But what I am worried about is that as humans are outpaced and rendered obsolete in the most parts of cognitive labour, the incentives to maintain the institutions and practices that strengthen our minds so that we can even have the imagined intent, taste and capacity to wisely steer vastly more intelligent machines, will fade. A myriad of questions seem unanswered to me: Where will the human’s purpose come from? Through what experience will his taste have matured? What will enable him to recognise that the advice he receives is mistaken, or that the goal he has chosen is unworthy of pursuit? To separate our capacity to decide and exercise “agency” from our ability to think seems to me like making the old educational error in a more radical form. Educational reformers once imagined that knowledge and memorisation could be ignored, while at the same time leaving something called “critical thinking” intact. We are now being asked to believe that thinking itself can be handed over while human agency somehow would somehow survive untouched. But agency is not an abstract faculty floating free of everything else we do. Our purposes are inextricably linked to our acquisition of knowledge, to our thinking. Agency, considered in the abstract, tells us little about what a person will do with it. Someone intelligent and well-read may devote extraordinary energy to building a library that enlarges the lives of his compatriots, while someone unintelligent and indoctrinated by religious extremism might use their agency to wage jihad against infidels. Not only that: by delegating the work of understanding, we may also relinquish some of the means through which we discover what is worth doing. The promise that humans will supply the goals while machines do the thinking treats our purposes as though they were already fully formed, awaiting the means of execution.

The world's top thinkers on AI are posting on Substack. Bookmark this thread for your weekend reading on pacing the frontier from Noah Smith, Matt Yglesias, Ruxandra Teslo, Dwarkesh Patel, Dean W. Ball, and more:

@RuxandraTeslo argues the most neglected AI safety risk isn't a robot takeover, it's the gradual surrender of human thinking: "A calculator relieves us of a quite narrow operation whose purpose we already understand, whereas AI systems can increasingly take over large swathes of mental activity. Often, these are the very activities through which our understanding develops in the first place."

It’s the “Brave New World” versus “1984” debate all over again. Big AI Is Watching You (and can kill you) versus everyone takes a little AI soma and is perfectly happy with their lot in the dystopia.

"It is far from preordained that AI will make us intellectually weaker. But without active effort, the temptation to outsource our thinking will prevail, because it appeals to a common human weakness: the desire to spare ourselves effort."

Last week, a letter from mathematicians concerned about the effects of AI was dismissed as “protectionist”. I fear that what is happening to maths is a canary in the coalmine for a progressive hollowing out of intellectual institutions in the age of AI. Amidst discussions about AI’s potential to escape human control, a far more mundane but in my mind worse outcome gets slightly ignored: the danger that we might actually prefer to relinquish control to the machines, as human cognitive capacities atrophy. Optimistic accounts of an AI economy imagine machines doing cognitive work, while humans choose goals or exercise taste and agency. The problem I see is that the judgement, taste and purposes required for those roles develop through intellectual work and knowledge accumulation; they cannot be cleanly separated from these activities. So if we want humans to exercise wise control and ultimately, any control at all we have to preserve their cognitive abilities. But the intellectual communities through which these develops are threatened and I think the mathematicians’ letter is warning in that direction. For maths, the model seems to be following: If AI makes results cheap, the institutions supporting that training could lose their justification and funding, even as mathematical output multiplies. I worry that the maths community is the canary in the coalmine for a potential broader hollowing out of intellectual life. Human intellectual cultivation has historically been sustained indirectly by economic and social hierarchies, most recently through universities. In the most recent arrangement, access to desirable white-collar careers required passing through universities and society effectively subsidized institutions in which scholarship could flourish. This also had other positive spillovers: the preservation of a sense of a “unified civilization”, with a shared vocabulary of meaning, values and inherited traditions. I am by no means a supporter of universities in their current form and have criticized them at length (particularly because they have increasingly departed from the mission I outlined above). That being said, we do need some intellectual institutions. AI threatens the current order. If degrees become less necessary for employment, or if entry-level knowledge work falters, the economic incentives that support universities and other institutions of cultivation could weaken. This to me seems like one of the most important questions regarding “AI safety”. I know “cognitive decline” does not sound as scary or totalizing as existential risk, but the quality of human life matters just as much as the quantity of it. And to me, the value of technology is determined by its ability to elevate humans. I do not think that cognitive atrophy is pre-determined. I believe we can and should build institutions that preserve and encourage the cultivation of the human mind. But we have to take it seriously.

Last week, a letter from mathematicians concerned about the effects of AI was dismissed as “protectionist”. I fear that what is happening to maths is a canary in the coalmine for a progressive hollowing out of intellectual institutions in the age of AI. Amidst discussions about AI’s potential to escape human control, a far more mundane but in my mind worse outcome gets slightly ignored: the danger that we might actually prefer to relinquish control to the machines, as human cognitive capacities atrophy. Optimistic accounts of an AI economy imagine machines doing cognitive work, while humans choose goals or exercise taste and agency. The problem I see is that the judgement, taste and purposes required for those roles develop through intellectual work and knowledge accumulation; they cannot be cleanly separated from these activities. So if we want humans to exercise wise control and ultimately, any control at all we have to preserve their cognitive abilities. But the intellectual communities through which these develops are threatened and I think the mathematicians’ letter is warning in that direction. For maths, the model seems to be following: If AI makes results cheap, the institutions supporting that training could lose their justification and funding, even as mathematical output multiplies. I worry that the maths community is the canary in the coalmine for a potential broader hollowing out of intellectual life. Human intellectual cultivation has historically been sustained indirectly by economic and social hierarchies, most recently through universities. In the most recent arrangement, access to desirable white-collar careers required passing through universities and society effectively subsidized institutions in which scholarship could flourish. This also had other positive spillovers: the preservation of a sense of a “unified civilization”, with a shared vocabulary of meaning, values and inherited traditions. I am by no means a supporter of universities in their current form and have criticized them at length (particularly because they have increasingly departed from the mission I outlined above). That being said, we do need some intellectual institutions. AI threatens the current order. If degrees become less necessary for employment, or if entry-level knowledge work falters, the economic incentives that support universities and other institutions of cultivation could weaken. This to me seems like one of the most important questions regarding “AI safety”. I know “cognitive decline” does not sound as scary or totalizing as existential risk, but the quality of human life matters just as much as the quantity of it. And to me, the value of technology is determined by its ability to elevate humans. I do not think that cognitive atrophy is pre-determined. I believe we can and should build institutions that preserve and encourage the cultivation of the human mind. But we have to take it seriously.

I am increasingly frustrated about the state of conversation about AI in the UK. It feels like we are in never ending cycles of deluded thinking. Under current trends, Britain will be defenceless against the transformative effects of AI and have no leverage over development at the frontier. We are the most exposed economy to AI in the world. We have very little data centre capacity and few credible plans to expand – and our high energy costs mean we would struggle to even if we tried. I am publishing a paper today on what the UK should do to build that leverage, let the economy capture value from AI and prepare the state to adapt, however AI develops. The three strategies are as follows: 1. Build the infrastructure that gives us leverage. Fix the grid, fix planning, make Britain a place frontier AI can run. 2. Let the economy capture the value of AI. Reform the regulatory, labour and talent barriers that stop British firms adopting AI. 3. Build a state that can adapt to a changing world. Build a tax base that survives the labour market changing, public services that use AI to deliver better, and capacity to weather the upcoming geopolitical storms. Two years ago, we published a similar report on AI - many recommendations of which ended up in the action plan. I remember feeling hopeful, but sadly the fundamental problems facing the country on AI have not shifted. I truly hope that this week of momentum culminates in action. There’s still a narrow path for the UK, but everyday that path is narrowing.

Remarkable how people will just make up things like, “Europe doesn’t have data centers because they can’t securitize them.” Lots of American data center builders are active in Europe. Fluidstack pulled out of France and OAI out of Britain because permitting and time-to-power were too slow. In places where those problems don’t exist — i.e. Scandinavia — lots of stuff is getting built. European data center builders will complain about “financing,” but what they mean is that, under the prevailing political, permitting, and energy environment, no one wants to buy the damn things!

AI allows us to design & develop life-saving medicines in months, yet India takes a year to approve clinical trials – Australia & China take weeks. Honoured to co-write an op-ed in @EconomicTimes with @KiranShaw on decentralising our trial framework to accelerate Indian biotech.

Partly good news for accelerating phase-1 trials, by new FDA appointees: - Reduced requirements for "QRIs" (supposed to be the equivalent of Australia's ethics boards whose approval is mostly accepted as "go ahead" for phase-1 trials) - FDA only wants a pilot program with 10 companies, and not give away any authority (which Australia doesn't either, it just sets a culture of trust in their boards). That leaves is partly open to how it will be done in practice how successful it will be. I still think it's not enough: Australia has done it successfully for 30+ years. Just skip the pilot, move right to accepting applications, helping the initial QRIs get it right so it's easy for you to say yes, publish success rates of QRI-to-phase-1 acceptance @endpts @maxonwifi @houmanhemmati

OpenAI Foundation owns 26% of OpenAI. Could be the richest nonprofit/charity in the world? This year, it's staffing up and started giving grants. We wrote about grants to ramp up biological data production.

Listen if your model of what has happened is that the EAs have launched a coup I think you have very bad judgement. Here is what actually happened: a very large number of people have slowly grown aware of the potential AI has to radically transform human society. Most people who contemplate this are uncomfortable with this—because they do not like change, because they do not like ceding agency and control to something they do not understand, because they distrust Silicon Valley, because they fear the apocalypse, because they fear the dystopian-lite future many AI futurists advocate as a the “good” outcome. Take your pick, it could be any of those. The point though is that the majority of people who become aware of the technology’s trajectory are not comfortable with it. For a long time this did not seem to matter because people kept these feelings to themselves. They weren’t confident they understood the issue; the entire problem seemed far-out there and sci-fi; they didn’t want to look bad; and if they were politicians, they did not want to needlessly upset an engine of the National economy. Nevertheless the unease has been growing for months and months and months now and if you did not realize this you were living in a bubble. Well now your bubble has popped. An event occurred that made it suddenly OK for folks to express the anxieties that had slowly been building up this year, and the expression came in one big flood. It is a classic “preference cascade.” The people who are trying to gin this up to some master plan of a group of activists have their head stuck in the ground—it reminds me of the liberals who refused to see Trump’s genuine grassroots popularity until they were whacked in the head by it.

Starting a new thing at UNC with @BenjaminGVincen to figure out which tumor-specific pMHCs are actually on tumor cells & which vaccine platforms are more immunogenic. Lots of long-read WGS/scRNA, targeted mass spec, tumor-specific TCRs coming your way in the near future

In my latest @nytimes article, I wrote how Phase I trials in the US are broken and this being the biggest blocker to cancer cures. The good news: we can borrow from Australia, which keeps patients safe while moving trials faster. W/ @kroetscha for @IFP, we mapped out how. Before getting into the weeds: why do Phase I trials matter so much? 1/ Phase I trials enable iterative loops of learning. Each patient generates information researchers can use to refine the drug, rethink the target, adjust the dose, or redesign the next experiment. Faster Phase I trials mean faster feedback between the clinic and the lab and better drugs. 2/ Phase I is also a crucial financing milestone. Small biotechs often have only a limited runway and getting encouraging human data can unlock the capital needed for further development. This is often the difference between survival and death for a small biotech. 3/ Reforming Phase I trials would pave the way to personalised medicine. Sequencing, biological engineering and A.I. make increasingly personalized therapies possible. But our regulatory system was mostly built for standardized drugs tested in large populations. Crucially, Australia is an important counterexample to the idea that faster trials must mean less safety. We can learn from them! 1/ Depending on modality, Phase I trials can begin 6–12 months sooner there, at substantially lower cost. Australia has run more than 18,000 trials since 2006, with no evidence that this faster system has produced worse safety outcomes. 2/ Australian trial volume has increased 2x in the last decade, driven by American companies taking their studies there. What does Australia do well? 1/ Most important, is Australia’s Clinical Trial Notification Pathway (CTN). Instead of submitting an IND package to their national drug regulator, sponsors simply notify the regulator that they are starting the study. Studies are reviewed by local ethics boards. 2/ Australia embraces a more risk-proportionate approach to study oversight. Requirements for Phase I should not be the same as those for later stage trials commercial drugs, and Australian system implements this distinction well. 3/ In Australia, Phase I studies are formally exempt from full manufacturing requirements, which can 10x costs for some drugs. In the United States, the situation is more complicated. Most sponsors end up implementing near commercial-scale manufacturing. So what can the United States do? 1/ First: create an Australian-style notification pathway for appropriate Phase I trials. Instead of requiring every study to pass through the full traditional FDA review pathway that American companies have to go through, qualified institutions could oversee scientific and ethical review. 2/ Formally exempt Phase I trials from commercial-scale manufacturing requirements and replace them with phase-appropriate manufacturing standards. 3/ Make FDA expectations much more explicit. Sponsors over-engineer studies because they do not know what reviewers will accept. FDA should publish much clearer standards of what is expected. 4/ Operation TrialBlazer, launched by HHS in June, is important for achieving this, by proposing an Australian-like Expedited IND pathway. 5/ But more lasting reform will require Congress. First, the FDA will need explicit authorization to rely on the judgments of local institutions to determine whether a trial may proceed. Second, Congress should formally amend the statutory full manufacturing requirements currently codified in section 501(a)(2)(B) of the FD&C Act (21 U.S.C. § 351(a)(2)(B)) to exempt early-phase trials.

We are on the cusp of a wave of new therapies for some of the worst diseases. But the world won’t benefit unless the US fixes its drug regulatory system. My new essay for @nytimes, on how slow clinical trials are now the biggest obstacle to curing cancer. - I interviewed dozens of researchers, especially oncologists at leading U.S. centers. A striking consensus emerged: science is no longer the main bottleneck to new cancer drugs. It is our ability to test discoveries in patients through clinical trials. - The cost of starting a Phase 1 trial in America has roughly doubled over the past decade. As a result, companies increasingly take early trials abroad: Australia’s Phase 1 trial volume has nearly doubled in a decade, driven mainly by U.S. companies. - Unfortunately, the underlying incentives are badly asymmetric: Institutions can be blamed for harms caused by moving too fast, but almost no one is blamed when patients deteriorate during avoidable delays. One doctor called the emerging system “ritualized safety over actual risk assessment.” Or as, @DavidHongMD put it: "We often forget that the biggest risk is the cancer itself." - This problem is becoming more urgent because medicine itself is changing. Sequencing, biological engineering and A.I. make increasingly personalized therapies possible. But our regulatory system was mostly built for standardized drugs tested in large populations. - @sytse, the co-founder of GitLab, shows what personalized medicine can achieve: after relapsed osteosarcoma and being told there were no options left, he pursued a highly individualized approach and has now been cancer-free for a year. But doing so required extraordinary resources and regulatory expertise. - Pierce Ogden’s father was less lucky. After molecular analysis identified a drug that might target his glioblastoma, the manufacturer agreed to provide it. But administrative barriers delayed access until it was too late. “My dad was ready to try anything,” Pierce told me. “But the system is paternalistic.” - The A.I. revolution is making this bottleneck more important, not less. A.I. relies on relevant data. Information from early-stage trials could compound with A.I. tools to achieve truly revolutionary medicines. Without the data, this is far less likely to happen. - Another important shift is that innovation increasingly comes from academic labs and small biotech companies rather than Big Pharma. These small companies find it far harder to unable to absorb delays and regulatory barriers. - Apart from cancer, China is the biggest winner from America's outdated medical regulations. China has a much faster trial system, with testing often starting a full year earlier. This allows Chinese pharmaceutical companies to experiment and improve medicines while American companies play with mice. Today, half of all drugs licensed by major pharmaceutical companies originate there, up from less than 5 percent only a decade ago. - But we don't need to copy China. The best model is Australia: lots of on-site scientific and ethics reviews, and requirements that are proportionate to small, early-stage trials. Phase 1 studies there begin roughly 6–12 months faster, without any notable increases in adverse safety events. - Operation TrialBlazer, a 2026 HHS initiative is a good start in this direction, but we need legislative action by Congress to truly make Phase I trials faster and more efficient! I want to thank everyone who helped me with this article: everyone I interviewed and the amazing editors at the Times. This is the result of a months long journey of extensive interviews and research. Special thanks go to those who came on the record. One of the features of the system is an atmosphere of fear, where practitioners are afraid to publicly come out and explain these issues. So anyone who does is a hero in my book!

“Why are people not happier about A.I.? It might cure cancer” is a very one-dimensional view of human beings. It assumes that humans want only material comfort, disregarding their fears around loss of agency and purpose. Wrote an essay on this: There is an enormous backlash against data centres. Many in the tech world have converged upon "curing cancer" or "curing disease" as the positive message to be said around AI. I want to clarify that as someone coming from a biology background, I consider "curing disease" as a very noble mission. I devote a large part of my time to thinking about how to make that go faster, via Clinical Trial Abundance. Yet I still believe that the vague promise of "curing disease" is fundamentally not worth it if it comes with enslavement and permanent deprecation of human existence, when it comes to AI. I think many feel the same. Some points: 1. Americans now appear more hostile to new data centres than to nuclear power plants. Their stated concerns are practical, chiefly water and electricity usage. But I think these objections are at least partly respectable vessels for a deeper and more diffuse fear: that the machines inside these buildings may eventually make human beings redundant. 2. One specific instantiation of this fear is that of job loss. In Anthropic’s 2026 survey of nearly 52,000 Americans, 64% said they were worried about AI-induced job loss, making it the most common concern in every state. I think this is not just about loss of income: when people are offered a choice between government income support and creating jobs, they overwhelmingly prefer the jobs. This suggests people value work beyond its “earning a paycheck” function. 3. The technology industry realizes it has a “PR crisis”, so it has gone into “portraying a more optimistic message” mode. The central point of this optimistic messaging strategy increasingly seems to be that AI will cure cancer (and all disease). Curing cancer is obviously an extraordinary good. Whether it will happen and whether mass unemployment is the price is not the point of my essay. 4. What I am interested is the popularity of “Curing cancer” as the ultimate good that we can hope for. It betrays a very uni-dimensional way of seeing the world, seeing humans as beings with material needs to be satisfied and only that and forgetting that they also want agency and control over their lives. It also shows how much we have come to equate human welfare with material welfare: health, comfort, consumption and the relief of suffering. 5. This reminds me of Dostoevsky’s Grand Inquisitor. In Dostoevsky’s parable, Christ comes back to Earth in the time of The Inquisition. The Grand Inquisitor arrests him and argues made a terrible mistake by asking human beings to bear the burden of freedom. By refusing Satan’s temptations, bread, certainty and worldly power, Christ chose dignity and self-determination over comfort and obedience. The Inquisitor thinks the world would be better off if humans got the opposite bargain: feed them, protect them, relieve them of responsibility, and they will willingly surrender their freedom. 6. In Dostoyevsky’s imagination the possibility that humans might prefer to be well-fed hamsters to exercising their conscience is horrifying. But almost two centuries later, we seem to have accepted this. Indeed, many optimistic visions of a post-AGI future seem to offer The Inquisitor’s solution: a world of extraordinary abundance and conquered disease in which people are nevertheless less necessary and consequential, and less able to shape the institutions governing their lives. And this is seen as good. 7. I think Silicon Valley is unusually prone to this mistake because it has become extraordinarily good at satisfying material wants (which is a good thing, in and of itself!). What is interesting is that its builders often approach technology with almost religious intensity, and they take their spiritual needs from the act of building. Yet what they offer everyone else is not the act of building themselves, but only the results, which are only satisfying to the flesh: chiefly longer life, greater convenience and cheaper goods. 8. But I do not think this will be enough to quell anxieties. Humans may not be entirely as Christ wants them to be, but neither are they as the Grand Inquisitor sees them. The anxiety here is about loss of purpose and meaning, too. One way to understand this is by looking at what work means beyond “earning a paycheck” and why people fear unemployment so much, even when their material needs might be taken care of. 9. First, contrary to what many people say, people derive meaning from their work. Roughly 70% of Americans say their work gives them a sense of purpose; 51% say they are highly satisfied with their jobs; and 39% say work is central to their identity. The idea that 80–95% of people regard work as meaningless drudgery is not really consistent with the evidence. 10. More importantly, work has second-order functions that surveys about “job satisfaction” barely capture. It supplies status, independence, recognition, obligation, bargaining power and the feeling of being needed. A society in which most people earn their place through participation may be politically and psychologically very different from one in which they are supported materially, but no longer needed. I will discuss three of these social and political embeddedness roles of work. 11. Work is one of the central mechanisms of social mobility. From Jefferson’s distinction between a “natural aristocracy” of talent and an “artificial aristocracy” of birth, through Tocqueville’s observation that Americans honored labor, to Lincoln’s ideology of free labor, to the more modern American Dream the American ideal has been that one should be able to improve one’s station through effort. It is deeply ingrained in the national conscience and it’s hard to see how that could be abandoned. 12. Careers also function as ladders of agency. You begin by performing narrow tasks and over time, you acquire judgment and tacit knowledge, and begin to be trusted with increasingly consequential decision, acquiring status within a bounded community. AI could take that source of status and more broadly, if it removes the lower and middle rungs of these ladders, we may also destroy the process through which humans learn to exercise authority in the first place. 13. Finally, labor can also be seen as political leverage and a world in which labour does not count might be a world without political rights. History offers some interesting examples to learn from. After the Black Death made labor dramatically scarcer in Europe, workers could demand better terms, and the resulting shift in bargaining power helped undermine serfdom. I believe that if valuable labor can strengthen political rights, the opposite can also be true. 14. It may well be that widespread job loss never materializes. That is not the point of this exercise. The point is that figures like Elon Musk are openly predicting it, while we have remarkably few serious answers — or even serious public discussions — about what should follow. And no, framing this as a “PR problem” does not count. That is not to say that discussions approaching this don’t exist – earlier this year, Phillipp Trammell @dwarkesh_sp published an essay asking what policy should look like in a world where human labor is no longer economically important and capital becomes the dominant source of income and power. They consider various economic mechanisms that might prevent such a transition from producing an extraordinary concentration of wealth and political power. But they are few and far between and it seems like our political institutions are too exhausted and battered to do much about this, anyway. And it also seems to me that these discussions are mostly about policy, rather than about building anything resembling a broader moral framework or organized system of meaning.