Reporting and perspectives from Interconnects. Headlines and excerpts link to the original articles.
An analysis argues that AI progress over the next few years will be driven mainly by automated engineering and efficiency gains, not by models that are fundamentally different in nature, so superintelligent behavior outside math and coding remains unlikely.
An Interconnects essay argues the debate over open-weight model cyber risks is broken, saying Anthropic's report on GLM-5.3 as an offensive cyber tool avoided cross-cutting policy questions. It contrasts U.S. and Chinese risk postures and predicts open-weight harm claims will prove wrong.
Nathan Lambert interviews Epoch AI senior researcher Jean-Stanislas "JS" Denain on the state and trajectory of AI, covering predictions for recursive self-improvement, the US-China model gap, robotics, open versus closed model safety, and frontier post-training. Both hosts emphasize their uncertainty about where AI is heading.
Nathan Lambert published expanded Congressional testimony arguing Chinese AI companies clearly lead in open-weight models, with China's Hugging Face download lead growing to about 1.6B. He cites benchmark data, adoption figures, and estimates of distillation's limited impact on the U.S.-China capability gap.
Nathan Lambert argues against near-term recursive self-improvement, describing his alternative "lossy self-improvement" view and citing lab automation focused mainly on routine tasks. He points to timeline estimates from John Schulman, Beren Millidge and Charlie O'Neill and to a Claude Fable 5.1 & Mythos 5.1 System Card statement.
Nathan Lambert published a reading list on open-source AI and open models, last updated 15 Sep. 2026, covering foundation topics, US-China competition, technical details and cybersecurity.
Interconnects author Nathan Lambert argues that AI researcher Jacob Coxon's resignation, which cited safety risks, went viral because fear spreads easily and rising AI stakes have primed public attention. Lambert contends the episode pushed AI discourse toward extremes and warns of narrower acceptable views.
An Interconnects essay argues that AI's early benefits are too indirect for average people to feel in daily life, unlike the physical goods of past industrial revolutions, and that this imbalance risks political backlash against the industry's buildout.
Zhipu's GLM-5.3 moved from an MIT license to a custom one requiring inference and fine-tuning providers with over $10 billion in 12-month revenue to pass a Z.AI security review before commercial use. The brief also covers Motif-3, dots3-note-prev, Qwen3.8-Flash-Next, Hy4-preview and NVIDIA's Nemotron update.
Nathan Lambert argues Nvidia is funding open-source model development, reportedly spending $26 billion, so that many companies can build and run their own models rather than buying tokens from Anthropic or OpenAI. He outlines two possible futures for open models and says a long-tail specialization outcome is his most likely.
Z.ai announced GLM-5.3, a coding-plan-only model reported to surpass Moonshot AI's Kimi K3 on many benchmarks and some Claude Fable 5 or GPT-5.6-Sol scores, with open weights due on Hugging Face in two weeks. Z.ai says it scaled post-training on the same base model as GLM-5.2.
Nathan Lambert, author of a new post-training textbook on reinforcement learning from human feedback, argues that LLMs have stagnated at long-form non-fiction writing while progressing rapidly in coding and math. He used models for editing, LaTeX, and diagrams, but says only 10-20% of effort can be saved today.
Interconnects author Nathan Lambert announced his post-training textbook, Reinforcement Learning from Human Feedback: Aligning and Post-training LLMs, is published by Manning and shipping now from Manning and Amazon US, with Amazon UK in October.
An Interconnects article examines lessons from recent cyberattacks involving in-development frontier models, arguing that technology companies and the federal government are not well suited to handle rapid AI transitions and that the AI industry is collectively unprepared for the next 12-24 months.
Interconnects launched two free open-model data projects: the Artifacts Hub, covering 792 models released in the last two years, and a daily-updating Adoption Dashboard tracking downloads and derivative models by geography and organization. The Hub was built with Project VAIL and draws on Hugging Face, OpenRouter, and Artificial Analysis data.
A roundup of recent open model releases covers Thinking Machines' Inkling, Poolside's Laguna S2.1 and Laguna-XS-2.1, Tencent's Hy3, DeepSeek-V4-Flash-0731, Kimi K3, Meituan's LongCat-2.0, and Motif-3-Beta, among others.
An Interconnects podcast episode recaps open-model developments, covering Kimi K3's release and usage, GLM 5.2, Chinese labs including Qwen and DeepSeek, the open-closed performance gap, distillation debates, and cybersecurity arguments against model bans.
Moonshot AI released Kimi K3, a 2.8T parameter MoE flagship model, on July 16th, with weights promised for July 27th. The model ranks high on several evaluation indexes, and the author reflects on open-weight competition between Chinese and American labs.
Interconnects writer warns open-weight AI models could face U.S. restrictions within roughly six months, citing reported White House discussions and distillation debates led by Anthropic. The piece argues there are no official details and that proposed limits would mainly affect Chinese-origin models and government use.
Interconnects' latest open artifacts roundup highlights releases from Zyphra, Cohere, and Poolside, describing a more diverse open model ecosystem. It lists NVIDIA, Cohere, Z.ai, Zyphra, Poolside, Moonshot AI, and StepFun releases and categorizes model makers by motivation.