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The avalanche that hit math is coming for AI development
Many thanks to Max Farrens, Gabe Fried, and Thomas Larsen for pairing with me through significant parts of writing and researching this essay.
You can watch or listen to me narrate this essay on YouTube, Spotify, and Apple Podcasts.
If you asked me earlier this year how far away we are from superintelligence, I’d probably have said at least the better part of a decade. But over the last three months, I’ve updated my views a ton. I’ve been debating people about this question, both on and off the podcast. And I’ve been watching the progress of AI on verifiable domains and thinking about what that implies for AI research itself.
I’ve now come to the view that we’re only a couple of years away, and possibly only a couple of months away, from AIs that are as good as the best humans at AI research. I’ve also come to believe that automating AI research will compress so much progress into a single year that, soon thereafter (so by 2030, but 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 of months ago. Let me walk you through how my thinking has changed.
Before we get into more speculative arguments, I want to point out that we have a lot of evidence that AI is already dramatically speeding up further AI progress at the frontier.
For example, METR, which is an independent evaluation organization, concluded after three months embedded inside Anthropic that Anthropic’s overall rate of progress on well-scoped tasks is 2x what it was a year ago, and that a “majority of this acceleration today is likely attributable to Anthropic’s extensive use of AI to do R&D.”
OpenAI published a blog post about this in September, where they showed that the heaviest 10% of AI users among OpenAI’s researchers went from using around $1,000 a day of internal coding agents at the beginning of the summer to over $7,000 a day by the end of August.
In September, Anthropic put out a similarpost on its own AI uplift. They reported that, as of August, Claude “leads” more than 26% of Anthropic’s AI R&D work—up from under 1% as recently as February.1
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Math is a preview of what will happen to AI research
When I think about how big a deal it could be if AIs automate AI research, and how soon that could happen, a really important update for me has been just seeing how quickly AI capabilities in mathematics have exploded. Current AIs are slicing through well-scoped problems in mathematics 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 different ideas and make new discoveries. And lo and behold, here we are: In a wholly verifiable domain like mathematics, AIs are doing this 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 as side quests tohype up future model releases.
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