GPT-5 is what you’ve been waiting for – it defines and extends the cost-intelligence frontier across model sizes today.
it’s been a long journey, and we’ve landed pivotal improvements across many axes in the whole GPT-5 family.
and hey no more model picker (by default)!
Boris is a thoughtful and positive-sum leader. I couldn’t have asked for a better first manager, and I can’t imagine anyone better-suited to the job of careful last-mile AI deployment in this critical period.
Congrats!! 💥
We now know that with an appropriate harness both Mythos and GPT-5.5 can reproduce what our internal model did in one-shot for the unit distance problem. Clearly there is an insane overhang of capabilities with this generation of models, and no ceiling in sight for what scientific advances they can bring. You can go and try to discover new things with 5.5 right now!
“sure the models are good but they aren’t creative” is the most recent iteration of human exceptionalism
creativity is high-powered search. we deem something “creative” when we can’t see the path that led to it
this proof marks a big step towards demonstrating AI “creativity”
Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946.
For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids.
An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better.
This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.
@benchatenu as long as AI remains an amplifier for technical employees, you’d increase the strength of your preference for hiring the most productive folks, right up until AI replaces them/us altogether
if dario believes (and indeed he can’t seem to stop saying it) that humans are only useful for technical work for a short order of months this seems like a reasonable policy
why spend half the new hires’ remaining tenure training them when you can hire staff+ to race to RSI
might need a “research mode” where the models throw caution to the wind on gnarly problems and send it on a crazy approach with the understanding that it’s ok to come up short on this try
possibly equivalent to getting them to utilize more TTC effectively (better search)
Introducing FrontierSWE, an ultra-long horizon coding benchmark.
We test agents on some of the hardest technical tasks like optimizing a video rendering library or training a model to predict the quantum properties of molecules.
Despite having 20 hours, they rarely succeed
interesting finding suggesting codex is more collapsed, higher floor/lower ceiling
seems aesthetically similar to when models have to be coaxed to solve problems that are easily found to be “open”