Google, OpenAI and Anthropic draft a self-regulatory AI safety body
The three largest US AI labs have stopped waiting for a regulator and started naming their own. On the same day, a survey of 685 CIOs put a number on how little enterprises know about the agents they already run.
Google, OpenAI and Anthropic are pushing ahead with an industry-run standards body for frontier AI — tentatively the "Standards Authority for Frontier AI," or SAFA — aiming to launch it by the end of 2026 or early 2027, and have approached former White House AI policy adviser Sriram Krishnan about running it, The Information reported Thursday. The body is meant to operate without direct government oversight and to fill the gap Washington has left. It would write voluntary safety and security standards for frontier models, back third-party testing before a model is deployed, standardize how labs report safety incidents, and set qualification standards for the independent auditors who would check the work.
Two questions are still open inside the drafting: whether SAFA runs its own safety and capability tests, or only certifies other people's, and who decides which models clear the "frontier" threshold that brings them into scope at all. Membership beyond the three founding labs, and how the body divides work from existing regulators, are unresolved too.
The strategic turn is the story. Earlier plans for a public-private body with government participation stalled, and the labs have moved to self-regulation instead — the outcome critics of this month's pacing push predicted when subscribers to ChatGPT, Claude, Grok and Gemini sued four labs over their slowdown pledge, citing among other things a July working group on an industry standards body. We laid out the argument ten days ago — The Take — Forget the pause. The fight is over the spec — that whoever writes the tolerance ranges owns the category. What is new is that the drafting pen now has a letterhead, a launch window and a candidate chief executive.
Treat the specifics as reported rather than settled. Every detail traces to a single paywalled report; no lab has confirmed the name, the timeline or the recruitment on the record, and Krishnan has not been reported as accepting.
A Dataiku-commissioned Harris Poll of 685 CIOs in eight countries found that 81% lack complete oversight of agents built outside approved systems and formal channels, while 84% say employees are creating agents faster than IT can govern them — and 90% of the same CIOs say they are confident they have complete tracking of all the agents they run. Those two numbers sit in the same study, and the distance between them is the finding. Nearly three in four (72%) cannot consistently confirm whether their agents deliver the outcomes they were built for, 60% have no central governance layer spanning their enterprise apps and IT environments, 67% estimate 51 or more agents running in production, and 47% have already decommissioned more than 20 agents this year.
Dataiku announced the survey alongside an October general-availability product, Agent Management, which scans agent platforms including AWS Bedrock, Databricks Agents, Google Vertex, Microsoft Copilot Studio and Azure Foundry, Salesforce Agentforce and Snowflake Cortex into one cross-vendor inventory, priced per instance annually with monitoring metered per agent. Read the survey as vendor data: Dataiku sells the fix, and its published methodology names the eight countries and dates but states no revenue floor for the sample. The direction still matches the market. IBM research the company cites puts complete, current AI inventories at fewer than one in five organizations, and the network-layer version of the same problem is already shipping — Broadcom wants to catch shadow AI in the packet path.
A free, anonymous model with a one-million-token context window went live on OpenRouter and OpenCode on Wednesday as Space Bunny Alpha, has already processed roughly 1.6 trillion tokens, and is being talked about for a chain of thought that comes out compressed into fragments — one r/LocalLLaMA poster described it as caveman mode, reasoning in clipped shorthand to save tokens, with no effect on the final answer. OpenRouter's listing takes text, images and video, forces reasoning on, and labels the provider as one "who has chosen to remain anonymous during this preview." OpenCode is offering it free for a limited window with zero data retention.
The attribution is the weak part. Reddit and a tokenizer fingerprinting analysis point to MiniMax's M3.1 family — the analysis matched the MiniMax vocabulary on all 24 sampled tokens but classes that as a family-level guess, and a competing fingerprint argues the model looks Western rather than Chinese-trained. MiniMax has confirmed nothing. This is the Ox Alpha playbook from August — free preview, unknown lab, sleuths moving in, as with Mystery model Ox Alpha tops GPT-5.6 — sleuths say it's Zhipu's GLM — and the checkable facts here are the listing, the price and the token count, not the identity.
What to watch: whether Krishnan takes the SAFA job or the body launches without a public membership list, and whether OpenRouter names Space Bunny's provider before the free window closes.
If three labs write the safety standard and also decide which models it applies to, who is the auditor actually working for? Tell us in the comments.
