Reading up on Terence Tao

Terence Tao uses a Bronze Medal meme to argue that AI-generated mathematical proofs are just the first step, with true value requiring verification, clear explanation, and eventual textbook canonization.

AI’s ability to solve high‑profile math problems erodes puzzle‑solving as a proxy for expertise, risking the loss of conceptual insight and idea generation in mathematics.

AI’s success in solving high‑profile math problems threatens the puzzle‑solving proxy that signals mathematical expertise, risking erosion of genuine idea generation and progress.

LLMs serve best as tools for deepening understanding through precise, iterative questioning, making code merely a byproduct of the insight they help construct.

The article argues that domain expertise, not just prompting skill, determines how much value users can extract from LLMs, as experts steer models more effectively.

Scientific discovery emerges from a loop of human question-posing, LLM proposal, external verification, and human curation, not from AI alone.

AlphaEvolve, a Gemini-powered coding agent, has optimized algorithms across genomics, quantum physics, and infrastructure, achieving significant improvements.

A 23-year-old with no math background used ChatGPT Pro to solve a 60-year-old Erdős problem, producing a novel method that experts believe may have broader applications.

Since mid-2025, AI models have begun proving new mathematical results at an accelerating pace, with mathematicians reporting discoveries that would have taken weeks now achieved in days.

The space of cognitive tasks is high-dimensional, so low-dimensional narratives comparing AI and human intelligence are significant oversimplifications.