Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI | Lex Fridman Podcast #472

Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI | Lex Fridman Podcast #472

Lex Fridman PodcastJun 14, 20253h 14m

Lex Fridman (host), Terence Tao (guest), Narrator

Boundary problems in mathematics (Kakeya, critical vs supercritical PDEs)Navier–Stokes regularity, blowup, and fluid dynamics as computationRandomness, universality, and the behavior of primes (twin primes, Riemann hypothesis, Goldbach)Collatz conjecture, cellular automata, and undecidabilityGeneral relativity, Ricci flow, and Perelman’s proof of the Poincaré conjectureStyles of doing mathematics: hedgehogs vs foxes, structure vs randomnessFormal proof assistants (Lean), experimental mathematics, and AI‑assisted research

In this episode of Lex Fridman Podcast, featuring Lex Fridman and Terence Tao, Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI | Lex Fridman Podcast #472 explores terence Tao on hard math, fluid chaos, AI, and human insight Terence Tao and Lex Fridman range across some of the hardest problems in mathematics and physics, from Navier–Stokes and Ricci flow to the Riemann hypothesis, primes, and the Collatz conjecture.

Terence Tao on hard math, fluid chaos, AI, and human insight

Terence Tao and Lex Fridman range across some of the hardest problems in mathematics and physics, from Navier–Stokes and Ricci flow to the Riemann hypothesis, primes, and the Collatz conjecture.

Tao explains how deceptively simple puzzles like Kakeya and Collatz connect to deep questions about singularities, turbulence, and undecidability, and why ‘supercritical’ nonlinear systems are so hard to tame.

He describes his problem‑solving style (a “fox” connecting many fields rather than a single‑minded “hedgehog”), his use of tools like Lean and large language models, and how formal proof and AI may transform mathematical practice.

The conversation also delves into the philosophy of mathematics versus physics, the role of randomness and universality, famous breakthroughs like Perelman’s and Wiles’s, and what emerging AI means for future collaboration and discovery.

Key Takeaways

Hard problems live at the boundary between solvable and hopeless.

Tao emphasizes that the most interesting problems are those where existing techniques do 80–90% of the work but fail on a crucial remaining piece, like Kakeya or Navier–Stokes; these boundary cases expose where our methods and intuitions truly break down.

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Supercritical nonlinear systems are inherently difficult and often unpredictable.

In equations like 3D Navier–Stokes, nonlinear transport dominates dissipative effects at small scales, allowing energy to cascade into finer structures and potentially blow up; this same supercriticality underlies why we can forecast planetary motion far ahead but not weather beyond about two weeks.

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Designing counterexamples is just as valuable as finding proofs.

Tao’s averaged Navier–Stokes blowup construction shows that many tempting proof strategies for global regularity must fail, because slight variants of the equation already blow up; such “obstructions” prune whole families of doomed approaches and sharpen what a successful proof must exploit.

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Universality explains why simple laws and Gaussian behavior appear everywhere—but can dangerously fail.

The central limit theorem and related universality principles show why bell curves and simple macroscopic laws emerge from vast micro‑complexity, yet Tao notes that when hidden correlations or systemic shocks exist (e. ...

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Structure versus randomness is a central organizing theme in modern math.

Results like Szemerédi’s theorem, Tao’s work on primes in arithmetic progressions, and inverse theorems show that objects are either highly random or secretly structured (and thus near a simpler model); leveraging this dichotomy lets mathematicians prove robust patterns in primes and dense sets.

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Formal proof assistants and AI are starting to reshape how math is done.

Lean can turn proofs into checkable certificates and support large‑scale, trustless collaboration (e. ...

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Human “mathematical smell” and strategic cheating remain hard to automate.

Tao stresses that humans excel at sensing when an approach is wrong, breaking hard problems into selectively simplified sub‑problems (“installing cheats”), and switching problems before obsession becomes a “mathematical disease”; current AIs can generate plausible text and code but lack reliable meta‑judgment about their own dead ends.

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Notable Quotes

What’s really interesting are the problems just on the boundary between what we can do perfectly easily and what are hopeless.

Terence Tao

Mathematicians are one of the few people who really care about whether 100% of all situations are covered.

Terence Tao

The beauty of mathematics is that you get to change the problem—change the rules—as you wish. It’s like trying to solve a computer game where you have unlimited cheat codes.

Terence Tao

The most incomprehensible thing about the universe is that it is comprehensible.

Albert Einstein (quoted by Terence Tao / Lex Fridman context)

Humanity plural has much more intelligence, in principle, on its good days, than the individual humans put together.

Terence Tao

Questions Answered in This Episode

How might AI systems develop the kind of “mathematical smell” Tao describes, and what training data or feedback would they actually need to get there?

Terence Tao and Lex Fridman range across some of the hardest problems in mathematics and physics, from Navier–Stokes and Ricci flow to the Riemann hypothesis, primes, and the Collatz conjecture.

Get the full analysis with uListen AI

If the Riemann hypothesis or P vs NP were suddenly resolved tomorrow, which areas of mathematics and technology would be transformed first and most dramatically?

Tao explains how deceptively simple puzzles like Kakeya and Collatz connect to deep questions about singularities, turbulence, and undecidability, and why ‘supercritical’ nonlinear systems are so hard to tame.

Get the full analysis with uListen AI

To what extent should mathematicians embrace formal proof systems like Lean as a default, and how might that change the culture, incentives, and pedagogy of the field?

He describes his problem‑solving style (a “fox” connecting many fields rather than a single‑minded “hedgehog”), his use of tools like Lean and large language models, and how formal proof and AI may transform mathematical practice.

Get the full analysis with uListen AI

Can we ever rigorously rule out the kind of “Maxwell’s demon” conspiracies Tao invokes—both in fluids and in number theory—or must some phenomena remain probabilistic beliefs rather than theorems?

The conversation also delves into the philosophy of mathematics versus physics, the role of randomness and universality, famous breakthroughs like Perelman’s and Wiles’s, and what emerging AI means for future collaboration and discovery.

Get the full analysis with uListen AI

Are there ethical or societal risks in building fluid or physical systems that are Turing-complete, as in Tao’s “fluid computer” thought experiment for Navier–Stokes blowup?

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Transcript Preview

Lex Fridman

The following is a conversation with Terence Tao, widely considered to be one of the greatest mathematicians in history, often referred to as the Mozart of Math. He won the Fields Medal and the Breakthrough Prize in Mathematics, and has contributed groundbreaking work to a truly astonishing range of fields in mathematics and physics. This was a huge honor for me, for many reasons, including the humility and kindness that Terry showed to me throughout all our interactions. It means the world. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description or at lexfridman.com/sponsors. And now, dear friends, here's Terence Tao. What was the first really difficult research-level math problem that you encountered? One that gave you pause maybe?

Terence Tao

Well, I mean, in your undergraduate, um, education, you learn about the really hard impossible problems, like the Riemann hypothesis, the twin primes conjecture. You can make problems arbitrarily difficult, uh, that's not really a problem. In fact, there's even problems that we know to be unsolvable. What's really interesting, uh, are the problems just at the... on the boundary between what we can do perfectly easily and what are hopeless. Um, but what are problems where, like, existing techniques can do like 90% of the job and then y- you just need that remaining 10%. Um, I think as a PhD student, the Kakeya problem certainly caught my eye, and it just got solved actually. It's a problem I've worked on a lot in my early research. Historically, it came from a little puzzle by the Japanese, uh, mathematician Soichi Kakeya, uh, in like 1918 or so. Um, so, the puzzle is that you, you have, um, a needle, um, in s- on the plane, um, or- or think like- like- uh, like driving on- on- on a road or something, and you want to- to execute a U-turn, you want to turn the needle around, um, but you want to do it in as little space as possible, uh, so you want to use this little area, uh, in order to turn it around. So, um, but the needle is infinitely maneuverable. So you can imagine just spinning it around its, um... uh, it's a unit needle. You can spin it around its center, um, and I think, um, that gives you a disc of- of area, I think, pi over 4. Um, or you can do a three-point U-turn, which is what they- we teach people in- in the driving schools to do, uh, and that actually takes area pi over 8. Um, so it's- it's a little bit more efficient than, um, uh, rotation. And so for a while people thought that was the most efficient, uh, way to turn things around. But Bazhukovich, uh, showed that in fact, uh, you could actually, uh, turn the needle around using as little area as you wanted.

Lex Fridman

Mm.

Terence Tao

So .001, there was some really fancy multi, um, uh, um, back and forth U-turn thing that you could- you could do, that- that you could turn the needle around. And in so doing, it would pass through every intermediate direction.

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