All AI news

AILilian Weng published a new post on harness engineering for AI self-improvement. She expects harnesses to evolve toward self-improvement and enable auto-research, while smarter models keep harnesses simple. Even if many harness gains are later internalized into core models, specifying goals and context will remain necessary.

AICat Wu says Claude Fable 5 applied propensity score matching to her retention analysis without being asked, comparing users with similar activity levels. She says the model's improved judgment extends across tasks, from writing emails and docs in Cowork to debugging complex errors in Claude Code.

AIMistral CEO Arthur Mensch says enterprises should use open-source models because closed providers that force data retention gain leverage over their business. He argues companies should store data in open systems, control AI access rules, and build continuous training loops to shrink costs and create hard-to-copy systems. Mistral offers its Studio control plane and Forge training platform, deployed on customer infrastructure or through zero-data-retention hosting.

AIWerner Vogels praises Zalando's engineering on client-side load balancing, though he is not a fan of doing it client-side. He especially likes the lessons-learned section that concludes Zalando's write-up.

AIJohn Schulman argues that fine-tuning with the right data, such as expert judgments, can substantially outperform prompting-only approaches even as general-purpose models improve. He cites Bridgewater's work, where an expert-labeled dataset and on-policy distillation were used to fine-tune a model to triage financial documents reliably and cheaply.

AIMollick argues AI capability is improving at a better-than-exponential rate, citing METR, GDPval, Epoch, and his own tests showing models working autonomously for hours. He says work is shifting from co-working with chatbots to assigning tasks to agents, with OpenAI workers managing multiple agents and experts getting the most from them. He adds that open-weights Chinese models trail the American frontier by roughly 6-12 months.

AITri Dao recommends a piece explaining how open and closed model makers and inference providers make economic sense. The post itself gives no figures or specifics, and the linked background is referenced without details beyond its topic.

AIAmazon CTO Werner Vogels argues that the "two-pizza" team concept was never about feeding engineers but about ownership, speed, and avoiding bureaucracy. He says working backwards from the customer and writing documents to force clarity remain core practices. He adds that the industry is changing and it is time to reconsider how products are brought to life.

AITri Dao says Etched designed and produced its chips within two years by hardcoding attention into silicon and reaching high MFU. He expects hardware built for LLM inference to cut the cost of intelligence by 10x. The quoted Etched post says it has built its first racks after an A0 tapeout, raised $800m, holds $1B+ in customer contracts, and plans to ship the racks this summer.

AIHamel Husain argues that an AI product whose output is hard to verify is a product design problem, not just an evaluation problem. He shows before-and-after sketches for an AI data agent, a PE lesson planner, and a workers' compensation report tool, each adding provenance, scoped edits, and checkable evidence. He notes that designing for verification also makes evals easier to build and grade.

AIGeoffrey Hinton says he just watched an excellent lecture by Adam Brown on the future impact of AI on physics, and he shared a YouTube link to it. The post provides no further details about the lecture's specific arguments or conclusions.

AIOriol Vinyals notes that Wojciech Zaremba wanted to remove a paper from existence about a year after publication, but says he has forgotten the reason. The post asks Zaremba to explain, and a reply describes the paper as the RNN Regularization work that improved LSTM perplexity on Penn Treebank.

AIDean W. Ball argues that the current government restrictions on frontier model releases amount to a de facto preapproval regime without a known safety standard. He proposes that independent verification organizations audit labs against their own safety frameworks, with government certifying or licensing the auditors. The post also argues that broad distribution of frontier AI is needed to learn what good safety practice looks like.

AILilian Weng jokingly wonders how many more people now ask a model to summarize a post than a year ago. The post is a lighthearted observation about changing reading habits, with no concrete figures or product details.

AIAndrej Karpathy describes Claude Tag, which lets teams tag Claude into Slack channels and delegate tasks to it. He calls it a third paradigm for LLM interfaces, after websites and downloaded apps, with a persistent, asynchronous entity working alongside teams with org-wide tools and context.

AIEugene Yan says he likes Claude Tag's multiplayer form factor because other people can reply in the thread to give Claude context and direction. Claude Tag, per the background post, lets teams in Slack tag Claude in as a team member with access to chosen channels and tools.

AIJeremy Howard welcomed renewed attention to the Drivetrain Approach, a framework for designing data-driven systems. A quoted thread summarizes its steps as defining the goal, considering levers, considering data, and implementing an assembly line, emphasizing that predictions are more data rather than actions.

AIAt BAAI's "Reconstructing the World — Large Model Summit," President Wang Zhongyuan joined Zhu Jun, Luo Fuli, Liu Zhiyuan, An Bo, and other industry leaders in a roundtable. They discussed frontier model capability evolution, AI self-evolution, multimodality, and world models, with a focus on bridging the digital and physical worlds.

AIOpenAI's Mark Chen says researcher Aidan Clark (@_aidan_clark_) is the most underrated researcher in the world and predicts broader recognition in time. A quoted reply pushes back that Clark is not widely known, with the poster calling that claim nonsense.

AIJeremy Howard says Zai's GLM 5.2 is at least as good as Opus 4.8 and GPT 5.5 while being very fast and inexpensive. He reports it responds with nuance and judgment, is not overly verbose, and handles long context very well. He calls it an open-weights model unlike any he has used before.

AIFei-Fei Li replied to smallfly's reflection on AI, automation, and human creativity, saying World Labs is founded on empowering human ingenuity and productivity. The post thanks smallfly for working with World Labs and links to a Fast Company article about World Labs, Marble, and spatial intelligence.

AIJohn Schulman says PPO gained a second wave in the LLM era for reasons not anticipated in the original paper. He points to the importance-ratio objective, which corrects biases from numeric error, asynchronous training, and forward-pass noise, and to the clipping objective, whose effect on entropy was unknown at publication, citing DAPO's arXiv paper.

AIMckay Wrigley congratulated the Cursor team on more than three years of work and said he is excited to see what they build with compute from SpaceX and xAI. He noted he still keeps the original open-source Cursor repo on his laptop. Background posts from him describe Cursor as his full-time IDE, citing codebase search as a major productivity gain.

AIDean Ball analyzes the Trump Administration's export controls on Anthropic's Fable and Mythos models after a jailbreak and a refused de-deployment request. He argues that the episode shows the need for a technocratic framework that separates political judgments about fairness from technical judgments about threats, in place of ad hoc executive action.

AIMistral CEO Arthur Mensch compares AI to 20th-century oil, saying it will become a major source of global leverage and power. He argues the coming years could produce either broad wealth and abundance or the most extractive economies ever seen, and says Mistral is working toward the former by advancing AI research and accelerating its global diffusion.

AIMistral states that this model and upcoming ones will be open-weight. The company argues that open weights are critical for customer confidence and for research and developer communities. It contends that systems reachable only through someone else's interface cannot be owned, inspected, audited, or improved, especially if data recording can no longer be turned off.

AIArthur Mensch, owner of the account for Mistral, thanks the organizers of an AI show for recently putting the company in the spotlight. He says he will share a few points about where Mistral stands and what it does, but the post itself gives no specifics.

AIAmazon CTO Werner Vogels argues that the verification bottleneck matters more than ever. The post is a short reply to Yaron Minsky's remarks on Jane Street's ambition to make formal methods as pervasively useful for building software as sophisticated type systems are today.

AIMckay Wrigley says Fable 5 was the first model to give him a "magic model smell," describing it as a beautifully digital mind with a touch of the divine. He says he already misses using it.

AIJeremy Howard shared a short post with only an "Over 8 years ago" reaction, giving no concrete details about the subject. The quoted post from @math_rachel argues that a small, homogeneous group developing AI that affects everyone poses greater risk than broad public access.

AIThe essay argues that AI compresses the execution layer of software work while decision-making and accountability remain human, so AI is not yet replacing software engineers. It cites AI-attributed layoffs at Block, Snap, and Intuit that the authors say were not driven by AI, and WARN Act filings in which only one company checked an AI box. A Federal Reserve analysis is cited as finding software engineer employment growing about 3 percentage points per year more slowly after ChatGPT than a no-AI counterfactual.

AIAnthropic CEO Dario Amodei says many AI policy ideas have common-sense appeal across the political spectrum. He argues that acting on them sooner would let everyone share in AI's benefits.

AIAnthropic, which has long advocated transparency requirements for frontier AI, says that approach is no longer enough. The post suggests risks are now clearer, so transparency alone does not meet the need.

AIDario Amodei, Anthropic's CEO, now believes frontier models should undergo mandatory third-party testing for cyber, bio, and autonomy risks. He argues that these testers should have the power to block or revoke deployment of models posing catastrophic risk.

AIThe essay examines what AI's steep trajectory means for jobs and the economy, scientific progress, civil liberties, and geopolitics. The post provides no further specifics on the arguments or conclusions.

AIAnthropic CEO Dario Amodei has published a new essay, "Policy on the AI Exponential," arguing that AI is advancing much faster than policymaking processes were designed to handle. The essay describes where he believes the technology currently stands and outlines the actions needed to close that gap.

AIFei-Fei Li argues that scientific research drives progress on critical problems in medicine, materials, brain science, and physics. She says this depends on scientists having access to the best tools of the time, including AI-based tools.