[AINews] Open Models, Model Labs vs Agent Labs, and the Untrainable★ 72
Latent Space3 days agoCommentaryThis AINews issue uses Sarah Guo’s essay as a lens for current AI industry debates: where open models matter, how agent labs differ from model labs, and what cannot be trained away. It also recaps discourse around Anthropic Fable/Mythos, Fable 5’s capabilities, Google’s DiffusionGemma, and maturing agent infrastructure. The central takeaway is that durable value may lie in integration, customer translation, maintenance, and intent rather than model scores alone.
Upgrading agentic coding capabilities with the new Devstral models★ 72
Mistral AI News6 days agoReleaseMistral AI announced two Devstral updates focused on agentic coding workflows: Devstral Small 1.1 and Devstral Medium. Devstral Small 1.1 remains a 24B Apache 2.0 open model and reaches 53.6% on SWE-Bench Verified. Devstral Medium reaches 61.6%, is available through Mistral’s API, and supports private deployment and custom finetuning for enterprises.
Mistral AI partners with NVIDIA to accelerate open frontier models★ 74
Mistral AI News6 days agoBusinessMistral AI announced it is a founding member of the NVIDIA Nemotron Coalition, a global initiative for open frontier foundation models. The partnership combines Mistral AI’s model architecture, training techniques, multimodal capabilities, and enterprise fine-tuning tools with NVIDIA compute, development tools, and synthetic data pipelines. The coalition’s first initiative is a DGX Cloud-trained base model that will support the upcoming NVIDIA Nemotron 4 family and be open-sourced for specialization.
Open and closed models are on different exponentials
Interconnects (Nathan L.)13 days agoCommentaryNathan L. argues that open and closed models are developing along different exponential curves. The key question is whether marginal gains in model intelligence translate into practical value. Some use cases may reward small capability improvements, while others may not benefit proportionally from additional intelligence.
Some ideas for what comes next, May 2026
Interconnects (Nathan L.)19 days agoCommentaryNathan Lambert argues that 2026 AI progress is becoming higher-stakes, with model capabilities, work patterns, economics, and real-world risks all escalating. He says open models still lack a true Claude Code and Opus 4.5-style agent moment, and Gemini has no clear competitor to Claude Code or Codex yet. The essay also tracks Mythos, American open-model momentum, frontier-lab competition, and mounting intervention from governments and other power structures.