大佬观点

Fable 5 is state-of-the-art on nearly all tested benchmarks, with exceptional performance in software engineering, knowledge work, scientific research, and vision. The longer and more complex the task, the larger Fable 5’s lead over our other models.

Super excited to announce seven new world-class MAI models today. They represent what we consider a new era in AI designed to keep you in control and on the frontier. First is our text foundation model, MAI-Thinking-1, exceptionally strong on reasoning and SWE tasks. - It’s a 35B active parameter MoE with a 256K context window. Independent human raters on Surge prefer it for overall quality in blind side-by-sides versus Sonnet 4.6, and it’s achieved 97% on AIME 2025, the key measure of its general-purpose reasoning abilities. - It's at 53% on SWE Bench Pro, placing it right alongside Opus 4.6 on one of the toughest coding benchmarks. - And since we co-designed our models with our own silicon, MAI-Thinking-1 is optimized on our MAIA 200 chip. Benchmarking head-to-head against the GB200, we see 30% better performance per dollar as well as a 1.4x performance-per-watt gain when running our MAI models on the MAIA 200 end-to-end. Next is MAI-Image-2.5 and its Flash variant. Two super strong models now at #2 on the leaderboards, surpassing the score of Nano Banana 2 on image editing. Last for now is MAI-Code-1-Flash, our new inference efficient coding model, especially tuned for VS Code and GitHub Copilot CLI. - Code-1-Flash achieves 51% on SWE Bench Pro, despite having just 5B parameters, putting it closer to Haiku in size but cheaper in cost. All of this is the foundation for Microsoft Frontier Tuning. It lets you customize our models to create custom, company-specific agents that only you control. You can make our model, your model. Your data. Your agents. Your moat. Early adopters are already seeing a difference. When we tuned our models for McKinsey’s tasks, MAI delivered the highest win rate, outperforming GPT-5.5 on quality, while being 10x lower on cost. Also really excited to be collaborating with the amazing team at Mayo Clinic to jointly train a new frontier AI model for healthcare. Our announcements today mark another milestone on the road to humanist superintelligence. You can learn more and about our other new models in our latest blog: microsoft.ai/news/building-a…

1/ We’ve raised over $1B at a $26B valuation, led by @Lux_Capital, @generalcatalyst, and @8vc. Our enterprise usage has grown >10x since the start of this year, and our run-rate revenue grew to $492 M. We launched Devin two years ago as the first AI software engineer. Since then, cloud agents have gone from niche to mainstream, and today they are the fastest growing way to create software.

1/ We’ve raised over $1B at a $26B valuation, led by @Lux_Capital, @generalcatalyst, and @8vc. Our enterprise usage has grown >10x since the start of this year, and our run-rate revenue grew to $492 M. We launched Devin two years ago as the first AI software engineer. Since then, cloud agents have gone from niche to mainstream, and today they are the fastest growing way to create software.

🔬Doing Vibe Physics The full story of how GPT‑5.x derived new results in theoretical physics and quantum gravity, live on our Science pod today! latent.space/p/lupsasca our conversation with @ALupsasca, an award winning theoretical physicist on his AGI-pilling journey applying GPT5 to physics problems (with a nudge from @markchen90)! Timestamps 0:00 Introduction to Al's impact on physics research 0:43 Guest introduction: Alex Luposka 2:49 Alex joining OpenAl and the shift in physics research 4:08 The release of GPT-5 and the shift in capabilities 10:05 Explaining Quantum Field Theory and amplitude calculations 14:20 Overview of gluons and the strong force 14:38 Discussing the first research paper on single-minus gluon tree amplitudes 20:56 How ChatGPT helped solve a year-long physics puzzle 23:02 Complexity of manual calculations in physics 26:12 The history and mechanics of Feynman diagrams 27:44 The Parke-Taylor formula and the quest for simplification 31:26 Using ChatGPT to find the simplification in the special phase space region 38:07 Proving the formula from scratch to ensure validity 41:00 Determining the scientific impact and future research 42:27 Introduction to the second paper on graviton amplitudes 45:41 | Defining particles, irreducible representations, and symmetry 47:46 How GPT Pro generalized the research to gravity 53:57 The epistemological shift: Is this a new way of doing physics? 59:27 The use of Al as a 'scout' for research directions 1:01:44 The role of 'taste' and collaboration with Al 1:10:23 Personal evolution from Al skeptic to resident scientist 1:12:46 Solving a black hole perturbation problem with GPT-5 1:16:34 Discussing whether Al can make original, conceptual leaps 1:20:09 Challenges of 'Al slop' and the future of academic publishing 1:23:13 The bottleneck of writing academic papers 1:30:19 Final takeaways and looking ahead to the next year Video