Google, Cohere, and vLLM sign open-weight AI letter

• The live signatory list got much bigger than Jensen Huang’s launch image: the Microsoft-hosted letter now lists Google, Cohere, OpenAI, OpenClaw, Periodic Labs, Ollama, AMD, AI21, and more, while vllm_project said Inferact signed on behalf of the vLLM community.
• The policy payload is the distillation paragraph: natolambert highlighted language calling distillation a widely used model-development technique, separate from unlawful extraction.
• Anthropic became the obvious pressure target after natolambert called it the only major lab without a single open model, while GergelyOrosz named Anthropic and Amazon as the two major AI and cloud companies still not indicating support.
• The stronger engineering thread moved past weights: percyliang named open weights, datasets, software stacks, and process knowledge as the four frontier-level ingredients.
• The safety split stayed live: emollick framed the disagreement around whether people believe lab-level AGI and biosecurity risks are real, while RyanGreenblatt backed broad use of released open-weight AIs but also supported slowing Chinese AI progress.

The letter includes a live roster, not just the first NVIDIA-hosted PDF that spread on X. It also puts distillation in the normal engineering bucket, while Lawfare described parallel U.S. and Chinese signals about controlling open-weight models. In the same window, Hugging Face published The Stack v3, a 15.9 TB training split with roughly 4.9 trillion code tokens.

The live Microsoft page defines open-weight models as systems anyone can download, inspect, modify, and run on their own infrastructure. The signatory list now includes Google, Cohere, OpenAI, OpenClaw, Ollama, Periodic Labs, Nous Research, Prime Intellect, AMD, AI21, Cloudflare, GitHub, and others.

Google’s public support came with receipts: demishassabis pointed to JAX, Transformers, AlphaFold, and Gemma, then clarified that the Gemma series had crossed 900M downloads in a follow-up.

The other new public support signals were spread across company accounts and project accounts:

• Cohere said it signed and tied the letter to sovereign AI cohere.
• nickfrosst said Cohere releases open-weight and open-source models nickfrosst.
• OpenClaw said open weights protect user choice and let people run, study, and build AI on their own terms openclaw.
• Periodic Labs posted that it supports open models LiamFedus.
• Ollama said it signed and framed its mission as making open models accessible to every developer ollama.
• vLLM said Inferact signed the open letter on behalf of the vLLM community vllm_project.
• MiniMax publicly aligned with the open-weights push and the SF march language MiniMax_AI.

OpenAI was the correction case. An early r/OpenAI post said OpenAI had refused to sign, then a later r/OpenAI post pointed back to the Microsoft page showing OpenAI on the list.

The letter’s sharpest paragraph separates ordinary distillation from misappropriation. The Microsoft-hosted text calls distillation “widely used” for model improvement, evaluation, and validation, while saying unlawful extraction should be handled through targeted legal and commercial frameworks.

That clause landed directly after U.S. officials accused Moonshot AI of distilling Anthropic’s Fable model to build Kimi K3. kimmonismus shared an Axios excerpt saying the White House backed open-weight models while describing large-scale covert industrial distillation as unacceptable.

Jensen Huang made the broader engineering argument in an Axios interview. rohanpaul_ai quoted him saying AI systems will constantly learn from other AI systems because the internet itself is becoming AI-generated.

Gergely Orosz used the car-industry analogy: GergelyOrosz asked why taking apart a rival car is accepted while inspecting a model through outputs becomes a “distillation attack.” Jürgen Schmidhuber went further, saying he supports open-source models distilling what commercial companies distilled from the internet SchmidhuberAI.

Anthropic absorbed most of the pressure because the big public names around it moved. Google joined the roster, OpenAI appeared on the live Microsoft page, and Cohere, Ollama, OpenClaw, Periodic, and vLLM all posted support.

The cleanest version of the holdout claim came from GergelyOrosz, who named Anthropic and Amazon as major AI and cloud infrastructure companies that had not indicated support for open weights. koltregaskes added a longer caution list that included Apple, Salesforce, Intel, Qualcomm, Oracle, Adobe, and AMD before several later additions changed the roster.

Julian Schrittwieser’s CUDA and Windows posts turned into the day’s most quoted backlash object. He later said people were jumping to the conclusion that he wanted to ban open-weight models, adding that open models can be useful while historically anti-open-source companies were suddenly favoring openness Mononofu.

AndrewYNg drew the line many commenters used against that argument: AndrewYNg said everyone has the right to keep code private, and the problem is trying to stop others from opening their work. koltregaskes also noted that Anthropic has given the ecosystem MCP and Agents.md, but still no open-source models koltregaskes.

The best technical framing came from Percy Liang: weights are only one renewable resource in an open AI stack.

His four ingredients were:

1. Open-weight models
2. Open training datasets
3. Open software stacks
4. Open process knowledge

That list explains why the letter became bigger than a signatory screenshot. hwchase17 argued that every company building around AI will need to “own their intelligence,” and Vtrivedy10 unpacked that as open models, open harnesses, owned infrastructure, fine-tuning, open data, open recipes, and inspectable context.

Compute was the obvious missing fifth item. JJitsev argued that full-stack openness without open compute still cuts academia off from studying frontier systems.

NVIDIA’s own ecosystem argument was blunt. ctnzr said NVIDIA is the biggest institutional contributor on Hugging Face and will keep publishing because when AI grows, NVIDIA’s opportunities grow too.

Open-data work moved in parallel with the policy fight. The most concrete artifact was The Stack v3.

The Hugging Face dataset card describes the training split as 15.9 TB of source code across 713 languages, from 173M repositories, at roughly 4.9T tokens. Two changes matter for code-model builders:

• File contents are inline, so the dataset is self-contained.
• Each row is grouped at repository level, which preserves full-repo context.

lvwerra framed it as a cyber-defense release: lvwerra said open code models need datasets like this, and his follow-up called code data rich because code, docs, issues, and PRs contain structured problem-solving recipes.

Traces were the smaller, messier dataset thread. badlogicgames said traces are a building block for OSS models and hard to bootstrap, then badlogicgames linked a tool to remove sensitive material and upload traces to Hugging Face.

The harness thread answered a different question: what part of the agent stack can users actually inspect or swap?

OpenAI’s Codex CLI became one example because reach_vb called it a fully open-source harness that lets users audit prompt handling and run open-weight models. LangChain’s position was similar at the framework layer: LangChain said open models bring choice, innovation, and flexibility through Deep Agents and the LangChain ecosystem.

AndrewYNg announced OpenWorker as the desktop-agent version of the same idea. AndrewYNg described an open-source AI coworker that runs on Mac, supports GPT, Claude, Gemini, open-weight models, and Ollama, and keeps data local except through the chosen model provider and integrations.

Infrastructure vendors made the production version concrete. togethercompute said its next inference platform can test changes on real traffic, swap models behind a stable endpoint, autoscale on TTFT, latency, and throughput, and use pre-optimized deployment profiles.

The open-weight coalition did not erase the safety split. It made the split easier to locate.

emollick argued that many open-weight supporters do not deeply believe the AGI or ASI risk model held inside labs, especially semi-autonomous biosecurity risk. his follow-up said if those risks seem unreal, lab warnings look like commercial FUD for protecting margins.

RyanGreenblatt took a narrower policy position. RyanGreenblatt said broad restrictions on released open-weight AIs would be bad policy, while export controls and defenses against distillation could still slow Chinese AI progress; his related post said government employees and defense contractors may reasonably be blocked from using adversary-trained open or closed models for automated work.

andy_l_jones put the timeline pressure on it. andy_l_jones estimated open source is less than 12 months behind the frontier and listed three possible escape routes: frontier-model desensitization, an industry-benefit case strong enough to survive incidents, or a research program for open models as safe as closed models.

BlackHC’s version was operational: BlackHC distinguished published models from unreleased research models, and BlackHC pointed to checklists, training, monitoring, and auditing for higher-risk experiments.