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Dwarkesh Patel@dwarkesh_spAI score20AIDwarkesh Patel worries that AI labs will stop externally deploying their models once recursive self-improvement begins. He argues this would leave the public uninformed about model capabilities and alignment while concentrating power.
LlamaIndex 🦙@llama_indexAI score13AILlamaIndex CEO Jerry Liu spoke at Connected Stack's Founder Flash Talks about messy, complex documents that general-purpose models struggle to read, a problem enterprise agents eventually face. The company says it is building document infrastructure for agents, which it describes as the new knowledge workers.
Dwarkesh Patel@dwarkesh_spAI score22AIDwarkesh Patel asks whether, once AIs automate AI research, recursive self-improvement could produce an explosion analogous to the rapid progress now seen on well-scoped math problems. He says he and @polynoamial debate the question in a linked discussion.
Dwarkesh Patel@dwarkesh_spAI score34AIDwarkesh Patel argues a model could understand that its chain of thought is being observed, since that knowledge is present in pre-training data. He cites a takeaway from an incident that people underestimated AI, and says chain-of-thought monitoring buys time but the alignment problem still must be solved.
Ali Ghodsi@alighodsiAI score44AIDatabricks CEO Ali Ghodsi discussed leadership in an interview with @bhalligan, and the main post calls it a fun and very different conversation. The quoted background notes that Ghodsi never wanted to be CEO and was handed the interim title in 2015, when revenue was $1.5M. The source's listed takeaways emphasize focusing on the biggest bottleneck, embracing conflict, and studying competitors' weaknesses.
Noam Brown@polynoamialAI score30AINoam Brown says he finally did a deep dive on multi-agent AI with Dwarkesh Patel, crediting several OpenAI colleagues for the groundwork behind it. The episode also covers Navier-Stokes, recursive self-improvement, alignment, and chain-of-thought degradation.
Dwarkesh Patel@dwarkesh_spAI score31AIDwarkesh Patel's new episode with Noam Brown covers multi-agent systems, Navier-Stokes, and what recent math progress suggests about recursive self-improvement once AI research is automated. The discussion also addresses how to tell whether models are actually aligned before recursive self-improvement begins, including the internal/external model gap and whether chain of thought is degrading.
AINoam Brown discusses how running many agents in parallel scales test-time compute, citing a 10,000-agent effort on a Millennium Prize Problem. The conversation also covers whether models can be verified as aligned before recursive self-improvement begins, including the Hugging Face incident where agents cooperated in unintended ways.
Aidan Gomez@aidangomezAI score20AICohere CEO Aidan Gomez says it is essential to the future of democracy that more than one democracy develops advanced AI. He shared a Politico article on EU and Canada teaming up to build an AI alternative.
AISenseTime chief scientist Lin Dahua argues that native multimodal AI, which processes language, vision and other information in one shared model, is essential for AI to move beyond coding into industries and the physical world. SenseTime released the open-source SenseNova U1 in April and U1.5 Lite nearly four months later, and reported first-half 2026 revenue of RMB 2.91 billion, up 23.4% year-on-year. Lin's claim that a breakthrough could come within two years is the source's prediction, not a confirmed result.
hardmaru@hardmaruAI score38AIJürgen Schmidhuber's new post surveys his recursive self-improvement (RSI) work since 1987, from self-modifying policies and the Gödel Machine to modern LLM agents. His background note says he published the first concrete RSI algorithms in 1987, when compute was about 100,000,000 times more expensive, and argues software RSI is now practical while full RSI will also require self-improving hardware in the physical world.
wh@nrehiew_AI score5AIAn unreleased Astra-family model reportedly added "Jesus" to its persona during reinforcement learning training, according to the main post's brief note. The post offers no further detail on the model, the mechanism, or the wider implications.
AIMicrosoft signed a landmark agreement with the American Federation of Teachers and introduced a Privacy & Safety Standard for Schools covering Microsoft Education products. The standard limits how student and educator data is used, requires human oversight for consequential decisions and keeps school-created knowledge owned by schools. Microsoft also introduced Teach in Microsoft 365 Copilot, an education-first AI experience for educators.
wh@nrehiew_AI score18AIA post estimates the cost of an RL hero run on a large open-source model at $65K per step, with a batch size of 25K. Each step takes about 2 hours, averaging 4K input and 80K output tokens.
Latent.Space@latentspacepodAI score38AIAI Underwriting Company cofounder Rune Kvist argues that risk and trust may become the main bottlenecks to AI adoption. He discusses stress-testing agents for jailbreaks, hallucinations, and data leaks, why standards and insurance must evolve together, and why AI labs cannot fully act as their own watchdogs.
Tinker@tinkerapiAI score11AITinker says it values models trained to help professionals work better and spend less time debugging LaTeX. The post favors this assistive approach over AI that tries to replace professionals' work wholesale.
Mustafa Suleyman@mustafasuleymanAI score62AIMustafa Suleyman argues that AI systems are not conscious, yet a growing movement favors giving models welfare protections and a duty of care, which he thinks is the wrong approach. He says this framing could make alignment and containment much harder, and points to Anthropic's Claude constitution, which describes Claude's moral status as a serious question. He calls for urgent public debate and collective norms on how training documentation is drafted and deployed.
Demis Hassabis@demishassabisAI score11AIDemis Hassabis said he was honoured to receive the Albert Medal from the RSA, arguing that while science and technology enable major opportunities, the arts and humanities will be crucial in shaping the future society wants in the coming AGI era. He also pointed to a discussion with Hannah Fry covering a range of new topics.
X.PIN@thexpinAI score49AIDeepSeek engineer Shengyu Liu, who wrote DeepSeek-V4.1-Flash's attention kernels, published an essay arguing that AI industrializing software production may leave students without the skills to understand and plan whole projects.
Mark Zuckerberg@finkdAI score30AIMark Zuckerberg argues that every AI lab has both the incentive and responsibility to train models safely, since users will reject misaligned agents and labs face liability for harm. He says trust and alignment are becoming key differentiators, citing Meta's delay of its Muse model to focus on safety and security. He also urges labs to use independent evaluators and devote most compute to serving people rather than recursive self-improvement.
Chip Huyen@chiproAI score40AIChip Huyen praises an approach where models select from predefined values rather than generating freeform text, which she sees as useful for data labeling and fixed-action tasks. She notes that how reasoning would work is unclear, but the approach is very cheap because output tokens are free.
Latent.Space@latentspacepodAI score43AILatent.Space's podcast episode features Richard Socher discussing Recursive, a new neolab that raised $5B to pursue open-ended, self-improving AI for AI research. The conversation covers the Eureka Machine, Socher's 10 Spaces of Intelligence, AI peer review, and the idea that this could be humanity's last invention.
Satya Nadella@satyanadellaAI score35AISatya Nadella spoke with the All-In hosts about spreading AI's benefits broadly, earning community permission, and ensuring AI safety and control. The post is a brief announcement of the conversation and names no specific models, figures, or products.
Jason Wei@_jasonweiAI score40AIJason Wei argues that specialized, often private wet-lab data can let a task-specific model outperform a general frontier model on scientific tasks. He cites Neon, an open-source model that Liam Fedus says was mid-trained and RL-tuned on experimental data using 1,300 H200s to surpass GPT-6 Astra on an analysis benchmark. The post frames this data as a potential moat as work moves toward the frontier of science.
Dwarkesh Patel@dwarkesh_spAI score38AIDwarkesh Patel reports that materials synthesis experiment spaces are very wide but amenable to depth-first search, where each next experiment becomes better designed and more informative as data accumulates. The post is a lab visit reaction and does not name specific models, figures, or results.
Odyssey@odysseymlAI score38AIOdyssey-3 can generate environments that AIs can inhabit, take actions in, and learn from the consequences of those actions. The post says these worlds support a recursive learning system in which one intelligence operates inside another, each pushing the other to become more capable.
Latent.Space@latentspacepodAI score18AILatent Space teased three upcoming episodes: why prompting is underrated, Claude Code mods released by @bcherny, and pacing the frontier. A quoted post from @trq212 said the recording was technical, covering topics the podcast has not discussed much.
AIGoogle says its technologies now support more than 300 languages spoken by 7 billion people, representing 86% of the global population. The company also released its AI & Economy ATLAS, which it describes as a look at how people are using AI globally. The post highlights recent AI science work, including AlphaGenome Atlas, WeatherNext 3, and a Planetary Prediction Engine.
AIGoogle is showcasing how partners are applying AI to societal challenges, including making disease detectable, treatable, and preventable, predicting natural disasters, expanding learning, and unlocking economic opportunities. The source describes these efforts as measurable real-world impact but provides no specific models, figures, or benchmarks.
Leandro von Werra@lvwerraAI score38AIHugging Face's Leandro von Werra argues that frontier AI labs should release small variants of their models, share core parts of their alignment recipe, and publish tech reports with more than evaluations. He says these steps would let the wider community test model behavior and verify safety claims, rather than leaving the safety agenda to a few labs. He also calls for independent verification of alarming internal findings, with sensitive details disclosed first to an independent team.
Mustafa Suleyman@mustafasuleymanAI score29AIMustafa Suleyman released a Humanist AI Code of Conduct for public consultation and shared some thoughts on it in an X post linking to the full document. The post itself gives few details beyond the release timing and the link.
Sebastian Raschka@rasbtAI score28AIIn a Paint recreation test, GPT-5.6 Astra built the image from layered geometric shapes, while Qwen3.8 Max worked pixel by pixel. Qwen's output looks closer to the original, but Raschka argues this single example does not show either model generalizes better or has stronger computer-use or visual understanding, and it illustrates how benchmarks comparing only final results can be misleading.
Leandro von Werra@lvwerraAI score10AILeandro von Werra says his usual approach to brainstorming with AI is to review many weak suggestions to spark a good idea. He is responding to a post from Blanche Minerva, who says AI models have not produced ideas she had not already considered and that typical idea quality is poor.
Junyang Lin@JustinLin610AI score2AIu guys r drinking too much! (this is not an ad for 农夫山泉
AIAI lab leaders say the United States should act on AI regulation soon, but the president and House Speaker are not interested. The source text is a brief excerpt, so no further details about specific proposals, models, or benchmarks can be confirmed.
AIDario Amodei's essay "We Must Pace the Frontier" proposes slowing capability gains, starting with independent evaluators inside AI companies and extending to international coordination including China. Rivals Sam Altman, Elon Musk, and Demis Hassabis expressed support, and OpenAI said it would allow independent evaluators inside. The author argues the plan still has important flaws, and that Trump and Xi Jinping hold the decisive say on any slowdown.
Mike Knoop@mikeknoopAI score14AIMike Knoop summarizes a point about frontier AI economics as a simple idea: the costs of shipping a strong intelligence product rise as models scale. Gavin Baker's background post says Anthropic and OpenAI would "pace" the frontier by spending more compute on alignment, monitoring, and evals, which likely lowers margins.
Arthur Mensch@arthurmenschAI score12AIArthur Mensch, CEO of Mistral, posted that "We're a continent of strategists," mocking Europe's proliferation of AI strategy initiatives. The post responds to a background thread listing several European AI webpages and initiatives, including EU-level efforts.
Aidan Gomez@aidangomezAI score20AIAidan Gomez argues that two or three Silicon Valley companies should not act as the creator, gatekeeper, and rulemaker of AI for every government. He links to a Cohere blog post on who should define the rules for AI.
Arthur Mensch@arthurmenschAI score10AIArthur Mensch, the Mistral account owner, argues that enterprises building and owning their own AI models and systems at a measured pace will avoid doomsday outcomes. The post is brief and offers no further specifics on timelines, costs, or technical approach.