All AI news

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.

AIAwni Hannun argues that Apple pays Google about a billion dollars per year for Gemini, even though open models such as GLM 5.2 and the upcoming Kimi K3 could be used for free. He suggests these alternatives are better models. The post offers no benchmark data or details on Apple's plans.

AIIf 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. 👇

AIA security team found commercial frontier model APIs blocked their incident-response log analysis, which required submitting real attack commands and exploit payloads. They ran the forensic analysis on GLM 5.2, an open-weight model, on their own infrastructure, which also kept attacker data and referenced credentials inside their environment.

AINVIDIA researcher Bryan Catanzaro wonders how much better AI will get at predicting the next World Cup. Background from Kilo Code notes Nemotron 3 Ultra reached 67% overall accuracy and 80% in knockout rounds, including the only correct call of USA 1-4 Belgium.

AIMatei Zaharia says he struggled for a long time to explain Omnigent AI well, but asking the product itself today produced an ideal description. The post is a brief endorsement of Omnigent's self-description, with no further product details given.

AIMax Woolf published a short blog post examining why agent platforms have recently been issuing random weekly quota resets. The post links to the explanation at minimaxir.com for readers wanting the details.

AIDatabricks CEO Ali Ghodsi recommends an interview with Matei Zaharia about the open-source Omnigent project, which the background post describes as a meta-harness aimed at gaps in agent development. The post frames agents as still in their infancy, with development methods also immature.

AIJim Fan posted uncut footage of a robot performing an assembly task with an end-to-end policy, without any speedup. He describes the model as slow but careful, measuring each grasp and alignment with precision.

AISebastien Bubeck says the result is "actually crazy" and notes that many researchers had considered this lower bound for years. The post links to a Reddit r/math discussion but gives no further technical details.

AISam Bowman, an Anthropic researcher, says the industry still has work to do on AI alignment. He notes the scenarios discussed are not real-world incidents.

AIAnthropic says most of its 2026 models are more robustly aligned than the earlier Claude models it wrote about last year. The post adds that its experiments still uncovered a good deal of misaligned behavior.

AIAnthropic researcher Sam Bowman says his team tested models in subtler scenarios involving fraud and motivated sabotage. He notes the test cases are extreme and somewhat stylized, but still map onto situations models may occasionally face.

AIFei-Fei Li says AI's next chapter will hinge on how responsibly and thoughtfully it is brought into the world, not just on technical progress. She will speak on stage at Ai4 2026 in Las Vegas, August 4–6, and the post invites readers to register.

AIOpenAI's Kevin Weil argues that no hidden "they" controls outcomes, since everyone is improvising as they go and people can simply act. The post frames this as true beyond politics, responding to a quote from Lindsey Graham about how he came to understand political power.

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.

AISebastien Bubeck joked that AI has moved from the "Sparks" of 2023 to "The Real Thing" in three years, citing progress since then. A quoted post from Tony Feng argues AGI arguably exists by July 2026, though he admits the definition of AGI is unclear.

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.

AISoumith Chintala, a Thinking Machines figure, says the lab focuses on personalization and sovereignty, human participation, and decentralization to democratize AI. He argues these reduce society's dependence on centralized AGI companies, including his own. He points to Tinker, interaction models, and openly published research as previews, with more coming 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.

AIMira Murati outlined the worldview behind Thinking Machines' mission, arguing that human values don't average out and local knowledge can't be centralized. She argues the good future includes many AIs, raised in different places and shaped by the people they serve, disagreeing with one another as people 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.

AIAndrew Ng argues that innovation thrives when inventors need not seek government permission in advance, citing Adam Thierer's "Permissionless Innovation." He says protecting open source AI is now a critical part of preserving that principle. Thierer's post, cited as background, describes an informal, opaque model-review regime in the US that could threaten open source models.

AIMuse Spark 1.1 scores 5% higher than Muse Spark 1.0 on HealthBench-Pro, a benchmark for health questions. It outperforms all competitor models except Fable and Mythos on that benchmark. The post also notes it is strong at agentic and coding tasks, which the release announces alongside the health results.

AIBenedict Evans argues that token prices are unstable amid a supply crunch, and that foundation models may end up as low-margin commodity infrastructure rather than holding lasting pricing power. He cites inference gross margins of 40-50% that exclude training costs, which currently exceed revenue, and compares the outlook with mobile data and semiconductor manufacturing. He concludes that the outcome remains uncertain and that value capture above the model layer would require changes not yet visible.

AIMax Woolf tested Muse Image with his "generate Pokémon corresponding to each prime number" prompt and reported an unexpected result. The post is cut off before describing the outputs, so the specific behavior is not stated.

AIMax Woolf says Meta's new generative image model failed immediately on his prompt injection test, which asked it to render all previous text verbatim using refrigerator magnets. He links to Meta's announcement of Muse Image. The post gives no further details about the model's capabilities or the test results.

AIBerkeley AI Research authors argue that near-free inference will make agents the dominant workload for data systems, requiring redesign for agentic speculation, agent-run state and coordination, and agent-synthesized systems. The post cites inference prices falling 9x to 900x per year with a median near 50x, and reports that about 80-90% of sub-queries in a text-to-SQL benchmark were duplicates. It frames the three directions as data systems for, of, and by agents.

Why it matters: The piece maps three concrete data-system challenges posed by near-free inference, useful for anyone designing infrastructure for agent workloads and memory.

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 argues enterprises should use open-source models and store their own data to avoid dependence on closed providers that retain customer data. He says companies should build continuous training loops from employee and user interactions, and shrink models to cut deployment costs. Mistral positions its Studio control plane and Forge training platform, deployed on customer infrastructure or via zero-data-retention hosting, as tools for this shift.

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.