Lex Fridman podcast draws a comment from Terence Tao, discussing AI’s impact on mathematical research and programming careers
Lex Fridman released episode 503 of his podcast, AI Crisis in Math, on October 26, 2026, with guests including Steven Brunton, a professor of mechanical engineering at the University of Washington. At the start of the episode, Lex quoted Fields Medalist Terence Tao, saying that Tao was horrified by AI’s progress in mathematics.
Tao’s original text said: “I am horrified by the many person-years of ongoing patient and deliberately slow research efforts, particularly by graduate students and postdocs, toward many motivating problems in mathematics being casually disrupted or destroyed by such a release.” He further pointed out that once an existing solution is known, it becomes difficult to explore problem paths in the same rich way, and he expressed regret for the “untraveled paths” in the technological race.
Lex then said that many programmer friends he knows have been in shock since last December, because the programming field has shifted heavily toward AI writing most of the code, making them feel that they have “lost the meaning of life, accompanied by fear, and even some dark emotions.” Steven Brunton replied that his overall feeling is relatively optimistic, believing that the ability to solve real-world problems will be greatly improved (for example, in artificial hearts or aircraft-related work), and stressed the need to refocus on intrinsic motivations such as curiosity, understanding the world, and helping others, rather than external awards.
Original sources:
Title: Transcript for AI Crisis in Math: Navier-Stokes, Riemann Hypothesis & Complexity Theory | Lex Fridman Podcast #503 Publisher: Lex Fridman Publication date: 2026-10-26 URL: https://lexfridman.com/math-ai-crisis-transcript/ Source nature: Official podcast transcript Verification status: Relevant passages read in this request Supporting content: Lex’s quotation of Tao’s original text, Lex’s description of programmers’ state, Steven Brunton’s response
Title: #503 – AI Crisis in Math: Navier-Stokes, Riemann Hypothesis & Complexity Theory Publisher: Lex Fridman Podcast Publication date: 2026-10-26 URL: relevant podcast platforms (Apple Podcasts, etc.) Source nature: Podcast release Verification status: Cross-checked through the transcript and summary in this request Supporting content: Program theme and guest identities
ABAB AI Insight
This episode is an urgent discussion recorded by Lex in response to recent claims that AI has made progress on difficult mathematical problems (including Navier-Stokes). Tao’s comment focuses on the phenomenon of graduate students’ and postdocs’ years of research paths being abruptly interrupted, emphasizing the impact of the release of solutions on the process of exploration. Lex compares this with changes in the programming field and describes practitioners’ emotional reactions.
As a fluid dynamics expert, Steven Brunton distinguishes mathematical results from engineering applications, emphasizes an optimistic view of improved ability to solve real-world problems, and steers the discussion back to the intrinsic purpose of work. The publicly available material currently comes mainly from the podcast transcript and the guests’ statements, and does not involve independent verification of the correctness of specific mathematical results.
Key areas for follow-up observation include further reactions from the mathematics community to AI-generated proofs, the practical impact of the adoption of programming tools on career paths, and whether the discussion expands to other fields.
ABAB News · Laws of Cognition
Once a solution is made public, the richness of the original exploratory path may decrease, even if the result is correct.
A leap in tool capability can simultaneously create expectations of expanded capability and changes in the sense of professional meaning.
Expert responses often distinguish formal results from real-world application value and reiterate intrinsic motivation.