic, less open, and less resilient. When the cost of hosting speech rises high enough, platforms host less of it.

The internet did not become a central forum for public discourse by accident. It developed under legal rules that allowed experimentation, risk-taking, and a wide range of voices. Courts, litigants, and government actors now place those rules under sustained pressure.

If this moment marks a turning point, the lesson is straightforward. When the law treats speech as a liability to manage rather than a value to protect, it does not improve speech. It reduces it.

The post Treating Speech as a Bug, Not a Feature appeared first on Truth on the Market.

In a recent post, we looked at a small cluster of systems that try to use markets to correct misinformation.

Start with a simple analogy: bad information is a kind of pollution, a familiar problem in law & economics. In this case, the pollution manifests as a market failure in journalism and social media. A well-designed “truth-bounty” system could, in theory, reward those who earn public trust and capture attention by being right. That would increase the production and spread of high-quality news, while pushing misinformation to the margins.

Some of these proposals cut out authors and publishers altogether. Prediction markets and “retrodiction markets” would let anyone with a view—and some cash—bet on what’s true. Think the moon landing was faked? Buy “Yes” or “No.” Maybe the 1977 film “Capricorn One” (about a staged Mars landing) reflected conspiracy culture. Or maybe it was, depending on your priors, closer to documentary.

This is not entirely hypothetical. Financial markets already host a version of it. Certain firms do investigative work to uncover fraud and mismanagement in publicly traded companies, then take positions that pay off when the information becomes public. Short-selling ahead of the reveal can be quite profitable. That’s the “Hindenburg model,” named for Hindenburg Research, an early and prominent practitioner.

All of this sounds elegant in theory. In practice, these incentive-aligned truth machines face serious obstacles—especially when one tries to export them from finance into the messier world of politics and social discourse, where reliable information is in short supply and trust is even scarcer.

Truth Is Not Self-Executing

A core problem with “truth bounties” sits right there in the label. Everyone understands what a bounty is. Truth is another matter. Much of what drives disagreement does not sort neatly into true or false.

Statistics make the point nicely. Lies and damned lies are told with statistics [apologies]. Cancer deaths continue to rise—true—unless one uses age-standardized rates, in which case the claim becomes false. Non-falsifiable statements create even more room for mischief. “A lot of people are saying this is the best blog post ever” gestures at consensus without committing to anything testable. It’s not true, but somewhere out there may be a sufficiently large—and sufficiently obscure—group to make the statement technically defensible.

Our own research on the quality of news and commentary drives this home. We look at logical argumentation, evidentiary sufficiency, predictive accuracy, and similar markers. Much of what passes for analysis, though, is emotive content built on in-group premises. “Democracy is doomed” may resonate strongly within a shared ideological community. Analytically, it says very little.

At that point, truth and falsity never really make it onto the workbench.

Truth Markets Need More Than Theory

Any truth-bounty system will need a standard for resolving disputes. Odds are, it will look something like the “materially misleading” standard from fraud and consumer-protection law. That works fine in court. It is a heavier lift for the general public. Asking ordinary participants to apply lawyerly standards—especially in a high-conflict environment—is optimistic. Many will see a truth-bounty regime not as neutral adjudication, but as another elite project to define and enforce “fake news.”

Then comes the question of who decides. Every system needs an arbiter. Retired judges would bring credibility, but not cheaply. Subject-matter experts bring knowledge, but also baggage. Fair or not, many people already distrust them as part of the system. In a polarized setting, participants may accept dubious claims about arbiters as readily as they accept conspiracy theories about pizza parlors in Washington, D.C. And once arbiters matter, they become targets of pressure, persuasion, or worse.

Some models try to sidestep the problem by letting the market decide. In retrodiction and securities markets, participants rely on price signals. The price of a contract reflects a rough consensus about the underlying question, whether that is a contested factual claim or a company’s prospects.

That works better in finance than elsewhere. Outside financial markets, trading can be thin. Thin markets are easier to manipulate and harder to trust. There are potential fixes, but they introduce their own tensions. It would be more than a little ironic to import insider-trading rules into truth markets when the most valuable traders—the ones with the best information—are, by definition, insiders.

Thin markets also blunt incentives. Low expected returns will not draw out high-value information. As the prior post put it, “A prediction market that allows the evidence-holder to profit from the information will draw more information out faster.” True enough—but only if the rewards are meaningful. A congressional staffer who can expose her boss’ bribe-taking is not going to do it for $10,000 if it costs her job.

It is tempting to treat market-based solutions to misinformation as elegant, engineerable systems. In reality, they have to operate in a social environment that resists clean design. Truth markets would enter a society that is, in many ways, comfortable with the status quo. Not everyone is eager for a new mechanism to surface uncomfortable facts. Some will see innovation. Others will see risk, moral decay, or something worse.

When Markets Incentivize Mayhem

One standard criticism of prediction markets is that they do not just incentivize good behavior—like exposing corruption—but can also incentivize very bad behavior.

The classic example is the “assassination market.” The idea is straightforward and unsettling: someone could profit from correctly predicting a political leader’s death—and, in the extreme case, could bring that outcome about. To get ahead of this, prediction market Kalshi has adopted a policy of refusing to pay out on predictions made correct by a death. That stance drew attention from legislators and litigation from users when Kalshi declined to pay out on a contract asking whether Ayatollah Ali Khamenei would be out as Iran’s supreme leader before March 1, 2026. He was out, alright. In a body bag.

That policy addresses the most obvious edge case. It does not eliminate the broader moral overhang. These market-oriented truth mechanisms sit at an uneasy intersection of investing, journalism, and gambling. If they start to look like the sportsbook at the Circa Resort & Casino in Las Vegas, many people will not treat them as a serious response to misinformation. Add in the risk of a new class of “problem gamblers,” and public acceptance becomes even more fragile.

The law already draws a line here. “Games of skill” get more leeway, in part because they can generate social value—say, when better-informed traders improve capital allocation. “Games of chance” fare worse, at least outside their entertainment value, especially under more restrictive regulatory frameworks.

Regulators are beginning to test those boundaries. Some jurisdictions have moved to limit or prohibit wagers on granular events, such as “which team will score the next field goal.” Arizona has sued Kalshi, alleging illegal gambling and election wagering. But prohibition tends to displace, not eliminate, activity. With multiple jurisdictions and a largely borderless internet, prediction markets will migrate. Some will include unsavory topics. Others will happily take bets on the next basket in tonight’s game.

That reality points in a different direction. It is better to have most prediction-market activity in stable, lawful jurisdictions such as the United States than to push it offshore. Keeping markets domestic reduces the risk of highly liquid activity in less-regulated venues and makes oversight more feasible. Participants who place large, well-timed bets—say, on an assassination or other harmful event shortly before it occurs—often reveal themselves through their trading patterns. Investors piling into a stock just before a merger announcement tend to do the same.

You Can’t Bounty the News Cycle

There are other social frictions. The “truth-bounty” concept treats news stories like discrete products—small, shelf-stable units, like jars of pickles, olives, or peppers.

Journalists do not see their work that way. They provide a service. A subscription buys more than the news of the day. It buys travel coverage, crossword puzzles, and weather reports. More importantly, it funds ongoing work—someone digging into corruption at the statehouse and in the boardroom, someone reporting on the lives of women in sub-Saharan Africa, someone tracking the environmental toll on Everest. Journalism is a public service, not a jar of pickles.

Even at the story level, the model does not quite fit. A news article—the “first draft of history”—is rarely final. It evolves hour by hour, day by day. Which version earns the bounty? The first? The corrected? The updated?

Do not expect journalists to rush toward truth bounties or other market-based mechanisms. The industry already faces pressure from failing subscription and advertising models. Newsrooms are shrinking. Revenues are thin. And now the proposal is to layer on additional risk—chasing bounties in a system they do not control. That is not an easy sell.

Truth Has a Demand Problem

The biggest drag on market-based solutions to misinformation may not be the mechanisms. It may be the consumers.

Much of the literature assumes that people want the truth when they consume news and information. That assumption does a lot of work. It is also, at best, incomplete.

Americans love bullshit. It is easy to blame media outlets—new entrants that ignore journalistic standards, or social media platforms that amplify clickbait and outrage. But demand matters at least as much as supply. Bullshit persists because people want it. It reinforces prior beliefs. It flatters the home team. It offers a way to snub authority.

In practice, many consumers prioritize social belonging, stability, and intellectual ease over truth-seeking.

That complicates the case for market-oriented fixes. These proposals often assume latent demand for truth just waiting to be unlocked. The reality is more nuanced—and more uncomfortable. People are not just consumers of information; they are social actors with preferences that do not map cleanly onto accuracy.

That leaves a set of open questions. Can social incentives shift behavior toward better thinking? Can truth be made “cool”? If a committed group of truth-seekers emerges, will others follow? Is “putting your money where your mouth is” enough to generate real demand for accuracy?

If you think you know the answers, there is a market for that. Place your bets.

The post Truth Markets and Their Discontents appeared first on Truth on the Market.

The framework sketches broad principles to guide federal policymaking on a technology at risk of a balkanized patchwork of state-level rules. Left unchecked, those rules could curb development, competition, and innovation by layering cumulative compliance burdens on businesses.

But the framework itself is thin on details, and Congress may never translate it fully into law. Still, as Kristian Stout notes elsewhere in these pages, it commendably advances a “light-touch federal approach, grounded in existing legal doctrines, and focused on harms rather than speculative risks,” while avoiding premature or overly centralized interventions.

Even so, the framework leaves out several policies and ideas policymakers should consider. Without them, regulatory guardrails for responsible AI development and deployment could end up constraining American competitiveness, innovation, and the economic opportunities that follow.

Threading the Federalism Needle

The framework preserves a role for states. They retain authority over how AI infrastructure—such as data centers and utility poles—gets deployed within their borders, and they can continue to use their police powers to prosecute criminal conduct facilitated by AI technologies.

At the same time, the framework aims to preempt a growing patchwork of state-level AI regulations that constrain nationwide development and innovation. Companies operating across jurisdictions must otherwise navigate disparate, overlapping, and often inconsistent rules. Those requirements raise the cost of doing business and divert resources away from improving products and tools.

The framework also calls for clear standards that avoid ambiguous interpretations, which can invite excessive and costly litigation—particularly in areas like child protection. In copyright law, it takes a generally permissive approach to using content to train AI models, while leaving specific questions—such as whether the scope of the “fair use” exception covers AI training—to courts.

Courts remain well-suited to that task. They tend to account for commercial realities and have repeatedly shown an ability to apply general principles and extensive precedent to new technologies and market developments—creatively, but pragmatically. They also face less risk of regulatory capture or political bias than legislatures or regulatory agencies.

In the end, the framework tries to balance community concerns about emerging harms with skepticism of regulatory overreach, and federalism and local autonomy with the Constitution’s protection of interstate commerce. That balance matters for cost-efficient development and deployment.

Consistent with these principles—and with the goal of holding bad actors accountable without undermining innovation and competition—policymakers should, however, also consider several other areas of emerging concern.

Consent Fatigue Meets Compliance Costs

Conspicuously absent from the framework is any mention of a federal data-privacy standard. That omission matters. State privacy laws vary widely, and those differences shape companies’ ability to access depersonalized data needed to train AI models, improve accuracy, and serve users effectively. Without representative data, model performance suffers, and existing biases can worsen—undermining tools such as fraud detection, tailored recommendations, and advanced analytics.

More than 20 states have enacted comprehensive data-privacy standards, creating a fragmented regulatory landscape. That patchwork raises compliance costs and complexity for businesses. It also hits “little tech” hardest, as smaller firms lack the scale to spread fixed regulatory costs. As Jennifer Huddleston of the Cato Institute observes:

Because [different state privacy] laws have different models, businesses may not merely be able to comply with the most restrictive one. Instead, they will likely incur additional costs and require additional time for each law rather than developing the best privacy and security options for their product’s intended audience more generally.

These barriers to competition grow when privacy regimes become especially burdensome. One 2022 study found 33% fewer new apps entered the European mobile app market than expected following adoption of the European Union’s General Data Protection Regulation (GDPR). The California Consumer Privacy Act (CCPA) alone imposed an estimated $55 billion in compliance costs, affecting between 15,643 and 570,066 businesses.

Privacy rules can also degrade the user experience. Consent pop-ups introduce friction, increasing the time and effort required to navigate digital services, without delivering commensurate benefits. Studies show users routinely dismiss these interruptions, signaling a preference for streamlined experiences over additional, low-value choices. That behavior has downstream effects: fewer users consent to depersonalized data collection for AI training, further limiting data quality.

The consequences fall unevenly. When friction drives users away from major platforms, smaller businesses suffer most—especially those that rely on platform access, lack brand recognition, or depend on data-driven tools to reach customers. The same dynamic affects firms increasingly reliant on AI-enabled tools, whether integrated into platforms or deployed independently.

The framework’s preference for federal preemption of state AI rules points in a clear direction: Congress should adopt a federal privacy standard that similarly preempts state laws. Done right, such a standard could balance user autonomy and informed consent with the need for access to depersonalized data—without imposing the kind of excessive costs that only large incumbents can absorb.

Recent legislative efforts suggest a less promising path. Under the Biden administration, Congress considered the American Data Privacy Protection Act (ADPPA), which did not preempt state laws and took a more heavy-handed approach. The bill would have required firms holding large volumes of data to submit impact assessments to the Federal Trade Commission (FTC) for nearly all algorithmic activities, increasing administrative costs and uncertainty. Regulators could have rejected those submissions as insufficiently detailed, exposing even good-faith actors to significant litigation risk.

The ADPPA ultimately failed to advance. But without a workable federal standard, future proposals may revive similarly burdensome approaches—raising the stakes for getting privacy policy right now.

The Case for Letting Employers Do the Training

The framework also favors “non-regulatory methods to ensure that existing education programs and workforce training and support programs, including apprenticeships, affirmatively incorporate AI training.” That likely reflects the administration’s broader AI action plan, which urged the U.S. Treasury Department to clarify that AI literacy and skills programs qualify as “educational assistance” under Section 132 of the Internal Revenue Code. That change would allow employers to offer tax-free reimbursement for AI-related training.

Additional tax reforms could go further. My Mercatus Center colleague Revana Sharfuddin has argued for policies such as full business expensing to encourage employer investment in AI-related workforce training. Employers increasingly report that many college graduates lack the skills to use AI tools effectively—tools that now play a central role across industries. As a result, firms must shoulder more of the burden to train and upskill their workforce.

Full expensing would change those incentives. If employers could fully deduct the cost of training programs, they would have stronger reason to invest in workforce development. That approach would also reduce pressure for government-run training initiatives, avoiding the need for additional federal overlays.

It could also help workers adapt to technological change. As AI reshapes the labor market through automation, expanded access to training can prepare workers for new—and potentially more lucrative—roles, including entrepreneurial opportunities.

Build the Pipes Before Regulating the Flow

In the same vein, policymakers should extend full business expensing to a broader range of AI-enabling infrastructure. That includes utility poles for broadband deployment, which would advance the goals of the $42.5 billion Broadband Equity, Access and Deployment (BEAD) program. BEAD funds states and localities to expand high-speed internet access, largely through private-sector partnerships.

The AI implications are substantial. Many rural and regional communities still lack reliable high-speed internet, which underpins applications such as industrial automation, advanced health care, and remote education. For latency-sensitive uses—such as autonomous vehicles—even fractions-of-a-second delays can carry serious consequences.

BEAD funding should also come with conditions that encourage states and localities to reduce unnecessary regulatory barriers to infrastructure deployment, particularly where the costs outweigh the benefits. Critics may argue that such conditions infringe on state autonomy, especially given the framework’s emphasis on federalism in areas like zoning. But conditions on federal grants do not prevent states from spending their own funds as they see fit. They simply aim to ensure federal taxpayer dollars deliver maximum value.

Tax policy can also support AI infrastructure more directly. Expanding deductions for data center investment would complement state and local efforts, which increasingly rely on tax incentives to attract these facilities. Data centers are essential for AI and cloud computing, both of which demand ever-greater computational capacity. They also require significant capital and operating expenditures.

With that said, data center development remains politically contentious, particularly due to concerns about energy use and resource demands. The framework addresses these concerns by emphasizing that new construction should not raise local electricity costs, consistent with the Ratepayer Protection Pledge that several companies have adopted. In practice, data centers can also reduce costs—for example, by investing in grid infrastructure, supporting new energy generation, or absorbing excess capacity.

Finally, the framework calls for “streamlin[ing] federal permitting for AI infrastructure construction and operation.” That goal is important, but it does not go far enough. Policymakers should improve coordination across federal, state, and local permitting regimes, as well as among the agencies involved. The same applies to environmental reviews.

Duplicative processes based on inconsistent standards do more than increase costs. They inject uncertainty into project timelines and final expenses—exactly the kind of friction that slows infrastructure deployment and, in turn, AI development.

Fishing Expeditions Won’t Build AI

Recent analysis cited by the Computer & Communications Industry Association (CCIA) estimates that “AI-related products and improvements will contribute $15.7 trillion to the global economy by 2030, including $3.7 trillion to the U.S. economy (14.5% of total estimated GDP).” Those gains could be undermined by costly, ill-advised antitrust litigation and “fishing expedition” investigations driven more by speculative theories of monopolization than by evidence of actual competitive harm.

Concerns raised by U.S. and international competition enforcers about concentration across the AI stack—from data to cloud computing—often overstate the risks. Our research finds these markets remain dynamic and competitive. Partnerships between AI startups and large technology firms illustrate the point. Startups bring ideas and talent; incumbents bring capital, infrastructure, and access to millions of users. These arrangements lower entry barriers, extend the benefits of network effects and scale, and ultimately serve consumers, innovation, and competition.

By contrast, the specter of preemptive antitrust intervention—aimed at “correcting” markets before they supposedly tip—creates real costs. It injects uncertainty into capital-intensive AI investment decisions and diverts resources away from developing and refining new technologies.

That does not mean enforcers should stand down. Agencies such as the FTC and the U.S. Justice Department (DOJ) should continue to monitor AI markets and conduct targeted studies. But enforcement should rest on concrete evidence of practices that materially harm competition—such as conduct that excludes rivals without offsetting consumer benefits—not on abstract theories of future harm or incomplete understandings of evolving technologies.

A more disciplined approach would also encourage firms to experiment and compete, confident that novel business practices will not trigger enforcement based on conjecture, rather than evidence.

Rules That Don’t Break the Engine

The national AI framework marks a step in the right direction. Policymakers should treat it as a foundation for future legislation—not a final word. It outlines broad principles, not prescriptive rules, and largely embraces a “permissionless innovation” approach that targets concrete harms, while remaining wary of regulatory overreach.

Getting the next steps right will require more than siloed expertise. Policymakers must understand not only the distinct issues at each layer of the AI tech stack, but also how those layers interact. Only then can they weigh the benefits of new rules against their likely costs.

Done well, the resulting framework can give businesses the certainty they need while mitigating real harms. It can also preserve the conditions for dynamic competition and continued innovation—unlocking tools with the potential to expand educational, economic, and health-care opportunities for hundreds of millions of Americans.

The post Guardrails, Not Roadblocks: Improving the AI Framework appeared first on Truth on the Market.

I don’t claim expertise across every issue they covered—and, unlike them, I won’t pretend otherwise. But I have spent considerable time studying the evolution of online consumer protection: how trust emerged in a radically new environment, how entrepreneurs built the mechanisms that made online commerce possible, and how those mechanisms continue to evolve in response to AI. On those questions, the Klein-Wu-Doctorow narrative gets the story—and its causes—wrong.

We Built This Internet

The podcast’s central conceit—that “we didn’t ask for this internet”—reflects either a category error or a simple mistake.

Start with the category error. “We” (society?) never made a substantive, collective request for a particular kind of internet. At most, our elected representatives made a high-level choice in the early 1990s to move the internet out of government and academic control and open it to commerce. Congress amended the National Science Foundation’s (NSF) statutory authority in 1992 to permit “additional purposes.” Three years later, the NSF shut down NSFNET, and the acceptable-use policy that had limited commercial activity fell away. Everything else followed.

If “asking for” the internet means revealed preferences—what users chose through their behavior—then today’s internet is, quite literally, what we asked for. It emerged from the actions of billions of individuals, including a small share who try to exploit others, and a set of entrepreneurs who figured out how to counter that behavior by building tools that protect users from malicious actors.

How the Internet Solved for Trust

Klein, Wu, and Doctorow describe the modern internet as if it sprang from a conspiracy between large tech firms and a passive government. The reality is more interesting—and more encouraging.

Commerce depends on trust. Each party must trust the counterparty. In person, that trust grows through repetition, reputation, reliable identification, and legal enforcement. Online, those mechanisms appear only in fragments. Entrepreneurs filled the gap.

Start with identity and security. Taher Elgamal and his team at Netscape developed SSL, using public-key infrastructure to authenticate websites through digital certificates and asymmetric cryptography. That small padlock in your browser bar marks one of the most consequential innovations in the history of commerce.

Then came user ratings. Pierre Omidyar’s feedback forum on eBay, launched in 1996, created a decentralized reputation system that allowed strangers to transact with confidence. Variants of that system now underpin Amazon, Uber, Airbnb, and countless other platforms.

Payment systems followed. Credit-card networks adapted their rules for “card-not-present” transactions, and intermediaries like PayPal emerged to stand between buyers and sellers.

Each innovation solved a specific trust problem. None came from a government committee. Entrepreneurs identified real needs and built tools to meet them.

No Data, No Defenses

Here is where Klein, Wu, and Cory Doctorow’s analysis goes most seriously off track. They treat the collection and use of consumer data as purely extractive—as if firms harvest data only for their own benefit, offering nothing in return. That framing ignores the basic economics of multisided markets operating on open, distributed networks.

Consider bots. Since at least 1997—when website operators tried to game AltaVista’s rankings by submitting massive numbers of URLs—malicious bots have posed a persistent threat to the internet’s functioning. The countermeasures that followed—CAPTCHA, reCAPTCHA, hCaptcha, and Cloudflare’s suite of protection tools—all rely on collecting and analyzing user data.

Google’s reCAPTCHA used user responses to improve machine word recognition for Google Books and Maps. hCaptcha funds its privacy-preserving bot detection by selling labeled data to train AI models. Cloudflare, which now handles an estimated 21% of global internet traffic, uses analytics to distinguish humans from bots with minimal friction. In each case, user data serves as the fuel that powers the protection system.

It is hard to see how email spam, website infiltration, distributed denial-of-service (DDoD) attacks, and the many other threats to online commerce could have been addressed without tools that monetize—or otherwise leverage—user data. This is the quid pro quo of open, distributed networks. You can dislike it, but you cannot wish it away without also wishing away the protections it makes possible.

The Internet Has Been Here Before

The most productive part of the Klein-Wu-Doctorow conversation focused on AI. Ironically, the strongest rebuttal to their pessimism lies in the very trust ecosystem they discount.

AI poses three familiar trust problems: Can we trust that content is real? Can websites trust that visitors are human? And can either side trust the counterparty’s identity? These challenges are serious, but not new. They are updated versions of problems entrepreneurs have been solving for three decades.

Start with content authenticity. Digital content credentials—the C2PA standard, backed by Adobe, Google, Meta, Microsoft, OpenAI, and Sony—and digital watermarking tools like Google’s SynthID aim to verify provenance. Adoption remains early, but the pattern looks familiar. SSL began as a niche solution for high-value transactions, then spread as costs fell and benefits became clear. Cloudflare’s free Universal SSL reportedly doubled adoption almost overnight in 2014.

Identity presents a parallel challenge. Deepfake-enabled fraud has accelerated demand for multi-factor authentication, digital identity systems like Estonia’s X-Road architecture, and emerging zero-knowledge proof systems that let users verify claims without disclosing sensitive personal data. The next frontier—trusting AI agents that act on users’ behalf—will likely rely on intermediary-trust models similar to those that enabled online travel agencies: accredited, bonded corporate entities that stand behind the transactions their bots execute.

None of these solutions is perfect. All remain in development. But they are developing—driven by the same entrepreneurial forces that created SSL, eBay’s rating system, CAPTCHA, and Cloudflare.

The Unspecified Better Option

The core problem with the “we didn’t ask for this internet” framing is its premise. It assumes a better alternative existed—and that someone blocked it. But what, exactly, is the counterfactual? A government-designed internet? A network where commerce remained off-limits? An internet without user ratings, without SSL, without the data-driven protections that keep most users safe most of the time?

Tim Wu and Cory Doctorow, in particular, have long advocated more aggressive regulation of technology firms. That approach may make sense in some contexts. But their critique consistently discounts the role of decentralized, market-driven innovation in solving the very problems they highlight. User-rating systems—now continuously refined and verified through AI-based fraud detection—often outperform top-down licensing regimes. Entrepreneurial bot-detection tools adapt faster, and more precisely, than regulatory mandates.

The internet took its current form through the interaction between online protection and the use of consumer data—an interaction driven by the inherently multisided nature of markets and the distributed nature of networks. The resulting trust ecosystem is imperfect. It is also remarkable. And it continues to evolve to meet the demands of the AI era.

The Case for This Internet

We should be grateful—not just for the internet’s enormous benefits today, but for the fact that the trust mechanisms built over the past three decades can continue to evolve to meet the risks of the AI era. That requires preserving the conditions that made this progress possible: enforceable contracts, relatively open markets, and a regulatory environment that does not choke off the entrepreneurs who keep building architectures of trust.

Klein, Wu, and Doctorow are right about one thing: the internet has real problems. But those problems do not reflect a fundamental system failure. And they do not show that “we didn’t ask for this.” We did. 

This is the internet we asked for. On balance, it is rather glorious.

The post The Myth of the Unwanted Internet appeared first on Truth on the Market.

But they are real. Evidence shows guarantees can signal quality and encourage transactions. Firms that guarantee products or services often see substantial gains in business.

At a recent conference in Boston, we examined proposals for “truth guarantees” and similar market-oriented approaches aimed at mitigating “misinformation.” Guaranteeing truth could improve content quality, increase trust in journalism, and bolster the reliability of social media—especially if platforms boost guaranteed items. These proposals, however, face several challenges.

Putting a Price on Truth

Boston University Professor Marshall Van Alstyne has conducted extensive research on addressing misinformation by warranting the validity of claims. He analogizes misinformation to pollution. Just as air pollution contributes to lung disease, bad information produces externalities—insurrectionist activity, vaccine resistance, the assassination of health insurance CEOs, and other forms of social disorder.

The standard responses to externalities are familiar. In the information context, however, speech values and the First Amendment sharply—and appropriately—limit them. Direct government regulation of communications is off the table; it amounts to censorship. Pigouvian taxation fares little better. Imposing costs on producers of misinformation to compensate for harm is a content-based burden on speech that would likely fail constitutional scrutiny. In both cases, empowering a central authority to determine truth is poor policy and likely to deepen distrust.

Ronald Coase offers an alternative. A better-functioning market could enable trades that reduce misinformation or internalize its costs.

What scarce resource anchors such a market? Van Alstyne points to attention. Information consumes attention. A more efficient market for attention should improve information quality, reduce attention devoted to bad information, and mitigate associated externalities.

Free-speech norms still apply. Listeners—those who supply attention—must remain free to choose their sources. They may use that freedom to remain in filter bubbles.

Speakers, however, want to be heard. They want to penetrate those bubbles.

The question, then, is how to bring speakers and listeners together on better terms. How can incentives shift so that speakers share—and listeners engage with—more truthful content?

Research suggests modest interventions help. Prompting social media users to consider truth and falsity reduces the sharing of false information. Requiring users to certify that shared content is true reduces falsity further. These measures, however, also slightly reduce the sharing of true information.

Van Alstyne proposes a contractual right to accurate information. Speakers—here, authors—would post a “truth bounty,” effectively placing funds in escrow to back their claims. If a claim proves true, the speaker recovers the bounty after reaching audiences they might not otherwise reach. If the claim proves false, the speaker forfeits the escrow. The system raises the cost of spreading false information.

His research suggests this approach works. When speakers face rewards for truth and penalties for falsity, they share more accurate information. When listeners see that a speaker has warranted a claim, they tend to trust it more.

In “Certifiably True: The Impact of Self-Certification on Misinformation,” Van Alstyne and his co-authors find that engagement increases when truth bounties are available—more information gets shared. Lowering the cost of truth and raising the cost of falsehood yields more accurate content. Unlike efforts to cajole or coerce speakers or platforms, truth guarantees align with both the spirit and the letter of free speech.

This approach is not a cure-all. It will not eliminate uncertified false information. And when speakers falsely warrant claims, listeners may initially find those claims more credible—at least until they are challenged. Still, the results are promising and intuitively sound.

Skin in the Game—or Don’t Play

Van Alstyne is not alone in developing this approach. Yonathan Arbel of the University of Alabama School of Law and Michael Gilbert of the University of Virginia School of Law propose a contractual mechanism that allows speakers—individuals, media outlets, and others—to signal credibility through financial commitments.

In “Truth Bounties: A Market Solution to Fake News,” Arbel and Gilbert lay out a detailed framework for guaranteeing truth. They address the full lifecycle: staking money on a claim’s accuracy, contesting claims, arbitrating disputes, and resolving outcomes. Their core insight is simple: “Having something to lose … signals to consumers that [speakers] tell the truth.”

Their proposal engages closely with institutional design, including how challenges should work.

Challenges could proceed in different ways, but for communications on the internet, a straightforward way would involve clicking on [an] icon. Doing so could bring challengers to a website, where information on the story—title, date, publisher, author—would load automatically, allowing the challenger to pursue her complaint. The challenge window would remain open for a set duration, similar to a statute of limitations—for example, one year.

…Whether out of malice or ignorance, people could clog the system with meritless challenges. To mitigate this problem, the system could charge a challenge fee. The fee would force the challenger to put skin in the game. Like court fees, paying the challenge fee signals that the challenger has confidence in the merits of her claim. 

Arbel and Gilbert recognize the familiar tradeoff. Fees must deter weak claims without blocking strong ones.

A small fee would fail to screen out meritless challenges, but a large fee could block even meritorious ones. One approach would be to set the challenge fee as a single-digit % of the bounty, subject to a minimum. In any event, experience would inform the optimal amount.

Betting Against the Tin Foil Hat

Prospective guarantees are not the only path. Enrique Guerra-Pujol of the University of Central Florida proposes an ex post approach. In “Betting on Conspiracy Theories: Kurt Gödel and ‘The Leibniz Cover-Up,’” he suggests that communities—whether or not speakers participate—can sort truth from falsity through modified prediction markets.

The paper ranges widely, drawing on Michel Foucault, Richard Dawkins, and others. Guerra-Pujol uses logician Kurt Gödel’s fixation on an unlikely conspiracy against Gottfried Leibniz as a starting point for a broader method to discipline conspiratorial thinking. The core idea will sound familiar: put skin in the game.

[I]nstead of trying to censor or suppress conspiracy theories, why not allow people to bet on them? A betting market would aggregate all available information about the truth-values of various conspiracy theories by allowing bettors to bet on their beliefs about past events.

Guerra-Pujol sketches a “retrodiction market” in which participants buy and sell contracts tied to specific conspiracy theories. Each contract presents a binary outcome: true (T) or false (F). Buying either contract amounts to a wager on the correct answer.

[I]f some bettors believe that Lee Harvey Oswald was part of a conspiracy or that 9/11 was an inside job, they could buy T contracts on these topics, and conversely, if other bettors think that Oswald indeed acted alone or that 9/11 was not an inside job, bettors could buy F contracts. The prices of these bets would then be based on supply on demand, depending on how many bettors buy T or F contracts. If more bettors believe Oswald was part of a conspiracy, the price of the T contract will rise, while if more bettors believe Oswald acted alone, the price of the F contract will rise. Furthermore, the more participants or bets there are, the more likely that prices will reflect the true probabilities of the various conspiracy theories being bet on.

These markets would do more than aggregate beliefs. They would create incentives to surface and promote evidence. Participants with money at stake would work to persuade others and uncover facts that support their positions. In a sufficiently liquid market, with real stakes on the line, even long-disputed questions—such as who killed John F. Kennedy—might converge toward clearer answers.

Markets Don’t Lie—They Pay

Does Guerra-Pujol ask us to imagine things that never were and wonder why not? [apologies] Hardly. A version of the retrodiction market already exists.

Hindenburg Research combined investigative journalism with short selling. The firm took positions against companies based on research into alleged fraud or mismanagement, then publicized its findings to regulators and the press. As stock prices fell, Hindenburg profited. When the firm shut down in early 2025, founder Nate Anderson credited its work with contributing to nearly 100 civil or criminal charges “including billionaires and oligarchs. We shook some empires that we felt needed shaking.”

Again, money where one’s mouth is creates incentives to uncover and disseminate truth. Successor firms have emerged. The information environment improves.

Guerra-Pujol’s retrodiction markets aim to do something similar for conspiracy theories and false beliefs. The Hindenburg model shows how financial incentives can discipline corporate misconduct. Prediction markets—such as Polymarket and Kalshi—extend the same logic to other domains.

Consider a hypothetical. An investigator or congressional staffer uncovers clear evidence that a member of Congress accepted bribes. A prediction market that allows the evidence-holder to profit would accelerate disclosure. “Will Congressman Crooked serve out his term?” When self-interest aligns with accurate information, the result can be faster revelation and broader dissemination. The potential gains are significant—reducing the externalities that information asymmetries create across many domains.

Truth bounties, retrodiction markets, and the Hindenburg model all rely on the same mechanism: markets that reward accuracy and penalize error. They are absolutely positively 100% guaranteed to work.*

*Not a guarantee.

In a forthcoming post, we will examine the challenges these approaches face.

The post 100% Guaranteed (Not a Guarantee): Putting a Price on Truth appeared first on Truth on the Market.

In FTC v. Southern Glazer’s Wine & Spirits, the FTC alleges that the distributor violated the Robinson-Patman Act by offering better prices to some retailers than others, harming competition. The Robinson-Patman Act putatively aims to protect smaller businesses from discriminatory pricing. Southern Glazer’s responds that, after years of investigation, the FTC still cannot identify a single unlawful transaction.

Recent discovery disputes suggest the company may be right.

17 Million ‘Paired Transactions,’ Zero Clear Violations

The FTC’s case rests heavily on 17 million “paired transactions”—comparisons between prices offered to different customers. In theory, that approach could flag discriminatory pricing. In practice, the analysis appears detached from economic reality.

As part of a joint discovery stipulation, the FTC and Southern Glazer’s embedded Vimeo links to deposition testimony directly into the record, allowing the court to view exchanges in real time. The clips depict an agency unable—or unwilling—to answer basic questions about its theory of liability.

When asked whether the FTC could identify any specific diverted customer, an agency witness responded: “The FTC cannot identify any diverted customer. As of today, discovery is ongoing.”

Pressed on whether the FTC could identify a retailer that lost profits due to alleged price discrimination, the same witness admitted: “I can’t identify an instance of a retailer suffering… lost profits as a result of discriminatory conduct by Southern.”

This is more than an evidentiary gap; it is a chasm. The FTC’s answers suggest it lacks evidence to satisfy the core elements of a Robinson-Patman claim. Even after years of investigation, the agency cannot point to a single concrete example.

The deposition conduct only heightens the concern. As Southern Glazer’s filing explains:

FTC counsel’s interruptions included over 80 instructions not to answer, sometimes referencing the witness’s status as trial counsel. In total, counsel objected at least 160 times, often at such frequency and length that the witness forgot the question posed.

That pattern raises obvious questions about whether the agency is testing its case—or shielding it from scrutiny.

The FTC’s Mismatched Pairs

The FTC’s methodology presents a deeper problem. To identify allegedly unlawful price discrimination, the agency paired transactions between entities that do not compete.

One pairing compared a major supermarket chain with a restaurant and lounge. Another matched a wholesale retail giant with an airport hotel described during questioning as an “adult-themed fantasy hotel.” These businesses do not compete for the same customers in any meaningful sense. Big-box retailers offer a wide array of goods and services. “Adult-themed fantasy hotel” experiences are not among them.

Southern Glazer’s counsel captured the issue succinctly:

Is the FTC’s testimony under oath that a consumer deciding whether to buy alcohol thinks to themselves, “Gee, I’m really torn between the Publix supermarket or Truth Nightclub to pick up my bottle of vodka?”

The FTC objected to the question and declined to answer.

The Robinson-Patman Act does not prohibit price differences in the abstract. It applies when sellers discriminate between competing purchasers—businesses that actually compete for the same customers. Pairing entities with fundamentally different business models, customer bases, and purchasing contexts stretches that concept beyond recognition.

One might ask: Where, exactly, is the beef?

Despite relying on millions of data-driven pairings, the FTC has yet to explain whether those comparisons reflect real-world competition. The agency’s posture resembles another recent case. In litigation that invalidated the FTC’s new Hart-Scott-Rodino reporting form, the court observed that “although repeatedly asked, counsel for the FTC could not identify a single illegal merger in the forty-six-year history of the prior Form that the Final Rule’s new form would have prevented.”

When Discovery Becomes the Theory

The FTC appears to still be determining which transactions matter. As one witness explained, “Closer to trial, we will determine which transactions would be the focus of the trial.”

That approach inverts the purpose of discovery. Discovery tests defined allegations; it does not supply them. Even now, after years of investigation and litigation, the agency has not identified the core of its case. It cannot point to specific unlawful transactions, specific injured parties, or even defensible comparisons between competitors.

So what, exactly, is being enforced?

The FTC has framed its renewed Robinson-Patman enforcement as a return to protecting small businesses. The approach here suggests something else: an expansive enforcement model untethered from actual evidence.

Aggregating millions of “paired transactions”—some facially implausible—while deferring judgment on which violate the law invites overreach. That approach risks sweeping in standard competitive practices, such as bulk discounts and volume-based pricing, without evidence of harm to consumers or competition.

Antitrust enforcement works best when grounded in identifiable harm, coherent economic reasoning, and administrable limits. The record here raises serious questions on all three fronts.

These shortcomings lend weight to FTC Chairman Andrew Ferguson’s earlier dissent when the prior administration filed the case. As the litigation proceeds and the absence of evidence becomes harder to ignore, the FTC may need to revisit that assessment.

The post 17 Million Pairings, Zero Proof appeared first on Truth on the Market.

In recent years, policymakers across various jurisdictions have proposed restricting or banning minors’ access to social media platforms. Governments across a growing number of jurisdictions are considering age-based restrictions, mandatory parental-consent requirements, or outright bans for younger users. Recent global tracking indicates that at least 42 countries are considering, proposing, or implementing some form of social media age restriction. Several have already enacted or adopted legislation limiting minors’ access to online platforms. In the United States, two jury trials this week found Meta and Google liable for harms to children tied to product design, including claims that the companies failed to adequately verify users’ ages before allowing them to create profiles.

This surge in regulatory activity reflects a broader policy narrative that social media drives rising rates of adolescent anxiety and depression. As concerns about youth mental health and online safety intensify, policymakers have turned to blunt regulatory tools, including blanket bans and parental-consent regimes with strict age-verification requirements, to address perceived harms.

Despite strong political momentum, the empirical basis for broad restrictions remains contested. The causal relationship between social media use and teen mental health remains uncertain. At the same time, the rapid expansion of regulatory initiatives across jurisdictions risks creating a fragmented global policy landscape.

Anxiety, Apps, and Assumptions

Many proposals to restrict teens’ access to social media rest on the assumption that these platforms drive rising anxiety and other mental-health problems among adolescents. Media coverage often reinforces this narrative. For example, reports on proposed restrictions in Europe claim that “hours of scrolling over harmful content is rewiring young brains and causing anxiety and other health hazards, experts say, compelling European governments to act.”

The empirical evidence for a strong causal link, however, remains unsettled. Existing research identifies correlations between social media use and negative outcomes, not causation.

Adolescent mental health reflects a wide range of influences, including family environments, school-related pressure, economic conditions, and broader social changes. This complexity makes it difficult to isolate the specific effects of social media. The National Academies of Sciences, Engineering, and Medicine’s “Social Media and Adolescent Health” report concluded that “[t]he committee’s review of the literature did not support the conclusion that social media causes changes in adolescent health at the population level.”

Emerging research further suggests a more nuanced relationship between social media use and well-being. Some experts find that nonuse may also correlate with negative outcomes.

Not Just Doomscrolling

Public debate about social media and youth mental health focuses almost exclusively on potential harms. This framing overlooks the benefits these platforms provide to many adolescents. For teenagers, social media often extends their existing social lives, allowing them to stay connected with friends, share experiences, and participate in communities beyond their immediate offline environments.

Online platforms can also create connections that may not exist locally. Teenagers who feel isolated at school or in their communities may find peers online who share similar interests, identities, or experiences. In this way, social media can facilitate forms of social support that would otherwise be difficult to access. As the U.S. Surgeon General noted in a 2023 advisory, social media can enable community building and connections among peers with shared identities and interests.

Many adolescents report that these platforms help them feel more accepted, supported during difficult periods, and more connected to friends.

These benefits matter for policy. If social media generates both costs and benefits, policies that treat these platforms as inherently harmful risk ignoring their positive role in adolescents’ social lives. Proposals that focus only on harms present a one-sided approach to a more complex issue.

When ‘Kids Today’ Isn’t a Category

Research consistently finds that the effects of online platforms depend heavily on individual characteristics and context. As the American Psychological Association explains, “in most cases, the effects of social media are dependent on adolescents’ own personal and psychological characteristics and social circumstances,” as well as the specific content and features of the platforms they use.

This variation produces very different outcomes across users. Some teenagers experience harm from excessive or unhealthy use, while others benefit from social connection, access to information, or community support. Outcomes often turn on factors such as age, personality, family environment, and patterns of platform use.

The Organisation for Economic Co-operation and Development (OECD) similarly observes that vulnerability to problematic digital-media use often reflects broader personal and environmental factors outside the digital environment. As the OECD notes, “various personal and environmental factors in the non-digital world can make children more vulnerable to problematic digital media use.”

Blanket bans assume that all teenagers face similar risks and should be treated alike. The evidence suggests otherwise. Research on digital literacy shows that young people vary significantly in their ability to navigate online environments. One analysis finds that children with stronger digital skills are better equipped to manage online risks, while those with limited digital literacy face greater vulnerability.

These differences complicate the case for broad prohibitions. When outcomes vary widely, treating all young users as a single group risks restricting beneficial uses for those who navigate platforms responsibly while targeting harms experienced by a smaller subset. This heterogeneity points toward a more nuanced policy approach—particularly when considering who is best positioned to manage these risks.

Regulators Aren’t the Least-Cost Avoiders

From a law & economics perspective, variation in risk across individuals shifts the focus to identifying the “least-cost avoiders”—the parties best positioned to prevent harm at the lowest cost. In this context, teenagers—and the actors closest to their daily lives, including parents, families, schools, and community institutions—are often better positioned than distant regulators to manage risks associated with social media use.

These actors possess far more information about teenagers’ behavior, maturity, and social environments than regulators designing uniform rules. Parents can set screen-time limits, supervise platform use, and restrict certain applications based on individual circumstances. Schools and local communities can also shape norms around responsible online behavior. This informational advantage and proximity allow these institutions to identify problems early and respond in ways tailored to individual needs.

Policy debates often overlook these community-level institutions. As Mario Zúñiga observes, discussions of online harms tend to shift quickly toward government intervention while neglecting the role of families, schools, and social norms that have historically guided youth behavior. As he writes:

[T]he debate over social media harms is a case in point. Faced with new technologies that challenge traditional parenting, many have leapt to call for state intervention (bans, mandates), while neglecting the role of families, schools, and social norms, the community institutions that have historically guided youth behavior.

Courts have also recognized the importance of parental authority in this area. In NetChoice v. Griffin, the court noted that parents can regulate their children’s social media use by limiting time on platforms, restricting accessible content, and supervising online interactions. In practice, a range of tools already allows parents to monitor and manage their children’s digital activity. Social media services themselves provide parental-control tools that enable families to supervise platform use. Against this backdrop, age-verification and parental-consent requirements are, as U.S. District Court Judge Timothy L. Brooks put it, “more restrictive than policies enabling or encouraging users (or their parents) to control their own access to information, whether through user-installed devices and filters or affirmative requests to third-party companies.”

What Happens After the Ban

Even when well-intentioned, blanket bans on teenagers’ use of social media may produce unintended consequences. One concern is displacement. Restrictions may push young users toward less regulated or harder-to-monitor online spaces. Teenagers may circumvent age restrictions or migrate to alternative platforms that operate outside mainstream services. As one analysis notes, “under-16s will simply switch to other platforms that are less studied and less well-regulated.”

If activity shifts to encrypted or underground platforms, parents and educators may lose visibility into teenagers’ online behavior, making supervision more difficult. Broad prohibitions can therefore risk “making it harder for parents and educators to identify and address issues.” These measures may also alienate young users, who could view them as intrusive and dismissive of their autonomy.

Social media bans also raise concerns about access to information and participation in public discourse. For many teenagers, these platforms serve as key channels for communication, news consumption, and engagement with social and educational communities. Restricting access may limit their ability to participate in discussions that increasingly take place online.

In jurisdictions with strong free-speech protections, such as the United States, broad limits on access to communication platforms may also raise constitutional questions. Several courts have already held that parental-consent and age-verification requirements conflict with First Amendment jurisprudence. As U.S. Supreme Court Justice Brett Kavanaugh explained, “[g]iven those precedents, it is no surprise that the District Court in this case enjoined enforcement… and that seven other Federal District Courts have likewise enjoined enforcement of similar state laws.”

These proposals also present important privacy tradeoffs. Many social media bans rely on strict age-verification systems that require users to submit personal identification or other sensitive information to prove their age.

While platforms already use various methods to estimate or verify age, additional regulatory mandates may expand the collection and storage of personal data. These requirements risk creating new privacy and data-security concerns, particularly when large volumes of sensitive information are handled by private platforms or third-party verification services.

Regulation Without Borders

Governments worldwide are experimenting with policies aimed at protecting minors online, but these approaches vary significantly across jurisdictions. In the European Union, platforms operate under the Digital Services Act, which imposes obligations related to transparency, risk assessment, and protections for minors. In the United States, policymakers have taken a different approach, focusing on proposals such as the Kids Online Safety Act and a range of state-level initiatives addressing youth access to social media. Other countries are considering or implementing age-based restrictions, parental-consent requirements, or broader platform obligations.

These diverging approaches create challenges for platforms operating globally. Companies must navigate a growing patchwork of national and regional rules that differ in scope and design. Requirements for age verification, parental consent, platform design, and access restrictions may vary by jurisdiction. This fragmentation increases compliance complexity and may produce inconsistent rules governing who can access online services and under what conditions.

In response, platforms may adopt the most restrictive standards across larger markets to simplify compliance. This approach can extend stricter rules beyond the jurisdictions that enacted them. As a result, minors may face access restrictions—including removal from platforms—even in places that have not adopted such bans. In this way, blanket prohibitions in one jurisdiction can generate spillover effects that reach well beyond the regulators that imposed them.

A Blunt Tool for a Complex Problem

Proposals to ban teenagers from social media rest on a simplified narrative that online platforms are the primary driver of declining youth mental health. The empirical evidence does not support such a clear causal claim. Instead, the research points to a far more complex relationship, with outcomes that vary significantly across individuals and contexts.

Because social media generates both costs and benefits, blanket bans are a blunt tool. Treating teenagers as uniformly vulnerable ignores differences in digital literacy, family environments, and patterns of platform use. Such restrictions risk eliminating beneficial uses of online platforms while targeting harms that affect only a subset of users.

These policies also carry unintended consequences. Strict age-verification requirements raise privacy concerns. Access restrictions may push users toward less regulated online spaces that are harder for parents and educators to monitor.

If the goal is to mitigate harm, policymakers should be cautious about one-size-fits-all restrictions. Teen social media use reflects a wide range of influences that broad prohibitions cannot effectively address. A more effective approach would preserve the benefits of online participation while targeting specific harms as they arise.

Regulating social media as if all teens face the same risks may feel decisive—but it is neither precise nor effective.

The post Social Media Bans and the Problem of One-Size-Fits-All Policy appeared first on Truth on the Market.

Ongoing Tunney Act proceedings tell a different story. In United States v. Hewlett Packard Enterprise Co., No. 5:25-cv-00951-PCP (N.D. Cal.), the court continues to review the settlement, including an evidentiary hearing during the week of March 23. Recent developments underscore the transaction’s procompetitive benefits—and confirm that the DOJ was right to approve it.

As Antitrust Meets Geopolitics, Huawei Looms Large

The merger combines HPE’s expertise in enterprise and cloud infrastructure with Juniper’s hardware and AI-driven networking management. That combination positions the firm as a stronger global competitor in AI-driven networking infrastructure. By integrating complementary assets, the deal also enables a scaled U.S. company to compete more effectively with Huawei—the China-based, state-backed telecommunications-equipment manufacturer and the largest firm in that market. Both the executive branch and Congress have identified Huawei as a national security threat.

The intelligence community appears to agree that the merger advances U.S. national security interests. According to recent reports, Central Intelligence Agency (CIA) Director John Ratcliffe concluded that blocking the deal would create national security risks, while allowing it to proceed would mitigate them. That assessment contrasts with earlier statements from the Antitrust Division’s former leadership, which reportedly—and incorrectly—indicated that the intelligence community had no concerns about stopping the transaction.

The DOJ appears to have relied heavily on that intelligence assessment in agreeing to a proposed consent decree. That reliance undercuts critics’ central claim that the settlement reflects improper political influence, rather than a legitimate enforcement judgment.

The decision also aligns with a broader U.S. policy objective: encouraging allies to adopt American, rather than Chinese, technology. U.S. technology exports project American influence and values, including a commitment to democratic governance. U.S. firms operate in an environment defined by openness and freedom of expression; Chinese technology firms operate under significant state control. As one technology executive explains:

The number one factor that will define whether the U.S. or China wins this [AI] race is whose technology is most broadly adopted in the rest of the world … Whoever gets there first will be difficult to supplant.

A Merger Below the Line and a Theory That Stretches It

Even apart from national security, the merger posed, at most, modest antitrust risk in a market with strong existing competition. Cisco holds roughly 50% of the wireless enterprise networking market, while Hewlett Packard Enterprise and Juniper Networks account for about 17% and 9%, respectively.

In United States v. Philadelphia National Bank, 374 U.S. 321 (1963), the Supreme Court suggested in dicta that a merger may trigger a presumption of illegality if it produces a combined market share of 30% or more. The HPE–Juniper merger yields a combined share of about 26%—well below that benchmark. Courts and commentators have also criticized the 30% threshold as arbitrary in the decades since.

Commentators further note that the DOJ’s initial emphasis on market shares reflected Cisco’s entrenched dominance more than any meaningful increase in market power from the merger itself. By challenging a deal that falls below even basic structural benchmarks under Section 7 of the Clayton Act, 15 U.S.C. § 18, the DOJ risked an adverse ruling that could have narrowed future enforcement authority.

The Tunney Act Is Not a Do-Over Machine

Against this backdrop, the merger should readily satisfy review under the Tunney Act, 15 U.S.C. § 16(e), which requires courts to determine whether a proposed consent decree is “in the public interest.” Congress enacted the statute in 1974 to address concerns about backroom dealmaking in antitrust settlements. Amendments in 2004 direct courts to consider factors such as the decree’s competitive impact, the adequacy of enforcement mechanisms, and the likely effects of alternative remedies.

The statute gives courts a limited role. As the U.S. Court of Appeals for the D.C. Circuit explained in Massachusetts v. Microsoft Corp., 373 F.3d 1199 (D.C. Cir. 2004), the reviewing court asks whether the decree addresses the competitive harms alleged in the complaint—not whether the court would have made the same enforcement decision. A court should reject a decree “only if any of the terms appear ambiguous, if the enforcement mechanism is inadequate, if third parties will be positively injured, or if the decree otherwise makes a mockery of judicial power.”

Measured against that standard, the proposed consent decree is straightforward. It requires HPE to divest its Instant On and wireless local area network business and to license key Juniper software to competitors. These targeted remedies address the horizontal overlap identified in the complaint.

Courts almost never reject DOJ merger settlements under the Tunney Act’s public interest standard, and evidentiary hearings remain rare. The 2019 proceedings in the CVS–Aetna matter stand as a notable exception.

The state attorneys general’s petition also raises broader concerns. Their core claim—that lobbying activity justifies full-scale judicial relitigation—would convert the Tunney Act into a vehicle for political actors to second-guess enforcement decisions. Antitrust agencies routinely hear from lobbyists about a merger’s potential effects. That reality does not undermine the public interest.

The states’ intervention rests on the theory that the DOJ abandoned a meritorious case for political reasons. The emerging record suggests the opposite. The Antitrust Division’s initial challenge may have relied on an incomplete understanding of the intelligence community’s views. If so, the subsequent decision to settle reflects a legitimate course correction, not an improper concession.

A Deal That Checks Every Box

The proposed consent decree satisfies the Tunney Act’s public interest standard on every relevant measure. The combined firm’s market share remains well below the threshold discussed in Philadelphia National Bank, and the decree imposes targeted divestitures and licensing commitments that address the horizontal competitive concerns.

The national security case strengthens the result. The intelligence community appears to have confirmed that the merger enhances the ability of a U.S. firm to compete globally with Huawei, a Chinese company designated as a national security threat.

Taken together, the record supports approval. The proposed consent decree advances competition, reinforces national security interests, and serves the public interest.

The post When Antitrust Meets National Security and Gets It Right appeared first on Truth on the Market.

Last week, my International Center for Law & Economics (ICLE) colleagues Ben Sperry and Jeff Westling published a post here at Truth on the Market titled “The FCC’s Sleeping Power over the Press.” It’s not about antitrust, but it’s well worth your time. Super-attentive readers of my agency roundup posts may recall my occasional forays into First Amendment issues (here and here, with links to journal articles here and here). Everyone needs a hobby. Mine happens to be the Bill of Rights. TMI? Perhaps. In any event, there’s a Federal Trade Commission (FTC) hook here. I’ll get to that.

Ben and Jeff focus on the administration’s “willingness to combat so-called ‘fake news’ through a little-used and difficult-to-enforce” policy recently invoked by Federal Communications Commission (FCC) Chairman Brendan Carr. As they note, Carr posted a screenshot of a Truth Social message from President Donald Trump criticizing press coverage of the conflict in Iran (not a war, even if it often looks like one). The president has every right to kvetch about coverage of himself, his policies, or anything else. There’s nothing objectionable about that, whether or not we agree with his views. I don’t take Ben or Jeff to suggest otherwise.

Carr, however, did more than amplify the president’s complaint. He added this on social media:

Broadcasters that are running hoaxes and news distortions-also known as the fake news–have a chance now to correct course before their license renewals come up. The law is clear. Broadcasters must operate in the public interest, and they will lose their licenses if they do not.

That sounds like something more than a personal gripe. It’s certainly not a factual correction—Carr identified no specific errors or corrections. If he meant to engage in jawboning—or to signal something more pointed—the vagueness may be the point. As King Lear put it (not at his most lucid):

I will do such things/What they are, yet I know not; but they shall be/ The terrors of the earth

Better to avoid that sort of madness—and the time and expense of thorny constitutional litigation. You have Ben and Jeff’s post, so I won’t rehash it here. Jumping to their recommendations, they offer two sensible proposals: first, the FCC should repeal its “news distortion policy” through its “Delete, Delete, Delete” proceeding; second, Congress should repeal the statutory authority said to undergird that policy.

One more point. Neither Carr nor the Trump 2 administration invented jawboning or government efforts to influence the press. These practices didn’t begin with the Biden administration either, though they continued there. Murthy v. Missouri set a high bar for challenging “backdoor” government efforts to censor speech, over a sharp dissent from Justice Samuel Alito, joined by Justice Neil Gorsuch and Justice Clarence Thomas (Ben has a helpful post on the decision). But that case turned on standing. And National Rifle Association v. Vullo—also decided in 2024—took a less forgiving view of government pressure conveyed through “guidance letters” (see Ben and R.J. Lehmann’s post on Vullo). All worth keeping in mind as we turn to…

Where Antitrust and Speech Collide—Sort Of

Several of my earlier forays into speech regulation were prompted by the FTC and the U.S. Department of Justice (DOJ) Antitrust Division. One followed an FTC announcement seeking public comment on “technology platform censorship.” That request suggested that content moderation by tech platforms might violate either—or both—the competition and consumer-protection prongs of Section 5 of the FTC Act.

Another, co-authored with Ben Sperry, examined a statement of interest the Antitrust Division filed in a private antitrust case against several major news publishers. The division did not argue that the alleged facts established anticompetitive conduct—specifically, an agreement in violation of Section 1 of the Sherman Act. It argued only that they could.

We found that argument both narrow and ill-advised. Once again, you have the link, so I won’t rehash it here. If you want a fuller treatment, we cover this ground—and more—in our Concurrences article, “Is There an Empty Set at the Intersection of Antitrust and Content Moderation?”

Apple News, Section 5, and a Theory That Doesn’t Quite Land

A Feb. 12 press release from the FTC reports:

Federal Trade Commission Chairman Andrew N. Ferguson issued a letter to Apple CEO Tim Cook, reminding him of Apple’s obligations to its customers. The letter follows reports that Apple News systematically boosts left-wing sources and suppresses right-wing sources. The letter points out that if Apple misrepresents Apple News or violates its terms of service, it could be violating the FTC Act.

A reminder. How thoughtful. 

The letter itself states that “there have been reports that Apple News has systematically promoted news articles from left-wing news outlets and suppressed news articles from more conservative publications.” It continues: “[t]hese reports raise serious questions about whether Apple News is acting in accordance with its terms of service and its representations to consumers, as well as the reasonable consumer expectations of the tens of millions of Americans who use Apple News.”

“Serious questions”? Right. I doubt it.

That’s not to say I endorse Apple News’ editorial choices, or that the platform is unbiased or politically neutral. I’m not even sure what, precisely, “neutrality” would mean in this context.

What Apple Promises (Hint: Not Much)

Let’s take this in four parts: (1) the terms of service, (2) the “reports” cited by Ferguson, (3) possible Section 5 liability, and (4) the First Amendment.

Start with the terms of service. These documents—drafted, edited, and redrafted by counsel—rarely promise much. Apple’s are no exception. They say little of substance, aside from noting that Apple’s privacy policy applies, with the usual caveat:

by using the Site, you acknowledge and agree that Internet transmissions are never completely private or secure. You understand that any message or information you send to the Site may be read or intercepted by others . . . 

And, in all caps—so YOU KNOW THEY MEAN IT:

APPLE DOES NOT PROMISE THAT THE SITE OR ANY CONTENT, SERVICE OR FEATURE OF THE SITE WILL BE ERROR-FREE OR UNINTERRUPTED, OR THAT ANY DEFECTS WILL BE CORRECTED, OR THAT YOUR USE OF THE SITE WILL PROVIDE SPECIFIC RESULTS. 

Otherwise, we get the standard limitations and disclaimers. What we don’t get is any assurance of political neutrality—however defined—or even objective truth.

Ferguson’s letter says little about the terms. It notes, accurately but generically, that they address “a consumer’s use of the site, prohibited conduct, privacy and data security, and dispute resolution.” That doesn’t get us very far.

The Evidence (Such as It Is)

What about those troubling “reports”? Two come from Heather Moon at a site called MRC Free Speech America. I hadn’t heard of it either. It is sponsored by the Media Research Center, which bills itself as “AMERICA’S MEDIA WATCHDOG,” dedicated to “Shattering Liberal Media influence and Exposing Big Tech censorship that threaten America’s core values.” Moon’s posts rely on an MRC “study.”  Ferguson also cites a New York Post article, but that piece simply summarizes the same MRC findings.

Moon claims:

Apple News stubbornly refrained from using any right-leaning outlets in the top 20 articles of its morning editions between Jan. 1 and Jan. 31, 2026. Of the 620 top stories featured by the news app in the first month of the year, not a single one was from a right-leaning media outlet. 

Maybe. But there’s no objective metric for what counts as “left-” or “right-leaning.” The MRC relies on bias ratings from AllSides. Those ratings strike me as highly subjective—not quite what one expects, even from “qualitative” social science. Still, AllSides does explain its method and publishes its classifications. To its credit, it notes that it “doesn’t rate accuracy or credibility, because we’re not a Ministry of Truth. A publication can be accurate yet still biased.”

On that scale, the Wall Street Journal—my paper of choice, though one of three we subscribe to—is rated “Center,” alongside outlets like Forbes, Reuters, MarketWatch, and Reason. There are also categories for Lean Right and Right, and for Lean Left and Left. For example, the New York Times is rated Lean Left for news and Left for opinion.

None of this is especially satisfying. It’s subjective, contestable, and—frankly—the point. There’s no shortage of disagreement about media bias, including my own views.

For what it’s worth, I spot-checked Apple News. On March 22, its Top Stories included Fox News (Right, per AllSides), Reuters (Center), Bloomberg (Left), and the Los Angeles Times (Left), alongside sports and entertainment content from outlets that AllSides doesn’t rate. On March 23, the top included the Wall Street Journal, Reuters, and ABC News. Scrolling further yields more of the same: a mix of sources, categories, and topics.

If there’s a clear pattern, I didn’t find it.

Trying to Fit Editorial Judgment into UDAP

What about Section 5 of the FTC Act? Ferguson appears to focus on the consumer-protection prong—unfair or deceptive acts or practices (UDAP). More specifically, unfairness:

Big Tech companies that suppress or promote news articles in their news aggregators or feeds based on the perceived ideological or political viewpoint of the article or publication may violate the FTC Act if that suppression or promotion (1) is inconsistent with the terms and conditions of service; (2) is contrary to consumers’ reasonable expectations such that failure to disclose the ideological favoritism is a material omission; or (3) when those practices cause substantial injury that is neither reasonably avoidable nor outweighed by countervailing benefits to consumers or competition.

How would the FTC get an unfairness case off the ground here?

The terms of service say nothing about ideological neutrality. They don’t promise balance, accuracy, or any particular editorial approach. The only statement touching accuracy is the disclaimer quoted above.

So what’s the theory? A material omission? By which version of the product? Relative to what “reasonable expectations”? Established how? And what, exactly, should Apple have disclosed?

As Ben and I have argued elsewhere:

At bottom, filtering the news—and sources of the news—is central to the product or service that news organizations provide in the market. The notion that they best provide that good or service by publishing everything … is risible, if not incoherent. 

Substitute “balance” for “diversity,” and the problem remains. Editorial selection is the product, not a defect. The fact that some users prefer different editorial choices does not transform those choices into an unfair practice.

I’m not saying a Section 5 theory is impossible. I am saying I don’t see one here—and that’s before we get to the First Amendment.

The FTC Is Not the Speech Police—But…

Ferguson includes a paragraph that, standing alone, sounds entirely reasonable:

The FTC is not the speech police; we do not have authority to require Apple or any other firm to take affirmative positions on any political issue, nor to curate news offerings consistent with one ideology or another. But Congress has mandated that we protect consumers from material misrepresentations and omissions, including when the product or service offered to consumers is a speech-related product.

The first sentence is exactly right.  I like it so much that I’ll repeat it, bolded, and in italics:

The FTC is not the speech police; we do not have authority to require Apple or any other firm to take affirmative positions on any political issue, nor to curate news offerings consistent with one ideology or another. 

The second sentence is also true, as far as it goes. The First Amendment does not immunize all speech. Fraud, for example, is not protected (so says the Supreme Court) and falls squarely within the FTC’s UDAP jurisdiction. See, e.g., Tim Muris testifying before the Senate in 2010, and more recent remarks by Chris Mufarrige, director of the FTC’s Bureau of Consumer Protection.

There is also precedent for the proposition that the First Amendment does not protect criminal or civil conspiracies that violate Section 1 of the Sherman Act or Section 5 of the FTC Act—this time, the unfair methods of competition (UMC) prong. The Supreme Court’s 1945 decision in Associated Press is the usual citation. The First Amendment does not absolutely prohibit all congressional (and, via the Fourteenth Amendment, state) regulation of speech acts. But you knew that.

Still, protected speech is protected speech. And the exercise of editorial discretion strengthens First Amendment claims. The space for a viable Section 5 complaint—under either the UMC or UDAP prong—is narrow. Which is to say, Ferguson is a bit breezy here:

The First Amendment protects the speech of Big Tech firms. But the First Amendment has never extended its protection to material misrepresentations made to consumers, nor does it immunize speakers from conduct that Congress has deemed unfair under the FTC Act, even if that conduct involves speech.

Congress has not deemed Apple’s conduct unfair under the FTC Act. Section 5 provides that “[u]nfair methods of competition in or affecting commerce, and unfair or deceptive acts or practices in or affecting commerce, are hereby declared illegal.”  (15 USC § 45(a)(1)). It also limits the commission’s authority under the unfairness prong:

The Commission shall have no authority … to declare unlawful an act or practice on the grounds that such act or practice is unfair 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.

That’s Section 5(n) (15 U.S.C. § 45(n)). There’s more to Section 5 than subsections (a)(1) and (n), but none of it specifies which acts or practices are unlawful. That work is left to the FTC and the courts.

You might call that a quibble. Courts have upheld enforcement of antitrust and FTC Act violations notwithstanding First Amendment protections. But First Amendment doctrine suggests it’s not so simple. The precedents Ferguson invokes do not obviously do the work he assigns them.

A Boycott Is Not a News Feed

Ben Sperry and I discuss Federal Trade Commission v. Superior Court Trial Lawyers Association elsewhere, so I’ll be brief here. The case involved a group boycott. As Justice John Paul Stevens explained for the Court:

Pursuant to a well-publicized plan, a group of lawyers agreed not to represent indigent criminal defendants in the District of Columbia Superior Court until the District of Columbia government increased the lawyers’ compensation.

The Court had little trouble characterizing the conduct. “[T]he horizontal agreement among these competitors was unquestionably a ‘naked restraint’ on price and output.” The lawyers argued that their boycott was expressive conduct—protected speech. The Court disagreed.

As Stevens put it:

Every concerted refusal to do business with a potential customer or supplier has an expressive component. At one level, the competitors must exchange their views about their objectives and the means of obtaining them. The most blatant, naked price-fixing agreement is a product of communication, but that is surely not a reason for viewing it with special solicitude.

To treat such conduct as protected speech, the Court concluded, “exaggerates the significance of the expressive component in respondents’ boycott.”

That analysis does real work—but in a very specific setting: a horizontal agreement among competitors to restrain trade. It does not follow that any activity involving speech, or even expressive judgment, loses First Amendment protection simply because it has communicative elements. And it is a long way from a coordinated refusal to deal to editorial judgment about which stories to feature on a news platform.

Fraud Is Not Protected Speech, But This Isn’t Fraud

The citation to the FTC’s POM Wonderful case ultimately returns us to the same point: fraud is not protected speech.

The commission’s complaint targeted advertisements for pomegranate-based products that claimed to treat, prevent, or reduce the risk of heart disease, prostate cancer, or erectile dysfunction—without adequate substantiation. Some allegations proved too ambitious and were rejected on appeal. Others were straightforward. For a careful treatment, see Maureen Ohlhausen’s opinion for the commission and her concurrence, which parses the strengths and weaknesses of the claims.

At bottom, the commission found liability based on numerous “advertisements containing false and misleading claims.” As alleged, POM:

disseminated advertising and promotional materials representing that consumption of certain doses … treats, prevents or reduces the risk of heart disease, prostate cancer, or erectile dysfunction … without having a reasonable basis to substantiate these claims.

A panel of the U.S. Court of Appeals for the D.C. Circuit—Judge Douglas Ginsburg, Judge Merrick Garland, and Judge Sri Srinivasan—agreed on the core findings. While rejecting some of the FTC’s substantiation standards, the panel concluded that: 

the  Commission’s  findings  of deception are supported by substantial evidence in the record; and  we  would  reach  the  same  conclusion  even  if  we  were  to  exercise  de  novo  review,  at  least  with  respect  to  the  nineteen ads determined  misleading  by  the  administrative  law  judge and held by the Commission to form a sufficient basis for its liability determination and remedial order. 

The court applied the Supreme Court’s Central Hudson framework for commercial speech, which offers significant protections, but less protective than the First Amendment’s treatment of core expressive activity.

That matters. This was not about political, social, or religious speech. It was not about academic or artistic expression. It was not about “pure speech.” And, to put it bluntly, it was not about the news.

It was about false advertising.

Fraud often takes the form of words—speech acts—but it is not protected by the Speech Clause of the First Amendment. That’s doing the work in POM Wonderful. It’s not obvious that it does any work here in the Apple News matter.

Editorial Discretion Still Means Something

Associated Press doesn’t get us much further. We should read it in light of 80-plus years of subsequent First Amendment jurisprudence. That body of law includes cases involving news organizations, such as Miami Herald Publishing Co. v. Tornillo, and content moderation by platforms, such as Moody v. NetChoice.

Those cases protect editorial discretion.

In Moody, the Supreme Court struck down Florida and Texas laws regulating large social media platforms, emphasizing:

On the spectrum of dangers to free expression, there are few greater than allowing the government to change the speech of private actors in order to achieve its own conception of speech nirvana.

The Court reiterated that the government may not “restrict the speech of some elements of our society in order to enhance the relative voice of others.” Buckley v. Valeo, 424 U.S. 1, 48–49 (1976) (per curiam).

That principle applies with force to both publishers and platforms.

Ben Sperry and I have made the same point in the antitrust context (here and here). The First Amendment protects editorial choice. It does not empower the agencies to supervise it.

All Signal, No Case

Do we know where courts—or ultimately the Supreme Court—would draw the line in some hypothetical Section 5 unfairness case raising First Amendment issues? We don’t. But we do know the direction of travel: the Court has been far more protective of speech—and editorial discretion—than of government intervention in both news media and content moderation.

Do we know what unfairness case the FTC might bring against Apple News? No. Chairman Ferguson’s letter doesn’t say, and it’s hard to derive one from the terms of service.

In theory, one could imagine a case. If a platform’s editorial practices diverged sharply from specific, material representations in its advertising or marketing, that gap might support a fraud theory. That could, in turn, violate Section 5’s prohibition on “unfair or deceptive acts or practices in or affecting commerce” without triggering First Amendment protection or running afoul of the statute’s limits. Possible, yes. Likely, no. And it doesn’t describe the conduct Ferguson identifies.

Warning letters can serve a useful function. They can provide targeted guidance—a form of soft law that signals potential enforcement. The FTC, including under this administration, has issued such letters before. But it’s hard to see much guidance here, if any. For that matter, on “guidance,” see Vullo.

No doubt, some users feel aggrieved by Apple’s editorial choices. Ferguson may be among them. But if the foregoing is right, what is the point of the letter? The charitable view is political posturing, with no enforcement action to follow. Perhaps Tim Cook understands that, with or without the advice of counsel.

The less charitable view is jawboning—or something close to it: an attempt to exert pressure, backed by the implicit threat of investigation, to achieve indirectly what the chairman concedes the FTC cannot do directly.

The post Speech, Section 5, and Some Curious Scribbling: A First Amendment Story appeared first on Truth on the Market.

Acquihires are transactions aimed primarily at acquiring a firm’s workforce, rather than its products or other assets. They differ from “license-and-hire” agreements, in which the acquirer also licenses the target’s technology. In recent years, acquihires have drawn increasing policy attention. Critics argue they allow large incumbents to consolidate power, hoard talent, and sidestep merger-control thresholds. Federal Trade Commission (FTC) Chair Andrew Ferguson captured this concern when he announced that the agency would investigate such deals “to make sure they are not a way to get around” merger review.

These concerns are especially acute in fast-growing sectors like artificial intelligence (AI), where human capital drives innovation. Recent transactions have served to intensify the scrutiny. Microsoft hired Inflection AI’s top talent while licensing its technology. Google entered a similar arrangement with Character.ai, and Amazon with Adept.

This debate echoes earlier concerns about killer acquisitions. That literature focused on dominant firms acquiring nascent rivals to shut them down and preempt future competition. Empirical support emerged in pharmaceutical markets, but evidence from other sectors like digital markets remained limited. That distinction did little to slow policy momentum. Regulators and commentators increasingly treated large technology acquisitions as inherently suspect, even when the empirical record was mixed.

Acquihires and similar arrangements now play a comparable rhetorical role, particularly in discussions of AI. Commentators sometimes describe AI as a “first-class fire accelerator” of anticompetitive conduct. This framing elevates a transaction form that is neither new nor empirically settled into a systemic threat.

That narrative risks repeating the analytical missteps of the early killer acquisition debate. The point is not that acquihires cannot be anticompetitive. Rather, current discussions often rest on questionable assumptions. This post examines three: that acquihires are designed to evade regulatory scrutiny; that they are inherently anticompetitive; and that existing enforcement tools are inadequate, requiring regulatory expansion. A more effective approach is to analyze these transactions within the existing merger-control framework.

Not Just an End Run

A central claim in the current debate is that acquihires (and license-and-hire agreements) are designed to evade merger control. Because these transactions may involve limited asset transfers, no clear change of control, or targets with negligible turnover, they can fall below notification thresholds. Some authorities therefore characterize acquihires as an “avoidance strategy,” rather than a legitimate business practice.

Firms may, of course, structure transactions to reduce regulatory scrutiny. But as with killer acquisitions, that explanation is incomplete. Firms pursue acquihires for a range of reasons that reflect competitive strategy, not circumvention.

One driver is strategic repositioning. In innovation-driven markets, firms often face constraints not in physical or financial capital, but in specialized human capital. Acquihires allow firms to pivot quickly into emerging technological domains by bringing in cohesive teams with relevant expertise. That flexibility is critical in fast-moving environments.

A second driver is information costs. Knowledge in high-tech sectors is often tacit, ambiguous, and embedded in organizational routines. Hiring individuals one by one rarely transfers that knowledge effectively. Acquiring an intact team can offer a more cost-effective way to internalize capabilities that cannot be easily codified or licensed.

A third, often overlooked rationale is failure management. In high-tech environments, failure rarely marks a definitive endpoint. Acquihires allow startups to redeploy talent even when their products or business models do not scale. They can also reflect the final stage of experimentation within large firms, where exploratory units are spun out and later reabsorbed once they demonstrate value.

These dynamics complicate efforts to infer anticompetitive intent from transaction structure alone.

Effects, Not Assumptions

Competition law focuses on effects, not intent. The acquihire debate points to several ways these transactions could harm competition, but the evidence is more equivocal than often claimed.

One concern is talent foreclosure. Some theoretical and empirical work suggests firms may pursue inefficient acquisitions to deny rivals access to scarce talent. That theory depends on a key condition: scarcity. Foreclosure matters if the relevant input is limited or shrinking. That makes this an empirical question: is talent scarce? In AI labor markets, the answer is not obvious. Rapid wage growth, retraining, and new entry all point toward expanding, not constrained, talent pools. If supply grows, the case for systematic talent preemption weakens.

A second concern centers on financial asymmetries. Large technology firms can draw on deeper capital reserves and may outbid smaller rivals for talent. But financial resources are an imperfect proxy for access to workers. Empirical research shows that highly skilled employees often trade compensation for autonomy, mission-driven work, or startup culture. Anecdotal evidence from AI markets points in the same direction. If workers value more than pay, incumbents’ ability to foreclose rivals through acquihires becomes less clear.

The takeaway is straightforward: acquihires can be benign, beneficial, or harmful, depending on context. That is exactly the type of case-specific analysis that modern merger control is designed to perform.

When Hiring Becomes a Merger

If acquihires raise risks in some cases, does that justify expanding merger control?

Many competition authorities already have tools to review at least some of these transactions. Several European authorities can scrutinize sub-threshold deals through transaction-value thresholds and call-in powers. The United Kingdom’s Competition and Markets Authority (CMA) has used its flexible jurisdictional framework to examine similar arrangements.

At the European Union level, the picture is similar. To be clear, transaction value does not provide a reliable jurisdictional hook, as the General Court noted in Cisco Systems. But the European Commission is not without options. Under the Digital Markets Act (DMA), designated gatekeepers must report a broad range of concentrations, including those below notification thresholds. The Commission can share that information with national authorities, which may then refer cases back for review. As more authorities gain these powers, their use becomes increasingly likely.

Further expansion of merger review would carry real costs. Some, such as increased legal uncertainty, were highlighted by the Court of Justice in Illumina/Grail. Others deserve equal attention. From an economic perspective, the market for corporate control reallocates assets to their highest-valued use. In technology markets, human capital is often a firm’s most valuable asset. An acquihire can offer a lower-cost mechanism to transfer that asset: rather than negotiating dozens of individual contracts, a firm can secure an entire team through a single transaction.

Line-drawing presents an additional challenge. There are few clear limiting principles that separate acquihires (where merger review might apply) from ordinary hiring arrangements, where it likely would not. That ambiguity risks overreach. If acquiring a workforce qualifies as a merger, where does enforcement stop? Does hiring a high-profile CEO amount to a transfer of control? What about recruiting a four-person data science team?

Diagnosis Before Prescription

The growing scrutiny of acquihires reflects legitimate concerns about the concentration of power in innovation-intensive markets, particularly within the booming AI sector. But the rush to cast these transactions as a systemic threat risks repeating the analytical missteps of the “killer acquisitions” debate.

Acquihires are not inherently anticompetitive, nor do they fall outside the reach of existing law. They are complex organizational mechanisms that often solve real economic problems. They facilitate the transfer of tacit knowledge, enable the rapid redeployment of cohesive teams, and provide a necessary “soft landing” for failed startup experiments.

If the past decade of merger policy offers a lesson, it is this: effective enforcement requires careful diagnosis, not rhetorical momentum. Existing competition tools, at various levels of government, can address clear cases of foreclosure. Before policymakers pursue costly reforms to address acquihires, they should ensure the cure is not worse than the disease.

The post Acquihires and Other Antitrust Ghost Stories appeared first on Truth on the Market.

Whether Congress can translate that posture into durable legislation remains an open question. But as a statement of direction, the framework gets more right than wrong.

Start with what is likely to be the least controversial pillar: children and parental control. The framework emphasizes empowering parents with tools to manage minors’ digital environments, alongside age-assurance mechanisms and protections against exploitation. This largely tracks the trajectory of existing law, which has increasingly moved toward giving parents the primary role in shaping children’s online experiences. Importantly, the framework also avoids vague or open-ended liability standards for content, a choice that should reduce the risk of over-deterrence and constitutional conflict.

More interesting is the framework’s approach to infrastructure. It explicitly recognizes that AI development is constrained not only by algorithms and data, but by energy and physical capacity. The call to streamline permitting and allow data center operators to develop or procure their own power (particularly behind-the-meter generation) reflects a pragmatic understanding of where real bottlenecks lie. If taken seriously, this could help avoid the emerging dynamic in which AI policy debates fixate on model governance while ignoring the physical constraints that will ultimately shape deployment.

On intellectual property, the framework adopts a notably restrained posture. It expresses the administration’s view that training on copyrighted material does not violate copyright law, but defers to courts to resolve the issue and cautions Congress against intervening prematurely. This is the correct instinct. The fair-use question is already being actively litigated, and premature legislative intervention risks freezing the law before courts have had an opportunity to develop doctrine in response to real disputes. 

At the same time, the framework appropriately shifts IP-related attention to model outputs. It emphasizes protecting creators from infringing AI-generated content and contemplates a federal right covering name, image, and likeness, with explicit carveouts for parody, satire, and news reporting. The balance of targeting downstream harms while preserving expressive uses is far more defensible (and administrable) than attempts to regulate training inputs directly.

Still, it is difficult to imagine Congress entirely avoiding the training question. The economic stakes are too large, and the litigation trajectory too uncertain, for lawmakers to remain indefinitely on the sidelines. The framework is best understood, therefore, as buying time for courts to develop the relevant principles and factual records, rather than as a permanent settlement.

The framework’s free-speech provisions are also notable, particularly in their explicit rejection of government “jawboning.” By prohibiting federal agencies from coercing platforms to alter or suppress content based on ideological considerations, the framework attempts to draw a bright line around state action in the AI context. Given the tumultuous state of First Amendment doctrine in platform governance, this is a meaningful commitment, though one that will depend heavily on how courts interpret both coercion and collaboration.

On institutional design, the framework continues to resist calls for a centralized AI regulator. Instead, it endorses a sectoral approach, relying on existing agencies with subject-matter expertise, while supporting tools like regulatory sandboxes and shared federal datasets. This is almost certainly the right model. AI is not a single industry or product but a general-purpose technology, and attempts to regulate it through a single agency risk both overbreadth and irrelevance.

The endorsement of data access initiatives, including federal datasets and sandbox environments, is particularly promising. If implemented effectively, these could lower barriers to entry and mitigate concerns about concentration by enabling broader participation in AI development. That said, the success of such efforts will depend on execution: poorly designed data-sharing regimes can easily become either unusable or captured by incumbents.

Perhaps the most consequential and contentious element of the framework is its approach to federalism. The proposal would preempt state laws that impose “undue burdens” on AI development, while preserving traditional state police powers and authority over areas such as zoning and public-sector use of AI. The goal is clear: to avoid a fragmented regulatory landscape that could impede national competitiveness in the development of AI while leaving room for states to regulate the deployment of AI.

This approach, rooted in layered competency,  is a defensible position. AI development is inherently interstate, and a patchwork of inconsistent state regulations could function as a de facto barrier to entry. At the same time, the framework attempts to preserve space for states to regulate harmful conduct, including fraud and child exploitation, and to govern their own use of AI systems.

The difficulty, as always, will be in the line-drawing. What counts as an “undue burden”? When does a state law regulating conduct become, in effect, a regulation of AI development itself? These questions are unlikely to admit of clean answers, and they will ultimately be resolved through litigation as much as legislation.

Taken as a whole, the framework reflects a coherent theory of AI policy: regulate harms rather than technologies, rely on existing legal institutions where possible, and avoid premature or overly centralized interventions. It is a theory grounded less in fear of AI than in concern about regulatory overreach.

That does not mean it is complete. The framework leaves unresolved questions about copyright, about the practical limits of preemption, and about how to ensure that pro-innovation policies do not simply entrench existing incumbents. But it does set the debate on a more productive footing.

The real test will come in Congress. It is one thing to articulate a forward looking framework; it is another to resist the political incentives that tend toward complexity, precaution, and interest-group bargaining. If lawmakers can hold to the principles outlined here, the result could be a durable and innovation-friendly AI policy regime.

If not, this framework may be remembered less as a foundation than as a road not taken.

The post A Sensible Federal Framework for AI (If Congress Can Stick to It) appeared first on Truth on the Market.

The DMA is often framed as a pro-consumer reform—a necessary intervention to “rein in gatekeepers” and restore fairness and contestability in digital markets. That framing obscures a more ambitious—and more troubling—project. At its core, the DMA empowers EU regulators to dictate the architecture of the digital experience itself.

The DMA reflects a deep suspicion of integration. Services that work seamlessly together—search and maps, maps and bookings, messaging and payments—once stood as user-facing innovations. Regulators now treat them as potential forms of “self-preferencing,” and therefore as threats to competition or, at minimum, to “fairness.”

This approach drives a clear regulatory vision: digital services must either interoperate with rivals on mandated terms or remain artificially separate.

Markets do not typically evolve this way. But it is how regulators design systems.

When Seamlessness Becomes Suspicious

Consider a simple example. A user searches for a restaurant on Google and sees a map, reviews, and an option to book a table. For consumers, that integration is the point. It lowers transaction costs and streamlines the path from discovery to action. Under the DMA, however, the same design risks being labeled self-preferencing if Google favors its own booking tool over rivals.

The implication is fragmentation. Instead of a seamless experience, users must move across multiple services—search in one place, maps in another, bookings somewhere else. A once-integrated workflow becomes a patchwork of disconnected steps.

The DMA extends this logic across the digital ecosystem. Integration between search and maps becomes suspect. Preinstalled apps that function well together appear potentially exclusionary. A platform that builds complementary services around its core offering risks designation as a “gatekeeper” leveraging its position.

The DMA thus encodes a preference for modularity over integration. Not because users demand it—the success of the regulated platforms suggests the opposite—but because regulators fear the competitive and, increasingly, the moral implications of integrated ecosystems.

The Fantasy of Neutrality

This reflects a deeper conceptual shift. Traditional competition law asks whether conduct harms consumers—by reducing output, raising prices, or diminishing innovation. The DMA instead asks whether platforms act with sufficient “neutrality” toward rivals.

But neutrality, in this context, is a mirage.

Digital platforms are not passive conduits. They are products—designed, curated, and continuously optimized. Demanding neutrality requires platforms to stop functioning as products and instead operate as regulated utilities that host competitors on equal terms. It treats Amazon.com like a 19th century railroad.

This logic applies even—especially—when integration improves functionality, lowers costs, or enhances the user experience. Curation—the exercise of judgment, i.e., the absence of perfect neutrality—is the core feature of most digital platforms. Users do not want an alphabetical list of search results on Google, nor do they want to cycle through three separate apps to find, book, and get directions to a restaurant.

EU regulators, however, focus less on user preferences than on reshaping digital markets to reflect their preferred structure and normative vision.

Making Innovation Irrational

The consequences are predictable. By limiting how platforms integrate their own services, the DMA reduces the incentive to build those services in the first place. Why invest in a better mapping tool, payments system, or booking feature if firms cannot fully integrate it into a broader ecosystem?

Digital competition policy increasingly embraces this kind of “leveling down.” Regulators restrict successful firms’ ability to capitalize on their investments and require them to share functionality or data with rivals.

This marks a sharp break from the classical liberal view of competition as a dynamic discovery process—one in which firms experiment, integrate, and innovate under uncertainty. The DMA replaces that process with a set of ex ante constraints designed to shape outcomes in advance.

But competition is not a static condition to engineer. It is a messy, decentralized, and often unpredictable process. EU regulators have struggled to grasp that basic point.

The Illusion of the ‘Correct’ Price

There is also a more practical—and more fundamental—problem. The DMA’s vision of interoperability and neutrality demands constant oversight, technical standard-setting, and ongoing regulatory intervention. This is not a one-off fix; it is a permanent administrative project. In practice, it places the European Commission in the role of deciding which prices and pricing structures are “correct.”

Consider Apple’s App Store fees. If Apple reduces its commission from 30% to 27%, is that sufficient? What if it drops to 17% but expands the base of transactions subject to fees? What if it adopts a different pricing model altogether—lower headline rates paired with higher charges elsewhere? Each approach redistributes value among developers, users, and competitors.

Who decides which outcome is right?

Markets once answered those questions. That view now falls out of favor, dismissed as a relic of the “neoliberal era.” Dirigisme has returned. Regulators in Brussels now assume responsibility for steering digital markets toward their preferred outcomes.

There is just one problem: no neutral answer exists. Pricing structures necessarily involve tradeoffs. Some favor large developers; others benefit smaller firms. Some deliver short-term gains for consumers; others support long-term investment and innovation. The DMA effectively hands regulators the authority to resolve these tradeoffs.

In practice, the primary beneficiaries are often not consumers, but competitors—particularly those that gain access to platform resources or more favorable terms without making comparable investments.

Back to Basics—If We Can

None of this denies that digital markets raise real competition concerns. But micromanaging product design or imposing a one-size-fits-all model for how services must interact is not the answer.

A more coherent approach would return to first principles. Regulators should focus on demonstrable harm, apply effects-based analysis, and preserve the incentives that drive innovation. Intervention in the business models of private, for-profit firms should occur only when actual harm can be shown. That premise may seem unremarkable, but it has become contested.

This discipline matters most in fast-moving sectors, where rigid rules quickly become obsolete, or counterproductive. The emergence of OpenAI and the rapid rise of generative artificial intelligence may render key aspects of the DMA outdated sooner than even its most fervent critics anticipated.

The User Experience, Regulated

The DMA reflects a distinct vision of the digital economy—one that treats integration as suspect, scale as dangerous, and success as something to constrain through ongoing regulation. In that framework, consumers adapt to regulators’ conception of markets, not the other way around.

That vision carries real costs. By favoring fragmentation over integration and neutrality over functionality, the DMA risks degrading the user experience that made digital services valuable in the first place.

In seeking to discipline how platforms compete, EU regulators increasingly dictate how users interact with technology. The result is a managed, fragmented, and less efficient digital environment—designed not by markets, but by regulators.

That outcome should give policymakers pause.

The post The DMA’s Case Against Seamlessness appeared first on Truth on the Market.

This post examines what those experiments reveal. It explores the shift toward ex ante regulation, the tension between openness and design, the rise of regulatory benchmarking across jurisdictions, and the evolving economics of the so-called “Apple Tax.” Along the way, it asks a broader question: whether regulators can—and should—be calibrating complex digital ecosystems, or whether that task is better left to competition, experimentation, and consumer choice.

Integration on Trial

Japan’s Mobile Software Competition Act (MSCA) represents one of the clearest efforts outside Europe to import the European Union’s regulatory model for digital platforms. Enacted against the backdrop of growing global concern over the market power of large technology firms, the MSCA targets so-called “gatekeeper” platforms—particularly in mobile ecosystems—by imposing upfront obligations on how they design and operate their services.

In doing so, the Act adopts a distinctly European-style approach. It treats vertical integration in mobile ecosystems as presumptively problematic and responds with ex ante unbundling, rather than case-by-case enforcement. That marks a clear departure from both economic theory and traditional antitrust practice, which have generally evaluated integration based on its actual competitive effects rather than assuming harm at the outset.

Antitrust law has never treated vertical integration as inherently anticompetitive. To the contrary, a substantial body of evidence shows that integration often reduces friction, lowers transaction costs, and delivers simpler, more reliable user experiences (here). Courts and enforcers have therefore assessed vertical integration contextually, intervening only when it excludes rivals or harms consumers in specific circumstances.

The MSCA—like the EU’s Digital Markets Act (DMA)—takes a different approach. It restricts forms of integration precisely where platforms often integrate for efficiency. The law limits self-prioritization, constrains control over distribution and payments, and mandates greater openness to third parties. These interventions assume, rather than demonstrate, that platform control is more likely to suppress than enhance competition.

That assumption sits uneasily with the empirical literature on digital platforms. Research shows that platform entry into complementary markets can expand demand, improve quality, and even spur innovation by third-party developers, rather than crowding them out.

To be sure, unbundling can increase competition within a platform by lowering entry barriers for alternative app stores or payment providers. But those gains come with costs. Apple’s integration of distribution, discovery, and payments reflects more than technical necessity—it is a core product-design choice. iOS centers on curation, security, and clear lines of responsibility.

Many consumers choose Apple precisely because they value a single, reliable intermediary and know where accountability lies when something goes wrong. Splitting these functions across multiple actors fragments responsibility, complicates enforcement, and shifts costs to users through added complexity, risk, and friction.

The MSCA also raises a broader competitive tradeoff. Forcing Apple to loosen its curation may increase intra-platform competition, but it risks reducing inter-platform competition by making iOS more like Google LLC’s Android operating system. Regulators may gain competition within iOS at the expense of competition between differentiated ecosystem models.

That outcome is not obviously welfare-enhancing. It also raises a classic error-cost concern: the risk of condemning a differentiated, consumer-valued design choice rather than genuinely exclusionary conduct.

One Opening, Global Fallout

Japan’s intervention also has implications beyond its own borders. Because Apple operates a global platform, changes imposed in one jurisdiction inevitably shape how the company defends its design choices elsewhere. If Japan forces Apple to open its ecosystem in Tokyo, how can the company defend its “technical necessity” argument in Mumbai or Beijing?

This tension highlights a broader problem of regulatory spillovers. Concessions made in one market can weaken a firm’s credibility in another, even when legal obligations differ. Japan’s approach therefore complicates Apple’s position abroad, but it does not resolve it. Instead, it raises a deeper question about whether platform design can remain globally coherent in a world of increasingly fragmented regulatory demands.

Apple’s core claim has not been that opening iOS is impossible. Instead, the company argues that doing so degrades the user experience—making devices less safe, less private, and more confusing as responsibility fragments across multiple app stores and payment systems. Apple has also framed some concerns in technical terms, noting that it has not identified ways to open certain functions without increasing risks to user data.

The issue is less “possible versus impossible” and more about design tradeoffs: how much openness, under what safeguards, and in which enforcement environment. As I argued in my op-ed in Japan Today, the MSCA—and similar ex ante regimes such as the DMA and the United Kingdom’s Digital Markets, Competition and Consumers Act (DMCC)—is best understood as an experiment.

We do not yet know how these tradeoffs will play out in practice. We only know they will exist. That uncertainty matters because the MSCA rests on assumptions that depart from traditional antitrust analysis, including treating self-preferencing and certain forms of vertical integration as presumptively harmful.

Ultimately, the act will be judged not by how many new intermediaries it enables, but by whether users are better off once security, reliability, and accountability are taken into account. It also gives regulators significant authority to determine how open a platform must be and how much control its owner may exercise.

Different regulators will answer those questions differently. Outcomes in Tokyo may not map neatly onto Mumbai or Beijing.

Pricing Under Pressure

Apple’s reported 15% commission for WeChat “mini-apps” in China creates the impression of a strategic retreat, particularly given the company’s long-standing defense of its standard commission structure. The move follows mounting pressure from Chinese authorities and the unique importance of Tencent Holdings Ltd.’s WeChat ecosystem, where “mini-apps” function as an embedded platform within a platform—blurring the lines between app distribution, payments, and services.

But this is not just a story about a single concession in a single market. It illustrates something broader than a straightforward economic compromise. It highlights how platform pricing and governance are increasingly shaped by regulatory context, bargaining dynamics, and local ecosystem power—rather than by a uniform global business model.

China has developed a regulatory style that relies heavily on discretion and ambiguity, where the threat of intervention can be as influential as the law itself. That dynamic is not unique to China. The DMA and the MSCA similarly empower regulators to intervene when fees are deemed “too high” or terms “unfair,” while offering limited guidance on what those standards mean in practice.

The result is a governance model in which regulated firms are expected to justify and continuously adjust their business models, often while second-guessing whether they have gone far enough.

From a law & economics perspective, the more salient issue is institutional, not geopolitical. There is no objectively “right” platform commission that regulators can discover. Outcomes will tend to reflect bargaining power, political salience, and local institutional context, rather than clear economic benchmarks.

Even when headline fees fall, platforms can rebalance costs elsewhere in the ecosystem. Apple’s Core Technology Fee in the EU is a good illustration of how cost incidence can shift in ways that benefit some firms while disadvantaging others, particularly smaller developers or those with different business models.

More fundamentally, this reflects a broader shift from market-enabling regulation toward active market-shaping. Regulators increasingly influence pricing, access, and platform design in complex, fast-moving ecosystems.

Borrowed Benchmarks

The spread of these regulatory interventions does not occur in isolation. As more jurisdictions experiment with platform rules, regulators increasingly look to one another for reference points. The Competition Commission of India (CCI), for example, reportedly treats Apple’s concessions in Japan as a benchmark for what is “economically viable.”

That approach reflects a growing tendency toward regulatory benchmarking across borders. But it also raises a basic question: Does it make sound economic sense to use a fee structure extracted in one jurisdiction as “Exhibit A” in another, or does that risk ignoring the distinct economic conditions and tradeoffs that shape each market?

From a law & economics perspective, the answer is clearly the latter. A fee structure that emerges under regulatory pressure in one jurisdiction is not evidence that it is efficient or welfare-enhancing—even there, much less elsewhere. It reflects a specific mix of enforcement capacity, payment infrastructure, fraud risks, market structure, and bargaining dynamics.

Treating it as a benchmark risks a cascade effect. Once one regulator extracts a concession, others may treat it as a floor rather than a ceiling, even when underlying conditions differ. Lower commissions in one country may be absorbed relatively smoothly; in another, they may increase fraud, disputes, and consumer friction.

Platforms may also respond by shifting costs elsewhere—through per-install fees, compliance charges, or security-related requirements—often with uneven effects across developers.

The result is growing fragmentation. Although regimes such as the DMA are often presented as imposing common standards, their international diffusion may instead produce locally tailored outcomes.

That shift contrasts with the consumer welfare standard, which has long provided a relatively consistent benchmark across jurisdictions. Moving toward open-ended notions of “fairness” or “appropriate” pricing risks replacing that common framework with a patchwork of negotiated outcomes.

Same Fee, New Wrapper

South Korea provides a concrete example of how these regulatory interventions play out once implemented. In 2021, the country amended its Telecommunications Business Act to require Apple and Google to allow third-party in-app payment systems—an early test case for opening mobile ecosystems through legislation.

In practice, however, the results have been more nuanced. Apple now permits alternative payment methods, but still imposes a 26% commission on those transactions, in addition to the fees charged by payment processors. A similar pattern emerged in the Netherlands, where regulators required changes for dating apps, yet Apple maintained a substantial commission alongside new conditions.

This does not necessarily reflect bad faith. It is, instead, a predictable economic response. Apple will continue to charge for access to iOS—its intellectual property, tools, distribution, security, and infrastructure. When regulators constrain one revenue stream, the firm has strong incentives to reprice elsewhere.

The EU’s Core Technology Fee made that dynamic especially visible. Debate often focuses on headline commissions rather than on how costs shift across developers and users.

In that sense, these laws change the form of the fee more than its existence. They encourage “compliance by reclassification,” with more complex pricing structures.

That complexity creates its own costs. Country-specific regimes increase legal risk, engineering overhead, and compliance burdens, and raise the likelihood of inadvertent noncompliance. Over time, fragmentation may make international operations more expensive than operating under a more uniform U.S. framework.

Openness vs. Coherence

Apple CEO Tim Cook has repeatedly argued that greater “openness” can invite malware and undermine user security—a position that has featured prominently in Apple’s response to regulatory efforts in the United States, Europe, and Asia. But this argument is best understood not as a claim that Apple’s system is perfect, or that open systems cannot be safe. Rather, it is a claim about platform governance and the tradeoffs embedded in different design choices.

From its inception, Apple has prioritized a curated, tightly integrated ecosystem over maximum openness. That model emphasizes control over app distribution, review, and payments as a way to deliver a simpler, more secure, and more predictable user experience. Some users clearly value that approach. Others prefer more open systems, such as Google’s Android operating system, which offer greater flexibility and choice. Both models coexist in the market, reflecting different consumer preferences, rather than a single “correct” design.

Curation relies on centralized control over distribution, review, and payments. When laws mandate sideloading, third-party app stores, or alternative payments, that model becomes more fragmented. Even with safeguards, Apple’s ability to control what reaches users—and to ensure a consistent experience—declines.

That does not guarantee harm, but it increases the likelihood of lower-quality or riskier apps at the margins. The extent of that effect remains uncertain.

The larger issue is coherence. Apple continues to emphasize curation in the United States while implementing legally required openness elsewhere. That creates a fragmented product identity.

More broadly, these regimes assume regulators can identify the “optimal” level of openness, pricing, and control. That premise is questionable. In practice, those balances are typically discovered through competition, experimentation, and consumer choice.

By shifting toward ex ante calibration, laws such as the MSCA and the DMA move those decisions toward regulators. Whether that tradeoff is justified is not self-evident—and raises familiar concerns about error costs and unintended consequences.

Barrier or Backbone?

The so-called “Apple Tax” sits at the center of many of these debates, but it is often invoked more as a slogan than as a clearly defined concept. Critics frame it as a toll that raises costs and limits entry, while defenders characterize it as the price of accessing a global distribution and payments infrastructure. In reality, it can function as both a barrier and an enabler, depending on the developer, the business model, and the market context.

That ambiguity is compounded by a lack of precision in how the term is used. “Apple Tax” is often treated as a catch-all for any fee associated with the App Store, even though Apple’s pricing structure is more nuanced. Clarifying what the fee actually covers—and how it operates—is essential to evaluating its effects on competition, entry, and innovation.

Apple does not charge a per-app listing fee, although developers typically pay about $99 per year for the Apple Developer Program. Free apps that do not sell digital goods pay no commission. The relevant fee is the commission on paid apps and in-app purchases, with a reduced 15% rate for eligible small developers.

For some developers, that commission can be a marginal barrier. For others—particularly those outside major tech hubs—it functions as an enabling infrastructure fee.

It supports distribution, discovery, payments, fraud management, and access to a trusted marketplace. It allows a developer in Vietnam to reach a customer in New York without having to build the entire commercial stack herself.

The more important question is the net effect on entry and innovation. Lowering headline commissions does not eliminate costs; it merely shifts them.

The relevant benchmark is not whether the fee is “high,” but whether the overall system promotes access and innovation without undermining the trust and infrastructure that make the ecosystem valuable.

Regulation as Experiment

Across jurisdictions, one pattern emerges: regulators are not converging on a single model of platform governance—they are running parallel experiments. Each reflects different assumptions about competition, control, and the role of the state in shaping digital markets.

Those experiments carry real tradeoffs. Expanding openness may increase entry and reduce certain frictions, but it can also fragment governance, shift costs, and erode product coherence. Lowering headline fees may benefit some developers, while redistributing costs to others. And greater regulatory discretion may correct perceived imbalances, but it also introduces uncertainty, negotiation, and the risk of error.

What makes this moment unusual is not just the scale of intervention, but its ambition. These regimes do not simply police conduct; they attempt to redesign markets and products ex ante. That requires confidence not only in legal authority, but in the ability to calibrate complex, evolving ecosystems.

Whether that confidence is warranted remains an open question.

For now, the “Apple Tax,” the “walled garden,” and the push for openness are not being resolved—they are being renegotiated, jurisdiction by jurisdiction. And in that process, the real risk is not that regulators or platforms get everything wrong, but that in trying to optimize everything, they end up flattening the very differences that competition is supposed to reveal.

The post Opening the Walled Garden: Global Regulation and the Unbundling of Apple’s Ecosystem appeared first on Truth on the Market.

As International Center for Law & Economics (ICLE) scholars explained in comments to the FCC in a separate proceeding, intercarrier compensation (ICC) rules still on the books for some rate-of-return regulated carriers—particularly rural and competitive local exchange carriers (LECs)—create perverse incentives. These rules encourage legacy carriers to maintain copper networks to generate revenue from ICC fees.

The FCC should complete ICC reform by fully transitioning to a bill-and-keep model. Under this approach, carriers recover service costs from their own customers, rather than extracting call termination and origination fees from interconnecting carriers. This shift would eliminate remaining arbitrage schemes in which legacy carriers partner with high-traffic callers to stimulate access charges, imposing additional costs on carriers that have already transitioned to IP-based networks.

The agency should also phase out Universal Service Fund (USF) distributions to legacy carriers that rely on subsidies to offset lost ICC revenue. Doing so would reduce strain on the USF and help address concerns about rising contribution fees.

The Per-Minute Model Meets Its Expiration Date

Voice telephony historically operated as a regulated utility. Incumbent providers served defined geographic areas, and regulators set the rates those firms could charge. When a call originated on one network and terminated on another, the originating carrier paid termination fees to the receiving carrier under a “calling-party-pays” regime. Regulators also subjected these fees to price-cap regulation.

That system created opportunities for arbitrage. Some local exchange carriers (LECs)—particularly rural LECs—partnered with call centers and other high-traffic businesses to inflate terminating minutes and maximize access-charge revenue. In some cases, these arrangements became central to their business models.

As competition in voice communications increased, the case for strict price controls weakened. Consumers could switch providers if prices rose too high. At the same time, many telecommunications companies retired legacy switched telephone networks and shifted to IP-based networks that deliver traffic over fiber. These networks differ fundamentally from time-division multiplexing (TDM)-based systems. The relevant constraint is no longer minutes of use, but bandwidth. Per-minute price caps on intercarrier exchanges no longer fit this technological reality.

IP-based networks instead rely on a bill-and-keep model, under which carriers exchange traffic without charging termination fees. Each provider recovers its costs from its own customers and retains the associated revenue. This approach strengthens incentives to reduce costs and improve service quality. It also reduces compliance and transaction costs associated with ICC, which requires extensive tracking, auditing, and administrative oversight. Bill-and-keep largely eliminates these burdens and better aligns with the economics of digital networks.

How ICC Still Pays to Stay in the Past

The FCC has taken several steps to facilitate the transition to IP-based networks and, indeed, ICLE scholars have consistently highlighted the benefits of an all-IP communications grid. Despite this progress, ICC rules still create incentives for some legacy carriers to maintain TDM-based and copper networks. These incentives come at the expense of their own customers, as well as customers of interconnecting networks, and delay the broader benefits of modern communications infrastructure.

Although the FCC has eliminated many terminating charges, some remain. In particular, when a terminating carrier does not own the tandem switch—an intermediary switch between two LECs—that carrier may still collect terminating access fees, including tandem-transport fees for delivering the signal from the tandem switch to the end user. By routing calls through an intermediate tandem, whether independent or affiliated, a terminating carrier can ensure that both tandem-switching and tandem-transport fees apply.

LECs also retain opportunities to exploit 8YY toll-free charges. Unlike standard calls, the receiving party pays for 8YY calls. This structure has long encouraged partnerships between 8YY service providers and LECs to increase revenue from originating end-office charges. In 2020, the FCC moved many of these charges to a bill-and-keep framework, but some elements remain. These include a nationwide joint tandem-switched-transport rate capped at $0.001 per minute and a database-query charge capped at $0.0002.

Legacy carriers can also continue to extract rents from TDM-based signaling services. These networks rely on Signaling System 7 to route calls, and carriers assess fees for these services. In an all-IP environment, these charges would disappear.

Taken together, the ICC regime functions as a de facto subsidy for legacy TDM technology. The remaining fees apply only to TDM networks and would vanish in a fully IP-to-IP environment. Carriers that depend on ICC revenue—whether through legitimate charges or arbitrage—therefore have incentives to delay network upgrades and force incoming communications onto TDM infrastructure.

Paying Twice for Yesterday’s Network

Interconnection rules that require conversion to TDM force many carriers that have upgraded to fiber to maintain parallel TDM networks in parts of their service areas. Maintaining these duplicative systems imposes significant costs and can slow fiber expansion.

Copper networks depend on electrically powered equipment, and signal quality degrades over distance due to resistance in the wire. Carriers compensate with repeaters, amplifiers, remote terminals, and TDM switches that clean and retransmit signals. They also maintain large switching offices. Fiber networks eliminate much of this powered equipment, reducing energy use, lowering costs, and improving reliability. Copper infrastructure also attracts theft due to high scrap value, often forcing carriers to replace stolen lines, rather than upgrade to fiber.

Recent data underscore the scale of these costs. In 2024, AT&T reported roughly $6 billion in annual direct operating costs to maintain its copper network—about 5% of its $122 billion in annual revenue. These costs persist even as the company lost nearly 1 million subscriber lines year over year, leaving about 3.3 million network-access lines. Verizon has migrated 4.5 million circuits from copper to fiber and fully retired 36 central offices, generating approximately $180 million in annual operating savings. Fiber deployment has reduced maintenance dispatches by about 60%. In a 2017 FCC filing seeking approval to retire copper in eight Northeast markets, Verizon reported 3.4 million fewer repair and troubleshooting dispatches between 2012 and 2016 than would have occurred had those customers remained on copper. Decommissioning TDM switches also allowed the company to consolidate facilities that once required up to 13 floors of equipment into just one or two.

Maintaining copper networks ultimately raises costs for consumers. IP-based networks offer greater reliability than legacy TDM systems, deliver higher call quality, and enable more effective robocall mitigation. Where carriers delay transition, customers remain on outdated technology that cannot meet modern needs. In competitive markets, consumers can switch providers. In many rural areas, however, a single carrier may dominate, leaving customers with no alternative to copper service. While carriers ultimately decide whether to upgrade, the FCC should act when ICC-driven arbitrage—not consumer demand—drives the decision to delay investment.

Turning Off the Subsidy Spigot

The FCC’s NPRM proposes to eliminate the remaining ICC fees that have not yet transitioned to a bill-and-keep framework. Completing this transition would remove incentives for carriers to maintain copper networks solely to extract rents under ICC rules. As more legacy carriers upgrade, providers that have already transitioned will be able to retire larger portions of duplicative copper infrastructure maintained only for interconnection. These cost savings should support further investment in communications networks, including broadband and mobile data services. Reduced reliance on copper networks would also limit theft and service disruptions tied to copper infrastructure.

Pulling the Plug on a ‘Temporary’ Subsidy

As the FCC began the transition to bill-and-keep, some legacy carriers that had not upgraded to IP-based networks lost ICC-related revenue. In response, the commission implemented a temporary safety net. CAF ICC—a component of the Connect America Fund (CAF), itself part of the Universal Service Fund (USF)—allows eligible carriers to recover a portion of lost revenue.

Under this framework, carriers first attempt to recover revenue through a capped “Access Recovery Charge” (ARC) on customers’ monthly bills. If the ARC does not cover the full amount of “eligible recovery” under the FCC’s formula, the carrier may receive the remaining balance through CAF ICC support.

This approach was understandable as a transitional measure. But CAF ICC was always intended as a temporary stopgap to limit consumer disruption. Today, it applies only to rate-of-return carriers—typically small, rural incumbent LECs. The NPRM would finally phase out this subsidy over two years. Carriers would receive 66% of baseline support in the first year, 33% in the second, and no support by the third.

Killing Arbitrage, Easing the Squeeze

Eliminating CAF ICC would serve two key purposes.

First, it would reduce the arbitrage opportunities embedded in the current system. Legacy carriers can still recover costs through mechanisms other than customer payments, which weakens incentives to upgrade legacy TDM networks. Even as ICC reforms advance, some carriers may rely on USF support to sustain outdated infrastructure. Phasing out CAF ICC would remove both ICC-based and USF-based incentives to delay investment. Carriers could still maintain TDM networks where consumer demand or economic conditions justify it, but market forces—not regulatory arbitrage—would determine those outcomes.

Second, reducing USF expenditures would help ease mounting pressure on the program’s funding mechanism. Under the Communications Act, USF contributions are assessed primarily on telecommunications services, especially voice. Consumers, however, have shifted toward data services that support voice, video, and other applications. While these data services benefit from USF programs, they do not contribute to the fund, placing increasing pressure on a shrinking base of voice-service revenues.

Reform proposals have focused on expanding the contribution base, but reducing program costs offers a more immediate and practical step. Eliminating CAF ICC would save more than $300 million annually—nearly 7% of the USF high-cost program budget. Lower costs would ease pressure on the contribution factor, reduce the burden on telecommunications customers, and create more flexibility for policymakers considering broader USF reform.

The Sky Isn’t Falling

One concern with the proposed action—particularly the elimination of CAF ICC—is that legacy carriers may not recover sufficient revenue from their customers to cover costs, potentially undermining their ability to serve their communities. Some also argue that certain carriers cannot profitably upgrade to fiber. These concerns, however, are largely overstated.

First, most rural and competitive LECs have already begun the transition to IP-based services. Carriers have been on notice of the shift to bill-and-keep since 2011. Today, 86% of rural local exchange carrier (RLEC) customers are served by fiber-to-the-premises networks, and 83% of carriers have IP-enabled switching capabilities within their networks. These trends reflect both consumer demand for higher-quality service and the operational efficiencies of modern networks. Most carriers already have the capacity to complete the transition.

Second, the FCC’s proposal does not require carriers to undertake unprofitable network upgrades. It requires only that carriers recover their costs from their own customers, rather than through intercarrier payments or subsidies. Carriers that choose not to upgrade can continue to provide voice service over copper networks, while their customers remain free to switch to alternative providers.

If prices rise, many consumers may turn to mobile voice services offered by nationwide or regional carriers. As of June 2024, retail switched-access lines accounted for only about 8% of subscriptions, most of which were likely business lines, rather than residential. Consumers have already shifted toward mobile voice, and those whose wireline providers fail to meet their needs have viable alternatives. This competitive pressure will push legacy carriers to reduce costs and improve service quality.

Time to Let Copper Go

The FCC’s efforts to facilitate the retirement of copper networks have advanced many of the agency’s goals. But outdated ICC rules remain a barrier to a fully IP-based telecommunications environment in the United States. The FCC’s NPRM would remove regulatory incentives that encourage legacy carriers to maintain outdated infrastructure. By accelerating the transition to fiber networks, these reforms would reduce costs across the system and allow customers of legacy networks to realize the full benefits of modern communications technology.

Some carriers will lose revenue if the FCC adopts these changes. But revenue loss does not mean widespread service failure or loss of coverage. Consumers today have a wide range of communications options, and the data show that continued support for carriers that have not transitioned to IP-based networks is not necessary to ensure access. Phasing out CAF ICC—always intended as a temporary measure—may reduce revenue for carriers that decline to upgrade, but it will deliver net benefits to American consumers.

The post Subsidizing Obsolescence: How FCC Rules Keep Copper Alive appeared first on Truth on the Market.

More than a year ago, Dirk Auer and I challenged the “hyperbolic and dystopian” narrative dominating discussions about competition in artificial-intelligence (AI) markets. We argued that concerns about competition risks—and calls for tougher enforcement—were unwarranted or overstated. AI markets were, in general, highly competitive. More importantly, the AI revolution offered an opportunity to revisit some of the assumptions driving competition concerns in digital services, including data as a barrier to entry and the role of network effects.

AI markets continue to evolve at breakneck speed. This makes it useful to revisit recent developments through the lens of economic and legal principles that inform competition policy. This post is the first in a biweekly series tracking those developments.

Rather than chase the news cycle, I will focus on concrete changes across the AI stack—compute, models, data, integration, and governance. The aim is to reassess, and where necessary push back on, common claims about competition, concentration, and market power. Over time, the series will examine infrastructure investment, talent mobility, model development, new products and services, partnership dynamics, and regulatory responses.

The goal is to connect real-world developments to the competition-policy and enforcement debate, and to test maximalist claims about inevitable concentration or competitive harm against observed market evidence.

The guiding question remains straightforward: Has the AI industry evolved toward monopolistic control of key inputs or demonstrable harm to competition, or toward layered, dynamic competition?

A Lot of Scrutiny, Little Evidence of Harm

A striking feature of the AI competition-policy landscape is the gap between enforcement activity and outcomes. Agencies have devoted significant resources to AI cases, yet have issued few decisions finding anticompetitive conduct or reduced competition. This is especially true of partnerships between incumbent firms and AI startups, which have drawn sustained scrutiny. Despite this, policymakers often acknowledge—at least implicitly—that AI markets remain competitive, even if popular media accounts suggest otherwise.

Over the past two years, competition authorities on both sides of the Atlantic have repeatedly raised concerns about AI partnerships and investments. In January 2025, the Federal Trade Commission (FTC) published a staff report summarizing preliminary findings from an inquiry launched a year earlier. The report examined “relationships between the world’s current largest Cloud Service Providers (‘CSPs’) and two of the most prominent AI model developers” and identified three “areas to watch going forward”:

• The partnerships could affect access to key inputs, such as computing resources and engineering talent.
• The partnerships could increase contractual and technical switching costs for AI developers.
• The partnerships could give CSPs access to sensitive technical and business information unavailable to rivals.

The report, however, offered no concrete evidence of competitive harm in generative AI markets. It did not find that these partnerships created dominant positions or even that dominance was a likely outcome. In his concurring and dissenting statement, then-Commissioner and now-FTC Chair Andrew Ferguson underscored the point:

[T]he limited, brief nature of the study should foreclose the drawing of broad conclusions about the AI industry and its future, or even about the partnerships themselves. The staff report acknowledges as much, deploying hedging language throughout Section 5 and making clear that “an analysis of the impact that these partnerships might have on competition is beyond the scope of this report.” The Commission therefore should not have published speculation about what the future may hold. Readers should skip Section 5 of the Report, or read it with tremendous skepticism.

The UK Competition and Markets Authority (CMA) has taken a similarly cautious approach after investigating multiple AI partnerships, including Microsoft’s deals with Mistral AI, Inflection AI, and OpenAI, as well as Amazon’s partnership with Anthropic.

The CMA cleared the Microsoft-Mistral AI deal in May 2024. It found that Microsoft’s potential shareholding—less than 1%—did not confer influence over Mistral’s policy or board. The agreement’s compute and distribution commitments were also unlikely to create dependency sufficient to affect Mistral’s commercial strategy.

The CMA also cleared Microsoft’s “acquihire” of Inflection in September 2024. It concluded the transaction would not substantially lessen competition in either consumer chatbots or foundational models, citing Inflection’s small market share and early-stage models. That same month, the CMA cleared Amazon’s $4 billion investment in Anthropic, finding that Anthropic UK did not meet the relevant turnover threshold.

Following a 15-month pre-notification investigation, the CMA concluded in March 2025 that the Microsoft-OpenAI partnership did not give rise to a relevant merger situation. It found no “change of control by Microsoft from material influence to de facto control over OpenAI.”

The European Commission has reached similar conclusions. It reviewed NVIDIA’s acquisition of Run:ai and cleared the deal unconditionally in December 2024, after a referral from Italy’s competition authority under its call-in powers. Although the Commission found that “NVIDIA likely held a dominant position in the global market for discrete GPUs for use in datacentres,” it also found that Run:ai lacked a significant position in “GPU orchestration software.” The transaction therefore raised no competition concerns.

Separately, after an extensive review of the Microsoft-OpenAI partnership, the Commission concluded in June 2024 that Microsoft had not acquired lasting control over OpenAI. The partnership did not qualify as a merger. The Commission continued to assess exclusivity provisions under abuse-of-dominance rules but never opened a formal investigation or brought charges. In any event, those concerns appear to have diminished as the partnership evolved—from cloud-service exclusivity to a right of first refusal.

In Brazil, the Conselho Administrativo de Defesa Econômica (CADE) opened administrative proceedings in mid-2024 to assess whether Amazon, Microsoft, and Google should have notified their AI partnerships. The agency focused on potential gun-jumping under Brazilian law. The investigations—covering Amazon-Anthropic, Microsoft’s arrangements with Mistral AI and Inflection AI, and Google’s deal with Character AI—remain ongoing, but have not produced enforcement actions.

Regulatory scrutiny also extends to conduct-based theories of harm, though these cases remain unresolved. In July 2025, Italy’s competition authority opened proceedings into Meta’s preinstallation of Meta AI within WhatsApp. The authority alleged that Meta abused its “dominant position in the market for consumer communications apps” by placing its AI services in a “prominent position.” According to the agency:

Meta appears capable of channelling its customer base into the emerging market, not through merit-based competition, but by “imposing” the availability of the two distinct services upon users, potentially harming competitors.

It later imposed interim measures, ordering Meta “to immediately suspend the WhatsApp Business Solution Terms in order to preserve access to the WhatsApp platform for Meta AI’s competitors.” CADE and the European Commission have initiated similar actions.

As Giuseppe Colangelo notes in recent International Center for Law & Economics (ICLE) comments to the French competition authority, these theories of harm may not align with market realities. While the cases “resemble longstanding debates over vertical integration and self-preferencing,” he argues that “AI markets remain highly dynamic and characterised by considerable competitive uncertainty. Even if Meta seeks to leverage messaging dominance into AI services, success cannot be presumed.” Even in jurisdictions more inclined to intervene in digital markets, agencies face an uphill battle in proving harm.

The pattern is consistent. After extensive scrutiny, agencies have uncovered no solid evidence of anticompetitive harm. Some investigations remain open, but enforcement outcomes appear unlikely. This does not prove that anticompetitive conduct is absent. Still, given the scale of regulatory effort—and the lack of findings—it is reasonable to infer that such outcomes are, at minimum, unlikely in AI markets.

Schumpeterian Competition in Real Time

Stepping back, the broader picture matters. Since November 2022, the AI industry has seen rapid improvement, shifting market shares, and constant entry. This is high-stakes Schumpeterian competition—not creeping monopolization—and the dynamic is well captured by a now-familiar internet meme:

A brief look back makes the point. When OpenAI launched ChatGPT in November 2022, some predicted “first-mover dominance” of the kind often associated with digital markets. Within two months, ChatGPT reached 100 million users—the fastest adoption of any consumer application on record.

The launch jolted incumbents. Within weeks, Google declared a “code red.” CEO Sundar Pichai reassigned teams and brought back co-founders Larry Page and Sergey Brin for emergency strategy sessions—even though Google had invented the transformer architecture underlying modern large language models (LLMs). The rushed February 2023 launch of Bard, Google’s ChatGPT competitor, produced a factual error in its first demo. A startup had made a tech giant scramble.

Entry and expansion followed quickly. Anthropic released Claude in March 2023, iterating rapidly and positioning itself around safety, long-context processing, and enterprise reliability. By late 2025, it reportedly captured about 40% of enterprise LLM spending, surpassing OpenAI. Elon Musk’s xAI launched Grok in November 2023 and integrated it into X with real-time platform data. Perplexity, founded in August 2022 by former OpenAI, Google, and Meta researchers, grew into an AI search alternative valued at $18 billion by July 2025, handling more than 780 million queries per month.

Google, despite its early stumble, regained ground. Competition around ChatGPT has intensified:

The top chatbot’s market share fell from 69.1% to 45.3% between January 2025 and January 2026 among daily U.S. users of its mobile app. Gemini, in the same time period, rose from 14.7% to 25.1% and Grok rose from 1.6% to 15.2%.

The data, obtained by Big Technology from mobile insights firm Apptopia, indicates the chatbot race has tightened meaningfully over the past year with Google’s surge showing up in the numbers. Overall, the chatbot market increased 152% since last January, according to Apptopia, with ChatGPT exhibiting healthy download growth.

On desktop and mobile web, a similar pattern appears, according to analytics firm Similarweb. Visits to ChatGPT went from 3.8 billion to 5.7 billion between January 2025 and January 2026, a 50% increase, while visits to Gemini went from 267.7 million to 2 billion, a 647% increase. ChatGPT is still far and away the leader in visits, but it has company in the race now.

As of this writing—after the company’s feud with the Department of War—Anthropic’s Claude ranks No. 1 among free iPhone apps on Apple’s App Store. In early February, it ranked 42.

The pace of entry and the volatility of market shares point to low barriers to entry and intense competition. Google’s experience underscores the point. Despite access to Search, YouTube, Android, and Chrome—data advantages some viewed as insurmountable—it took more than a year to catch up to a well-funded startup. When it did, it competed on the merits: better models (Gemini 3 Flash, Nano Banana) and improved distribution (AI Overviews), not exclusionary use of data.

Model performance has also improved at an unprecedented pace. Ethan Mollick’s “Otter test”—asking image models to generate an otter on a plane using Wi-Fi—illustrates the speed of progress.

He also points to benchmarks such as GDPval, where industry experts compare AI and human performance on complex tasks. Leading models now match or exceed top-tier human practitioners 82% of the time.

Competition has also come from unexpected quarters. In January 2025, DeepSeek—a Chinese startup founded in 2023—released its R1 reasoning model. It matched or exceeded OpenAI’s o1 model at a fraction of the training cost (about $6 million). As RAND observed, this “demonstrates that the barrier to entry is low enough that new entrants can be competitive,” making it “too soon to pick winners and losers.” Even allowing for uncertainty around DeepSeek’s reported costs, its emergence suggests that early leaders will struggle to build durable moats or enjoy the “quiet life” of a monopolist.

Open-source models reinforce this conclusion. Meta’s Llama family, Mistral’s models, and a wave of Chinese open-weight releases have enabled thousands of developers to build competitive applications without massive proprietary infrastructure.

Low switching costs further intensify competition. Jason Furman, former chairman of the Council of Economic Advisers, recently noted in The New York Times that switching costs have “forced companies to pass the gains from innovation on to users.” In his words:

There are no lazy monopolists in the A.I. space coasting on past advantages. Over the past year, the top spot on the Arena leaderboard has moved among those three companies, with strong performances from newer arrivals such as the Chinese company DeepSeek and the French firm Mistral — many of which require far less capital than earlier generations of A.I. companies.

Moreover, no single company dominates across A.I. areas. Anthropic currently leads in text and coding, OpenAI in text-to-image generation, xAI (founded three years ago) in image to video and Google in search-integrated A.I.

This assessment is notable coming from Furman, who co-authored the “Unlocking Digital Competition” report for the United Kingdom’s Treasury—a report often associated with a “winner-take-most” view of digital markets. As he acknowledges, many expected AI to reinforce those dynamics. So far, it has not.

Competition has also driven sharp price declines. Inference costs have fallen dramatically since late 2022. OpenAI’s most capable model at the time cost $20 per million tokens. GPT-4o-mini now delivers better performance for $0.15-$0.60 per million tokens—a reduction of more than 97%. Research from Epoch AI finds that the cost of achieving GPT-4-level performance has fallen roughly 40x per year, or about 70% annual deflation.

This discussion has focused on consumer-facing markets, where entry and market-share volatility are most visible. But similar dynamics appear across the AI stack. At the infrastructure layer, concerns about graphics processing unit (GPU) concentration and cloud dependencies may carry more weight. NVIDIA’s position in AI accelerators, for example, raises potential input-foreclosure questions that do not arise in chatbot markets—although Google’s tensor processing units (TPUs) and Amazon’s Trainium chips provide meaningful competition.

Data advantages may also matter more in specialized domains, such as medical imaging or legal research, than in general-purpose models. Future posts will examine each layer—compute, cloud infrastructure, training data, and applications—to assess whether apparent concentration translates into durable market power, or whether competitive dynamics resemble those observed in consumer-facing markets. 

The Dystopia That Didn’t Arrive

In short, the dystopian narrative—that data-driven network effects would create insurmountable moats, that partnerships would foreclose competition, that big tech would inevitably dominate—has not materialized.

Even so, as of August 2025, some were continuing to call on policymakers to “break up Big AI.” Those calls sit uneasily with the reality of AI markets: dynamic competition, sustained entry, rapid innovation, significant infrastructure investment, and shifting market shares.

None of this rules out anticompetitive conduct. Firms can engage in exclusionary behavior, and when they do, enforcement agencies should act. But that is a far cry from claims that concentration and competitive harm are inevitable.

The post The Great AI Monopoly That Wasn’t appeared first on Truth on the Market.

In the executive order “Restoring Freedom of Speech and Ending Federal Censorship,” the White House asserted:

The First Amendment to the United States Constitution, an amendment essential to the success of our Republic, enshrines the right of the American people to speak freely in the public square without Government interference.  Over the last 4 years, the previous administration trampled free speech rights by censoring Americans’ speech on online platforms, often by exerting substantial coercive pressure on third parties, such as social media companies, to moderate, deplatform, or otherwise suppress speech that the Federal Government did not approve.  Under the guise of combatting “misinformation,” “disinformation,” and “malinformation,” the Federal Government infringed on the constitutionally protected speech rights of American citizens across the United States in a manner that advanced the Government’s preferred narrative about significant matters of public debate.  Government censorship of speech is intolerable in a free society. 

The order declared it the policy of the United States to “ensure that no Federal Government officer, employee, or agent engages in or facilitates any conduct that would unconstitutionally abridge the free speech of any American citizen.”

Despite that stated commitment, the administration has signaled a willingness to combat so-called “fake news” through a little-used and difficult-to-enforce news-distortion policy. Federal Communications Commission (FCC) Chairman Brendan Carr recently amplified that position on social media, posting a screenshot of President Donald Trump’s Truth Social statement criticizing coverage of the conflict in Iran and warning broadcasters: 

Broadcasters that are running hoaxes and news distortions – also known as the fake news – have a chance now to correct course before their license renewals come up. The law is clear. Broadcasters must operate in the public interest, and they will lose their licenses if they do not.

The problem requires precision. The FCC may plausibly claim legal authority to pursue this approach. That possibility makes the policy more—not less—concerning. This is not merely regulatory overreach. It is a long-dormant power that has remained on the books for decades, available to any administration willing to use it.

Congress should revisit that authority and eliminate it. No matter which party controls the White House, empowering a federal agency to make open-ended judgments about what qualifies as legitimate news coverage conflicts with core free-speech principles.

This discussion proceeds in two parts. First, it traces the origins of the news-distortion policy and explains why it no longer fits the modern media landscape. Second, it shows that, even under existing legal standards, no alleged facts would suffice to establish news distortion in broadcasters’ coverage of the conflict in Iran.

A Rule Built for Rabbit Ears

Section 309(a) of the Communications Act authorizes the FCC to grant a broadcast license only if doing so serves the “public interest, convenience, and necessity.” The news-distortion policy emerges from that broad standard, which permits the agency to review certain content decisions. On its face, that authority sits uneasily with the First Amendment. The Supreme Court, however, has upheld it in the broadcast context by distinguishing broadcasting from other media.

In Red Lion Broadcasting Co. v. FCC and FCC v. Pacifica Foundation, the Court relied on spectrum scarcity. Because the government allocates limited broadcast frequencies, it may impose license conditions that would not apply to other forms of expression. In Red Lion, for example, the Court upheld the fairness doctrine as applied to a radio station that refused to provide reply time to an individual it had “personally attacked.”

Both the fairness doctrine and the news-distortion policy rested on the same rationale: “[a] licensee would be abusing his position as a public trustee . . . were he to withhold from expression over his facilities relevant news or facts concerning a controversy or to slant or distort the presentation of such news.”

That rationale eroded over time. In 1987, the FCC concluded that the fairness doctrine “on its face, violates the First Amendment and contravenes the public interest.”  In 2011, the agency formally repealed the underlying rules. The news-distortion policy, by contrast, remains on the books—but largely in name only. The FCC has rarely enforced it and has not attempted to do so in more than 30 years.

The broader legal foundation for special regulation of broadcast licensees has also weakened. As we have argued previously:

[I]nsofar as FCC authority over broadcast ever made sense, it certainly makes very little today. Broadcasters no longer have the market power they once did, when television consumers only had ABC, NBC, CBS, and PBS—later to be joined by Fox, UPN, the WB, and the CW. Cable, satellite, and all the offerings of internet-video services are stiff competition for network television. Even content from network broadcasters is largely accessed today through cable, satellite, or the internet. Very few customers rely on over-the-air antennas for television at all anymore. 

The media landscape bears little resemblance to the world of the 1960s and 1970s, when the Court decided Red Lion and Pacifica. Broadcast outlets now compete directly with cable networks, streaming platforms, podcasts, and digital publications—and often lose that competition. Treating ABC differently from CNN because it remains theoretically accessible via over-the-air antennas no longer reflects market reality.

The same logic that doomed the fairness doctrine applies here. As Eric Fruits put it: 

The original justification for some broadcast regulation stemmed from the limited number of available broadcast licenses. But such scarcity has been eliminated in the Digital Age, with countless online news outlets and platforms available to consumers… This abundance of information sources diminishes the need for government intervention to ensure the public receives diverse and accurate news.

In short, the news-distortion policy rests on increasingly unstable legal ground. Its decades-long dormancy underscores that point. There is little justification for treating a segment aired on ABC or NBC differently from a video on YouTube, a podcast on Spotify, an article in The Wall Street Journal, or a post on The Huffington Post.

All Allegation, No Proof

Even if the news-distortion policy remains valid law, the allegations here fall well short of what enforcement would require.

Given the constitutional concerns at stake, the FCC and reviewing courts have emphasized that the policy has “extremely limited scope.” A viable claim must satisfy two elements: (1) the broadcaster must have deliberately intended to slant or mislead, and (2) the distortion must concern a significant event, not a minor or incidental aspect of the report.

The intent requirement sets a high bar. It is “not enough to dispute the accuracy of a news report… or to question the legitimate editorial decisions of the broadcaster.” A claimant must instead produce “extrinsic evidence”—evidence beyond the broadcast itself—such as internal directives, outtakes, or proof of bribery demonstrating intentional distortion.

No such evidence has been alleged here. Carr’s tweet relies solely on a screenshot of the president’s Truth Social post criticizing allegedly misleading headlines in newspaper coverage of the conflict in Iran. That falls short in two respects. The news-distortion policy does not apply to newspapers or their online equivalents. And even if it did, such allegations would not constitute extrinsic evidence of deliberate distortion.

Past cases underscore the gap. Many involved broadcasters staging or reenacting events depicted in news reports. Even then, the FCC distinguished between material distortions and “incidental” production choices. No comparable conduct has been alleged here.

Carr’s comments also raise a separate concern. They go beyond describing the law and suggest prejudgment. His references to “legacy media” as untrusted purveyors of “fake news” imply that the outcome has already been decided and that the agency is merely awaiting a formal complaint to act. That posture could complicate any eventual enforcement action in court.

The Problem Is the Power

Carr is once again warning broadcast licensees that their coverage may fail to satisfy his view of the public interest. The FCC’s public-interest standard is broad, but it is not boundless. Public statements suggesting that news coverage violates the news-distortion policy create a real risk of backdoor censorship.

Rather than invoke that authority, the FCC should repeal the policy through its “Delete, Delete, Delete” proceeding. The agency should acknowledge the weak legal foundation for treating broadcasters differently in today’s media marketplace. The First Amendment protects the marketplace of ideas. It does not empower regulators to decide what is true.

Agency restraint, however, is not enough. The problem is structural. As long as the policy remains on the books, any administration—of either party—can use it to pressure the press over unfavorable coverage. Congress should eliminate that authority altogether.

The First Amendment should not depend on the goodwill of regulators. It should depend on law that leaves no room for them to decide what counts as the truth.

The post The FCC’s Sleeping Power Over the Press appeared first on Truth on the Market.

The clearest example comes from midcentury enforcement. In 1956, the U.S. Department of Justice (DOJ) imposed a sweeping antitrust remedy on AT&T. The DOJ consent decree required AT&T’s research arm, Bell Labs, to license its existing patents for free and to grant access to future patents on reasonable terms. Those two measures made a difference. But the most consequential element was a third prong: a bar on entering industries not directly related to telecommunications.

The goal was straightforward—to prevent a monopolist from leveraging its market power into adjacent and emerging markets. The effects were anything but modest. Shielded in part from AT&T, International Business Machines Corp. (IBM) consolidated its position in computing. Intel, Microsoft, and Apple followed. The “quarantine” remedy, as it came to be known, helped shape an entire industry—and, given the ubiquity of computing, much of the modern world.

That approach, however, did not last. The quarantine remedy has largely fallen out of favor in the United States. Some scholars—notably Tim Wu—have urged its revival  in digital platform markets, but those proposals have gained little traction with enforcers. The European Union has never formally adopted this type of remedy. Although some have proposed other, arguably equally “radical,” tools to address anticompetitive conduct, quarantine remedies remain outside the EU’s competition-law toolkit.

Yet abandoning the label has not eliminated the underlying effect. Modern enforcement often produces functionally similar outcomes. Even when agencies do not describe them in these terms, several actions have blocked or significantly impeded dominant firms from entering emerging markets.

This disconnect reflects a familiar feature—perhaps a bug—of antitrust enforcement. Antitrust rules often diverge from how authorities apply them, as Dan Crane has shown. Remedies designed to address one concern can produce very different effects in practice. In particular, efforts to level the playing field in digital markets may create de facto quarantine remedies. Those outcomes can limit entry into adjacent markets and weaken competition.

Quarantine by Another Name

One pathway runs through self-preferencing rules. These interventions often aim to ensure fair treatment within platforms. But they can also restrict how dominant firms expand beyond them. The European Commission’s Google (Shopping) decision illustrates the point. The Commission required Google to treat rival comparison-shopping services on equal terms in its general search results. The remedy sought to restore fair competition on Google’s platform. Its structural effect went further: it constrained Google’s ability to leverage Search into the emerging “vertical” product-search market—a space where Amazon was consolidating its position.

A similar dynamic may arise in cases involving Apple Inc.’s App Tracking Transparency (ATT) framework. Critics often portray ATT as exclusionary, arguing that it made it harder for advertisers to obtain user consent for third-party tracking. But Apple’s privacy-centric strategy may also have been an effort to build a differentiated advertising product and enter Google and Facebook’s core market. Apple had a large, privacy-sensitive user base to support that move. Enforcement that constrained how Apple implemented this strategy may therefore have disrupted that entry.

What links these examples is not an explicit prohibition, but a practical constraint. The Google (Shopping) remedy did not state that “Google shall not enter vertical search markets.” Nor did actions against Apple formally bar entry into advertising. Instead, they imposed conditions that made entry less viable. In effect, they signaled: you may enter, but not on terms that make entry worthwhile. The label is absent, but the effect resembles a quarantine.

A second pathway operates through merger control. In recent years, enforcers on both sides of the Atlantic have increased scrutiny of below-threshold mergers. U.S. agencies have focused on “acquihires,” while the European Commission has used merger-regulation tools and related authorities to expand oversight of transactions that historically escaped review.

That scrutiny can be justified. Some below-threshold deals raise real competitive concerns. But acquisitions also serve as a key entry strategy. Firms often acquire startups to build capabilities, combine assets, and pivot into new markets. When enforcement chills these transactions—as empirical evidence suggests—it closes off a primary route for incumbent diversification. The result mirrors a quarantine: dominant firms face stronger disincentives to enter adjacent markets.

Fairness That Fences Everyone In

These dynamics highlight a broader tradeoff. Self-preferencing rules and stricter merger review can address genuine harms. But they also shape how firms compete across markets. Current debates tend to emphasize intra-platform competition—fairness within a dominant firm’s ecosystem—while paying less attention to inter-platform competition. Restrictions that improve the former may weaken the latter by limiting incumbents’ ability to challenge one another in adjacent markets.

Management scholarship helps frame this risk. Research in business strategy has long observed that when incumbents avoid entering each other’s markets, the outcome may reflect “mutual forbearance”—an implicit accommodation in which firms respect each other’s domains and soften competition. The literature on “multimarket contact,” dating back to Corwin Edwards and to Douglas Bernheim and Michael Whinston, treats the absence of cross-market entry as a warning sign. If competition enforcement inadvertently produces this dynamic, it may allow dominant firms to enjoy the “quiet life.”

This concern becomes more concrete in digital markets. One common response is that constraining dominant firms preserves space for startups and smaller rivals. That holds when credible entrants are small firms. But in many digital markets, the most significant threats come from other incumbents—Google entering e-commerce, Amazon entering streaming, Apple contesting advertising. When enforcement blocks or deters these cross-market moves, the primary beneficiary may be the incumbent already dominating the target market, not a startup. In that setting, a quarantine does not reduce concentration—it can entrench it.

The Case for Letting Titans Fight

The European Commission’s recent investigation into Meta Platforms Inc. underscores the point. The case concerns changes to Meta’s policy governing artificial-intelligence products on WhatsApp. The Commission has warned that the policy “may prevent third-party AI providers from offering their services through WhatsApp,” while “Meta’s own AI service ‘Meta AI’ would remain accessible to users on the platform.”

At the same time, it bears noting that Meta is not leading the artificial-intelligence race. Its prospects of catching frontrunners such as Anthropic and OpenAI appear to be narrowing. Against that backdrop, Meta may be using the assets it still controls—most notably, WhatsApp’s large user base—to compete. The Commission’s investigation may be justified on its own terms, particularly if it could restore intra-platform fairness on WhatsApp. But if it also limits Meta’s ability to challenge other large firms, it risks confining the company to a losing position in a market where scale matters.

Sometimes it takes a titan to challenge another titan. If digital markets often operate that way, enforcement should account for it. The functional quarantine framework outlined here captures one side of that equation. Ignore it, and competition policy may end up protecting markets from competition itself.

The post Keeping Titans in Quarantine appeared first on Truth on the Market.

U.S. District Judge Arum Subramanian, who reportedly described the DOJ–Live Nation/Ticketmaster settlement as “absolutely unacceptable,” will continue to preside over the trial with the existing jury. Reports indicate that the judge was frustrated by what he described as a “lack of communication” regarding the DOJ’s settlement negotiations with Live Nation.

The handling of the LNTM case raises serious questions about economic efficiency and the separation of powers. A judicial order requiring divestiture of Ticketmaster also would create significant antitrust federalism tensions.

A Settlement Without a Breakup

Key terms of the tentative DOJ–Live Nation/Ticketmaster settlement—outlined in a term sheet filed with the court—reportedly include the following:

• No Divestiture: Live Nation would not be required to divest Ticketmaster. The remaining state plaintiffs, by contrast, continue to press for divestiture.
• Ticketing Choice: Live Nation would allow venues to use competing ticketing platforms, such as SeatGeek or AXS, enabling up to 50% of tickets to be sold through rival services.
• Venue Divestitures: Live Nation agreed to divest ownership or control of 13 amphitheaters, including venues in Milwaukee; Cincinnati; Syracuse, New York; and Austin, Texas.
• Fee Caps: Ticketmaster’s service fees at Live Nation-owned amphitheaters would be capped at 15%.
• Financial Penalty: The agreement would establish a $280 million settlement fund to pay civil penalties to states that join the deal.
• Behavioral Changes: The settlement would allow artists to rent Live Nation-owned venues regardless of whether they use Live Nation’s promotional services. It also would require termination of Ticketmaster’s ticketing agreement with Oak View Group.

Breaking Up the Band Isn’t Always Procompetitive

Based on what we know so far, there is little reason to view the proposed DOJ–Live Nation/Ticketmaster antitrust settlement with alarm. The agreement appears likely to generate economic benefits, as discussed below. Removing contractual barriers that restrict venues from working with multiple ticket vendors and promoters—changes the settlement reportedly requires—would promote competition.

By contrast, requiring Live Nation to divest Ticketmaster, as the DOJ originally sought, could have eliminated cost-saving efficiencies from vertical integration without materially enhancing competition. Ticketmaster already held a large market share before Live Nation acquired it—indeed, slightly larger than today—and it is far from clear that ticketing competition would increase if Live Nation and Ticketmaster were separated. Market realities also matter: popular artists exert substantial influence over ticket pricing and sales terms, and that dynamic would remain regardless of the outcome of the antitrust case. (See this 2024 Truth on the Market commentary on LNTM.)

A divestiture, moreover, would replace a streamlined integrated system with a fragmented one, likely increasing costs for artists and fans. Live Nation’s integrated structure generates several types of economic benefits.

Elimination of Double Marginalization

Vertical integration allows a single firm to coordinate pricing across multiple stages of the supply chain—promotion, venue operation, and ticketing.

In a fragmented market, each independent firm—such as a standalone promoter and a separate ticketing service—adds its own markup. That dynamic creates “double marginalization,” in which successive firms each impose their own margin. Divesting Ticketmaster could therefore raise final ticket prices for consumers compared with an integrated firm that optimizes a single combined margin.

The ‘Flywheel’ Efficiency Model

Live Nation operates a “flywheel” model in which higher-margin segments—such as ticketing and advertising—help subsidize lower-margin activities like concert promotion.

Vertical integration allows Live Nation to offer artists larger financial guarantees while recouping costs through other parts of the ecosystem, including venue concessions and sponsorship deals. Control across multiple stages of the business also helps the company absorb the substantial risks of concert promotion, which often operates on thin—or even negative—margins for individual shows.

Transaction-Cost Reduction

Bringing promotion, venue management, and ticketing under one corporate umbrella reduces the time and expense required to negotiate complex agreements among separate firms.

An integrated platform also can streamline tour planning and ticket distribution, which proponents argue leads to better-attended events.

Technology and Data Integration

An integrated Ticketmaster can use Live Nation’s extensive consumer data to target advertising more effectively, potentially lowering marketing costs for artists.

Breaking up the companies could sever these data links, forcing venues and promoters to rely more heavily on third-party marketing services.

Investment and Stability

The certainty created by Live Nation’s integrated structure encourages long-term investment in venue upgrades and new technologies.

Separation could introduce significant economic uncertainty for investors, reducing capital flows into the live music industry and potentially leading to fewer shows or less-innovative venues.

Courts Aren’t Super-Prosecutors

A strong legal argument holds that a federal judge’s opposition to an antitrust settlement proposed by the DOJ may exceed the court’s Article III authority and create a separation-of-powers conflict. The concern rests on a basic constitutional principle: the executive branch—acting through the DOJ—has exclusive authority to decide how to enforce federal law and allocate enforcement resources. (See here, for example.)

Prosecutorial Discretion

The U.S. Supreme Court has long recognized that the executive branch has broad—indeed “absolute”—discretion to decide whether to bring a case and how to resolve it. If a judge compels the DOJ to continue litigating a case it no longer wishes to pursue, the court effectively assumes the role of a super-prosecutor.

Resource Allocation

Only the executive branch can decide how to allocate its limited budget and personnel. Blocking a settlement forces the DOJ to devote public resources to litigation it has already concluded should end.

Policy Balancing

Antitrust settlements often reflect complex economic tradeoffs. These judgments involve policy choices that traditionally belong to the political branches—not to the judiciary, whose role is to resolve disputes rather than make enforcement policy.

The Tunney Act’s Constitutional Tightrope

The Tunney Act, 15 U.S.C. § 16 (1974, as amended), requires federal courts to determine whether a proposed antitrust consent decree is in the “public interest.” The Supreme Court has never invalidated the statute. Legal scholars—and even some former DOJ officials—nevertheless argue that it sits uneasily with the Constitution. The statute also raises concerns from a law & economics perspective.

Creating a ‘Chancellery of the Public Interest’

One critique holds that the Tunney Act effectively turns judges into “ministers of industry.” Instead of resolving cases or controversies—interpreting and applying the law—the act invites judges to evaluate the wisdom of a negotiated settlement. That inquiry risks pushing courts into a legislative or executive role by asking them to decide what outcome is “best” for the economy.

Infringing on the Unitary Executive

If the president, acting through the attorney general, concludes that a settlement advances the government’s enforcement objectives, the act effectively gives a non-elected official (the judge) a potential veto. Critics argue that this arrangement conflicts with the unitary-executive theory, which holds that the president must retain full control over the execution of federal law.

Narrow Judicial Scope (The ‘Rubber Stamp’ Tension)

Courts have long recognized these constitutional concerns. Appellate decisions therefore instruct trial judges to give the DOJ “broad latitude” and “substantial deference” when reviewing consent decrees under the Tunney Act. Tension arises when a court applies the statute aggressively. If a judge treats the public-interest inquiry as a license to rewrite a settlement or demand a specific remedy—such as a breakup—the court risks substituting its judgment for that of the president’s appointees.

A Law & Economics Perspective

Active judicial review of antitrust consent decrees—negotiated between the Department of Justice (DOJ) and private parties—can result in higher transaction costs, slower resolution of anticompetitive issues, and inferior economic remedies compared to the expertise-driven approach of the DOJ.

The Current Context

In the Live Nation case, Judge Arum Subramanian’s frustration over a reported “lack of communication” from the DOJ appears to reflect a procedural grievance. A different issue would arise if the court invoked the Tunney Act to block the settlement on the ground that only a breakup would suffice.

Live Nation’s lawyers could then pursue an interlocutory appeal, arguing that the court violated separation-of-powers principles by displacing the DOJ’s enforcement judgment. They could contend that the court’s role is limited to determining whether the settlement falls “within the reaches of the public interest,” not whether it represents the best possible outcome.

Such an argument could draw support from Justice William Rehnquist’s dissent in Maryland v. United States. It could also rely on the U.S. Circuit Court of Appeals for the D.C. Circuit’s reasoning in U.S. v. Microsoft, which emphasized that although the Tunney Act requires courts to assess whether a consent decree serves the public interest, that authority does not permit a court to conduct a full trial or rewrite the agreement.

Too Many Cooks in the Antitrust Kitchen

The states’ decision to continue seeking divestiture of Ticketmaster from Live Nation after the DOJ settled its claims also raises significant tensions in antitrust federalism.

U.S. antitrust enforcement operates through a distributed-enforcement architecture in which federal and state authorities share enforcement power. Their objectives often align, but they can diverge on remedies for the same alleged conduct.

Under principles of competitive federalism, state attorneys general possess independent discovery and enforcement authority under their own statutes. They may pursue structural remedies even when the federal government opts for behavioral ones, so long as they assert independent grounds under state or federal law.

That framework creates significant complications in a case like LNTM. The litigation’s outcome will shape nationwide practices in entertainment promotion, tour scheduling, and ticketing markets. Divergent remedies across jurisdictions could therefore produce regulatory fragmentation in an industry that operates on a national—indeed global—scale.

Don’t Turn a Settlement Into a Constitutional Problem

The uncertainty created by the negative reaction of the trial judge and most state co-plaintiffs to the DOJ–Live Nation/Ticketmaster settlement is troubling. It discounts sound economics. Serious separation-of-powers and federalism questions also would arise if Judge Subramanian ultimately authorized the forced divestiture of Ticketmaster. The better course would be for the remaining state plaintiffs to reconsider their position and endorse the tentative settlement now on the table—with the court’s approval.

The LNTM matter illustrates federalism and separation-of-powers dilemmas—though not yet crises—that are likely to challenge the U.S. antitrust system in the years ahead. Federalism concerns are especially salient, given the growing tendency of state antitrust officials and legislators to expand state-level enforcement activity.

Congress is unlikely to preempt—or significantly limit—state antitrust law in the near future. Greater attention to Dormant Commerce Clause and conflict-preemption issues in antitrust enforcement may therefore become increasingly important. (See here, for example.)

Put simply: antitrust enforcement works best when economics guides remedies, the executive branch sets enforcement policy, and courts resist the temptation to rewrite negotiated settlements.

The post Antitrust Encore: When a Settlement Isn’t the End of the Show appeared first on Truth on the Market.

In a public notice, the FCC seeks comment on sports-broadcasting practices and market developments. The agency focuses on the growing shift of live sports from national broadcast networks to online streaming services—either simulcast alongside local broadcasts or offered exclusively online. The FCC worries that this shift could weaken local broadcasters’ ability to remain solvent and to fund local news and public-affairs coverage.

Those concerns carry some weight. Viewers have steadily moved away from traditional linear television toward on-demand streaming. As audiences fragment, linear-TV viewership has fallen sharply. Live sports have long resisted that trend. Because sports unfold in real time, they remain one of the few categories that still anchor the linear-TV model. As leagues and distributors move those games to streaming platforms, local broadcasters risk losing both viewers and the advertising revenue that supports their local programming.

The FCC’s concern, though, applies to local broadcasting as a distribution channel—not to the underlying production of local content. Just as television shows and movies migrated online, local news and community commentary increasingly appear on digital platforms. Local bloggers, YouTubers, and independent outlets now perform many of the same functions once dominated by local television anchors, often tailoring coverage to niche community interests. Even if local broadcast stations struggle, the overall production of local content may continue to expand.

Broadcast television could still play a role in the sports ecosystem. Many fans do not want to subscribe to multiple streaming services just to follow their teams. For those viewers, some franchises might prefer broadcast deals with local stations that offer broader reach. League-wide rules often restrict those arrangements. Ordinarily, such collective restraints would face antitrust scrutiny. Joint agreements among teams in the four major U.S. professional leagues—the National Football League (NFL), National Basketball Association (NBA), Major League Baseball (MLB), and National Hockey League (NHL)—receive special treatment under federal law. The Sports Broadcasting Act (SBA) exempts certain league agreements for “sponsored telecasting” from antitrust liability.

If federal policymakers want to give local broadcasters a fair chance to compete with digital rivals, Congress should reconsider that exemption. Repealing the SBA would subject league-wide broadcast restrictions to antitrust law’s rule-of-reason analysis. If those restraints genuinely enhance competition, courts will uphold them. If they do not, the law should not shield them.

The Last Thing People Still Watch Live

In 1939, about 400 households across New York City tuned in to watch the first live televised sporting event: Princeton’s 2–1 baseball victory over Columbia. From the start, sports proved uniquely suited to television. Big games became appointment viewing—events audiences planned to watch as they happened.

Early broadcasters quickly recognized the opportunity. NBC and CBS experimented with live sports to encourage Americans to buy television sets. The broadcasts were technically crude, but the draw was undeniable. Sports also created a natural partnership with advertisers. Brands discovered that sports fans offered a loyal, predictable, and highly engaged audience.

As television revenue grew, leagues began to worry about competitive balance. Early on, most leagues lacked centralized media deals. Each franchise negotiated its own broadcast contracts, effectively acting as its own media company. Teams in larger markets—or with more national appeal—earned far more media revenue than smaller-market rivals. That gap threatened the competitive and financial stability of the leagues.

The NFL responded by restricting how teams could sell broadcast rights. One rule barred franchises from entering broadcast agreements that reached into another team’s territory. The policy aimed to protect smaller-market teams by preserving the value of their local audiences. In practice, it also limited teams’ ability to compete for viewers outside their home territories.

Those restrictions soon drew antitrust scrutiny. In United States v. NFL, courts held that many league broadcasting rules violated the Sherman Act. The ruling effectively blocked the NFL’s effort to sign a league-wide contract with CBS in 1961—even as the rival American Football League secured its own collective deal with ABC.

The NFL turned to Congress for relief. Lawmakers responded quickly. Congress enacted the Sports Broadcasting Act of 1961, allowing professional sports leagues to sell “sponsored telecasting” rights collectively to television networks, which could then select games for local affiliates. The statute reflected the technological realities of the time. Cable television barely existed, so the act focused narrowly on broadcast television.

League media deals expanded rapidly in the decades that followed. The NFL eventually signed nationwide contracts with multiple networks. Demand for televised games continued to grow. “Monday Night Football,” originally broadcast on ABC, now airs on ESPN following a series of corporate mergers—though some games still simulcast on ABC. The NFL Network later secured rights to Thursday-night games. No matter how many games the league offers, fans seem to want more.

Streaming services now see live sports as a powerful subscription driver. Broadcast networks increasingly place marquee events on their own streaming platforms. Netflix and Amazon Prime Video carried NFL games on Christmas Day, while Amazon holds the primary rights to “Thursday Night Football.” NBCUniversal, which owns rights to English Premier League soccer, streams some high-profile matches exclusively on Peacock. The NBA offers League Pass, which streams out-of-market games directly to consumers.

The appeal is straightforward: live sports remain the last major form of appointment viewing. Miss an episode of a serialized television show and you can stream it later. On-demand viewing has fueled widespread cord-cutting, as consumers abandon traditional pay-TV bundles. Sports work differently. Fans generally want to watch the game as it unfolds.

That dynamic puts live sports at the center of the modern media economy. Streaming platforms need them to attract subscribers. Linear television providers need them to remain relevant in an on-demand world.

An Agency in Search of Authority

The FCC frames its inquiry as a competition issue in the video marketplace. Legally, though, the agency has little authority to regulate contracts between professional sports leagues and streaming platforms. The FCC’s jurisdiction centers on broadcast licensees, not the broader ecosystem of digital distribution.

The agency could attempt a different path. Because it regulates broadcast licenses, the FCC might invoke its “public interest” authority to discourage networks from shifting games allocated to broadcast television onto streaming platforms.

Part of that authority historically centers on protecting localism in broadcasting. The FCC emphasized that point in its public notice:

At the same time, sports remain inherently local, despite the increasingly national nature and reach of both professional and college sports events. Just as communities turn to their local TV broadcasters for news, weather, and emergency information, they do the same for coverage of their local sports teams. Many sporting events that were previously available through free broadcast and traditional pay-TV packages, are now only available through a myriad of stand-alone subscription streaming services. This shift has led to notable frustration among many consumers and sports fans. Sports fans are increasingly left with a fragmented ecosystem that requires them to subscribe to multiple services to watch their favorite teams.

The FCC followed with a pointed question:

To what extent do current sports media rights contracts conflict with or impede TV broadcasters from meeting their public interest obligations? How should these arrangements be considered in the context of broadcasters’ public interest obligations and the FCC’s duty to ensure licensees meet their statutory requirements?

The notice suggests the FCC views professional sports as part of a broadcaster’s local-service mission. Consider a hypothetical: if the NFL and CBS shifted a locally allocated game exclusively to Paramount+ instead of airing it on CBS affiliates, fans in that team’s home market would need a subscription, rather than simply tuning in to their local station.

The FCC could argue that a broadcaster’s license carries a public-interest obligation to air local-team games on broadcast stations. Alternatively, the agency might claim that moving games to streaming platforms reduces local-station revenue, weakening their ability to fund news and other community programming.

Both theories face serious legal hurdles.

The Limits of the Scarcity Rationale

In Red Lion Broadcasting Co. v. FCC, the Supreme Court upheld the FCC’s authority to regulate broadcast content based on the “scarcity” of the electromagnetic spectrum. Because only a limited number of broadcasters could operate without interference, licensed stations were treated as “public trustees.” That logic allowed speech regulations that would violate the First Amendment if applied to print media.

Even at the time, that authority had limits.

First, Red Lion involved a rule requiring broadcasters to present contrasting viewpoints on public issues to ensure balanced coverage. Congress had explicitly recognized similar principles in the statutory equal-time framework. The Supreme Court emphasized that legislative backing when upholding the FCC’s rule.

No comparable statutory directive exists requiring broadcast networks to air local sports games, rather than stream them. The Sports Broadcasting Act, if anything, cuts the other way. It allows leagues to negotiate collective broadcast arrangements and even to black out games under certain circumstances.

Second, the scarcity rationale itself fits poorly in the modern media environment. The doctrine emerged when broadcast frequencies were genuinely scarce. That premise eroded with the rise of cable television, satellite distribution, fiber networks, and—most decisively—the internet.

Today, consumers carry devices that provide instant access to millions of voices and platforms. Treating broadcast speech as uniquely scarce now conflicts with technological and economic reality. If the First Amendment adapts to new communications technologies, it should expand protections—not preserve restrictions based on outdated assumptions about media scarcity.

The Major Questions Problem

Recent Supreme Court doctrine adds another complication. The Court’s “major questions doctrine” requires clear congressional authorization when federal agencies claim authority over issues of vast economic and political significance.

Few markets qualify more clearly than live-sports media rights. Those rights sit at the center of competition among broadcast networks, cable providers, and streaming platforms.

In West Virginia v. EPA, the Supreme Court identified several factors that signal a major-questions problem:

1. The agency claims to discover sweeping new regulatory authority in a long-extant statute.
2. The asserted authority comes from an ancillary or broadly worded provision.
3. Congress has repeatedly declined to enact legislation addressing the issue directly.

Those factors do not favor the FCC here. The agency’s public-interest authority applies to broadcast licensees, not to streaming platforms or the broader video marketplace. Attempting to regulate how leagues distribute games across platforms would represent a significant expansion of that authority.

Congress also has legislated directly in this area. The Sports Broadcasting Act reflects a deliberate decision to facilitate collective sports-broadcasting arrangements. If lawmakers wanted to empower broadcast stations relative to streaming services, they could amend the statute.

For now, they have not.

A Weak Legal Foundation

The FCC may worry about the effects of streaming on local broadcasters. The agency’s legal tools to stop that shift are limited. Stretching the public-interest standard to control sports-media contracts would face serious statutory and constitutional challenges.

In short, the FCC can ask questions about the market. It likely cannot reshape it.

Saving Local TV Isn’t the Same as Saving Local News

At the core of the Federal Communications Commission’s (FCC) public notice lies a familiar concern: communities will suffer if local broadcast stations weaken. The evidence suggests otherwise. Local content is not disappearing as broadcast stations face economic pressure. It is migrating.

Consumers increasingly get local news from digital sources—social media, local websites, newsletters, and independent online outlets. Some apps even aggregate local news broadcasts from stations across the country. The traditional gatekeeping role once held by broadcast stations and newspapers has largely vanished. Today, anyone can launch a local blog, newsletter, or video channel and reach an audience directly.

That shift lowers barriers to entry and expands the range of voices covering local issues. Stories that once might never have made it past an editor’s desk can now find an audience online. Local broadcasters still play a role in the media ecosystem. The structure of information distribution, though, has changed.

The FCC’s concern for local stations also risks conflating two distinct issues: protecting local content and protecting a particular technology. Propping up broadcast distribution at the expense of competing platforms would distort the marketplace and ultimately reduce consumer welfare.

If broadcast stations lose access to some sports-related revenue streams, they will need to compete on what differentiates them—local reporting, trusted on-air talent, and coverage that national outlets cannot replicate. If broadcast television continues to deliver value to viewers, the market will sustain it. If not, policy should not freeze the media landscape in place.

More Games, More Screens, More Choice

The FCC’s concern about sports migrating to streaming does not arise in a vacuum. Members of Congress have raised similar complaints, arguing that sports-media rights scattered across multiple platforms increase consumer costs and make it harder for fans to figure out which services they need to follow their teams.

Those concerns capture only part of the story. In many respects, the shift toward streaming has expanded consumer choice.

For decades, NFL games appeared almost exclusively on Fox, CBS, NBC, and ABC (via ESPN). Fans typically could watch only four or five games live each week, and coverage focused heavily on local markets. Out-of-market games were largely unavailable.

NFL Sunday Ticket changed that. The package allowed fans to watch nearly every out-of-market game—something previously unavailable at any price. Today, YouTube’s version of Sunday Ticket expands the experience further. Viewers can watch multiple games simultaneously, jump automatically to key plays, or monitor several streams through multi-view features.

The growth of streaming has also increased the total number of games available to viewers. Thursday-night matchups, Christmas Day games, and other special broadcasts supplement the traditional Sunday slate. Rather than replacing existing viewing opportunities, these additions give fans more live content.

Streaming also reflects how consumers actually watch sports today. The FCC’s nostalgic model assumes viewers sitting at home in front of a television. If fans left the house, they missed the game—or listened on the radio.

That constraint has largely disappeared. Many fans now watch games on smartphones, tablets, or laptops while commuting, traveling, or multitasking. Delivering games through platforms consumers prefer expands access, rather than restricting it.

Leagues also have incentives to experiment with new distribution models. Streaming platforms may offer broader reach, innovative features, or higher licensing fees. If media-rights revenues increase, leagues can invest more in players, facilities, and the on-field—or on-court—product. Collective licensing also spreads revenue across teams, helping smaller-market franchises remain competitive.

Fans may reasonably dislike juggling multiple subscriptions to follow every game. That frustration does not necessarily mean consumers are worse off overall. Streaming has expanded viewing options, added features, and increased the volume of live sports available.

In the end, the market—not regulators—should decide which distribution models prevail. Broadcast television, streaming platforms, and hybrid arrangements will compete for viewers. Policymakers should resist the temptation to tilt the playing field toward any particular technology.

Don’t Regulate Streaming—Repeal the SBA

If Congress wants to expand local access to live sports, the most direct solution would be to repeal the Sports Broadcasting Act. The statute currently grants antitrust immunity for league-wide broadcast-television deals, but not for agreements involving cable, satellite, or streaming platforms. Eliminating the exemption would place all distribution technologies on the same legal footing and subject league-wide media agreements to Section 1 scrutiny under the Sherman Act’s rule-of-reason analysis.

In practice, that means courts would evaluate the competitive effects of league-wide broadcast deals. Many of these agreements exist to preserve competitive balance among teams. Without revenue-sharing arrangements tied to league-wide contracts, franchises in larger markets could enjoy a substantial financial advantage over smaller-market rivals. That imbalance could reduce on-field competition and diminish the overall product offered to fans.

The ongoing litigation over NFL Sunday Ticket illustrates the point. YouTube TV’s current Sunday Ticket package faces a Sherman Act challenge. An amicus brief filed by the International Center for Law & Economics (ICLE) explains several procompetitive features of the arrangement. Bundling out-of-market games can promote efficient pricing and expand access to games that otherwise would not be available. Exclusive licensing can also mitigate free-rider problems associated with relationship-specific investments, allowing distributors that invest in production quality and new features to capture the returns from those investments.

Exclusivity has historically encouraged innovation in sports broadcasting. DirecTV, for example, used NFL Sunday Ticket to attract subscribers and compete with dominant cable providers. YouTube TV has followed a similar strategy, pairing the package with features such as multi-view and dynamic game switching. Although the litigation remains on appeal, these consumer benefits illustrate why exclusive agreements can survive rule-of-reason scrutiny even without a statutory exemption.

None of this guarantees that league-wide deals always benefit competition. Courts could conclude that certain arrangements impose greater harms than benefits. League rules that prevent teams from negotiating their own broadcast deals may limit franchise revenues and discourage investment in teams or facilities. A nationwide agreement that ultimately pushes games behind streaming or cable paywalls could also reduce value for viewers who previously relied on free broadcast television.

Repealing the SBA would not eliminate league-wide media deals. It would simply subject them to the same antitrust framework that governs other joint ventures. Courts would weigh the agreements’ competitive harms against their benefits and uphold only those restraints that promote competition. Just as important, repeal would align the legal analysis across broadcast, cable, satellite, and streaming technologies—rather than privileging one distribution model over the others.

Let the Market Call the Play

Live sports long served as the liferaft of the broadcast-television industry. Changes in viewing habits and the rise of digital-streaming platforms have weakened that model’s dominance. The FCC’s inquiry—and similar concerns voiced in Congress—reflect a desire to protect local broadcasters as the market evolves.

Regulators should tread carefully. Policies that privilege one distribution technology over others risk harming consumers and distorting competition. The FCC’s authority is also limited. Efforts to steer sports content back to broadcast television would stretch the agency’s statutory powers and raise serious First Amendment concerns for media companies and sports leagues.

If lawmakers want to revisit the legal framework governing sports-media rights, Congress should start with the SBA. Repealing the statute would create a unified framework for evaluating league-wide media agreements under existing antitrust law.

Courts would then apply the rule-of-reason analysis that already governs sports deals involving cable, satellite, and streaming platforms. Many of those arrangements produce substantial consumer benefits—expanding access to games, encouraging investment in production quality, and supporting competitive balance among teams. When those procompetitive benefits outweigh potential harms, the agreements survive antitrust scrutiny.

The goal should not be to preserve a particular technology. It should be to ensure that the rules governing sports-media rights promote competition and consumer welfare—no matter how fans choose to watch the game.

The post The 65-Year-Old Law That Still Shapes How You Watch Sports appeared first on Truth on the Market.

The DSA is an EU regulation governing how online intermediary services operate, including social-media platforms, online marketplaces, app stores, and online booking services. Because it is a regulation rather than a directive, it applies directly across all EU Member States and does not require each country to pass its own implementing law. Rather than focusing primarily on whether specific pieces of content are legal or illegal, the DSA targets how platforms are structured and run. Oversight extends to how “very large online platforms” (VLOPs) design their interfaces, how transparent they are about advertising, and how accessible their data is to outside scrutiny. The DSA classifies platforms with at least 45 million monthly active users in the EU as VLOPs.

The European Commission’s enforcement action against X marks the first major attempt to apply this framework in practice. In April 2023, the Commission designated X (formerly Twitter) as a VLOP pursuant to Article 33(4) of the DSA. In December 2025, the Commission fined X €120 million for “breaching its transparency obligations under the Digital Services Act (DSA).” The Commission cited three alleged infringements: the design of X’s “blue checkmark,” deficiencies in its advertising-transparency tools, and restrictions on researchers’ access to public data.

The decision offers an early case study of how the DSA may be enforced. It also raises broader questions about the scope of EU platform regulation, the role of transparency as a regulatory tool, and the growing divergence between European and U.S. approaches to governing digital markets.

Understanding the decision requires understanding what the DSA does—and does not—do. The regulation does not redefine what speech is legal or illegal; those determinations remain governed by existing EU and national law. Instead, the DSA imposes procedural and structural obligations on large platforms aimed at creating a safer, more transparent, and more accountable online environment. In practice, that means requiring transparency tools, systemic-risk assessments, and data-access mechanisms designed to make platform operations more visible and subject to outside scrutiny.

Brussels vs. the Blue Checkmark

The European Commission’s first finding concerned the design of X’s verification system. Article 25(1) of the DSA prohibits platforms from designing or operating interfaces that deceive or manipulate users, or that materially distort their ability to make free and informed decisions. Recital 67 elaborates on this prohibition by targeting so-called “dark patterns”—interface designs that impair users’ autonomous decision making through their structure or presentation, even if the effect is indirect or unintentional.

The Commission focused on how the blue checkmark functioned within X’s interface. Under Twitter’s earlier system, the checkmark signaled that an account’s identity had been verified. After the platform transitioned to X, verification became subscription-based and involved less vetting. The visual presentation of the “verified” status, however, remained largely the same.

In the Commission’s view, this continuity preserved the checkmark’s historical meaning, even though the underlying verification process had changed. As a result, users could continue to interpret the symbol as indicating that an account had undergone the same identity checks as before:

(87)“The results demonstrate a significant mismatch between perceptions and reality: more than half of respondents misunderstand Twitter’s blue check verification policies to still require proof of identity”

The Commission also evaluated the design in its broader context, as Recital 67 requires. Verification symbols, it noted, function across platforms as indicators of authenticity. Major services commonly use them to signal that an account belongs to a genuine public figure, brand, or institution:

(91) “The Commission therefore took the view that the design, organisation and operation of X’s online interface is deceptive to X’s recipients, since the provider of X materially changed the verification process as compared to cross-industry standards”

X did take steps to clarify the new meaning of the “verified” label. The platform provided explanatory information and introduced certain safeguards after redesigning the verification system. The Commission nevertheless concluded that these measures did not overcome the visual and historical cues associated with the symbol. The decision also did not explain how a platform could measure or neutralize such effects ex ante, leaving the compliance standard somewhat unclear.

The Commission further emphasized that verification status interacts with platform architecture. Accounts labeled “verified” may receive greater visibility or perceived authority through ranking and recommendation systems. In that context, ambiguity about the symbol’s meaning could have an outsized effect on user decision making. Disclosure measures, the Commission concluded, did not adequately prevent users from treating the label as a signal of authenticity.

Notably, the Commission did not claim that X’s verification system facilitated illegal or harmful speech. The concern instead centered on how the design affected users’ understanding of information. The persistence of the verification symbol, in the Commission’s view, preserved a meaning that no longer corresponded to the underlying verification process.

Diverging Regulatory Philosophies

This reasoning reflects a broader divergence between European and American approaches to regulating digital platforms.

Under the DSA, large-scale distortions in user understanding can justify regulatory intervention even without a demonstrated connection to unlawful outcomes. The focus lies on platform design and systemic effects.

U.S. constitutional law takes a different starting point. Lawful speech is presumptively protected under the First Amendment, even if audiences misunderstand it. The constitutional framework assumes that individuals may encounter misleading claims and evaluate them independently without government redesign of the communicative environment.

Misunderstanding alone rarely justifies regulation under U.S. law. Even commercial speech—subject to somewhat reduced constitutional protection—can generally be restricted only when it involves materially misleading claims that risk concrete consumer harm. The U.S. Supreme Court has also recognized that some false statements fall within First Amendment protection. In United States v. Alvarez, the Court held that the First Amendment protected a man’s false claim that he had received the Congressional Medal of Honor, emphasizing that the Constitution does not allow the government to suppress speech simply because it is false.

A similar principle appears in U.S. consumer-protection law. Liability under Section 5 of the Federal Trade Commission Act typically requires a materially misleading practice that causes—or is likely to cause—concrete consumer harm. The Federal Trade Commission’s (FTC) deception standard focuses on material misrepresentations that consumers rely on to their detriment, not mere misunderstanding.

Viewed through that lens, user confusion about verification badges reflects uncertainty about a platform signal, rather than unlawful conduct. The contrast highlights a broader shift that commentators have identified in European competition policy. Recent analyses—including discussions surrounding the Commission’s draft guidance under Article 102 of the Treaty on the Functioning of the European Union (TFEU)—suggest that enforcement increasingly relies on structural presumptions rather than effects-based analysis centered on demonstrated consumer harm.

From an American perspective, requiring platforms to mitigate the effects of lawful but misunderstood signals risks blurring the line between regulating platform architecture and indirectly shaping permissible expression.

Extraterritorial Effects

This broader debate has drawn attention in the United States. Some lawmakers have characterized the Digital Services Act as part of what the House Judiciary Committee described as a “foreign censorship threat.” Their concerns extend beyond the removal of illegal content. Critics argue that provisions such as Article 22—which grants priority status to government-designated “trusted flaggers”—may encourage platforms to align moderation practices with European regulatory preferences.

The evidentiary basis in the decision reinforces these tensions. In paragraph 87, the Commission cites a study finding that more than half of respondents misunderstand the blue-checkmark system. The study surveyed 299 U.S. residents, even though the enforcement action concerns systemic risks to EU users.

The DSA applies to companies outside the European Union that offer services to EU users, so relying on non-EU evidence is legally permissible. At the same time, the regulation was designed to address risks affecting “European users in their everyday lives.” Evidence drawn exclusively from American respondents complicates the claim that the enforcement action targets harms specific to the European market.

U.S. lawmakers have seized on this dynamic. Critics argue that “the threat to American speech is clear,” contending that European regulators may classify political speech, humor, and other First Amendment-protected expression as “disinformation” or “hate speech,” and then require platforms to adjust their global moderation systems accordingly.

Reporting in The New York Times suggests that some EU officials and experts hope the DSA’s influence will extend beyond Europe. The goal, in that view, is to shape platform policies globally.

If so, the implications extend well beyond the European Union. Structural obligations under the DSA could reshape platform architecture worldwide, influencing speech environments in jurisdictions built on materially different constitutional traditions.

Advertising Transparency, No Harm Required

The Commission’s second finding concerned X’s alleged failure to comply with the DSA’s advertising-transparency requirements. These rules reflect the European Union’s concern that large-scale online advertising systems may generate systemic risks.

Recital 68 treats advertising transparency as a tool to address risks associated with targeted advertising, including illegal advertising, discriminatory outcomes, and the amplification of harmful content. Recital 95 emphasizes that advertising systems operated by very large online platforms (VLOPs) pose particular risks because of their scale and their ability to target users based on behavior both within and beyond the platform.

To address those risks, the regulation requires heightened transparency and oversight. Article 39 mandates that VLOPs maintain a publicly accessible advertising repository. In practical terms, this repository functions as a public database of advertisements displayed on the platform. It allows outside observers to see which ads ran, who paid for them, when they appeared, and how they were targeted. The goal is to make advertising practices visible enough to permit public scrutiny and regulatory oversight.

Why the Commission Found X Noncompliant

Article 39 also specifies how the repository must operate. Under Article 39(1), it must be searchable and reliable. Article 39(2) requires platforms to allow searches and filtering across multiple criteria, including advertiser identity, subject matter, and time period, and to make the system robust enough to support systematic analysis. The regulation also requires access through an API—an application programming interface—that allows researchers and regulators to retrieve large volumes of advertising data automatically.

Applying this framework, the Commission concluded that X failed to comply with Article 39. Although the platform maintained an advertising repository in formal terms, the Commission found that it did not function as an effective transparency tool.

The repository did not permit comprehensive multicriteria searches across all the information required under Article 39(2). The Commission also found that the API lacked access to the full set of advertising data. As a result, researchers and regulators could not conduct systematic or large-scale assessments of advertising practices on the platform. In the Commission’s view, these limitations substantially reduced the repository’s practical value and undermined the transparency goals of Article 39.

Architecture Regulation vs. Harm-Based Enforcement

This finding reflects a regulatory approach that prioritizes structural transparency in platform architecture. Enforcement does not require proof that any particular advertisement was unlawful. A platform may face liability even when no illegal advertisement has been identified and no specific harm has been demonstrated.

The U.S. regulatory framework approaches advertising infrastructure differently. In the United States, online advertising practices are typically governed through unfairness, deception, and privacy standards. Liability generally arises when advertising is misleading, disclosures are inadequate, or consumer data is misused. The law rarely imposes affirmative design mandates on how an advertising system must be structured or publicly disclosed absent such wrongdoing.

Advertising systems can also intersect with expressive concerns. Decisions about how advertisements are selected, categorized, prioritized, and presented may involve elements of editorial discretion. In the United States, those choices can raise First Amendment considerations, even if indirectly.

For that reason, structural transparency mandates imposed without a showing of harm can appear disproportionate from an American perspective. Disclosure becomes an end in itself, rather than a tool for identifying unlawful conduct. When no illegal advertisement has been identified, requirements aimed at redesigning platform architecture may seem less like consumer protection and more like regulatory oversight of lawful activity.

The DSA’s Researcher Data Pipeline

The European Commission also relied on Article 40(12) of the Digital Services Act, which requires platforms to grant qualified researchers access to publicly accessible data in order to facilitate research into systemic risks associated with very large online platforms.

“Publicly accessible data” refers to information visible to ordinary users of the service, such as public posts, engagement metrics, and other content that can be viewed without special privileges. Although individual users can see this information through the platform interface, researchers typically require technical tools—such as APIs—to collect and analyze it at scale.

Article 40(12) creates a formal access mechanism for that purpose. Platforms must provide access without undue delay when a researcher satisfies the criteria for designation as a “Qualified Researcher.” These criteria are designed to ensure that the research serves a legitimate public-interest purpose and is conducted responsibly.

To qualify, researchers must demonstrate independence from commercial interests, disclose their funding sources, and commit to complying with data-protection, confidentiality, and cybersecurity requirements. They must also show that the requested data access is necessary and proportionate to a defined research project aimed at studying systemic risks under the DSA, such as the spread of illegal content, the effects of recommender systems, or risks to public discourse and electoral processes.

Researchers who meet these conditions may obtain structured access to platform data in order to conduct large-scale analysis.

The Commission’s Findings

The Commission concluded that X’s implementation of Article 40(12) fell short in several respects.

First, it found that X interpreted the eligibility criteria for Qualified Researchers too narrowly (Section 6.3.2.1). According to the Commission, X imposed additional conditions related to institutional affiliation, geographic location, and disclosure requirements that are not explicitly required by the DSA. While Article 40 allows platforms to verify applicants, the Commission determined that X’s approach went beyond verification and materially reduced the pool of eligible researchers.

Second, the Commission found that access granted to approved researchers was too limited in scope and duration. Restrictions on scraping and API functionality made large-scale analysis of public data difficult. In the Commission’s view, access that exists in theory but cannot be used effectively in practice does not satisfy Article 40(12).

These findings have become central to debates surrounding the Digital Services Act. X and several American commentators argue that provisions like Article 40 effectively require predominantly U.S.-based platforms to provide large-scale data access under EU regulatory supervision.

Disclosure Without Demonstrated Harm

The legal baseline in the United States differs significantly. Even when information is publicly visible to users, platforms generally retain broad discretion to restrict scraping, limit API access, or condition access on contractual or commercial terms.

Several legal and policy considerations help explain this difference.

First, mandatory API access can weaken proprietary control over commercially valuable data systems. In the United States, disputes over large-scale data access usually arise under private-law mechanisms—such as terms-of-service agreements and computer-access rules—rather than public-law obligations to provide standardized APIs.

Second, broad API mandates may create security risks. As cybersecurity analysts note, “the ubiquity of APIs brings security risks by expanding an organization’s attack surface, making them a prime target for hackers.”

Third, disclosure mandates can expose commercially sensitive information. Transparency rules that reveal advertiser identity, campaign activity, or targeting parameters may disclose proprietary strategies.

U.S. courts typically require a close connection between compelled disclosure and a concrete regulatory objective—such as preventing fraud, avoiding corruption, or informing voters in campaign-finance contexts. Disclosure obligations generally serve as tools to address identifiable harms.

Article 40 reflects a different regulatory logic. The DSA treats structured data access as a preventive mechanism designed to enable oversight and research into potential systemic risks. Critics argue that this approach risks turning disclosure into an end in itself rather than a tool for addressing concrete wrongdoing.

The Preventive Turn in EU Platform Regulation

The European Commission’s decision illustrates how the Digital Services Act governs platforms through an ex ante, risk-based regulatory model. The finding against X does not rest on evidence of concrete harm. Instead, the Commission concluded that certain design choices, transparency failures, and restrictions on data access could contribute to systemic risks affecting public discourse.

This preventive logic marks a notable shift from frameworks that intervene primarily in response to demonstrated harm. Rather than focusing on identifiable injury, the DSA adopts a more top-down approach to platform governance. Authorities may impose obligations based on assessments of systemic risk and presumptions about potential harm. Under this model, platform practices may be sanctioned because they could distort user understanding or limit external scrutiny at scale.

The same ex ante logic appears in other EU digital regulations, including the Digital Markets Act (DMA). Taken together, these rules suggest a broader shift in the European Union’s regulatory philosophy toward preventive oversight of digital platforms.

Viewed from the United States, the approach looks different. American law generally affords private platforms greater discretion over design choices and the organization of speech-related services. Transparency mandates and data-access obligations imposed without evidence of harm therefore raise questions about the appropriate balance between preventive regulation and platform autonomy.

Those tensions become particularly salient where expressive activity is involved. When regulatory obligations reshape platform architecture, they can indirectly shape the conditions under which speech is distributed and understood.

The post When a Blue Checkmark Becomes a €120 Million Problem appeared first on Truth on the Market.

The CCCC on Feb. 17 announced an investigation into Meta Platforms Ireland Ltd. for alleged abuse of dominance. The probe stems from Meta’s October 2025 amendment to its WhatsApp Business Solution Terms. Those changes prohibit providers of general-purpose artificial-intelligence (AI) services from accessing WhatsApp’s Business Application Programming Interface (API), while—according to the commission—“preserving and preferentially integrating Meta’s own AI service.”

The European Commission and Italy’s Autorità Garante della Concorrenza e del Mercato (AGCM) had already opened proceedings on the same issue. COMESA’s notice followed, echoing those concerns.

Competition investigations of large technology platforms have become routine. Routine scrutiny does not, however, guarantee sound analysis or procompetitive outcomes. The COMESA notice appears to apply the wrong doctrinal standard—a mistake that could undermine the investigation’s aim of protecting competition and consumers.

Even setting that error aside, the available theories of harm face serious obstacles on the current facts. This post examines those difficulties and asks whether any coherent theory ultimately survives.

A Merger Test in a Dominance Case

The most immediate problem with the COMESA notice is its standard of harm. The investigation proceeds under Regulation 36 of the COMESA Competition and Consumer Protection Regulations 2025—the provision governing abuse of dominance. Yet the notice claims the conduct is “likely to substantially lessen competition in the Common Market.” That is a merger-control standard, not an abuse-of-dominance standard.

Some jurisdictions apply the substantial-lessening-of-competition (SLC) test outside merger control. Australia and New Zealand do so for certain restrictive agreements, and the United States applies it under Section 3 of the Clayton Act. The COMESA regulations themselves, though, show that policymakers chose not to apply the SLC standard in dominance cases.

Regulation 33, which governs restrictive agreements, explicitly adopts the SLC language. Regulation 36—addressing abuse of dominance—does not. The SLC test also appears nowhere in COMESA’s 2019 Abuse of Dominance Guidelines. The relevant question, therefore, is not whether conduct substantially lessens competition, but whether it is exclusionary or exploitative.

If the commission aims to develop a coherent and analytically sound body of competition law for digital markets, it must begin with the correct standard. Applying the wrong test from the outset risks distorting the analysis and undermining the investigation’s purpose.

Chasing the Antitrust Unicorn

The dominant theory of harm suggested by COMESA’s notice is refusal to deal. The commission describes WhatsApp as “a crucial gateway [for artificial intelligence service providers] to access their customers in the Common Market.” That framing points toward an essential-facilities theory.

Labeling WhatsApp an essential facility is notable because the doctrine itself remains controversial. As Robert Bork and Gregory Sidak famously observed:

The essential facilities doctrine is the unicorn of antitrust law. Everyone knows what an essential facility looks like, but precious few have seen one in the flesh.

Courts have treated the doctrine cautiously. Some have avoided endorsing it outright—most prominently, the U.S. Supreme Court in Verizon Communications Inc. v. Law Offices of Curtis V. Trinko, LLP. Others recognize it only under exceptionally narrow conditions, such as the Court of Justice of the European Union in Oscar Bronner GmbH & Co. KG v. Mediaprint Zeitungs- und Zeitschriftenverlag GmbH & Co. KG, and the 7th U.S. Circuit Court of Appeals in MCI Communications Corp. v. AT&T Co.

Where the doctrine applies, courts impose demanding criteria. A claimant must show:

1. The facility is controlled by the dominant firm;
2. The facility is unique or exceptionally difficult—if not impossible—to replicate;
3. Access to the facility is indispensable for competitors seeking to compete in the relevant market; and
4. Denial of access forecloses competition in that market.

COMESA’s notice does not yet define the relevant market—a prerequisite for any coherent abuse-of-dominance analysis. Even if it did, establishing WhatsApp as an essential facility in the COMESA region would be difficult. Control and possible foreclosure might be arguable. Demonstrating uniqueness and indispensability would be far harder, given the range of alternative communication platforms and distribution channels available across COMESA member states.

Aspen’s Shadow

Another path for COMESA would be to follow the approach taken by the AGCM. In a November 2025 announcement expanding its investigation into Meta’s integration of Meta AI into WhatsApp, the AGCM relied on language drawn from the European Court of Justice’s (ECJ) Google Android Auto decision:

Denying access to a digital infrastructure originally developed by a dominant company not solely for the needs of its own activities, but with a view to allowing third-party companies to use that infrastructure, as is the case with the WhatsApp platform, is abusive when it has the actual or potential effect of excluding, hindering, or delaying the market development of a product or service that is, at least potentially, in competition with a product or service provided or that can be provided by the dominant company, and constitutes conduct that limits competition on the merits, potentially causing harm to consumers. [Translated in part using Google Translate.]

The Google Android Auto case arose from a complaint by Enel X Italia, the developer of the electric-vehicle (EV) charging app JuicePass. The app allows EV drivers to locate and manage charging stations. Enel X sought to make JuicePass available through Android Auto, Google’s in-car interface that mirrors smartphone apps on a vehicle’s dashboard display.

At the time, Google permitted only certain categories of apps—such as navigation and messaging—through standardized templates. It declined to provide a template that would allow EV-charging apps to run on Android Auto. JuicePass therefore could not operate on the platform, even though Google’s own services, such as Google Maps, remained accessible.

In paragraph 49 of its decision, adopting the opinion of Advocate General Laila Medina, the ECJ held:

Article 102 TFEU must be interpreted as meaning that the refusal, by an undertaking in a dominant position which has developed a digital platform, to ensure, at the request of a third-party undertaking, that that platform is interoperable with an app developed by that third-party undertaking is capable of constituting an abuse of a dominant position even though that platform is not indispensable for the commercial operation of that app on a downstream market, but is such as to make that app more attractive to consumers, where that platform has not been developed by the undertaking in a dominant position solely for the needs of its own business.

Under this reasoning, a platform that initially permits third-party access may abuse its dominance by later restricting that access. Applied to the present case, the argument would be that because WhatsApp once allowed third-party integration, a subsequent decision to bar certain uses could be exclusionary.

Whether WhatsApp was designed with such openness in mind is doubtful. More importantly, COMESA should approach the Google Android Auto logic with caution. The decision rests on shaky doctrinal ground. In essence, it borrows the structure of the U.S. Supreme Court’s decision in Aspen Skiing Co. v. Aspen Highlands Skiing Corp., while stripping away the elements that justified liability in that case.

The 1985 Aspen Skiing decision carved out a narrow exception to the general rule that firms have no duty to deal with rivals. Liability rested on a specific set of facts: a prior, voluntary, and profitable course of dealing; termination of that relationship without a legitimate efficiency justification; and conduct consistent with sacrificing short-term profit to harm a rival.

That willingness to forgo revenue without a legitimate business reason was the lynchpin. It distinguished exclusionary conduct from the ordinary exercise of unilateral business judgment—decisions courts are rightly reluctant to second-guess.

The Supreme Court itself treated Aspen Skiing as exceptional. In Verizon Communications Inc. v. Law Offices of Curtis V. Trinko, LLP, the Court confirmed that view, describing Aspen as sitting “at or near the outer boundary of Section 2 liability.” Extending it further, the Court warned, risks forcing firms to subsidize rivals and chilling investment. As the Court emphasized, the Sherman Act generally “does not restrict the long recognized right of [a] trader or manufacturer engaged in an entirely private business, freely to exercise his own independent discretion as to parties with whom he will deal.”

The Google Android Auto decision adopts Aspen’s structure—substituting prior platform openness for a prior course of dealing—while removing its limiting principle. The result is a theory under which a dominant firm that once opened its platform cannot later withdraw access or decline new integrations without risking abuse allegations. Evidence of naked exclusionary intent—the centerpiece of Aspen—drops out of the analysis.

The implications are significant. Treating an initial decision to open a platform as creating a quasi-permanent obligation of openness discourages experimentation with open architectures. A firm told that openness today constrains product decisions indefinitely has a rational incentive never to open at all. The doctrine effectively taxes openness and rewards closed, walled-garden designs from the outset.

To be sure, open and closed ecosystems generate different competitive dynamics. Open platforms often create broader ecosystem value by enabling third-party innovation and complementary services. Closed systems restrict participation and require the platform owner to shoulder more of the innovation burden. The tradeoff lies between ecosystem-wide innovation through openness and tighter value capture through closure, which may carry weaker long-term innovation incentives.

The Google Android Auto approach also raises a deeper concern. It deprives firms of the autonomy to redesign or manage their own products without regulatory oversight. A platform that cannot close what it once opened—absent an “objective justification” judged by an external authority—no longer competes freely. It operates under ongoing administrative supervision.

Supervising the design of complex technical platforms in fast-moving markets is a demanding task for any competition authority or court. Such intervention risks substantial error costs, often exceeding institutional expertise. COMESA should be wary of importing a doctrine with those characteristics into a competition regime that is still developing its institutional foundations.

A Self-Preferencing Theory in Search of a Platform

The COMESA notice’s claim that Meta is “preserving and preferentially integrating Meta’s own AI service” while excluding rivals suggests a self-preferencing theory of harm. That theory typically arises where a dominant platform serves as a critical gateway between users and downstream services and then uses control of that intermediation layer to favor its own offerings.

The European Commission’s decision in Google Shopping illustrates the point. There, the Commission argued that Google presented its general search engine as a neutral index of web content, but then departed from that neutrality by favoring its own comparison-shopping service over rival services that depended on Google for traffic. The alleged abuse thus lay in the discriminatory use of a supposedly neutral gateway—not in the mere fact that Google promoted its own product.

WhatsApp Business does not appear to share that baseline. Meta has not presented the WhatsApp Business API as a neutral platform through which competing AI providers reach users. Rival AI services have not historically relied on the API as a distribution channel.

As Todd Davies and Marina Iskander observed, a stronger case might exist if services such as ChatGPT or Claude had launched on Meta’s platform and then faced exclusion. That is not the situation here. For that reason, characterizing Meta’s conduct as self-preferencing may be misplaced.

Tying: The Least Bad Theory

Of the available theories of harm, tying may be the most plausible—though its viability depends on defining the relevant market and demonstrating foreclosure, neither of which COMESA has yet done. As noted in a previous article, tying requires three elements: separate products, coercion, and foreclosure.

The separate-products element would likely be satisfied. There is plausibly distinct consumer demand for the WhatsApp messaging platform (the tying product) without integrated Meta AI, as well as for Meta AI as a standalone service (the tied product). One counterargument is that Meta AI’s integration differs from a typical chatbot. It assists directly with drafting, summarizing, and responding to messages within the app—functions closely tied to WhatsApp’s core messaging features. Whether that distinction alters the analysis remains unclear.

The coercion element may also be met, though not through classic bilateral conditioning (“buy A to get B”). Instead, coercion could arise from the structure of the integration itself. In its Facebook Marketplace decision, the European Commission—citing the Court of First Instance in Microsoft Corp. v. Commission—observed that:

…compulsion or coercion can still exist where the party accepting the tied product is not required to use it or is entitled to use the same product supplied by a competitor of the dominant undertaking.

Under that reasoning, excluding rival AI services while integrating Meta AI into a dominant messaging platform could structurally favor Meta’s product. The convenience and seamless integration might steer users toward Meta AI, potentially satisfying the coercion element.

The foreclosure element presents the most serious difficulty. Tying requires more than coercion; it requires foreclosure of rivals in the tied-product market as a result of the tie. That claim is difficult to sustain against global AI providers such as OpenAI, Google Gemini, and Anthropic. These firms possess substantial resources and numerous distribution channels—including standalone apps, web access, and enterprise sales—and do not depend on the WhatsApp Business API for viability.

The more relevant question for COMESA’s mandate may involve local dynamics. The foreclosure analysis could look different if the relevant rivals are not global incumbents but smaller, regionally focused AI providers building products for the COMESA common market. For such nascent firms, WhatsApp might represent more than one distribution channel among many.

Even so, the argument carries risks. Protecting potential entrants who may never become efficient competitors risks sliding from competition policy into economic protectionism. The relevant legal question cannot be merely whether WhatsApp access would help local AI providers. It must be whether those providers cannot achieve economic viability without it.

If supported by concrete market evidence, that argument—focused on nascent regional competition—may offer COMESA the most defensible path toward a viable tying theory.

Borrowing Doctrine Means Borrowing Trouble

COMESA has an opportunity to develop competition doctrine for its digital economy that is both analytically disciplined and attentive to the market realities of the region it serves. That opportunity would be poorly served by importing contested European frameworks whose doctrinal foundations remain unsettled even in their home jurisdiction.

A more careful approach would ground enforcement theories in coherent economic logic and recognize the right of independent traders to decide how to deploy their resources. Administrative restraint, in that sense, should not be mistaken for passivity. It is often a hallmark of sound administration.

The post COMESA, WhatsApp Business, and Antitrust in Search of a Theory appeared first on Truth on the Market.

The filing suggests the current administration may be moving to restore that approach. If so, the implications for patent remedies—and for innovation policy more broadly—could be significant.

A brief look at the economic role of patent injunctions, and at what the Collision Communications statement of interest (CCSOI) actually says, helps explain why. It also raises the possibility that even stronger pro-patent initiatives may soon follow.

Why the Right to Exclude Matters

The primary economic justification for routine injunctions is protection of the “right to exclude.” That principle is grounded in the U.S. Constitution and lies at the core of the patent system.

Routine injunctive relief transforms a patent from a mere right to collect royalties into a robust property right. It allows innovators to commercialize their inventions with confidence while requiring competitors to contribute new advances to the marketplace, rather than free ride on existing ones. The result is stronger incentives for research and development (R&D), greater investment, and more durable innovation cycles.

Routine injunctions also strengthen innovation incentives in several ways. First, they help innovators recoup the substantial costs of developing new technologies. In high-risk sectors such as pharmaceuticals, firms often invest hundreds of millions of dollars to bring a product to market. Injunctions help preserve market exclusivity, allowing innovators to recover those costs and earn a return on their investments.

Second, strong patent protection reinforced by injunctive relief signals a firm’s R&D capabilities to outside investors. By reducing information asymmetries, credible patent rights can increase market valuations and improve access to the capital needed to finance future innovations.

Third, injunctions deter “predatory infringement”—situations in which competitors use patented technology without authorization and force the patent holder to pursue compensation through litigation. By preserving the rewards promised by the intellectual-property system, injunctions help sustain a continuous cycle of innovation.

Competition in the Shadow of Injunctions

Injunctions also play a critical role in facilitating efficient market negotiations. They establish the conditions for private bargaining over licensing, rather than leaving courts to estimate damages after the fact. An injunction preserves the property owner’s ability to refuse an inadequate offer, preventing coerced transfers of intellectual property.

Absent the credible threat of exclusion, potential infringers may find it profitable to use a technology and delay payment until litigation compels them to pay damages. That dynamic risks turning patents into de facto compulsory licenses. Injunctions push licensing negotiations back into ordinary market contracting.

They also reduce valuation errors. Courts often struggle to calculate accurate monetary damages for unique, non-fungible inventions. Injunctions instead apply a property rule that leaves valuation to the market participants best positioned to assess a technology’s worth.

Finally, the threat of an injunction can stimulate competition by encouraging design-arounds. Competitors excluded from using a patented technology must develop alternative solutions. That process can generate different—and sometimes superior—technologies.

Design-arounds also expand the range of technological approaches and products available in the marketplace. The result is greater product diversity, stronger competition, and increased long-term dynamism in innovation-driven markets.

A Welcome Return to Patent Basics

The CCSOI filed by the DOJ and USPTO underscores the central role of injunctive relief in promoting innovation. The statement frames strong injunctions as an essential mechanism for protecting patent holders’ property rights and preserving the incentives that drive research, investment, and technological progress. The position is also consistent with the DOJ’s June 2025 statement of interest in the Radian Memory Systems case.

First, the agencies emphasize that the patent holder’s right to exclude lies at the core of the patent system. Unduly restricting access to injunctions weakens that right and, in turn, diminishes the incentives for future inventors to invest in R&D. The constitutional structure of the patent system reflects the expectation that exclusive rights will encourage innovation by allowing inventors to capture the value of their discoveries.

The CCSOI also highlights the role injunctions play in encouraging market-based licensing. According to the agencies, injunctions are “effective tools” for pushing parties toward private negotiations grounded in market principles. A negotiated licensing agreement is “far preferable,” the statement notes, to a “judicial guesstimate” of a reasonable royalty—an estimate courts may struggle to calculate accurately for unique technologies.

The agencies also reject categorical limits on injunctions for non-practicing entities (NPEs), such as Collision Communications. Monetary damages may not fully compensate patent owners for ongoing infringement, particularly when infringement forecloses licensing opportunities or undermines the technology’s market value. Denying injunctions solely because a patent owner does not manufacture products would improperly narrow the scope of the right to exclude.

The statement’s recognition of the importance of standard-essential patents (SEPs) is also notable. Inadequate compensation for SEPs discourages participation in standard-setting and weakens incentives to contribute advanced technologies to widely adopted technical standards. Over time, that dynamic risks degrading standards quality, slowing innovation, and reducing the quality of downstream products and services. (A November 2022 International Center for Law & Economics “Issue Spotlight” provides additional information on the benefits of SEPs and on the erroneous nature of the attacks on those important property rights.)

Finally, the agencies stress that meaningful access to injunctions helps deter “efficient infringement.” Without the credible threat of exclusion, well-resourced firms may find it profitable to copy patented technology and treat damages as a cost of doing business. Preserving injunctive relief helps ensure that smaller innovators can protect their inventions and compete on the merits, rather than on litigation budgets.

Three Ways to Put Teeth Back in Patents

The administration’s recent SOIs in trial courts represent a welcome signal of support for stronger patent rights. Even so, additional steps could reinforce that message and strengthen the role of injunctions in the patent system.

First, the administration could support legislation that restores a presumption in favor of injunctions once a court finds patent infringement. Proposals such as the RESTORE Patent Rights Act would move patent remedies closer to traditional property rules and reinforce the patent holder’s right to exclude. (See, for example, this analysis of the RESTORE Patent Rights Act.)

Second, the White House could issue a policy statement endorsing the principles of the New Madison Approach. That framework emphasizes several core propositions: hold-up is not an antitrust problem, so antitrust law should not govern disputes between SEP holders and standard implementers; standard-setting organizations should not facilitate collective action by implementers that disadvantages patent holders; the right to exclude remains a fundamental element of patent rights, including SEPs; and unilateral, unconditional decisions not to license a patent should be per se lawful.

Third, the administration could take affirmative steps to challenge foreign policies that weaken U.S. patent rights. The forthcoming administration “Special 301 Report” is expected to identify jurisdictions whose policies undermine patent protections. Where appropriate, the United States could pursue trade responses—including the possibility of sanctions—to encourage reforms.

The Trump administration has taken constructive steps by signaling its patent-policy views through SOIs. The next step may be to articulate a clearer, governmentwide commitment to strong patent rights. A robust patent system supports innovation, investment, and long-run economic growth (see, for example, here and here)—all consistent with the goal of “making the American economy strong again.”

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In a new enforcement policy statement on the Children’s Online Privacy Protection Act (COPPA), the FTC signals that it will decline to pursue enforcement actions against companies that collect personal information for age-verification purposes before obtaining verifiable parental consent (VPC). In effect, the agency is inviting companies to gather additional data—including from children—in order to determine users’ ages.

That move sits uneasily with COPPA’s statutory design. It also reflects a broader policy shift: rather than mandating age verification directly, the FTC appears to be encouraging it indirectly through selective non-enforcement.

When the Courts Say No, Try Policy Statements

Across the country, federal courts have repeatedly struck down state age-verification mandates on First Amendment grounds, whether aimed at social-media platforms or app stores. These laws may be well intentioned, but courts have concluded that they impose unconstitutional barriers to accessing lawful information online.

As I’ve observed previously, the shift in strategy by age-verification advocates has been evident for some time:

The fight has shifted. Lawmakers have moved their focus from social-media platforms to app stores. But the basic problem hasn’t changed: the First Amendment still stands in the way.

By now, the pattern is clear. Courts have held that age-verification and parental-consent mandates for social media restrict minors’ access to lawful speech and fail constitutional scrutiny because they do not employ the least-restrictive means.

Courts have also pointed to those less-intrusive alternatives. Parents and teens can rely on device-level controls, platform tools, and other user-managed safeguards to address online risks without requiring every user to verify their age simply to access lawful speech.

Justice Brett Kavanaugh made this point explicit when reviewing the Supreme Court’s First Amendment precedents, including Free Speech Coalition. As he explained, “[g]iven those precedents, it is no surprise that the District Court in [NetChoice, LLC v. Fitch] enjoined enforcement of the Mississippi law and that seven other Federal District Courts have likewise enjoined enforcement of similar state laws.”

The FTC now appears to be pursuing a different route to the same objective. The commission’s COPPA Enforcement Policy Statement references the wave of state laws requiring age verification but does not acknowledge that many of those laws have been enjoined or remain under active constitutional challenge.

The timing is notable. Having struggled to enact age-verification mandates that survive First Amendment scrutiny, policymakers now appear to be encouraging the same outcome through regulatory non-enforcement.

That strategy may not be unconstitutional. It nonetheless raises serious concerns. The agency appears to be using regulatory discretion to achieve what Congress has not enacted—and in a way that cuts against COPPA’s core privacy protections.

COPPA Has No Pre-Consent Clause

COPPA’s rule is straightforward: operators must obtain verifiable parental consent before collecting personal information from a child under 13. The statute contains no exception that allows companies to collect data first in order to determine whether a user is a child.

That distinction matters. COPPA regulates the collection of children’s data; it does not function as a general age-verification statute. Its companion legislation, the Children’s Online Protection Act (COPA), did attempt to regulate access to certain online material through age verification. The U.S. Supreme Court ultimately struck down COPA as a violation of the First Amendment.

The FTC’s policy statement moves COPPA closer to that model through enforcement discretion. The agency declares it “will not bring an enforcement action” against companies that collect personal information for “Age Verification Purposes” before obtaining VPC.

That position sits uneasily with the statute’s text. COPPA requires parental consent before collecting children’s personal information. By promising not to enforce the rule when companies collect additional data to verify age, the FTC effectively invites conduct the statute appears to prohibit.

The legal footing for that invitation is thin. The policy statement itself acknowledges its limited legal force, noting that “[t]his Enforcement Statement does not create any substantive rights or entitlements, and the Commission retains the right to investigate and bring actions for violations of the COPPA Rule in individual cases.”

The issue is not that the statement itself is unconstitutional. The FTC is not mandating age verification, and enforcement discretion falls within executive authority. The concern is that the agency appears to be using non-enforcement to encourage the very policy outcome courts have repeatedly blocked when legislatures attempted to impose it directly.

The Collect-It-All Solution

The FTC argues that the policy will “promote innovation” in age-verification technology. In practice, it encourages a “collect-it-all” model.

Reliable age verification often requires biometric identifiers, government-issued IDs, or behavioral “age inference” signals. To determine who might be under 13, companies would likely collect such information from anyone who lands on a site.

That approach reverses COPPA’s logic. The statute was designed to prevent the collection of children’s personal information without parental consent. Age-verification systems, by contrast, operate by collecting information first and determining age afterward. In many cases, a child’s data could be collected before a parent even knows the child visited the site.

The implications extend beyond children. Because age-verification systems must screen every user to identify minors, they necessarily collect information about adults as well. What begins as a rule aimed at protecting children becomes broad-based data collection affecting everyone online.

Large-scale age-verification databases also create security risks. Even with “reasonable security safeguards,” repositories containing biometric identifiers, government IDs, or similar signals would present attractive targets for hackers.

The Long Way Around the First Amendment

The FTC’s approach resembles a familiar regulatory maneuver. When courts reject direct mandates, agencies sometimes pursue similar goals indirectly—here, by signaling that certain conduct will not trigger enforcement.

Encouraging age verification through regulatory forbearance risks achieving through policy signals what legislatures have repeatedly failed to impose through law. At the same time, the approach undermines COPPA’s central safeguard: limiting the collection of children’s personal information without parental consent.

Congress should instead revisit the issue directly. Protecting children online is an important objective, but policies that raise serious First Amendment concerns while expanding data collection are unlikely to survive judicial scrutiny—or improve privacy outcomes.

A more durable approach would empower parents, rather than expand surveillance. Tools that allow families to control how and when minors share age-related information can address safety concerns without requiring universal age verification or testing constitutional limits.

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It also left a durable misunderstanding.

Many assume economic analysis purports to prove that existing institutions are good, justified, or normatively desirable. That confusion surfaces most clearly in debates over Richard Posner’s wealth-maximization rule, among critics and supporters alike.

In policy discourse, “efficiency” often functions as an evaluative conclusion—a way to endorse current arrangements or to claim that legal and political disputes can be resolved by neutral technocratic criteria. Read that way, economic analysis looks like a theory of justification.

Within the economic framework, though, efficiency is evaluatively empty by design. It operates inside the model. It does not rank alternative social states, and it does not supply an independent moral criterion. Efficiency follows from the assumption of constrained maximization. It describes the internal coherence of a model, not the justice of an outcome.

Posner acknowledged that limit. He did not treat efficiency as self-justifying. Instead, he defended wealth maximization as an adjudicative decision rule grounded in liberal commitments—not as a claim that “efficiency” itself carries independent normative force.

Why Inefficiency Cannot Follow from Maximization

Michael Staten and John Umbeck (1989) provide the clearest account of why “inefficiency” cannot emerge from a model built on constrained maximization. They distinguish two common meanings of inefficiency and show that each conflicts with the logic of the economic framework. Their point is not that outcomes cannot be regrettable, or that institutional reform cannot improve welfare in some broader sense. It is that the economic model, taken on its own terms, cannot generate inefficiency as a theoretical conclusion.

If that is right, efficiency cannot serve as an evaluative criterion—unless the analyst excludes relevant constraints or draws arbitrary categorical lines among them. Once an economist labels a legal regime “inefficient,” she has stepped outside the model, whether she admits it or not.

Start with the traditional Pareto conception. An allocation is Pareto (P) efficient if no reallocation can make one person better off without making someone else worse off. To call an allocation P inefficient is to claim that mutually beneficial trades remain unrealized.

Staten and Umbeck (1989, p. 62) argue that this result cannot follow from constrained maximization because “if theoretical actors are assumed to maximize, then they must maximize.” Once the objective function and relevant constraints are specified, “the solution of a constrained maximization process must always be P efficient.” Alleged inefficiencies, they conclude, are “illusions” produced by a “failure to fully specify all of the relevant constraints.”

Their example is the familiar deadweight-loss triangle in the monopoly model. The standard story has the monopolist restrict output until marginal revenue equals marginal cost. Arnold Harberger’s triangle then appears as unrealized gains from trade.

But the textbook setup embeds a hidden assumption. Marginal revenue lies below demand only because the model forces the monopolist to charge a single price per unit. It “implicitly assumes away price discrimination.” If alternative pricing schemes permit additional mutually beneficial exchanges, a maximizing monopolist must adopt them. The alleged P inefficiency disappears.

For the traditional marginal-revenue curve to remain relevant, some constraint must block price discrimination. Yet once that constraint enters the model, marginal cost “is no longer the correct marginal cost curve,” because it “fails to fully measure the cost of producing and selling extra units.” In a fully specified model, the original marginal-revenue and marginal-cost curves cannot both survive. Fix either inconsistency, and the inefficiency vanishes.

Economists often respond by shifting from Pareto inefficiency to comparisons across equilibria. Monopoly is “inefficient” relative to perfect competition, adverse selection relative to perfect information, externalities relative to zero transaction costs. Staten and Umbeck call this N (Nirvana) inefficiency, invoking Harold Demsetz’s (1969) critique of the nirvana approach to comparative analysis.

That move does not solve the logical problem. It either repeats the same specification error—by excluding binding constraints from the comparison—or it amounts to little more than regret that the world is not ideal. Wishful thinking does not supply a decision rule.

Does this render efficiency meaningless? As a moral verdict, yes. As a decision rule, also yes. Efficiency cannot perform evaluative work if every outcome is efficient relative to its constraints.

Yet that very emptiness gives the concept analytic bite. An alleged inefficiency is not a conclusion. It is a signal. It prompts the analyst to ask which constraint the model has omitted. If the shortest path from A to B is a straight line, but we observe a winding road, the Chicago move is not to infer irrationality. It is to infer an obstacle—information costs, bargaining costs, enforcement costs, organizational limits, political constraints, strategic behavior, distributional conflict, or related frictions.

This leaves economists with a choice. They can acknowledge the evaluative emptiness of efficiency, recognizing that contrary claims rest on arbitrary exclusions or incomplete specification. Or they can abandon maximization as the core behavioral assumption of the model. In the Chicago view, the latter abandons the economic approach itself.

An economist who wants to evaluate law while remaining faithful to that approach must therefore adopt a separate normative criterion—state it explicitly, and defend it on independent grounds.

Posner’s Wealth Maximization as Political Philosophy

Critics often argue that law & economics treats wealth maximization as a rough proxy for welfare—a practical stand-in for efficiency because “costs and benefits are both hard to measure, and transfers (if ever actualized) hard to achieve” (Jebediah Britton-Purdy et al., 2020, p. 1797). On that account, Posner becomes the cautionary tale: an effort to resolve contested moral questions by appealing to a crude metric dressed up as neutral economics.

Read through a Chicago lens, the charge shifts. Posner appears to misunderstand—or strategically evade—efficiency’s evaluative emptiness, using economic language to smuggle in normative conclusions. That reading misses the mark. Whatever one thinks of his economics or his moral priors, Posner exemplifies evaluative law & economics done well. He does not launder wealth maximization through “efficiency.” He defends it as a normative decision rule.

As a leading figure in the Chicago tradition, Posner (1985, p. 91) understood that efficiency has no independent evaluative content. He rejected the idea that analysts can restore that content by treating some constraints as categorically distinct. “Transaction costs … are just like any other costs—like transportation costs, for example—and when added in, show that the [claimed benefit from reallocation] really has no net value.” Transaction costs differ from transportation costs, he explains, not in their effect on efficiency, but “because they sometimes can be circumvented by substituting a legal institution for the market as a method of allocating resources.”

The move is not to ignore transaction costs in order to compare reality with an ideal. It is to recognize that, when bargaining breaks down, law must allocate entitlements. That choice carries normative implications. The question is not whether a reallocation is “efficient.” It is how courts should exercise coercive power when private ordering fails.

Posner’s answer is wealth maximization. Not because wealth approximates welfare. Not because he endorses utilitarianism, even if “wealth and happiness are positively correlated” (Posner 1985, p. 88). He offers wealth maximization as a rule of adjudication suited to conditions of deep moral disagreement—a way to prevent judging from dissolving into ad hoc particularism.

He is explicit about the nature of the claim: “It must also be emphasized that it is a political philosophy that I am expounding” (Posner 1985, p. 103, original emphasis). Wealth maximization is “merely a way of operationalizing, of making concrete, the idea of [liberalism].” It serves as an “analytic tool” whose “primary” function is to “strip away distributive considerations,” including “the complications introduced in public policy analysis when you think of dollars as being worth different amounts, in some ethical sense, depending on who has each dollar” (Posner 1990, p. 167).

Posner grounds the rule in liberal and pragmatic commitments. Liberalism, he writes, “is the political philosophy best suited for societies in which people don’t agree on the foundations of morality, and pragmatism is the philosophy of living without foundations,” because both reject “the idea of using some comprehensive doctrine… to supply the answers to questions about either reality or personal conduct” (Posner 1996, p. 29). Wealth maximization provides a publicly intelligible judicial rule under those conditions, without asking courts to resolve first-order moral disputes.

In his account, the rule advances liberal values by taking voluntary exchange and consent as the baseline, treating individual rights as constraints on state action, and orienting adjudication toward rules that expand material prosperity. It also reflects institutional humility. His claim is not that wealth is the “only social value that government ought to pay attention to, but that it is the only such value … that courts can do much to promote” (Posner 1984, p. 133, original emphasis).

One need not share Posner’s commitments to grasp the methodological lesson. Economic analysis can clarify tradeoffs and constraints. It cannot supply the evaluative rule. Posner’s defense of wealth maximization models evaluative law & economics at its most transparent: it uses economics to map the terrain, then takes responsibility for the normative choice that economics itself cannot make.

Further Reading:

•   Harold Demsetz, “R. H. Coase and the Neoclassical Model of the Economic System,” Journal of Law and Economics, Vol. 54, No. 4 (2011)
•   Gary Lawson, “Efficiency and Individualism,” Duke Law Journal, Vol. 42 (1992)
•   Peter T. Leeson, “Logic Is a Harsh Mistress: Welfare Economics for Economists, Journal of Institutional Economics, Vol. 16, No. 2 (2020)
•   Richard A. Posner, “Review: Pragmatic Liberalism Versus Classical Liberalism,” The University of Chicago Law Review, Vol. 71, No. 2 (2004)
• Richard A. Posner, “Law, Pragmatism, and Democracy,” Harvard University Press (2005)

The post The Evaluative Emptiness of the Economic Approach to Law appeared first on Truth on the Market.

The pitch sounds simple: give users control over their information. A closer look tells a different story. The bills never identify a clear market failure. Their interoperability mandates expose nonconsenting users to significant privacy risks. Their artificial-intelligence (AI) provisions do not cohere into a workable regulatory scheme. And lawmakers are moving ahead despite little evidence that consumers actually want social-media interoperability.

Copy-Paste Federalism

As noted above, Utah set the template. Gov. Spencer Cox signed H.B. 418 in March 2025, with an effective date of July 1, 2026. The statute requires full social-media interoperability: users must be able to download and transfer their content and interactions, and platforms must maintain “transparent, third-party-accessible… interfaces using open protocols.”

Lawmakers almost immediately discovered the problems. Utah is already advancing a follow-up measure, H.B. 408, to patch obvious gaps, including adding consent protections for users whose data would be pulled into another user’s transfer. The need to amend the law within a year of enactment should itself raise alarms.

Other states have, nonetheless, quickly followed into the breach. Virginia’s SB 85 passed the Senate 40-0 earlier this month and is now moving through the House. It goes beyond Utah by applying interoperability mandates to AI “model operators,” requiring portability of “contextual data,” including prompts, chat histories, uploaded files, and model-generated inferences.

South Dakota’s SB 111 cleared both chambers, passing the Senate 34-0 and the House of Representatives 62-3. New York has companion bills—A8963 in the Assembly and S7476 in the Senate—pending in committee. California Assemblymember Josh Lowenthal plans AB 2169, which would amend the California Consumer Privacy Act to mandate portability of social-graph and AI contextual data. New Hampshire’s HB 1589 stands as the lone rejection, dying in committee on a 16-0 vote recommending against passage.

The speed is notable. The similarity is more so. Across states, the bills use nearly identical language, structure, and underlying theory.

Portability Without Purpose

The threshold question these bills never answer is simple: what consumer harm are they fixing?

Lawmakers assume platform ecosystems are a barrier to welfare. Users treat them as a benefit. People keep separate identities across services because the services do different things. A pseudonymous discussion on Reddit serves a different purpose than professional networking on LinkedIn or sharing selfies on Instagram. These are not interchangeable experiences trapped behind technical barriers; they are differentiated products users intentionally choose. Forced real-time interoperability would collapse distinctions users actively maintain.

Supporters invoke competition policy and “walled gardens,” often analogizing to telephone-number portability. The comparison fails. Telephone numbers are standardized identifiers within a regulated utility network. Social-media platforms are heterogeneous products built around distinct norms and interactions. The claim that a user’s data should move seamlessly from Reddit to X (formerly Twitter) to Instagram to LinkedIn assumes a functional equivalence that does not exist. A Reddit comment thread organized around pseudonymous, upvote-driven discussion does not resemble an Instagram story or a LinkedIn endorsement. As Gus Hurwitz has observed, the analogy breaks down because social-media content and interactions lack any comparable standardization process.

Implementation makes the problem clearer. What would it mean for a Reddit post—embedded in a topic-based, pseudonymous community—to appear in an Instagram feed optimized for visual engagement or a LinkedIn timeline curated for professional signaling? The content would lose the context that gives it meaning. Interoperability assumes user-generated content has value independent of the platform in which it arose. In social media, that assumption is usually wrong.

Nor is there strong evidence of consumer demand. Users already have options. Existing privacy regimes, including the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act, allow data downloads. Users routinely maintain accounts across multiple services. Multi-homing is the norm, which undermines claims that network effects meaningfully lock users in. The real constraint is not the inability to export data; it is that the data has little value outside the environment that produced it. A follower list from one platform rarely translates into engagement on another.

The economic literature is, at best, ambivalent. Research on China’s mandatory interoperability regime found it provided “very limited convenience to consumers” and “hardly facilitat[ed] entry of small operators,” instead benefiting other large platforms seeking expansion. Geoffrey Manne and Sam Bowman likewise find mixed results in the United Kingdom’s Open Banking initiative—the closest real-world analogue—enabling some fintech entry while creating adverse distributional effects for privacy-conscious consumers.

Your Privacy Is Not Yours Alone

The bills’ most serious flaw is third-party privacy—or, more accurately, the lack of it. Each proposal defines a user’s “social graph” broadly to include connections, posts, comments, reactions, shares, and associated metadata. New York’s bill is typical. It expressly covers “secondary users’ responses to the covered user’s content,” along with the metadata attached to those interactions.

The problem is structural. When User A exports a social graph, the transfer necessarily includes User B’s data—comments, interactions, and relationship information—whether User B consents or not. This is not hypothetical. Utah’s follow-up measure, H.B. 408, tacitly concedes the issue by trying to add consent requirements. The fix does not work. Requiring consent from every affected user would make interoperability practically impossible, because every social interaction involves multiple parties.

The risks multiply once the data leaves the platform. Outside a platform’s controlled environment, the recipient is bound only by its own privacy policy. Virginia’s SB 85 would open a substantial loophole, effectively converting personal information into a portable resource detached from its original context. The recipients may be lightly regulated startups, foreign actors, or firms with minimal privacy commitments. Broad interoperability interfaces also create attractive attack surfaces, while enforcement authorities cannot realistically detect or stop misuse in real time—especially when the actors operate outside the United States.

Moderation Meets the Open Gate

Mandated interoperability would also disrupt content moderation. Platforms have spent billions building detection and removal systems. The Congressional Research Service reports that Meta removes about 90% of violent or graphic content automatically, while Reddit removes roughly 72% through automation. Those tools are platform-specific, trained on each service’s norms, signals, and user behavior. Forcing content to move across platforms means forcing moderation systems to operate outside the environments they were designed to govern.

The “fediverse”—a network of federated social-media servers connected through the ActivityPub protocol—offers a preview. One study found that its 18,000-plus independently operated servers create persistent moderation conflicts. When a user encounters harmful content originating on another server, local moderators often cannot compel action. Federated systems make moderation harder because no single authority can enforce rules across the network.

The implications are serious. A platform such as Facebook could be required to interoperate with services that apply minimal moderation standards, effectively bypassing safeguards its users expect. The risks are especially acute for minors. Platforms that invest in age-based protections could be forced to maintain open channels with services that lack comparable protections altogether.

Port My Prompts, Break the Model

Some proposals go further. Virginia’s SB 85 and California’s proposed AB 2169 extend interoperability mandates to AI “model operators.” They require portability of “contextual data,” including prompts, chat histories, uploaded files, preferences, metadata, and model-generated or inferred information. The provisions suggest little understanding of how AI systems work.

No standard format exists for transferring a user’s full AI interaction history across models. The premise is confused. A conversation with a large language model reflects not just the user’s inputs, but the model’s training data, fine-tuning, and architecture. Moving the transcript does not reproduce the experience; it produces data detached from the system that generated it. Transferring a ChatGPT conversation to Claude or Gemini would leave the receiving model without the context necessary to interpret model-specific outputs.

Near-real-time interoperability would also create operational and security risks. It could expose proprietary model characteristics, degrade performance, and conflict with user expectations about how AI interactions are handled. Requiring disclosure of intermediate “reasoning” processes, for example, would create a substantial vector for extracting proprietary information, similar to recent incidents involving bot-based probing of AI systems. These provisions read less like workable regulation and more like aspirational language appended to a social-media bill to capture the politics of AI governance.

A Solution Still Looking for a Problem

The state “Digital Choice Act” movement shows how attractive rhetoric can mask weak policy. “User empowerment” sounds compelling, but these bills never identify a concrete consumer harm. Instead, they attempt to impose a single technical framework on heterogeneous products that users deliberately treat as different services. Social media is not a standardized utility, and interoperability cannot manufacture equivalence where none exists.

The costs are clearer than the benefits. Exporting a “social graph” inevitably transfers information about nonconsenting users. Open interfaces enlarge security vulnerabilities and create enforcement gaps once data leaves a platform’s controlled environment. Cross-platform data flows would also disrupt content-moderation systems that depend on platform-specific norms and tooling, weakening safeguards that users—including minors—rely on. The AI provisions compound the problem by mandating portability of interaction data that has meaning only within the model that generated it, while creating new risks of proprietary information leakage.

Nor is there strong evidence of consumer demand. Users already multi-home, maintain distinct online identities, and can download their data under existing privacy regimes. The central difficulty is not technical lock-in; it is that user-generated content derives value from context. Moving it does not recreate the experience.

Before adopting sweeping mandates, lawmakers should ask two questions: what market failure requires intervention, and will interoperability actually solve it? So far, the bills answer neither. They promise portability, but deliver privacy risks, moderation conflicts, and technical incoherence—while solving no clearly identified problem.

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In its settlement with Express Scripts Inc. (ESI) and its affiliated entities, the agency says the deal will force fundamental changes to ESI’s business practices, increase transparency, and reduce patients’ out-of-pocket drug costs—including insulin—by as much as $7 billion over 10 years. Perhaps it will, although they don’t show their work on that one. It also promises new revenue for community pharmacies and aligns, according to the FTC, with the Trump administration’s health-care priorities.

But here’s wrinkle number one: the FTC settled only part of a larger case.

On Sept. 20, 2024, the FTC filed a complaint against the three largest prescription-drug benefit managers—Caremark Rx, Express Scripts (ESI), and OptumRx—along with their affiliated group purchasing organizations (GPOs). The agency alleged anticompetitive and unfair rebating practices “that have artificially inflated the list price of insulin drugs, impaired patients’ access to lower list price products, and shifted the cost of high insulin list prices to vulnerable patients.”

Here’s the redacted complaint. 

The new agreement resolves the claims against ESI and its affiliates alone. The cases against the other PBMs remain pending, and the FTC has said nothing yet about how—or whether—those will be resolved.

The One-Commissioner Commission

The Federal Trade Commission, in this instance, effectively meant FTC Chairman Andrew Ferguson flying solo. As the agency explained, “[t]he Commission vote to accept the consent agreement for public comment was 1-0, with Commissioner Meador recused.”

A body Congress designed to be bipartisan suddenly became merely bipedal. That is unusual. It may be lawful, and it may or may not have affected the settlement’s terms.

Is there such a thing as a one-member “commission”? Maybe. I’m not entirely sure.

Section 1 of the Federal Trade Commission Act provides that the commission:

…shall be composed of five Commissioners, who shall be appointed by the President, by and with the advice and consent of the Senate. Not more than three of the Commissioners shall be members of the same political party. 

The statute clearly envisions a multi-member body and, if not strictly bipartisan, at least one spanning more than one political party (the commission has even had nominal independents at times). But the text does not set an explicit quorum requirement or invalidate actions taken with fewer than five commissioners.

The statute’s staggered terms, combined with the Senate’s advice-and-consent requirement, also suggest Congress anticipated vacancies from time to time.

The FTC’s own rules add a quorum requirement, though not with perfect clarity. Under 2005 amendments to the rules of practice:

A majority of the members of the Commission in office and not recused from participating in a matter (by virtue of 18 U.S.C. 208 or otherwise) constitutes a quorum for the transaction of business in that matter.

Is one a majority when the number of participating members equals one? It is certainly more than half of one (and by half). Whether that fits ordinary usage is another question.

For a thoughtful discussion, see a 2018 Law360 article by Stephen Calkins and John Villafranco. They doubt the legality of the FTC’s rule and recommend revising it. They may be right. I remain agnostic.

The FTC has operated with reduced membership before. We have seen four-, three-, and two-commissioner agencies. From roughly February 2017 to April 2018, the Commission consisted of two members: Republican Acting Chairman Maureen Ohlhausen and Democratic Commissioner Terrell McSweeny. The agency did not grind to a halt. It voted, brought cases, and conducted ordinary business. I worked in the Office of Policy Planning at the time, and operations continued—surprisingly smoothly.

We have also seen one-party commissions. After Commissioner Christine Wilson departed in 2023, only three commissioners remained, all Democrats: Chair Lina M. Khan, Commissioner Rebecca K. Slaughter, and Commissioner Alvaro Bedoya.

Following Khan’s departure and President Donald Trump’s dismissal of Bedoya and Slaughter (at least de facto, in Slaughter’s case—the Supreme Court is expected to address whether it was de jure in Trump v. Slaughter by June 2026), the commission dropped to two members: Chairman Andrew Ferguson and Commissioner Melissa Holyoak, both Republicans. The agency briefly returned to three Republicans with Mark Meador’s appointment, then fell back to two after Holyoak departed to become a U.S. attorney.

Single-vote action is also not entirely unprecedented. In 2018, one commissioner voted to modify a prior order while Commissioner McSweeny was recused.

So this situation is rare, but not wholly unique. It is odd—and possibly lawful, or possibly not.

Section 5 Without a Theory

Moving from process to substance, I’m not at all sure this was a good case. I sympathize with some of the immediate beneficiaries—especially patients without adequate prescription-drug coverage—but I’m inclined to doubt it.

This is a standalone Section 5 case: one count alleging a violation of Section 5’s prohibition of “unfair methods of competition” (UMC), and two counts alleging “unfair” practices under Section 5’s ban on “unfair or deceptive acts or practices.” No other counts. The FTC’s initial (redacted) complaint is here.

Let’s start with competition. As one might expect of a standalone Section 5 case, the complaint does not allege any violations of the Sherman Act or the Clayton Act. And even allowing that Section 5 may reach somewhat beyond those statutes, it is hard to see a clean competition theory on the face of the pleading. As Herbert Hovenkamp put it when the FTC brought the case:

The complaint does not allege an anticompetitive cartel agreement. conscious parallelism? shared monopoly? no single firm apparently has enough share for monopolization. I’m struggling with the theory of action.

The FTC alleges that “[a]lthough the Respondents claim to prioritize patient well-being, their actions reveal a pattern of anticompetitive and unfair conduct.” The anticompetitive part is where I get stuck. What, exactly, is the residual prohibition in a Section 5 UMC case—at least in this one—the part that does not overlap with Sherman Act liability, but is still supposedly violated by the PBM conduct at issue?

This is not, for example, an invitation to collude—say, to engage in horizontal price-fixing—where there is no agreement (and thus no Section 1 violation), but where the conduct is an attempt to form an agreement that that would be per se unlawful because it “always or almost always” harms competition and consumers, and where unsuccessful attempts, like successful ones, lack a procompetitive rationale. 

Now turn to consumer protection. The alleged violations here appear to be strictly “unfairness” claims, which matters because Congress placed real limits on Section 5 unfairness authority. Section 5 provides:

The Commission shall have no authority … to declare unlawful an act or practice on the grounds that such act or practice is unfair 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. In determining whether an act or practice is unfair, the Commission may consider established public policies as evidence to be considered with all other evidence. Such public policy considerations may not serve as a primary basis for such determination.

In other words, unfairness under the FTC’s UDAP authority requires substantial consumer injury (actual or likely), not reasonably avoidable by consumers, and not outweighed by countervailing benefits “to consumers or to competition.”

That brings us back to the same sticking point: if the FTC’s consumer-protection theory turns on an analysis of competitive effects, it necessarily recalls the question of the scope of the agency’s UMC authority, and the extent to which the FTC’s twin missions under the FTC Act work as complements, rather than as a mash-up.

As it happens, the scope of UMC has been contentious for years—at least since the FTC, under then-Chair Khan, issued its “Policy Statement Regarding the Scope of Unfair Methods of Competition Under Section 5 of the Federal Trade Commission Act” in November 2022. In my view, that statement is rubbish (here’s a piece I wrote with Gus Hurwitz and a short explainer from Geoff Manne and me). And in any case, there’s precious little jurisprudence to settle the matter. 

Let’s take this in three steps. First, what do PBMs do? Second, how does the FTC allege that the PBMs’ conduct harms competition and consumers, in violation of the FTC Act? Third, how is the settlement likely to affect competition and consumer welfare?

PBMs Explained (Without the Pitchforks)

PBMs are complicated. They provide a complex range of management services—one that varies somewhat across PBMs, but also across PBM clients. But we can at least sketch the basics.

The FTC is right that PBMs provide services to health-plan sponsors that provide prescription-drug coverage. To do so, PBMs contract with health plans/insurers, pharmacies (and pharmacy networks), and drug manufacturers. 

The agency is also right that parts of this system look opaque to some contracting parties—and certainly to downstream beneficiaries. And yes, there is a great deal of money involved. Concern about high drug prices, and about whether competition and antitrust enforcement can constrain them, is perfectly reasonable.

For readers who want a deeper dive, the best starting point remains the FTC’s 2005 PBM “mail-order” report. It is old, but still valuable. Congress requested it (by statute), and the staff conducted a substantial empirical study. The report gives a clear description of PBM services and presents research findings on their aggregate effects.

More recent work includes a 2019 U.S. Government Accountability Office (GAO) report and a 2024 study by Compass Lexecon economists Dennis W. Carlton, Mary Coleman, Nauman Ilias, Theresa Sullivan, and Nathan Wilson (now at Econic Partners). The FTC itself has also released two “interim staff reports” based on a more recent study: “Pharmacy Benefit Managers: The Powerful Middlemen Inflating Drug Costs and Squeezing Main Street Pharmacies” (2024) and “Specialty Generic Drugs: A Growing Profit Center for Vertically Integrated Pharmacy Benefit Managers” (2025). In my view, the first reads more like a polemic than a study, while the second offers a narrow and selective view of the industry, particularly given the breadth of data the FTC collected under 6(b) orders and the allegations in its complaint.

For those who prefer the short version, I offer this.

PBMs administer prescription-drug benefits for private-sector plan sponsors—employers, unions, and managed-care organizations, among others. Their services include claims processing and adjudication, rebate negotiation, retail-network formation and management, benefit-design consulting, and drug-formulary design and management.

The precise bundle varies across clients. The FTC’s 2005 report describes the core functions succinctly:

Plan sponsors often hire PBMs to manage these pharmacy benefits on their behalf. As part of the management of these benefits, PBMs assemble networks of retail and mail pharmacies so that the plan sponsor’s members can fill prescriptions easily and in multiple locations. PBMs also negotiate with pharmaceutical manufacturers for payments that can lower the price that plans and members pay for prescription drugs.

Formulary design is especially important. In consultation with the plan sponsor, the PBM determines which drugs are covered for each condition and on what terms. Plans typically use tiers—lower (sometimes zero) copayments for preferred drugs and higher copayments for less-preferred drugs or channels. As the FTC’s complaint notes, PBMs offer standardized formularies that sponsors can adopt “off the shelf,” but they also help design customized formularies tailored to particular clients. Large plans may heavily influence the process or handle portions of plan design themselves.

Retail pharmacy networks work similarly. They may be open, tiered (preferred pharmacies get lower copayments), or restricted “narrow networks.”

Plans also pick and choose services. PBMs are not the only firms that provide them. The GAO’s 2019 report found that “Part D plan sponsor contracts varied by the number of services provided by PBMs” and that “[p]lan sponsor contracts varied in the number of PBMs used to provide one or more of the drug management services.”

Concerns about vertical integration predate the current case. Congress ordered the FTC’s 2005 study through the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 because PBMs owned mail-order pharmacies, raising conflict-of-interest concerns. The FTC’s 2024 report also notes broader integration: “[d]ownstream, PBMs are vertically integrated with large health insurers which, through their health plans and plan sponsor services, provide coverage for hundreds of millions of Americans.”

Vertical integration can be anticompetitive, but it usually is not (see, e.g., Francine Lafontaine and Margaret Slade; James Cooper, Luke Froeb, Dan O’Brien, and Michael Vita; David Reiffen and Michael Vita). And the FTC’s own 2005 research did not confirm the fears that had motivated the inquiry.