Andy

AI models, and specifically large language models, are used to do spreadsheets, and now they're becoming more and more competent in doing those kinds of financial analysis-type things.

But when you get to the realm of pure mathematics, where mathematicians are working on proofs and developing new theorems in sort of the ethereal world of mathematics, LLMs are not very impressive.

And one of the world's biggest mathematicians, a fellow named Joel David Hamkins, has slammed AI models used for solving mathematics and calls them zero and garbage, adding he doesn't find them useful at all.

He highlighted AI's frustrating tendency to confidently assert incorrect conclusions and resist correction.

They'll argue with him.

And he said, quote, if I were having such an experience with a person, I would simply refuse to talk to that person again.

Now, bring on Axiom Math.

Founded by 24-year-old dropout from Stanford, Karina Hong, it raised a $64 million seed round.

Wow.

Build an AI mathematician.

And major investors are behind this, including Graycroft and Menlo Ventures, a couple of VC firms whose names I recognize.

And its core architectural idea is to move from generic next token prediction, which creates hallucinations, as we know, in LLMs, and instead use a stack that tightly couples a language modeling algorithm

sort of kernel with formal proof systems and programmatic reasoning from mathematics so

It's not trained on the broad web and conversational data, so it's not going to spin out and you can't kind of jailbreak it and have it talk about politics or anything.

It's a math-specific shell of formal mathematical languages, proof checkers, and verification-driven training signals that goes beyond standard LLMs.

So...

It doesn't have all those problems, and as a result, because each reasoning step is meant to be checked by a proof engine as it's running, it is virtually free of hallucinations that are common in generic LLM outputs.

So, David...

You know, check it out.

Axiom Math.