Tao and Fields Medalists' two objections to AI in math, and why they're weak
RexDouglass · x · 2026-09-12
Stefanos Batzoglou summarizes and pushes back on the misalignment points Terence Tao and other Fields Medalists see in AI's use in mathematics:
- Advancing understanding: solving problems is only a proxy for developing theories and methods, and AI solutions may be unreadable/incomprehensible.
- Training mathematicians: AI stronger than humans that clears human-accessible open problems reduces the incentive to train mathematicians in constructing hard proofs.
He argues both are weak: mathematicians themselves show little interest in communicating results understandably — even top researchers struggle with adjacent fields — and AI may actually vastly improve the comprehensibility of math. The thread stems from a Terence Tao reference shared by Andrew Curran.
More from AGI Musings
- Even AI Critics Find the Fields Medalists' Letter 'Disgusting', AI Twitter Shows Little Sympathy — iruletheworldmo · 2026-09-12
- Anti-pause take: AI slowdown calls ignore game theory, incentives and economics — nptacek · 2026-09-12
- repligate explains why he still respects Eliezer: rare pre-AI alignment thinking — repligate · 2026-09-12
- tszzl: America's free speech culture quietly underpins the entire AI safety discourse — tszzl · 2026-09-12
- Alignment discourse's blind spot: both camps treat AI as a tool — RileyRalmuto · 2026-09-12
- Blogger accuses top AI risk PR funder of running a 'deliberately deceptive recruiting tool' — kevinnbass · 2026-09-12