With July 4th looming, it’s tragically poetic that the country is again facing the question of how to constrain the exercise of arbitrary power. The Trump administration’s decision — based on questionable justifications — to block foreign nationals from using two Anthropic models indicates that AI governance is being shaped by actors who wield incredible influence over this critical domain and are yet subject to few effective constraints. This episode strengthens the case for Congress to step up and pass an AI governance framework that will reduce the frequency with which AI policy is breaking news.

What Happened

A brief review of the known facts (of which there are not many—itself a troubling sign from a rule of law perspective) surfaces the many ways in which highly consequential AI policy decisions seem disconnected from a reasoned, transparent, and thorough process. Commerce Secretary Howard Lutnick sent Anthropic a letter on Friday detailing that their latest models—Fable 5 and Mythos—are now subject to export restrictions. 

Mythos drew popular attention following documented instances of its being able to detect and exploit cyber vulnerabilities. Anthropic decided to share that model with a select set of trusted partners rather than risk bad actors leveraging the technology for nefarious ends. Fable 5 is a version of Mythos with several additional safeguards and was therefore made popularly available. In fact, Anthropic reports that Fable 5’s safeguards are “substantially more effective than those of any previously deployed model.”

The administration’s imposition of an export restriction followed from evidence of a potential narrow, non-universal jailbreak of Fable 5. Amazon researchers probing the model determined that it could provide users with information about cyber vulnerabilities when directed at websites. Yet, as Katie Moussouris, chief executive with the cybersecurity firm Luta Security, told the Wall Street Journal, such information is far more valuable to defenders of Internet systems than attackers. 

Nevertheless, the administration’s directive means that Anthropic can no longer make those models available to foreign nationals, theoretically including their own employees. Anthropic responded by blocking everyone’s access to Fable 5 and Mythos to ensure it does not violate the administration’s expansive directive.

A Failure to Adhere to the Rule of Law

In conjunction with my frequent co-author Alan Rozenshtein, I have previously reported on the ways in which AI governance by ad hoc executive action (or, in many cases, inaction) and by exclusively private efforts raise rule-of-law concerns. The rule of law can be defined in many ways by myriad actors, but most would agree that its core tenets include publicity, prospectivity, and legality. Publicity mandates that the affected party know the rules and laws that govern their conduct. Prospectivity ensures that the affected party is able to align their behavior with any applicable rules and laws, rather than be held accountable for prior acts. Legality confines every actor to only operate where they have clear authority to do so. 

Failure to adhere to those tenets undermines the extent to which individuals can live an ordered life and monitor those with extensive influence over their well-being. Government officials opting not to adhere to the rule of law also imposes significant costs on our economy. “The rule of law is the most influential factor for long-term economic growth and societal wellbeing,” as Annie (Yu-Lin) Lee and Joseph Lemoine summarized in an Atlantic Council report. Perpetuating an AI governance approach in which enforcement decisions are publicly announced on a Friday during the middle of a World Cup game is unlikely to safeguard individual liberty or encourage innovation and entrepreneurialism. 

That’s precisely why the administration’s recent action requires scrutiny. And that’s precisely why Congress must act so that such consequential governance decisions are not disclosed via X posts and are instead made in alignment with the Rule of Law. 

On publicity, the administration’s decision to take extraordinary action against Anthropic was disclosed by Anthropic—not the administration. Of course, the government necessarily withholds information or delays public announcements of enforcement actions in many cases. When it comes to decisions that may deny millions of people access to AI technology that is already shaping our economy, such opacity is in immediate conflict with the rule of law.

On prospectivity, a scroll of X makes clear that no one saw this coming. That’s not a good thing. While some AI scholars have flagged that export control authorities may one day be used to block individuals from accessing certain AI models, it has not been the topic of popular discussion. In turn, it has not been a potentiality actively shaping the decisions of AI labs. This surprising turn to export restrictions—even after Anthropic and other labs have engaged with the administration on numerous occasions about the cyber risks posed by their models and the safeguards taken to mitigate those concerns—instead suggests that labs will have to constantly be looking over their shoulder for what ‘creative’ enforcement decisions may come down the pike. 

On legality, research on the export restrictions raises significant doubts about whether these provisions are intended to reach AI models. As Joe Khawam and Tim Schnabel wrote in Just Security, “[T]hese frameworks, designed for discrete transfers of static information between known parties, are ill-suited to govern AI systems that generate unlimited, dynamic outputs on demand for potentially anonymous users worldwide.” Perhaps the administration has a clearer explanation as to why the legal authority it’s relying on in this case fits the facts. However, that explanation has not yet been made available. 

What has been made clear is that if the administration thinks such restrictions are legally available and necessary, it ought to limit access to many more such models offered by other labs. Anthropic is not alone in offering models with advanced cyber capabilities, which were purportedly the basis of the administration’s decision, as former AI czar David Sacks spelled out on X. The related rule-of-law concept of generality mandates that like cases be treated alike, which implies that the administration should be in the process of sending letters to other labs.

Conclusion

This is the second time in as many weeks that I’ve written about wild swings in AI policy. It should not be the case that the government needs to be reminded by private entities about the rule of law. Yet, here’s what Anthropic had to say on Friday: “[W]e believe the government should have the ability to block unsafe deployments as part of a statutory process that is transparent, fair, clear, and grounded in technical facts. This action does not adhere to those principles.” 

Transparency, fairness, clarity, and evidence-driven enforcement should be core components of AI governance. So long as AI policy decisions are driven by a few actors taking action behind closed doors in response to non-public reports, we will be far short of that standard.

Artificial Intelligence (AI) policy shouldn’t begin with the presumption that an emerging technology requires new forms of government control. In fact, the history of American technology policy shows that a light-touch approach allows consumers and innovators to find the best uses for technology. The light-touch approach enables companies to respond to the problems and demands of their consumers rather than those of the government, helping American companies become industry leaders.

Yet concerningly, a new bad policy idea intended to support American leadership in AI is emerging on both the Left and the Right. President Trump has floated a possible federal “partnership” with major AI companies, in which the public could receive “pieces” of those companies and benefit from their success. The details are unclear, but all signs point to the administration seeking to acquire equity stakes, which it has done with over 20 companies starting last year. 

On the Left, Senator Bernie Sanders has been more explicit. His proposed American AI Sovereign Wealth Fund Act would impose a one-time 50 percent tax on the largest AI companies, paid in stock. The government would then use voting shares and board representation to block decisions it deemed harmful and push decisions it deemed beneficial. 

These proposals come from different political instincts. Sanders wants redistribution and government control. Trump appears more interested in dealmaking and public buy-in. Both risk transforming AI policy from a framework for innovation into a vehicle for state control. 

This convergence didn’t come from nowhere. As Tad recently argued, Trump’s equity stake agenda helped create the precedent that Sanders is now trying to use more openly. Once the federal government starts taking ownership stakes in private companies, the remaining fight is over who gets to exercise the power and toward what ends. 

Legal Concerns

Sanders’s proposal raises the clearest legal concerns. As our colleague Ilya Somin argues, forcing companies to hand over half their stock would likely run afoul of the Fifth Amendment Takings Clause, which prohibits the government from taking private property without paying just compensation. Labeling the transfer a “tax” doesn’t eliminate the problem, as “Stock is private property, and seizing 50% of the stock value of major firms is a pretty obvious case of confiscation.”

Trump’s intentions are harder to evaluate because they apparently haven’t been fleshed out. But a “voluntary” equity deal with companies subject to federal contracts, antitrust scrutiny, energy and data center policies, and other regulatory approvals wouldn’t occur on ordinary market terms, given the government’s ability to affect their costs, markets, and future growth. Meanwhile, the administration’s existing equity deals have proceeded without clear government-wide statutory authority. 

Government Should be a Neutral Party

Government ownership would blur lines that should remain clear. Namely, the government should act as a regulator, the market should allocate capital, and private firms should remain independent developers of technologies. 

The federal government already regulates AI companies. It can also use procurement and contracting decisions in ways that affect a firm’s ability to compete. The recent Pentagon-Anthropic dispute shows how quickly those powers can become a fight over product design, market access, and constitutional rights. As Jennifer has written, the government labeled Anthropic a “supply-chain risk” because the company refused to change its product to meet Pentagon demands, even though the government could have just canceled the contract and chosen another vendor. 

If the government also becomes a shareholder, it would have financial and political incentives tied to the success of some firms over others, possibly making it harder for smaller or newer companies to challenge government-backed incumbents and weakening the market’s competitiveness. 

A regulator may hesitate to enforce rules that could reduce a government-backed company’s valuation. A procurement office may favor a company in which the government has a direct interest. Or a president may pressure companies to serve political goals while presenting that pressure as stewardship of the public’s investment. 

This is the broader danger of the government-ownership model: it shifts corporate decisions toward political rather than market-driven incentives. 

It is a particularly dangerous time to make such a decision around AI winners and losers. The industry is in an early and dynamic phase. As the history of innovation shows, it is not always clear who the long-standing giants will be or what all the potential applications of a general-purpose technology are. Determining winners and losers now through government investment risks putting a thumb on the scale in favor of certain companies and, more generally, disrupting private investment, as our colleague Scott Lincicome has written regarding the Intel stake.

The risk of the government picking the wrong winner at an early phase is high. In many ways, AI in 2026 is where the internet was in 1996. We can now laugh at headlines about “Will MySpace Ever Lose Its Monopoly” or how “Yahoo Won the Search Wars,” but what if the government had invested in them as the chosen champion? 

While we, as a society, may occasionally be nostalgic for the Y2K internet, it was innovation and entrepreneurs who found better ways to respond to consumers’ desires, not the government.

Free Expression Concerns

Beyond that, a government interest in AI could also raise unique free expression concerns. Particularly if the government were to make the type of significant investment under Sanders’s proposals, it raises the question of how the government could potentially dictate what is and is not allowed under an AI’s algorithm. This could allow the government to potentially limit the use of such tools for criticism or counter viewpoints. It could also enable controlled or limited access to certain information. This is not because chatbots or AI algorithms have speech rights, but because the humans designing AI algorithms have free expression rights in designing the algorithm, as Jennifer and our Center for Constitutional Studies have argued. For example, how might this play out in the friction between Anthropic and the Pentagon if the government were both a significant shareholder and a customer?

There are also free expression and privacy concerns about what access to user data such actions could give the government. Could a government stake in AI companies be the new data broker loophole to access data on citizens that the government would not otherwise be able to obtain? Users have speech rights under the First Amendment, including the right to ask questions and to engage in anonymous speech. Could government control or ownership impact privacy and speech?

AI Needs a Framework, Not Federal Ownership

Rejecting government ownership doesn’t mean rejecting any AI policy framework. It means asking what kind of framework is appropriate for a general-purpose technology. 

As Jennifer articulated, AI policy should follow four broad principles. Policymakers should first ask what existing law already covers. They should avoid a disruptive patchwork of state rules. They should improve AI literacy and education to address concerns about labor disruption and disinformation. And they should place safeguards on the government’s own use of data and AI to protect civil rights and liberties. 

This type of light-touch approach to a general-purpose technology does not direct potential applications. It instead directs appropriate responses to specific application concerns, such as fraud, civil rights abuses, or cybersecurity. Such an approach focuses on the specific applications at the heart of the concerns without blocking innovation.

Policymakers should approach AI with the same regulatory humility they should have brought to the early internet. Policymakers shouldn’t presume that they know in 2026 where AI is headed any more than they could have known in 1996 where the internet was headed. 

If the government directs the path of innovation, AI is less likely to develop in ways that meet the needs of consumers, workers, and entrepreneurs.

AI’s “Problems” Have Better Policy Solutions than AI Nationalization

Humility matters for the underlying tech, labor, and infrastructure policy debates at the heart of much of the AI debate as well.

Many AI concerns aren’t unique AI questions—they are extensions of ongoing policy debates over speech, online youth safety, privacy, cybersecurity, labor markets, and government use of technology. AI may change how those questions arise, but that doesn’t mean Washington should own the companies building the tools. If anything, as we described above, government investment could lead to more problems on key issues such as privacy and speech.

Similarly, a more complicated picture of AI and labor is emerging. AI may change work, but that’s not the same thing as an economy-wide jobs collapse. A recent paper finds that AI exposure can reduce demand for certain tasks, but AI adoption can also increase productivity and labor demand. That supports a careful response focused on adjustment, education, and AI literacy rather than a permanent government ownership regime. 

The same is true of data centers. As with any infrastructure element, there are debates about the impact on resources. Electricity and water concerns can be real in particular communities, but the backlash is outrunning the evidence. Concerns about water use are frequently overstated, and evidence that data centers are broadly driving up electricity prices remains at best mixed. If electricity demand is the concern, the answer is electricity abundance, faster permitting, better siting, and more flexible grid policy. Our colleague Travis Fisher’s proposal for consumer-regulated electricity offers one possible way to let large users finance their own power needs without shifting costs onto households. 

Conclusion

Policymakers often describe AI as a race with China, but there is no finish line. It’s an ongoing process of innovation, adoption, infrastructure buildout, and improvement. We are only at the beginning of understanding how AI may be applied across many industries and in our daily lives.

The US won’t strengthen its position by making leading firms more dependent on federal permission and boardroom influence. As technology and innovation expert Adam Thierer emphasizes, the US “became a technological leader because it developed policies that encouraged robust private-sector entrepreneurialism and openness to new ideas and change.” 

A better AI agenda would keep barriers to entry low, enforce existing laws against concrete harms, expand energy supply, allow infrastructure to be built, and avoid policies that lock in today’s largest players. That way, AI’s benefits can be developed through experimentation and competition rather than dictated by politicians and bureaucrats in Washington.

Since the days of powdered wigs, so-called experts have predicted that economic growth would soon collide with hard physical limits. According to some intellectuals, the world has always been running out of food, oil, and just about everything that advances civilization.

The predictions change over time, but the unwavering belief is that improvements in living standards are not sustainable. Economist Thomas Malthus warned in 1798 that population growth would overwhelm food production, leading to widespread starvation. The 1970s brought “The Limits to Growth,” a report by the Club of Rome that predicted resource depletion would lead to “a rather sudden and uncontrollable decline.” 

Later came peak oil, climate alarmism, and activism against hydraulic fracturing and data centers. Just like the centuries-old claims of limits to growth, these theories fall apart when we consider the historical record and account for human adaptation.

The late economist Julian Simon (David’s father and fellow at the Cato Institute) spent years arguing that human beings are not merely consumers of resources but also discoverers of scientific knowledge and innovative thinkers. Human ingenuity, Simon argued, is the ultimate resource—one that has no limit. 

When Simon’s book The Ultimate Resource was published in 1981, people scoffed at his optimism. But he was right—the state of the world was improving and would continue to improve. Global poverty collapsed. Food production surged faster than population growth. Resource scarcity diminished. Human life expectancy rose dramatically. Even as industrial output expanded, the developed world became cleaner, wealthier, and safer.

The shale revolution shows how resourceful we can be. Not long ago, experts confidently argued that the US was running out of economically recoverable oil and natural gas. Then, entrepreneurs and engineers transformed global energy markets through hydraulic fracturing and horizontal drilling. Rather than continuing our slow decline in oil and natural gas production, the US now leads the world. 

The important lesson is more philosophical than technical. The shale revolution was not centrally planned into existence. It emerged from decentralized experimentation, private investment, risk-taking, and a government that respects property rights and otherwise gets out of the way. In other words, it emerged from the exact kind of society Simon believed produced human progress.

Few public officials understand that lesson better than Secretary of Energy Chris Wright.

Long before entering government, Wright argued that abundant energy is the foundation of modern human flourishing. Clean cooking, refrigeration, transportation, clean water systems, modern medicine, industrial production, and computing power all depend on enormous amounts of energy.

The optimistic worldview shared by Simon and Wright has taken decades to reach the mainstream and influence policymakers. However, today, it looks like the future.

Billions of people around the world will need vastly more energy to achieve modern living standards. In the US, energy-intensive growth industries, such as the technology sector, are racing to expand infrastructure, strengthen electric grids, and secure reliable fuel supplies. The common denominator is the need for vast amounts of new energy, and the political mood is shifting toward optimism. 

Consider two recent examples. The concept of “energy superabundance” would have sounded politically radioactive only a few years ago, but now it’s the title of a series of workshops held by governors from the American West that concluded last week. Likewise, the Operation Gigawatt Summit, hosted by Utah Governor Spencer Cox, featured prominent officials, including Wright and EPA Administrator Lee Zeldin. 

The climate debate reflects this broader shift in perspective. Last year’s Department of Energy (DOE) report, commissioned by Secretary Wright, eviscerated many of the catastrophic climate claims that had dominated public discussion for years. The data tell the story: global warming has some negative consequences, but in the most important ways, and on net, it is beneficial. As humanity became wealthier, more technologically advanced, and more energy-abundant, natural disaster-related deaths plummeted worldwide. Human beings are increasingly resilient, safe, and in control of their own destiny.

Secretary Wright has put us on the path toward energy liberty, but the work is not done. We will know freedom is truly winning when Congress limits the size and scope of the federal government’s role in the energy sector. That would mean curtailing emergency authorities and taking the DOE budget to new lows (yes, zero should be on the table). For now, we are extremely lucky to have a freedom fighter at the DOE. 

Secretary Wright wrote in the foreword of the DOE report, “misguided policies based on fear rather than facts could truly endanger human well-being.” Reforming these policies is imperative. But the great news is that, for the first time ever, doomslayers like Secretary Wright are guiding American energy policy. We think Julian Simon would have enthusiastically cheered Chris Wright, and we credit Simon with building the philosophical foundation for today’s energy abundance.

Travis Fisher is the Director of Energy and Environmental Policy Studies at the Cato Institute.

David M. Simon is a senior fellow with Unleash Prosperity, a lawyer in Chicago, and a writer at www​.dmswrit​ings​.com.

House Representatives Jay Obernolte (R‑CA) and Lori Trahan (D‑MA) introduced a 300-page draft bill to establish a framework for the United States on Artificial Intelligence (AI). The bill comes amid an increasingly fierce debate over a range of topics, from data centers to child safety to government investment in AI companies. It also comes as a growing number of states continue to consider their own AI frameworks.

There is considerable debate over what a policy framework for AI should do. Back in 2023, I laid out four principles that I still believe would continue the US’s history of a light-touch approach to innovation policy that has allowed entrepreneurship to flourish and consumers to benefit from technology far more than a government-dictated approach. While AI technology and adoption have advanced in that time, these core principles still hold enormous relevance for today’s policy decisions. 

They consist of: considering existing regulations that might already cover concerns, preventing a patchwork of state policies that could disrupt innovation, improving AI literacy and education to combat concerns about labor market disruption and misinformation, and considering safeguards for the government’s own use of data and AI to protect civil rights and civil liberties. 

Many of the questions that have emerged about AI are not AI questions but rather ongoing debates about speech and youth online safety or data privacy. At an individual level, AI may change how one thinks about the risks and benefits of certain choices. On a regulatory level, the rapid advancement of AI also illustrates the pacing problem in some of these underlying areas where technology moves faster than law. As the inability to launch various AI products in Europe early on due to GDPR concerns illustrated, a regulatory approach built for one particular technology can come at the expense of innovation and better solutions in the future.

It is important that any policy framework consider not only technology as it exists in 2026 but also what a better future might be prevented by an overly prescriptive approach. Presuming to know everywhere AI is headed in 2026 is like presuming to know everywhere the internet was headed in 1996. There may be a need to address specific concerns and unique challenges, such as cybersecurity or government use, but a policy framework that presumes a general-purpose technology is free for entrepreneurial use and responds narrowly to specific targeted concerns about specific applications delivers the greatest benefits for both innovators and consumers.

The US took such an approach when the internet emerged, and that is part of what created an environment that enabled its innovators to become global leaders.

The Obernolte-Trahan bill, regardless of its specifics, is an important moment because it signals Congress recognizes that a state patchwork or a series of impermanent White House executive orders are not the right approaches to such an important issue. It is constitutionally correct that if there is to be a policy framework on AI, it is Congress’s role.

Cato’s technology policy scholars will analyze the bill’s details in the coming weeks, focusing on its potential impact on AI innovation and other principles, such as free expression. 

Reps. Ro Khanna (D‑CA) and Delia Ramirez (D‑IL) introduced a bill last month promising to raise the federal minimum wage to $25 per hour for large businesses by 2031 and for other employers by 2038. That would peg the federal minimum wage higher than any existing state or locality’s minimum wage. The large business minimum would beat any local wage floor even if it grows to match the Congressional Budget Office’s (CBO) projected inflation in the coming years.

That $25 per hour marketing is somewhat misleading, though. The Living Wage for All Act would primarily set the minimum wage at two-thirds of national median hourly earnings, subject to a phase-in cap not to exceed $25 by 2031.

For most years, that’d just make the minimum wage two-thirds of the national median, and the “$25 by 2031” pledge little more than a red herring. Minimum wages wouldn’t reach $25 until 2038—much later if median wage growth significantly lags our upper-bound estimates based on CBO projections.

Notwithstanding the phase-in cap, pegging the minimum wage to two-thirds of the national median would’ve meant a $16.34 minimum wage in 2025 and a wage floor close to $20.22 in 2031. That would still be an extraordinary increase, which previous evidence suggests could be very destructive.

Khanna’s launch video offers two justifications for such an aggressive move: the need for the federal minimum to catch up to rising prices and the desirability of minimum wages tracking worker productivity.

Inflation-Adjustments

First, he says the inflation-adjusted 1968 federal minimum wage was $14 per hour in modern, 2026 dollars. Methods of inflation-adjusting can yield slightly different results, but using the Consumer Price Index, we think the real February 1968 Fair Labor Standards Act (FLSA) wage floor was actually a bit higher at $15.22 in inflation-adjusted, January 2026 dollars.

And even though the federal floor today sits at just $7.25, that doesn’t make his conclusion that “workers today are making half what they made in 1968” true. For starters, just 1 percent of workers made the federal minimum wage in 2024, as market wages have grown significantly without changes to the federal floor. But also, state and local policymakers have raised their own minimum wages significantly, making the federal floor irrelevant for most workers.

As we show in a recent briefing paper, the population-weighted effective minimum wage—the greatest of each locality’s federal, state, or local floor averaged across all working-age Americans—was $12.13 in January 2026. The population-weighted median was $13.73 (more Americans live in states with $15 or more minimum wages than the federal floor). Most Americans today therefore live under a minimum wage much closer to the real 1968 value than Representative Khanna lets on.

And why does Khanna compare the current federal floor to the 1968 one? Probably because 1968’s FLSA regime was the highest real wage floor in US history. Had he averaged over the full 90-year history of the federal minimum wage, the figure is only $9.92.

Even if you cut off the historically low, depression-era minimum wages, the average real minimum wage since 1950 is $10.49. And even taking off the period since 2010, the average only rises to $11.16.

The modern effective minimum wage factoring in state and local laws is higher than all of these. You’d need to cherry-pick only 1960–1980 to average a higher federal floor of $12.78, and even that is far afield of Khanna’s $14. The real federal minimum wage has almost never been as high as $14.

Productivity-Adjustments

Rep. Khanna doesn’t just want a return to the 1968 wage floor, though. His second justification brings in productivity. Since average real labor productivity has increased 2.5 times, he argues that “the average worker is producing two and a half times more value than they were producing in 1968, yet they’re being paid half as much.” That justifies a $25 minimum wage for him.

The problem is that comparing average productivity to the minimum wage is apples and oranges. Minimum wages don’t bind average workers. They affect the lowest-paid workers, who tend to cluster in sectors with productivity levels, and sometimes productivity growth, well below the economy-wide average.

Historic productivity gains in oil and gas extraction, for example, raise economy-wide labor productivity, but they have little direct bearing on the productivity of those in lower-wage sectors such as fast food, childcare, or retail cashiers.

Labor productivity of couriers and messengers or warehouse and storage employees have decreased since 2010. Demanding higher minimum wages in these industries, even while productivity has fallen, is a recipe for layoffs or lower hiring.

In truth, even sectoral productivity is too blunt a benchmark. Within any given sector, productivity gains accrue unevenly. Even if median productivity rises, a similar number of workers’ marginal product can remain just at or above the minimum wage if the variance of worker productivity also expands. That’s exactly what we might expect from a dynamic and diversifying economy.

Tying the minimum wage to middle-band worker productivity therefore risks overshooting the marginal product of the least-productive workers. The result we’d expect is “disemployment,” whether through layoffs, hour cuts, or lowered future hiring. That’s because paying above a worker’s marginal product will be a losing proposition for employers, so they will reduce demand for workers, at least if they can’t recoup costs by trimming back on other aspects of their employment bill.

Consequences of a 67 Percent of Median Hourly Pay Wage Floor

Minimum wages set too high or increased aggressively have a real capacity to do harm. Studies of even moderate minimum wage increases tend to find degraded noncash compensation or working conditions to offset the cost. That’s one reason why, if we are to have this form of price control, at least a federalist approach might limit the damage.

Local leaders inevitably will have more intimate knowledge of their labor markets and can (at least in theory) tailor their intervention to local conditions. A $25 federal floor would override all that local calibration while hitting hardest in low-cost areas where market wages are furthest from two-thirds of the national median.

Representative Khanna’s district would be relatively fine; the San Jose–Sunnyvale–Santa Clara metropolitan statistical area (MSA) regularly has the highest median hourly wage of any metro area in the country, $40.41 in 2025. But other MSAs with local economies less productive than Silicon Valley inevitably won’t fare as well. As we show in our policy brief, the Living Wage for All Act would raise minimum wages as high as 95 percent of local median wages in America’s most-affected MSAs. They wouldn’t be lifted up, so much as priced out.

The United Kingdom currently has just this national policy of linking the country’s minimum wage to two-thirds of median hourly pay. Even the UK government’s reports on the policy note a host of problems created through wage “compression,” whereby the policy distorts relative prices of different jobs and discourages people to move up and take more responsibility.

Such a policy across the diverse US economy would be extremely risky indeed.

The Securities and Exchange Commission (SEC) has proposed ending quarterly reporting and allowing public companies to submit financial reports on a semiannual basis. This proposal has been a long time coming. Last September, President Trump announced his disdain for quarterly reporting on Truth Social, with SEC Chairman Paul Atkins quickly seconding the president’s vision.

Both the president and the chairman share the same vision for quarterly reporting mandates (ending them), though not the same reasoning. Trump calls for semiannual reporting to reduce short-termism—executives’ pursuit of short-term results over long-term financial health. Atkins, on the other hand, stands on the more principled case that the SEC’s rules have “prevented companies and their investors from determining for themselves the interim reporting frequency that best serves their business needs.”

Does Quarterly Reporting Cause Short-Termism?

While short-termism is a politically convenient justification for reducing reporting mandates, the empirical case for short-termism rests on shaky ground. For starters, US financial markets and corporate investment (research and development) have grown significantly since the modern introduction of quarterly reporting in 1970. Although that relationship is surely overly simplistic, a 2018 review of the empirical literature finds that the macro predictions of the short-termism hypothesis are “largely undemonstrated, implausible, or untrue.” Even the proof most commonly cited as evidence of short-termism—share repurchases—fails to support the cause. Recent work by Harvard’s Elliot Tobin and Charles Wang, for example, finds that, across 17 countries, legalizing buybacks is followed by an 8 to 10 percent rise in corporate investment, not a decline.

It is also somewhat surprising that the administration would rest on short-termism as the reasoning for reducing reporting mandates. Some lawmakers have used short-termism to justify increased government oversight, enhanced reporting mandates, and even higher taxes. 

Nevertheless, the record against short-termism does not undermine the case for reducing reporting mandates.

Should the SEC Even Set the Frequency?

Chairman Atkins, in his statement accompanying the release, framed the proposal to move to semiannual reporting as restoring self-determination. In other words, if companies and investors wish to report quarterly, they should be allowed to. And if they wish to report semiannually instead, they should be allowed to do so. That argument follows the right principle.

However, this principle extends beyond the proposal. Disclosure has historically arisen from self-interest, exchange listing rules, or statute. Before Congress wrote interim-reporting authority into Section 13(a) of the Securities Exchange Act of 1934, it was already common for companies listed on the New York Stock Exchange to report quarterly (albeit the content of financial reports was quite different).

More recently, the United Kingdom’s experience supports letting financial markets calibrate themselves. The UK mandated quarterly reporting in 2007, then dropped the mandate in 2014. Roughly 91 percent of UK firms continued to report quarterly through the end of 2015. By 2017, about 40 percent of Financial Times Stock Exchange 100 firms had moved to semiannual. And what happened? Well…nothing. A Goldman Sachs report found that reporting frequency had no impact on valuations.

But reporting frequency has impacted costs. The SEC’s own economic analysis estimates that reporting semiannually would save a company roughly $198,000 per year. And those savings won’t fall evenly, as the SEC also finds that “smaller issuers may face relatively higher compliance burdens” that scale poorly with revenue. 

Where the Debate Should Go

Quarterly reporting hasn’t produced the short-termism President Trump has claimed. The SEC’s own analysis shows quarterly reporting adds significant costs for no measurable benefit, and evidence from the UK reveals that giving companies more freedom to choose didn’t affect company valuations. Yet even with this evidence, short-termism remains a political scapegoat.

To their credit, Chairman Atkins and the SEC have taken a principled approach. Rather than debating whether short-termism is real, the proposal rests on the principle that reporting frequency is a question companies and investors should answer for themselves. Atkins is correct: How often a company reports to its investors should be a decision between the company and its investors, not the government. 

Last Friday, the Trump administration quietly moved a little closer to granting temporary relief from Obamacare. That action is not useless, but it does appear to signal a wasted opportunity.

The administration is preparing a regulation to change how the federal government interprets the law governing so-called “short-term limited duration insurance,” or STLDI. Only a few million people enroll in STLDI plans. The market has outsized importance, however, because it is one of only two kinds of health insurance that are exempt from Obamacare’s costliest health insurance regulations, the other being health insurance in US territories. Unlike territorial plans, STLDI is exempt from both Obamacare and nearly all other federal health insurance regulations. That is why STLDI plans can offer comprehensive health insurance to many or most Obamacare enrollees at premiums 60 percent (Congressional Budget Office) to 66 percent (Kaiser Family Foundation) below the lowest-price Obamacare plans. 

Trump’s greatest health care victory was a 2018 regulation that provided relief from Obamacare by adding consumer protections to these plans. In 2016, President Obama tried to force reluctant consumers into overpriced, low-quality Obamacare plans by arbitrarily limiting STLDI plans to three months. The Obama rule tossed patients out of their coverage after they got sick, leaving them with nothing. In 2018, Trump clarified that it was perfectly consistent with federal law for STLDI contracts to last 12 months, for insurers to renew the initial contract for up to 24 additional months, and for insurers to issue separate renewal guarantees—so that when consumers reached the 36-month limit, they could enroll in a new STLDI plan at healthy-person premiums, even if they had fallen seriously ill. Trump clarified, and two federal courts affirmed, that federal law effectively leaves this market enough freedom to offer consumers long-term health insurance protection. 

The Trump rule was a success. Premiums for comprehensive coverage fell. Consumers could purchase health insurance free from Obamacare’s quality-reducing regulations. Obamacare premiums did not spike and enrollment did not fall, as critics predicted. Instead, while the Trump rule was in place from 2018 to 2024, Obamacare premiums stabilized and enrollment increased. 

I don’t think the rule caused either development. But neither did it produce the dire consequences critics feared.

Nevertheless, in 2024, President Biden rescinded the Trump rule. Like Obama, Biden wanted to force reluctant consumers into overpriced, low-quality Obamacare plans. He limited STLDI plans to four-month contracts and prohibited all renewals—federal court rulings notwithstanding.

The only flaw in the Trump rule is that it was not permanent. While it clarified that insurers were free to offer renewal guarantees, the fact that it came from administrative rulemaking rather than legislation meant that the next president could take that freedom away at the stroke of an autopen. So insurers did not have sufficient incentive to invest in renewal guarantees.

For months, the Trump administration has been debating how to promote health insurance affordability. On one side are those who support a regulation-only strategy by which the administration reissues the 2018 rule, even though the new rule may last as little as two years. This side is apparently unaware that tremendous gains in freedom and affordability would come from codifying the Trump rule, that they are sitting on a winning messaging strategy, and that experience with the Trump rule has already negated critics’ fearmongering. The savvier side of this debate advocates for a legislative strategy by which Trump pushes Congress to make his greatest health care victory permanent and pursues regulation only as a backup. Trump himself appeared to be on that side of the debate:

Unfortunately, the regulation-only side appears to be winning. The president’s Great Health Care Plan doesn’t mention STLDI, much less demand that Congress codify the Trump rule. I have seen no indication from the administration that it plans to push Congress to do its job. 

It is a tremendous waste of an opportunity. The administration can reissue the 2018 rule. It can even improve on that rule. But any relief it provides would disappear with the next Democratic administration. 

President Trump needs to push legislators to legislate. I’ve already provided the arguments, data, talking points, polling, poster child, graphs, and legislative language the administration needs.

People are still struggling under the high cost of Obamacare. They need permanent relief.