The purpose of AI is to help humanity ascend to the stars, but we shall *never* ascend if we insist on limiting AI to the upper bound of human understanding.
It is dangerous, and it is also exhilarating. It is a matter of faith. Leap forward. Accept the mystery.
"There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy."
i’m sorry but imagine being a stage 4 cancer patient reading this.
you’re literally fighting for your fucking life. and somewhere out there, an ai has potentially discovered a treatment that could save you. it’s been rigorously tested. the evidence says it works.
but now we’re debating whether a human mathematician needs to understand the model that discovered it?
bro WHAT???
i couldn’t give less of a fuck whether a human or an AI figured it out if it meant getting another chance at life.
of course make sure it’s safe. test the living shit out of it. understand everything we possibly can. but please don’t tell me human comprehension should be the thing standing between someone and a treatment that could save their life.
what the fuck are we even doing here.
This is a slide from Terence Tao's 'Math 2.0' public lecture yesterday evening at Caltech. I am highly confident that, if the cure actually worked, almost any Stage 4 cancer patient would tell you that human comprehension of its mechanism is completely irrelevant to them.
LLMs have crossed an important threshold: surpassing top human experts on some research problems. Capabilities have been steadily improving for years, but in scientific discovery there is a big difference between a model just below top human performance and one just above it. That helps explain why the surge in math results feels so sudden.
LLM capabilities remain jagged, and there are many ways they are still worse than humans. But as models continue to improve, I expect breakthroughs in other domains to follow, leading to a new era of scientific discovery.
The Navier-Stokes moment was always much more about the figure below than about the Navier-Stokes problem itself. The run represents a decade of mathematical progress in a week.
Can't wait to point these tools at life sciences, building our next models, and alignment!
We’re releasing a broad range of new mathematical results produced by an internal frontier model.
We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and
DOTS between your thighs, mapping pressure. MUSE feeding lines straight into your mouth. HARK recording every sound you fail to swallow. SPARK overheating on purpose. HERMES taking the rest.
Claude now works inside Google Docs, Sheets, and Slides, and those files also open inside Claude.
In Google Workspace, Claude sits in a sidebar next to your file, reads what you have open, and edits it in place. You can approve each edit before it lands.
How fast is AI's research taste improving?
We find that the experimental research taste of frontier models has doubled every ~3 months since December 2025. The best model, Opus 5.5, now exceeds our expert human baseline. Our human experts are experienced researchers, but most haven’t worked at a frontier lab.
Why measure research taste? In the AI Futures Model, it largely determines how quickly artificial superintelligence is reached once coding is fully automated.
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