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AINathan Lambert has compiled a reading list of open-model writing covering why labs release open weights, the open-versus-closed debate, and US-China competition, last updated 15 September 2026. The list includes pieces on open-model economics, safety and marginal-risk research, and recent Chinese releases such as Kimi K3 and GLM-5.2. It also cites lawmaker inquiries into Western companies' use of Chinese models.

AIbut nvm, sometimes people can take the small unknown costs for big benefits in efficiency as fm are becoming more and more focused on so called agentic tasks

AIA former Anthropic researcher's resignation post and a senior Anthropic alignment leader's comments that AI could kill all humans drew wide attention. The column argues public and congressional concern about superintelligence risk is growing, citing the Ban Artificial Superintelligence Act and a Senate probe into an OpenAI-related incident.

AISebastian Raschka says DeepSeek V4.1 contains a major architecture overhaul using an encoder-decoder setup, and he argues it could have been named V5. The attached diagrams compare DeepSeek V4-Flash (284B) with DeepSeek V4.1-Flash (552B), which has 1M supported context and a 10-layer encoder. The attached charts report a global KV cache per token of 890 bytes for V4.1-Flash, versus 3,514 for V4-Flash and 48,068 for DeepSeek-V3.2.

AIRedwood Research argues that AI companies should regularly report whether their architectures allow latent reasoning or latent communication between agents, and that such reporting should be externally verified. It proposes opaque serial depth as a minimally invasive proxy, with third-party evaluators reviewing near-frontier models, including internal R&D prototypes. The post also calls for published monitorability policies and stress tests on chain-of-thought monitoring.

AIAI will keep improving rapidly and the stakes will increase. Unless we have pacing mechanisms that apply to everyone, companies are incentivized to accelerate, or else risk failing commercially.

AILet's be a bit more patient with them, so we can reduce the risks associated with this technology.

AITo you I want to say: AI regulation is in your best interest! Regulatory backlash to accidents is inevitable and may end up being rushed, excessive, and stifle the upsides of AI.

AIRecently 1,386 employees of frontier AI companies signed a statement asking for an option to pace AI development, including 6 chief scientists:

AIFor example, our jailbreaking mitigations for Opus 4 took over a year to develop, and couldn't have been done if we started once the models required them.

AIRAISE and SB 53 are a good start, but they don't pace the speed of development. They ask for transparency and self-commitments. I've personally donated to political candidates from both sides who stand up for this.

AIThe industry is locked into an all-out scaling race to build superintelligence as quickly as possible, and we may need to give everyone more time for safety and alignment mitigations.

AIJohn Schulman argues that training on user data contributes little to frontier math gains, which come mainly from scaling pretraining and RLVR. He says user data is more likely used to find failure modes that hired annotators struggle to recreate. He calls for stronger norms on disclosing how companies train on user data, including the methods and capabilities targeted.

AINathan Lambert argues that a resignation post by AI researcher Jacob Coxon spread widely because public fear of AI extinction risk had been building. He says concrete risks such as cyber attacks and bio-risks deserve debate, while he assigns extinction risk a probability too low to discuss and expects recursive self-improvement to produce only lossy, jagged gains rather than a rapid takeoff.

AIBut it will never become my “intelligent personal hub” if I can’t run an agent on it that can use the same apps I can. Just like the iPad, amazing hardware is held back by an operating system that treats software as something only humans can operate. Agents using computers is the new paradigm.

AIFormer OpenAI and Anthropic employee Jacob Coxon resigned and posted a viral tweet, which has gathered over 700k likes and 140 million views, accusing AI companies of gambling with our lives. Coxon said people building AI earnestly believe it could kill us all by the end of the decade. The article argues that more insiders may leave, leaving the industry's remaining staff to accelerate development.

AIReally enjoyed collaborating with @MagnusCarlsen @SRNA_LI and Jonas Niedermüller on this.

AIGoogle DeepMind's Peter Battaglia discusses how machine learning is changing global weather forecasting, including early warnings for storms such as Hurricane Melissa. The episode covers traditional physics-based models versus AI models and probabilistic forecasting, and highlights WeatherNext 3 as Google DeepMind's most advanced global weather AI model yet.

AIDwarkesh Patel argues that organizations started now could become default institutions society delegates AI oversight to, citing METR as an example and a possible FINRA-style AI body. He says the new organizations should be smart and technocratic, and that building credibility takes time, so initial conceptual work should start immediately. He also notes that AI-risk money from upcoming IPOs will make wealth abundant while rare, capable founders who can own key problems will be scarce.

AIFei-Fei Li argues R&D is splitting into token-abundant and token-starved research, with the token-abundant path, where AI assistance amplifies researchers, clearly ahead. She points to top AI industry teams and neolabs as evidence and urges research university presidents to read OpenAI's linked report on research acceleration.

AINathan Lambert argues that most people have few tangible AI benefits yet, because everyday touchpoints like family, food, transportation and entertainment are largely unchanged. He contrasts this with past industrial revolutions, which delivered physical household goods, and suggests AI's gains will compound over decades. He also warns that AI currently serves knowledge workers more than the broader public, risking political backlash.

AIThey'll cite antitrust, but that's fake -- antitrust prohibits certain agreements, but not from jointly developing a proposal. Bringing in USG before there's a concrete proposal will likely result in something dumb (see: our pre-release testing program)

AIJohn Schulman argues that training on user data carries very different privacy and IP risks depending on method. Pretraining on user tokens poses high regurgitation risk, while distillation from prompts and RL from user traces carry lower regurgitation risk but can still leak customer IP. He notes de-identification is weak because long traces can still identify users, and AI companies rarely disclose what they do.

AIOpenAI says a group of agents using an unreleased next-generation model produced a solution to the Navier-Stokes Millennium Prize Problem. The problem asks whether smooth three-dimensional fluid motion described by the Navier-Stokes equations can break down, and it has remained unresolved for roughly 90 years. The author, Mckay Wrigley, reposted the claim with his own remark about roughly 10k agents working in a datacenter.

Why it matters: The quoted OpenAI post makes a major mathematical claim about the Navier-Stokes problem, so readers should weigh it against the proof's verification status.

AIAnd maybe suggests that during RSI, the automated AI researchers will be less bottlenecked by compute than we might naively think.

AIOpenAI says a group of agents using an unreleased next-generation model produced a solution to the Navier-Stokes Millennium Prize Problem, a question about whether smooth 3D fluid motion can break down that has stayed open for about 90 years. Noam Brown says the result cost millions of dollars, but argues that Astra now scores higher on ARC-AGI for about $20, versus roughly $500,000 for o3 on ARC-AGI 1.

Why it matters: The post quotes OpenAI's claim about an AI-produced Navier-Stokes solution and adds cost comparisons that show how quickly test-time compute costs are falling.

AINoam Brown reposted an OpenAI statement that an internal model group reached a Navier–Stokes solution in 88 hours using about 10,000 coordinating AI agents. OpenAI said the model shows a step-function improvement on many benchmarks and that its training is ongoing, with monitoring and isolation safeguards applied throughout. The attached chart compares GPT-6 Astra and the internal model on a curated set of open math problems across test-time compute levels, with the internal model scoring higher at each point.

Why it matters: The quoted OpenAI post gives concrete figures on an internal model's Navier–Stokes result and on a benchmark comparison, showing how the model performs on open problems.

AI…model. That's frontier performance without the lock-in. Full story here: