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News of the day
1. OpenAI's new AI model has reportedly solved the Navier-Stokes Millennium Prize Problem, a 90-year-old mathematical challenge. → Read more
2. OpenAI launches Flare for faster image generation and Sunburst for precise edits with ChatGPT Images 2.5, but access varies. → Read more
3. Kepler Computing emerges from stealth with a novel 3D stacking approach to high-bandwidth memory, potentially easing AI chip supply bottlenecks without EUV lithography. → Read more
4. Instacart introduces Clementine, an AI assistant that transforms grocery lists, recipes, or conversations into ready-to-buy carts, simplifying meal planning and shopping. → Read more
Our take
Hi Dotikers!
Well, that escalated quickly. OpenAI claims that a swarm of 10,000 AI agents cracked the Navier-Stokes problem in 88 hours. Not a benchmark, not a coding puzzle, but one of the seven Millennium Prize Problems, the kind of question that has resisted the sharpest human minds for over a century. If the proof holds up, this is one of the biggest scientific headlines of the decade, full stop.
But here is where it gets messy, and honestly, where it gets interesting. Mathematician Tristan Buckmaster has publicly pushed back, pointing to his own work with Levent Alpöge and raising uncomfortable questions about timelines, training data and who actually deserves credit. Because if your model was trained on years of human research that walked right up to the finish line, calling it a solo AI victory feels a bit like taking credit for the marathon because you drove the last hundred meters.
The real story, in my view, is not the flashy 88-hour figure. It is the Lean formalization. A proof verified line by line by a machine is a proof you can trust, regardless of who or what wrote it. That part is genuinely new and genuinely exciting. What is less exciting is the pattern we keep seeing: labs announcing breakthroughs on X before the scientific community has had a chance to breathe, let alone review.
So yes, celebrate the milestone if it survives scrutiny. But the future of AI in science will not be machines replacing mathematicians. It will be an uneasy, productive and occasionally bruising collaboration, and the sooner the labs acknowledge the humans in the loop, the better it will go for everyone.
Let's dig into what actually happened, what the math says, and why the credit fight matters more than the headline.
Alex.
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