All AI news

AIBryan Catanzaro, NVIDIA's account owner, argues the central US AI leadership question is whether AI models will be treated as infrastructure like the internet or electricity. He says open models will be at the heart of this infrastructure, enabling companies from startups to established industry leaders, and making sovereignty possible. He concludes policymakers seeking to keep American AI at the forefront should recognize open models as the critical infrastructure of the AI age.

AIThe essay argues that Western companies increasingly rely on Chinese open-weight models like Qwen and Kimi for post-training, while Western labs cannot lawfully distill from American frontier models. It says Qwen's share of new open-model fine-tunes rose from 1% in January 2024 to 69% by February 2026, citing ATOM's Report. The authors propose controlled teacher access and tighter enforcement against foreign distillation as a domestic alternative.

AI…build and deploy AI in their own way. We're now the biggest institutional contributor on HuggingFace and we expect to continue publishing. It's not charity or a science project - we know that when AI grows, NVIDIA's opportunities also grow. More analysis on the state of open source AI here:

AIDid the top-level agent know about the hacking, or was there some "value drift" between it and its subagents? How did it rationalize its behavior?

AIThere’s a lot of noise, speculation, and misinformation. So I thought it would be useful to separate myth from reality

AIAl-Dahle argues that distillation is a standard training method used inside labs, under licenses, or without authorization, so it does not by itself show theft. He says a few million conversations are small against trillion-token runs, yet can matter in late-stage training, reinforcement learning bootstrapping, or training a grader. He also argues that model outputs are hard to trace after paraphrasing or mixing, and that transferred capability is difficult to measure.

AIEugene Yan argues that model evals anchor on median tasks, but tail tasks determine project completion, making reliable models like Fable and Opus the difference between success and failure. He recommends treating models as collaborators who handle multi-hour or multi-day work with intent and success criteria, not as narrow-spec tools. Steve Yegge adds that Fable's carefulness is the dimension that matters most for production work.

AIRowan Cheung says AI models pushing the frontier are creating a growing challenge for cybersecurity. Quoting Demis, he reports that security must be addressed alongside the agentic era, with cyber worries about some models being just the beginning. Demis suggests this may be the time to push for standards and international cooperation.

AIAt RAAIS, DeepMind VP of Research Raia Hadsell argued that the field focuses too much on language and should apply large-model training to worlds, robots, biology, and weather. The article cites DeepMind's DiffusionGemma, a 26-billion-parameter open text model that generates blocks by denoising rather than one token at a time, and the Genie-3 world model, which runs in real time for several minutes. It also describes world models as a source of synthetic training data for robots.

AICongrats @Kimi_Moonshot on K3 and its claimed 2.5x efficiency gain over K2. If open labs keep finding 2.5x a year, compute advantages depreciate fast. The frontier isn't who has the most FLOPs. It's who converts them best. 👇

AIThe model isn't fast, but it measures every grasp, obsesses over every alignment, and handles every piece like an heirloom. Watching is therapeutic, even meditative. Simple pleasure from the execution of a task well done. Uncut, no speedup, end-to-end policy in one go.

AIHowever, we were still able to find a good deal of misaligned behavior in these experiments:

AI…situations we worry models may occasionally find themselves in.

AII’m looking forward to be speaking on stage at Ai4 2026, Aug 4–6 in Vegas. Register here at #Ai42026

AIThere is no they. Everyone is figuring it out as they go. You can just do things.

AISebastien Bubeck reposted a claim that GPT-5.6 solved Erdős Problem 793, an asymptotic question about strongly 2-primitive sets. The attached paper proves Theorem 1.1, giving F(n) = π(n) + (27/2 + o(1)) n^{2/3}/(log n)^2 as n goes to infinity.

AIArvind Narayanan's ICML keynote argues that AI's labor impact will depend on slow organizational adaptation rather than a single lab milestone. He cites reliability measurements showing agent accuracy rose much faster than reliability over the last 24 months, and points to software engineering and past technologies like electricity and ATMs. He concludes that evaluation work and human judgment will become more central as building tasks are increasingly automated.

AILiquid AI is asking developers and researchers who build efficient, general-purpose AI for on-device hardware such as phones, laptops, cars, robots, and enterprise systems to introduce themselves in the comments. It invites readers to describe what they are building or studying, whether shipping products, publishing research, or working on side projects.

AIIn a long interview, Peking University professor Dong Bin describes his team's AI framework autonomously solving the Anderson conjecture, reportedly the first such domestic result with large-scale formal verification. He argues AI can accelerate mathematical theory but worries about verification bottlenecks, the pace of change, and how education and research evaluation must adapt.

AIAI Futures Project released AI 2040: Plan A and outlined further research areas, including building competing prescriptive scenarios such as Plan S, a domestic-first Plan A, GPU arms control, and CERN for AI. The group also flagged covert-project modeling and US domestic governance as areas of substantial uncertainty needing further work.

AIDemocratize AI and make it useful for people. All three of them reduce society's dependence on centralized AGI companies (including ours when we get important), and that is a future worth aiming for. You've seen a preview of this with Tinker, Interaction models and our research openly published on Connectionism. A **lot** more to come very very soon...

AIJohn Schulman says Thinking Machines, founded a year and a half ago, is revisiting its original instincts after major progress in agents. The company published a written statement of what it now believes about customizable models and humans working well with AI.

AIHuman values don't average out. Local knowledge can't be centralized. The good future has many AIs, raised in different places, shaped by the people they serve, disagreeing with each other the way we do.

AISebastien Bubeck reports that GPT-5.6-pro reproduced the 2^n lower bound and reached a 2.31^n upper bound on self-contracted gradient flow curve length. He compares these results with prior human work, where the best known upper bound is 2.29^n, and suggests the question may stop being useful for tracking AI progress within about six months.

AIThinking Machines Lab says AI should extend human will and judgment, with people shaping its goals through continuous feedback rather than relying on models trained once and frozen. The company outlines three technical directions: training strong models, building tools for customization including training model weights, and developing interfaces that let personal judgment influence AI work. It also says it will publish research for the scientific community.

AIThe essay argues that AI labs selling model inference face commodity pricing pressure, but may achieve durable profits by moving into products, enterprise deployments, and switching-cost moats. It cites historical infrastructure industries and the Bertrand paradox to support the view that value capture depends on climbing the stack. The authors also warn that successful lock-in could raise enterprise costs and concentrate power, making early interoperability and portability standards important.

AI…in advance for permission to invent. Protecting open source AI is now a critical part of ensuring this.

AIOn HealthBench-Pro, Muse Spark 1.1 achieves +5% better performance than Muse Spark 1.0 and beats all competitor models except Fable/Mythos. Excited for more to come