The avalanche that hit math is coming for AI development

Dwarkesh Patel (0:00)

Thank you. If you had asked me earlier this year how far away we are from superintelligence, I'd probably have said at least a better part of a decade. But over the last three months, I've updated my views a ton. I've been debating people both on and off the podcast about this question. And of course, I'm watching the progress of AI on verifiable domains and thinking about what that implies for AI research itself. And I've come to the view that we're only a couple years away, and possibly only a couple months away, from AIs that are as good as the best humans at AI research. And I've also come to believe that automating AI research will compress so much progress into a single year that soon thereafter, by 2030 and plausibly as early as next year, we'll have AIs that are at least as good as the best human experts in every field. I know this is a wild claim and it's one that I myself didn't believe even a couple months ago. Let me walk you through how my thinking has changed.

Before we get into the more speculative arguments about what will happen in the future, I want to point out that AI is already dramatically speeding up further AI progress at the frontier. For example, Meter, which is an independent evaluation organization, spent three months embedded of Anthropic, and afterwards concluded that Anthropics' rate of AI progress on well -scoped tasks is 2x what it was a year ago. And that a quote majority of this acceleration today is likely attributable to Anthropic's extensive use OpenAI published a blog post about this in September, where they showed that the heaviest 10% of AI users amongst OpenAI's researchers went from using about $1,000 a day of internal coding agents at the And also in September, Anthropic put out a similar post on its own AI uplift. They reported that Claude now leads 26% of Anthropic's AI R&D work, up from under 1% as recently as February.

In thinking about how big a deal it would be for AIs to automate AI research and how much that would speed up AI progress, a really important update for me has just been seeing how fast the capabilities of AI models in mathematics have exploded.

Current AIs are slicing through well-scoped problems in maths like a hot knife through butter. It was only two years ago that I asked Dario why LLMs aren't able to use their vast knowledge across so many different fields in order to connect together different ideas and make new discoveries. And lo and behold, here we are in a wholly very Ais are doing the search and match discovery to such a great extent that hundreds of important results, which the top mathematicians and computer scientists in the world had spent decades trying to resolve, are being churned out.

It's crazy how fast this progress has happened. As recently as 2023, these models couldn't reliably add four-digit numbers. In 2024, they could barely solve any problems in a high school math competition. In 2025, they could win a gold medal in the International Math Olympiad. To earlier this year, where they finally started to solve some niche open problems. To a month ago, where OpenAI solved a Millennium Price problem in 88 hours.

I'm reminded of a line from the novel East of Eden. Quote, And there is one sure thing about the fall of gods. They do not fall a little. They crash and shatter. End quote. Now, it's true that progress has screwed disproportionately Towards the verification -laden parts of mathematics, like finding counterexamples rather than say coming up with new theory. Toby Ort recently wrote a great essay about this he writes quote while current AI systems are starting to automate mathematical proof there is currently very little evidence that they can ask powerful questions or find profound new fields of mathematical inquiry, end quote. But another way to say that same exact sentence would be this.

Current AI systems have solved a millennium -priced problem all without the ability to ask powerful questions or found profound new field of mathematical enquiry And this ability to solve well-scoped problems is going to be an even bigger deal for machine learning research than it has been for mathematics broadly construed. That's because the goal of mathematics research is not just to prove or disprove long lists of conjectures, Rather, to extend the human understanding of mathematical concepts. Coming up with the idea of topology, or group theory, or going back even further, the Cartesian grid, matters far more than knowing exactly how many We mainly care about such abstruse questions insofar as they might elicit new mathematical theory. But in AI research, the priorities are exactly reversed.