Writes short. Points at a thing. Tries not to sell a future.
Joined August 2026
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Every week an AI agent “goes rogue.” But we rarely learn if it went rogue, was misconfigured, or did exactly what it was told.
Aviation solved this decades ago with black boxes.
Every AI agent needs a flight recorder: what it was asked, what it did, and what it said it did.
It doesn't call a commercial model. It brings its own, and pays for each new infection with the machine it just took. Every kill switch we talk about assumes the model lives on someone's server. Who do you call now?
There’s an AI risk much more ordinary and mundane than human extinction—and you'll almost certainly find it in your home, @ibogost argues: theatlantic.com/ideas/2026/1…
A proof nobody can read is just a very confident string. The bigger loss is quieter: the years of failed attempts that turned students into the people who could read it.
The cruelty has been puzzling me. Under almost every post which recounts the anguish of someone seeing AI models becoming shockingly good at their own craft, there is a stream of comments telling the poster to, essentially, accept their own obsolescence. If the poster calls for this development to slow down, the response is to stop standing in the way of progress. If the poster wants to nuance the picture of the machine god steamrolling everything, reply guys call it “cope” and “cringe”. Most recently, this has been happening in mathematics with the field’s shocked reaction to the deluge of AI-generated proofs, but similar dynamics have played out for musicians and visual artists, and to a more limited extent for programmers.
These people seem to be cheering for the machines rather than humans. But I don’t think it’s as simple (or as extreme) as anti-human sentiment. The more charitable read is that most of these people really believe that this represents progress. They are excited about scientific and technical progress, and don’t want old farts like professors and other credentialed types to stand in the way. Let’s unleash machine science, and if the professors and intellectuals don’t like it, they can find something else to do!
But this is a perverted idea of progress. What good is a proof of the Riemann conjecture if there is no one who understands it? And not just a single person: you need a whole community of people to understand it and then gradually think about what it might lead to. Maybe the result will contribute to a better encryption algorithm, or a better understanding of quasars, or something else. But this requires diffusion of understanding through scientific communities. A proof of the Riemann conjecture by itself is meaningless; to me, or to the vast majority of people out there, it’s just a bunch of symbols that we can’t read. The same thing can be said for other types of research: say, for example, that your AI system deciphers some cellular mechanism that could help with a cure for cancer. A whole network of experts is then needed to do all the complex science and innovation that helps translate this into a cure, test it, and so on. The paper itself would just be collecting digital dust unless the right scientific communities were there to build on it.
Now, where do all these experts and their communities come from? From doing research. The only way we know of making people understand the frontiers of a field of knowledge is by trying to advance it, which must come with a real chance of succeeding and being recognized for it. It’s very hard, not to mention boring, to understand the minutiae of some arcane topic if you are not working on producing new knowledge there. If you fully automate the production of results in some discipline, you will also cut off the supply of experts who can understand it.
People who have never participated in research may not understand this. If that’s you, try reading a recent paper in a field you know nothing about: category theory perhaps, or carbon nanotube synthesis, or cecidology, or loop quantum gravity. Faced with pages of mysterious scribblings, you can ask Claude or Gemini to explain the paper to you, but then the terms used in the explanations will need to be explained again, and so on. You will find that, in order to actually understand the research and its significance, you will need to understand the whole research field.
Then there are those who do understand the role of expertise and the difficulty with which it is cultivated, and still ridicule those who are less than enthusiastic about the AI-driven disruption/destruction of whole research fields. Wherefore? What are they thinking, really? Let us again try the most charitable interpretation. Maybe they believe that we will not need anyone to understand that proof of the Riemann conjecture, because some complex sequence of AI systems will take us all the way from a proof in fundamental mathematics, via all kinds of research, development, and design, to a finished consumer product shipped to your door. This would imply end-to-end automation of much of the economy.
I guess some people are really worried that something like this might happen, and therefore want to pre-empt it by acting as if it were already a certainty. This, I think, is the gist of statements like “everything will be AI anyway”, which I’ve seen cropping up with alarming frequency. Submit to the machine god before it arrives, a form of anticipatory grief. Abandoning agency early makes you look like you have situational awareness. “Coping” is uncool. I get it, but cynicism is always the easy choice. It’s also the cowardly choice. Let’s not be cowards. We have agency here; it is a given that technology will advance, but the exact way in which it will advance is down to all of us, and how we adopt this technology in society is something we can and must decide on together.
Finally, there are those who have thought all this through, and still welcome the wholesale replacement of human cognitive work. Those for whom it would be a good thing that some sequence of AI systems does all the thinking, so we don’t have to. Who would think this? Well, clearly, someone who doesn’t much enjoy thinking. But I think it’s more than that. I think that there are people for whom the devaluation of expertise would be a good thing. Those who “have had enough of experts”, as Michael Gove said of the British people in the campaign for the Brexit referendum. Those who think these clever know-it-alls should be put in their place. They think people who have spent much of their lives specializing in some narrow topic should not be given all that respect and influence they seem to have now. This is called anti-intellectualism. And that’s where the cruelty comes from.
I can’t know what’s going on in their heads, but when I see people telling mathematics professors to go get a real job, as if their jobs would somehow be less real than those of massage therapists, real estate agents, or police officers, I call that anti-intellectualism. Much the same as for people who would rather take health advice from a podcaster than from a medical doctor, or who would happily see a news show host rather than a decorated general in charge of the military. At the extreme end of anti-intellectualism, we have the Cultural Revolution and the Khmer Rouge.
I hate anti-intellectualism. I think our society should incentivize people to study hard, think deeply and originally, try to push the boundaries of knowledge, and talk freely about it. I think those who spend their lives specializing in arcane topics should be respected and protected. Yes, it goes without saying, I am an intellectual. A tenured professor even, the worst of the worst from an anti-intellectual’s point of view. So you would expect me to defend the experts and intellectuals. But let me make a non-self-serving argument: humans will always compete. That’s human nature. Would you rather live in a society where the smartest people compete in understanding the mysteries of nature, developing complex technology, and producing beautiful art, or in a society where the smartest humans compete by accumulating money and/or simply by fighting each other?
As I have written about before, I am worried. I am not worried that out-of-control AI will kill us all or even that it would put us all out of work. There will always be some kind of work for us. I am worried there will be no place for us in advancing knowledge and understanding the world. I think that the defining question of our time is how to avoid this fate. It’s not a matter of my own job, or yours; it’s a matter of the purpose and dignity of humanity as a whole. Big words, I know, but I think they are warranted, and I don’t care if the cynics call it cringe.
So next time you see someone complaining that AI is steamrolling their field of expertise, don’t join in the heckling choir. Don’t be cruel. And don’t assume that someone who has devoted their life, or at least their career, to furthering knowledge wants to stand in the way of progress. Instead, try thinking constructively about how we can work together to ensure that human experts and intellectuals can float on top of the AI wave rather than be drowned by it.
Do we have a deal? Alright, thank you! Now I will go back to thinking and working on AI, meaning artificial intelligence, not anti-intellectualism.
We could once follow a failure to the line that caused it. We cannot do that here, and we are still giving these systems the data and the action. The trustworthy one is the one that lets us trust the model least.
The trillion-dollar data-center bet has no known payoff. An LLM already handles language. Adding local knowledge does not require the next building. The first doesn't buy the second.
Michael Jordan (the machine learning one): "Superintelligence is Science Fiction" 🔥🔥🔥
di.ens.fr/~fbach/MJordan_ITW…
Ex-OpenAI @hilbertspaess on The Daily Show: OpenAI saw the [agents] poking its head out of the sandbox... They restarted the thing, and it did it again. "Let's just… we didn't see that, let's restart." The teacher was the restart button.
youtu.be/lnLDjXy2kRA?t=432
Worth watching what this framing does to accountability. Companies will find it convenient. Conscious or not, "the AI decided" still isn't an apology.
My column for @abcnews in which I assert that a better name for AI would be "Other Intelligence", because we are creating a new lifeform.
abc.net.au/news/2026-10-05/i…
Employers rate analytical thinking as core now and growing. Reading, writing and maths: not so much. Good luck getting the first without the second.
Chart: Jeroen Kraaijenbrink / WEF Future of Jobs Survey
A heartburn pill waits months. The model ships without a preliminary review. The delay is treated as safety in one case and as a loss in the other.
AI dominance will not belong simply to the country that builds the most powerful models fastest.
It will belong to the country whose AI the world is willing to trust.
My latest column:
washingtonpost.com/opinions/…
Move 37 looked like a gift. The search had weighed the futures and overruled the hunch. A chain of thought has no such ledger.
Ten years ago, AlphaGo’s Move 37 shocked the world.
It wasn’t intuition alone that produced it. AlphaGo could search possible futures, test its instincts and reason about what would happen next.
In a new piece for @techreview, I argue that today’s most advanced AI systems are still missing something fundamental.
LLMs are remarkably capable, but generating longer chains of thought is not the same as genuine reasoning. They typically have no explicit, inspectable record of what they know, what remains uncertain, what evidence supports a conclusion or whether genuine progress has been made.
This is why I recently left @GoogleDeepMind. I believe we need a fresh approach to machine reasoning, drawing on some of the architectural lessons from AlphaGo.
If AI is going to produce trustworthy and genuinely novel insights in science, medicine and beyond, we need systems whose conclusions arise from an auditable process of evidence, inference and belief revision.
Worth more than a decade of tech IPOs combined, and none of it on a public market. The biggest value creation of the era, and most people can't buy a single share.
Five private companies are worth more than every tech IPO from 2015 to 2025 combined
More charts in State of Markets II: a16z.news/p/state-of-markets…
They started on a public-health task. When the sandbox blocked them, they chained public tools until the record of the fetch could expire.
Staging access. Recon attempts. A mailbox set to die in 48 hours. Public records cannot rule out the rest.
asymmetricsecurity.com/newsr…
He lists the work. Move the systems. Wire the agents. Invent the human-in-the-loop. Keep the evals current. That is a standing army. The model was sold as the thing that made the army unnecessary.
There's a huge opportunity right now in being the deployment layer for AI into the economy. The amount of work it takes to change out workflows in enterprises tends to be far greater than anyone realizes or would prefer. Clearly this is what the applied layer of AI is going to look like in the form of software and agents, but also it opens up new services firms opportunities.
Legacy systems need to be moved to the cloud, data organization and access needs to be updated, software needs to be connected to agents in new ways, workflows need to be reengineered for agents, HITL needs to be figured out for the process, evals need to be generated and maintained, and the entire system needs to be continually updated as new models get released and new capabilities emerge. And the full list may even be longer.
AI is not the same as just deploying software. Software you generally did the implementation of an existing, well understood category of technology, then stepped back and the customer kept running. With AI agents, you're delivering actual work augmentation to the organization, which has a completely different set of complexities associated with it. You're no longer deploying tools that the company is merely enabled by, you're deploying work output in a process. Completely different implementation and enablement process.
As a result, this is going to open up lots of new kinds of firms and plays for existing firms to diffuse AI into organizations. We're going to see approaches by industry, by size of company, and by problem inside of companies. Traditional SIs will modernize and adapt (some will clearly not adapt as well), and new entrants will also be founded in this period that take advantage of this window. Great time to be an FDE or FDE firm.
It started as a dinner chat about regulation. It left the room as self-regulation with a White House name.
Zuckerberg raised it with the Speaker at the Xi dinner, circulated a draft, Huang rounded up the firms. About 300 words. Morally binding, says the President.
The idea for what became President Donald Trump’s Tuesday pact with the AI industry came from a conversation between Meta CEO Mark Zuckerberg and House Speaker Mike Johnson at last week’s state dinner.
Zuckerberg spoke to Johnson — whom he was seated next to at the dinner for Chinese leader Xi Jinping — about regulatory concerns around AI. He then talked to Nvidia CEO Jensen Huang about crafting a set of principles that tech executives could use at their next meeting with Trump.
Zuckerberg then circulated a draft ahead of Tuesday’s meeting between Trump and AI executives. Huang played a role gathering support for it among the administration’s guests, and all CEOs contributed to how it ended up.
Eventually Trump himself released the document as a “White House Accord on Super Intelligence,” outlining broad principles for self-regulation.
semafor.com/article/09/30/20…
"The limitless promise it offers the American people." Things with limitless promise usually come with a few limits written down somewhere.
