Terence Tao – Kepler, Newton, and the true nature of mathematical discovery
- Source
- dwarkesh.com
- Author
- Dwarkesh Patel
- Date
A sharp counter-argument to the 'AI accelerates science via fast verification loops' narrative, using the Kepler/Newton history to show why frontier discovery resists RL-style optimization — useful for anyone building or reasoning about AI research agents and .
- eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
“I think AI has driven the cost of idea generation down to almost zero, in a very similar way to how the internet drove the cost of communication down to almost zero.”
Terence Tao
“we’re going through a cognitive version of the Copernican revolution, where we used to think that human intelligence is the center of the universe”
Terence Tao
“whenever we do a systematic study, on any given problem an AI tool has a success rate of maybe 1% or 2%. It’s just that they can buy scale, and you just pick the winners.”
Terence Tao
“The progress is simultaneously amazing and disappointing. It is a very strange feeling to see these tools in action.”
Terence Tao
“2026-level AI would be stunning in 2021. A lot of it—face recognition, natural speech, doing college-level math problems—we just take for granted now.”
Terence Tao
- 00:00:00Kepler was a high temperature LLM
- 00:11:44How would we know if there's a new unifying concept within heaps of AI slop?
- 00:26:10The deductive overhang
- 00:30:31Selection bias in reported AI discoveries
- 00:46:43AI makes papers richer and broader, but not deeper
- 00:53:00If AI solves a problem, can humans get understanding out of it?
- 00:59:20We need a semi-formal language for the way that scientists actually talk to each other
- 01:09:48How Terry uses his time
- 01:17:05Human-AI hybrids will dominate math for a lot longer
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