The draft guidelines span more than 100 pages and raise a host of issues. This post zeroes in on one that should give competition lawyers pause: the quiet politicization of competition law through soft-law instruments that sidestep—and ultimately erode—the coherence of established frameworks.

On the surface, the document updates the European Union’s merger-review framework. Read more closely, and a different project emerges: a systematic effort to advance industrial policy through competition law, dressed up as technical refinement.

What follows breaks down how the guidelines do this, the mechanisms they deploy, and why that trajectory should concern you.

Nothing New Here (Except What’s New)

It is true, as some have argued, that much of the document codifies existing case law. Its treatment of market shares and Herfindahl-Hirschman Index (HHI) thresholds, the counterfactual framework, the failing-firm doctrine, and much of the foreclosure analysis tracks established European Commission practice and court-endorsed principles. Guidelines that do this perform their proper function, i.e., they codify rather than create new law.

The problem lies in what’s actually new. Those elements appear to do something quite different.

The most consequential expansion concerns “parameters of competition.” Paragraph 20 states that: 

Non-price competition can encompass a variety of parameters to the extent they are relevant for the competitive process in the markets at hand, including output and quality in various aspects, also covering choice (which may include media and cultural diversity), capacity, investment, innovation , privacy, sustainability, resilience (including security of supply). A merger can have an impact on one or multiple parameters of competition, and that impact is subject to an overall assessment. Many non-price parameters are not readily subject to quantification. Some of them, among other things, may also reflect the objectives of EU policies recognised by the Treaties.

In addition: 

The Commission enjoys a margin of discretion in weighing such price and non-price parameters of competition in the balancing exercise in order to establish an overall assessment of the merger’s effects on businesses and consumers.

It is true that, in prior Commission decisions cited in the guidelines—including Ball/Rexam, Tata Steel/thyssenkrupp (later upheld by the General Court and the Court of Justice of the European Union (CJEU) on conventional horizontal-effects grounds), and Norsk Hydro/Alumetal—security of supply and sustainability appeared as factual elements within a standard competitive analysis. Customers valued reliable local production as a product characteristic; sustainability credentials shaped purchasing decisions. But the alleged harm was always reduced to familiar concerns, most often higher prices, fewer suppliers, or reduced output. These concepts fed into a standard significant impediment to effective competition (SIEC) assessment. They did not operate as independent grounds for intervention.

On a first reading, the draft guidelines appear to construct something qualitatively different. They place resilience and sustainability on par with price, quality, and innovation.

There are several other signals that corroborate this reading. Paragraph 55 defines market power to include the ability to reduce resilience or sustainability “below competitive levels,” elevating these concepts to the foundation of the analytical framework, not merely contextual factors.

Paragraph 23 lists harm to resilience and sustainability as bases for what reads like a standalone theory of harm. Paragraph 299 treats them as verifiable efficiencies capable of supporting approval. And Paragraph 342 subjects them to open-ended balancing at the Commission’s discretion, where they may be weighed against e.g., price, output, innovation, or quality.

In the Commission’s own words:

In its prospective analysis of concentrations, the Commission disposes of a margin of discretion and weighs up different, sometimes incommensurable price and nonprice parameters of competition.

The result is a doctrinal shift. Resilience and sustainability can now independently tip a merger decision toward prohibition or approval, without being translated into price or output effects. An alternative reading is that traditional competition concerns automatically dovetail with resilience and sustainability so that what is good for competition is good for resilience and sustainability. But  if these references are merely obiter dicta, then why include them at all?

If my reading holds, the implications are significant. A merger could, in principle, be blocked because it reduces supply diversity—for example, by concentrating sourcing in a non-European supplier. Or it could be approved because it increases that diversity, e.g., by combining complementary European assets.

That is not simple codification. It is the construction of a new, standalone merger-control variable from concepts that prior case law treated as factual context. The Commission goes just far enough to claim it is codifying existing law, while taking an additional step that, so far as I can tell, no court has endorsed.

Innovation Shield for Some, Entrenchment for Others

We’ve covered the legal-interpretive move that lets the draft guidelines shift the law while claiming continuity. The shift runs in one direction: toward greater discretion. By expanding the universe of potential harms, the guidelines create a larger menu of interventions—and more room for selective, asymmetrical enforcement.

Consider the growing list of competition parameters: innovation competition, dynamic foreclosure, entrenchment, portfolio effects, resilience, sustainability, competitiveness. Each can operate as a standalone theory of harm or combine with others. Together, they give the European Commission enough analytical flexibility to reach almost any outcome while maintaining a veneer of neutrality.

The asymmetry becomes clear in the treatment of startup acquisitions.

The guidelines introduce an “innovation shield”—a safe harbor under which acquisitions of small, innovative firms presumptively do not raise competition concerns, provided market-share thresholds are met and enough independent R&D competitors remain. That is a genuine improvement. It reflects sound law & economics: credible exit options sustain upstream venture-capital investment, and overenforcement in innovation markets is costly.

But Paragraph 192 immediately narrows the shield. It excludes acquisitions by “the largest firm in the relevant market or a gatekeeper,” as defined by the Digital Markets Act. In practice, that means that the largest and most successful tech firms get no shield. They instead  face a distinct theory of harm—entrenchment—with no equivalent safe harbor and a clear tilt toward false positives (i.e., making acquisitions harder). 

The entrenchment theory (Paragraphs 252-256) sets a low bar. The Commission need only show that the acquirer is dominant in a “core market,” that the target operates in a “related” market—not necessarily horizontal or vertical—and that the target is “important to effectively compete.” There is no market-share floor or structural constraint. If the markets are “related” and the asset is “important,” entrenchment becomes plausible.

In digital ecosystems, that standard sweeps broadly. It can capture almost any acquisition by a large platform. And if the target were not “important,” why acquire it in the first place? Smaller acquirers, by contrast, can invoke the innovation shield for the same transaction.

That asymmetry creates a basic economic problem: scale is secular. The efficiencies from acquisition—cost reduction, integrated R&D, capital deployment, access to complementary pipelines—do not depend on the acquirer’s nationality, size, or regulatory status under the DMA.

Mario Draghi’s report identified Europe’s lack of scale as a central competitiveness problem. The guidelines seem to translate that into a different directive: permit scale when it benefits European firms, restrict it otherwise. 

At points, the text says as much. Paragraph 13 states:

Mergers increasing the competitiveness of European industry, particularly in global markets, improving the conditions for growth and raising the standard of living in the Union are welcomed.

Other provisions reinforce the theme. Paragraph 15 outlines when scale-enhancing mergers may advance European competitiveness, but the categories are so broad and open-ended that they could justify almost any outcome in the name of “European competitiveness.”

It is far from clear that the Draghi report supports this reading. One could just as easily conclude the opposite. When a large U.S. technology firm acquires a European startup and integrates it into a global pipeline, that still generates scale. It still benefits European consumers. It still signals to venture-capital investors that exit options remain viable.

The counterfactual also does not hold up. Blocking a foreign acquisition does not mean a European buyer steps in or that the startup grows into a continental champion. Often, neither happens. The startup may fail to find a buyer at all, or it may settle for one with fewer resources and weaker integration capacity. In the worst case, it exits the market.

The guidelines’ asymmetrical treatment of acquirers does not guarantee more European scale. It risks the opposite: fewer viable exits, weaker venture-capital incentives, and a less dynamic European innovation ecosystem—the very outcome the Draghi agenda aims to avoid.

Industrial Policy in a White Glove

The expansion of competition parameters raises concerns that go beyond policy disagreement. As argued above, the guidelines equip the European Commission with a toolkit broad enough to approve European consolidation and block foreign acquisitions of European assets—while invoking the same analytical framework in both cases.

That, it seems, is how the Commission has read the Draghi report’s mandate.

The guidelines operationalize that approach. Theories layer thickly enough that almost any outcome becomes defensible. Resilience can sink a foreign deal; competitiveness can save a European one. The result is industrial policy in a “white glove” of technical neutrality—one that systematically advantages European acquirers while placing procedural and analytical hurdles in the path of foreign firms. Those firms appear less as contributors to European scale than as threats to European strategic autonomy.

Whether that is good policy is a fair question. I don’t think it is.

The harder question is whether it is legal. Again, I have my doubts.

The European Union Merger Regulation (EUMR) sets SIEC as a clear test. The CJEU has long drawn a line between competition analysis and broader public-interest considerations, reserving the latter for narrow, reviewable interventions—most notably, Article 21 of the EUMR, which allows Member States to invoke legitimate interests.

By recasting resilience and competitiveness as “dimensions of competition,” the Commission appears to fold policy goals it lacks authority to pursue under Article 2 of the EUMR into the competition analysis itself.

Several legal constraints come into view.

Article 2 of the EUMR limits the merger test to competition. Article 119 of the Treaty on the Functioning of the European Union (TFEU) commits the union to an open-market economy with free competition—a principle not easily satisfied by redefining competition to include its policy substitutes. Articles 63–66 of the TFEU guarantee the free movement of capital between Member States and third countries; restricting foreign acquisitions requires a clear legal basis that competition law does not obviously provide. And the Meroni doctrine bars EU institutions from expanding their mandate through interpretive discretion—though courts have read that constraint narrowly.

These are not trivial issues.

The Commission had other tools. Article 21 of the EUMR allows Member States to block mergers on legitimate-interest grounds, subject to proportionality and judicial review. Perhaps that route seemed too decentralized, too uneven, or too limited.

Instead, the Commission expanded the definition of competition and produced guidelines that—according to one senior antitrust partner quoted in Politico—are “a bit like the Bible: everybody can read anything into it.”

That is not a fringe critique. The Commission effectively acknowledges the point—and appears to embrace it. Paragraph 342 states that “the Commission disposes of a margin of discretion and weighs up different, sometimes incommensurable price and non-price parameters of competition.”

When parameters are incommensurable and discretion at the balancing stage is unconstrained, the guidelines do more than guide. They function as a delegation of power—one that allows the Commission to reach its preferred outcome, shielded from meaningful judicial review and justified by an ever-expanding set of “parameters” that stretch competition analysis beyond its legal and conceptual limits.

A Compromise That Doesn’t Cohere

There is a version of these guidelines that would deserve straightforward praise: one that genuinely codifies established case law, sharpens the efficiency framework, introduces a meaningful innovation safe harbor, and preserves the boundary between competition and other policy goals.

The draft does some of that. The theory-of-benefit framework improves the analysis. The innovation shield—scope problems aside—points in the right direction. The distinction between direct and dynamic efficiencies reflects modern industrial-organization thinking.

But the genuinely new elements pull the document elsewhere.

Observers of the Brussels policy scene will not be surprised. According to Politico, the guidelines reflect a compromise between European Commission President Ursula von der Leyen’s industrial-policy agenda and Executive Vice President Teresa Ribera’s defense of competition-law orthodoxy. The result tries to serve both—and commits fully to neither.

Where von der Leyen’s priorities prevail, the guidelines expand competition parameters, introduce asymmetrical theories of harm, and preserve a margin of discretion that makes the framework difficult to review. Where Ribera’s influence shows, the text gestures toward legal certainty, codification, and continuity with established case law.

Those two strands do not cohere. And when a framework embeds irreconcilable objectives, the side that preserves discretion usually wins in practice. Here, that is the industrial-policy side.

If the Commission wants to pursue industrial policy—steering which firms scale in Europe, limiting foreign acquisitions of European assets, favoring European consolidation—it has tools to do so. Article 21 of the EUMR. Sector-specific legislation. Foreign-investment screening with democratic accountability.

Redefining competition until it does all that work is not one of them.

Whether the courts will accept that move remains an open question. The harder truth is that the challenge is already written into the text.

The post Merger Guidelines for the Industrial Policy Curious appeared first on Truth on the Market.

Late last year, the Brazilian government submitted the bill to the Chamber of Deputies as part of its “Digital Brazil Agenda.” The proposal borrows from Europe’s Digital Markets Act (DMA), but it is not a straight copy. Its structure more closely resembles the United Kingdom’s Digital Markets, Competition and Consumers Act (DMCC).

Unlike the DMA, the bill would not impose a fixed list of obligations as soon as a company is designated. Instead, it creates a second-stage process in which the Administrative Council for Economic Defense (CADE) would study the designated firm’s markets and then decide which firm-specific duties to impose.

That may sound more restrained. It is still a major shift in Brazilian competition policy.

The bill would amend the Brazilian Competition Law (BCL) to create a new Digital Markets Superintendency (SMD) within CADE. It would empower CADE to designate firms as having “systemic relevance in digital markets” for up to 10 years. It would then allow the agency to impose tailor-made “special obligations” drawn from an open-ended statutory menu.

I have previously written here at Truth on the Market about several problems with this proposal. The bill risks quietly pushing aside the consumer-welfare standard and replacing it with vague goals like “the protection of the competitive process” and “the promotion of freedom of choice.”

Lazar Radic and I have also examined the institutional risks of creating the new SMD. That office would duplicate much of the work of CADE’s existing General Superintendence (SG), rather than building digital-market expertise inside CADE’s current investigative body.

The scale of this proposed overhaul deserved a more comprehensive look. To that end, Geoffrey Manne, Dirk Auer, and I recently published an International Center for Law & Economics (ICLE) white paper, “Digital Overreach: A Premature Turn to Ex Ante Regulation in Brazil.” Policymakers, legal practitioners, and academics should consult the full paper for a detailed economic and institutional assessment of the proposed regime.

This post highlights several of the paper’s central claims. Bill 4,675/2025 raises serious institutional concerns, and it may be unnecessary, given Brazil’s existing antitrust toolkit.

Europe’s early experience also offers a warning. Importing a DMA-style model could bring meaningful tradeoffs, including higher compliance and operational costs, more user friction, and further strain on Brazil’s already notorious “Custo Brasil”—the regulatory and structural cost of doing business in the country.

With that in mind, here are several points the Brazilian Congress should consider before enacting an ex ante regime like Bill 4,675/2025.

Ex Post, Not Exaggerated: Brazil’s Antitrust Tools Already Deliver

The premise behind Bill 4,675/2025—like other ex ante regimes—is familiar: traditional, ex post antitrust enforcement moves too slowly and cannot keep up with digital markets.

Brazil’s recent experience does not clearly support that claim.

In December 2025, CADE reached two landmark settlements—Termos de Compromisso de Cessação (TCCs)—with Apple and Google. TCCs are settlement agreements in which companies commit to stop or change conduct under CADE supervision.

In the Apple settlement, CADE required the company to permit third-party app stores, enable alternative payment processors, and remove anti-steering restrictions on iOS. For a deeper dive, see my Truth on the Market post with Mario Zúñiga, “Apple in Brazil: Ex Post Antitrust Meets Ex Ante Ambitions.”

CADE’s settlement with Google followed a similar path. It prohibited Google from conditioning Play Store licensing on the preinstallation or preferential placement of proprietary apps like Google Search and Chrome.

These outcomes mirror the kinds of structural remedies Bill 4,675/2025 would impose. The key difference is how CADE got there. Traditional antitrust enforcement allowed the agency to tailor remedies to the facts, security concerns, and competitive dynamics of each case, while keeping consumer welfare as the guiding principle for unilateral-conduct enforcement.

That flexibility matters. In the Apple case, privacy and security concerns led CADE to drop a sideloading requirement that had appeared in an earlier interim measure. A rigid ex ante regime would have a harder time making that kind of security-sensitive adjustment.

CADE has continued to move aggressively. In January 2026, it opened an administrative inquiry—alongside an interim measure—against Meta over new WhatsApp terms and the integration of AI chatbot tools.

The Administrative Tribunal upheld the interim measure in March 2026, citing irreparable harm and a likelihood of success on the merits. CADE has since sanctioned Meta for breaching that order. ICLE will publish a more detailed analysis of this case in the coming weeks.

CADE has plainly stepped up digital-market enforcement in recent months. But the number of settled or fully adjudicated cases remains limited. That makes it hard to justify a sweeping legislative overhaul aimed at market failures that have not yet proven beyond the reach of ex post enforcement.

Put differently, the bill tries to solve problems Brazil’s existing framework may already be able to handle. CADE’s recent actions suggest as much. If speed is the concern, the better response is to give CADE more resources and streamline its procedures—not to build a parallel regulatory bureaucracy.

Europe’s DMA: More Clicks, Less Competition

Supporters of the Brazilian bill often point to the DMA as a model. As noted, however, the bill’s architecture more closely tracks the UK’s DMCC because it uses a two-step process.

Even so, Europe’s early experience with ex ante regulation gives Brazil good reason to pause. Early evidence suggests that some DMA obligations create user friction and compliance burdens, while doing little to make markets more contestable. 

The consumer-welfare evidence makes the point concrete. Louis-Daniel Pape and Michelangelo Rossi studied the DMA’s restrictions on self-preferencing in the context of Google Maps. By forcing Google to remove the Maps tab from EU search pages, the DMA increased mapping-related queries by 21%. Rival mapping services, however, saw virtually no traffic gains. Users simply had to take more steps to reach the same destination. More clicks, same map, no competitive upside.

Other evidence points in the same direction. Chiara Farronato, Andrey Fradkin, and Alexander MacKay examined the effects of restrictions on Amazon’s private-label products, a common target of self-preferencing rules. They found that such a ban reduced consumer surplus by 5.5%, largely because consumers lost product variety and cost advantages.

Consumers appear to feel these changes. A European Center for International Political Economy (ECIPE) survey found that 39% of EU users reported more cumbersome online experiences after the DMA took effect. At the same time, 80% had no idea the law existed.

The effects extend beyond user experience. Ex ante rules can operate as an “innovation tax,” raising the cost and uncertainty of launching new products. Apple has delayed the European rollout of features such as AirPods live translation, iPhone mirroring, and Apple Maps enhancements, citing DMA compliance uncertainty. Meta postponed the launch of Threads in the EU, and Google delayed certain AI features.

Mario Draghi, former president of the European Central Bank, underscored the broader stakes in his widely cited 2024 report. He linked Europe’s persistent productivity gap with the United States in large part to the tech sector, and identified “inconsistent and restrictive regulations” as a major barrier to scaling innovative firms.

Brazil wants to attract technology investment and raise labor productivity. That makes the lesson straightforward: policymakers should think carefully before importing rules that may dampen innovation and growth.

More Rules, Same Bottlenecks

If the DMA has proved difficult to implement in a well-resourced, highly institutionalized setting like the European Union, transplanting a similar model to Brazil would likely create even greater risks.

Brazil ranks 78th out of 143 countries on the 2025 World Justice Project Rule of Law Index, and 124th on the World Bank’s 2020 Doing Business ranking. In that environment, broad, discretionary regulatory regimes invite strategic behavior and rent-seeking. In simpler terms: rivals may try to use the regulator to hobble competitors, rather than to help consumers.

Brazil also contends with the well-known “Custo Brasil”—the thicket of structural and regulatory inefficiencies that raises the cost of doing business. Those inefficiencies cost an estimated R$1.7 trillion annually, or roughly 19.5% of GDP in 2022. Adding a complex, compliance-heavy ex ante digital regime would likely make those burdens worse, not better.

Capacity constraints raise a related concern. Even in Europe, the European Commission has struggled to staff its DMA enforcement unit, filling only 19 of 80 planned positions. The shortfall shows how hard it is to recruit the technical expertise these regimes require.

CADE faces an even steeper challenge. It would have to absorb major new responsibilities through internal reallocations, with no additional funding. That leaves Brazil with an uncomfortable tradeoff: either the new regime remains under-resourced, or CADE diverts resources from its core antitrust mission.

Beyond the False Choice: Fix the Framework, Don’t Import It

The push for Bill 4,675/2025 rests on what amounts to an “imaginary antitrust consensus.” It also frames the choice too narrowly, as if Brazil must either import a DMA-style regime or leave digital markets untouched.

That is a false binary.

Before making a structural shift of this magnitude, policymakers should consider narrower, more coherent options. Our white paper lays out a sequence of steps Congress could take short of adopting a sweeping ex ante regime.

The first—and best—option is to rely on Brazil’s existing ex post framework. Recent cases show that CADE can address alleged digital-market concerns through tailored, case-specific remedies, including negotiated outcomes like the Apple and Google TCCs.

If lawmakers still decide more intervention is necessary, they should favor targeted measures over economy-wide rules. Japan’s Mobile Software Competition Act offers one useful example. It focuses on smartphone software ecosystems, accounts for cybersecurity, and avoids broad mandates that risk degrading the user experience or undermining the benefits of integrated systems.

If Congress proceeds with Bill 4,675/2025, it should at least add meaningful guardrails to reduce error costs and improve institutional coherence.

Start with the standard. The bill should restore consumer welfare as the lodestar, tying both its objectives and enforcement to demonstrable consumer harm. Without that anchor, enforcement could drift toward protecting less-efficient rivals or chasing vague notions of “the competitive process.”

The bill also needs an explicit efficiencies defense. As drafted, its omission sits uneasily with Article 36 of the Brazilian Competition Law. Designated firms should be able to show that challenged conduct—such as self-preferencing or vertical integration—creates net benefits for users, including quality improvements, lower prices, better security, or reduced search costs. Remedies should reflect those benefits.

The bill also needs tighter designation criteria. Designation should rest on established antitrust principles, including evidence of durable market power and a plausible theory of competitive harm—not proxies like turnover, scale, or cross-market presence.

Institutional design matters, too. Congress should house any digital-markets function within CADE’s General Superintendence, likely as a specialized unit. It should not create a parallel bureaucracy that risks duplication, fragmentation, and internal conflict.

Finally, legislation of this scope should not move forward without a rigorous regulatory impact assessment (RIA). An RIA is a structured analysis of a proposal’s likely benefits, costs, enforcement demands, and market effects. The Brazilian Economic Freedom Act already embraces this logic in Article 5 for major regulatory measures. Congress should hold itself to the same standard here.

The European Commission conducted an impact assessment before adopting the DMA. Brazil has offered no comparable, publicly grounded analysis for Bill 4,675/2025—covering expected benefits, compliance costs, enforcement capacity, and likely effects on consumers, business users, and innovation.

Mind the ‘Brussels Defect’

Brazil need not choose between complacency and comprehensive ex ante regulation. It already has a sophisticated competition authority. It already has legal tools capable of addressing digital conduct. And it has ample reason for caution before adding more discretionary oversight to an economy that suffers from too much regulatory friction, not too little.

Before creating a new digital-markets regime, Brazil should first show that the existing one is failing. So far, the evidence points the other way.

A better path remains available. Brazil can reinforce existing competition-law tools, exercise regulatory humility, and resist the urge to import “Brussels Effect” rules that increasingly resemble a “Brussels Defect.” That approach would better serve Brazil’s 212 million citizens.

No overhaul required—just a skilled and steady hand.

The post Before Brazil Scrubs In: The Case Against Digital-Market Surgery appeared first on Truth on the Market.

A recent Economist essay by a former competition lawyer offers a clean example.  Writing in the magazine’s By Invitation section, Marie Potel-Saville argues that digital platforms engage in “cognitive exploitation” through infinite scroll, dark patterns, and dopaminergic feedback loops—practices that, in her view, erode the conditions necessary for functioning markets. Her proposed fix would flip the burden of proof, forcing platforms to show they are “not predatory by design” before deployment. The piece is polished and clearly motivated by real concern. It also neatly illustrates the problem it sets out to diagnose. 

When Analogies Do All the Work—and None of the Proof

This style of argument usually rests on a familiar rhetorical move: analogize the conduct at issue to a well-established legal category, then let that category do the normative heavy lifting. The analogy makes intervention feel legally natural, even when it does not hold up. Neo-Brandeisians often reach for infrastructure comparisons—treating e-commerce platforms like Amazon as if they were railways, telephone lines, or electricity grids. The comparison sounds intuitive. It is also deeply flawed.

In the Economist essay, the chosen analogy comes from securities regulation. When a trader manipulates stock prices, the argument goes, the law treats it as structural harm to the market: a corrupted price no longer conveys reliable information. Cognitive exploitation, on this view, works the same way. If platforms “manufacture” the preferences of billions of users, then consumer signals lose their informational value.

It is a tidy parallel. It is also technically unsound—and the problem runs deeper than a loose comparison.

Securities law can identify manipulation because it has an objective benchmark: the unmanipulated price. Courts and regulators can specify a counterfactual and test against it. “Manufactured preferences” offer no such anchor. There is no observable, measurable “authentic preference” that a regulator can point to or that a court can adjudicate against. What would an unmanipulated preference for social media consumption look like? How would anyone identify it? The analogy does not say, because it cannot. Without a workable counterfactual, the framework collapses at the threshold.

This flaw shows up across the genre. The Digital Markets Act (DMA) starts from a similar premise: that platform “gatekeeper” power mirrors traditional bottleneck monopoly, and so justifies ex ante obligations without proof of harm in individual cases. The German Federal Cartel Office (Bundeskartellamt) takes a comparable tack in its Amazon decision, treating price-prominence effects as if they were exclusionary conduct—without establishing the competitive harm that analogy requires. In each instance, the analogy carries the argument where the evidence does not. 

Deception Isn’t the Same as Desire

The deeper problem is a conflation that runs through the Economist essay—and much of the broader literature. It treats dark patterns and engagement optimization as the same phenomenon when, in analytically relevant respects, they are opposites.

Dark patterns work against user preferences. Hidden unsubscribe buttons, fake urgency timers, deliberately obscured cancellation flows—each steers users toward outcomes they did not choose and would not endorse if the interface were honest. The Federal Trade Commission’s (FTC) case against Amazon, which resulted in a $2.5 billion settlement in September 2025, targets this kind of conduct. It focuses on interface design—specifically, the “Iliad Flow,” a four-page, six-click cancellation labyrinth that impedes a decision the user is actively trying to make. The multistate lawsuit against Meta Platforms Inc. (Meta) by 42 attorneys general raises related issues, particularly around disclosure and consent for minors. These cases may rise or fall on the merits, and the legal standards remain unsettled (here and here). But they at least point to a coherent target: conduct that allegedly deceives users about what they are getting. 

Engagement optimization moves in the opposite direction. A recommendation algorithm that learns, in real time, what a user watches, clicks, and dwells on—and then delivers more of it—matches preferences with a level of precision no earlier market mechanism could achieve. Users do not stay on social media because they have been tricked. They return, often for hours, across demographics and income levels, despite abundant alternatives, because the product delivers what they demonstrably want.

Lumping these phenomena together under the label “cognitive exploitation,” as the Economist essay does, lets a defensible claim about deception do the work for a much more contestable claim about efficient engagement. That move is rhetorical, not analytical. It posits a structural unfairness across the entire platform and hands regulators a mandate far broader than the specific harms could justify.

There is a further irony—one that warrants its own treatment. The platforms drawing the most scrutiny often have the strongest internal incentives to limit manipulative design. Facebook and Amazon depend on sustained user engagement and trust; dark patterns that alienate users undermine both advertising and subscription models. The more aggressive dark-pattern ecosystem—fake close buttons, deceptive download prompts, cookie-consent walls designed to exhaust rather than inform—thrives in the long tail of the web, among publishers and ad networks with no ongoing relationship with users and no reputational stake. If the goal is to curb deceptive design at scale, current enforcement priorities look almost exactly backward.

From Choice to Paternalism

Once you separate the two concepts, the engagement-optimization claim runs into a problem its proponents rarely face head-on: revealed preferences.

Users stay. They return. What critics call manufactured compulsion looks, from a standard welfare perspective, like a product doing its job—matching supply to demand with a level of efficiency earlier markets could not reach. To reframe that as exploitation, you have to argue that revealed preferences count for less than some other set of preferences that should replace them. Proponents rarely say what those substitute preferences are, let alone defend why they should override observed behavior.

What they are reaching for—without quite saying so—is the distinction between first- and second-order preferences, i.e., between what people want in the moment and what they would endorse on reflection. That is a serious idea with a substantial literature, from Harry Frankfurt’s work on second-order volitions to Cass Sunstein and Richard Thaler’s ”nudge” framework, which at least states its normative commitments openly. But this is political philosophy, not competition or consumer-protection law. It requires answers to hard questions: Which preferences count? Who decides? On what basis should a regulator’s view of reflective endorsement displace what consumers actually do? Dressing the argument in market-failure language does not answer those questions; it sidesteps them. 

This is the same move I have identified elsewhere as a hallmark of anti-consumer-welfare antitrust: substituting regulatory preferences for consumer preferences, justified by an abstract appeal to what consumers would want if they wanted differently. Whether the vehicle is neo-Brandeisian antitrust, the DMA’s choice-architecture mandates, or “cognitive exploitation” theory, the logic is the same. Consumers generate outcomes critics dislike—platform dominance, high engagement, preferences for convenience and integration—and the framework steps in to override them, with “market failure” standing in for what is, at bottom, a dispute about values.

A Rule Without Edges

Arguments like this share a second structural flaw: they lack any limiting principle.

The Economist essay’s test—design that engineers behavior users might not endorse on reflection—does not describe anything unique to digital platforms. Loyalty programs create switching costs. Scarcity cues create urgency. Aspirational advertising creates desire. Subscription defaults exploit inertia. Loss-leader pricing skews comparison. If dopaminergic feedback loops are disqualifying, why not manufactured brand attachment? Why treat the sensory engineering of a restaurant, mall, or store as categorically different from the algorithmic engineering of a social media feed? The line never appears, because the framework offers no principled way to draw one.

This is not just an academic concern. Courts and regulators need administrable standards—rules with enough substance to produce predictable outcomes and to constrain enforcement discretion. “Predatory by design” offers neither. Who determines compliance? Against what baseline? Using what method? The vagueness hands regulators sweeping discretion without guidance on how to use it, creating what Brian Albrecht and Erik Hovenkamp have described as a wishing well “into which one may peer and find nearly anything he wishes.” Far from checking platform power, a standard like this injects uncertainty that hits smaller firms hardest while giving regulators a tool they can wield selectively against disfavored companies.

The Startup Filter Disguised as Safety

The proposed remedy adds a structural problem to the analytical one. Precautionary, pre-deployment review—i.e., requiring platforms to prove they are not harmful before launch—is a standard typically reserved for irreversible, catastrophic risks: pharmaceuticals, nuclear plants, medical devices. Extending that framework to consumer software would impose compliance costs that only established incumbents can bear. A startup building a new social app cannot finance the legal and regulatory apparatus needed to clear a “not predatory by design” standard before it even reaches users. Meta can. Google can

The predictable result is higher barriers to entry—high enough to keep out the very challengers such rules claim to protect. The policy would entrench the incumbents it purports to discipline. We have seen this dynamic before. The General Data Protection Regulation (GDPR), for example, has imposed compliance burdens that fall hardest on smaller firms, often foreclosing the competitors most likely to disrupt larger platforms. 

The progressive framing should not obscure the bottom line. This is incumbency protection by another name.

When Getting What You Want Is the Harm

There is a more candid version of this argument—and it is worth stating directly.

The concern is not that markets are failing. It is that they are working—efficiently allocating attention to content that humans, given their cognitive architecture, tend to overconsume. On this view, the problem is not deception. Users are getting exactly what they want. The problem is that what they want may be bad for them in ways they do not fully appreciate. That concern has familiar roots. It echoes long-running debates over sugar, gambling, alcohol, and cheap calories—markets where the interaction of human preferences and efficient supply produces outcomes critics find troubling. None has been “solved” by regulating product design.

That is a legitimate debate. But it is not a competition or consumer-protection argument. It is about the limits of preference satisfaction as a measure of welfare, about paternalism and autonomy, and about whether—and when—second-order preferences or “human flourishing” (as defined by regulators) should override first-order ones. Those are questions of political philosophy and political economy. Labeling them “market failure” does not make them so. It just obscures the normative premises doing the work. 

If the claim is that human wants can conflict with human flourishing—and that platforms efficiently satisfying those wants create a problem—then the argument should say so, and defend it on those terms. That would at least let readers evaluate whether the proposed remedy—regulatory override of consumer choice, implemented through discretionary design standards and precautionary, pre-deployment review—is proportionate to the concern, and whether it fits within the legal tradition invoked to justify it. Or, for that matter, within a liberal polity at all.

Call the Problem by Its Name

The preference-substitution problem in platform regulation is not, at bottom, about platforms. It is about analytical honesty. Conflating deceptive design with efficient engagement, leaning on analogies that collapse on inspection, skipping any limiting principle, ignoring entry effects—these are features of a style of argument, not defects in any single proposal.

Where dark patterns actually exist—where design works against what users are trying to do—existing consumer-protection frameworks can respond, if applied with care. The Amazon and Meta lawsuits show as much, whatever their ultimate merits. That is no small point.

What those frameworks cannot do—and what no legal regime can do without stating its premises—is condemn preference satisfaction at scale because regulators dislike the preferences being satisfied. Every proposal in this space ultimately turns on a simple question, and most try not to answer it: are digital markets failing—or succeeding too well?

The post The Hidden Premise: Smuggling Paternalism Through the Back Door appeared first on Truth on the Market.

As AI spreads, it has sharpened an old question in a new setting: How should competition policy preserve room for innovation while also addressing the anticompetitive risks new technologies can create? The pace of AI development raises an even more pointed concern. Can recently adopted ex ante rules—rules meant to prevent competitive harm before it occurs—remain “future-proof” in digital markets? The DMA did not anticipate AI’s rapid rise. It may age faster than expected.

That concern looks more urgent with the emergence of assistive and agentic AI. These tools could reshape core digital intermediation functions, including web browsing, online search, and e-commerce. Put less grandly: They may change how users find, choose, and buy things online. AI assistants and agents increasingly act as standalone interfaces, allowing users to access third-party goods, services, and content without leaving a conversational environment. That shift could reconfigure where—and how—competitive power concentrates.

This backdrop highlights a familiar tension. Competition law moves slowly, but it adapts. Its open-ended standards can evolve with changing market realities. Sector-specific regulation works differently. It can target problems quickly and directly, but it often locks in assumptions about how markets operate. When technology shifts, those assumptions can break.

The DMA illustrates the tradeoff. Despite its recent adoption, the regime already risks rapid aging. Lawmakers designed it for a digital ecosystem that AI-driven intermediation may soon transform.

Against that backdrop, the European Commission asked stakeholders whether the DMA can address AI-powered services as they roll out. It also considered whether to revise the list of core platform services—the categories of digital services covered by the DMA—and the obligations attached to them. 

New AI entrants sit at the center of that inquiry. When existing gatekeepers integrate AI into their ecosystems, the DMA may capture those services. The regulation does not clearly reach new, standalone AI operators.

The report tempers expectations. So far, the Commission has taken a cautious line. It views the DMA as fit for purpose and does not propose amendments. In the Commission’s view, the regulation has proved adaptable enough to keep pace with developments like AI. Even so, its analysis focuses mainly on how existing gatekeepers deploy AI within designated core platform services.

That leaves a mixed picture. The Commission is, at once, both right and wrong.

Where the Commission Gets It Mostly Right

AI services may fall within the DMA through two main paths. First, an AI provider might itself offer a core platform service and meet the thresholds for gatekeeper designation. Second, an existing gatekeeper might embed AI features into an already designated core platform service, bringing those features within the DMA’s obligations.

On the second path, the Commission is largely right. This scenario does not pose especially novel problems for applying the DMA, as written, to AI-related markets. The familiar competition concerns remain. Gatekeepers benefit from structural advantages, including privileged access to cloud infrastructure and data. They also can integrate AI assistants and agents directly into their core platform services. In principle, the regulation’s existing provisions can address those risks.

The Commission’s recent specification proceeding against Google illustrates the point. The Commission seeks to ensure that Google gives third-party AI-service providers access to the same Android operating-system features and functionalities available to its own services. It also aims to require Google to grant third-party online search-engine operators—including AI chatbot providers that offer search functionalities—access to anonymized ranking, query, click, and view data on fair, reasonable, and non-discriminatory (FRAND) terms. At the same time, the Commission is examining whether Google’s integration of AI Overviews into Google Search complies with the DMA, particularly the ban on self-preferencing in Article 6(5). 

The harder question concerns new AI entrants. The DMA may not reach them at all. Standalone AI applications do not fit neatly within any of the core platform service categories listed in the regulation. Unless those services can be folded into existing categories—such as online search engines, web browsers, or virtual assistants—providers cannot be designated as gatekeepers based solely on their AI offerings. The result may be a split regulatory landscape, where large incumbent platforms and new AI entrants face different rules.

That gap matters most for AI assistants and agents. These systems could reshape competitive dynamics and market structures in fundamental ways. They may become new gateways—ones the DMA did not anticipate—and place AI providers in a role functionally similar to gatekeepers.

When the Model Doesn’t Fit the Models

As we argue in a forthcoming paper, agentic AI should prompt a rethink of the DMA’s overall architecture.

The regulation reflects a different technological and economic moment. Lawmakers designed it around the structural features of earlier digital markets—features that helped identify both gatekeepers and core platform services. In that sense, the DMA embodies a competition-policy framework rooted in a Big Tech-centered view of digital competition. But AI may not follow the same path. Foundation models—the large AI systems that power many downstream applications—differ in important ways from the platforms that shaped that earlier paradigm.

Some familiar concerns remain. Access to key upstream inputs—computing power, data, and highly skilled labor—still matters. So do the incentives of large incumbent digital firms to fold AI into their existing products and services. Those dynamics can reinforce existing advantages.

But the downstream picture looks different. Competition in AI markets appears active and crowded. New entrants continue to emerge. Many have achieved commercial success and attracted significant investment. Barriers to entry, at least for developers, may be lower than initially feared. And the range of downstream applications suggests that foundation models will not produce uniform winner-takes-all outcomes across sectors.

The DMA, however, targets a specific problem: the gatekeeping power of vertically integrated firms that operate in a dual role. These firms control access to an important platform while also competing on that platform. Much of the DMA focuses on limiting leveraging strategies, especially self-preferencing. That logic fits traditional digital platforms. It fits less comfortably with the structure and behavior of many new AI entrants.

The Clock Keeps Ticking as Brussels Buys Time

In principle, the logic behind the DMA supports extending it to new AI players. The regulation rests on a simple premise: Traditional antitrust enforcement often arrives too late in digital markets, after positions have hardened and become difficult to dislodge. That concern could apply with equal force to AI, given its potential to produce new gatekeepers. Unless European policymakers intend to abandon the strategy that has defined the Brussels approach to digital markets, they will need to rely on the DMA as the primary framework governing competition in the AI era.

But that instinct runs into a real risk: acting too soon. The AI sector remains in flux. Technologies, business models, and market structures have not yet settled. Extending ex ante regulation to foundation models and AI services at this stage could constrain innovation by limiting developers’ ability to experiment with new features, design choices, and platform architectures.

It would also force policymakers to predict not just the pace, but the direction of technological change in fast-moving and uncertain markets. That is a tall order. Misjudging where market power will emerge—or how it will operate—could carry real costs for innovation, competition, and consumer welfare.

The harder question, then, is one of timing. Intervene too late, and the DMA risks irrelevance. Intervene too early, and it risks distortion.

That is what makes the first review of the DMA more than a routine stocktaking exercise. It surfaces a deeper dilemma for a regime that remains, in many respects, still young. The issue is not just whether specific provisions work as intended. It is whether AI forces policymakers to reopen the legislative framework itself.

For now, the Commission has chosen to buy time. It is sticking with its Big Tech-centered approach and continuing to monitor how AI develops. That may be prudent in the short term.

But the underlying dilemma will not wait.

The post The DMA’s AI Dilemma: Too Soon, Too Late, or Both? appeared first on Truth on the Market.

My recent Albany Law Journal of Science and Technology article, “Is Data Really a Barrier to Entry? Rethinking Competition Regulation in Generative AI,” co-authored with Satya Marrar, challenges that assumption head-on. We argue that fears of data scarcity and monopolization are overstated—and that premature regulation may do more to stifle AI innovation than to protect competition. (See here for a March 2025 Mercatus Center Working Paper version of this article.)

Yes, data matters. But data is not destiny. Standing alone, it is not a sound basis for sweeping ex ante regulation or speculative antitrust attacks on generative AI markets.

Data Moats Aren’t Castles

The “anticompetitive data moat” theory holds that a firm can secure durable market power by controlling a large, unique, hard-to-replicate dataset that rivals need to build or improve competing products. (See, for example, here.)

The story has intuitive appeal. Large digital platforms have amassed vast quantities of user data. Generative AI systems train on data. So the firms with the most data will dominate AI. On this view, antitrust enforcers and sectoral regulators should move early to stop large firms from locking up the market.

That syllogism is too crude. It treats “data” as a homogeneous commodity. It is not. Data is a heterogeneous input whose value turns on context, quality, legality, format, freshness, and use case. A large trove of low-quality or irrelevant data may be far less valuable than a smaller, well-curated, domain-specific dataset. Data that matters for consumer search may have little bearing on legal research, medical coding, cybersecurity, logistics, chip design, industrial maintenance, or accounting. An advantage in one AI application does not necessarily translate into dominance in another.

This distinction matters because antitrust law does not punish firms for possessing useful assets. It targets exclusionary conduct that harms the competitive process. An incumbent’s control of an input that rivals would like to have does not establish monopoly power. It does not show foreclosure. In generative AI, the data-input story weakens further because developers have multiple paths to competitive products: open datasets, licensed corpora, synthetic data, enterprise-specific data, retrieval-augmented generation, fine-tuning, distillation, open-weight models, and vertical specialization.

A static view of data also misses the dynamic nature of AI competition. The relevant question is not whether some firms have more data today. It is whether rivals can obtain, generate, license, substitute, or work around the data they need to compete tomorrow. On that score, the market evidence points away from fatalism.

First, Do No Harm—to Competition

Antitrust should promote competition, not precautionary industrial planning. In an October 2025 Truth on the Market post on AI and antitrust, I criticized the “precautionary instinct” as inconsistent with the “American economics-oriented, case-and-fact-specific antitrust-enforcement philosophy,” which demands evidence that a challenged practice is actually harming or likely to harm competition.

That framework fits generative AI. Enforcers should not ignore exclusionary conduct. They should scrutinize acquisitions that eliminate significant nascent rivals, exclusive contracts that substantially foreclose critical inputs, collusive arrangements, deceptive practices, and technical restrictions that block interoperability without legitimate justification.

But scale alone is not a violation. Data possession is not presumptively unlawful. And speculative future bottlenecks do not justify present regulatory mandates.

The costs of getting this wrong are high. Heavy regulation in an emerging technology market can entrench the very firms it targets. Large incumbents can absorb compliance costs, legal uncertainty, reporting obligations, audits, and licensing burdens far more easily than startups. A rulebook meant to check “Big Tech” dominance can become a moat around Big Tech.

That result is not a paradox. It is a recurring feature of regulation. Fixed compliance costs fall more heavily on smaller firms. Complex access mandates demand armies of lawyers, engineers, and compliance officers. Rules that restrict data use can make it harder for entrants to train, test, and adapt models. Mandatory sharing obligations can weaken incentives to collect, license, clean, and curate data in the first place. The likely outcome: less entry, less experimentation, and less competitive pressure.

The Moat Is Shrinking Faster Than You Think

The strongest case against data fatalism is the speed at which AI capabilities are improving—and costs are falling. Stanford University’s 2025 AI Index reports that the cost of running a system at roughly GPT-3.5 level dropped more than 280-fold between November 2022 and October 2024. It also finds that the performance gap between leading closed-weight and open-weight models on some benchmarks shrank from 8.04% in January 2024 to 1.70% by February 2025.

Those numbers do not fit a story of durable, data-driven foreclosure. If incumbents’ data advantages were decisive, the gap between closed models and open challengers would widen—or at least hold steady. It has not. Open-weight models have improved rapidly. Smaller models have become more capable. Inference costs have collapsed. Developers can increasingly build useful AI applications without owning massive proprietary data troves.

None of this makes frontier-model development cheap. It is not. Training state-of-the-art models still demands significant investment in compute, talent, infrastructure, and evaluation. But antitrust law distinguishes between high cost and unlawful entry barrier. Many industries require large, upfront investment. That alone does not turn successful firms into regulated utilities.

More important, frontier training is only one slice of the market. The AI stack spans chips, cloud services, data centers, foundation models, open-weight models, inference providers, model-routing tools, enterprise applications, consumer chatbots, developer platforms, fine-tuning services, synthetic-data providers, evaluation tools, and vertical AI products.

Competition can thrive at one layer even if another is concentrated. A firm that leads in general-purpose chat may lose in legal research, pharmaceutical discovery, financial compliance, or customer-service automation. A model that lags on general benchmarks may still win on privacy, latency, cost, customization, or local deployment.

That layered reality makes broad regulation especially risky. The law should not assume that a single market structure governs all AI applications.

Valuable Doesn’t Mean Essential

A core mistake in the data-moat theory is to confuse value with indispensability. Data can be valuable without being essential. It can confer an advantage without foreclosing rivals. It can be hard to obtain in one form, yet available through substitutes in another.

Consider a legal AI product. Success may turn less on a vast general corpus than on access to statutes, cases, regulations, treatises, firm documents, citation tools, expert feedback, and workflow integration. Consider a medical AI product. The differentiators may be clinical validation, privacy-preserving deployment, specialized labeling, and integration with hospital systems. Consider a manufacturing AI product. The key inputs may come from a customer’s own sensors, machines, manuals, and maintenance records.

In each case, the relevant asset is not “data” in the abstract. It is the ability to combine specific data with effective engineering and customer-specific knowledge.

Generative AI also lets firms economize on data. Retrieval-augmented generation can connect models to external databases at inference time, rather than embedding all knowledge in pretraining. Fine-tuning can tailor a model to a narrower domain. Distillation can transfer capabilities from larger models to smaller ones. Synthetic data can supplement scarce real-world examples. Human feedback and evaluation can improve outputs without requiring ownership of all underlying source material.

These techniques are not magic. They do not eliminate the need for data. But they weaken the claim that only firms with the largest preexisting datasets can compete. They make data advantages contestable.

If Data Is ‘Essential,’ Everything Is

Some proposals implicitly treat AI data as an essential facility. If leading firms control data that rivals need, the argument goes, those firms should have to share it, license it, or make it available on regulated terms. (See, for example, here, here, and here.)

That temptation should be resisted. Essential-facilities-style duties are hard to administer even in mature infrastructure markets. They fit poorly in a fast-moving innovation market. (See the U.S. Supreme Court’s 2004 decision in Verizon v. Trinko and Phillip Areeda’s 1990 Antitrust Law Journal piece “Essential Facilities: An Epithet in Need of Limiting Principles.”) Courts and regulators are not well positioned to decide which datasets are essential, what access terms are reasonable, how to protect privacy and intellectual-property rights, how to maintain data quality, or how to preserve dynamic incentives.

Compelled access also risks undercutting the very investments that make data valuable (see here). Raw data rarely has value until firms collect it lawfully, clean it, structure it, label it, filter it, update it, and integrate it into a model-development process. If firms must later share the results on regulated terms, their incentive to make those investments may diminish.

Traditional antitrust offers a better path. If a firm uses exclusive contracts to foreclose rivals from truly indispensable inputs, enforcers can investigate. If an acquisition eliminates a meaningful future competitor without offsetting efficiency justifications, enforcers can challenge it. If firms collude to divide AI markets or suppress competition, antitrust law can respond. But remedies should follow evidence. They should not start from the premise that data ownership is itself a competitive wrong.

Don’t Mistake Collaboration for Collusion

My April 2026 post on competitor collaboration in the AI era makes a related point: competition policy should “complement—not undermine—efforts to promote innovation and economic growth,” and antitrust should not stand in the way of lawful, output-expanding AI infrastructure development.

That logic extends beyond infrastructure. AI development often depends on collaboration among firms with complementary capabilities: cloud providers, chipmakers, model developers, data licensors, universities, startups, enterprise customers, and safety researchers. Some collaborations raise legitimate antitrust concerns. Many do not. They can be procompetitive—allowing firms to share risk, pool complementary assets, accelerate deployment, and challenge better-capitalized incumbents.

A regulatory posture that treats AI collaboration with suspicion risks undermining entry. Smaller firms often need partnerships to secure compute, distribution, or specialized data. Universities and nonprofits may rely on private-sector support. Enterprise customers may need model providers to coordinate with software vendors and cloud platforms. Reflexive hostility to collaboration will not promote competition. It will fragment the ecosystem and raise costs.

The same holds for data licensing. Exclusive or semi-exclusive agreements may, in some cases, be anticompetitive. But they can also be efficient. They can incentivize data owners to make lawful datasets available. They can protect privacy, security, and quality. They can fund curation and labeling. And they can give entrants access to valuable inputs without requiring them to build data-collection operations from scratch.

The right question is not whether a data arrangement involves a large firm. It is whether it substantially forecloses competition and lacks offsetting procompetitive benefits.

The 1990s Called—They Warned Against This

There is a useful historical analogy. In a February 2025 post, I argued that “promoting competition, not regulation,” is key to U.S. AI leadership and pointed to the Clinton administration’s relatively deregulatory approach to the early internet as a model of permissionless innovation.

The analogy is not perfect. AI raises different risks than the early internet, including concerns about safety, security, copyright, misinformation, privacy, and labor-market disruption. Some risks may warrant targeted legal responses. But the competition-policy lesson holds: premature regulation can lock in mistaken assumptions about technology and market structure.

In the 1990s, regulators could not have predicted the evolution of search, social media, e-commerce, smartphones, cloud computing, streaming, or app-based services. Had policymakers imposed rigid rules based on early internet business models, they might have stifled later innovation. Generative AI sits at a similarly early stage. Regulators should be humble about their ability to predict which models, firms, architectures, and business strategies will prevail.

The goal is to preserve the conditions for rivalry: entry, experimentation, investment, contracting, interoperability where justified, and enforcement against actual exclusion. It is not to redesign AI markets before they fully form.

Not All AI Roads Lead to Bigger Models

Another weakness in data-centered AI regulation is that it bakes in assumptions about how the technology will evolve. If regulators treat access to training data as the core competition problem, they risk privileging large-scale pretraining as the dominant model. But AI competition may not hinge on ever-larger general-purpose systems. It may turn on smaller models, specialized models, agentic workflows, private deployment, model routing, tool use, retrieval systems, and industry-specific applications.

A startup building a highly reliable tax-compliance assistant does not need to outgun the largest general chatbot. A medical-device company using a narrow AI model probably doesn’t need internet-scale training data. A law firm may prefer a private system grounded in its own documents. A manufacturer may prioritize a model that runs locally and protects trade secrets. In each case, the competitive question is not who has the most data. It is who best solves the customer’s problem.

Competition policy should not assume that the largest model is the relevant unit of analysis. Nor should it treat access to general training data as the decisive input. The market should be allowed to discover which approaches work.

Antitrust, Not AI Central Planning

A sound AI competition agenda would look different.

First, it would distinguish scale from exclusion. Large investments in compute, talent, and data may reflect vigorous competition. Size alone is not a sin.

Second, it would focus on conduct. Exclusive dealing, tying, predatory strategies, discriminatory access, collusion, and anticompetitive mergers should be assessed under established legal standards.

Third, it would account for efficiencies. AI partnerships, data licenses, cloud agreements, and model-distribution arrangements can expand output, reduce costs, improve safety, and accelerate deployment.

Fourth, it would resist turning antitrust into general AI regulation. Privacy, copyright, national security, and consumer-protection concerns belong to the legal regimes designed to address them—not to competition law stretched beyond its institutional competence.

Fifth, it would preserve room for experimentation. In fast-moving markets, false positives carry real costs. Blocking benign or procompetitive conduct can deprive consumers of innovations that never come to market.

In short, when assessing the role of data in AI, enforcers should prioritize evidence before intervention, actual competitive effects before remedies, and consumer welfare before regulatory ambition.

Data Isn’t Oil—and It Isn’t Destiny

The debate over generative AI needs less metaphor and more economics. Data is not oil. It is not a single resource, not necessarily scarce, not necessarily exclusive, and not necessarily decisive. Firms can copy, license, generate, clean, degrade, specialize, substitute, and combine it with other inputs in ways that reshape its competitive value.

The fact that data is useful in AI does not make it a durable barrier to entry. The fact that large firms hold data does not mean rivals cannot compete. And the importance of AI does not justify abandoning case-specific analysis for precautionary control.

The right policy is not passivity. It is disciplined enforcement. Antitrust agencies should target real exclusion, not hypothesized dominance. They should protect the competitive process, not particular competitors. They should recognize that innovation often comes from unexpected entrants, technical workarounds, open models, specialized applications, and new contractual arrangements.

Generative AI remains young, fluid, and highly contestable. Premature regulation built on an exaggerated data-bottleneck theory could slow the very forces lowering barriers and expanding access. The better course is to let competition work—and step in only when the evidence shows it is being unlawfully suppressed.

The post AI, Antitrust, and the Mirage of Data Dominance appeared first on Truth on the Market.

Across the continent, regulators are reaching for Europe’s most ambitious digital frameworks. The Common Market for Eastern and Southern Africa (COMESA) Competition and Consumer Commission (CCC) recently overhauled its regime to introduce gatekeeper regulation modeled on the European Union’s Digital Markets Act (DMA). Nigeria’s Federal Competition and Consumer Protection Commission (FCCPC) fined Meta million for what it framed as a privacy and competition violation grounded in European-style reasoning—albeit flawed. South Africa’s Competition Commission (SACC) has moved from market inquiry to enforcement on digital platforms.

These moves may signal sophistication—and, at first glance, progress. The reality is less encouraging. African institutions are investing heavily in rulemaking while systematically underinvesting in the capacity to administer those rules with competence. The result is not rigorous enforcement, but mimicry. Stripped of the analytical discipline that gives it value, regulation becomes a set of empty forms.

Following Europe Down the Wrong Path

The model being copied is itself contested. When the so-called “Draghi Report” was released in 2024, it gave official imprimatur to a longstanding critique: Europe is in relative decline, and its appetite for overregulation is part of the problem. Eurozone labor productivity has grown just 0.7% annually since 2020—less than half the U.S. rate. A slow digital transition and a fragmented tech sector explain part of the gap. Overregulation does the rest, steering Europe away from high-growth sectors and widening the divide.

The innovation numbers tell a similar story. According to the Stanford AI Index Report, U.S.-based institutions produced 40 large AI foundation models in 2024. China produced 15. The EU produced just three. At the same time, Europe’s tech-startup ecosystem remains volatile. Founders are leaving for more hospitable regulatory environments at rates that concern even European policymakers.

Survey data reinforce the point. A European Investment Bank study found that founders of startups and scale-ups view the European regulatory environment as overly restrictive in key sectors, particularly deep-tech, digital technologies, biotech, and clean tech. Recent frameworks like the Artificial Intelligence Act (AI Act) and the General Data Protection Regulation (GDPR) impose heavy compliance burdens and often misalign with the realities of building breakthrough technologies. Innovation ecosystems depend on speed and flexibility. Europe offers neither.

Yet regulatory ambition travels well. When the European Commission rolls out digital competition rules—or when national authorities open high-profile investigations into Big Tech—other regulators take notice. They often respond in kind. African competition authorities are no exception.

The problem is not regulatory ambition as such. Digital markets warrant oversight, and African authorities are right to focus on them. Nor is the specific choice of rules dispositive. Capable administrators—agencies and courts—can work around imperfect frameworks. They can interpret narrowly, enforce selectively, and build doctrine incrementally. They can course-correct. What they cannot do is manufacture institutional competence from thin air. An understaffed administration armed with a flawless framework will still struggle. The reverse does not hold.

Power Without Capacity Is Just Paper

History bears this out. Daniel Carpenter’s comparative study of American federal agencies during the Progressive Era shows that formal authority, untethered from institutional capacity, produces ineffectiveness. The Interior Department wielded more discretionary power than any agency of its time, yet remained institutionally weak—unable to plan, resist political pressure, or execute the sophisticated mandates Congress assigned it. By contrast, agencies that succeeded built capacity first. As Carpenter shows, that capacity cannot be imported or engineered into existence. It must be built. 

The problem, then, is one of sequencing. Authorities are adopting the architecture of advanced regulation without first constructing the institutions needed to make it work. A recent Lagos State High Court decision shows why that matters.

If You Can’t Find the Speaker, Sue the Stage

The facts of Femi Falana v. Meta Platforms., decided by Justice Olalekan Oresanya of the Lagos State High Court, are striking.

In January 2025, an AI-generated deepfake video appeared on Facebook under a page called “AfriCare Health Centre.” It used the image and fabricated voice of Femi Falana—one of Nigeria’s most prominent human-rights lawyers—to claim falsely that he had suffered from prostatitis for 16 years. Falana’s counsel, Olumide Babalola, tried to identify the page operator and came up empty. As he  put it: “We searched everywhere, on Facebook, outside Facebook, globally. There was no such entity.” With no identifiable third-party defendant, Falana filed a fundamental-rights application directly against Meta. 

The application invoked Section 37 of the 1999 Constitution, which guarantees the privacy of citizens, their homes, correspondence, and communications, as well as Sections 24 of the Nigeria Data Protection Act 2023 (NDPA). The court awarded $25,000 in damages and found that Meta: (i) could not rely on an intermediary defense because it monetizes content; (ii) acted as an agent of the anonymous page operator and was therefore vicariously liable; (iii) owed a duty to ensure the accuracy of third-party content; (iv) qualified as a joint data controller under the NDPA alongside the anonymous operator; and (v) violated Falana’s constitutional right to privacy under Section 37 by placing him in a false light. 

Judgment Without Justification

The High Court’s judgment is remarkable not for flawed reasoning, but for the near absence of reasoning at all. In common-law systems, adjudication functions as a kind of “reasonable custom,” emerging from a public exchange of arguments between counsel and the court. As Martin Golding observed, judicial reasoning aims to produce conclusions that independent observers—judges, lawyers, scholars—can find intelligible and defensible. 

That justificatory function is largely missing here. The court dispenses with tradition but keeps its hauteur, issuing conclusions from on high without attempting to persuade.

Start with the constitutional privacy claim. The court quoted Section 37 verbatim, noted that health information is private, observed that the deepfake placed Falana in a false light, and declared a constitutional violation actionable per se. According to the court:

It is my well-considered view that a [s]tatement or publication in a video made without the consent of the party concerned, irrespective of whether the statement be true or false, which contains an imputation that the person has a terminal or contagious disease, has the tendency to put that person in a false light and amounts to a violation of the right to privacy of that person and is actionable per se i.e., even without proof of actual damage or injury, and also created a potential case of strict scrutiny on the part of the person who made the statement or publication.

The court reached this conclusion without analyzing Section 37’s scope or the elements of the false-light tort.

Assume arguendo that Section 37 applies horizontally and, read expansively under the Fundamental Rights Enforcement Rules (2009), could encompass multiple torts. False light is not one of them. Section 37 protects against intrusion into a private sphere—the state reading your correspondence, entering your home, surveilling your communications. False light addresses something else entirely: misrepresentation in the public sphere. It sits closer to defamation, guarding against distortion of public image, rather than invasion of private life.

That raises a threshold question: should Nigeria recognize the false-light tort at all? Even in the United States, where the tort originated, several states have rejected it as incompatible with free expression or as a source of excessive liability. English courts have taken a  similar view (see John McCamus for a comprehensive analysis)

The High Court, in short, grafted a contested—and in Nigeria largely nonexistent—tort onto a constitutional provision that serves a different function. It elevated false light to constitutional status without grappling with the tension between privacy and freedom of expression.

The agency and vicarious-liability findings fare no better. The court held that Meta, as a platform hosting third-party content, was “presumed to be acting on behalf of an unnamed or undisclosed principal,” such that the agent’s acts bound the principal. But agency requires consent and control—specifically, that the principal authorizes the agent to act on its behalf and subjects the agent to its direction. Neither element appears here. 

The platform-user relationship is contractual and arm’s-length. Users receive reach and distribution; platforms receive a broad license over content and data. Neither party owes fiduciary duties, loyalty obligations, or a duty to act in the other’s best interest. Agency doctrine does not fit this arrangement.

When a user posts on Facebook, they do not direct Meta. Meta provides a service under terms that disclaim the very obligations agency would impose. If courts begin reading agency into standard platform agreements, the implications are sweeping: digital-services contracts could become agency relationships, with attendant fiduciary duties, disclosure obligations, conflict-of-interest rules, and indemnity exposure.

The court seems to recognize the analytical strain, yet proceeds anyway. It concludes—without explanation—that Meta is vicariously liable. Accepting Meta’s lack-of-control argument, the court suggests, would be “unjust,” leaving Falana without recourse against an unknown tortfeasor. In the court’s words:

[T]o my mind, this cannot be a just, fair, and reasonable interpretation of the Law as a tool of balancing the competing and often conflicting interests of members of civilized society.

But as U.S. Supreme Court Justice Antonin Scalia observed, fidelity to the law sometimes produces outcomes a judge may find unappealing.

Balancing interests has consequences. Here, the court inverts the basic logic of platform liability, collapsing the distinction between platform and publisher and exposing intermediaries to potentially boundless liability. The implications extend well beyond Meta—to YouTube, X, TikTok, local marketplaces like Jumia or Konga, gig-economy platforms like Bolt, and even software-as-a-service providers whose tools users might misuse.

The investment consequences could be significant. Platforms depend on predictable liability to operate and attract capital. The agency theory advanced here is neither insurable nor readily quantifiable. Any investor conducting due diligence on a Nigerian tech company must now factor in the possibility of open-ended liability for user-generated content—simply because the platform generates revenue.

Ignore those implications, and both the credibility of the Nigerian judiciary and the country’s emerging innovation economy come under strain.

A System Set Up to Struggle

The errors in Falana are not just the product of a bad judge. They reflect a system asked to do more than it is equipped to handle. Worse decisions have emerged from Nigerian High Courts—unsurprising, given that many High Court judgments never make it into law reports and therefore escape scrutiny and criticism.

Delay compounds the problem. Litigation routinely takes years to resolve. In one arbitration matter—where speed should be the point—it took 12 years for the Supreme Court of Nigeria to uphold the award. After spending five to 10 years in the High Courts, many litigants simply give up on appeal. 

That Falana concluded within a year likely reflects the use of the Fundamental Rights Enforcement Procedure (FREP) Rules. But speed is the exception, not the norm.

Capacity constraints are severe. Nigerian judges are among the most overburdened in any common-law system. As of 2019, the Supreme Court reportedly had more than 10,000 pending appeals. At the trial level, judges lack the institutional support U.S. courts take for granted—no law clerks conducting independent research, no robust administrative infrastructure. In many courtrooms, judges still record proceedings longhand. 

The cognitive load is immense. A generalist judge, operating under those conditions, must navigate technically complex questions at the intersection of constitutional law, data protection, and platform architecture. Even a diligent judge will struggle to produce careful, coherent doctrine.

The structural deficits are well documented: underfunded court libraries, limited access to comparative-law databases, and minimal exposure to specialized expertise. In that environment, doctrinal errors—confusing constitutional privacy with false light, or agency with platform liability—are not aberrations. They are predictable. They will recur. And as regulatory frameworks grow more complex, they will compound. 

This is the mimicry problem. Nigeria, like many African jurisdictions, is adopting the analytical vocabulary of the EU’s DMA, the GDPR, and the German Bundeskartellamt’s data-practices jurisprudence. But that vocabulary emerged in institutions with deep, specialized capacity—staff economists, data scientists, expert tribunals, and courts with decades of precedent. Transplant the vocabulary without the underlying capacity, and you get something that looks like sophisticated regulation, but cannot deliver it.

Overreach Beats Underthinking

The mimicry critique does not apply with equal force across the continent. South Africa offers a useful counterexample. The Competition Commission of South Africa (SACC)’s Media and Digital Platforms Market Inquiry, finalized in November 2025, and its Online Intermediation Platforms Guidance Note, gazetted in February 2026, reflect a more credible approach. These are the products of multi-year inquiries staffed by economists, grounded in extensive public participation, and shaped through provisional reports and public responses. The remedies remain tethered to the existing Competition Act framework.

South Africa’s Competition Tribunal (SACT) also brings something many peers lack: institutional memory built over decades of antitrust enforcement, along with a demonstrated capacity for serious economic analysis.

That does not make the South African model immune from criticism. Competition policy there often leans toward overreach. In part, that tendency reflects the text and structure of the Competition Act of 1998, which directs authorities not only to promote and maintain competition, but also to “ensure that small- and medium-sized enterprises have an equitable opportunity to participate in the economy” and to “promote a greater spread of ownership, in particular to increase the ownership stakes of historically disadvantaged persons.” That mandate reflects the country’s apartheid history, and similar commitments appear in Sections 9(2) and 217 of the Constitution of the Republic of South Africa, 1996 (see also).

But overreach is a different problem from the one identified here. An institution that overreaches can be checked—by courts, and sometimes by sustained criticism. An institution that lacks the capacity to reason through the doctrine it applies cannot be corrected so easily. 

Mind the Capacity Gap

To be clear, everyone starts somewhere. This is not an argument for indefinite regulatory delay. It is an argument about priorities: capacity before mimicry.

That means investing in the basics. Judicial research clerks for technically demanding cases. Continuing education for judges and agency staff on digital markets and platform economics. Transcription technology to replace longhand note-taking. Legal databases that give courts and regulators access to the comparative jurisprudence they are already trying to apply.

The gap between mandate and capacity remains the central problem. Until it closes, new frameworks will yield enforcement theater, rather than real market governance. Decisions like Falana will continue to pile up, each one chipping away at the credibility that effective enforcement depends on.

As authorities reach for their pens, a threshold question should come first: are we institutionally equipped to do this? The gap between regulatory vocabulary and regulatory competence is not static. It widens with each new framework, each technically demanding case before an underprepared court, each judgment that substitutes decisiveness for analysis. The costs are asymmetric. A poorly reasoned decision is easy to issue and hard to unwind. Its effects—on investment, doctrine, and institutional credibility—linger long after the case is forgotten.

The continent does not need less ambition. It needs institutions that can carry it. Until then, digital competition frameworks will keep promising more than they can deliver.

The post Africa’s Imitation Game in Competition Law appeared first on Truth on the Market.

First, the DOJ claims Visa imposes “de facto exclusivity” on merchants. The theory: merchants route nearly all debit transactions through Visa to hit volume thresholds that unlock loyalty discounts on transaction fees. That, in turn, deprives rival debit networks of the scale they need to compete.

Second, the DOJ alleges Visa neutralizes potential competitors—such as Apple—by sharing monopoly profits through agreements that turn would-be entrants into partners, rather than threats in fintech (i.e., using online technology to deliver financial services).

As to the first theory, the complaint zeroes in on Visa’s use of loyalty discounts, or rebates, to steer transaction routing. Merchants receive these discounts only after hitting volume thresholds—often 90% or more of total debit transactions. Route more to Visa, pay less. Route less, lose the discount. The DOJ argues this structure operates as a de facto exclusive-dealing arrangement that deters merchants from sending transactions to rival networks.

Step back for a moment. Why do merchants have any routing choice at all? If a consumer uses a Visa debit card at the point of sale, doesn’t the consumer decide the network?

Not quite.

In 2010, Congress enacted the Durbin Amendment as part of the Dodd-Frank Wall Street Reform and Consumer Protection Act; it took effect in 2012. The amendment requires that each debit card connect to at least two unaffiliated networks capable of processing transactions. Typically, one network appears on the “front of the card”—Visa, Mastercard, American Express, or Discover—and another on the “back of the card.” That second option usually involves a PIN-debit network with roots in ATM transactions, such as STAR, NYCE, Accel, Pulse, or Shazam. At the point of sale, the merchant—not the consumer—chooses how to route the transaction.

This post advances three considerations for assessing the legality of loyalty discounts.

First, antitrust law generally resists punishing firms for offering lower prices. As a result, claims of predatory pricing (prices so low they force rivals to exit) or exclusionary discounting (pricing structures that induce buyers to concentrate purchases with a single supplier) face well-defined legal and economic hurdles.

Second, the legality of loyalty discounts often turns on two factors: whether the discounts span multiple products, and how much of a buyer’s demand remains “contestable” versus “non-contestable.” For example, if a merchant processes 100 debit transactions per period but only 20 can be routed to a PIN network, the contestable share is 20%.

Third, the DOJ’s emphasis on harm through denying rivals “scale” raises a threshold problem. The complaint never defines scale, specifies the level required for competition, or ties the concept to market-specific evidence.

The High Bar for Attacking Low Prices

As a general principle, theories of harm premised on giving too much of a good thing—e.g., low prices or generous discounting—face an uphill battle in court. Courts hesitate for good reason. Aggressive pricing often reflects the very competition antitrust law seeks to protect, not punish.

Herbert Hovenkamp puts it plainly:

The great majority of discounting practices are procompetitive. Discounts are the age-old way that merchants induce customers to purchase from them and not from someone else or to purchase more than they otherwise would.

To be sure, discounting can harm consumers in narrow circumstances, as the next section explains. The harder problem lies in identifying those cases with any confidence. That task carries real administrative costs. Worse, mistaken enforcement—false positives—can impose significant “error costs” by chilling pricing practices that benefit consumers.

Courts have responded by setting a high bar for claims built on low prices. Predatory-pricing claims, for example, require proof that a firm priced below cost to drive out rivals and later monopolize the market. That standard reflects a broader skepticism: low prices, standing alone, rarely signal anticompetitive conduct.

The same logic applies to rebates. Firms design rebates to induce buyers to purchase more of their product—that is their purpose, whether or not the firm has market power. As a result, analyzing aggressive discounting often mirrors the predatory-pricing inquiry. Discounting may make life harder for rivals, but difficulty for competitors does not equal harm to competition.

As long as the net price remains above cost, the case for liability weakens considerably. As Hovenkamp notes, “above-cost discounts on single products should be regarded as lawful.” (See the section below on why the single-product versus multiple-product distinction matters.) At a minimum, courts should apply a strong presumption of legality and place the burden squarely on the plaintiff to demonstrate anticompetitive harm.

Not All Discounts Are Created Equal

Put simply, loyalty discounts offer lower prices to buyers who meet a purchase threshold. More precisely, Bruce Kobayashi describes them as “a particular form of non-linear pricing in which the unit price of a good declines when the buyer’s purchases meet a buyer-specific minimum threshold requirement.” A 5% discount triggered when a customer sources 80% or more of its requirements from a single seller fits the bill.

These discounts take different forms. When tied to a single product, they are “single-product” discounts. When they span multiple products, they become “multi-product” discounts. For example, a 5% discount that applies only if a buyer sources 80% of both Product A and Product B from the same seller qualifies as a multi-product loyalty discount.

Structure matters, too. Some programs use “all-units” discounts, where the lower price applies retroactively to all units once the threshold is met. Others use “incremental” discounts, where the lower price applies only to units purchased above the threshold. For instance, an incremental schedule might price the first 100 units at $10, the next 100 at $9, and all additional units at $8. An all-units version would apply the $8 price to all units once the buyer exceeds 200.

Under certain conditions, Kobayashi explains, all-units discounts can exclude an equally effective competitor. A key condition is capacity. If a rival cannot match the incumbent’s output, it may struggle to compete for the marginal units that determine whether the buyer hits the threshold. That challenge intensifies with multi-product discounts, where rivals must match capacity across several markets, not just one. Capacity constraints matter most in industries with high fixed and variable expansion costs—e.g., adding production lines or building new plants. They matter less where marginal costs remain low after large upfront investments.

That raises a threshold question for debit-card markets: can rival networks handle significantly more transactions, or do capacity constraints meaningfully limit expansion?

A related issue—and a central feature of the DOJ’s theory—is the distinction between “contestable” and “non-contestable” transactions. The complaint asserts that PIN-debit networks cannot process a meaningful share of transactions for various reasons—e.g., merchants may avoid routing higher-dollar transactions to those networks. From that premise, the DOJ argues that any price-cost test should apply only to the contestable share. If a large portion of transactions is deemed non-contestable, allocating discounts solely to the contestable portion increases the likelihood that the test will show below-cost pricing.

That framing raises its own question: is the DOJ analyzing the right level of competition? Even if certain PIN networks cannot process specific transactions, larger “front-of-card” networks—such as Mastercard, Discover, and American Express—can. The primary competitive battleground may therefore sit at the front-of-card level. If so, that is where contestability should be assessed.

Courts generally evaluate loyalty discounts along one of two paths. If the program reflects legitimate price competition—call it “genuine discounting”—courts apply a price-cost test, which provides a safe harbor when prices exceed an appropriate measure of cost. The details matter, including whether the discounts involve single or multiple products and how courts treat contestable versus non-contestable sales.

If, instead, the program reflects “exclusionary pricing,” courts analyze it under the rule of reason, much like an exclusive-dealing claim. On June 23, 2025, the district court denied Visa’s motion to dismiss. In doing so, the court signaled its approach, finding that “the Government has alleged a plausible exclusive dealing claim.” That does not render the price-cost test irrelevant. It does, however, confirm that the court will evaluate Visa’s pricing in a broader competitive context.

If Everything Is About Scale, Nothing Is

An integral piece of the DOJ’s loyalty-discount theory is the claim that discounting deprives rivals of “scale.” The complaint leans on the concept heavily, alleging—“[p]erniciously”—that Visa’s program “prevents its current and potential rivals from gaining the scale, share, and data necessary to erode Visa’s existing dominance.” The word “scale” appears 40 times.

That emphasis exposes two problems.

First, denying rivals scale—or sales—often reflects ordinary competition. Firms win business; rivals lose it. That is the point. Hovenkamp underscores the issue:

Indeed, one of the problems with the theory that discounts deprive rivals of economies of scale is that the theory does not require a discount at all. The seller who simply sold all of its product at the fully discounted price, without requiring any purchase commitment, would also be depriving rivals of economies of scale.

Put differently, conduct that reduces rivals’ sales may be entirely legitimate. The theory does not distinguish well between hard competition and exclusion.

Second, if “scale” does real work in the analysis—whether as a mechanism of harm or a barrier to entry—the plaintiff must define it and prove it. That requires, at a minimum: (a) specifying the relevant concept of scale (economies of scale, minimum-viable scale, or something else), (b) identifying the level of scale needed to compete or enter, and (c) showing that rivals fall below that threshold.

The DOJ does none of this. Despite invoking “scale” repeatedly, the complaint never defines the term or identifies how much scale competition requires. That omission matters. Replace “scale” with “sales,” and the theory reduces to a familiar—and largely uninformative—claim: discounting “denies rivals sales.” That tells a court little about whether the conduct violates the antitrust laws.

This rhetorical move has become common. Recent complaints from the DOJ and the Federal Trade Commission (FTC) rely heavily on “scale” while leaving it undefined. In the DOJ’s challenge to Google’s search-distribution agreements, “scale” appears 36 times, without definition or supporting evidence. In the DOJ’s Google AdTech complaint, the count rises to 70. The FTC follows the same template. Its complaint against Amazon uses “scale” 76 times, again to argue that the company’s conduct “denies scale” to rivals. The FTC’s Facebook complaint uses the term 24 times—without specifying what level of scale “meaningful” competition requires. Brian Albrecht has noted the same trend in platform cases.

Courts should treat these claims with skepticism. Without a market-specific, evidence-based account of scale, the concept does little analytical work. Hovenkamp makes the point directly:

[The] measurement of scale economies across the full range of a firm’s activities is extraordinarily difficult. A federal court could never apply such theories, particularly in a jury trial, without creating the ‘intolerable risk’ that the Supreme Court feared in Brooke Group, of chilling procompetitive behavior.

Even setting aside the definitional gap, the DOJ’s theory sits uneasily with market realities. Visa’s rivals—Mastercard, American Express, and Discover—process trillions of dollars in transactions each year across debit and credit networks. Calling that a lack of “scale” stretches the concept beyond recognition.

A Test Case for Modern Antitrust

The DOJ’s case against Visa’s debit-card business tees up a central question for modern antitrust: how far courts should go in policing loyalty discounts and scale-based theories of harm. The answer will not stop with Visa. It will shape how enforcers—and courts—approach large, multisided platforms across the economy.

If “scale” remains undefined and discounts remain suspect simply because they work, the risk is clear: antitrust drifts from protecting competition to second-guessing it.

The post From Competition to Exclusion: Can Discounts Go Too Far? appeared first on Truth on the Market.

Instead, the Italian government’s latest energy measures suggest something else: when prices rise, the instinct is not just to subsidize costs, but to supervise how those costs flow through the system—down to how firms bid, price, and earn margins. The result looks less like market oversight and more like a slow drift toward price control, dressed up as “pass-through” enforcement.

That shift matters. It reflects a broader belief that competition can be fine-tuned from above by monitoring costs, constraining pricing, and scrutinizing margins. The two decrees at the center of Italy’s response—the Decreto Carburanti and the Decreto Bollette—put that belief into practice.

Two Decrees, One Direction

As energy prices soar, the Italian government has moved to make energy products more affordable, including road fuels, electricity, and natural gas. It has temporarily cut excise taxes by €0.20 per liter of road fuels (down from the ordinary €0.67 per liter for both gasoline and diesel) and reinforced a prior decree aimed at trimming certain components of electricity bills.

But the government has not limited itself to taxes and levies. Both the Decreto Carburanti (road-fuel decree) and the Decreto Bollette (power-bills decree) introduce measures with clear competition-policy implications.

The Decreto Carburanti requires oil companies to publish their suggested gasoline and diesel prices online each day, and bars them from raising prices more than once daily. It also directs the Ministry for Industry and Made in Italy to monitor prices at individual gas stations. If the ministry detects an “anomalous or sudden price increase” relative to international benchmarks, it must ask the Guardia di Finanza—Italy’s financial and fiscal police—to investigate the entire value chain and report its findings to the national competition authority. These measures broadly track the approach discussed in a prior Truth on the Market post on Germany’s Fuel Market Intervention Package by Mario Zúñiga and Dirk Auer.

The Decreto Bollette goes further.

Issued Feb. 20—before the onset of the Iran crisis—the decree aims to close a persistent gap between Italian electricity prices and those in the rest of Europe, a gap widely seen as putting Italian firms at a competitive disadvantage. The energy-price shock triggered by the Third Gulf War has only heightened its political urgency.

To bring prices down, the decree introduces a complex mechanism—loosely inspired by the “Iberian exception” adopted during the 2022 crisis—that subsidizes gas-fired generators. The subsidies offset part of their marginal costs, including the cost of carbon-dioxide allowances under the EU Emissions Trading System and certain network charges.

The decree then attempts to ensure that these cost reductions translate into lower prices through full pass-through at both the wholesale and retail levels. In practice, however, the mechanism comes close to imposing price controls under a system that formally preserves free pricing.

At the wholesale level, the regulator must “verify” that generators fully reflect the subsidies in their bids. If they do not, generators must repay the subsidies, plus a sanction to be determined. The decree also requires the regulator to monitor bidding strategies to prevent “economic withholding” of capacity—conduct that, when it departs from the “fair interplay of demand and supply,” may amount to unlawful market manipulation. To do so, the regulator must assess whether bids align with estimated marginal costs.

At the retail level, suppliers must submit periodic reports—at least annually—detailing their profit margins by type of offer and customer.

When ‘Economic Withholding’ Becomes a Cost-Based Straitjacket

At the wholesale level, the decree intervenes in two ways. First, it requires the regulator to ensure full pass-through of subsidies to gas-fired generators. Second, it directs the regulator to prevent “economic withholding” of capacity.

The concept of economic withholding comes from the EU Regulation on Wholesale Energy Market Integrity and Transparency (REMIT, Regulation (EU) 1227/2011), which defines it as:

Actions undertaken to offer available generation capacity at prices which are above or at the market price and do not reflect the marginal cost (including opportunity cost) of the market participant’s asset, which results in the related wholesale energy product not being traded or related asset not being dispatched.

As Luca Lo Schiavo has argued, three conditions must hold simultaneously to establish economic withholding: a price condition (“the offered price must be greater than or equal to the zonal market price”); a cost condition (“the zonal price must be greater than the short-run marginal cost of the production unit, including opportunity costs”); and a rejection condition (“the offered quantity must be rejected by the market algorithm”). When all three are met, a bid above marginal cost may indicate market manipulation, insofar as it contributes to higher prices by keeping otherwise available capacity out of the market.

REMIT does not prohibit economic withholding per se. It does so only within a framework of market manipulation—and only where that manipulation is established. Even then, liability does not attach if “the person who has entered into the transaction or issued the order to trade demonstrates that his reasons for doing so are legitimate” (Article 2(2)(a)(ii)). In other words, proving economic withholding is not enough; the operator may still show a legitimate justification. (See European Agency for Cooperation of Energy Regulators (ACER), Guidance on the Application of Regulation (EU) No 1227/2011 (2024) para. 281.)

Critics have argued that this framework lowers the standard of proof relative to traditional antitrust enforcement or shifts the burden onto the operator. Even so, it still requires case-by-case analysis under due process—not merely an algorithmic check of the three conditions. That requirement helps explain the relatively small number of economic-withholding cases brought under REMIT in the European Union.

More importantly, European regulators already have the tools to address manipulation through economic withholding. Only ACER may issue guidance under Article 16(1) of REMIT to ensure coordinated and consistent enforcement. Italy’s regulator, the Regulatory Authority for Energy, Networks and Environment (ARERA), conducted a fact-based investigation of the day-ahead market in 2025, covering 2023-24, which raised suspicions of potentially illicit behavior. It has not yet opened individual cases. A separate case involving alleged capacity withholding in 2022 remains pending, with results expected soon.

The Decreto Bollette takes a different approach. It effectively assumes that capacity withholding is generally illicit or harmful and instructs the regulator to “adopt one or more measures for the assessment of economic withholding practices by wholesale market operators.” Those measures “shall provide that, with reference to sell offers submitted in the day-ahead market, opportunity costs that can be estimated at the time of trading constitute the only legitimate economic justification for offering at a price above the marginal cost of generation capacity.”

The implications are hard to miss. First, the regulator must systematically estimate generators’ marginal costs. Second, market participants must calculate and document their opportunity costs—i.e., the value of alternative uses of capacity—with sufficient rigor to withstand scrutiny. Third, in a case-by-case review, operators may have to justify their bids based on those costs. If bids exceed estimated costs without adequate justification, regulators may presume unlawful capacity withholding—at least where offers were rejected and prices covered costs.

This framework raises at least three major concerns.

Start with substance. If day-ahead bids must align with estimated costs, generators cannot pursue bidding strategies beyond cost recovery. Once firms procure inputs upstream, the downstream market ceases to play any meaningful role in price formation. The market no longer allocates resources; it simply mirrors costs.

Next, the decree assumes that regulators can observe—or reliably estimate—those costs for each generating unit, in every market time unit (MTU), typically every 15 minutes. That assumption stretches credibility. It also treats each MTU in isolation, ignoring how firms optimize across time. Generators do not bid for a single 15-minute interval in a vacuum; they manage portfolios subject to technical constraints, intertemporal tradeoffs, and expectations about future demand, supply, and network conditions.

Finally, the decree assumes that regulators can systematically estimate not only actual costs—fuel, carbon allowances, transport rights, hedges—but also opportunity costs. In practice, opportunity costs depend on forward-looking strategies and alternative uses of assets, which are inherently firm-specific and context-dependent.

Push the logic a step further. If regulators can observe costs and opportunities with this level of precision, why rely on markets at all? Regulators could simply dispatch plants, procure inputs, and optimize capacity use directly—both in the short and long run.

Of course, regulators are not—and cannot be—omniscient. A legal regime that restricts bids to observable costs, while presuming that regulators can observe those costs, effectively denies any meaningful role for markets. It replaces decentralized decision-making with centralized estimation—and assumes away the very information problems that markets exist to solve.

Chasing ‘Margins’ in a Market That Sells More Than Megawatts

The Decreto Bollette also reaches into the retail market. It tasks ARERA with collecting—at least annually—extensive data from electricity and gas retailers on their “profit margins by type of offer and by type of customer.”

As at the wholesale level, this requirement assumes that “profit margins” can be observed and, once observed, will yield insights useful to “promote transparency, competition, and the correct functioning of energy markets,” as the decree’s preamble puts it.

That assumption runs into at least two problems.

Start with scale. Italy has roughly 630 electricity and gas retailers, offering thousands of service and price offers. Offers typically fall into four categories: variable-price and fixed-price, each either with or without additional services (such as green-energy guarantees, bundles, or loyalty programs). Many include temporary discounts. On top of that, suppliers serve multiple customer types—residential users, micro-businesses, small and medium-sized enterprises, and large industrial consumers. The decree asks suppliers to estimate a “profit margin” for each combination of offer and customer.

Then comes the harder question: what, exactly, is a “profit margin”?

A natural reading would treat it as a gross margin on energy supply. But that raises immediate complications. How should firms allocate fixed costs? How should they account for financial hedges? How should regulators compare offers with different features? Two examples illustrate the point.

Example 1: Fixed-price contracts as risk transfers. In 2024—the most recent year with available data—about 54.8% of residential customers chose fixed-price contracts. These contracts embed a wager: if wholesale prices rise above the agreed price, the customer benefits; if not, the supplier does. Under normal conditions, fixed-price contracts cost more than variable-price ones, because they bundle energy supply with a financial hedge against price volatility. But when shocks hit—as in 2022 or after the Third Gulf War in 2026—fixed-price customers gain protection. Looking only at gross margins in “normal” periods misses the point. Many customers willingly pay a premium for insurance against price spikes. What appears as a higher margin may simply reflect a different allocation of risk—not exploitation.

Example 2: Bundles and value-added services. About 98% of fixed-price customers and 72% of variable-price customers opt for bundled offers that include additional services. The most common is a guarantee that electricity comes from green sources. These “green” offers typically carry a premium. If margins are calculated as the spread between retail and wholesale electricity prices, they may appear unusually high—but that may say little about profitability once procurement costs for green energy enter the picture. The problem only deepens with more complex bundles, such as those including hardware or energy-efficiency advisory services. A supplier might charge higher per-unit prices while helping customers reduce overall consumption or shift usage to lower-cost periods. The supplier earns higher margins; the customer still saves money. Margin data alone misses the mutual gains.

The broader point is straightforward: estimating profit margins by offer and customer type is inherently difficult. Profitability depends on procurement strategies, customer loyalty, pricing structures, and operational efficiency—factors that often emerge only at the aggregate level, in financial statements. The problem worsens when offers change frequently in response to market conditions and consumer preferences.

But there is a deeper issue. Even if regulators could obtain precise, offer-level margin data, it would reveal little of value for “transparency, competition, and the correct functioning of energy markets.”

Transparency is already high. Italy has multiple price-comparison tools, including one run by the state-owned Acquirente Unico and overseen by ARERA. Suppliers must upload their offers to this public portal as they take effect, and the data are available in open format for third-party comparison tools.

As for competition and market functioning, profit margins alone say very little. ARERA and the Italian Competition Authority (AGCM) already have the tools to detect and sanction misconduct—and they use them. ARERA’s investigation of the day-ahead wholesale market in 2023-24 (discussed above) and AGCM’s enforcement actions against retail abuses make the point. Adding offer-level margin data does not meaningfully improve regulators’ ability to identify wrongdoing. It risks generating noise without insight.

If Regulators Know the ‘Right Price,’ Why Have Markets at All?

The new provisions—aimed at preventing (almost unconditionally) economic withholding in wholesale electricity markets and extracting granular “profit margin” data at the retail level—rest on a simple premise: competition can be engineered from the top down. If that were true, there would be little point in maintaining markets at all. Regulators could just as well take over suppliers and manage the entire value chain.

The problem runs deeper than administrative burden or questionable compatibility with European law, which assigns price formation to markets, not regulators. The real flaw lies in the premise itself. Markets exist precisely because no central authority can know the “right” price of goods and services in advance.

Italy—and many other countries—has already tested vertically integrated, state-directed energy systems. They did not work. Reintroducing them, even under the banner of “free pricing,” will not change the outcome.

Dress it up as oversight or call it transparency—but if regulators set the terms, police the bids, and second-guess the margins, the market becomes a formality. And when markets become a formality, they stop doing the one thing we need them to do: discover prices.

The post The Price of Watching Prices: Italy’s Slow Slide from Markets to Management appeared first on Truth on the Market.

The White House’s AI Action Plan takes a lighter touch. It directs the Federal Trade Commission (FTC) to prioritize innovation, which has translated into more measured enforcement and fewer regulatory burdens. So far, the FTC and the U.S. Justice Department (DOJ) have resisted calls to recast the agencies as all-purpose AI regulators.

Algorithms Aren’t the Enemy—Overreach Is

In a recent case, the DOJ took a tailored approach that preserved room for innovation in data and technology. In its dispute with RealPage—which uses algorithms to generate recommended pricing—the DOJ reached a measured settlement. It allows the company to continue offering pro-competitive services, rather than imposing a scorched-earth remedy that could have chilled the use of algorithms in a wide range of neutral, benign applications.

As one former DOJ appointee put it, the settlement:

…offers the first substantive signal from federal enforcers about how they intend to approach algorithmic pricing going forward. For the many industries in the economy today that utilize algorithmic pricing, … the settlement itself … does not treat algorithmic pricing as inherently unlawful under U.S. antitrust law.

The agreement reflects a basic economic reality: well-structured data-sharing arrangements can promote innovation, efficiency, and the development of complex products. Businesses can use reasonable agreements to tackle common challenges without undermining competition. Sharing historical, aggregated, or anonymized data—especially through independent third parties—can improve industry standards and streamline supply chains. Done right, these arrangements benefit consumers; benchmarking reports, for example, can sharpen insight into market trends and consumer preferences.

The settlement also stops short of condemning the underlying technology. It includes no finding that algorithmic rent-setting violates the Sherman Act and leaves much of RealPage’s business intact. Courts have likewise resisted the theory that independent use of sophisticated pricing software satisfies the “contract, combination, or conspiracy” required under Section 1. Earlier this year, the 9th U.S. Circuit Court of Appeals reaffirmed that using the same vendor or pricing tool does not establish an agreement or transform parallel conduct into collusion.

Stay in Your Lane (and Let Congress Drive)

New technologies do not require Congress to sweep aside centuries-old legislative frameworks. Congress exists to act as a level-headed lawmaker. As the people’s representatives, legislators have both the authority and the institutional capacity to craft clear, durable rules.

FTC Chairman Andrew Ferguson recently underscored the point: “Congress has to set the rules of the road.” That tracks with the FTC’s design. From the start, it was meant to be a small, nimble regulator. President Woodrow Wilson, who signed the FTC Act into law, described the commission as a body that could provide industry with “the advice, the definite guidance and information” it needed. The mandate was straightforward: learn, measure, and correct.

The FTC largely follows that roadmap today. It has leaned on its convening power to gather expert input on emerging issues. In recent months, the commission has brought together scholars, policy analysts, and technologists to weigh in on artificial intelligence, Section 230, and more. That deliberate, information-gathering approach gives Congress a stronger foundation for legislation than any rush to regulate.

The FTC has also focused its enforcement where it counts: bad actors. Last year, the commission settled with an AI company that made false claims about its products’ capabilities. Chairman Ferguson has signaled that the FTC will continue to police similar conduct—without drifting into the kind of regulatory helicoptering that keeps innovation stuck in the garage.

Other agencies have shown similar restraint. The U.S. Labor Department, for example, recently launched an AI-literacy initiative aimed at easing adoption and helping workers build new skills. A more skeptical agency might have reached for barriers instead. This administration has taken the opposite tack, instructing agencies to stay in their lanes and avoid stifling development.

Technological change always tempts regulators to consolidate power. But durable governance depends on preserving institutional roles, not collapsing them. Congress should set the rules of the road. Agencies like the DOJ and the FTC should keep learning from markets, measuring real-world effects, and acting with precision under existing authority.

That division of labor may lack drama. It also works.

The post No-Fly Zone: Why AI Doesn’t Need Helicopter Regulation appeared first on Truth on the Market.

It’s a—maybe not compelling—but a story. A 2023 New York Times op-ed argued that this mechanism drives high grocery prices, noting that “as suppliers cut special deals for Walmart and other large chains, they make up for the lost revenue by charging smaller retailers even more, something economists refer to as the water bed effect.” The Organisation for Economic Co-operation and Development (OECD) has raised concerns about it for years. The United Kingdom’s Competition Commission has investigated it.

Regulators that have actually examined the waterbed effect tend to be skeptical. In its 2008 groceries investigation, the UK Competition Commission considered the theory and declined to rely on it, finding the evidence insufficient. Two years earlier, the UK Office of Fair Trading concluded that “there are theoretical questions that would need to be resolved before concluding that the price differentials observed are evidence of a waterbed effect.” As Eric Fruits put it on this blog, the waterbed was a notion without a model—and without a model, it was headed the same way as the real-world waterbed.

Then it got a model.

In 2011, Roman Inderst and Tommaso Valletti published a paper in the Journal of Industrial Economics that gave the waterbed a formal theoretical foundation. Their model features a supplier selling to a large buyer and smaller rivals. The large buyer’s scale gives it bargaining leverage, so the supplier compensates by charging the smaller rivals more. In the model, that is exactly what happens: the large buyer pays less, and the smaller rivals pay more. That result is straightforward.

The paper goes further. It claims the waterbed harms consumers—not just smaller firms, but shoppers at the checkout, who face higher prices on average. That is the result that matters for antitrust, which turns on consumer harm. It is also how the authors close their abstract.

I have a new working paper that shows consumer harm is impossible in their model.

The Waterbed Breaks Before Consumers Do

The waterbed operates through the small firm’s wholesale price. More precisely, it does not turn on the supplier needing to “make up” lost profits. It turns on bargaining. Once the small firm faces a lower-cost rival, its bargaining position with the supplier weakens, and the supplier can charge it more.

But the small firm is not captive. It can reject the offer and source inputs elsewhere. The supplier may want to squeeze harder, but push too far and the small firm walks.

For the waterbed to harm consumers overall, the squeeze has to be extreme. The small firm’s retail-price increase must outweigh the large firm’s price decrease. The walk-away option blocks that outcome. The supplier hits a ceiling before the waterbed grows strong enough to raise average prices.

In the Inderst and Valletti model, these forces pull in opposite directions. The small firm’s cost disadvantage must stay limited so the firm prefers the supplier’s deal over walking away—that is what keeps the waterbed in place. But consumer harm requires a large enough disadvantage that the small firm’s price increase swamps the large firm’s decrease. The first constraint caps the disadvantage below what the second requires. The waterbed and consumer harm cannot coexist.

This is not a math error. Inderst and Valletti’s consumer-harm result takes the form of an if-then: if a certain condition holds, then consumers are worse off. The logic is sound. The condition never holds. No equilibrium in the model satisfies the “if.” Nowhere in their model does the waterbed harm consumers.

The wholesale waterbed is real in their setup. Large buyers pay less, and smaller rivals pay more. The model even allows the small firm to raise its retail price—despite competing against a lower-cost rival, it still chooses to do so.

You can call that a waterbed. But a higher price at the small firm is not the same as consumers being worse off.

The Policy That Outruns Its Math

A throwaway line in a 15-year-old paper would not usually matter. This one does. The waterbed effect has hovered over antitrust for two decades. As Eric Fruits has noted on this blog in the context of the proposed Kroger-Albertsons merger, it surfaces in merger review and policy debates. It came up repeatedly in congressional hearings on that deal.

Its largest policy footprint may be the push to revive the Robinson-Patman Act. The RPA prohibits suppliers from charging competing buyers different prices for the same goods. That is the waterbed: a supplier charges Walmart less and the corner grocery more. If that price discrimination harms consumers, you have a consumer-welfare case for enforcement.

The logic chain runs like this: the waterbed harms consumers; supplier price discrimination is the mechanism; RPA enforcement is the fix. If the waterbed does not harm consumers in the very model that supplies its theoretical foundation, the first link in the chain breaks. What remains is a statute that bars supplier discounts to large buyers without a consumer-welfare rationale. As Alden Abbott has argued, reinvigorated RPA enforcement risks raising prices for the consumers it is supposed to protect.

None of this proves the waterbed could never harm consumers. A different model, with different assumptions, might get there. The point is narrower. The specific claim that this model—the one people cite—shows consumer harm does not hold. Before building policy on a formal result, check that the result actually follows. Here, it doesn’t.

The waterbed was a notion without a model. Now it has a model—but it doesn’t show what people think it shows.

The post The Waterbed Effect Doesn’t Hold Water appeared first on Truth on the Market.

Modern communications infrastructure doesn’t stand still. Providers must keep investing in upgrades and expansion to meet consumer demand. Next-generation applications—artificial intelligence (AI), artificial reality (AR), and virtual reality (VR)—will require robust infrastructure to support them. That infrastructure depends on sustained investment from a wide range of firms, including broadband providers.

Those providers invest only when they can expect a return. Policymakers who want to unlock the benefits of next-generation infrastructure should focus on reducing the transaction costs tied to broadband deployment. Lower those costs, and more projects will pencil out.

Permitting stands out as one of the most significant of those costs. Before breaking ground or attaching wireless facilities to buildings and towers, providers must secure approvals from multiple local and federal authorities. These costs include not just permit fees, but also the uncertainty of denial and the delays that can stall projects.

H.R. 2289, the American Broadband Deployment Act (ABDA), offers one path forward. Sponsored by Rep. Earl L. “Buddy” Carter (R-Ga.), the bill would impose shot clocks on local permitting authorities to accelerate approvals and franchising, and require fees to remain cost-based and reasonable. More notably, it would streamline federal environmental and historic-preservation review, significantly reducing the burden on providers. The House Energy and Commerce Committee has cleared the measure, and the full House is expected to take it up in the coming weeks.

If the United States wants to lead in next-generation applications, it needs the infrastructure to match. Broadband deployment represents just one piece of that puzzle, but it’s a critical one. Reducing transaction costs will do more than ease deployment—it will encourage the investment needed to build the networks of the future.

High Costs, Higher Risks—and Plenty of Red Tape

Broadband deployment requires substantial upfront investment. Providers must incur high fixed costs before serving a single customer. That dynamic introduces real risk: firms rely on future subscriptions to recover those costs, and demand is never guaranteed. As competition has increased in recent years, capturing a sufficient customer base has become more uncertain.

Much of that investment is also sunk. If a project fails, providers can’t easily recover or redeploy key assets. Fiber, once buried, is costly to extract and reuse elsewhere. Labor and regulatory expenses are unrecoverable. When a deployment falls short, investors have little ability to mitigate losses, which raises the stakes of every build.

Economies of scale help offset these risks. Providers spread large, upfront costs across a broader customer base, lowering per-user expenses. Areas with higher population density and greater willingness to pay offer more reliable returns. By contrast, rural and lower-income communities present thinner margins and greater uncertainty.

These cost dynamics create a classic underinvestment problem. Broadband generates substantial social value that providers cannot fully capture. The benefits—improved education, higher property values, better health care delivery—accrue broadly, while deployment costs remain concentrated on the firm. A project may be socially efficient when total benefits exceed total costs. But if private returns alone fall short, rational firms will not build. Investors cannot capture the value that spills over into a student’s GPA, a farm’s valuation, or a rural hospital’s budget.

Transaction costs further complicate the picture. In a frictionless world, demand for high-speed broadband would justify investment. In practice, deployment requires navigating a dense web of property rights and regulatory approvals. Providers must identify asset owners, whether utility poles shared among electric utilities and legacy telecommunications providers, private easements for rural fiber routes, or the relevant authority over a given right-of-way. They must then negotiate access agreements, incurring legal, administrative, and time costs, and ensure compliance with those agreements.

These transaction costs often rival—or exceed—the cost of the hardware itself. A mile of standard 144-count fiber optic cable costs roughly $5,000 to $8,000 at wholesale, and can reach $30,000 at market rates. Make-ready work for a single pole ranges from $600 to $6,000, while replacing an unsuitable pole can cost $5,000 to $15,000. Permitting and access fees for public rights-of-way typically run $20,000 to $40,000 per mile—and continue to rise. Large projects also require extensive coordination and labor, from installation crews to traffic control, further driving up costs.

Death by Permits: The Real Cost of Getting to ‘Yes’

In a frictionless market, broadband providers would weigh material and labor costs against expected returns and build where the numbers work. In reality, transaction costs complicate that calculus. Permitting stands out as one of the most persistent and burdensome.

Start with access to public rights-of-way—the strips of land along roads and highways where providers must route cables. States and municipalities typically charge for that access, using a mix of one-time application fees, annual lease payments, or a % of gross revenues. The structure and magnitude of those fees vary widely.

Section 253 of the Telecommunications Act prohibits state and local requirements that effectively block telecommunications services. The Federal Communications Commission (FCC) has interpreted that provision to limit fees to amounts roughly tied to the government’s actual costs. In practice, enforcement remains uneven. Some localities continue to impose fees that exceed any plausible cost basis.

Cable providers operate under a slightly different regime. They typically secure access through local franchise agreements, which cap franchise fees at 5%. Localities, however, often sidestep that cap through in-kind contributions or additional taxes. The FCC has taken steps to curb those workarounds, but the tension persists.

Zoning presents another hurdle. Installing above-ground equipment—cabinets, small cells, towers—often requires multiple approvals, public hearings, and aesthetic review. Each layer adds time and expense. Delays can stretch for months or years, tying up capital that could otherwise fund additional deployment. Some states have adopted “shot clock” rules that impose deadlines on local decisions, but those timelines vary, and providers often must litigate to enforce them.

Construction permitting adds yet another layer. Before trenching or boring begins, providers must secure approvals from public works or transportation authorities governing street cuts, lane closures, and restoration. Restoration requirements—how a road must be repaired after construction—drive significant cost variation. Even when standards are clear, the permitting process itself creates delays, increasing carrying costs and complicating workforce scheduling.

Environmental and historic-preservation review introduce a distinct set of constraints. The National Environmental Policy Act (NEPA) requires federal agencies to assess the environmental effects of major federal actions. Broadband projects that receive federal funding typically trigger that review. Wireless deployments often do as well, due to the FCC’s role in spectrum management, though the agency is revisiting that process.

Section 106 of the National Historic Preservation Act (NHPA) imposes similar obligations. Federal agencies must consider the effects of projects on properties listed in, or eligible for, the National Register of Historic Places. A Section 106 consultation can take six months or longer. In areas with dense historic resources, these reviews can become the binding constraint on deployment.

The costs add up quickly. Environmental and historic-preservation review alone is expected to cost mobile wireless providers $2.2 billion, with higher totals likely when wireline projects receiving federal support are included. The FCC and the National Telecommunications and Information Administration (NTIA) have issued rules and guidance to streamline these processes. But without statutory reform, these reviews remain a durable source of delay and expense.

Less Waiting, More Building: A Permitting Reset

Congress has no shortage of proposals aimed at reducing permitting costs. ABDA provides a useful lens because it takes an expansive view of the problem, addressing both wireline and wireless infrastructure.

Start with state and local siting. The bill would amend Section 332 of the Communications Act to prohibit local zoning authorities from discriminating among providers and to impose firm shot clocks on siting decisions. Localities would have 90 days to act on requests for standard wireless facilities attached to existing structures, and 150 days for other facilities. For small cells, the deadlines shrink to 60 and 90 days. The bill would also extend shot clocks to wireline facilities, paired with deemed-granted remedies and cost-based fee standards. The goal is straightforward: reduce delays that tie up capital, increase risk, and discourage expansion.

The bill also revisits Section 6409 of the Middle Class Tax Relief and Job Creation Act of 2012. That provision requires approval of “eligible facilities requests”—projects that do not substantially change the physical dimensions of existing infrastructure, such as adding a small antenna to a macro tower. ABDA would extend that framework to wireline facilities. The logic tracks: if a project leaves existing infrastructure largely unchanged, it should move forward with minimal friction. The FCC is already examining how some localities attempt to evade Section 6409 by arguing that modifications defeat concealment and therefore count as “substantial” changes. The bill would narrow that maneuvering room and reduce the cost of incremental network upgrades.

Title II of the bill turns to cable franchising, aligning it more closely with other technologies. The bill would impose a 120-day shot clock on new franchise applications, with a deemed-granted remedy if authorities fail to act. It would also harmonize permitting rules for placing, constructing, or modifying cable equipment, applying the same 90- and 150-day shot clocks and cost-based fee requirements. This technology-neutral approach would further encourage competition and entry—trends that have already delivered lower prices and more choices for consumers.

Finally, ABDA targets federal environmental and historic-preservation review under NEPA and the NHPA. The bill would exempt a broad category of “covered projects” from review. That category includes collocations and modifications of existing wireless facilities, wireline deployments on eligible support infrastructure, small-cell installations, projects on brownfield sites or within floodplains, disaster-recovery work, and replacement facilities in public rights-of-way that are substantially similar to existing structures. It also covers projects on existing towers or buildings and facilities operating under geographic-area licenses that do not require antenna-structure registration. For easements on federal property, the bill would exempt from both NEPA and NHPA review any easement on property that already hosts a communications or utility facility, as well as easements within public rights-of-way.

Taken together, these reforms aim to do one thing: make it faster, cheaper, and more predictable to build broadband networks.

Faster Builds, Fewer Vetoes: The Local Control Tradeoff

Reforms like those in ABDA would lower deployment costs, add certainty, and encourage further network expansion. But they would also shift authority away from local governments. That tradeoff matters.

Many communities resist broadband projects for familiar reasons: construction disrupts daily life, and new infrastructure can alter local aesthetics. Local governments also incur real costs from these projects. If reforms limit their ability to recover those costs, local taxpayers may end up footing the bill.

ABDA attempts to preserve some local control. It allows jurisdictions to impose objective, reasonable, and nondiscriminatory aesthetic and concealment requirements. Even so, those constraints may not give local officials enough flexibility to address community-specific concerns.

There’s no question that a patchwork of local rules increases costs and can stall deployment. Federal policy aims to expand broadband access nationwide. Still, any effort to streamline permitting must grapple with the tradeoff: faster, more predictable deployment on one hand, and reduced local control on the other.

Build Faster or Stay Stuck: The Choice Ahead

To meet the demands of next-generation applications, the United States will need infrastructure that can handle significantly higher loads. Broadband sits at the center of that challenge. But building and upgrading networks requires sustained private investment—and today’s permitting regime makes that investment harder to justify.

The FCC has taken steps to rein in some of these costs. Still, meaningful reform will likely require congressional action. Local permitting processes, right-of-way access rules, and zoning requirements vary widely across jurisdictions, often with little connection to the actual costs imposed by a project. Delays can stall deployment altogether. Uncertainty adds risk, and risk dampens investment—especially when providers cannot fully capture the broader social benefits of expanded connectivity.

Environmental and historic-preservation review add another layer of delay and expense. These requirements can stretch timelines and push otherwise viable projects into the red. A more uniform and streamlined approach would lower costs, reduce uncertainty, and accelerate the buildout of infrastructure needed to support AI, AR, and VR.

There’s no free lunch. Streamlining permitting will inevitably curtail local authority. That tradeoff is real, and Congress should confront it directly when considering legislation like ABDA. If the goal is more broadband, faster, something has to give.

The post The Last Mile Is a Paper Trail: Why Broadband Gets Stuck appeared first on Truth on the Market.

In my role as chief executive of the Competition and Consumer Commission of Singapore (CCS), I oversee the administration and enforcement of Singapore’s competition, consumer protection, and legal metrology laws to ensure Singapore’s markets remain competitive and consumers are protected from unfair trading practices. This is my second stint with CCS. During my first stint, from June 2010 to August 2012, I served as director of legal and enforcement.

I have been serving in the Singapore Public Service for over two decades. I started my career as a justices’ law clerk to the chief justice and judges of the Supreme Court.

I previously served in the Attorney-General’s Chambers as a deputy senior state counsel and a deputy public prosecutor, where I specialized in white-collar and major crimes. I was also a judicial officer in the State Courts, where I presided over Penal Code offenses, civil disputes, and tribunal matters.

My career has taken me across various facets of government legal work, ranging from intellectual property, tax law, labor relations, and procurement.

Previously, I was a tribunal member with the Ministry of Defence’s Compensation Board, the Work Injury Compensation Board, and president of the General Court Martial. Currently, I sit on the Ministry of Law’s Copyright Tribunal and am also a Lien Fellow with Nanyang Technological University’s Nanyang Centre for Public Administration.

Singapore competition law is both highly relevant and relatively unfamiliar to many readers. Can you give a high-level overview of recent antitrust developments?

CCS has completed over 50 cases in Fiscal Year 2024, encompassing investigations, merger assessments, and government advisories, demonstrating our active enforcement approach across diverse sectors.

The momentum has continued strongly into 2025. We issued positive guidance in our inaugural case under the streamlined process for collaborations pursuing environmental sustainability objectives in January 2025. Our enforcement activities have included infringement decisions against contractors for bid-rigging in public-sector tenders in May 2025, and against Chinese yuan remittance service providers for anticompetitive information exchanges in July 2025. We also have several investigations that are currently underway.

In the aviation sector, we maintained a balanced and rigorous approach in our assessments. We completed our review of several joint-venture and commercial-cooperation proposals, including conditional approvals for the proposed expanded joint venture between Singapore Airlines and Deutsche Lufthansa AG in January 2025, and the proposed commercial cooperation between Singapore Airlines and Malaysia Airlines after accepting commitments in July 2025. In 2024, we released Qantas and Emirates from capacity commitments on the Singapore-Brisbane and vice versa routes.

We have continued to review and refine our competition framework to streamline processes and increase efficiency, maintaining Singapore’s reputation as a business-conducive jurisdiction while ensuring robust competition enforcement. In October 2025, we launched consultations on proposed changes to two key competition guidelines: the streamlining of the merger regime and a new settlement procedure. The proposed changes to the merger procedure guidelines are designed to ensure our merger regime continues to operate optimally, with merger notifications processed in a timely manner without compromising the robustness of our assessment. That has since been finalized and is slated to take effect May 1.

Under the proposed new settlement procedure, we intend to introduce a streamlined settlement procedure that will increase the maximum settlement-discount quantum to recognize the greater efficiencies reaped when parties successfully conclude an investigation via settlement, facilitate the ease with which parties can initiate settlement, and provide greater clarity on our position should a party that has entered into a settlement agreement subsequently appeal against our decision. The public consultation for that set of settlement guidelines is currently being evaluated.

Significant milestones in the past year included CCS’s 20th anniversary celebrations, the unveiling of our new logo, and hosting the 11th Association of Southeast Asian Nations (ASEAN) Competition Conference and CCS Conference in September 2025. The anniversary marked two decades of competition enforcement in Singapore, while the conferences provided invaluable platforms for competition practitioners across the region to discuss the latest policy developments, share best practices, and explore how competition enforcement can build a more resilient and inclusive regional economy. It also allowed us to examine emerging trends in competition and consumer protection compliance across Singapore and ASEAN.

These activities underscore Singapore’s role as a leading competition-policy hub in Asia, balancing robust enforcement with clear guidance to support business compliance and economic growth.

In 2020, CCS conducted a market study on e-commerce platforms. What did it find, and how has it shaped your approach to digital markets?

In 2020, we undertook a market study on e-commerce platforms to gain an in-depth understanding of the business models and operating environment of e-commerce platforms, which compete across multiple market segments offering distinct products and services.

Our market study in 2020 did not find any major competition concerns relating to e-commerce platforms in Singapore. We found that several e-commerce platforms compete aggressively for customers. While customers do display a certain degree of platform loyalty, a significant number practice multi-homing, in that they use more than one platform simultaneously to buy or sell. We also found that while data collected by e-commerce platforms benefits platform operators by allowing them to improve the quality of service offered to customers, the lack of data is not currently regarded by new entrants as an insurmountable barrier to entry.

We identified key areas in which further clarity and guidance by CCS could be beneficial to assist businesses in the application of the Competition Act in the digital platform economy. For example, in the context of merger assessment, to ensure that CCS’s merger regime remains well placed to address instances of “killer acquisitions,” we amended our guidelines on merger assessment to clarify our approach toward assessing mergers involving markets where innovation is an important feature of competition, and where one or more of the merger parties is an important innovator, even if they do not have a large market share.

We also updated our guidelines relating to how markets are defined for multi-sided platforms, so that businesses can better understand how CCS assesses markets involving multi-sided platforms or digital-platform companies. For instance, we have included specific features of multi-sided platforms (e.g., number of markets to be defined, indirect network effects, price structure) that may be taken into consideration when assessing whether there is a “product ecosystem” comprising multiple products and/or services sold by the same seller.

CCS flagged several AI-related theories of harm in its 2024 OECD report. Have any materialized?

We remain vigilant in monitoring developments in the AI sector. To this end, we commenced a market study into AI last year. The market study aims to provide an analysis of the current AI landscape in Singapore, identify emerging competition and consumer concerns, and recommend appropriate regulatory responses where necessary.

As part of this study, we have been actively engaging with stakeholders across the AI ecosystem to gather insights. This includes discussions with other government agencies, AI developers (both local and multinational developers), businesses utilizing AI technologies, and other relevant parties. We anticipate publishing our findings and recommendations later this year.

Beyond competition matters, we have also observed consumer protection issues related to AI materializing, for example, in the Lambency Detailing case, which involved AI-generated fake reviews.

In addition, in 2026, ASEAN member states will embark on an ASEAN market study into AI, with Singapore as the lead member state for this study. That process is currently underway.

Singapore’s AI Markets Toolkit allows developers to self-assess compliance. How does it work in practice?

I must say that the initiative has been well received. We actively address emerging competition and consumer protection issues in AI. We launched the AI Markets Toolkit in late September 2025, in collaboration with Singapore’s Infocomm Media Development Authority (IMDA).

The AIM Toolkit is a voluntary self-assessment tool that helps businesses evaluate their AI models and business practices for competition and consumer protection compliance through a series of process checks and technical tests. This toolkit runs entirely on the business’s local system, and neither CCS nor any third parties will have access to any of the data, model, or information uploaded. Upon completing the self-assessment, a report, comprising a summary of results and recommendations, will be automatically generated for the business. CCS will not be able to see the results of the toolkit.

Implementing the AIM Toolkit would minimize the risk of a business inadvertently engaging in anticompetitive or unfair trading practices. The AIM Toolkit is part of CCS’s modernized compliance program and is unique globally because it signals that we are here to work with industry, and not just act as an enforcer while companies flounder amid new technology. Usage of the toolkit can be considered a mitigating factor for the purposes of financial-penalty calculation in the event of an infringement. We hope that use of this tool can help businesses identify possible infringements early and take appropriate and timely remedial actions. 

Are there any notable recent or upcoming regulations in digital platforms, AI, or data?

The government has introduced regulations to address specific harms in the digital space. For instance, the Online Criminal Harms Act 2023 deals with criminal content and activities on digital platforms, while the more recent Online Safety Bill, introduced in November 2025, allows victims of online harm (such as harassment and doxing) to seek recourse, with an Online Safety Commission to be established to administer this act.

Singapore is also drawing up regulations for data centers and cloud-service providers under the Digital Infrastructure Act, which will, inter alia, require these providers to put in place cybersecurity and service-disruption measures to ensure network resiliency in Singapore. This legislation will be tabled in Parliament later this year.

These developments are of keen interest to CCS because issues in the digital space are often multidisciplinary and cut across different domains, including areas involving competition and consumer protection. This requires a whole-of-government approach to address these challenges. As an active participant in this space, CCS will need to review its toolkit and framework continuously to ensure they are fit for purpose and complement tools used by other government agencies. This includes being robust in enforcement approaches, including the use of nonregulatory tools.

For instance, CCS has recently updated Singapore’s national standards for e-commerce transactions, with best practices that e-marketplaces and e-retailers can adopt to enhance consumer protection and trust, as well as to foster an open and competitive e-commerce sector. These best practices were introduced in consultation with stakeholders in the e-commerce industry (e.g., online marketplaces, merchants, consumer representatives, government agencies, and industry experts) to achieve a consensus-based outcome, such that businesses can resolve issues without requiring stronger intervention. This reflects our approach of co-creating solutions with industry rather than relying solely on regulation. 

How does Singapore view the European Union’s Digital Markets Act? Are similar measures under consideration?

Over the years, CCS has observed that different jurisdictions have moved toward prescriptive ex ante regulation of digital platforms to supplement existing competition and consumer law frameworks, with the European Union’s Digital Markets Act (DMA) as a prime example. We note that several other jurisdictions, including Australia, the United Kingdom, Japan, South Korea, and certain ASEAN member states, are also developing their own regulatory responses to digital-market challenges, though their approaches vary considerably.

Although the jury on the overall efficacy of the DMA is still out, the issues it seeks to address are common across competition agencies. Smaller agencies such as CCS need to find ways to address these issues, recognizing that there is no one-size-fits-all approach. This is especially important for a small jurisdiction like Singapore, where such regulations can have a significant impact on the digital economy.

For this reason, CCS has explored innovative ways to achieve balanced solutions that work for Singapore. For instance, CCS has pursued voluntary industry initiatives to raise consumer protection and competition standards in the e-commerce sector by introducing guidance for businesses. This allows us to co-create solutions with stakeholders and resolve issues without regulatory intervention.

Concurrently, we are undertaking a systematic review of global regulatory frameworks to assess their relevance to Singapore. While we do not rule out regulatory approaches, any measures would need to be introduced in a calibrated manner, taking into account market conditions, the state of competition, and the impact on businesses. 

What role could industrial policy play in antitrust enforcement in Singapore?

I see industrial policy and competition policy as potentially complementary rather than at odds. Competition policy can inform industrial policy to bring about sustainable, long-term economic growth.

Conventional competition policy suggests that businesses should compete and grow on their own merit, with minimal government intervention, so that they can scale and compete effectively regionally and internationally. Naturally innovative and productive businesses will compete better in global markets. Markets, however, do not operate in perfect competition, and certain factors may hamper growth.

Market studies conducted by CCS can shed light on these factors and provide the government with insights into specific market dynamics. Armed with such insights, targeted industrial policies—such as grants, subsidies, and tax incentives—may be implemented to preserve a level playing field and encourage entry.

CCS also advises the Singapore government and other public agencies on competition-related aspects of policymaking. For example, CCS previously worked with another agency to improve capacity-sharing arrangements for container haulage to ensure the market remains competitive while encouraging resource optimization.

Through such efforts, a balance can be struck between industrial policy and competition policy.

Is there anything else you would like to add?

Looking ahead, CCS will continue to strengthen its regulatory capabilities through the strategic adoption of emerging technologies and AI tools.

CCS will continue to evolve its regulatory framework to meet the needs of Singapore in an increasingly complex global environment.

On the international front, CCS will continue strengthening its leadership through active participation in key forums. I was recently appointed vice chair of the International Competition Network (ICN) and look forward to advancing competition policy worldwide.

As vice chair of the ICN, I am committed to ensuring it remains relevant and valuable to competition agencies across all stages of development, sizes, and regions. My focus is on enhancing platforms that bridge existing gaps and enable agencies to participate meaningfully in tailored knowledge-sharing initiatives. The goal is to foster an inclusive environment where agencies not only learn from established authorities but also contribute their own perspectives and solutions to shared challenges.

The post The View from Singapore: A TOTM Q&A with Alvin Koh appeared first on Truth on the Market.

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.

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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.