The Counterexample Machine: Why AI's Math Doesn't Look Like Ours
from AI and the Human Future Hosted by Daniel Altman and Emily Carter
This spring, an internal OpenAI model killed a math conjecture that had been open since 1946—with a counterexample, not the proof everyone was chasing. Daniel and Emily unpack the 'hint sequence' theory of why AI is suddenly good at math, how its route differs from human mathematicians, and who gets to decide which problems the machines attack next. ## Hosts ### Daniel Altman Daniel Altman is a computer scientist and AI strategist with a background in engineering, quantitative research, and emerging technologies. He studies how technological breakthroughs reshape industries, markets, work, and human capabilities. ### Emily Carter Emily Carter is a business strategist and former technology product leader with experience building digital products and studying how innovation reaches millions of users. She focuses on AI adoption, business transformation, work, education, entrepreneurship, and leadership. ## Sources - https://latent.space/p/ai-is-getting-good-at-math/ - https://www.a16z.news/p/charts-of-the-week-attention-is-all ## Episode Information Language: en Published: 2026-10-23T21:51:54.343597+00:00
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