There are dozens of bills floating around Congress that would regulate tech companies, and a good chunk of them are aimed at changing the way social media companies moderate content on their platforms.
To learn more about the different proposals out there and how they interact with our First Amendment rights, we sat down with Ari Cohn, Free Speech Counsel at the technology think tank TechFreedom.
This morning’s announcement that Nvidia and AMD will resume selling AI chips to China on the condition that they surrender 15% of their revenue from those sales to the U.S. government marks a jarring inflection point in American industrial policy.
This is not just a transaction workaround for a particular situation. This is a major philosophical government policy shift.
What was once a matter of national security has now somehow become a transactional arrangement that is essentially a pay-to-export policy that blurs the line between protective regulation and economic extortion.
The deal, reportedly brokered after Nvidia CEO Jensen Huang met with President Trump, allows the companies to sell their H20 and MI308 chips in China, a market that previously accounted for billions in revenue.
There were two immediate responses from two former officials and trade experts who voiced strong concerns. Christopher Padilla, former head of the Commerce Department’s International Trade Administration, called the deal “astonishing,” likening it to “a mix of bribery and blackmail” and warning it may be “possibly illegal” and Peter Harrell, a fellow at the Carnegie Endowment for International Peace, said the arrangement sets a “terrible precedent,” noting that national security export controls should not be relaxed in exchange for financial concessions
The unintended consequences of a government policy are unknown, and the potential cost of access is steep, not just financially, but philosophically. This arrangement contradicts the very principles Republicans have long championed: free enterprise, limited government interference, and a clear separation between national security and commercial interest.
History provides some lessons.
Consider the Jackson-Vanik Amendment of 1974, which tied trade relations with the Soviet Union to human rights benchmarks, particularly the freedom of emigration for Soviet Jews. It was a moral stand, using trade as leverage to promote democratic values. But it was also transparent, legislated, and principled—even if imperfect.
Today’s chip deal with China lacks that moral clarity. It’s not about human rights or democratic norms. It’s about money. The 15% levy isn’t a tariff, a sanction, or a strategic investment. It’s effectively just a tax on corporations. And unlike Jackson-Vanik, it wasn’t debated in Congress or anchored in law. It was brokered behind closed doors, with the executive branch acting as both regulator and beneficiary.
This blurring of roles recalls the worst instincts of economic policy, where governments extracted rents from private enterprise under the guise of national interest. It also evokes the “crony capitalism” of post-Soviet Russia, where access to markets was contingent on political favor and financial tribute.
This policy sets a dangerous precedent by monetizing export permissions, effectively transforming the federal government into a tax collector on global commerce. It’s a dangerous slope. If the U.S. can demand a revenue cut for chip sales to China, what stops it from doing the same for pharmaceuticals to Europe, or software to Latin America? The logic of national security becomes a flexible pretext for economic leverage, and the private sector is left navigating a landscape where policy is shaped not by principle, but by deal-making.
This isn't an industrial strategy, but essentially it's economic policy to the highest bidder. And it sends a troubling signal to allies and adversaries alike: that American innovation is for sale, and its gatekeeper is no longer Congress or the Constitution, but the highest bidder in the West Wing.
If Congress is serious about reshoring supply chains, protecting intellectual property, and countering China’s techno-authoritarianism, then it must reject this ad hoc monetization of policy. True industrial strategy requires coherence, transparency, and a commitment to long-term national interest, not short-term revenue schemes. This should be embraced by Democrats and Republicans alike. Otherwise, we risk becoming the very system we claim to oppose: one where power is transactional, markets are politicized, and innovation is hostage to the whims of the state.
David Nevins is co-publisher of The Fulcrum and co-founder and board chairman of the Bridge Alliance Education Fund.
Generative AI is advancing at breakneck speed. Already, it’s outperforming doctors on national medical exams and in making difficult diagnoses. Microsoft recently reported that its latest AI system correctly diagnosed complex medical cases 85.5% of the time, compared to just 20% for physicians. OpenAI’s newly released GPT-5 model goes further still, delivering its most accurate and responsive performance yet on health-related queries.
As GenAI tools double in power annually, two distinct approaches are emerging for how they might help patients.
One path involves FDA-approved tools built by startups and established technology companies. The other empowers patients to safely use existing tools like ChatGPT, Gemini, and Claude.
Each path has advantages and tradeoffs. Both are likely to shape healthcare’s future.
To better understand what’s at stake, it’s first helpful to examine how generative AI differs from the FDA-approved technologies used in medicine today.
Narrow AI
Medicine has relied on “narrow AI” applications for more than two decades, using models trained to complete specific tasks with structured clinical data.
These tools are programmed to compare two data sets, identify subtle differences, and assign a precise probability factor to each. In radiology, for example, narrow AI models have been trained on thousands of mammograms to distinguish between those demonstrating early-stage breast cancer and those with benign conditions like fibrocystic disease. These tools can detect differences too subtle for the human eye, resulting in up to 20% greater diagnostic accuracy than doctors working alone.
Because narrow AI systems produce consistent, repeatable results, they fit neatly within the FDA’s current regulatory framework. Approval requires measurable data quality, algorithmic transparency, and reproducibility of outcomes.
Generative AI: A new kind of medical expertise
Generative AI models are built differently. Rather than being trained on structured datasets for specific tasks, they learn from the near-totality of internet-accessible content, including thousands of medical textbooks, academic journals, and real-world clinical data.
This breadth allows GenAI tools to answer virtually any medical question. But the large language model responses vary based on how users frame questions, prompt the model, and follow up for clarification. That variability makes it impossible for the FDA to evaluate the accuracy and quality of the tools.
Two distinct pathways are emerging to bring generative AI into clinical practice. Maximizing their impact will require the government to change how it evaluates and supports technological innovation.
1. The traditional path: FDA-approved, venture-backed
As medical costs rise and patient outcomes stagnate, private technology companies are racing to develop FDA-approved generative AI tools that can help with diagnosis, treatment, and disease management.
This approach mirrors the narrow AI model: high-priced tools that are highly regulated and largely dependent on insurance coverage for American families to afford them.
With venture funding, companies can fine-tune open-source foundation models (like DeepSeek or Meta’s LLaMA) using a process called “distillation.” This involves extracting domain-specific knowledge and retraining the model with real-world clinical experiences, such as tens of thousands of X-rays (including radiologists’ readings) or anonymized transcripts of patient-provider conversations.
Consider how this approach might impact diabetes management. Today, fewer than half of patients achieve adequate disease control. The consequences include hundreds of thousands of preventable heart attacks, kidney failures, and limb amputations each year. A generative AI tool trained specifically for diabetes could replicate the approach of a skilled chronic disease nurse: asking the right questions, interpreting patient data, and offering personalized guidance to help users better manage their blood sugar levels.
This path already appears to have federal backing. The Trump administration recently launched its Medicare-funded Health Tech Ecosystem initiative, partnering with more than 60 tech and healthcare firms to pilot AI-enabled tools for chronic disease management, including diabetes and obesity.
Although distillation is faster and cheaper than building an AI model from scratch, the timeline to FDA approval could still span several years and cost tens of millions of dollars. And any adverse outcome could expose companies to legal liability.
2. The alternate path: Empowering patients with GenAI expertise
This second model flips the innovation equation. Instead of relying on expensive, FDA-approved tools developed by private tech companies, it empowers patients to use low-cost, publicly available generative AI to manage their own health better. This can be accomplished through digital walkthroughs, printed guides, YouTube videos, or brief in-person sessions.
For example, a patient might input their blood pressure, glucose readings, or new symptoms and receive reliable, evidence-based advice from ChatGPT or Claude: whether a medication change is needed, when to alert their doctor, or if emergency care is warranted. Similarly, patients working with their physicians could use these LLMs to detect early signs of post-operative infection, worsening heart failure, or neurological decline.
With 40% of doctors already engaged in “gig work,” an ample supply of clinicians from every specialty would be available to contribute their expertise to develop these training tools.
This model would bypass the need for costly product development or FDA approval. And because it offers education, not direct medical care. It would create minimal legal liability.
Government support for both models
These approaches are not mutually exclusive. Both have the potential to improve care, reduce costs, and extend access. And both will benefit from targeted government support.
The traditional path will require companies to evaluate the reliability of their tools by testing the accuracy of their recommendations against clinicians. When these tools are equivalent, the FDA would give its approval.
The alternate path of educating patients to use existing large language models will benefit from educational grants and added expertise from agencies like the CDC and NIH, partnering with medical societies to develop, test, and distribute training materials. These public-private efforts would equip patients with the knowledge to use GenAI safely and effectively without waiting years for new products or approvals.
Together, these models offer a safer and more affordable future for American healthcare.
Robert Pearl, the author of “ChatGPT, MD,” teaches at both the Stanford University School of Medicine and the Stanford Graduate School of Business. He is a former CEO of The Permanente Medical Group.
Writer and educator Brent McKenzie shares on the importance of civic engagement and how he went from doomscrolling to creating a project called "Idiots & Charlatans."
The kids call it doomscrolling—that endless, glazed-eyed spin through terrible headline after terrible headline. And let’s be honest: content from today’s political circus is built for it. It’s so absurd, so extreme, that you find yourself muttering, “This can’t be real.”
But that’s the trap. The worse it gets, the harder it is to look away. And somewhere along the way, outrage starts to replace action. I know—because I caught myself doing it.
The Guardian. Axios. CNN. Social media. I’d bounce between them like I was looking for an emergency exit. Twenty minutes later, I’d be furious and discouraged—but no more informed, and no closer to doing anything about it. Just more adrenaline. And a little less hope.
And I came of age when activism mattered—when organizing, speaking up, marching, and writing letters could actually change things. I still believe that. But somewhere along the way, the energy that used to go into action started going into…scrolling.
That’s when I realized I needed a new focus. I needed to do something.
So I built something that could help me focus on what really matters—and tune out the rest.
For me, that meant building a small project called Idiots & Charlatans, not as a reaction website or think piece factory but as a tool to help me stay anchored in a few big values: democracy, truth, equity, climate, justice, and free speech. I call them my non-negotiables.
At first, I didn’t know where to start. Every headline felt catastrophic, and every issue felt urgent. But then I asked myself: which battles, if lost, will be the hardest to undo? That question helped me cut through the noise.
I made a short list—my non-negotiables:
🗳️ Democracy, because once it slips, getting it back is nearly impossible.
🌍 Climate, because the clock doesn’t reset.
🧑🏽🤝🧑🏼 Equity and justice, because the rollback is real and the pain is personal.
📢 Free speech and truth, because without them, we lose the ability to fight for anything else.
When a headline touches one of those, I pay attention. When it doesn’t, I don’t. And that shift alone has made all the difference.
That’s what helped me, but the bigger question is:
What will help you?
Because this isn’t just about how we cope with the news.
It’s about how we respond to it.
Reclaim that time—the time you’d normally spend doomscrolling—and do something. Anything.
Because let’s be honest: we’re not going to doomscroll our way out of this mess.
And as older Americans—people who’ve lived through enough to know what’s at stake—we can still show others what serious civic responsibility looks like.
We don’t need more commentary. We need more courage.
We don’t need more dread. We need more doing.
So if you find yourself scrolling tonight, just ask:
What else could I be doing instead?
Brent McKenzie is a writer and educator based in the United States. He is the creator of Idiots & Charlatans, a watchdog-style website focused on democratic values and climate change. He previously taught in Brussels and has spent the majority of his professional career in educational publishing.
"The stakes of AI policymaking are too high and the risks of getting it wrong are too enduring for lawmakers to legislate on instinct alone," explains Kevin Frazier.
Nature abhors a vacuum, rushing to fill it often chaotically. Policymakers, similarly, dislike a regulatory void. The urge to fill it with new laws is strong, frequently leading to shortsighted legislation. There's a common, if flawed, belief that "any law is better than no law." This action bias—our predisposition to do something rather than nothing—might be forgivable in some contexts, but not when it comes to artificial intelligence.
Regardless of one's stance on AI regulation, we should all agree that only effective policy deserves to stay on the books. The consequences of missteps in AI policy at this early stage are too severe to entrench poorly designed proposals into law. Once enacted, laws tend to persist. We even have a term for them: zombie laws. These are "statutes, regulations, and judicial precedents that continue to apply after their underlying economic and legal bases dissipate," as defined by Professor Joshua Macey.
Such laws are more common than we’d like to admit. Consider a regulation requiring truck drivers to place visibility triangles around their rigs when parked. This seemingly minor rule becomes a barrier to autonomous trucking, as there's no driver to deploy the triangles. A simple, commonsense solution, like integrating high-visibility markers into the trucks themselves, exists, yet the outdated regulation persists. Another example is the FDA's attempt to help allergy sufferers by requiring sesame labeling. Rather than simply labeling, many food producers responded by adding sesame to more foods to avoid non-compliance, a comical and wasteful regulatory backfire.
Similar legislative missteps are highly likely in the AI space. With Congress declining to impose a moratorium, state legislatures across the country are rapidly pursuing AI proposals. Hundreds of AI-related bills are pending, addressing everything from broad, catastrophic harms to specific issues such as deepfakes in elections.
The odds of any of these bills getting it "right" are uncertain. AI is a particularly challenging technology to regulate for several reasons: even its creators aren't sure how and why their models behave; early adopters are still figuring out AI’s utility and limitations; no one can predict how current regulations will influence AI's development; and we're left guessing how adversaries will approach similar regulatory questions.
Given these complexities, legislators must adopt a posture of regulatory humility. States that enact well-intentioned regulations leading to predictable negative consequences are engaging in legislative malpractice. I choose these words deliberately. Policymakers and their staff should know better, recognizing the extensive list of tools available to prevent bad laws from becoming permanent.
Malpractice occurs when a professional fails to adhere to the basic tenets of their field. Legal malpractice, for instance, involves "evil practice in a professional capacity, and the resort to methods and practices unsanctioned and prohibited by law." In medicine, doctors are held to a standard of care reflecting what a "minimally competent physician in the same field would do under similar circumstances."
While policymaking lacks a formalized duty of care or professional conduct code, we're not entirely without guidance. A related concept, though less familiar, offers a starting point: maladministration.
Maladministration encompasses "administrative action (or inaction) based on or influenced by improper considerations or conduct," indicating when "things are going wrong, mistakes are being made, and justifiable grievances are being ignored." While typically applied to administrative agencies and politicians, as the creators of such systems, they bear responsibility for anticipating and correcting these mistakes.
Given the inherent difficulties of regulating AI, policymakers should, at a minimum, demonstrate consideration of three key tools to reduce the odds of enacting misguided regulations. These tools align with core democratic values, ensuring policy promotes the common good.
First is experimental policy design via randomized control trials (RCTs). Legislators shouldn't assume one best way to test AI models or report their training. Instead, they should build experimentation into legislation. Some labs might follow steps A, B, and C, while others follow X, Y, and Z. The legislature can then assess which provisions work best, ideally transitioning all regulated entities to superior practices or amending the law. This fosters innovation in regulatory methods.
Second are sunrise clauses. These delay enforcement until prerequisites—basic conditions of good governance—are met. Unlike a simple future effective date, a true sunrise clause imposes a checklist: Is the implementing agency staffed and funded? Have regulated entities been consulted? Do stakeholders understand compliance? In AI policy, these questions are urgent. Enforcing complex laws before infrastructure exists is inefficient and undermines legitimacy. A sunrise clause ensures laws "land" effectively, demanding competence before policy becomes an enforceable rule. This promotes transparency and accountability.
Third are sunset clauses. If sunrise clauses delay a start, sunset clauses enforce an end unless actively renewed. This is critical for fast-evolving technologies. A sunset clause builds in mandatory reassessment: "This law expires in two years unless renewed." This isn't laziness; it’s disciplined humility. AI regulation shouldn't outlive its usefulness, and sunset clauses ensure laws earn their permanence, preventing outdated assumptions from locking in.
The stakes of AI policymaking are too high and the risks of getting it wrong are too enduring for lawmakers to legislate on instinct alone. While action bias is human, embedding it in law is neither excusable nor sustainable. At this early, uncertain stage of AI development, policymakers have a rare opportunity: to regulate with foresight, humility, and discipline.
Kevin Frazier is an AI Innovation and Law Fellow at Texas Law and Author of the Appleseed AI substack.
