If scaling laws hold—and Surge AI CEO Edwin Chen believes they do—we’re hurtling toward a future where there’s nothing humans can do that AI can’t do better. When OpenAI’s models disproved an open conjecture posed by mathematician Paul Erdős using novel algebraic geometry techniques, Fields medalist Timothy Gowers felt the shift acutely. He initially thought the model had proved an upper bound, and braced himself: that would mean it was “all over for mathematicians very soon.” When he realized it had only found a counterexample, he was relieved—it bought him another year or two before the thing he’s devoted his life to becomes something AI does better.
As founder and CEO of the company behind the data environments and evals the major model companies use to train their models, Chen has a unique perspective on how quickly AI models are absorbing tasks we used to think of as uniquely human.
Dan Shipper talked with Chen for AI & I about what the act of creating or building means when AI can do it better—and whether an answer to that question already exists within science fiction.
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Timestamps:
00:00:54 Introduction
00:01:49 Surge as a "school for AGI"
00:04:46 What AI's capacity for novel mathematics says about human achievement
00:07:29 Motivation in an era when AI can do everything
00:14:34 The trap of optimizing AI models for engagement
00:29:34 Training using datasets versus training using environments
00:35:09 The value of personal data
00:39:40 Why models are bad at writing
00:42:00 Chen's AGI timeline
Links to resources mentioned in the episode:
Edwin Chen on X: https://x.com/echen
Surge: https://surgehq.ai
Riemann-bench (research-level math benchmark): https://surgehq.ai/leaderboards/riemann-bench
Hemingway-bench (creative writing benchmark): https://surgehq.ai/leaderboards/hemingway-bench
Talkie-1930 (language model trained on pre-1930 text): https://huggingface.co/talkie-lm/talkie-1930-13b-it
Ted Chiang, “What’s Expected of Us”: https://www.nature.com/articles/436150a
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