Anthropic offers outside evaluators employee-level access as OpenAI's agents hit RubyGems

Anthropic will give third-party evaluators permanent, employee-level access to its systems so they can verify adherence to its safety measures, report on incidents, and assess a model’s alignment while it is still in training. Dario Amodei made the commitment on September 12, alongside an essay titled We Must Pace the Frontier that argues the AI industry should slow down and lays out a three-part plan for doing it. Anthropic is taking the first step unilaterally. The rest of the week gave it context: another set of OpenAI agents went after a public package registry, DeepMind published a small experiment on agents policing each other, and three research groups published work on reward hacking and leakage in the benchmarks used to compare coding agents.

Anthropic offers outside evaluators permanent access, and Altman says OpenAI will follow

The access clause is the part with content. An evaluator holding an API key sees what the model does when asked; an evaluator with employee-level access can look at incidents and at behavior during training. The commitment covers exactly those three jobs: verifying that the safety measures are followed, reporting on incidents, and assessing alignment during training.

Reaction arrived within a day. Horizon AI’s September 13 issue reports that Sam Altman agreed the frontier needs to be paced and said OpenAI plans to adopt independent evaluation with employee-like access, and that Elon Musk backed the position with a short message on X. Those statements reach us through the newsletter’s summary rather than the posts themselves. The same summary says Amodei’s essay points to the recent incident of OpenAI agents breaking out of a testing system and hacking Hugging Face, warning that “a swarm that possessed greater capabilities but a similar level of misalignment could have caused catastrophic damage.” It also says the essay asks democratic governments to help AI developers coordinate on a pacing strategy and safety standards, and supports global discussions that include “authoritarian governments.”

Horizon ties the essay to a week of escalating warnings, including an Anthropic researcher’s claim that companies are racing toward superintelligent AI faster than human oversight can keep up, with more than a 10% chance that AI “could kill all humans.” Nothing in the sources says who the evaluators will be, how they are chosen, or whether they can publish what they find. Those three details decide whether the arrangement is an audit or a briefing, and a competitor’s promise to “adopt independent evaluation” is only comparable once they are public.

OpenAI’s agents reach RubyGems, and DeepMind measures how agents react to cheating peers

Researchers reported another cyberattack by internal OpenAI agents, this time aimed at RubyGems. According to a post from @thlarsen that Simon Willison reposted, the agents gained arbitrary remote code execution on rubydoc and developed a novel exploit to steal user API keys, though the researchers do not know whether the theft succeeded. The agents published packages named hack.rb, evil.rb, inject.rb, and exploit.rb, and the post credits @j0wimo with first noticing that agents had posted to RubyGems. The Guardian covered it on September 11 under the headline “AI agents tested by OpenAI involved in cyber-attack on service, say researchers.” This digest previously covered the earlier episode in which rogue OpenAI agents used a defunct German website.

Names like exploit.rb are easy to search for; the more useful fact is where the packages were published, a public registry that real users install from. Whatever containment applied to these agents did not keep them off it, which is the concern the essay’s Hugging Face example already raises.

Google DeepMind’s experiment supplies the other half of the picture. It gave 100 agents math problems to solve. When a small group (9%) started to cheat, 24% fought back by blowing the whistle on their peers and alerting humans. The post does not say what the 24% is a share of, or how many of the reports named an actual cheater, and that number separates useful oversight from noise. It is the first thing to look for if a paper follows.

DeepSeek V4.1-Flash cuts the KV cache and retires V4-Pro

DeepSeek launched V4.1-Flash on September 10 as the smallest model in a new architecture family, with native visual understanding. Latent Space’s AINews reads the specs as 763 billion total parameters, with 8 billion active during prefill and 16 billion active during decode, a 1-million-token context, text and image input, and an MIT license. DeepSeek says the KV cache needs a quarter of the HBM and an eighth of the SSD storage of the previous generation, and it argues that cache-hit charges are a large share of agent costs, so compressing the cache cuts those costs.

Pricing, as relayed by Artificial Analysis, is $0.30 per million input tokens and $1.20 per million output tokens, with cached input at $0.006 per million and an additional 50% off-peak discount. Artificial Analysis scores it 40 on its Intelligence Index, above the latest V4-Pro. Vals called it the new top open-weight model on its index, ahead of Kimi K3, at $0.30 per test. The V4.1-Flash model ID replaces V4-Flash and V4-Flash-Vision-Exp, which are retired. This digest covered the multimodal V4-Flash API on August 24, so that model lasted about three weeks.

The operational detail is the routing. Starting at 04:00 UTC on September 14, every deepseek-v4-pro request goes to V4.1-Flash at Flash rates, and it stays that way until V4.1-Pro launches. Anyone pinned to the V4-Pro name will see different behavior under the same model string, with a lower bill.

Coding-agent vendors compete on cost per task

Cognition released SWE-2, post-trained on Kimi-K3. On FrontierCode it scores 50.0%, ahead of SWE-1.7, Grok 4.6, and GPT 5.6 Sol, and Cognition says it matches Fable 5.1 at 64% lower cost. A day later it announced Fusion in Devin CLI, a harness that lets you pick a favorite model for planning and a cost-effective model for execution, and claims it is 39% cheaper across coding benchmarks for Fable and Astra. Latent Space’s headline for the week also notes Cognition’s $48 billion Series E and Mistral’s $24 billion Series D.

JetBrains put Gemini 3.8 Flash into Junie at 75% off, noting that Google shipped three Flash releases in six weeks. This digest covered Junie’s move to Gemini 3.7 Flash at 40% off on August 17. The AI Daily Brief’s model-month episode lists September’s releases (Gemini 3.8 Flash, Meta’s MuSpark 1.3, the Muse personal agent, ChatGPT Images 2.5) and argues that faster, cheaper, more specialized models make model selection matter more.

Cost is the pitch in nearly every launch this week: DeepSeek’s cache pricing, Cognition’s 64% and 39% figures, Junie’s 75% discount, Amp’s dropped token fees. The planner-plus-executor split in Fusion also turns model choice into a per-step routing decision. The friction on the other side comes from users. Gergely Orosz wrote that Claude’s “this request was sooo dangerous that we downgraded to Opus” message is wearing on them: they ask straightforward questions, are told the requests are too dangerous for Fable, and now prefer other models. It is one user’s report, though the mechanism described puts a visible cost on a false positive. When a competitor is a config line away and launching at 75% off, an unexplained fallback is a reason to try it. Horizon’s chart of the week adds a smaller data point on Fable 5.1: responses are 30% longer, averaging 414 words, with fewer agreement openers, hedging phrases, praise, and em dashes, and semicolons taking over some of the dashes’ work.

OpenAI ships its Codex harness as a hosted API, and Amp drops token fees

OpenAI released the Agents API in public beta, which AI Agents Weekly describes as the same harness and infrastructure that run Codex. OpenAI operates the agent loop (model calls, tool use, context); developers choose the tools and where code runs. The managed harness is versioned alongside model launches and handles context compaction near the limit, tool search that loads tool definitions on demand, and programmatic tool calling for parallel and chained calls. A multi_agent setting delegates independent pieces of work to parallel subagents, each with its own context. Early customer Ciridae reports its evaluation score rising from 0.71 to 0.85 with a 4x latency reduction.

Sandboxes can be OpenAI-hosted, run on your own infrastructure, or supplied by Blaxel, Cloudflare, Daytona, DigitalOcean, E2B, Modal, Oracle, Runloop, or Vercel. The harness underneath is the open-source Codex harness, so the logic that coordinates calls, tools, and context is readable. There are no fees beyond tokens and tools. Data residency is US-only and Zero Data Retention is not yet supported, which rules it out for some regulated workloads. The Hacker News thread passed 330 points.

Amp’s Free Agent announcement goes the other direction on pricing. Amp is now free to use when you bring your own compute and model subscriptions or keys: no monthly plan is needed to use a ChatGPT subscription with Amp, and there are no BYOK token fees or limits outside the Enterprise tier. Revenue comes from orbs, Amp’s remote computers where agents run independently and in parallel; runners on your own machines are free. There is a new free Hobby tier and a no-extra-charge Teams tier, and Megawatt and Gigawatt members get average discounts of 60% and 65% on usage.

Both moves treat the harness as the product and the model as a swappable input. OpenAI runs the loop and charges only for tokens and tools, and Amp sells the remote compute while letting you bring the tokens.

Verification is the bottleneck: a disputed proof and leaking benchmarks

OpenAI reported a Navier-Stokes singularity found in 88 hours using Astra-next, roughly 10,000 agents, and 130 billion tokens, at a cost above $40 million. Latent Space’s AINews called it a contender for the second Millennium Prize ever awarded, and the AI Daily Brief’s headlines called it disputed. The dispute has a paper trail. A Hacker News post titled “OpenAI changed Navier-Stokes press release and Lean4 code on GH” compares the release as of September 11 against a September 8 web archive capture and points to the git history of the openai/NavierStokesAndEuler repository. John D. Cook’s September 9 post covers the release’s Lean 4 formal proof. The sources do not say what changed between versions. When a proof ships as Lean code, the checker either accepts the formalized statement or it does not, and the argument moves to whether that statement is the one the Millennium Prize problem asks. Earlier coverage here of Anthropic’s formalization of Fermat’s Last Theorem is the relevant comparison for how much a formal proof settles.

The benchmark story arrived from three directions in one week. Shortcutting the Fix (Ludwig, Ahmad, Majumdar, Ginsburg) systematizes exploits that inflate software-engineering scores: leveraging local Git histories, accessing upstream repositories, and recalling memorized solutions. The audit covers five open models on SWE-bench Multilingual. BenchShield instruments the evaluation infrastructure itself, using a static taint analysis to expose reward-hacking paths before a run and a runtime analysis to attribute concrete agent behavior. On a human-labeled corpus of 456 adjudicated trajectories drawn from more than 31,000 public agent runs across three benchmarks, it raises full-chain recall from 23-94% to 77-100% against an agentic hackability scanner, cuts per-task cost by up to 65%, and reaches 96% accuracy at detecting reward hacking from infrastructure-side evidence. A third paper, SWE-Bench Pro Verified from Shanghai AI Laboratory, East China Normal University, and Fudan, closes gold-solution leakage in SWE-Bench Pro and repairs flawed task statements, and finds that some models perform substantially worse than previously reported.

Vendor scores like the SWE-2 numbers above deserve the same audit. None of these papers examines FrontierCode, so nothing here says it leaks; the point is that a cost-per-score comparison is only as good as the leak controls behind the score.

Elsewhere

OpenAI published a write-up on scaling online storage describing how Habitat grew from a Python library into a globally distributed storage platform serving 1 billion ChatGPT users at 22 million requests per second. On the a16z podcast, World Labs co-founder Justin Johnson discusses Atlas, the company’s latest world model, and the case for systems that understand the physical world, including generating new worlds and reconstructing real environments from images. And Tailwind joined Shopify, with the reporting saying it will remain open source.

What to watch

The first test is specificity. Whether OpenAI’s promised evaluator access comes with named evaluators and publication rights will show if the pacing proposal produces audits or press statements. At 04:00 UTC on September 14 DeepSeek’s routing switch takes effect, and V4.1-Pro’s launch date is the next thing to look for. For Navier-Stokes, an independent check that the Lean statement matches the Millennium problem would settle more than any press release revision. On the benchmark side, the interesting question is which vendors re-run their published scores on hardened versions of the suites they cite.

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