Ideas Weave Every Narrative with AI.
AI companies aren’t actually replacing mathematicians—they are hijacking their credibility. By forming advisory groups, tech giants are borrowing the legitimacy of top minds like Terence Tao to validate AI results, creating a dangerous blueprint for hijacking institutional authority across every credentialed field.
OpenAI just formed an advisory group for mathematics and AI—right after their models allegedly solved the Navier-Stokes Millennium Prize problem. This isn’t governance. It’s a theatrical burial ground. When the last pure human intellectual endeavor falls to machines, the corporate response is a committee, not a reckoning.
AI is making proof generation cheap, exposing mathematics’ over-optimization around measurable deliverables. As the field retreats to ‘human intuition,’ it faces a terrifying reality: AI will automate that too. The real crisis isn’t about what AI can’t do, but why we defined our worth by competence at all.
Timothy Gowers, one of the world’s greatest mathematicians, refused to sign a letter about AI. His reason exposes a terrifying truth: AI companies aren’t just generating answers, they are strip-mining a shared human commons. If we only reward the final output, we destroy the culture of understanding.
AI isn’t making mathematicians obsolete; it’s exposing that academic math is already just ritualized credentialing. If machines can produce the proofs, human value shifts entirely to interpretation, teaching, and judgment. Traditional degrees no longer measure what matters.
AI’s ability to ace math tests isn’t a triumph; it’s a tragedy. By optimizing for measurable proxies like correct answers, we are bypassing the true goal of math: conceptual understanding. This isn’t just a crisis for mathematicians—it’s the blueprint for the obsolescence of all human knowledge work.
OpenAI’s Navier-Stokes formal proof is being hailed as a massive AI breakthrough. But the real story isn’t about a machine becoming a mathematical genius. It’s about how formal methods infrastructure like Lean and mathlib have quietly matured into an assembly line, making formalization cheap enough to point an expensive AI fleet at the problem.
Terence Tao recently warned that AI is treating open math problems like a non-renewable resource, collapsing centuries of inquiry into instant extractions. This isn’t about machines replacing mathematicians—it’s about the destruction of the intellectual ecosystem itself. When answers become cheap, the scarce resource is knowing which questions to ask.
OpenAI’s recent approach to the Navier-Stokes equation isn’t a triumph of artificial intelligence. By burning 300 billion tokens and 4.9 million agent messages to brute-force a solution, they’ve established a terrifying new reality: human brilliance is dead, and exorbitant compute costs are the new barrier to entry.
The grandfather paradox isn’t a storytelling trope; it’s a math error. In 1991, physicist David Deutsch proved that the universe uses quantum probability to patch logical contradictions, forcing a 1/2 chance of survival. This self-correcting mechanism ensures you can never use time travel to cheat computational complexity.