Notes

Ankur Moitra: So much of our society now is geared towards winning and looking good on paper, and I always wonder to what end. You start off in high school and you can start, as soon as you walk in the door, optimizing your APs and your extracurriculars so that you can get to an elite school. And then when you get to that elite school, you might skip classes because, you know, maybe the important thing is to prepare for your interviews so that you can get a job at a frontier lab, and then you can get some nice, you know, restricted stock units and live out your life. And that’s great. You won. You won at every stage, but so what? ...

Alex Kontorovich: I’m constantly talking to AI systems, and I’m learning a lot of math a lot faster than I’d be able to. Because the way math is written, you know, I sometimes give a paper to a grad student to read, and they come back a week later, and I say, “So how far did you get?” “Well, I didn’t really get past the first page, to be honest.” “Oh, really? Tell me.” “Uh, well, actually, I didn’t get past the first paragraph.” I say, “Okay, fine. Why not?” “Well, the first sentence is by the perturbation theory of non-local systems and I don’t know what any of that means, so that’s kinda where I’m stuck.” I’m like, “Okay. You don’t know how to read a math paper. You don’t– You skip the first couple of paragraphs. Go straight to, you know, the Lemma 3.” “And that’s where you can start working and work your way backwards to what the thing is doing.” ...

Alex Kontorovich: But we also don’t wanna outsource so much– nearly so much decision-making to these systems for exactly that reason. Steven Brunton: Yeah, we have infrastructure and protocols in place already for safety critical and high impact decisions. We have the FAA, we have the American Medical Association, the Bar Association for these things that affect humans. And I think of, you know, how do we incorporate AI into our general processes in the entire world on two axes. We have, you know, how much do we trust the output, so verifiability or trustworthiness, and on the other axis is how high stakes is it if it’s wrong. If AI spits out a wrong proof, nobody goes without power. It’s fine. So it’s very low stakes. And it is actually extremely verifiable, so you know very quickly if it’s right or wrong, and it’s low stakes if it’s wrong. So that’s the perfect use case is this open-ended design space. Designing a better vehicle, you know, a better car design that’s lower drag, more fuel efficient. Low stakes if it gets a wrong design because you can always test it in a wind tunnel and verify if it did or didn’t work. ...

Ankur Moitra: So the way that this works is it’s an example of something called online algorithms. So you have a sequence of options coming. One through N. They come in each time step, and your goal is, you know, when someone comes along, you know their ranking relative to everyone else you’ve seen before, but you don’t know their ranking relative to the people who are yet to come. And your goal is, you know, to make a decision when the person comes along, do you commit or do they move on, in such a way that you have a good chance of finding the absolute best candidate. And so the point is that you can actually get a constant competitive ratio algorithm that what you do, the best strategy is to wait some amount of the time, not make any decisions, don’t accept anyone. And then once that probationary window has passed, you take the first person who’s better than anyone you’ve seen thus far. And that’ll give you a good competitive ratio in the sense that you can prove theorems. Assuming the inputs arrive in a uniformly random order, you’re likely to do pretty well compared to as if you knew what that sequence of inputs was in advance. ...

Ankur Moitra: I think also students have so much anxiety about, you know, what publications are gonna look like that one of the hardest parts is, you know, when I’m thinking about a project, I’m really trying to think about it in a sort of multi-year time horizon because I have some meta vision that I wanna realize, like how can we use learning theory techniques in quantum many-body systems? And there are a lot of stepping stones on route to that, but some of the most exciting problems are, you know, many years out. But students are so worried about the incoming AI tidal wave, you know, the future of work, universal basic income. Are any of us gonna have jobs? That they’re really thinking in three- and six-month horizons, and then it’s hard to find problems that we both care about just because- You know, we’re thinking about different worlds. ...

Although, as of October 30, 2026, there is no evidence that the Binance hack had anything to do with AI, I believe it did. I think there is a good chance the hackers used AI, directly or indirectly, in the attack. As an engineer, I know full well that hackers can use AI to launch large-scale attacks with ease. Hacking is, at its core, finding vulnerabilities in software. In the past that process usually depended heavily on people and was expensive to carry out, and what AI is best at is reading code tirelessly, in many threads at once, line by line, to find vulnerabilities. Because computer software is so complex, hackers can use all kinds of techniques to get around the visible guardrails, directly or indirectly, so a very strict set of guardrails is necessary. That is why I think it is reasonable for Anthropic to strictly restrict users from hacking, even if the restrictions are sometimes too strict. Of course, until the whole world has been hit by large-scale hacking, they look too strict. ...

AI is Transforming Intelligence We believe that Powerful AI, a highly capable and impactful form of frontier AI characterized by the following key attributes, will become widely available within a few years: cognitive capabilities matching or exceeding those of top human experts across most professional and scientific disciplines; the ability to navigate all interfaces available to a human working virtually, including text, audio, video, and direct computer control; the ability to reason through complex tasks autonomously over extended periods—lasting hours, days, or weeks—and seek clarification and feedback when needed; the ability to coordinate across multiple instances and collaborate with both humans and other AI systems; and the ability to interface with the physical world by digitally controlling laboratory equipment, robotic systems, and other machines through digital connections. ...

Enterprise Strategy. Our enterprise strategy is built on the belief that frontier AI is most valuable in real-world settings, as deployment across enterprises is where economic impact is greatest and where the improvement loops from customer feedback are richest. Accordingly, we have organized our research priorities, our product roadmap, and our go-to-market and Applied AI teams around enterprise use cases, which range from routine knowledge work to the most complex problems. The customer feedback we gather directs our research focus and makes Claude more capable over time. This broadens where Claude can be deployed and further expands enterprise adoption, which helps ensure that the benefits of our AI reach people and organizations at scale. In the prospectus: Prospectus summary: Overview (original filing on SEC EDGAR) ...

We manage compute as a single, fungible pool from multiple strategic partners, and we dynamically direct available compute between workloads based on where the marginal unit is expected to be most productive at any given time. In the prospectus: Prospectus summary: Our Approach to Compute (original filing on SEC EDGAR) In other words, they’ve built a dynamic compute platform: when the consumer side needs more compute, more goes to the consumer side, and whatever frees up goes to research and training. We have achieved this through our research breakthroughs and our multi-chip compute architecture across AWS Trainium, Google TPUs, NVIDIA GPUs, and AMD GPUs, enabling us to orchestrate workloads to optimize for price and performance. In the prospectus: Prospectus summary: Our Approach to Compute (original filing on SEC EDGAR) ...

To build AI is to harness cognition itself, and anything so powerful can be used for ill as well as good. In the prospectus: The founders’ letter (page image) (original filing on SEC EDGAR)