In Brussels, Article 6(7) of the Digital Markets Act (DMA) has begun to suffer a similar fate. The DMA is the European Union’s flagship law for regulating large digital platforms, which it calls “gatekeepers.” Article 6(7) is supposed to require those gatekeepers to make certain hardware and software features interoperable—that is, usable by rival services—while still allowing them to protect security, privacy, and system integrity.

Increasingly, though, the provision gets invoked with great solemnity in every new specification proceeding the European Commission opens, while its actual content keeps shifting to mean whatever the Commission needs in a given case. The trajectory of enforcement—from Apple’s iOS connected-devices proceedings last year to the current Google Android artificial-intelligence (AI) proceedings—suggests the Commission increasingly treats Article 6(7) less like a legal text with an internal structure and more like a slogan: interoperability now, for everyone, on whatever terms the Commission prefers. 

That is unfortunate, because Article 6(7) is not a blank check. It has two distinct halves. The provision requires gatekeepers to provide “effective interoperability with, and access for the purposes of interoperability to, the same hardware and software features” enjoyed by their own services. But it also expressly permits integrity measures that are “strictly necessary and proportionate.” Recital 50 of the DMA confirms that integrity-preserving measures are a legitimate part of implementing Article 6(7), not a loophole to be sheepishly apologized for after the fact.

Properly read, the provision establishes a balancing test, not a maximalist openness mandate. “Effective” does not mean “identical,” and “identical” does not mean “unlimited.”

The Commission’s enforcement to date has steadily read the second half of the provision out of existence. The result is weaker security, less inter-platform competition, and a regulatory tool that increasingly puts a thumb on the scale in the rapidly unfolding generative-AI race, often to the detriment of European consumers.

The Curious Disappearance of ‘Proportionate’

The first real-world stress test for Article 6(7) arrived in late 2024 and early 2025, when the European Commission opened specification proceedings against Apple. Those proceedings concerned iOS interoperability with connected devices and the process through which third-party developers can request access to iOS features. 

The features at issue were relatively well-defined: near-field communication (NFC) pairing, Bluetooth, Wi-Fi accessory configuration, AirDrop, AirPlay, notification forwarding, and related functions. In ordinary English, this was about how well non-Apple devices—headphones, watches, fitness trackers, speakers, and the like—could work with the iPhone.

What made the case interesting—as the International Center for Law & Economics (ICLE) pointed out in its January 2025 comments—was that these features were already meaningfully open. Third-party headphones, fitness trackers, and smartwatches already worked on the iPhone. Pairing was generally reliable. Battery information was accessible. First-party apps from Sony, Bose, JBL, and Fitbit already extended the user experience further. 

What Apple’s own devices received on top of that was mostly a slightly smoother pairing process and a handful of additional display and design features. Those sorts of refinements are precisely the kind of product differentiation Apple plausibly needs to compete against the broader Android ecosystem in the first place.

Forcing parity at the level of system access—in pursuit of parity at the level of the pairing animation—was a textbook example of a measure that is not “necessary and proportionate.” The marginal gains for contestability, or the ability of rivals to compete, were trivial. The marginal risks were not.

The July 2024 CrowdStrike incident, which knocked airlines, hospitals, banks, and other critical systems offline for hours, illustrated the problem vividly. Investigations traced part of the issue to a 2009 European Union agreement requiring Microsoft to grant third-party security software the same kernel-level access as Microsoft’s own tools. The kernel is the core layer of an operating system—the part with near-total control over the device. Giving outside software kernel access is not like letting someone borrow a charging cable. It is more like handing over the master key to the building and hoping they do not trip over the wiring. 

Apple stopped giving third parties that level of access to macOS in 2020. Apple devices were not affected by the CrowdStrike outage.

The Commission’s March 2025 specification decisions nonetheless went well beyond what was necessary to achieve effective interoperability. The Commission’s June 2025 noncompliance decision against Meta then revealed why. In that decision, the Commission explicitly disclaimed any obligation to weigh the economic consequences of its enforcement choices on either the gatekeeper or third parties. 

That posture has now bled into the Commission’s interpretation of the integrity exception in Article 6(7). If the Commission need not consider tradeoffs, then “strictly necessary and proportionate” collapses into little more than “necessary in the Commission’s view.”

From Pairing Animations to Ambient Surveillance

If the iOS case was the rehearsal, the Android AI case is the main event. On April 27, the European Commission adopted preliminary findings outlining the measures it proposes Alphabet should implement under Article 6(7) for AI-facing features in Google Android. 

The measures listed in the annex would require Google to provide third parties access, on terms equally effective to those enjoyed by Google’s own services, to: :

• continuous background access to a device’s core ambient sensors, including the microphone, camera, screen, speakers, accelerometer, and GPS;
• centralized, concurrent access to data shared by other apps through on-device databases like AppSearch, including data shared by Google’s own first-party apps;
• custom always-on wake-word detection—the “Hey Google” or “Alexa”-style listening function—running on the device’s digital signal processor, including while the device is in battery-saver mode;
• the ability to take agentic control of other apps through screen automation, including observing screen content, imitating user inputs, and executing multi-step transactions in a background virtual window;
• system-privileged access to AICore, Gemini Nano, and the underlying neural-processing-unit (NPU), graphics-processing-unit (GPU), and random-access-memory (RAM) resources currently reserved for Google’s own on-device AI models; and
• expanded background-execution privileges equivalent to those enjoyed by Google’s own apps.

Some translation is useful here. “Ambient sensors” are the parts of a phone that can see, hear, locate, and measure the world around it. A “wake word” is the phrase that activates a voice assistant. “Agentic control” means software that does not merely answer a question but can take actions for the user, such as opening apps, reading screens, clicking buttons, and completing transactions. NPUs and GPUs are specialized chips that help run AI models efficiently on the device, rather than sending everything to the cloud.

Put differently: this is not just about whether a third-party smartwatch gets the pretty pairing animation. It is about whether third-party AI assistants should receive deep, persistent access to the phone’s sensors, app data, computing resources, and user interface.

That is a qualitatively different proposition. As Mikolaj Barczentewicz put it in his April 2026 post, this is “opening Pandora’s interface.” The features at issue involve sensors that run continuously, span the device’s entire app-data layer, and grant programmatic control over other applications. In the wrong hands, those capabilities enable mass surveillance, credential theft, and unauthorized transactions at scale.

Article 6(7) expressly recognizes those risks. The provision permits integrity measures that are “strictly necessary and proportionate.” But the Commission’s annex operationalizes that exception in a way that, as a practical matter, largely closes it off.

Section 5.3 requires that any integrity measure rest on “objective and verifiable evidence showing the existence and magnitude of the integrity risk,” apply symmetrically to Google’s own services, and remain capable of independent verification by parties other than Alphabet itself.

Each of those criteria sounds reasonable in isolation. Taken together, they make the most natural response to genuinely novel risks—declining to expose a sensitive feature until the threat landscape becomes better understood—effectively unavailable. By definition, evidence of harm cannot be “objective and verifiable” ex ante. Conservative assumptions about attacker behavior, which underpin modern operating-system security architecture, are not “objective evidence” under this framework.

A standard that treats the absence of demonstrated past harm as evidence that a restriction is unjustified would not have produced many of the security practices the European Union now takes for granted.

The symmetry requirement is equally perverse. Google typically distinguishes between its own first-party services and user-installed third-party apps across multiple dimensions of trust, including code-signing provenance, internal review processes, contractual liability with device manufacturers, and the ability to revoke access quickly when problems emerge. Code-signing, in simple terms, is a way to verify where software came from and whether it has been tampered with. These distinctions are not decorative. They are part of how modern platforms keep devices from becoming very expensive malware terrariums.

The annex insists those underlying trust distinctions are irrelevant: any restriction applied to a third-party app must also apply to Google’s own services.

That leaves Google with a binary choice. Either it extends sensitive capabilities to every third party on equal terms, or it strips those capabilities from its own services entirely. The first option may be unsafe. The second leaves European users with a worse product. Neither option is what users actually want.

Contestability Cuts Both Ways

There is another problem with the Android AI case, and it flips the usual DMA narrative on its head.

In the AI-assistant market itself, Google is the challenger, not the incumbent. Recent StatCounter data puts ChatGPT at roughly 70% of European Union AI-chatbot usage. Anthropic’s Claude has reportedly been adopted by eight of the Fortune 10 and was operating at a $14 billion annualized revenue run rate as of February 2026. Google’s Gemini, despite the company’s enormous investment in its integrated AI stack, arguably trails both.

Google’s competitive strategy depends precisely on that integrated stack: chips (Tensor and TPU), cloud infrastructure (Google Cloud), foundation models (Gemini), platform integration (Search, Maps, Calendar, Gmail, YouTube, and Photos), and distribution channels (Android, Chrome, and Play). Foundation models are the large AI systems trained on vast amounts of data that power tools like chatbots, coding assistants, and image generators. Google’s bet is that it can combine those models with the services people already use and the devices they already carry.

Deep system-level integration on Android—wake-word reservation, on-device database access, preferential NPU, GPU, and RAM allocation, along with structured App Functions—is one of the few ways Google can translate that stack into a differentiated user experience capable of competing with OpenAI’s and Anthropic’s dedicated assistants. App Functions are structured ways for apps to expose actions—say, booking a ride, sending a message, or editing a photo—so an assistant can perform those tasks reliably, rather than simply guessing where to tap.

To some observers, Google leveraging its ecosystem to compete more effectively in AI may sound like precisely the scenario the DMA was designed to prevent. But in a world where Google is playing catch-up to OpenAI and Anthropic, that integration may be the difference between having two major AI competitors and three.

That is difficult to square with the DMA’s contestability rationale. Proponents often invoke contestability as a tool for disciplining entrenched big-tech incumbents. But contestability cuts both ways. If promoting competition is genuinely the goal of the DMA, it should matter just as much when enforcement reduces rivalry as when it increases it.

The Android Article 6(7) proceedings are not unique in this respect. The same structural pattern is emerging in the Meta/WhatsApp proceedings, where enforcement nominally framed as protecting AI competition on a dominant European consumer platform has the practical effect of requiring the platform owner to grant equivalent system access to the very firms that already lead the AI-assistant market. 

It is also worth questioning the empirical premise underlying the Android case: namely, that Android currently functions as an “important gateway” for AI services. The Commission’s preliminary findings take that proposition largely for granted, but the available evidence looks shakier. AI services today are still consumed disproportionately on desktop devices, not smartphones.

In October 2025, Gemini reportedly recorded roughly 813 million monthly desktop sessions, compared to 369 million mobile sessions—a desktop-to-mobile ratio of more than two-to-one. Usage patterns across other AI services appear similarly lopsided. At present, most users access AI services primarily through desktop browsers. Mobile may become more important over time. It does not yet appear to be the indispensable distribution channel these proceedings assume. 

The broader lesson is uncomfortable, but important. Mandating openness to increase rivalry within Android can simultaneously weaken rivalry between AI firms. The DMA’s twin goals—contestability and fairness—can pull in opposite directions. Right now, the Commission appears to be privileging the wrong side of that tradeoff.

What Six-Seven Actually Means

Article 6(7) has the potential to become a powerful tool for promoting contestability in digital markets. But realizing that potential requires the European Commission to take seriously both halves of the provision: openness and integrity. It also requires the Commission to evaluate enforcement decisions based on their effects in the markets that ultimately matter to European consumers.

Concretely, that means doing three things the Commission has largely skipped so far.

First, reconsider the gateway premise. Article 3 of the DMA conditions gatekeeper obligations on a service functioning as an “important gateway for business users to reach end users.” In plainer terms, the DMA’s special obligations are supposed to attach to services that businesses really need in order to reach customers. If a particular class of business users—in this case, AI-service providers—primarily reaches users through other channels, the case for mandating risky system-level mobile access becomes weaker, not stronger. The Commission’s limited enforcement resources could likely be deployed more effectively elsewhere.

Second, account for cross-market effects. When the platform owner is the trailing competitor in the upstream market a remedy will reshape, the “fairness” gains from mandated equal access may be outweighed by reduced contestability among the firms the platform is trying to catch. That is not an argument for abandoning Article 6(7). It is an argument for applying the provision cautiously, with the recognition that competition policy often affects multiple markets simultaneously.

Third, take integrity seriously. “Strictly necessary and proportionate” cannot mean regulators demand proof of harm before the harm occurs. No modern security architecture operates that way. Many of the security practices the European Union now treats as commonplace were built precisely on precautionary assumptions about how systems might fail or be exploited. Time-limited restrictions on the most sensitive feature classes should remain available to gatekeepers without requiring an ex ante demonstration that the threat has already materialized.

The kids are wrong. “Six seven” does mean something. The Commission simply has to read the whole provision—not just the half it likes.

The post The European Commission’s ‘Six-Seven’ Theory of Interoperability appeared first on Truth on the Market.

When European settlers arrived in North America, they brought royal charters granting them ownership of vast tracts of land. Almost immediately, they encountered numerous Indian tribes that could credibly claim control over those same lands. Faced with these conflicting claims, both parties had to decide whether to press their claims or abandon them. If they pressed, they then had to decide whether to exchange the land peacefully or fight it out.

Contrary to the popular narrative, Indian-white relations were not violent from first contact. History is complex, but the general pattern is that Indian-white relations began fairly peacefully and worsened over time. At first, Indian tribes often appeared inclined to drop ownership claims in the face of settler intrusion. When Indians did press a claim, the typical result was peaceful negotiation and exchange. Anderson and McChesney show that, from the Founding era to about 1830, treaties between Indians and whites were frequent, while battles were few and far between. As the American frontier moved farther west, the dominant mode of settling disputes shifted to warfare.

Why did Indian-white relations move from the peaceful interactions symbolized by the first Thanksgiving to the Massacre at Wounded Knee? Or, as Anderson and McChesney put it: “If both sides prefer settlements to violence, what caused the increasing resort to warfare between Indians and whites?” More broadly, under what conditions might Europeans have settled North America more peacefully?

From Courtroom to Continent

Like early land disputes between Indians and whites, most modern legal disputes settle long before they reach court. William Landes, in his 1971 article, “An Economic Analysis of the Courts,” developed a formal model to explain why. In Landes’ model, the decision to settle or litigate turns on the stakes of the dispute, the relative costs of settlement and litigation, and each party’s estimate of its odds of winning. Landes, Richard Posner, and others used the model to explore how settlement rates might be affected by the bail system, court delay, prejudgment interest, pretrial discovery, and rules governing who pays trial costs.

Anderson and McChesney’s key insight is that the decision to trade or raid mirrors the modern decision to settle or go to trial. The Landes model, in other words, is a general model of dispute resolution. All disputes—not just modern legal ones—are resolved according to the relative costs and benefits of each available method.

Where Peace Has a Price

When European settlers occupied Indian territory, the tribe had to decide whether to assert its claim to the land and its resources. An Indian tribe would assert its claim if the marginal benefit of the land—the value of one additional unit—exceeded the marginal cost of asserting that claim. That might mean making a credible commitment to retake the land by force, if necessary.

On first contact with European settlers, Indian-held land was abundant and the marginal value of land to Indians was relatively low. We should therefore expect Indians to allow whites to settle fairly unimpeded. Indians would assert a claim only when the marginal benefit of the land exceeded the marginal cost of asserting and enforcing it.

The logic worked symmetrically. Whites would expand their territorial control only so long as the marginal benefit of acquiring an additional strip of land exceeded the marginal cost of claiming and controlling it. Between those two points lies what Anderson and McChesney call the “zone of controversy.” That is where all disputes between Indians and whites occur.

Within the zone of controversy, Indians and whites had to decide how to resolve individual disputes. A peaceful settlement required both parties to walk away with a surplus they would not have gained from fighting. Indians would choose peaceful negotiation if the costs of fighting, plus the value of reclaimed land, exceeded the costs of negotiation. Whites would go to the negotiating table if the costs of negotiation were lower than the costs of fighting, plus the losses they expected from fighting.

Thus, all else equal, as the cost of negotiation rises, so does the likelihood of violence. And as the cost of violence falls, violence becomes more likely.

Uncertainty about the outcome of conflict adds another wrinkle. If one party is substantially more optimistic than the other about its chances of victory, fighting becomes more likely—even when the expected costs of fighting exceed the costs of negotiation.

This point is about information asymmetry, not merely imperfect information. If faulty information about military capacity causes whites and Indians to hold incorrect but identical expectations about their chances in battle, peace can still prevail if fighting costs more than negotiation.

When the Frontier Moved, So Did the Math

As white settlers moved farther west, the costs of negotiation rose, the costs of fighting fell, and information asymmetries widened. All of this meant more warfare.

Unlike the sedentary agricultural tribes in the east, Indian tribes west of the Mississippi were nomadic, like the buffalo they hunted. On the vast commons of the Great Plains, Indian tribes did not have neatly defined territorial ownership claims. The ownership rights they did have came from capture and possession.

The nomadic western tribes also lacked the more centralized political structures common among eastern tribes. Western tribal leaders did not fully control their members, and individuals routinely ignored treaties signed by their chiefs. Negotiation is difficult when both the party across the table and the object of negotiation are unclear. The western Indians’ nomadic lifestyle also made it harder to communicate with the tribe and “size up” its strength.

Over time, the party we have simply been calling “whites” was no longer an individual settlement community or local militia, but the growing federal government and the U.S. Army. Treaties signed by politicians in Washington were difficult to enforce locally when a citizen or rogue government employee violated their terms. The existence of a standing army—staffed by officers and bureaucrats whose careers benefited from fighting—also lowered the cost of violence.

It should not be too surprising, then, that whites shifted toward taking what they wanted by force.

Better Lawyers Than Molotovs

Stepping back, Anderson and McChesney implicitly teach us a valuable lesson about the importance of well-functioning legal institutions. Like it or not, violence is part of the human condition. If I find myself in a legal dispute with my neighbor, I must decide whether to negotiate and settle, go to trial and litigate, or throw a Molotov cocktail through his window.

Litigation might sometimes feel like combat. But compared with the alternatives, it is another way to resolve disputes peacefully. A capable legal system—one that upholds the rule of law and delivers fairly predictable judgments—lowers the costs of negotiation and raises the costs of fighting. That, in turn, reduces violence.

Further Reading

• Terry L. Anderson & Fred S. McChesney, “Raid or Trade? An Economic Model of Indian-White Relations,” The Journal of Law and Economics, Vol. 37, No. 1 (April 1994)
  
• William Landes, “An Economic Analysis of the Courts,” The Journal of Law and Economics, Vol. 14, No. 1 (April 1971)
• Robert D. Cooter & Daniel L. Rubinfeld, “Economic Analysis of Legal Disputes and Their Resolution,” Journal of Economic Literature, Vol. 27, No. 3 (September 1989)
• John Umbeck, “Might Makes Rights: A Theory of the Formation and Initial Distribution of Property Rights,” Economic Inquiry, Vol. 19, No. 1 (January 1981)
  
• Richard Posner, “An Economic Approach to Legal Procedure and Judicial Administration,”  The Journal of Legal Studies, Vol. 2, No. 2 (June 1973)
  
• George L. Priest & Benjamin Klein, “The Selection of Disputes for Litigation,” The Journal of Legal Studies, Vol. 13, No. 1 (January 1984)

The post ‘Raid or Trade? An Economic Model of Indian-White Relations’ by Terry L. Anderson & Fred S. McChesney appeared first on Truth on the Market.

Plenty of major AI figures seem to agree. Elon Musk says AI will “replace all jobs.” Anthropic CEO Dario Amodei regularly warns about mass job loss, framing AI as “a general labor substitute.” OpenAI investors talk openly about AI replacing “80% of all jobs by 2030.” These are influential people, not random bloggers. Still, they are not necessarily a representative sample of the world’s most careful economists.

And the fear itself is hardly new. Economist Wassily Leontief—best known for developing input-output analysis, a way of mapping how industries depend on one another—raised similar concerns in the early 1980s. If AI really were a perfect substitute for human labor, the logic would be straightforward. Any cost advantage would eventually drive firms toward 100% AI labor. You do not need a long essay to prove that result.

The problem is that the phrase “AI will eventually be a perfect substitute” does almost all the analytical work. That assumption hides a great deal: differences across tasks, industries, and workers; the many margins along which firms adjust; and the messy heterogeneity that makes the real economy more than a toy model.

How substitutable is AI today? What would need to happen for that substitutability to rise meaningfully? What other conditions would also need to hold? Even the historical analogy—“tractors could do what horses did, therefore horses became obsolete”—compresses several distinct steps into one neat sentence. “AI can do what humans do, therefore humans become obsolete” hides even more.

So let’s unpack those steps.

(This post draws on a new working paper that walks through the math and economics in detail. Really, though, it is mostly basic accounting.)

Before We All Become Horses

For those unfamiliar with the history of horses in the United States, the horse population actually rose for decades alongside industrialization. It increased from 4.3 million in 1840 to 27.3 million in 1920. The collapse came later, as tractors and motor vehicles displaced horses in agriculture and transportation. The number of farm horses and mules then fell to roughly 3 million by 1960.

Horses, in effect, had one main economic role, and that role disappeared. Humans are different. So before jumping from “AI can do tasks” to “humans become obsolete,” we should define carefully what that outcome would actually mean.

To keep things simple, suppose demand for human labor falls to zero. Not “low.” Zero. What would that require?

It would mean that no dollar spent anywhere in the economy passes through human labor at any point in the supply chain. Not the person who made the product. Not the person who shipped it. Not the person who designed it, marketed it, maintained it, or cleaned the building where it was assembled. Zero human labor embodied in final expenditure. That is the benchmark. That is what “humans become horses” would mean, stated precisely.

This is the input-output framework the aforementioned Wassily Leontief built his career on. The idea is straightforward: trace any final purchase backward through its supply chain and add up all the labor that contributed to it, both directly and indirectly. A cup of coffee includes the labor of the barista, but also the roaster, the truck driver, the coffee farmer, and the workers who built the truck. “Embodied labor” means all of it.

For labor demand truly to collapse, every one of those links would need to disappear across every good and service consumers buy. That is a much stronger claim than “AI can do some jobs.” The economy is not a single production function. It is a sprawling network of activities. When AI makes one activity cheaper, consumers do not simply buy more of the same thing forever. They redirect spending elsewhere.

Every dollar lands somewhere. Some spending flows into highly labor-intensive activities, such as restaurants, therapy, or home repair. Other spending flows into activities that require very little labor, such as cloud storage, automated checkout systems, or streaming subscriptions. So the relevant question is not merely: “Can AI do my job?” It is: “When AI makes some things cheaper, where does the saved money go next?”

Aggregate labor demand depends on at least three things: total spending in the economy, the share of spending that goes toward labor-intensive activities, and the amount of labor embodied in each activity. For labor demand to fall to zero, AI cannot merely displace workers in a few sectors. Every dollar of spending, wherever it ultimately lands, must shed all embodied human labor. The “humans become horses” story therefore requires three separate margins to collapse simultaneously.

A useful starting point is the simple observation that firms do not want labor per se. A restaurant does not want waiters because it enjoys employing waiters. It wants orders taken, customers reassured, mistakes fixed, and meals delivered. Labor demand is therefore “derived demand”—firms demand workers because workers help produce something else consumers value.

When AI can perform those underlying tasks more cheaply, two things happen at once. First, firms substitute AI for workers, reducing labor demand per unit of output. Second, lower production costs reduce prices, output expands, and that expansion tends to pull labor demand back upward. Whether total labor demand rises or falls depends on which force dominates.

Economists call this the Hicks-Marshall decomposition of derived demand into substitution effects and scale effects. The terminology sounds forbidding, but the intuition is simple: cheaper production reduces the need for workers in one sense, while expanding the market for output in another. That tension will organize the rest of the discussion.

When a dollar gets saved, where does it go? Into new tasks? New jobs? New industries? The money has to end up somewhere.

Your Job Is Not a Checklist

The case that AI can automate many tasks is not speculative anymore. This is obviously true to some extent, and it has been true for years.

Even early large language models (LLMs) showed substantial potential to affect workplace tasks. One widely cited paper by Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock estimated that roughly 80% of the U.S. workforce could see at least 10% of their job tasks affected by LLMs. When paired with complementary software tools, 86% of occupations crossed that 10% exposure threshold.

Since then, the empirical literature has grown rapidly, and the task-level evidence is hard to dismiss. In a large customer-support study, access to generative AI increased the number of issues resolved per hour by roughly 15%. In an experiment involving professional writing tasks, ChatGPT reduced average completion time by 40% while increasing measured output quality by 18%. In a controlled GitHub Copilot study, software developers completed coding tasks 55.8% faster. Those are not rounding errors.

But they are effects on tasks, not necessarily on jobs. That distinction matters. When a task gets automated, the saved dollar does not disappear into the void. Firms and workers often redirect it toward new activities within the same occupation: more client management, more review and verification, more coordination, more judgment calls, more customization.

Just as there is no fixed amount of demand in the economy, there is no fixed bundle of tasks that permanently defines a job. Jobs evolve. They absorb new responsibilities, shed old ones, and reorganize around whatever remains scarce and valuable.

The O-Ring Problem

There is a familiar ritual in AI discourse. Someone posts a demo. The demo performs a task associated with a particular job. People immediately conclude that the job is doomed.

Sometimes they are right. But that inference skips about 15 intermediate steps.

What does it actually cost to deploy the system once error rates are included? Do customers trust it? Can firms reorganize workflows around it? Does management even know how to integrate it effectively? A chatbot demo can appear overnight. A hospital cannot reorganize clinical liability around AI overnight.

That distinction matters because firms are not simply collections of isolated tasks. They are organizations. In many cases, the result will not be pure replacement, but rather a human-AI team producing output together. Economists call this complementarity: two inputs become more valuable when used jointly than separately.

But complementarity is not free. A human-AI pair that produces only marginally more value than the AI alone will not justify paying a full human wage. The human worker must contribute something the AI cannot reproduce cheaply or reliably.

That matters especially in high-stakes settings where errors are extraordinarily costly. Surgery, aviation, structural engineering, fiduciary advice, and many legal services all fall into this category. In these fields, the cost of failure can easily dwarf the savings from cheaper production.

That could eventually change. It probably will change in some areas over time. But it is not likely to change quickly.

This is essentially the “O-ring” logic from economics, named after the tiny rubber seal whose failure destroyed the Space Shuttle Challenger. When the value of the entire system collapses because one component fails, buyers do not focus primarily on sticker price. They focus on the expected cost of a system that actually works.

In those environments, human-supervised production can remain economically efficient even if AI itself becomes extremely cheap.

Horses Had Nowhere Else to Go

Suppose substitution effects really do dominate within most jobs. The saved dollar then escapes the workplace entirely. Where does it go next?

Most standard economic models collapse the economy into a single “final good,” which makes that question disappear by assumption. Real economies do not work that way. They contain many sectors, and every dollar eventually lands somewhere.

Start with software, which serves as a useful microcosm. Software-intensive industries have already undergone decades of automation through digital tools. If automation were going to drive human labor out of a sector entirely, this is where you would expect to see it first. The chart below groups industries according to how much software they purchase relative to value added: low, medium, and high software intensity. The result is striking.

The most software-intensive industries do not merely retain human labor. They actually devote a larger share of income to labor compensation—about 67%—than the least software-intensive industries, which devote roughly 55%. In other words, the industries that automated the most heavily also remained highly labor-intensive.

The same pattern appears in employment projections. The Bureau of Labor Statistics (BLS) projects total U.S. employment to increase by 5.2 million jobs between 2024 and 2034. Employment for software developers—a profession directly exposed to AI tools—is projected to grow 17.9%. BLS could ultimately prove wrong. Forecasting always carries uncertainty. Still, the evidence so far points strongly toward scale effects dominating in software-intensive industries. Automation reduced costs, output expanded, and labor demand remained robust.

Software may be an extreme case, but versions of this pattern appear across the broader economy and over much longer periods. Take the shift from goods to services. In 1929, most consumer spending went toward physical goods. Today, roughly two-thirds of consumer spending flows toward services. As manufacturing became dramatically more efficient, consumers did not respond by purchasing infinite refrigerators and toasters. Instead, spending shifted toward health care, education, restaurants, entertainment, travel, and personal services.

That is the “saved dollar” in action at the economy-wide level. Goods became cheaper. The substitution effect largely won within goods-producing industries. Employment growth in manufacturing did not continue indefinitely. But the freed-up purchasing power migrated elsewhere, and the scale effect emerged across sectors instead.

From a macroeconomic perspective, output expanded overall. Consumers simply redirected spending toward new categories of consumption. But migration alone does not help workers unless the destination sectors still contain substantial human labor. Did they?

Again, the answer appears to be yes.

Services consistently devote a larger share of value added to employee compensation than goods-producing industries do. Spending did not merely migrate. It migrated toward sectors where more of each dollar ends up in someone’s paycheck.

So yes, one could argue that this still resembles the horse story in one respect. The relative importance of goods production declined as productivity increased. The point, though, is that large, diverse economies contain adjustment margins that horses never had. There are escape valves.

Comparative advantage keeps reappearing. When automation makes some activities extremely cheap, spending tends to shift toward the activities that remain relatively expensive. And the activities that remain expensive are often the ones that are hardest to automate. Those are precisely the areas where humans continue to hold a comparative advantage—that is, where human labor remains relatively more productive or valuable than machine substitutes. The saved dollar therefore tends to drift toward areas where humans are still worth paying.

That is not technological optimism. It is simply the logic of comparative advantage.

James Bessen documents this dynamic sector by sector. In early textile manufacturing, power looms sharply reduced labor required per yard of cloth. But cloth became so much cheaper that demand exploded, and total textile employment increased for decades. Similar patterns appeared in steel and automobile production. Eventually, demand saturated. Prices stopped falling rapidly enough to offset labor-saving automation, and employment in those sectors declined.

The key question for AI, then, is not whether automation can destroy jobs. Of course it can. The real question is: Which sectors are in which phase? Where might AI-generated savings flow today?

Health care already accounts for roughly 18% of U.S. GDP, and that share continues to rise. Elder care will likely expand further as populations age. Personalized services, human-intensive care work, and new categories of consumption may absorb growing shares of spending.

Joel Mokyr, Chris Vickers, and Nicolas Ziebarth make this historical argument well in a Journal of Economic Perspectives article. Across prior waves of technological change, new tasks emerged, comparative advantage persisted, and entirely new categories of work appeared that earlier generations could not have anticipated.

Horses had no equivalent adjustment path. They did not move into elder care.

Will Humans Become a Luxury Good?

The saved dollar migrated toward human-intensive sectors last time. The strongest argument for why this time could be different comes from economist Philip Trammell’s paper, “Is Labor a Luxury in the Long Run?”

His answer is: probably not. Even if richer consumers initially spend more on human-intensive goods and services—live music, handmade products, personal care, bespoke experiences—four long-run forces may steadily erode that demand.

1. AI-generated variety keeps expanding. New AI-produced goods compete for every dollar that might otherwise land on a human-made product or service.
2. Human experiences carry opportunity costs. Time spent at a live concert is time not spent consuming some potentially superior AI-generated alternative.
3. Labor competes with other scarce goods for consumers’ willingness-to-pay premiums. Beachfront property, status goods, intellectual property, and research-intensive products may all absorb spending that might otherwise flow toward human labor.
4. Capital goods become cheaper over time. If investment opportunities continue expanding, the share of economic activity devoted to capital accumulation could grow indefinitely.

Trammell’s Coca-Cola analogy captures the intuition cleanly. Original Coke once held roughly 50% of the soda market. Then came Diet Coke, Cherry Coke, Pepsi Max, energy drinks, flavored sparkling water, and endless other varieties. Even with enormous brand loyalty and supply constraints, Coke’s market share fell below 20%.

The implication for AI is straightforward. Even if consumers initially prefer human-made goods, that preference may weaken as AI continuously generates new substitutes and varieties. Human labor does not need to become worthless. Its share can erode through dilution.

That is a serious argument, and I take it seriously. Still, notice what the argument requires. It is not enough for AI-generated variety merely to expand. That will almost certainly happen. The stronger claim is that AI-generated substitutes must expand broadly and rapidly enough to pull spending away from every human-intensive category simultaneously.

The real question is not whether AI competes with some human-produced goods. Of course it will. The question is whether any human-intensive islands survive. Does anyone still spend money on something with a person inside it?

The arithmetic quickly becomes more demanding than many “humans become horses” narratives imply. Suppose AI eventually captures 85% of economic activity. Software, accounting, logistics, medicine, law, management, and much of media production become almost fully automated. Human labor largely disappears from those sectors.

Now suppose the remaining 15% of spending flows toward activities that still contain at least 30% human labor: elder care, live entertainment, skilled trades, therapy, surgery, in-person education, luxury craftsmanship, status goods, and other relational or trust-intensive services.

The aggregate labor share would still equal at least:

S ? 0.15 × 0.30 = 0.045

That leaves labor with at least a 4.5% share of economic output. That may not sound comforting, but remember what this calculation is doing. It is merely establishing a lower bound under extremely aggressive automation assumptions. It is not utopia. It is not full employment. But it is also not zero. And a falling labor share does not necessarily imply falling labor demand if total output grows rapidly enough.

Alex Imas offers another reason to doubt the “humans disappear” story. As AI drives down the cost of commodities, real incomes rise. Historically, richer consumers tend to shift spending toward what Imas calls “relational goods”—goods and services whose value depends partly on human connection, scarcity, or social meaning.

That idea connects to a large economics literature on structural change. Over time, economies tend to shift from agriculture to manufacturing to services as incomes rise. The key debate is why. Do consumers simply buy more of whatever becomes cheaper? Or do rising incomes fundamentally change what people want?

Diego Comin, Danial Lashkari, and Marti Mestieri decompose those effects and conclude that income effects account for more than 75% of the long-run shift toward services. That distinction matters enormously here. If structural change were driven mainly by falling prices, then AI-generated abundance might pull spending overwhelmingly toward AI-produced goods. But if structural change is driven mainly by rising incomes and evolving preferences, then richer consumers may continue demanding more human-intensive experiences and services. Historically, that is exactly what has happened.

Experimental evidence points the same way. In one set of experiments, subjects learned that other people would be excluded from purchasing an otherwise identical product. Willingness to pay roughly doubled. The exclusivity itself created value.

Importantly, the exclusivity premium was stronger for human-made goods than AI-generated ones. Human-created artwork gained roughly 44% in value from exclusivity, compared with about 21% for AI-generated artwork. AI-made goods feel infinitely replicable. Human-made goods feel scarce, even when they technically are not. People value what other people cannot easily obtain. That impulse does not disappear as societies grow wealthier. If anything, it intensifies.

Perhaps AI-generated variety eventually overwhelms even those preferences. Maybe. Still, the structural-change evidence consistently suggests that income effects dominate price effects by roughly three to one. When basic goods become cheaper, humans do not announce that they are finally satisfied and stop developing new wants. They invent new forms of distinction, identity, taste, and status competition. The open question is where those new desires land. So far, the evidence points toward humans retaining an important role.

One final clarification matters here, because popular AI discussions often conflate two distinct claims. A falling labor share is not the same thing as falling labor demand. Labor’s share of national income can decline even while total employment and total wages continue rising, provided the overall economy grows fast enough. In that world, AI appears to “take over” a larger share of production while human workers still earn more in absolute terms because the economic pie itself expands dramatically.

That may well describe the phase we are currently entering. We already observe the basic pattern. Higher-income households consume more services, and service sectors remain relatively labor-intensive. Could that eventually reverse? Of course. But at the moment, this is the evidence we actually have.

The Horse Story Ends Here

Walking through all these layers—from tasks, where we are only beginning to see meaningful substitution, up through firms, sectors, and the macroeconomy—leaves me fairly skeptical of the “humans become horses” outcome. I know I have concealed that conclusion masterfully until now.

AI will absolutely perform many tasks. It will reorganize jobs, sometimes painfully. Some sectors may lose most of their human labor. Spending will often chase automation and lower prices. All of that can happen without driving human labor demand to zero. Because at every stage of the process, there is still a saved dollar looking for somewhere to land. And the same question keeps reappearing: Where does it go next?

For the horse outcome to occur, that saved dollar must eventually fail to find any activity with meaningful human labor embodied in it. Not some activities. All activities.

That is a very specific future. It is logically possible. But it requires substitution to dominate simultaneously across tasks, firms, sectors, and final consumption patterns, with no surviving human-intensive islands anywhere in the economy. The evidence we currently have—structural change, revealed preferences, comparative advantage, and experimental results—keeps pointing the other way.

Horses lost because the economy stopped needing horsepower. Humans are not just horsepower.

The post Why Humans Are (Probably) Not Headed for the Glue Factory appeared first on Truth on the Market.

Sharkey treats that mismatch as a design problem, not a terminological one. Her proposal separates two functions long bundled together under the “punitive” label: (i) punishment, which she calls “anti-social penalties,” and (ii) compensation for social harms that ordinary compensatory damages do not reach, which she terms “societal damages.”

The point is not to deny punitive damages’ deterrent role. Rather, Sharkey argues that once we concede punitive awards often respond to harms beyond those suffered by the named plaintiff, courts should stop laundering that broader social objective through a plaintiff windfall.

Methodologically, the paper is both doctrinal and institutional. It reads constitutional punitive-damages doctrine as a constraint and then asks what remedial architectures—split recovery, compensation funds, or aggregation substitutes—might better align punitive damages with their asserted social function.

Everybody’s Harm, One Plaintiff’s Check

The paper’s organizing claim is that legal actors already behave as if punitive damages incorporate a social-harm component. Sharkey opens with State Farm v. Campbell, where plaintiffs’ counsel urged jurors to punish a nationwide course of conduct, and the Utah Supreme Court defended a large punitive award by invoking harms to other insureds and to “society in general.” The system plainly reaches for a justification broader than the individual plaintiff.

Yet the system then does something inconsistent with that logic: it pays the money to the plaintiff. Sharkey’s move is to argue that courts and legislatures should make explicit what the doctrine already assumes implicitly—that part of the award serves a compensatory function for harms extending beyond the named plaintiff.

Once that premise becomes explicit, the next question becomes unavoidable: where should the “societal” portion of the award go?

Deterrence Is Only Half the Design Problem

The standard law & economics defense of punitive damages is the under-detection multiplier: when wrongdoing is difficult to detect or prove, compensatory damages alone will underdeter, so the remedy should be scaled up to bring expected liability closer to expected harm. Sharkey engages this view sympathetically, but presses on what it abstracts away: distribution.

The law & economics approach starts from the under-detection and under-deterrence problem and treats remedy design—especially who receives the money—as an input into deterrence, not merely a distributional afterthought. If the “extra” dollars exist to internalize social costs, paying them to a randomly positioned plaintiff may seem like a crude but tolerable shortcut.

Sharkey argues that shortcut carries consequences economic models often treat as secondary, but institutional design cannot. Recipient choice affects legitimacy, settlement leverage, litigation incentives, and the relationship between private enforcement and public objectives. Her societal-damages framing makes those consequences central, rather than incidental.

Iowa: Keep the Plaintiff, Split the Windfall

Iowa’s split-recovery approach requires a special finding about whether the defendant’s conduct was “directed specifically” at the claimant. If it was not, then—after specified costs and fees—no more than 25% of punitive damages may go to the claimant, with the remainder paid into a civil reparations trust fund. (Iowa Code § 668A.1).

The rule matters because it operationalizes the moral and economic intuition Sharkey wants doctrine to acknowledge. When the wrong is not specifically targeted—when it instead appears part of a broader pattern affecting others—the argument that the plaintiff should capture the entire punitive award becomes less persuasive. Iowa makes that intuition concrete through a binary interrogatory and a simple routing rule.

The predictable objection is incentive-based: reducing the plaintiff’s upside may weaken private enforcement. Sharkey’s response is not that incentives are irrelevant, but that they are variables to be engineered. The institutional challenge is to balance deterrence, administrability, and legitimacy.

The Ohio Supreme Court Creates a Public Fund

Sharkey also emphasizes that some courts have improvised “societal” routing even without statutory authorization. In Dardinger v. Anthem, the Ohio Supreme Court stated: “The plaintiff remains a party, but the de facto party is our society …” (Dardinger, 98 Ohio St. 3d 77 (2002)). The court then tried to implement that logic by remitting the punitive award and directing a substantial portion—after attorneys’ fees—to a court-created cancer-research fund.

One can object to the specifics. Courts are not natural fund administrators, and ad hoc earmarks invite arbitrariness. Even so, the episode is instructive for Sharkey’s purposes: once courts explicitly frame the wrong as social, they become pulled toward social distribution. The doctrinal vocabulary is already doing the work; the remedial architecture simply lags behind.

Catastrophic AI Risk Is Not Built for One-Plaintiff Remedies

Advanced artificial-intelligence risk—especially catastrophic misuse or large-scale misalignment, where an AI system pursues goals in ways its designers did not intend—creates a familiar enforcement problem for tort law. The harms that matter most may be practically noncompensable because they exceed feasible insurance limits, overwhelm defendant solvency, or occur in states of the world where adjudication is unavailable.

If expected social harm is driven by low-probability but catastrophic “tail” outcomes, insurance markets often fail to internalize those risks reliably. Diversification breaks down when risks are highly correlated; model uncertainty makes pricing difficult; and coverage is capped, excluded, or implicitly dependent on public backstops.

In that setting, two failures compound. First, relying on private insurance to price externalities becomes structurally unreliable. Second, relying on a plaintiff-windfall model to finance deterrence and redress becomes unstable. It routes public-harm money through sporadic, high-variance litigation outcomes while creating no durable institutional capacity for monitoring, remediation, or compensation in diffuse-harm cases.

Sharkey’s split also sharpens how punitive damages should be used—if at all—in frontier AI cases. If part of an award is meant to function as a deterrent sanction, it cannot be structured in a way that predictably allows the defendant to neutralize it through indemnification or contractual pass-through. A “punitive” price signal that can be shifted is not, in any robust sense, punitive.

By contrast, the portion of an award better understood as “societal damages” should be treated as a routing problem. If the justification is harm to nonparties or diffuse social costs, the remedy should not be delivered as a plaintiff windfall. Instead, it should go to institutions capable of converting those funds into public goods that reduce under-detection and manage diffuse harms, such as risk measurement, independent auditing capacity, and incident-response infrastructure.

The point is not that such institutions solve AI-governance problems. It is that once punitive damages are justified in social-cost terms, allocation becomes part of the mechanism, rather than an afterthought.

Conclusion

Sharkey’s contribution is to treat remedies as governance instruments with institutional form. Punitive damages are often defended as tools for internalizing externalities when compensatory damages underdeter. The societal-damages reframing asks a further question: if the function is partly social, why should the form remain purely private?

For correlated, partially uninsurable AI risk, that question is not academic. If courts continue to describe society as the relevant beneficiary of punitive awards, Sharkey’s framework forces a follow-on demand: build a remedial architecture that reflects that claim, and then defend it—doctrinally, constitutionally, and institutionally—on its own terms.

Further Reading

• Catherine M. Sharkey, “Punitive Damages as Societal Damages,” Yale Law Journal, Vol. 113 (November 2003).
• Catherine M. Sharkey, “The Future of Classwide Punitive Damages,” University of Michigan Journal of Law Reform, Vol. 46 (2013).
• A. Mitchell Polinsky & Steven Shavell, “Punitive Damages: An Economic Analysis,” Harvard Law Review, Vol. 111, No. 4 (February 1998).
• Gabriel Weil, “Closing the AI Accountability Gap: Strict Liability and Punitive Damages for Advanced Artificial Intelligence,” Oregon Law Review (forthcoming 2027)
• Gabriel Weil, “Overcoming Judgement-Proofness: The Law & Economics of Insuring and Mitigating AI Risk” (working paper, February 2026)

The post ‘Punitive Damages as Societal Damages,’ by Catherine M. Sharkey appeared first on Truth on the Market.

But the harder constitutional question is whether courts can do all that through broad, after-the-fact liability standards without telling platforms what the law actually requires. The legal world is currently watching a high-stakes collision between two foundational principles: the government’s power to protect children from allegedly “addictive” technology, and the constitutional requirement that laws give regulated parties fair notice of what conduct is illegal. As the Supreme Court  has explained, “we insist that laws give the person of ordinary intelligence a reasonable opportunity to know what is prohibited, so that he may act accordingly.”

That tension is now coming to a head. Appeals are likely in both New Mexico v. Meta and K.G.M. v. Meta, even as federal courts continue issuing rulings in the NetChoice litigation challenging state social-media laws, including in Arkansas. Together, these cases are exposing a basic fault line in modern tech regulation: Can courts impose liability on social-media platforms under broad “negligence” or “unfair trade practices” theories, or are those standards simply too vague to satisfy constitutional due-process requirements? 

The answer appears to turn largely on how courts characterize core platform features, such as algorithmic recommendations, infinite scroll, disappearing messages, autoplay, and push notifications. Put simply, are these features protected First Amendment activity, or merely product design?

That distinction matters enormously. If courts treat these features as protected speech or editorial discretion, then laws targeting them face constitutional scrutiny. Courts must ask whether the law restricts more speech than necessary, and whether the law—or its application—is too vague for regulated parties to know when they are crossing the line.

If, by contrast, courts treat these features as mere conduct, then the First Amendment largely drops out of the analysis. Courts instead presume that businesses have adequate notice of how broad negligence or consumer-protection laws apply to platform design choices.

Scrolling Into Tort Law

Before the jury trials in New Mexico and Los Angeles, courts largely treated social-media features as conduct, rather than speech, and allowed what amounted to product-liability theories to proceed. In denying Meta’s motion for summary judgment, the California court framed the issue this way:

[T]he allegedly addictive features of Defendants’ platforms (such as endless scroll) cannot be analogized to how a publisher chooses to make a compilation of information, but rather are based on harm allegedly caused by design features that affect how Plaintiffs interact with the platforms regardless of the nature of the third-party content viewed.

That framing matters because it shifts the case away from the First Amendment and into ordinary tort law. Under California negligence law—as in most jurisdictions—a plaintiff must show that the defendant: 1) owed a duty of care, 2) breached that duty, 3) proximately caused harm, and 4) caused legally cognizable damages. In ordinary commercial relationships, businesses generally must act reasonably under the circumstances to avoid foreseeable harm.

The theory against Meta therefore sounds familiar, at least at a high level. A car manufacturer may be negligent if it designs defective brakes. Likewise, plaintiffs argue, Meta may be negligent if it designs platform features that foreseeably contribute to harms like compulsive use, self-harm, or other mental-health injuries among minors.

From this perspective, the common-law “reasonableness” standard is not impermissibly vague because American courts apply it every day. Businesses are expected to behave reasonably, even when the standard itself cannot be reduced to a precise checklist.

The same basic logic applies to unfair-trade-practices claims. To prove “unfairness” under laws like New Mexico’s, the state typically must show that a defendant’s conduct: 1) caused substantial consumer injury, 2) inflicted harms consumers could not reasonably avoid, and 3) produced harms not outweighed by countervailing benefits to consumers or competition.

Again, the analogy is straightforward. If a company places an unreasonably dangerous product into the marketplace, that may qualify as an unfair trade practice. Plaintiffs argue that Meta’s allegedly addictive design features similarly create unavoidable and substantial harms for users.

Courts traditionally give regulators and plaintiffs considerable leeway when these standards apply to conduct rather than speech. The argument, in other words, is that negligence and unfairness doctrines provide sufficient notice to businesses, even if they operate through flexible, case-by-case standards.

That distinction also shapes vagueness analysis. Courts generally apply less demanding scrutiny to civil laws regulating economic conduct than to laws burdening speech. Even courts that have pushed back on the Federal Trade Commission’s (FTC) use of Section 5 unfairness authority have still recognized that principle. In FTC v. Wyndham, for example, a federal court explained that fair-notice standards are “especially lax for civil statutes that regulate economic activities.” Under that framework, a law fails for vagueness only if it is “so vague as to be no rule or standard at all.” 

The Constitution Hates Vibes-Based Liability

The analysis changes once courts conclude that speech—or at least editorial discretion—is involved. When laws burden First Amendment-protected activity, courts demand far more clarity about what conduct is prohibited.

As the Supreme Court explained in Vill. of Hoffman Ests. v. Flipside, Hoffman Ests., Inc.: 

[P]erhaps the most important factor affecting the clarity that the Constitution demands of a law is whether it threatens to inhibit the exercise of constitutionally protected rights. If, for example, the law interferes with the right of free speech or of association, a more stringent vagueness test should apply.

That heightened scrutiny reflects a familiar concern in First Amendment law: vague rules chill speech. Faced with uncertain liability, speakers and publishers often self-censor rather than risk litigation.

Two recent federal decisions striking down Arkansas social-media laws at the request of NetChoice turned heavily on that principle. In both cases, the U.S. District Court for the Western District of Arkansas concluded that heightened vagueness scrutiny applied because the laws targeted platform features intertwined with speech and editorial judgment. 

That makes sense. Social-media platforms necessarily organize, rank, filter, and recommend enormous amounts of user-generated content. The moment a law tells a platform how it may present speech, First Amendment questions arise.

The Arkansas court captured the problem vividly:

In a world where billions of pieces of content are posted on social media every day, social media would be functionally useless as a “vast democratic forum[ ]” if platforms were not allowed to use any algorithm—any system—for selecting and ordering content to display to users… If a social media platform was a library, banning algorithms would be roughly equivalent to requiring books be placed on shelves at random. Such a prohibition would burden users’ (or library patrons’) First Amendment rights by making it significantly more difficult to access speech a user wishes to receive, so a state probably could not constitutionally ban algorithms for the organization of speech (on social media or elsewhere) altogether…

[The law] does not prohibit all algorithms…[but it] forces covered services to restrict as to all users—minor or adult—anything that could have a forbidden effect (here, addiction) on any users—again, minor or adult.

That concern drove the court’s treatment of Arkansas Act 901, which imposed a negligence-style duty on platforms that use a “design algorithm, or feature” the platform:

[K]nows, or should know through the exercise of reasonable care, causes a user to: (1) Purchase a controlled substance; (2) Develop an eating disorder; (3) Commit or attempt to commit suicide; or (4) Develop or sustain an addiction to the social media platform.

The court concluded the law was likely “unconstitutionally vague” because it “fails to specify a standard of conduct to which platforms can conform.” Liability, the court warned, depended on “the sensitivities of some unspecified user” and on what a judge or jury later decided the platform “should have known” about those sensitivities.

In other words, ordinary negligence standards may work tolerably well for defective brakes or slippery floors. They become far murkier when applied to speech-ranking systems used by billions of people with wildly different psychological responses and preferences.

The court found Arkansas Act 900 even more problematic. Unlike Act 901’s negligence framework, Act 900 effectively imposed strict liability. It required platforms to “ensure” they:

[Do] not engage in practices to evoke any addiction or compulsive behaviors in an Arkansas user who is a minor, including without limitation through notifications, recommended content, artificial sense of accomplishment, or engagement with online bots that appear human.

The court concluded that “liability under Act 900 is even more uncertain than under Act 901.” The law extended beyond addiction to the platform itself, and imposed liability even where a company “could not have known through the exercise of reasonable care” that a feature would affect a particular child in a particular way.

That, ultimately, was the constitutional problem. The court found the statute likely void for vagueness because “[b]usinesses of ordinary intelligence cannot reliably determine what compliance requires.”

The Feed Is the Speech

The Supreme Court has already made clear that curating and presenting speech is itself protected First Amendment activity. Courts should follow the speech-based approach adopted by the federal court in Arkansas, not the conduct-based approach used by the state courts in New Mexico and Los Angeles.

In NetChoice v. Moody, the Court explained that “expressive activity includes presenting a curated compilation of speech originally created by others.” That principle applies directly to products like Instagram Feed. “[D]eciding on the third-party speech that will be included in or excluded from a compilation—and then organizing and presenting the included items—is expressive activity of its own.” The platform’s expressive product stems from Meta’s “choices about whether—and, if so, how—to convey posts.”

That last point matters. The First Amendment protects not only a platform’s decisions about what speech to display, but also how to present it.

As the International Center for Law & Economics (ICLE) argued in its amicus brief before the Massachusetts Supreme Judicial Court in litigation challenging Meta under the Commonwealth’s unfairness authority: 

[I]t is clear the First Amendment protects the right of newspapers to choose not only the content it will print, but “the decisions made as to limitations on the size” of the paper. Miami Herald Publishing Co. v. Tornillo, 418 U.S. 241, 258 (1974). Just as a government couldn’t tell a newspaper to expand its size, it couldn’t tell them to use smaller font, or to reduce its margins. In other words, how a newspaper presents its content is as much part of its “editorial control and judgment” as the content itself.

It is much the same here. The Commonwealth alleges that notifications, alerts, infinite scroll, autoplay, and ephemeral content are mere conduct rather than protected editorial choices. But this simply can’t be the case.

That does not mean social-media platforms are immune from regulation. It does mean that lawsuits targeting how platforms organize and present speech—whether styled as negligence claims or consumer-protection “unfairness” actions—must satisfy the more demanding vagueness standards courts apply when speech is at stake.

Anything less would create enormous pressure for platforms to over-censor. If avoiding liability becomes the overriding imperative, companies will inevitably restrict more user speech, suppress more borderline content, and redesign their services to be blander and less engaging in order to avoid allegations of “addictive” features.

That may make platforms less interesting. It would also make online speech less free.

Protecting minors online is a legitimate and important goal. So is giving parents better tools to supervise their children’s online activity. But education, parental controls, and user-empowerment tools are far more likely to survive constitutional scrutiny—and probably more likely to work—than vague liability regimes that effectively ask courts and juries to decide when an app has become “too engaging.”

The Constitution does not require the internet to be pleasant. It does require the government to speak clearly before punishing how platforms present speech.

The post Addicted to Vagueness: Lawmakers Can’t Regulate Social Media by Vibes appeared first on Truth on the Market.

That question has triggered the sort of debate that is easy to politicize and much harder to analyze carefully.

Within hours of Spirit ceasing operations, critics of the Biden administration’s antitrust policy cast the episode as a simple morality play: regulators blocked JetBlue’s acquisition of Spirit in the name of competition, Spirit then failed, therefore antitrust killed Spirit.

Defenders of the merger challenge answered with an equally tidy narrative. Spirit, they argued, was already in deep trouble—hamstrung by Pratt & Whitney engine groundings, mounting debt, soft demand, and rising fuel costs. In the words of Sen. Elizabeth Warren (D-Mass.): “Spiking fuel prices from Trump’s war was the nail in the coffin for twice-bankrupted Spirit airline.” On that account, antitrust policy had little to do with the carrier’s demise. 

Both stories contain some truth. Neither is fully satisfying.

It would be too easy—and almost certainly wrong—to say the U.S. Justice Department (DOJ) killed Spirit. The airline’s collapse was overdetermined. A Pratt & Whitney engine-inspection crisis grounded large portions of its fleet. The Big Four legacy carriers steadily cannibalized Spirit’s niche with their own basic-economy offerings. Fuel prices surged in the wake of the Iran war. Management’s attempt to reposition Spirit as a more premium carrier also failed to gain traction.

Still, it would be equally wrong to treat the merger challenge as irrelevant. The DOJ and the court should have taken more seriously the possibility that Spirit’s independence was, at best, precarious. That points to the more important question: whether merger doctrine, as currently applied, can adequately handle cases where the target firm is visibly distressed and where the realistic alternative to acquisition is not continued independent competition, but gradual attrition, restructuring, or outright exit from the market.

Scale or Die Trying

Critics of the Spirit-JetBlue transaction often frame it as JetBlue opportunistically swooping in on a vulnerable target. That framing misses what the deal actually represented. Both airlines came to the table because each had independently concluded that scale was becoming a survival imperative in an industry dominated by four major carriers.

For Spirit, the problem was deteriorating standalone economics. For JetBlue, the problem was different but related: its East Coast-focused, point-to-point network lacked the breadth needed to compete with the Big Four carriers for the high-value corporate and connecting traffic that drives airline profitability. 

Spirit itself had already acknowledged this reality before JetBlue entered the picture. In February 2022, Frontier and Spirit announced a stock-and-cash merger they described as creating “America’s most competitive ultra-low fare airline.” The companies promised roughly $1 billion in annual consumer savings and pitched the deal as a way to create a credible fifth-carrier rival to American, Delta, United, and Southwest.

That point matters. Spirit’s board was not defending independence as the optimal strategy. To the contrary, management was already telling the market that remaining a standalone carrier in the post-pandemic airline industry was an increasingly risky bet.

JetBlue entered the bidding in April 2022 with an unsolicited all-cash offer at a substantial premium, which it repeatedly sweetened over the following months. JetBlue argued that combining its product with Spirit’s fleet and lower-cost structure would create a “national low-fare challenger” capable of exerting competitive pressure on the Big Four, especially at a time when organic growth had stalled and aircraft were difficult to obtain on the open market. 

Spirit’s board initially rejected JetBlue’s offer—not because it preferred independence, but because it believed the transaction faced “substantial regulatory hurdles,” particularly while JetBlue’s Northeast Alliance with American Airlines remained in place. After Spirit terminated its merger agreement with Frontier on July 27, 2022, it signed the JetBlue deal the very next day.

That sequence matters, both legally and rhetorically. Spirit’s directors understood the antitrust risk, weighed it against JetBlue’s higher offer, and concluded the risk-adjusted value to shareholders favored JetBlue. They were also making that judgment against a backdrop of increasingly weak fundamentals.

The Pratt & Whitney geared-turbofan inspection crisis—which would eventually ground roughly one-quarter of Spirit’s neo fleet, on average, through 2024 and continue disrupting operations through 2026—was already emerging. Ultra-low-cost-carrier, or ULCC, margins were under pressure. Consolidation was the strategy management chose, and by mid-2022, JetBlue was the only realistic consolidation partner left standing. 

The mirror image of Spirit’s predicament is also worth emphasizing, because it highlights how poorly the conventional framing fits the underlying economics of the airline industry.

JetBlue’s interest in Spirit was not simple opportunism. Like Spirit’s interest in Frontier, it reflected a structural disadvantage. For much of the past decade, JetBlue has searched for ways to add the network depth that legacy carriers use to attract and retain lucrative corporate and connecting passengers.

Its 2020 Northeast Alliance with American was one attempt. Its bid for Spirit was another. Its May 2025 “Blue Sky” partnership with United Airlines—which combined reciprocal loyalty benefits, interline arrangements, and John F. Kennedy International Airport slot coordination, and took effect in October 2025—was a third. 

Different legal mechanisms, same commercial problem. JetBlue has long operated as a point-to-point carrier with a strong consumer brand, but a network too thin to compete effectively for the traffic that actually pays the bills. The Biden administration’s DOJ challenged the first two arrangements, and succeeded in blocking the second. The third moved forward under a Trump administration U.S. Department of Transportation (DOT) review without enforcement objection.

JetBlue’s repeated return to the same problem—using whatever legal instrument happens to remain available—suggests the underlying scale disadvantage is real. Blocking any single transaction does not make that structural problem disappear.

The Law Said Spirit Was Fine Until It Wasn’t

The DOJ filed suit on March 7, 2023, joined by Massachusetts, New York, and the District of Columbia. Four additional states joined later that month. The complaint portrayed Spirit as the nation’s largest and fastest-growing ULCC and credited it with generating a “Spirit Effect”—the tendency of Spirit’s entry into a market to drive down fares across competing airlines. 

The government argued the merger would eliminate roughly half of all ULCC capacity in the United States. It also emphasized that JetBlue planned to retrofit Spirit’s densely configured aircraft into JetBlue’s lower-density seating layout, reducing the number of available seats and, in the DOJ’s view, raising prices for the most price-sensitive travelers.

The DOT publicly endorsed the lawsuit the same day. That was notable in its own right. For decades, DOT had largely avoided intervening at the Hart-Scott-Rodino merger-review stage. Its support for the case reflected how fully President Joe Biden’s Executive Order 14036 and its “whole-of-government” competition agenda had reshaped federal antitrust enforcement. 

Politically, the center of gravity inside the Biden administration had already shifted firmly against the deal. That shift accelerated after the DOJ’s May 2023 victory against the JetBlue-American Northeast Alliance, which the agency appears to have viewed as evidence that JetBlue was no longer a disruptive outsider, but an emerging consolidator in its own right. Sen. Amy Klobuchar (D-Minn.) applauded the Spirit lawsuit. Sen. Warren had already urged DOT, in September 2022, to use its full authority to oppose the transaction. 

Judge William G. Young, a Ronald Reagan appointee sitting in the U.S. District Court for the District of Massachusetts, blocked the merger on Jan. 16, 2024, in a 109-page  opinion. The opinion is more nuanced than some critics admit. Judge Young expressly acknowledged that a combined JetBlue-Spirit carrier “would likely place stronger competitive pressure on the larger airlines”—precisely the out-of-market competitive benefit JetBlue had emphasized throughout the case. 

But the court ultimately concluded that those broader competitive benefits could not offset the loss of Spirit’s particular business model for highly price-sensitive travelers:

Although the Defendant Airlines provide ample evidence at the rebuttal stage that the anticompetitive harms of the proposed acquisition will be offset, both by new entries into the harmed markets and potential pro-competitive benefits, this evidence fails to establish that the proposed merger would not substantially lessen competition in at least some of the relevant markets.

Judge Young also addressed Spirit’s deteriorating financial condition directly and rejected the failing-firm defense:

Although Spirit is struggling, its executives testified that the airline had a long-term plan to return to profitability.

And:

JetBlue is also far from the only available purchaser, should Spirit find itself in dire need.

That last move is the legal hinge of the case, and it deserves closer attention. The defendants did not aggressively pursue a failing-firm defense at trial. Instead, they framed the transaction as affirmatively procompetitive. The court, meanwhile, analyzed Spirit’s financial distress within the narrow doctrinal framework established by Citizen Publishing Co. v. United States and its progeny. 

Within those doctrinal boundaries, Judge Young arguably reached a defensible conclusion based on the record before him. The harder question is whether those boundaries are themselves too cramped for analyzing mergers involving visibly distressed firms operating in industries already under severe structural pressure.

The Counterfactual Crashed Too

The chronology after Judge Young’s ruling is brutal.

JetBlue and Spirit terminated the merger on March 4, 2024. Spirit collected a $69 million termination fee and publicly recommitted to a standalone strategy. In May 2024, the airline eliminated change and cancellation fees. Two months later, it unveiled its “Go Big” and “Go Comfy” fare bundles—an attempt to move away from the bare-bones ULCC model and toward something resembling JetBlue Lite. 

In June 2024, Spirit CEO Ted Christie told shareholders at the company’s annual meeting that Spirit was not considering Chapter 11 bankruptcy. By Nov. 18, 2024, Spirit had filed a prearranged Chapter 11 petition anyway. 

The airline emerged from bankruptcy on March 12, 2025, after converting roughly $795 million in funded debt into equity and securing a $350 million equity infusion, and having rejected a renewed merger proposal from Frontier Airlines the prior month. By August 2025, Spirit had filed for bankruptcy again. On May 2, 2026, it ceased operations entirely after a last-ditch effort to secure Trump administration support reportedly collapsed because bondholders—including Citadel and Ares, according to published accounts—refused to approve the plan. 

The important point, viewed retrospectively, is not that Spirit’s May 2026 liquidation was specifically foreseeable. It was not. The more important point is that Spirit’s fragility—and the real possibility that an independent Spirit would shrink, restructure, or fail outright—became part of the public conversation almost immediately after Judge Young’s 2024 ruling.

Bloomberg Law captured the issue with striking economy on Jan. 17, 2024, less than 24 hours after Judge Young’s opinion, in a piece headlined: “Keeping Spirit Cheap for Flyers Threatens to Kill It Altogether.”

That same day, Brett Snyder’s Cranky Flier—arguably the most consistently informed publication in the airline trade press—warned that the ruling “isn’t great news for Spirit” and walked readers through plausible restructuring scenarios. Snyder also reproduced a same-day TD Cowen note from analyst Helane Becker stating that “a more likely scenario is a Chapter 11 filing, followed by a liquidation.” 

In other words, within 24 hours of the ruling, informed industry observers were already openly modeling not just bankruptcy, but liquidation.

The financial record confirms this was not merely hindsight bias. Spirit’s 2023 Form 10-K reported a net loss of $447.5 million and disclosed that Pratt & Whitney engine groundings were expected to sideline an average of roughly 25 aircraft throughout 2024, with continuing effects through 2026. By the time Spirit’s 2024 10-K disclosed a $1.23 billion net loss and included explicit going-concern warnings, the airline was already in Chapter 11.

But the trajectory had been visible much earlier—in Securities and Exchange Commission (SEC) filings, in trade reporting, and, if one reads the trial record carefully, in Spirit’s own pre-deal board materials (here and here).

None of this means the merger should automatically have been approved. Diana Moss’ commentary, William McGee’s ProMarket defense of Judge Young’s opinion, and Brad Shrago’s empirical research on the “Spirit Effect” all support the view that Spirit’s business model materially lowered fares for budget-conscious travelers and that those benefits would not necessarily survive absorption into JetBlue’s different product strategy. 

But those analyses shared an important assumption: they treated standalone Spirit as a stable competitive counterfactual, implicitly assuming the airline could continue offering something like its 2019-era fare structure indefinitely. The post-pandemic record suggests that assumption was already untenable.

Fuel shocks, labor-cost increases, and the Pratt & Whitney engine crisis forced fares upward across the entire ULCC segment, merger or no merger. The relevant question, therefore, was not whether Spirit’s low fares would survive the merger. It was whether Spirit’s low fares were likely to survive at all. By late 2023, the evidence increasingly suggested the answer was no.

What looks especially problematic in retrospect is the durability assumption embedded in the government’s theory of harm. The DOJ’s complaint treated Spirit as a persistent, forward-looking competitive constraint while largely sidestepping contrary evidence contained in Spirit’s own SEC disclosures. Judge Young’s opinion was more candid. It acknowledged cumulative losses approaching $2 billion since 2020, as well as the uniquely severe impact of the Pratt & Whitney geared-turbofan groundings.

Still, once the court concluded the strict failing-firm defense did not apply, most of that evidence effectively dropped out of the core competitive analysis. The “Spirit Effect” was treated less as a fragile market phenomenon than as a permanent feature of the airline industry.

That binary doctrinal framework—either a legally cognizable failing firm or merely background noise—fit awkwardly with the facts. What the case really called for was a probability-weighted assessment of competitive durability, not an all-or-nothing inquiry into whether Spirit had already crossed the formal threshold of failure.

The Theory Survived. Spirit Didn’t.

Three lessons stand out from the Spirit saga.

The first is that the failing-firm doctrine, as currently formulated, is too binary to do much useful work in industries defined by high fixed costs, capital-intensive operations, and large exogenous shocks. The doctrine asks whether a company faces imminent failure and lacks any alternative purchaser. If the answer is no, courts largely proceed as though the firm will survive indefinitely in its existing competitive form.

That framework collapses what is really a continuous question into a crude yes-or-no proxy. The relevant inquiry is not simply whether the firm is technically “failing,” but what the likely distribution of outcomes looks like over the next five or 10 years. Will the company remain an aggressive competitive force? Shrink? Restructure? Drift into irrelevance? Exit entirely?

Spirit was not a failing firm in the strict Citizen Publishing sense in January 2024. But it was plainly a fragile firm whose continued role as the marginal fare-disciplining force in low-end airline markets depended on contingencies largely outside its control. A doctrine that cannot account for that fragility will systematically overweight static structural harms while underweighting dynamic competitive risk.

The second lesson concerns out-of-market efficiencies in mergers involving differentiated firms. Judge Young’s opinion is unusually candid in acknowledging that a combined JetBlue-Spirit carrier would likely have imposed stronger competitive pressure on the Big Four legacy airlines. That benefit would have accrued to a broader population of travelers than Spirit’s traditional ULCC customer base.

Still, the opinion treated those broader competitive gains as legally subordinate to the loss of direct competition for highly price-sensitive flyers. As Daniel Gilman, Brian Albrecht, and Geoffrey Manne argued in a Truth on the Market post shortly after the ruling, the “any-market” logic associated with United States v. Philadelphia National Bank and United States v. Topco Associates tends to operate selectively rather than consistently. But where courts do apply it, localized competitive harm controls the analysis regardless of the magnitude of broader market benefits. 

Gilman later observed that Judge Young effectively recognized that the merger would likely be procompetitive on net at the national level—and blocked it anyway. Whatever one thinks of that conclusion doctrinally, it raises an uncomfortable policy question. Blocking a merger to preserve surplus for the very lowest-fare travelers may not be welfare-enhancing if the likely long-run result is both weaker competition against the Big Four and the eventual disappearance of the ULCC altogether. 

The third lesson is institutional humility. The Biden administration treated the JetBlue-American Airlines Northeast Alliance challenge and the JetBlue-Spirit merger challenge as components of a broader effort to re-discipline the airline industry by preventing further consolidation among smaller carriers.

But airlines are not an industry where firms can easily scale organically through grit and good intentions. Network depth matters. Aircraft-acquisition timing matters. Gate access matters. Frequent-flyer ecosystems matter. Scale itself is often a competitive asset.

In that environment, forcing smaller carriers to remain small in the name of preserving competition can, paradoxically, produce less competition over time—especially when the ULCC business model is already under pressure from legacy carriers copying its core pricing strategies.

None of this proves the JetBlue-Spirit merger should have sailed through unchanged. The government’s concerns about harm to budget-conscious travelers were plausible. The Northeast Alliance litigation weakened JetBlue’s claim to outsider status. And the defendants never fully developed the sort of failing-firm record that might have forced the court into a more dynamic analysis.

But the post-ruling history does suggest the framework applied in the case was too static and too confident about the durability of standalone ULCC competition.

The enforcers were right to ask whether the merger would eliminate a disruptive competitor. They were wrong not to ask, with equal seriousness, what would happen if that competitor was forced to go it alone.

Two-and-a-half years later, we have the answer. Spirit is gone. The “Spirit Effect” is gone with it. And many of the travelers the lawsuit purported to protect are now buying tickets from the same Big Four airlines the challenge was supposed to constrain.

The post Nonstop to Nowhere: Spirit, JetBlue, and the Limits of Merger Doctrine appeared first on Truth on the Market.

It’s an eloquent piece. Lindsey is a serious thinker, and I’ve long admired him and his work. But here his prescription would pull the movement in the wrong direction. The intellectual tradition he draws on also carries a troubling recent track record in policy.

(Lindsey refers throughout the essay to his recent book, “The Permanent Problem” (2026), which I haven’t yet read. What follows comes with an obvious caveat: the book may address some of these arguments, and I may be judging his views based on a necessarily truncated version of them.)

Liberalism Isn’t Morally Empty; It’s Morally Humble

Lindsey describes the progress movement as a “shriveled liberal faith”—“bloodless and technocratic” and allergic to “making substantive moral judgments about how people choose to live their lives.” He calls for “value rationality”: a framework that not only expands human capabilities but also takes a stand on what people ought to do with them. We should “lift up and improve the ends that we try to achieve,” he writes, and increase “the number of individuals living excellent, admirable, rewarding lives.”

That critique brushes aside too much of liberalism’s intellectual history. The classical liberal tradition that animates much of the progress movement is not morally vacant. It rests on one of the most demanding moral commitments in political thought: human flourishing requires decentralized, plural conceptions of the good life, and no authority—no matter how wise or well-intentioned—knows enough to dictate the right ends for others.

Friedrich Hayek made this point with characteristic depth in “The Constitution of Liberty” (1960). Lindsey invokes Wilhelm von Humboldt’s line—“the absolute and essential importance of human development in its richest diversity”—as evidence that liberalism once embraced a more substantive moral vision. John Stuart Mill placed that line at the head of “On Liberty.” And Hayek quotes it, too, giving it the final word in “The Constitution of Liberty” before his famous postscript, “Why I Am Not a Conservative.”

But both Mill and Hayek deploy the line to reach a very different conclusion. The emphasis falls on “human development in its richest diversity.” For Hayek, that insight cuts against the kind of moral project Lindsey advances. We need liberty because we cannot know in advance which ways of living—which experiments, values, and pursuits—will foster human flourishing. Progress, on this account, is evolutionary. Individuals try different paths. Most fail. Some succeed in ways no planner could have predicted.

James Buchanan, the Nobel laureate in economics (public choice), put the point starkly:

To lay down any “social” purpose, even as a target, is to contradict the principle of liberalism… that leaves each participant free to pursue whatever it is that remains feasible within the limits of the legal-institutional parameters.

This isn’t a narrow, technical claim about markets. It’s the core commitment of the liberal tradition. A progress movement that adopts “value rationality”—that takes positions on which ends people should pursue—would not deepen liberalism’s moral content. It would undercut liberalism’s defining insight.

None of this makes moral values unimportant. Like Lindsey, Hayek recognized that shared norms help sustain a functioning society. As he wrote in “The Constitution of Liberty”:

We understand one another and get along with one another, are able to act successfully on our plans, because, most of the time, members of our civilization conform to unconscious patterns of conduct, show a regularity in their actions that is not the result of commands or coercion, often not even of any conscious adherence to known rules, but of firmly established habits and traditions. The general observance of these conventions is a necessary condition of the orderliness of the world in which we live, of our being able to find our way in it, though we do not know their significance and may not even be consciously aware of their existence (p. 123).

But Hayek also stressed that trying to impose a preferred set of such rules is a mistake, in part because

they require knowledge which exceeds the capacity of the individual human mind and [because], in the attempt to comply with them, most men would become less useful members of society than they are while they pursue their own aims within the limits set by the rules of law and morals (p. 127).

The most relevant passage concerns the flexibility of voluntary norms:

There is an advantage in obedience to such rules not being coerced, not only because coercion as such is bad, but because it is, in fact, often desirable that rules should be observed only in most instances and that the individual should be able to transgress them when it seems to him worthwhile to incur the odium which this will cause. It is also important that the strength of the social pressure and of the force of habit which insures their observance is variable. It is this flexibility of voluntary rules which in the field of morals makes gradual evolution and spontaneous growth possible, which allows further experience to lead to modifications and improvements. Such an evolution is possible only with rules which are neither coercive nor deliberately imposed—rules which, though observing them is regarded as merit and though they will be observed by the majority, can be broken by individuals who feel that they have strong enough reasons to brave the censure of their fellows. Unlike any deliberately imposed coercive rules, which can be changed only discontinuously and for all at the same time, rules of this kind allow for gradual and experimental change. The existence of individuals and groups simultaneously observing partially different rules provides the opportunity for the selection of the more effective ones (pp. 123–24).

A progress movement that precommits to any single vision of the good life would shrink the diversity Humboldt celebrated and Hayek treated as the engine of progress. The movement’s allegedly “thin” framework—remove barriers to innovation, grow the pie, let people choose—is not an absence of moral commitment. It is the moral commitment: the conviction that human flourishing emerges from freedom and experimentation, not from imposing any particular vision of the admirable life.

When ‘Human Flourishing’ Becomes Policy Power

This might sound like an abstract philosophical dispute. It isn’t. We don’t have to guess what happens when policymakers swap measurable welfare standards for vague appeals to “human flourishing.” We can watch it unfold—most clearly in antitrust, the policy domain most closely tied to economic growth and consumer welfare.

Shortly after taking up his post as a newly confirmed Commissioner of the Federal Trade Commission (FTC) in May of 2025, Mark Meador published a broadside expounding his moral vision of antitrust, entitled “Antitrust Policy for the Conservative.” The essay defines conservatism as “the political, religious, and cultural project in the West of pursuing the just ordering of society that best facilitates human flourishing.” It “prioritized tradition and custom over newness for newness’s sake, and beauty and virtue over cold, calculated efficiency.” Antitrust should “reaffirm[] these first principles,” Meador writes, “to provide clarity and certainty around what antitrust law is and how the law should be enforced.”

In Meador’s framework, antitrust does more than protect competition. It advances a moralized vision of the economy. The promotion of free markets “is a legal and moral choice—not just an economic choice.” Innovation is no longer presumptively beneficial (“not all that is innovative is good”). Large firms are presumptively suspect (“big is bad”). And economics itself takes a back seat, because “[a]n obsessive preoccupation with efficiency is… incompatible with a humane way of living.”

The resulting approach to antitrust would entail a meaningful shift. As Gus Hurwitz and I have noted before:

This view contradicts the past century’s worth of experience and learning. It would require jettisoning the crown jewel of modern antitrust law—the consumer welfare standard—and returning antitrust to an earlier era in which inefficient firms were protected from the burdens of competition at the expense of consumers. And doing so would put industrial regulation in the hands of would-be central planners, shielded from any politically accountable oversight.

The parallels to Lindsey’s call for “value rationality” are hard to miss. Both seek to move beyond “purely materialist” measures. Both draw on classical and theological traditions. Both treat welfare maximization as morally thin. And both run headlong into the same critical question: whose vision of human flourishing governs?

As Corbin Barthold has observed, Meador’s antitrust is not neutral inquiry. It is “national conservatism, if not flat-out Christian nationalism” dressed up in antitrust clothing. Meador describes human beings as “embodied souls seeking communion with their fellow man and their Creator.” He declares short-form video “bad for the soul.” He speaks, Barthold writes, “with complete confidence in his own superior vision for the tech industry”: “The man is, apparently, a prophet.”

More troubling still, Barthold argues that Meador’s concerns are not really about products, but about people:

People shouldn’t like short-form video. The government, Meador seemed to suggest, must protect them from themselves. You might say that Meador wants to replace the consumer-welfare standard, under which the FTC protects markets that work to give people what they want, with a moral-welfare standard, under which the FTC pushes markets to give people what they are supposed to want—as determined by Mark Meador.

This mindset has already shaped policy.

In February 2025—just before Meador joined the agency, but under Chairman Andrew Ferguson, who shares his worldview—the FTC issued a Request for Public Comment Regarding Technology Platform Censorship. The agency framed it as an effort to “understand how technology platforms deny or degrade users’ access to services based on the content of their speech or affiliations, and how this conduct may have violated the law.”

The political subtext was obvious. Denial and degradation were assumed. The “speech or affiliations” in question were “conservative” ones. The question was not whether this assumed conduct violated the law, but “how.” As Dan Gilman and Ben Sperry observed:

It took little imagination to wonder whether an inquiry into “censorship” and how “technology platform” conduct “may have violated the law” may have skewed submissions to the agency and, perhaps, biased the inquiry itself. Encouraging input from “[t]ech platform users who have been banned, shadow banned, demonetized, or otherwise censored” (but no others) did not seem a neutral solicitation of public comment on the potential costs and benefits of platform conduct. Indeed, some of the Commission’s commentary seemed ominous. The FTC’s press release stated that “[c]ensorship by technology platforms is not just un-American, it is potentially illegal.”

The true intention may have been discernible (if obscured), but the legal authority was absent, as it must be: The First Amendment protects private speech from government interference. Yet here is an agency signaling it may coerce private platforms to carry speech the government favors—despite lacking statutory authority, and likely constitutional authority, to police the political content of private speech.

Later in 2025, after Meador had joined the FTC, the agency announced its Omnicom–IPG merger settlement. It allowed two large advertising firms to merge—but only if the combined company agreed not to base advertising decisions on a publisher’s political or ideological views. Again, the subtext was clear. As Barthold writes:

[t]he settlement is a transparent assault on advertising firms’ First Amendment right to boycott publishers on grounds of social or ideological principle. It is also a nakedly political effort to redirect advertising dollars toward right-wing outlets.

The agency has also waded into gender medicine. It held an all-day conference titled “The Dangers of ‘Gender-Affirming Care’ for Minors” and launched a public inquiry. As Barthold notes, “[t]he FTC is not a medical regulator; it has no expertise in this area. But transgender issues are at the center of the culture war, so the agency could not resist weighing in, thumb firmly on the scale for the political right.”

This is the risk of any effort to “thicken” the progress movement’s moral commitments. Lindsey’s vision of the good life is thoughtful and humane. Meador’s is, at best, contestable. But the institutional framework Lindsey proposes offers no way to distinguish between them.

Once policy turns on moral judgments about flourishing rather than economic criteria, those judgments track political power, not philosophical reflection. As Hayek warned in “The Road to Serfdom” (1944):

Once you admit that the individual is merely a means to serve the ends of the higher entity called society or the nation, most of those features of totalitarian regimes which horrify us follow of necessity (p. 209).

And despite Meador’s claims to be implementing a “conservative” antitrust, there’s arguably nothing at all conservative about it. As James Cooper and Thom Lambert have written:

In short, the FTC’s crusade against “big tech censorship of conservatives” risks converting antitrust and consumer protection law into tools for ideological regulation. That transformation would be bad policy, bad law, and a troubling departure from the principles of limited government and private ordering that conservatives themselves have long championed.

It is striking that these arguments have gained traction in the Age of Trump. Classical liberals (and even some conservatives) have long warned that empowering government to pursue moral ends also empowers it to decide which ends to pursue. You may not like the answer when your side loses.

Meador’s approach—however dressed up—amounts to politicized enforcement. In principle, it differs little from the more overtly partisan uses of state power seen elsewhere in the Trump era. It is hard to believe that advocates of a “less narrowly economic” policy framework—including Lindsey—had this in mind.

With respect to antitrust, Lazar Radic and I develop this critique at greater length in “Competition and Competition Law in the Classical Liberal Tradition” (forthcoming in the “Routledge Handbook of Classical Liberalism” (2026)). The central mistake behind projects like Meador’s, neo-Brandeisian antitrust, and the European Digital Markets Act is a conflation of standards and goals. Standards assess whether market processes work. Goals prescribe outcomes. Classical liberal systems rely on the former and resist the latter. As we put it there:

A classically liberal antitrust law does not contort market processes to achieve the government’s preferred policy outcomes. It sets out a general framework and employs standards to gauge market processes but does not impose “goals.”… Attaching any end goal to this inquiry vitiates its outcome and misconstrues the law’s purpose.

Once you import a “thick” moral vision—“value rationality”—antitrust becomes unavoidably discretionary:

[I]ll-defined, value-pluralistic antitrust law inevitably bestows enormous discretion on enforcers who, in weighing incommensurate goals and values, are ultimately required to act as regimenters and social planners…. [H]ow does one assess the welfare effects of conduct adjusting for all the possible facets of human well-being across different groups, all of which are presumably deserving of (varying degrees of) special protection?

A progress movement that takes positions on what counts as an admirable life—what ends matter, how to balance work and leisure—opens the door to an almost limitless range of moralized interventions. Each new commitment becomes a lever for rent-seeking and rent extraction. Interests aligned with the favored vision will push for subsidies and protections. Those that are not will face pressure and penalties.

A thinner, growth-focused agenda resists capture precisely because it offers less to grab onto. A thicker, value-laden one is an open invitation.

If Growth Is the Culprit, the Evidence Didn’t Get the Memo

At this point a sympathetic reader might object: fine—but what about Lindsey’s strongest claim? Growth, he argues, has decoupled from well-being for large segments of the population. Social atomization is rising. The progress movement shrugs.

Fair enough. Lindsey is pointing to something real.

Deaths from suicide, drug overdose, and alcohol abuse among working-age Americans—what Anne Case and Nobel laureate Angus Deaton called “deaths of despair”—more than tripled between 1992 and 2017. Loneliness isn’t imagined. The U.S. Surgeon General’s 2023 advisory found that roughly half of American adults reported feeling lonely even before the pandemic. Marriage rates have fallen. Community institutions have weakened. Life expectancy gaps by education have widened.

These are serious problems. Ignoring them would be willful blindness.

But the implied causal story—that a myopic focus on economic growth and the market order caused these pathologies, and that fixing them requires reorientating the progress movement’s goals—doesn’t hold up.

Start with deaths of despair. Christopher Ruhm’s paper, “Deaths of Despair or Drug Problems?,” finds that changes in economic conditions explain less than one-tenth of the rise in drug mortality between 1999 and 2015. The main driver was not economic “despair,” but changes in the drug supply—overprescribed opioids, followed by a surge of cheap heroin and fentanyl.

The pattern fits. The epidemic hit hard in relatively strong economies like Massachusetts. It didn’t recede as economic conditions improved. And it didn’t appear in other developed countries facing similar patterns of economic stagnation.

Case and Deaton themselves reject the simple story. “Like Ruhm, we directly contradict the idea that deaths are related to economic conditions from 1999 to 2015,” they write. Mortality rose steadily through the Great Recession, largely unaffected by the downturn. European countries with comparable economic distress saw no similar spike.

Their broader argument—that long-term institutional decline damaged working-class communities—is more plausible. But it’s also harder to test and far less clearly connected to any growth-versus-values reframing of policy.

The loneliness epidemic poses a similar problem. Its suspected causes are all over the map: technology, urbanization, the erosion of civic life documented by Robert Putnam, changing family structures, and the COVID-19 pandemic, among others. But the actual directional arrow of causation remains murky. Does growth produce atomization? Or do both reflect deeper, independent shifts?

The global data complicate the story. Loneliness rates are higher in low-income countries—about 24% versus roughly 11% in high-income countries, according to the World Health Organization’s 2025 Commission on Social Connection. If material progress drove disconnection, you’d expect the opposite.

Even the standard narrative of economic stagnation looks shakier when you examine living standards, rather than income. Bruce Meyer and James Sullivan find that while income inequality rose about 25% since the early 1960s, what they term “consumption inequality” rose only about 7%. Consumption—because it reflects taxes, transfers, borrowing, and assets that living-standard measures miss—better captures what people actually experience.

Meanwhile, Bruce Sacerdote finds that real consumption for families at the 25th percentile rose 164% between 1960 and 2015. That’s not a story of broad material stagnation.

None of this denies that social fragmentation, status anxiety, and weakening institutions are real problems. But the diagnosis matters enormously for the prescription.

If the drivers of these pathologies are primarily “supply-side” defects including drug-policy failures, housing constraints, bad urban design, and the disruption that comes with rapid social change—as the evidence suggests—then the response looks familiar: targeted, institutional reform. Remove barriers. Expand opportunity. Let people build the lives they want.

Indeed, as Paddy Maher argued recently in The Argument, financial distress is one of the most significant predictors of loneliness. If loneliness is your target, the best thing you can do may be to promote abundance, not to try to “fix” the morality of the market:

To be lonely is to be in need, and the loneliest group in society is young people who are about to miss rent. The new political focus on affordability is perfectly timed for anyone who wants to make a dent in loneliness and suicides; helping more people maintain economic security is one of the best levers we have.

From Policy to Lifestyle Policing

To see how quickly “value rationality” turns political, consider Lindsey’s own wish list. He writes:

We need to recover the physical fitness that once came naturally from an active life; we need to reclaim our attention spans and cognitive acuity from screen-addled brain rot; we need to revitalize neighborhoods and communities withered by atomization; and we need to re-center marriage and parenthood as cornerstones of normal adulthood.

Each of these claims carries normative baggage. Each depends on empirical assumptions that are far less settled than the essay’s confident tone suggests.

Take “screen-addled brain rot.” The research on screen time is, by most accounts, a mess—“conceptual and methodological mayhem,” as one comprehensive review puts it. That meta-survey finds little support for the standard narrative. There is no “undeniable evidence” that social media is broadly toxic. At most, “time spent on social media does not have a strong effect on the well-being of its users.”

The idea of widespread “social media addiction” is similarly shaky. Experts disagree on whether it exists, how to define it, and how to measure it. Once you account for the main drivers of well-being, social media use appears to be a negligible factor.

Much of the literature is correlational, which makes causal claims precarious. Do screens harm children? Or do children who are already struggling gravitate toward screens? Emily Oster puts it bluntly: “All of the studies we see of screen time are deeply flawed.” Different screen habits track different family environments, and those underlying differences, not the screens themselves, likely explain the observed effects.

None of this proves screens are harmless. But “screen-addled brain rot” reads more like a moral panic than settled science—the kind of contested claim a progress movement should hesitate to enshrine.

The call to “re-center marriage and parenthood as cornerstones of normal adulthood” raises similar issues. Stable two-parent families may benefit children, though even that literature is contested (see, among others, here and here). But declining marriage also reflects expanded choice, particularly for women.

Sociologist Andrew Cherlin describes marriage as having undergone “deinstitutionalization.” It is no longer a mandatory social arrangement, but one option among many. Whether or not that framing is complete, attitudes have clearly shifted. Many feminists and sociologists interpret declining marriage rates as a sign of greater autonomy, not social decay. Others—such as the Institute for Family Studies—see costs.

The point is not that marriage doesn’t matter. It’s that “re-centering” it as a policy goal reflects one contested moral vision among many. In practice, it would alienate large constituencies. Even among conservatives who share Lindsey’s instincts, there is little agreement about what government should actually do to raise marriage rates.

A growth-focused agenda sidesteps these fights. It expands people’s options and lets them decide what kind of lives to build.

When Moralism Gets in the Way of Living

Perhaps the strongest objection to this broader project comes not from libertarians, but from thinkers who share much of Lindsey’s worldview.

The essay takes aim at amoral “techno-capitalists” for believing that “[a] brighter future thus consists of better and more absorbing paid employment, not increased leisure to be filled with non-commercial pursuits.” But philosophers like Michael Sandel—the furthest thing from a free-market fundamentalist—offer a different moral vision. He emphasizes “the dignity of work,” arguing that “[w]ork is not only a way of making a living; it is also a way of contributing to the common good and earning social recognition and esteem for doing so.”

So which moral vision should prevail?

That question is not hypothetical. The debate over artificial intelligence is testing it in real time, and the evidence thus far cuts against the less-work ideal. A Stanford Institute for Human-Centered AI survey of 1,500 workers found that 94% prefer AI that augments human labor rather than replaces it. Workers want tools that free them up for higher-value tasks—not tools that eliminate work altogether. A Harvard Business School study finds similar resistance to full automation, even when AI can outperform humans at lower cost.

Many people value work itself, not just the income it produces.

Daron Acemoglu, a Nobel laureate and no market fundamentalist, makes the point plainly in his exchange with Sandel: “I completely agree that the market is not a perfect anchor. But I worry that the alternative would simply be what intellectual elites value.”

He offers an example that gets at the heart of the problem:

Let me give you an example that has long bothered me. Opera is often treated as high art and is heavily subsidized, even though it is largely consumed by the well-educated and the wealthy. Meanwhile, heavy metal, which came out of working-class pubs, is not. That is a judgment made by intellectual elites, and it translates into policy.

So, I’m always afraid that if we give intellectual elites too much power to decide, we’re going to end up with a lot of situations like this. You might like opera, but many people like heavy metal.

Timothée Chalamet may have put it more bluntly, but the point stands.

Acemoglu is echoing a much older warning emanating from the liberal tradition. Adam Smith captured it in “The Theory of Moral Sentiments” (1759) with his famous critique of “the man of system”:

The man of system… seems to imagine that he can arrange the different members of a great society with as much ease as the hand arranges the different pieces upon a chess-board; he does not consider that…, in the great chess-board of human society, every single piece has a principle of motion of its own, altogether different from that which the legislature might choose to impress upon it (Part VI, Section II, Chapter II).

Thomas Sowell later described “the anointed”—elites whose vision of progress resists empirical challenge. Frédéric Bastiat warned of “manipulators of society” who would reshape human life by force.

The critique is consistent across centuries. When elites define “human flourishing” and try to impose it through policy, people bristle. They have their own preferences, values, and plans. Lindsey’s vision—more stable families, stronger communities, healthier lives—is appealing. But it is still just one man’s vision. Building a political movement around it risks turning that movement into yet another group of “men of system,” rearranging the chess board.

This is the “who decides?” problem in its clearest form. And it connects directly to the Meador example. Once you build institutions that impose substantive moral visions, those institutions will be run by elites—and they will reflect elite preferences. Maybe that yields Lindsey’s humane vision. Maybe it yields something else: religious revivalism, nationalist priorities, social-justice maximalism, or whatever else happens to dominate politically. Meador’s “human flourishing” antitrust offers a preview.

You can see the dynamic even in smaller, more personal contexts.

Consider Lindsey’s call to recover “the physical fitness that once came naturally from an active life.” We are in the middle of a revolution in treating obesity and diabetes. GLP-1 receptor agonists like Ozempic and Mounjaro may be the most significant advance in metabolic medicine in decades.

And yet, a moralistic stigma persists. Users are often seen as taking an “easy way out.” Many people—including physicians—still frame weight loss as a test of personal discipline and view pharmaceutical help as “cheating.” As former Secretary of Health and Human Services Alex Azar has noted, there is little stigma around taking statins for cholesterol, but GLP-1s carry a moral taint.

I’ve seen this firsthand. My own doctor initially resisted prescribing a GLP-1 medication for my diabetes—not because of medical concerns, but because he believed there was something more virtuous about managing it through diet and exercise alone.

That is “value rationality” in practice. And it’s not benign.

I told him that I had spent decades failing at the “virtuous” path. I wasn’t going to suddenly succeed. The real choice wasn’t between doing it the “right” way or the “easy” way. It was between taking the medication or accepting a shorter life. That ultimately persuaded him.

There’s no virtue in dying of a preventable metabolic disease because you lacked the time, willpower, genetic luck, or simply the desire to meet an idealized standard. The virtue is in living. If a drug makes that possible, that is progress—full stop.

The nostalgia for fitness “that once came naturally” is not just historically naïve—the lives that produced it were often harsh and short. When translated into policy or medical practice, that kind of sentimentality can delay the adoption of life-saving innovation.

Apollo Shows the Problem with ‘Thick’ Progress

Lindsey points to the Apollo program—and the public’s eventual indifference to it—as evidence that technophiles are “mutants” who need a broader vision to win support. The descriptive point is fair. Most people aren’t technophiles. The progress movement has to show how its agenda improves ordinary lives.

But a “thick” agenda doesn’t solve that problem. It makes it worse.

Apollo is the perfect case study. It was driven by exactly the kind of value rationality Lindsey favors: a substantive vision of what was admirable and inspiring, tied up with national prestige and civilizational ambition. The result was extraordinary—and unsustainable. The public eventually saw it for what it was: a significant government expense, disconnected from everyday concerns.

As Boom Supersonic founder Blake Scholl put it, Apollo “pursued glory without regard to cost or practicality…. Glory is a dangerous goal, and when it is pursued without regard to pragmatic utility, much damage is done.”

A thinner progress agenda does a better job of motivating support because it delivers tangible gains. It focuses on investments in science and technology that generate broadly useful knowledge and lower costs across the board. Housing reform that lets people build. Energy policy that makes power cheaper. Regulatory reform that gets new medicines to patients faster.

These improvements don’t require anyone to buy into a particular vision of the good life. They expand options and let people decide for themselves.

Jason Crawford makes this point directly. The progress movement, he argues, should offer “a moral defense of material progress”—not chase prestige projects, but understand how progress happens and how to accelerate it:

What technology and wealth can do is empower us to pursue the goals we have chosen. When we view material progress this way, it is something much grander than a generator of comfort and leisure: it is the great liberator allowing us to pursue rich, full lives.

Crawford’s focus is practical. Remove institutional barriers. Enable discovery and diffusion. Not because of abstract ideology, but because that’s what actually improves lives. As he notes,

more lives have been saved and suffering relieved by efforts to pursue general growth and progress than direct charitable efforts…. A root-cause analysis on most human suffering, if it went deep enough, would blame government and cultures that don’t foster science, invention, industry, and business.

The unglamorous work of clearing those obstacles is what delivers results. Not moonshots chosen for their symbolic appeal. Not moralized agendas reflecting the tastes of whoever holds power.

Dystopia Is What Happens When Someone Decides for You

Lindsey invokes “Wall-E” and “Brave New World” to argue that a materialist theory of progress “can’t distinguish between eutopia and dystopia”—and is therefore “worse than useless”:

Both of these dystopias feature impressive technological advances—but advances twisted into the service of human degradation. Humanity’s collective capacities may have expanded, but individuals’ opportunities to develop and exercise their own personal capacities have been crushed.

But this misses the mark. No one in the progress movement is arguing for a world of passive consumption and automated complacency. The case for material progress is about expanding choice sets—not dictating how people use them.

Jason Crawford, for example, advocates “techno-humanism,” the view that “science, technology, and industry are good—because they promote human life, well-being, and agency.” That is a moral claim. But it differs sharply from Lindsey’s “value rationality.” Crawford does not prescribe a particular vision of the good life. He defends a framework in which people can pursue their own:

A better guide to well-being and human progress is whether people can achieve their goals and fulfill their values. The good life is one of constantly discovering, pursuing, achieving, and maintaining values.

Now look more closely at Lindsey’s dystopias. What do they actually have in common? Not too much technology, but too much control. In both worlds, a central authority has decided what the good life should be—total leisure in “Wall-E”; pharmacological contentment in “Brave New World”—and imposed it across society.

In other words, they depict exactly the kind of “thick,” substantive vision of flourishing Lindsey wants to import into policy.

The lesson is not that we need more “value rationality.” It’s that we should be wary of anyone who claims to know, in advance, what ends other people ought to pursue.

The Moral Case for Keeping Progress ‘Thin’

Lindsey worries that a vision of progress grounded in material advance alone is too thin. That concern is understandable. But his alternative—a progress movement that takes substantive positions on how people should live—is worse. It invites political capture, fractures coalitions, and accelerates the kind of moralized policymaking already distorting key domains.

The social problems he highlights—atomization, weakening institutions, the erosion of shared meaning—are real. He is right to push the progress movement (and everyone else) to take them seriously. But the most effective response is not to prescribe better lives. It is to remove the barriers that prevent people from building the lives they want: build more housing, reform drug policy, fix permitting, expand access to medical innovation. These reforms improve lives without requiring anyone to sign on to someone else’s vision of the admirable.

Adam Smith, as reported by Dugald Stewart, captured the core insight in 1755:

Little else is requisite to carry a state to the highest degree of opulence from the lowest barbarism, but peace, easy taxes, and a tolerable administration of justice; all the rest being brought about by the natural course of things. All governments which thwart this natural course, which force things into another channel or which endeavour to arrest the progress of society at a particular point, are unnatural, and to support themselves are obliged to be oppressive and tyrannical.

It would be a mistake to read Smith as a mere instrumentalist about wealth. As Jacob Viner observed, Smith’s defense of laissez faire also rested on moral grounds: it preserved the “natural system of liberty” to which individuals have a right. He likely would have resisted extensive state direction even if it increased aggregate wealth.

Hayek was explicit in “The Road to Serfdom” that liberalism rests on individualism and personal liberty:

Economic liberalism… regards competition as superior not only because it is in most circumstances the most efficient method known but even more because it is the only method by which our activities can be adjusted to each other without coercive or arbitrary intervention of authority (p. 110).

Milton Friedman, when pressed in the final episode of Free to Choose (1980) on whether he would trade freedom for prosperity, chose the “human and ethical and moral values” of the former.

This is the answer to the charge that a growth-focused agenda is “bloodless and technocratic.” The classical liberal case has always been consequentialist and moral—a double helix running from Smith through Mill, Hayek, Friedman, and others.

The progress movement inherits this tradition. Its agenda is not thin because it lacks moral content. It is thin because its moral content—growth, diversity, self-direction—requires restraint. A plurality of ends is not a problem to solve; it is the engine of progress.

Grow the pie. Let people decide for themselves what to do with their slice.

The post Grow the Pie, Skip the Sermon appeared first on Truth on the Market.

That is the question now before the California Supreme Court, which heard oral argument yesterday in Gilead Tenofovir Cases (S283862). The case asks whether California law recognizes a previously unheard-of “duty to commercialize a safer alternative drug.” Under the First Appellate District Court of Appeal’s rule, that duty arises whenever a pharmaceutical manufacturer allegedly “knows” that another formulation in its pipeline is at least as effective, and safer, than the product currently on the market. 

The International Center for Law & Economics (ICLE) filed an amicus brief in the case urging reversal. The brief argues that the appellate court’s rule effectively eliminates the longstanding product-defect requirement, rests on a misreading of  Brown v. Superior Court, and—most interestingly from a law & economics perspective—would invite courts and juries to second-guess R&D decisions that the patent system and U.S. Food and Drug Administration already heavily structure. 

Oral argument, of course, is no oracle of case disposition. Even so, two lines of questioning stood out to me, and both, I think, vindicated concerns we raised in the brief.

The Duty That Couldn’t Be Defined

The most revealing stretch of argument came when the justices repeatedly asked plaintiffs to explain, with any precision, what their proposed duty would actually require pharmaceutical manufacturers to do. The court never got an answer it seemed able to work with.

Plaintiffs argued that manufacturers owe “a duty to act reasonably in making commercialization decisions about an allegedly safer and at least equally effective alternative drug.” But when the justices pressed them on what that meant in practice, plaintiffs largely circled back to the same formulation: manufacturers must “act reasonably.”

The bench kept returning to the obvious follow-up: reasonable according to what standard?

At one point, the court tested the theory with a hypothetical. Could a jury find a pharmaceutical company acted unreasonably by pursuing one research path over another—perhaps backing a less promising candidate that ultimately failed—based on how it allocated scarce R&D resources? Plaintiffs said yes.

That answer immediately raised another concern: hindsight bias. Once a lawsuit is filed years later, after more data emerges and one research path succeeds while another does not, what exactly is the legal rule supposed to tell drug developers ex ante? What is the operating instruction?

Notably, defense counsel faced no parallel interrogation. The court’s questioning there focused mostly on how Gilead’s position fit within existing California law.

The exchange tracked almost perfectly with what our brief predicted. Plaintiffs’ theory is, as we argued, “a recharacterization of a traditional products liability claim, attempting to achieve the same result while avoiding the required showing of defect.”

California has long required plaintiffs alleging injury from a product to prove that the product was defective for a reason. A free-floating “duty to act reasonably,” untethered from any product defect, gives juries little meaningful guidance and leaves manufacturers with no workable way to structure their conduct in advance.

The justices’ discomfort echoed concerns the California Supreme Court itself expressed in Brown v. Superior Court. There, the court warned:

Perhaps a drug might be made safer if it was withheld from the market until scientific skill and knowledge advanced to the point at which additional dangerous side effects would be revealed. But in most cases such a delay … would not serve the public welfare.

The court also raised a closely related question: Why didn’t plaintiffs plead a design-defect claim in the first place?

The answer is straightforward. Plaintiffs have repeatedly conceded—including again at oral argument—that the drug at the center of the case is not defective and should not be removed from the market.

But that concession is the whole problem.

Without a defective product, plaintiffs are left arguing for what our brief describes as “an unprecedented expansion of tort liability” that “would expose manufacturers to potentially unlimited liability for products that are reasonably safe and defect-free.”

The justice who asked the question almost certainly understood that. In many ways, the question itself was the point.

A Jury Is Not a Drug-Development Committee

The second revealing exchange came from a different line of questioning, one aimed squarely at institutional competence. The court asked plaintiffs how their proposed duty fit alongside the patent and regulatory regimes that already govern pharmaceutical development.

Plaintiffs responded largely by criticizing the pharmaceutical industry’s allocation of resources between R&D and marketing. In a sense, that response went directly to one of the core concerns in our amicus brief: a rule like the one adopted in Gilead would effectively give plaintiffs’ attorneys and juries a veto over enormously complex, expensive R&D decisions.

Under the court’s framing, though, the problem ran deeper. The patent system already reflects a deliberate legislative bargain about innovation incentives. Congress decided how long manufacturers receive exclusivity for new inventions. Overlaying that framework with a tort duty requiring companies to commercialize drugs on some judicially determined timetable would replace an explicit statutory scheme with ad hoc, case-by-case litigation judgments.

The court appeared deeply uneasy with that prospect. At one point, the justices described the proposition, in so many words, as “uncomfortable.”

As our amicus brief explained, plaintiffs’ theory “would set an unrealistic and dangerous standard of perfection for drug development.” More fundamentally, though, the theory largely ignores the patent system that already shapes the timing of pharmaceutical commercialization.

That matters because patent terms run from the filing date, not from the date a drug reaches the market. In practical terms, sitting indefinitely on a superior product is not some masterstroke of profit maximization; it is usually economically irrational. Every year spent delaying commercialization is a year of patent exclusivity burned off the clock.

In other words, plaintiffs’ suppression theory depends on a view of the patent system that does not fit how pharmaceutical markets actually work.

As we noted in the brief, if Gilead truly believed Tenofovir alafenamide (TAF) was both safer and equally effective, the company generally would have had every incentive to bring it to market sooner, not later.

A Tough Day for an Expansive Tort Theory

Of course, oral argument is famously unreliable tea-leaf reading. The justice grilling you at the lectern may ultimately write the opinion in your favor; the justice tossing softballs may end up ruling against you.

Still, the substance of the questioning mattered. Plaintiffs repeatedly struggled to explain what “reasonable” commercialization behavior would actually require in practice. At the same time, the court repeatedly drifted toward concerns about institutional competence and the role of legislatures, regulators, and the patent system in structuring pharmaceutical innovation.

That combination feels significant.

If I had to guess, reversal now seems more likely than not. The most plausible paths are the two the defense laid out at argument: either the court reaffirms the defect requirement as the exclusive framework for product-based negligence claims, or it recognizes some broader duty in principle before concluding—under the familiar Rowland framework discussed extensively at argument—that this particular theory goes too far.

Either way, the argument suggested a court searching less for a way to expand tort law than for a limiting principle to stop it from expanding indefinitely.

And that may tell us all we need to know.

The post With Gilead’s Reasonableness Standard, Side Effects May Vary appeared first on Truth on the Market.

A growing share of scholars, advocates, and policymakers now rejects much of conventional antitrust doctrine and method. They argue that this framework enabled the rise of purported digital monopolies in platform markets and contributed to a broader, purported decline in competition.

Among the favorite targets is the “error-cost” principle: the view that the costs of antitrust overenforcement typically exceed the costs of underenforcement, in part because market forces tend, over time, to compete away inefficient practices. If that proposition is correct, regulators and courts should hesitate before condemning business practices as antitrust violations without compelling evidence of anticompetitive harm. Much of federal antitrust case law reflects this prudential approach.

Critics of the error-cost principle have offered little by way of hard numbers to show the competitive harms supposedly caused by caution in antitrust enforcement and adjudication. Nor have they seriously grappled with the countervailing costs of overenforcement. Even so, competition agencies in the United States, European Union, and United Kingdom have made significant interventions and pursued far-reaching remedies on this theory, brushing aside concerns about false diagnoses of anticompetitive maladies.

In the antitrust equivalent of Silicon Valley’s “move fast and break things,” agencies have been especially eager to block mergers based on inherently conjectural theories of potential future harm. Debates over whether such challenges are premature rarely yield clear answers at the time, precisely because they require crystal-ball predictions about future market trajectories. But now that critics of conventional antitrust have led several major competition agencies around the world, we have some accumulated evidence to inform the policy debate.

That evidence casts doubt on the merger-review strategy of “challenge first, ask questions later,” and on the assumption that false-positive error costs can be ignored or heavily discounted. It now appears that several recent merger challenges by U.S., EU, and U.K. regulators destroyed significant economic value—often accompanied by substantial losses in employment and related business activity—without any clear improvement in competitive conditions. In at least one case, those conditions worsened.

JetBlue-Spirit: The Maverick That Got Grounded

Let’s start with Spirit Airlines’ recent shutdown, which came just a few years after JetBlue’s failed 2022 attempt to acquire the low-cost carrier. The U.S. Justice Department (DOJ) and seven states challenged the transaction in court, arguing that it would eliminate a “maverick” carrier that disciplined pricing by larger airlines.

Concentration metrics offered only limited support for that theory. As the district court observed, Spirit accounted for about 4% of the U.S. passenger-airline market, and the combined firm would have represented only about 10%. That was far smaller than the market shares held by each of the four largest airlines—United, American, Delta, and Southwest—which together accounted for about 80% of the domestic market. The JetBlue-Spirit combination would, however, have produced high concentration levels on certain routes, particularly those connecting some Northeast cities with destinations in Florida and the Caribbean.

JetBlue argued that the combined entity would still face pricing pressure from the four largest airlines, as well as from smaller carriers such as Alaska and Frontier, in relevant geographic markets. It also argued that, for a smaller low-cost airline like Spirit, which tended to operate on thin margins, consolidation into a larger entity could provide the scale needed to improve economic viability and operational continuity for consumers.  At the same time, acquiring Spirit would enable JetBlue to compete more vigorously against the legacy carriers.

To address concerns about competition on routes with high overlap between acquirer and target, JetBlue committed in the acquisition agreement to divest assets—slots and routes—to secure regulatory approval, “up to a material adverse effect” on the combined airline. As the district court acknowledged, JetBlue also agreed to asset divestitures in the Boston, New York, and Fort Lauderdale markets and, at the time of the litigation, had entered into divestiture agreements with several low-cost airlines.

Despite these concessions, the district court insisted that the merger would do “violence to the core principle of antitrust law to protect the United States’ markets . . . from anticompetitive harm.” Recent developments suggest the decision helped produce something closer to the opposite result.

Several economic factors contributed to Spirit’s demise. But its complete exit from the market raises serious questions about whether the DOJ’s challenge and the court’s decision harmed the very consumers antitrust law is supposed to protect. Blocking the JetBlue acquisition most likely contributed, at least in some measure, to Spirit’s inability to weather volatility in the air-travel market. The result was value destruction for shareholders, lost jobs for Spirit’s workers—about 15,000 layoffs at the time of the airline’s shutdown—and lost business for the airline’s contractors and suppliers.

In the JetBlue acquisition, Spirit shareholders would have received $3.8 billion in consideration. Today, Spirit shares are largely worthless, as the company enters bankruptcy. It is impossible to know precisely how much blocking the merger contributed to Spirit’s demise. But the realized outcome is plainly worse for consumers than the alternative remedy on the table: a merger with asset divestitures to competitors.

That targeted intervention would have produced a combined entity better positioned to harness the scale efficiencies and financial stability of a larger carrier, while using divestitures to mitigate potential competitive harms on specific routes.

Illumina-Grail: The Cancer-Test Merger Regulators Diagnosed Too Early

Let’s turn to Illumina/Grail, which involved Illumina’s reacquisition of Grail, an emerging firm it had spun out a few years earlier. Illumina is the world’s leading producer of DNA-sequencing equipment.

Grail had developed a pioneering multicancer early detection, or MCED, test with the potential to save lives by identifying cancers earlier, enabling earlier treatment and improving the odds of success. After the approximately $8 billion deal was announced in September 2020, regulators in the United States and European Union challenged the transaction. Their theory was that Illumina, as the leading DNA-sequencing platform, would have incentives to foreclose access to that platform for Grail’s competitors in the nascent MCED-test market.

Keep in mind: Grail’s MCED test was not yet commercially available, and the MCED-test market was still in its earliest stages.

Illumina sought to allay regulators’ concerns about the acquisition’s effect on this emerging market. First, Illumina argued that it would have no incentive to foreclose other MCED-test providers, because doing so would likely produce revenue losses exceeding any prospective gains from excluding rival test developers. Second, Illumina publicly issued a binding “open offer” to maintain access for all other test providers on fair, reasonable, and nondiscriminatory terms for 12 years.

As I have shown elsewhere, platform providers often make this type of commitment voluntarily to encourage adoption of new technology—such as MCED tests—and to seed the market by assuring users that access will remain available on comparable terms going forward.

The agencies were unconvinced. In September 2022, the Federal Trade Commission (FTC) lost its challenge before an administrative law judge. The commission reversed that decision, and Illumina appealed to federal court.

In the EU, the European Commission accepted “referrals” from national competition authorities to review the transaction, even though Grail had no EU revenue and the deal therefore did not meet EU or national reporting thresholds. When challenged in court, the commission’s assertion of jurisdiction was initially upheld. On appeal, the European Court of Justice annulled it in September 2024.

By then, Illumina had already elected to terminate the transaction and divest Grail. The company faced the Commission’s decision to block the deal and impose a €432 million gun-jumping fine for closing the acquisition before securing EU merger approval, ongoing FTC litigation, and intense shareholder pressure amid mounting regulatory costs and delays. In June 2024, Grail was divested as an independent public company.

This two-front international regulatory challenge coincided with a precipitous decline in Illumina’s stock price. Illumina’s stock fell by approximately 52% between the deal’s announcement on Sept. 21, 2020, and its termination on Dec. 17, 2023, using the closing price on Dec. 18, 2023. That compares with an approximately 11% gain in the iShares U.S. Medical Device ETF (IHI) over the same period.

This is not to say regulatory challenges were mostly responsible for this exceptional decline in Illumina shareholder value. But they likely accounted for some material part of it, especially given Illumina’s dramatic underperformance relative to the broader medical-device and equipment sector.

In retrospect—which admittedly benefits from hindsight—the regulators’ foreclosure theory has lost even more force. During the Illumina/Grail litigation, the FTC asserted that Illumina controlled approximately 90% of the global market for high-throughput next-generation DNA-sequencing systems, leaving MCED-test developers with no comparable sequencing platform as a viable alternative.

That market share was critical to the agencies’ theory that Illumina would have an incentive to forfeit sequencing-platform revenue by excluding MCED-test developers, thereby capturing supracompetitive gains from Grail’s product. Today, Illumina faces increasing competition across segments of the next-generation DNA-sequencing market from BGI/MGI, Thermo Fisher, PacBio, Oxford Nanopore, Element Biosciences, and others.

At the same time, Grail faces growing competition from multiple MCED tests, although none—including Grail—have yet secured Food and Drug Administration (FDA) approval. For 2025, Grail reported revenue of $147 million and a net loss of $408 million—hardly the stuff of a dominant provider.

None of this could have been known with certainty at the time of the Illumina/Grail transaction. But the FTC specifically dismissed the possibility that new competitors to Illumina’s platform would emerge, stating that Illumina had failed to show that “new entry of an MCED test that does not rely on Illumina’s [] platform would be timely, likely, or sufficient to offset the anticompetitive effects” of the acquisition.

Subsequent developments suggest the FTC understated both the pace of technological change in the sequencing-platform market and the pace of adoption and clinical success in the testing market. About five years after the FTC challenged the Illumina/Grail deal, adoption of MCED tests has proceeded more slowly than expected, reflecting in part continued uncertainty about their clinical utility. Given the market’s uncertain trajectory, any sequencing platform would seem to have little incentive to limit access for MCED-test developers.

The Illumina/Grail litigation and its aftermath suggest that regulatory caution is especially warranted when a market remains in its infancy. That is precisely the opposite of the view now fashionable among some regulators, who seek to act preemptively as a kind of social planner steering the market’s future course.

At a minimum, this still-developing market does not appear to present the level of anticompetitive risk needed to justify the investment of significant taxpayer resources by two major competition agencies—especially in exchange for little, if any, apparent improvement in competitive conditions.

Amazon-iRobot: Antitrust Vacuums Up the Roomba Deal

In August 2022, Amazon announced an agreement to acquire iRobot, producer of the Roomba robotic vacuum and a pioneer in the home-robotics market, for approximately $1.7 billion. The transaction drew scrutiny from the FTC, the European Commission, and the UK Competition and Markets Authority (CMA).

Regulators relied on a foreclosure theory of anticompetitive harm. They focused on Amazon’s purported incentive to favor Roomba after the acquisition and disadvantage rival robot-vacuum manufacturers on Amazon’s platform. In broad strokes, the concern was that Amazon could leverage its position in online marketplace services to restrict competition in the vertical market for robot vacuums, hampering rival suppliers’ ability to compete on a level playing field.

That theory depended on two key assumptions: first, that Amazon could charge supracompetitive prices for Roomba; and second, that the additional revenue from Roomba sales would exceed the losses from depressing sales of rival robot-vacuum products.

Both assumptions were hard to square with the competitive reality of the robot-vacuum market, even at the time of the proposed acquisition. The market included multiple competitors, including larger and more established firms such as Dyson, SharkNinja, and Ecovacs, as well as lower-cost entrants. Those firms also had several channels to reach consumers, including direct-to-consumer websites, other online marketplaces such as Walmart, big-box retailers such as Best Buy, Target, and Costco, and brand-owned stores and showrooms.

Given those alternative distribution channels, and consumers’ resulting ability to vote with their feet—or their digital equivalent—Amazon likely had limited ability or incentive to disadvantage rivals. It would more likely do best by maximizing sales of robot vacuums across the market. Regulators also seemed to place little weight on the efficiencies that could come from integrating iRobot into Amazon’s product-development and distribution infrastructure, especially given iRobot’s precarious financial condition at the time of the acquisition.

The CMA eventually withdrew its objections. The FTC and European Commission persisted, and in January 2024, Amazon abandoned the transaction in light of regulatory scrutiny.

Here, the evidence more clearly indicates that regulators’ challenge played a significant role in iRobot’s post-termination financial decline. Immediately after the acquisition fell apart, iRobot laid off about one-third of its workforce. In the ensuing months, it continued to face serious financial distress—due in part to the very competition the agencies had downplayed.

The company filed for bankruptcy in December 2025, wiping out shareholders’ investment. iRobot was then acquired by a China-based manufacturer in January 2026, raising concerns about the security of users’ home-mapping data.

Qualcomm-Autotalks: Regulators Pump the Brakes on V2X

Let’s end the review with Qualcomm’s proposed acquisition of Autotalks. Qualcomm is a leading chip developer focused on the mobile-communications device market. Autotalks is a fabless semiconductor startup that designs pioneering vehicle-to-everything, or V2X, chipsets for the automotive market.

The technology is compatible with both leading V2X standards and enables cars to communicate with other vehicles, road infrastructure, pedestrians, and wireless networks. That, in turn, can improve road safety and support advanced driver-assistance and autonomous-driving systems. The acquisition appears to have been part of Qualcomm’s strategy to expand beyond telecommunications and deploy its connectivity solutions in the automotive sector.  For Autotalks, it was a mechanism to realize the value on the innovative technology that its founders and employees had been developing since 2008—a classic startup story.

Qualcomm announced the proposed acquisition in May 2023. The deal would have paid $350 million to $400 million to Autotalks’ founders, employees, and investors. Despite that relatively small deal value, the transaction attracted close scrutiny from the FTC, European Commission, and CMA. The agencies expressed concern that Qualcomm’s acquisition of a leading independent supplier of V2X chips could help it secure a dominant position in the automotive-connectivity market.

As in the other merger challenges, regulators seemed to give little weight to the synergies that could come from combining a small startup’s innovation with a larger company’s scale economies, infrastructure, and institutional expertise. As I have documented elsewhere, significant evidence shows that platform-startup acquisitions—a regular feature of healthy tech ecosystems—typically accelerate diffusion of the target’s technology through new products and services for businesses and consumers.

Here, the transaction likely would have promoted the build-out of connectivity solutions for assisted-driving and self-driving technologies, while encouraging adoption by original-equipment manufacturers  and Tier 1 suppliers and broader market growth.

Regulators’ opposition to the Qualcomm-Autotalks acquisition rested on the implied premise that Autotalks could efficiently develop and diffuse its technology as an independent firm.  Yet Autotalks had been attempting to do just that for about a decade since its first “design win” in 2016.  If the premise was unfounded, the merger challenge risked suppressing the startup’s technology or slowing market adoption—the sort of cure that leaves the patient wondering why they came in.

Qualcomm terminated the deal in March 2024 amid regulatory scrutiny. It ultimately completed the acquisition in June 2025 at a substantially reduced price—reportedly less than $100 million—and under terms that rendered the startup employees’ stock options worthless.

That steep decline in consideration implies a more than two-thirds reduction in deal value relative to the original transaction. The reduced valuation suggests Autotalks continued to struggle to deploy the technology independently after the initial deal was withdrawn. It also likely reflects the company’s limited exit options under the cloud of regulatory scrutiny.

The transaction eventually closed. But the regulatory delay may have slowed deployment of V2X technology in the automotive market and effectively shifted economic value from a startup to the larger acquirer.

When Error Costs Come Due

A significant share of antitrust scholars, advocates, and policymakers now argues that the error-cost principle—specifically, its emphasis on avoiding overenforcement relative to underenforcement—helped produce purported increases in concentration and entrenchment in tech and other markets. They would therefore discard it.

The practical result is a green light for regulators to act with little concern for the costs of getting it wrong, reinforced by a policy audience that sometimes seems to welcome intervention—especially against the largest firms—for its own sake.

Even without the benefit of hindsight, the transactions discussed here rested on tenuous factual grounds for inferring a material competitive threat. In two cases—JetBlue-Spirit and Illumina/Grail—regulators rejected significant concessions from acquirers that likely would have preserved the transactions’ efficiencies while safeguarding against anticompetitive side effects.

This rush to intervene reflects too little attention to the error costs of unwarranted action, especially in cases involving nascent markets, as in Illumina/Grail; financially precarious targets, as in Amazon-iRobot and, to a lesser extent, JetBlue-Spirit; or small acquisitions, as in Qualcomm-Autotalks. Those costs seem to have been overlooked by regulators as they challenged transactions based on largely conjectural theories of anticompetitive harm.

The interventions discussed here did little to enhance competition. In JetBlue-Spirit, intervention likely reduced it. They also contributed to significant losses in shareholder value and employment, reduced startup valuations, and delayed technological deployment.

Some scholars, advocates, and policymakers may place little weight on the costs of erroneous intervention. These merger challenges show why that confidence is misplaced. Error costs are not an abstraction; they are borne by the consumers, entrepreneurs, and workers that regulators purport to protect.

The post False Positives, Real Casualties: The High Price of Populist Antitrust appeared first on Truth on the Market.

That is the problem now facing the Federal Communications Commission’s (FCC) Lifeline program, which was designed to ensure that low-income Americans can connect to the communications networks modern life depends on. As the program has expanded to cover new services, the Universal Service Fund (USF) contribution factor—essentially a tax on consumer phone bills—has climbed to 37%. That makes every dollar lost to fraud more than an accounting error. It is a charge passed on to the consumers funding the system.

Reasonable people can debate the merits of the existing USF structure, and of broadband-subsidy programs more generally. But if regulators want to continue supporting broadband deployment and adoption, those programs should maximize benefits while minimizing costs.

USF costs have risen largely because support has expanded to broadband. But waste, fraud, and abuse still drive costs higher without producing any offsetting public benefit. Most notably, the FCC Office of Inspector General (OIG) found that providers in states that opted out of federal verification systems collected nearly $5 million in reimbursements tied to more than 116,000 deceased individuals over just five years. Roughly 40% of those payments went to individuals who may have died before they ever enrolled.

Eliminating waste, fraud, and abuse matters. But doing so also carries costs. Compliance burdens that are too heavy, eligibility rules that are too blunt, or verification requirements that are too cumbersome can prompt eligible households to abandon enrollment. They can also push providers out of thin-margin markets rather than absorb mounting administrative overhead.

Fortunately, the FCC could take two immediate steps to address this specific fraud with minimal burden on consumers.

First, the FCC should require all states to use federal verification systems, ensuring that up-to-date mortality information is used to verify claims. Second, it should require a secondary consent mechanism to confirm that the individual subscribed to a Lifeline-supported service is actually eligible and receiving that service.

Taken together, these reforms would directly target one of the Lifeline program’s most significant fraud vectors without undermining participation in the program. 

The Fine Line Between Oversight and Overkill

Fraud reduction and program access are not competing values. Still, policies designed to reduce fraud can also make it harder for legitimate recipients to access the program. Section 254 of the Communications Act requires the FCC to promote affordable access to quality communications services. That means every proposed reform should be judged not only by how much fraud it eliminates, but also by the burdens it imposes on eligible households and the providers that keep the program running.

Eliminating waste, fraud, and abuse has obvious value. Every dollar paid to a deceased subscriber, a fictitious enrollee, or a provider that never delivered service is a dollar that did not reach a qualifying family trying to afford a phone bill. With the USF contribution factor now at 37%, the surcharge has become one of the largest hidden taxes on American telecommunications consumers. Improper payments therefore impose a direct and ongoing financial cost on the very people subsidizing the system.

Reducing waste, fraud, and abuse also stretches existing program dollars further, lowers the contribution burden on ratepayers, and helps restore the legitimacy that any subsidy program needs to maintain public and political support.

At the same time, anti-fraud reforms are not costless. Compliance burdens that appear modest on paper can become decisive in thin-margin markets. When the FCC layers on enhanced compliance plans, mandatory audits, expanded financial disclosures, and duplicative reporting obligations, providers must decide whether participation in Lifeline still makes economic sense.

When the answer is no, providers scale back operations, exit high-cost markets, or leave the program altogether. The subscribers they serve then lose access. Overly rigid rules can produce the same result through a different mechanism, deterring eligible households from enrolling or remaining enrolled.

The goal of Lifeline reform should be a program that is harder to defraud and easier to use—not one that replaces a fraud problem with an access problem of equal or greater magnitude.

Fraud Thrives in Fragmented Systems

Earlier this year, the FCC OIG released findings that exposed a structural fraud problem in the Lifeline program. The report found that providers in states that opted out of the National Lifeline Accountability Database (NLAD) collected nearly $5 million in reimbursements tied to more than 116,000 deceased individuals over a five-year period. Roughly 40,000 of those enrollments appear to have occurred after the subscriber had already died. 

The OIG also uncovered widespread duplicate claims, including cases in which the same individuals were enrolled simultaneously in multiple states. Taken together, the findings describe a verification system with gaps large enough to sustain systematic abuse at scale, year after year, without detection.

The opt-out framework was intended to give states flexibility. In practice, the OIG’s findings suggest that flexibility came at a substantial cost. State-run systems lack the direct, real-time connections to federal mortality and identity databases that the National Verifier uses. States can also adopt rules that conflict with federal policy.

California offers a particularly striking example. The state eliminated requirements that applicants submit Social Security numbers, making it impossible for the Universal Service Administrative Co. (USAC) and the FCC to reliably verify Lifeline enrollment and reimbursement claims. These state-by-state variations have also produced uneven outcomes, creating inequities among participants depending on where they live. 

The most straightforward solution is to eliminate the opt-out framework entirely and require all remaining states to transition to the National Verifier on a clear and reasonable timeline. That reform would directly address the vulnerabilities identified by the OIG while imposing minimal burdens on legitimate participants.

Federal verification systems already perform functions that state systems have demonstrably failed to perform. The National Verifier runs automated death checks against authoritative federal mortality data, applies uniform identity-verification standards nationwide, and conducts real-time or near-real-time eligibility checks that do not depend on varying state policies or administrative capacity.

Centralizing verification would also reduce compliance burdens on providers. Right now, providers operating in multiple states must navigate different verification systems, documentation requirements, and reimbursement processes depending on the jurisdiction they serve. A single federal framework would replace that patchwork with a more predictable and administrable system. 

One More Click, Much Less Fraud

In addition to creating a single verification system, regulators should consider adopting a secondary-consent requirement. Under such a rule, any Lifeline enrollment or benefit transfer would require affirmative confirmation from the actual subscriber before completion.

The mechanics are straightforward. When an enrollment or transfer is initiated, the system sends a confirmation message to the applicant’s contact information on file. The transaction proceeds only if the individual responds affirmatively. The reform would not impose new documentation requirements or tighten eligibility standards. It would simply confirm that the person named on the application is the same person who actually wants the service.

That reform would target two common forms of enrollment fraud.

First, when fraudsters fabricate contact information, nobody can respond to the confirmation request, and the enrollment fails automatically. Second, when someone uses a real person’s information without that individual’s knowledge or consent, the legitimate subscriber receives an unexpected notification and can reject the transaction. What otherwise would have become a completed fraud instead becomes a detectable red flag that triggers scrutiny rather than reimbursement.

A secondary-consent mechanism would also help curb fraud in benefit transfers. Under the current NLAD framework, a provider can initiate a transfer of a subscriber’s benefit to a new carrier based largely on the provider’s own certification of consent. Applying secondary verification to transfers would reduce the ability of fraudulent actors to use benefit transfers as a workaround for existing verification checks. A subscriber who did not authorize a transfer would receive notice before it occurs and could block it.

As with eliminating state opt-out verification systems, the burden on legitimate users would be negligible. In most cases, the reform would require nothing more than a single tap or reply—the sort of action smartphone users perform dozens of times each day in other contexts. It would be far less onerous than the annual recertifications, officer certifications, and documentation requirements the program already imposes on both subscribers and providers.

Paired with the opt-out reforms discussed above, a secondary-consent requirement would directly target one of the Lifeline program’s remaining major fraud vectors without undermining participation. 

Dead Subscribers Make Bad Public Policy

The Lifeline program exists to ensure that low-income Americans can access the communications networks modern economic and civic life increasingly requires. If federal regulators intend to preserve the program, they must also ensure that it operates efficiently and does not impose costs that outweigh its benefits.

Every dollar lost to a deceased subscriber, a fictitious enrollee, or an unauthorized transfer is a dollar that never reached a qualifying household. It is also a cost shifted onto the ratepayers funding the system and another blow to the public legitimacy that keeps the program politically sustainable.

The reforms outlined above directly target the structural weaknesses identified in the OIG’s findings. Just as importantly, they do so without raising eligibility barriers, increasing documentation burdens, or imposing compliance costs that could drive providers out of thin-margin markets.

The broader lesson for Lifeline modernization is that the FCC does not have to choose between program integrity and program access. That tradeoff becomes unavoidable only when regulators ignore who bears the costs of reform and whether those costs are proportional to the expected gains.

A rigorous cost-benefit framework—one that weighs the harms of fraud and overregulation with equal seriousness—can produce targeted, high-impact, low-burden reforms. Done properly, Lifeline reform can create a program that is harder to defraud, easier to administer, and more reliably available to the households that depend on it most.

Because a subsidy program that cannot distinguish between a living subscriber and a dead one is not merely inefficient—it is unsustainable.

The post The Lifeline Program’s Afterlife Problem appeared first on Truth on the Market.

That trend creates a real risk of confusion. Antitrust is not supposed to function as an all-purpose ministry of industrial policy. At the same time, antitrust cannot pretend modern competition is captured by a static snapshot of domestic market shares. In innovation-driven global markets, firms compete through scale, capabilities, investment, and technological change—not just price levels in narrowly defined markets.

Policymakers increasingly invoke this sense of global “competitiveness” in debates over merger control, innovation, resilience, scale, and global rivalry. The term is often frustratingly imprecise. At its worst, it becomes a polite euphemism for protectionism: approve this merger, excuse this restraint, or subsidize this favored firm because it is “ours.” That version of competitiveness has no legitimate place in antitrust analysis. It substitutes national favoritism for competition on the merits.

There is, though, a more coherent—and more defensible—understanding of competitiveness. Properly defined, competitiveness refers to firms’ ability to succeed on the merits by operating efficiently, innovating, and serving customers effectively. Understood that way, competitiveness is not a rival to consumer welfare. It is one of the primary mechanisms through which consumer welfare emerges over time.

That distinction matters. The competitiveness concern worth taking seriously is not whether a domestic firm becomes larger, more politically influential, or better insulated from rivals. The relevant question is whether antitrust analysis accurately accounts for productive efficiencies, dynamic capabilities, innovation incentives, global competitive constraints, and the long-run process through which firms create value for consumers. That distinction should frame the current debate.

The ‘National Champion’ Temptation

There is nothing new about claims that antitrust enforcement may undermine national competitiveness. In the United States, merging parties and public officials have long argued that firms need greater scale to compete against larger domestic or foreign rivals. Courts have given those arguments a mixed reception, but the underlying legal principle has remained relatively stable: a merger that substantially lessens competition in a relevant market is not redeemed merely because it produces a larger or more prominent firm.

The canonical example is United States v. Philadelphia National Bank, where the Supreme Court rejected the argument that a merger was necessary to compete more effectively against larger banks. The decision gave rise to the now-familiar structural presumption, which continues to loom large over merger analysis. Critics have increasingly argued that the presumption encourages undue reliance on concentration metrics and industry structure, rather than evidence of actual competitive effects.

The problem is not that market structure is irrelevant. It is that structure is not destiny. A presumption that may have offered administrative convenience in 1963 fits poorly in industries shaped by rapid innovation, multisided platforms, intellectual property, global supply chains, and dynamic entry.

When “competitiveness” is invoked merely to wave away anticompetitive concentration, antitrust should reject it. But when the term is used to challenge overly mechanical reliance on market shares and to demand a fuller account of rivalry over time, antitrust should pay attention.

The European experience illustrates both the appeal and the danger of competitiveness rhetoric. The European Commission’s 2019 decision blocking the Siemens/Alstom Franco-German rail merger triggered calls for a more permissive policy toward “European champions.” That reaction raised legitimate concerns that competitiveness could become a backdoor around merger law. A large European firm shielded from European rivals may become less—not more—capable of competing globally. Protecting firms at home can dull the very competitive pressures that make them formidable abroad.

At the same time, Europe’s more recent debate has become notably more nuanced. The Draghi Report, the European Commission’s competitiveness agenda, and the Commission’s 2026 draft merger-guidelines process all reflect growing interest in scale, innovation, investment, resilience, and dynamic effects. That marks a meaningful shift, but not necessarily a retreat from competition. The critical question is whether those considerations enter merger analysis through disciplined economic reasoning or through ad hoc political exceptions.

Competition Is More Than Market Share

Competitiveness should mean the ability to win in the marketplace by offering better products, lower costs, higher quality, faster innovation, greater reliability, or more effective commercialization. Properly understood, that definition fits comfortably within a procompetitive consumer-welfare framework. Consumers benefit not only from lower prices today, but also from the new products, improved services, more resilient supply chains, and cost reductions that innovation generates over time.

This is where David Teece’s framework of dynamic competition becomes especially important. Teece argues that antitrust analysis has too often privileged static models centered on price, output, and concentration, while undervaluing innovation, entrepreneurship, and firm capabilities. In a 2025 Antitrust Law Journal article, he emphasizes that firms frequently compete for markets, across future product spaces, and against “unseen” potential rivals—not merely against visible incumbents in narrowly defined present-day markets.

That insight is not a license to abandon antitrust. It is an argument for doing antitrust better. A firm with a large market share is not necessarily insulated from competition if technological change, customer heterogeneity, and entrepreneurial entry constrain its behavior. By the same token, a firm with only a modest current share may possess assets or capabilities that make it a significant future competitive constraint. Antitrust analysis that ignores those dynamics risks condemning conduct that promotes innovation while approving conduct that suppresses it.

Teece’s argument also highlights the limits of merger analysis built primarily around concentration metrics. Dynamic competition depends on capabilities: R&D assets, engineering talent, data, manufacturing expertise, complementary technologies, commercialization capacity, and organizational adaptability. Current market shares do not always capture those realities.

In innovation-driven markets, the key question is often not “how many firms sell similar products today?” but “which firms possess the capabilities, incentives, and opportunities to constrain one another tomorrow?”

That is the competitiveness question antitrust should be asking.

Innovation Cuts Both Ways

The recent enthusiasm for “innovation competition” in merger enforcement has often become strikingly one-sided. Agencies increasingly argue that mergers may reduce innovation incentives, eliminate nascent rivals, or dampen future product development. Sometimes those concerns are well-founded. But an antitrust policy that recognizes only innovation harms while discounting innovation benefits is systematically biased against welfare-enhancing transactions.

In a 2024 Journal of Business & Technology Law article co-authored with Daniel Spulber, I argued that merger policy should not presume mergers either diminish or enhance innovation competition. The relevant inquiry is transaction-specific: how does a particular deal affect innovation incentives, investment, commercialization, and consumer welfare? The article also warns that presumptions of innovation harm may themselves suppress innovation by chilling transactions that provide innovators with capital, complementary assets, or viable exit opportunities.

That concern is not theoretical. Innovation is not magic. It requires financing, appropriability, complementary assets, managerial expertise, and viable routes to market. In some cases, a startup acquisition may eliminate a potential competitor. In others, it may encourage entrepreneurship by making entry more attractive in the first place. An acquisition may allow an invention to be scaled, manufactured, integrated, distributed, or commercialized far more effectively than the startup could achieve on its own.

The same logic applies to both horizontal and vertical mergers. A horizontal merger may reduce rivalry along some dimensions, but it may also combine complementary R&D programs, eliminate duplicative fixed costs, generate scale economies, or support more ambitious innovation investments. A vertical merger may raise foreclosure concerns, but it also may solve coordination problems, reduce double marginalization, protect relationship-specific investments, or improve the commercialization of emerging technologies.

The appropriate policy response is not blanket permissiveness. It is analytical symmetry. If agencies may advance innovation-based theories of harm, merging parties must be permitted to present innovation-based theories of benefit. Those benefits should not be discounted merely because they are dynamic, difficult to quantify, or likely to emerge over a longer horizon than short-run price effects.

Not Every Synergy Slide Is Fiction

A competitiveness-informed antitrust policy must take efficiencies seriously. That does not mean accepting every consultant slide labeled “synergy.” It means recognizing that efficiencies are often the reason firms compete more effectively and consumers benefit.

The current U.S. approach remains too cramped. The 2023 Merger Guidelines from the U.S. Justice Department (DOJ) and Federal Trade Commission (FTC) acknowledge innovation as a dimension of competition, but they place far greater weight on structural presumptions and theories of harm than on efficiencies, innovation benefits, or dynamic competitive gains. That imbalance carries real costs. It risks treating scale, scope, and integration as inherently suspicious simply because they make firms larger.

A better framework would distinguish procompetitive scale from market power. Scale can be harmful when it entrenches dominance, raises entry barriers, or facilitates exclusionary conduct. But scale also can be indispensable in high-fixed-cost, R&D-intensive, capital-intensive, and globally competitive industries. Semiconductor fabrication, next-generation wireless networks, pharmaceuticals, cloud infrastructure, aerospace, and advanced manufacturing all require enormous sunk investments and involve uncertain returns. In those settings, mergers that achieve efficient scale or combine complementary capabilities may strengthen competition rather than weaken it.

The European Commission’s 2026 draft merger-guidelines discussion appears to move in that direction. It treats scale, innovation, investment, and resilience as potentially procompetitive factors, while insisting those claimed benefits be distinguished from mere exercises of market power. That is the correct framework. The challenge is maintaining analytical discipline. Claimed efficiencies should be merger-specific, verifiable, and likely to benefit consumers. At the same time, agencies should not impose evidentiary burdens so demanding that dynamic efficiencies become effectively impossible to prove.

The same principle applies to out-of-market and long-term efficiencies. Traditional merger analysis is understandably reluctant to balance harms in one market against benefits in another. Antitrust should not casually sacrifice one group of consumers to benefit another. But rigid market-by-market analysis can break down in interconnected innovation ecosystems, multisided platforms, global supply chains, and international markets. A merger may generate dynamic benefits that do not map neatly onto the narrowly defined market in which short-run price effects are alleged.

That does not require embracing an unconstrained total-welfare standard. It simply requires recognizing that consumer welfare is forward-looking. Consumers are harmed not only by higher prices today, but also when antitrust blocks transactions that would have produced better products, faster innovation, more resilient supply chains, or lower future costs.

Administrability Is Not Accuracy

A dynamic approach to antitrust must also take error costs seriously. False negatives matter. Anticompetitive mergers and exclusionary conduct can distort markets and harm consumers. But in innovation-driven industries, false positives—condemning or deterring procompetitive conduct—are often even more damaging.

The reason is straightforward. Antitrust intervention can block or chill experimentation, integration, and investment whose benefits are uncertain, but potentially enormous. The costs of an erroneous prohibition are often invisible because the innovation never occurs, the product never launches, the startup never receives funding, or the efficiency is never realized. By contrast, many false negatives can be corrected over time through entry, technological change, follow-on enforcement, private litigation, or ordinary market adjustment. Not always, of course. But often enough that antitrust policy should avoid excessive confidence in static predictions about dynamic markets.

That point matters especially in merger review. Agencies must evaluate transactions quickly, under substantial uncertainty, and before the full effects of a merger can possibly be known. Those institutional realities naturally encourage administrable presumptions. But administrability is not the same thing as accuracy. A framework that systematically discounts efficiencies and dynamic competition will deter transactions at the margin. Firms will abandon deals, restructure investments, or avoid collaborations that could have improved consumer welfare.

A properly framed competitiveness lens should therefore make agencies more humble, not more passive. It should encourage better questions. Is the market genuinely domestic, or do global rivals meaningfully constrain behavior? Do current market shares tell the relevant story, or do capabilities, assets, and innovation pipelines matter more? Are claimed efficiencies speculative, or are they grounded in identifiable business realities? Would the merger expand output, accelerate innovation, or improve commercialization? Are there remedies capable of preserving competition while allowing efficiencies to proceed?

Those are not loopholes. They are what serious antitrust analysis looks like.

Remedies Are Not Theater

A competitiveness-sensitive approach also has implications for remedies. When a transaction threatens competitive harm but also promises meaningful efficiencies, agencies should ask whether a remedy can preserve rivalry without destroying the deal’s procompetitive rationale.

Structural remedies will often remain the cleanest option. But reflexive hostility toward behavioral or enabling remedies can be counterproductive. Access commitments, licensing arrangements, firewalls, interoperability requirements, or investment obligations may sometimes preserve competition while allowing the benefits of scale or integration to proceed. The United Kingdom’s recent merger-remedies reforms reflect this more pragmatic approach. Those reforms show greater openness to behavioral remedies at Phase 1 and distinguish between enabling remedies and more intrusive controlling remedies.

The United Kingdom Competition and Markets Authority’s (CMA) clearance of the Vodafone/Three merger is illustrative. The CMA initially raised concerns about a four-to-three mobile-network merger, but ultimately accepted commitments tied to substantial 5G investment. That does not mean every telecommunications merger deserves approval. It does suggest agencies can sometimes protect consumers more effectively by securing rivalry-enhancing investment than by blocking a transaction outright.

At the same time, remedies cannot become theatrical substitutes for competition. Investment commitments, price caps, and access obligations can prove difficult to monitor and enforce. They also may distort incentives or push antitrust agencies into the role of sector regulators. A remedy that merely papers over an anticompetitive merger solves little. But a remedy that preserves meaningful competitive constraints while allowing merger-specific innovation or scale efficiencies to proceed can remain fully consistent with consumer welfare.

The Qualcomm litigation offers a related lesson. Remedies affecting intellectual property can have profound consequences for innovation incentives, appropriability, and global technological leadership. A remedy compelling broad licensing of valuable technology may appear to expand access in the short run while weakening the incentives that made the technology possible in the first place. The 9th U.S. Circuit Court of Appeals’ rejection of the FTC’s theory in FTC v. Qualcomm avoided a remedy that could have undermined both innovation and competitiveness.

Antitrust Should Not Run on Vibes

The greatest danger in adding “competitiveness” to the antitrust vocabulary is not that agencies will discuss scale, innovation, or resilience. They should. The real danger is that competitiveness becomes a euphemism for unreviewable discretion.

An agency could define markets broadly to obscure competitive harm. It could accept weak efficiency claims because the merging firms are politically favored. It could approve a domestic “champion” while saying little about the resulting consumer tradeoffs. Or it could block a procompetitive transaction based on speculative innovation harms that no one can meaningfully test.

That is why transparency matters. If competitiveness refers to efficiency, innovation, investment, resilience, and dynamic rivalry, agencies should say so explicitly. They should explain how those considerations enter the analysis, identify the evidence necessary to establish them, and distinguish consumer-benefiting competitiveness from simple producer favoritism. And when agencies are making difficult tradeoffs, they should admit it.

A transparent competitiveness framework should rest on six principles:

• Competitiveness must remain tied to competition on the merits. Nationality alone should not qualify as a cognizable antitrust benefit. A firm does not deserve favorable treatment simply because it is domestic.
• Agencies should explicitly account for dynamic competition. In some markets, innovation pipelines, technological capabilities, potential entry, appropriability, and commercialization assets matter more than current market shares.
• Efficiencies should receive symmetrical treatment. If agencies are willing to credit speculative innovation harms, they also should be willing to credit well-supported innovation benefits.
• Evidentiary burdens should remain realistic. Dynamic efficiencies are often harder to quantify than short-run price effects, but that does not make them imaginary.
• Agencies should evaluate remedies pragmatically. The objective is to preserve competition and consumer welfare—not to maximize agency leverage or reflexively punish scale.
• Agencies must remain alert to public-choice risks. Competitiveness rhetoric can easily be captured by incumbents seeking protection from competition rather than competition itself.

Competitiveness, on Antitrust Terms

The best response to competitiveness concerns is not to weaken antitrust. It is to restore antitrust to its proper economic foundation.

A consumer-welfare framework is not limited to short-run price effects. Properly understood, it encompasses quality, innovation, variety, output, resilience, and long-run productivity growth. It asks whether challenged conduct harms the competitive process and thereby harms consumers. It does not condemn size for its own sake. It does not assume concentration is inherently harmful. It does not relegate efficiencies to an afterthought. And it does not mistake competitors’ complaints for harm to competition.

That framework is fully capable of incorporating competitiveness—provided competitiveness is defined correctly. Efficient firms are competitive. Innovative firms are competitive. Firms that use scale to lower costs, improve quality, or develop new products are competitive. Firms that succeed globally because they serve customers better are competitive. Antitrust should not obstruct those outcomes.

By contrast, firms seeking protection from rivalry are not competitive in any meaningful sense. Firms that merge to gain pricing power, suppress innovation, exclude rivals, or divide markets are not advancing competitiveness. They are reducing it.

The policy challenge is distinguishing between the two.

A dynamic, evidence-based antitrust policy would be more skeptical of structural shortcuts, more attentive to innovation and firm capabilities, more receptive to efficiencies, more pragmatic about remedies, and more cautious about false positives. It still would condemn anticompetitive mergers and exclusionary conduct. But it would not allow antitrust to become a barrier to the competitive process it is supposed to protect.

Competitiveness therefore belongs in antitrust analysis—but only on antitrust terms. Properly understood, it is not an industrial-policy escape hatch from consumer welfare. It is a reminder that consumer welfare emerges from firms competing, innovating, investing, and improving over time.

The post Competitiveness Without the Cronyism appeared first on Truth on the Market.

A recent post by Dean Ball, a senior fellow at the Foundation for American Innovation, tees up that familiar tension. On one hand, there is a strong case against broad, ex ante regulation of AI systems. On the other, highly capable systems may pose risks that are hard to dismiss—particularly in areas like cyber operations or biosecurity. The question is how to reconcile those instincts without defaulting to heavy-handed control. 

That framing is useful. It also leaves something important underspecified.

The debate still tends to treat “AI” as a single object of governance, rather than a layered system in which different interventions operate in very different ways. Knowing Dean Ball personally, I doubt he intended that simplification. But his post could leave that impression. It is worth unpacking why the distinction matters.

Once those layers collapse, any risk can justify sweeping oversight. Once they are separated, many proposed interventions look far less precise than their advocates suggest.

Intermediaries, Incentives, and Illusions of Independence

One proposal gaining traction in this space is the idea of independent verification organizations (IVOs). The basic structure is straightforward: governments set outcome-based safety goals, and licensed third parties verify whether AI systems meet them. Firms that opt in receive certification benefits, including reputational gains and, in some cases, legal safe harbors. 

At a high level, this moves beyond traditional command-and-control regulation. It aims to shape incentives rather than dictate design choices, while leveraging technical expertise outside the state. That has real appeal. In simulated environments, verification appears to push firms toward engaging underlying problems, rather than defaulting to defensive posturing.

Once you move from concept to implementation, though, the structure becomes clearer. Independent verifiers need licenses. They need access to proprietary systems. They need authority to compel information disclosure and, ultimately, to suspend or revoke certifications. Without those features, the framework does not work as intended.

In other words, what starts as a market-based mechanism depends on a fairly robust enforcement apparatus. The system does not replace state oversight so much as reorganize it around a new set of intermediaries. If the underlying problem is that legal categories fail to track capability, building a system that depends on those categories being precise enough to audit may not solve the problem—it may just relocate it.

That raises familiar institutional concerns. Verifiers are paid by the firms they assess. They operate in a setting where maintaining relationships can conflict with enforcing standards. The parallels to earlier verification regimes—most notably the credit-rating ecosystem—are hard to miss. Ensuring independence in that context is not a theoretical challenge. Relatedly, system-level interventions risk shaping competitive outcomes in ways only loosely tied to technical merit, with downstream effects across the broader ecosystem.

More fundamentally, the IVO model operates at a level that does not map well onto the risk profile or structural characteristics of AI systems. It evaluates systems as a whole, monitors deployments, and triggers recertification when systems change. That pulls policy toward continuous oversight of model behavior. It also blurs the line between public policy and private contracting. That boundary matters, particularly in an international context, where firms’ credibility as independent actors underpins their global position.

If the concern is misuse of specific capabilities—as it arguably should be—this is an indirect and often inefficient approach.

Ball’s recent comments on EconTalk underscore the tension. He flagged the mismatch between legal categories and technical capability, as well as the risks of state leverage over private firms. Those concerns complicate proposals that rely on formal verification regimes backed by enforcement authority. These frameworks aim to solve real problems, but they depend on precisely the kind of definitional clarity and institutional stability that remain unsettled. 

Mind the Gap Between Law and Capability

A more useful starting point is to ask where risk actually attaches.

In most of the scenarios driving concern, the issue is not the existence of a model. It is the availability of particular capabilities, and the conditions under which those capabilities can be accessed and deployed. Ball acknowledged this on EconTalk, noting that legal categories like “surveillance” increasingly fail to track real-world capability once AI scales analysis across commercially available data. That gap between law and capability is exactly where misuse risk emerges. 

That framing points to a different set of policy levers—ones focused on capability and access, rather than general system-level oversight.

For example, policymakers could develop more structured approaches to monitoring access to the most sensitive capabilities. Mechanisms that allow providers to detect anomalous use patterns or identify high-risk actors may play a role. Something analogous to know-your-customer (KYC) frameworks for certain high-end access points is at least worth considering, even if it raises its own concerns.

The point is not that this approach is obviously correct. It is that it targets the margin where misuse actually occurs.

The Best Defense Is More AI

There is also a broader dynamic that tends to get lost in these discussions: the same capabilities that create risk are also the tools needed to manage it. Defensive uses of AI are not peripheral. They are central to any plausible equilibrium.

Managing misuse will depend on the widespread deployment of defensive systems that can detect, interpret, and respond to anomalous behavior in real time. Those capabilities will need to spread across firms and, over time, to the local and individual level. Security in this environment will not be centralized. It will depend on the diffusion of capability across the ecosystem.

What changes is not just capability, but scale. Systems that once required scarce human attention can now operate across entire populations. As Ball put it, “AI [creates] ‘millions and tens of millions of analysts.’” That dynamic does not just increase risk; it raises the stakes for how widely defensive capabilities are deployed.

There is an intuitive way to think about this, drawn from early conceptions of networked computing in William Gibson’s cyberpunk novels. Users relied on persistent, individualized defensive systems operating at the edge. However stylized, that intuition points in the right direction. Security scales through distribution, not centralized control.

Frameworks that concentrate evaluation and oversight in a small set of licensed intermediaries risk slowing that diffusion. They may improve certain forms of accountability, but they also push the system toward centralization at precisely the moment when distributed defensive capacity matters most.

A simpler approach may prove more effective: establish clear rules of the road around specific forms of misuse, make those rules legible and enforceable, and then allow firms to operate within those constraints. That structure creates strong incentives to develop both offensive and defensive capabilities in response to changing conditions.

This approach does less from an institutional-design perspective. It does not try to build a comprehensive governance layer over AI systems. It does, however, align more closely with how these systems evolve in practice. It preserves flexibility, supports experimentation, and allows defensive capabilities to emerge and scale more organically.

When Oversight Becomes the Risk

None of this eliminates the tension Ball identifies. The need to manage real risks coexists with a well-founded concern about overbroad intervention. That tension is not going away.

The question is how to navigate it. The answer, in my view, is to be precise about where intervention occurs, resist the pull toward general system-level oversight, and avoid constraining the development of defensive capabilities in the name of safety.

If policy is going to shape this space, it should do so at the margins where it is most effective, while preserving the broader ecosystem that drives both innovation and resilience.

Get the margins right, and the system has room to adapt. Get them wrong, and oversight becomes the risk.

The post AI Risk and the Very Large Hammer appeared first on Truth on the Market.

As a description of how value gets created at the technological frontier in 2026, it is badly outdated.

Watch Jensen Huang’s recent conversation with Dwarkesh Patel and the gap becomes obvious. Asked whether NVIDIA risks commoditization, Huang does not just answer the question—he reframes it. On its face, the concern is reasonable. NVIDIA “sends a GDS2 file to TSMC,” which fabricates the dies, packages them with high-bandwidth memory (HBM) from SK Hynix and Micron, and hands them off to a Taiwan-based original design manufacturer (ODM). Meanwhile, NVIDIA “fundamentally makes software that other people manufacture.” 

The jargon is worth unpacking because it is the point. A GDS2 file is the final design file used to manufacture a chip. Taiwan Semiconductor Manufacturing Co. (TSMC) fabricates the chip dies. HBM is the fast memory stacked close to advanced processors. An ODM builds products for another company to sell under its own brand.

In other words, the “product” is not a thing NVIDIA simply makes and ships. It is a coordinated chain of design, fabrication, packaging, memory, systems, software, and deployment.

Huang’s response shifts the lens. NVIDIA’s job, he says, is to turn “electrons to tokens”—to do “as much as necessary, as little as possible.” Whatever the firm does not need to do, it pushes to partners. Whatever it does do, it structures so that everyone else can coordinate around it.

That’s the business.

NVIDIA’s moat is not the graphics processing unit (GPU). It is the coordination layer that makes hundreds of upstream and downstream actors rational in betting on its platform. That list runs long: TSMC, ASML, SK Hynix, Lumentum, Coherent, hyperscalers—the giant cloud providers that buy and operate massive computing infrastructure—artificial-intelligence (AI) labs, framework communities, application developers, financiers, and even, as Huang half-jokes, the plumbers and electricians wiring data-center buildouts.

In short, ecosystem orchestration—not discrete product innovation—is the dominant value-creation pattern in the AI economy.

That reality does not map cleanly onto the categories regulators reach for: single-product monopoly, classic two-sided “matching” platforms, or vertical foreclosure. It also cuts against policymakers’ instincts about where innovation comes from. If the underlying economic phenomenon is ecosystem coordination, analysis that fixates on discrete—and often poorly defined—“products” will miss the mark.

Get the framework wrong, and policy will not just misfire. It will penalize the coordination that drives growth.

The costs do not stay contained. Broad export controls aimed at slowing Chinese competition will also hit U.S. firms and erode the durability of American technological leadership. That tradeoff remains underweighted in a policy debate still anchored to a chip-centric view of the industry.

The Five-Layer Cake and the Real Business Beneath It

Huang’s mental model is that AI is “a five-layer cake.” Energy sits at the bottom—along with capital and other supply-chain inputs—followed by chips, systems and networking, the model layer, and, at the top, applications. NVIDIA’s job is not to own each layer. It is to make them mutually rational. 

That framing matters for competition policy, regulation, trade policy, and national-security policy in chips and AI. Huang’s interview surfaces four mechanisms that do the work.

Start with upstream–downstream coordination as market-making. Suppliers—Micron on memory, TSMC on logic and packaging, and the silicon-photonics ecosystem on networking—invest at scale because Huang has aligned them around a roadmap they trust. Downstream demand from hyperscalers and AI-first firms makes those forecasts credible.

NVIDIA’s GPU Technology Conference (GTC), on this view, is not just a conference. It is a full-scope market-making event—“the entire universe of AI all in one place”—where “the downstream could see the upstream, the upstream could see the downstream.” The coordination function is the product. 

Next is installed-base gravity. Even hyperscalers that can write their own kernels—the small pieces of code that tell processors how to perform specific computations—still want their software to run across the largest possible installed base: clouds, on-premises systems, robots, old GPUs, and new GPUs alike.

Compute Unified Device Architecture (CUDA), NVIDIA’s proprietary software platform and programming model, derives its value not just from kernel performance, but from portability across hundreds of millions of GPUs in varied environments. That is a standard platform network-effect story: the platform becomes more valuable as more users, developers, and tools build around it. The relevant question is not “whose chip is faster?” It is “whose installed base is bigger?”

A third mechanism: complementors as demand multipliers. Complementors are firms or developers whose products make another platform more useful. Much of the AI debate assumes “AI eats software”—agents replace tools, and the industry compresses. Huang argues the opposite. Agents will use Excel, Synopsys, Cadence, compilers, and design tools. That means more instances of those tools, not fewer.

Today, the number of users of Synopsys’ design compiler is capped by the number of human engineers. If agents can drive those tools, that cap moves up by orders of magnitude. AI does not displace complementors; it multiplies them. The ecosystem expands.

The same logic extends to labor markets. When AI complements tools rather than replaces them, demand rises for both the tools and the workers who use them. That aligns with early empirical evidence on AI’s labor effects and cuts against the zero-sum displacement story (see here and here). 

Finally, roadmap credibility becomes part of the product. Blackwell, Rubin, Rubin Ultra, and Feynman—NVIDIA’s successive GPU architectures and system platforms, announced on a predictable cadence to signal future performance and capability—anchor expectations across the ecosystem. Customers planning $100 billion AI factories need a supplier they can plan around. Predictability is a feature. “I can completely depend on them,” Huang says of TSMC. A credible roadmap makes upstream and downstream coordination self-reinforcing.

Put it together and NVIDIA’s market power, if that is the right term, is not “we make the best GPU.” It is “we make it rational for adjacent actors to coordinate around us.” That is a stronger—and more demanding—claim than CUDA lock-in or GPU path dependence.

In that sense, NVIDIA looks less like a traditional manufacturer and more like an ecosystem orchestrator. It resembles what Michael Cusumano, Annabelle Gawer, and David Yoffie describe as “platform leadership,” and what Marco Iansiti and Roy Levien call a “keystone.” It also tracks Alden Abbott’s observation that “firms that look like rivals in one dimension may act as complementors in another, and a static antitrust lens can easily misread value-creating coordination as competitive harm.”

This framing carries implications.

First, a two-sided-market analysis fixated on price differences across sides will miss the action. The relevant unit is the coordination surface connecting many specialized actors. Conduct that looks like self-preferencing or vertical foreclosure in a single-product frame often functions, in an ecosystem frame, as the set of hooks that makes complementor investment rational. Blanket bans on self-preferencing risk depressing integration and chilling that investment.

The depth of the moat depends as much on complementor switching costs as on user switching costs—and both may be lower than they first appear. 

More broadly, the regulatory, trade, industrial-policy, and national-security stakes do not reduce to any single layer of the stack. If the relevant economic unit is the ecosystem, policy that targets component shares, static market structure, or bilateral firm conduct risks aiming at the wrong object.

Antitrust can mistake coordination for exclusion. Trade policy can weaken the installed base, developer mindshare, and complementor density that sustain U.S. advantage. Industrial policy can overfocus on fabs—semiconductor fabrication plants—while underinvesting in standards, software, and downstream adoption. National-security policy can restrict hardware flows while conceding the broader ecosystem through which technological leadership reproduces itself.

The throughline is simple: Winning the chip layer is not the same as winning the system. The same holds for policy that tries to control it.

Models Compete, Ecosystems Compound

The pattern shows up clearly in a current example: Anthropic’s strategy around Claude is, at bottom, an ecosystem play.

Claude Code turns the model into a substrate that third parties extend through hooks, Model Context Protocol (MCP) servers, slash commands, and plugins. Cowork applies the same logic to non-developer knowledge work, offering a desktop layer where third-party plugins, skills, and connectors compose Claude into task-specific workflows—legal review, marketing analytics, financial modeling, and more.

MCP functions as an application programming interface (API) standard that lets this ecosystem scale without Anthropic building every integration itself.

The economics are straightforward. Each plugin, skill, and connector represents a specialized investment by an outside firm. Each one makes Claude more valuable for a particular use case. That raises users’ willingness to pay, which drives API usage, which, in turn, justifies more complementor investment.

That feedback loop—not any single feature—is what makes the position durable.

Against Claude’s recent gains, OpenAI is now visibly playing catch-up on this dimension. Not on raw model quality—where leadership shifts quarter to quarter—but on the complementor stack: custom GPTs, an apps platform, Operator, the ChatGPT enterprise tier, and Codex’s developer ergonomics.

Codex targets developers directly. At the same time, its streamlined modes suggest a push toward non-coding use cases—much as Cowork does.

This is exactly what Jensen Huang means when he warns that “ecosystems are hard to replace. It costs an enormous amount of time and energy.” In pure technological terms, OpenAI is not far behind Anthropic. But that is not the margin that decides the outcome.

The edge goes to whoever builds the largest installed base and the deepest set of third-party integrations that make users’ lives easier.

Competition, in other words, does not hinge on the model alone. It plays out across the ecosystem—and it is both intense and multidimensional.

A National Symbol Isn’t an Ecosystem

The clearest live case for the ecosystem framing—and the one with the highest policy stakes—is China’s own DeepSeek.

DeepSeek V4 launched April 23, 2026. In its technical report, the team concedes the model sits “3 to 6 months behind” the closed-source frontier. Outside observers suspect the gap is wider. CEO Liang Wenfeng framed the company’s mission around “hardcore research,” not products. He criticized Chinese tech for “freeriding on Moore’s Law” and positioned DeepSeek as a contributor to foundational innovation rather than an applications company. 

It was an earnest—and widely admired—vision.

It was also costly.

DeepSeek lost core talent to Tencent, ByteDance, Xiaomi, and DeepRoute.ai across large language models (LLMs), agents, optical character recognition (OCR), and multimodal systems, which process more than one kind of input, such as text and images. It missed China’s consumer-AI inflection point, as ByteDance’s Doubao became the most-downloaded chatbot, Alibaba’s Afu broke through in health, and firms like MiniMax and Z.ai moved toward public markets.

Operationally, it stumbled. A major training-run failure in mid-2025 hit as the firm migrated from NVIDIA to Huawei Ascend. By April 2026, DeepSeek had opened to external financing, no longer able to self-fund frontier-scale training. A Qwen employee, quoted in 36Kr, captured the shift: “the golden age of nonprofit AI development is over.”

In an ecosystem-driven economy, “failure to develop a vision of commercialization” is another way of saying “failure to build a compelling ecosystem.” Commercialization is not just revenue. It is how a frontier lab secures distribution, capital, complementor relationships, and the feedback loops that fund the next training run—and the next architectural bet.

DeepSeek did not need to become a marketplace. It needed to become an orchestrator: a substrate around which Chinese hyperscalers, application developers, chip vendors (Huawei Ascend, Cambricon, Biren), and end users could coordinate.

That role did not stay vacant. ByteDance (Doubao), Alibaba (Qwen), and increasingly closed-source Chinese labs filled it. DeepSeek became a national symbol without an industrial ecosystem.

The episode also underscores a broader point: Multiple ecosystems operate simultaneously at different layers of the stack. NVIDIA is one orchestration layer. So are firms higher up the stack. Each integrates across layers; none operates in isolation.

Huang flags this dynamic in both directions. Forcing U.S. firms out of China, he warns, risks backfiring: “if we are forced to leave China, it would be a policy mistake … it [will] accelerate their chip industry and force all of their AI ecosystem to focus on their internal architectures.”

The ecosystem argument cuts both ways. The failure of a Chinese lab to commercialize creates an opening for U.S.-aligned ecosystems. Policy that closes that window does so at a cost.

Closing the Window on Ourselves

Competition policy already understands ecosystem dynamics. Trade and national-security policymakers need to catch up.

U.S. policy on advanced-AI export controls has, by design, tried to shut the window through which American ecosystems might win. The logic is intuitive: restrict the flow of frontier accelerators—and the extreme ultraviolet (EUV) equipment used to produce them—to the People’s Republic of China (PRC), widen the capability gap, and give U.S. labs time to reach dangerous thresholds first and harden systems before adversaries catch up.

Run that logic through an ecosystem lens—and against the empirical record Huang describes—and two problems emerge. Neither is the one the policy is meant to solve.

First, the regime struggles on its own terms. Huang’s central move is to dissolve the chip-bottleneck premise. At the lowest layers of the stack, energy and chips function as partial substitutes. Where power is abundant, less-advanced chips can still deliver effective compute at scale.

The PRC has built massive excess generation capacity—“data centers that are sitting completely empty, fully powered… ghost cities… ghost data centers”—and is willing to absorb environmental, fiscal, and political costs U.S. operators will not. Under those conditions, “seven-nanometer chips are essentially Hopper… today’s models are largely trained on Hopper.” Huawei has shipped millions of accelerators, and its Ascend roadmap—now including FP4 support on the 950 family—is narrowing the per-chip performance gap export controls were meant to lock in. (FP4 refers to a very low-precision number format that can make AI computation cheaper and faster when models can tolerate the tradeoff.)

AI, as Huang puts it, is “a parallel computing problem.” More lower-end chips, paired with cheap power, can substitute for fewer high-end ones.

A second point follows. Frontier capability now turns more on algorithmic and architectural advances than on transistor scaling. Moore’s Law contributes roughly 25% annually; computer-science gains deliver order-of-magnitude improvements per generation. The International Center for Law & Economics (ICLE) has made a similar point about the limits of static performance metrics.

Roughly half of the world’s AI researchers are Chinese. The key levers—Mixture-of-Experts architectures, attention variants, low-precision numerics, and distributed training—are not export-controllable. Mixture-of-Experts models route different tasks to specialized parts of a model, making training and inference more efficient. Distributed training spreads computation across many chips or machines.

DeepSeek V4 provides a clean proof of concept. Its kernels no longer rely purely on CUDA, instead targeting a portable domain-specific language called TileLang. (A domain-specific language is a programming language designed for a narrow technical purpose.) Its post-training and inference pipelines use MXFP4 precision, another low-precision format, to run across heterogeneous chips—that is, different types of chips used together. Its kernel already runs on Huawei Ascend supernodes.

On this margin, the export regime is accelerating, not slowing, the indigenization of the Chinese stack.

The Mythos example sharpens the calibration problem. Huang notes that Mythos was trained on “fairly mundane capacity and a fairly mundane amount of it.” The cyber-offensive capabilities that motivate chip controls are not tightly gated by marginal access to frontier accelerators.

If the concern is models like Mythos, the relevant lever is the capability layer, not the chip layer. That means restrictions on releasing models with materially dual-use capabilities—models useful for both civilian and harmful purposes—combined with controls on remote access, something akin to know-your-customer (KYC) rules for APIs.

Anthropic’s decision to withhold Mythos from all users—not just Chinese ones—until vulnerabilities were patched offers a real-world example of capability-layer governance. ICLE’s comments to the National Telecommunications and Information Administration (NTIA) on dual-use foundation models and misuse risk sketch out the doctrinal framework. A regime that overweights chip controls while underinvesting in capability controls is mismatched to its own threat model. 

The second problem is more structural: The regime cedes the ecosystem layer to PRC-centered stacks.

Ecosystem analysis makes this visible in a way component-level analysis cannot. Durable advantage in AI comes from orchestrating capital, complementors, developers, standards, and customers around a credible roadmap. Export controls that wall U.S. firms out of the world’s second-largest computing market undercut that ecosystem across all of those margins.

Huang is explicit: “50% of the AI developers are in China. We don’t, we shouldn’t, the United States should not give that up.” And: “China is the largest contributor to open source software in the world… the largest contributor to open models in the world… today, it’s built on the American tech stack, NVIDIA.”

The question is not whether China reaches the frontier. It is which stack the global developer base optimizes for along the way.

If U.S. policy forces Chinese developers to build on domestic stacks—because they lack access to U.S. alternatives—those optimizations will propagate outward. As Chinese platforms mature, they diffuse into the Global South, the Middle East, Southeast Asia, and parts of Europe. Huang puts the endgame plainly: “AI models around the world… run best on not American hardware.”

That is the ecosystem-fragmentation cost the current regime imposes on its own intended beneficiaries.

The takeaway is not to abandon controls. It is to target the right layer.

An ecosystem-aware export-control regime would shift weight from chip-layer to capability-layer interventions. It would treat developer mindshare and framework standards as first-order policy concerns. It would recalibrate the Committee on Foreign Investment in the United States (CFIUS), the Bureau of Industry and Security (BIS), and outbound-investment regimes to reflect how ecosystems actually form—through partnerships, equity-and-compute deals, and standards bodies—not just through component flows.

This is not a call to “open the doors.” It is what follows from taking both the empirical and theoretical records seriously.

Coordination Eats the Widget

The garage-and-widget story always understated the roles of standards, capital markets, supplier coordination, and complementor mobilization. In an AI economy built on five-layer stacks, it is no longer even a useful approximation. The firms that lift the system are the ones that make it rational for everyone else to coordinate around them—and that means absorbing coordination costs the rest of the ecosystem would otherwise bear.

For competition policy, the question is not “is this firm dominant?” It is “is the ecosystem contestable, and does the orchestrator face enough discipline to keep it honest?”

For U.S.–China strategy, DeepSeek does not show that China cannot reach the frontier. It shows that reaching the frontier is not enough. The real contest runs through developer mindshare, capital flows, complementor density, and standards. Policies that concede those layers in the name of locking down the chip layer will miss their target—and, as Huang warns, risk achieving the opposite.

The firms that matter are no longer those with the cleverest widget. They are the ones that make every adjacent actor’s bet rational—“as much as necessary, as little as possible.”

That is the game now.

The post Stack Wars: The Garage Myth Meets the Five-Layer Cake appeared first on Truth on the Market.

The draft TTGs introduced a dedicated section on LNGs and, more notably, offered a soft antitrust safe harbor. In practice, qualifying LNGs would have avoided a full case-by-case assessment if they satisfied a defined set of conditions.

That approach did not come out of nowhere. A few months earlier, the European Commission signaled its position in an informal guidance letter issued jointly with the German competition authority, addressing the creation of the Automotive Licensing Negotiation Group.

That episode sets the stage for this post. It begins by situating LNGs within the broader SEP debate. It then examines the competition-law risks they raise, the limits of analogizing them to patent pools, and their uneasy fit with the Huawei framework.

Finally, it turns to the final TTGs. While the Commission dropped the proposed safe harbor, it kept a dedicated section on LNGs—raising the obvious question: was the intervention worth it?

LNGs: Technical Fix or SEP Policy Redux?

The Commission’s approach drew sharp criticism. Many viewed LNGs not as a technical clarification, but as the latest move in the longrunning SEP wars. On that account, the proposal looked less like guidance and more like a backdoor revival of debates that followed the collapse of the highly contested EU SEP Regulation.

As argued in a previous paper, LNGs resemble a consolation prize for the automotive sector. Firms in that sector had strongly backed the withdrawn Regulation. LNGs would deliver part of what that proposal promised: greater collective leverage for implementers in negotiations with SEP holders.

The logic tracks the earlier proposal. Both rest on the view that SEP licensing suffers from hold-up. The remedy, in turn, shifts bargaining power from SEP holders to licensees—ostensibly to rein in what critics characterize as excessive licensing prices.

When ‘Collective Bargaining’ Starts to Look Like a Cartel

Granting favorable antitrust treatment to LNGs is no neutral move. Collective buying arrangements raise familiar competition-law concerns. They can facilitate exchanges of commercially sensitive information and enable coordination on core competitive parameters, including price and output.

U.S. antitrust authorities have framed the issue more bluntly. In recent remarks, the U.S. Justice Department (DOJ) singled out the Commission’s comfort letter for the Automotive LNG as a regulatory intervention that appears to permit collusive licensing negotiation groups—conduct it characterized as buyer-cartel behavior under U.S. antitrust law.

The concern extends beyond any single dispute. By offering regulatory comfort to coordinated bargaining, such interventions risk spillover effects. They can anchor expectations and encourage firms to seek similar carveouts, rather than comply with antitrust law.

Reverse Patent Pools? Not So Fast

The proposed LNG section in the revised TTGs rests on a simple claim: the logic of technology pools extends to other forms of aggregation. On this view, LNGs deliver similar procompetitive benefits. They act as a one-stop shop, lower transaction costs, and function as a kind of reverse patent pool—counterbalancing the alleged market power of SEP holders or existing pools.

That analogy does not hold. Pools and LNGs aggregate fundamentally different things.

Patent pools combine complements. Each patent covers a distinct slice of a standard, and licensees need all of them to implement the technology. By bundling these inputs, pools address the familiar Cournot-complements and patent-thicket problems. The result is lower aggregate royalties and easier access to interlocking technologies. That market failure—and its fix—explains why antitrust law generally treats pools favorably.

LNGs do the opposite. They aggregate substitutes: competing implementers, each capable of negotiating its own license, band together to coordinate purchasing.

Pooling patents and pooling licensees are not mirror-image exercises. The former integrates inputs that are useless on their own. The latter consolidates buyer-side bargaining power among firms that would otherwise compete for the same inputs. The competitive effects run in opposite directions.

Pools mitigate double marginalization. LNGs risk creating monopsony power and providing cover for collusion among downstream rivals. The safeguards that discipline pools—limits to essential, complementary patents; fair, reasonable, and non-discriminatory (FRAND) commitments; and nondiscrimination duties toward licensees—have no real counterpart on the buyer side. The draft Guidelines’ procedural constraints—open access, limits on information exchange, and bans on collective boycotts—do not replicate them.

From Hold-Up to Group Hold-Out

To the extent LNGs rest on hold-up theory, they risk overlooking the mirror-image problem of hold-out. Worse, they may enable it—collectively. The risk is especially acute where LNG members can walk away after joint negotiations and pursue bilateral talks with the SEP holder. In that scenario, firms can free-ride on the group process, then delay—or refuse—to conclude individual licenses.

One obvious fix would have been to bind members to the outcome of joint negotiations. At a minimum, LNGs could require members to conclude licenses within a defined period after negotiations end—though even that would be a second-best solution compared to a world without LNGs.

The Commission did not go that route. It raised no objection to the Automotive LNG’s operating rules, which do not require members to accept the negotiated agreement. If a member rejects the outcome, the parties revert to the status quo ante, as if the LNG never existed, leaving the SEP holder and that firm to negotiate bilaterally. The final TTGs take the same approach. They impose no requirement that LNG members be bound by the result of collective negotiations.

That choice creates tension with the Huawei framework, which remains the central reference point for SEP licensing in the European Union. The framework aims to balance hold-up and hold-out through the willing-licensee test. How that test applies when negotiations proceed collectively—yet individual implementers remain free to reject the outcome and go it alone—remains an open question.

No Safe Harbor, Same Drift

In response to the criticism, the Commission ultimately dropped the draft safe harbor for LNGs. The final TTGs still include a dedicated section, though. It maps out potential pro- and anticompetitive effects, draws a line between genuine LNGs and buyer cartels, and identifies the factors relevant under Article 101.

The final text also engages—at least briefly—with Huawei. Paragraph 322 acknowledges that LNGs may facilitate coordinated delay during negotiations. It adds that implementers pursuing such a strategy may struggle to rely on Article 102 of the Treaty on the Functioning of the European Union (TFEU) as a defense against an injunction sought by an SEP holder that has committed to license on FRAND terms.

That is an improvement over the draft safe harbor. It does not, however, resolve the underlying problem. By giving LNGs a bespoke framework, the Guidelines normalize buyer-side coordination in SEP licensing. The debate shifts from whether such coordination should be allowed to how it should be designed.

The Commission’s safeguards—transparency, limits on information exchange, and case-by-case effects analysis—do not fully address the core concern. LNGs can aggregate implementers’ bargaining power while leaving them free to reject the outcome, prolong disputes, and push royalties downward.

The contrast with the United Kingdom is telling. In its draft Guidelines under consultation, the UK Competition and Markets Authority (CMA) declined to offer LNG-specific guidance. It opted instead for a case-by-case approach grounded in the facts and economic context of each arrangement. 

The safe harbor is gone. The detour remains. Alas, in these matters, detours have an unfortunate habit of becoming the road.

The post Reverse Patent Pools and Other TTBER Tall Tales appeared first on Truth on the Market.

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

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

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

Nothing New Here (Except What’s New)

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

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

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

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

In addition: 

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

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

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

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

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

In the Commission’s own words:

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

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

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

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

Innovation Shield for Some, Entrenchment for Others

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

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

The asymmetry becomes clear in the treatment of startup acquisitions.

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

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

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

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

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

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

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

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

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

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

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

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

Industrial Policy in a White Glove

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

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

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

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

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

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

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

Several legal constraints come into view.

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

These are not trivial issues.

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

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

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

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

A Compromise That Doesn’t Cohere

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

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

But the genuinely new elements pull the document elsewhere.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Europe’s DMA: More Clicks, Less Competition

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

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

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

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

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

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

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

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

More Rules, Same Bottlenecks

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

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

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

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

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

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

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

That is a false binary.

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

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

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

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

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

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

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

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

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

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

Mind the ‘Brussels Defect’

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

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

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

No overhaul required—just a skilled and steady hand.

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

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

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

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

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

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

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

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

Deception Isn’t the Same as Desire

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

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

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

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

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

From Choice to Paternalism

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

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

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

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

A Rule Without Edges

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

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

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

The Startup Filter Disguised as Safety

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

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

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

When Getting What You Want Is the Harm

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

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

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

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

Call the Problem by Its Name

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

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

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

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

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

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

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

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

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

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

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

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

Where the Commission Gets It Mostly Right

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

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

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

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

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

When the Model Doesn’t Fit the Models

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

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

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

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

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

The Clock Keeps Ticking as Brussels Buys Time

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

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

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

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

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

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

But the underlying dilemma will not wait.

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

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.