I feel like it's a sign of what's to come - large AI companies with resources academia never had, at some point deciding that selling access to AI is not as important as just doing the research themselves with models inaccessible to anyone else. The end game is all scientific advancement will be done by one or two companies, who will reap all the profits. Why would they give access to a model that develops a cure for cancer when they could develop the cure themselves and get all the money?
(Kind of like Amazon coming out with their own products - use the greater community for ideas, then become a competitor at a scale they often cannot match).
This is in notable contrast to the "PC revolution" in the late 70s/early 80s, the marketing was all about equipping people, empowering individuals. It was a "personal" computer after all, not a big mainframe owned by corporate... designed to make you more powerful.
That messaging is wildly absent from the EA/SV-centric LLM/AI boom.
Instead it's about replacing you, the singularity, inevitability, fear-mongering, etc. And there is much more of a flavor of information asymmetry to the business model because of the Internet connectivity and SaaS structure to it all designed to enable the fastest possible feedback loops for the inventors combined with a bunch of pinkie promises with no actual controls possible on the user side at least for the frontier large models. What exactly did OpenAI peek at when doing Navier-Stokes and do they even know or would you trust them to tell you? You are right, it is a very different vibe.
Such a depressing time to be a knowledge worker with domain expertise.
>If a human did this, it would be an instant Fields Medal, no questions asked.
- Alex Kontorovich, chair of the department of mathematics at Rutgers
Their prerogative, but no thanks.
Harvesting collective output of human consciousness to build AI that then replaces the very people who provided the input.
Google, in a large sense, replaced librarians, service desks, 411, and many other specialist people and services whose purpose was "The knowledge is somewhere, and there is an index, but most people don't know where the index is or how to read it."
But librarians aren't really diminished as a result (while service desks may have kind of quietly evaporated to go do something else).
If the system becomes "People talk about mathematics and then an analysis engine reads that zeitgeist much faster than people can and pulls it all together into new insights," that's not apriori bad... Except that we have to consider how many people were in that conversation because their ultimate goal is to have an insight tied directly to them, i.e. to become the Hamilton in "Hamiltonian operator." Because the reality is that people practice mathematics both to advance the sum total of human knowledge and to roll the dice on getting stamped into the history books as the closest form of immortality that people get, and we can't just ignore both drives.
Grant Sanderson (3blue1brown) expressed thoughts on this topic space in a recent video, where he suggested one possible shift might be from the focus in the field being on frontier-pushing to the focus being on making the frontier more "human-shaped." There's a huge gap between higher math and what most people understand, and possibly ample opportunity to bridge it.
That is mental if true. Though if once again most of them will lack a Lean proof, bit hard to feel confident about their correctness. They might trust their model, but given the community impressions on manuscript prose quality, I have a hard time imagining they are any better than its coding output, where closed loop verification continues to be essential. OpenAI might have great trust in their internal model, but this seems like a needlessly hazardous way test that trust out. The optics of those retracted papers from a few days ago were bad enough already.
I have separately heard of efforts regarding Lean proof optimization. Since it's all self machine checkable, might be valuable to do before a release like this too. I'd imagine a shorter Lean proof means a shorter manuscript too.
Making an English language description of a problem and proof steps match the Lean proof is apparently harder than the halting problem and is not something that a Lean proof alone or the current dump or even the Navier-Stokes dump solves.
A good breakdown of the problem is here: https://arxiv.org/html/2610.08144v1 or also see here: https://terrytao.wordpress.com/2026/10/09/what-mathematician...