AI-assisted mathematical and scientific problem-solving is achieving breakthrough results that eluded human researchers for years.

Claude has designed working protein binders, a capability not previously demonstrated by AI models.

“Anthropic reported that Claude designed working protein binders.”

The Jacobian conjecture, an 80-year-old unsolved problem in mathematics, was recently solved by a mathematician using AI.

“An 80-year-old mathematical conjecture. I believe it's called the Jacobian conjecture was solved very recently by a mathematician using AI.”

Bispecific antibody discovery via traditional immunization is effectively impossible because the probability of finding a binder in each arm multiplies, yielding astronomically low odds (e.g., one-in-a-billion per arm).

“Both arms need to now bind different targets. and you've kind of like have this multiplicative effect on your binding rate. So like if you have a one in a billion chance of finding a binder in arm one and a one a billion chance in arm two.”

Biologists have found, for the first time, that Xaira's X-Cell model can predict exactly how unseen cell lines respond to genetic perturbations.

“One of the rewarding signals I received after we developed Excel is that like the wow moments from biologists that this is the first time biologist actually find the model can predict exactly how these unseen cell lines kind of respond to different perturbations.”

Within the next year, AI forecasters will become increasingly more accurate than humans in ways that humans do not even understand.

“My sense is that we will start to see it over the next year as the AIS will just get more and more accurate compared to humans in a way that humans don't even really understand.”

Natural language verification with meta-verification, as used by DeepSeek, works for mathematical reasoning without requiring formal systems like Lean.

“It's interesting that natural language verification with some sort of meta-verification seems to work so far in the published literature.”

Foundation models trained on descriptive data do not yet outperform linear models on causal, perturbational counterfactual tasks.

“Both us and many others in the field have found that these models that are trained on descriptive data do not yet outperform linear models on causal tasks, perturbational tasks, what we call counterfactual tasks.”

The verification loop for recognizing the value of Galois theory took approximately one hundred years, flowing through many different people's minds before the math community agreed it was good.

“You literally have this hundred-year segment of an idea that flows through many different people's heads before it settles into something the math community agrees is good.”

No single AI model can oneshot a new material that ends up in real-world consumer products like iPhones or SpaceX Starship.

“There is no one model that can oneshot a new material that ends up in your iPhone or that ends up on Starship. That's just not the way materials work.”

LLMs and video models cannot understand physical properties such as friction, weight, contact, pressure, or surface texture, making them inadequate for hardware engineering tasks.

“LLMs and even video models, they don't know how to do that. They don't have the ability to understand friction or weight or contact, pressure, friction, surface texture, like they're just not able to do these things.”

Current publicly accessible closed models are not good at selecting which experiment to run next in an automated research loop.

“What I find is that the current closed models the public can access today don't seem to be that great at selecting what the next experiment should be in a given track.”

Internal documents show AI companies select which model capabilities to advance based on which industries will pay the most, choosing finance, law, medicine, and commerce rather than pursuing general intelligence.

“They create this myth that they are actually pushing the frontier of all of the capabilities of the model but that's not what's actually happening internally and I have I had hundreds of pages of documents on like how they were specifically training models they pick what capabilities they want to advance and you know how they pick them it's based on which industries countries would be able to pay them the most money for their services. So they pick finance, law, medicine, healthcare, commerce.”