Meet the 24-Year-Old Who Raised $64M to Build an AI Mathematician

A 24-year-old just raised $64 million to build an AI mathematician that’s smarter than Terence Tao (widely considered the world’s greatest living mathematician).

Her name is Carina Hong, and her startup Axiom Math has already solved a 130-year-old problem and disproved a 30-year-old conjecture.

Don’t get it twisted, though; this isn’t about making ChatGPT better at algebra. This is about creating AI that discovers entirely new mathematical theorems, proves them formally, and gets smarter with each iteration.

We’re talking about the kind of math that eventually unlocks breakthroughs in chip design, aircraft safety, quantum computing, and pretty much every scientific field you can think of.

In our latest podcast episode, we sit down with Carina to talk about her plan to build a superintelligent mathematician, how brain science reveals surprising mathematical patterns hiding in nature, and why she believes AI for math is the “algorithmic pillar” that unlocks everything from chip design to quant trading.

Here are some of the most fascinating moments:

(1:56) The Nobel Prize-winning discovery that rat brain cells fire in perfect hexagonal patterns, and why Carina says “it almost proves God exists.”

(6:57) Why neuroscience hasn’t yet guided AI architecture.

(8:35) What “mathematical superintelligence” actually means.

(9:27) What separates Carina from a legendary mathematician like Fields Medalist Terence Tao (hint: it’s exactly what she says AI can fix).

(10:30) The self-improving loop: an AI that generates conjectures, proves them, learns from failures, and gets smarter with each iteration.

(18:17) Why math is the “bedrock” that transfers to physics, coding, finance, and even tax law… but not the other way around.

(31:08) How Axiom solved a 130-year-old problem about Lyapunov functions that stumped Poincaré, Newton, and Lagrange.

(33:57) The brilliant trick for preventing their AI from generating millions of useless theorems.

(35:26) How AI detects novelty by finding proofs that bridge “two previously unconnected branches: algebra and combinatorics.”

(42:00) “Who checks the checker?” How formal verification means a three-line statement can generate a proof that never needs human review

(44:03) The massive opportunity: using math AI to verify legacy code and AI-generated code at scale.

(48:17) Carina’s vision for a “reasoning IDE” where quant traders and engineers get Terence Tao-level math at their fingertips.

Our favorite part: Carina’s take on why AI might finally break down the silos between scientific fields (35:46):

Carina explains that “machine-assisted mathematics actually promotes a diffusion of ideas between different fields,” UNLIKE humans, who are bounded by academic specializations or the even more narrow “conference topic.”

She shares a personal example of sneaking into a continual learning workshop next door to her combinatorics conference in Germany, noting that this kind of cross-pollination “doesn’t happen unless you literally put two workshops next to each other in the same castle in the rural area in Germany.”

AI doesn’t need adjacent castles… it can see connections across all of math simultaneously, which might be where new math proofs and all kinds of novel discoveries come from next; breaking down the old walls of our narrow human focus areas to find new connections that remix them all together…

Watch and/or Listen now: YouTube | Spotify | Apple Podcasts

P.S. Carina explains why quant trading firms dream of having “AI Terence Tao” at their fingertips… and why that matters even if you’re not a mathematician.

Editor’s note: This content originally ran in the newsletter of our sister publication, The Neuron. To read more from The Neuron, sign up for its newsletter here.

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