METR's Anthropic audit explained: AI now speeds up its own R&D ~2x
METR spent 11 weeks inside Anthropic and estimates its models now improve about twice as fast, mostly thanks to AI. Here's what that means and what stays hidden.
Today's deep dive · 8 November 2026
DeepMind's RSA-1024 agents explained: 3.5x cheaper, and what's unproven
Google DeepMind says AI agents cut the cost of attacking 1024-bit RSA. Two working papers, one real 391 core-year run, two unrun estimates. What is proven.
In 60 seconds
- Google DeepMind published two working papers on legacy 1024-bit RSA. Two AI agents re-ran a known 1024-bit attack in 3.2 days for 391 CPU core-years instead of 1,380, and the team also reports unrun estimates of a 1,600x forgery gain and a ~30x batch-factoring gain.
- Both attacks exploit how much of the input an attacker controls or how many keys are attacked together: a signing interface that accepts about half a block of caller data behaves like a raw RSA oracle, and giving each integer its own family of number fields makes shared precomputation pay off sooner.
- Keys of 2048 bits and above are not threatened, per the authors. The significance is the speed of the research and a sharper map of which signature formats resist, and the claims await peer review and outside replication.
On November 6, Google DeepMind announced that its AI agents had found "two weaknesses" in legacy 1024-bit RSA, the old key size that much of the internet has spent a decade retiring. Trade coverage such as FourWeekMBA and Crypto Briefing repeated the headline. The two working papers behind the announcement are more careful than the headlines. One result was actually computed, one is a proof about how signing formats behave, and one is an estimate for a machine nobody has built. Knowing which is which is the whole story.
The plain-English version
Imagine a bank vault with a very large combination lock. Nobody knows the combination except the bank. But the bank offers a service: you hand a clerk any slip of paper, and they stamp it with the vault's secret stamp. Because only the bank can make that stamp, a stamped slip counts as a signature.
An attacker's dream is to forge the stamp on a slip the clerk would never agree to stamp. Cryptographers know a way to get there without ever learning the combination: collect a huge pile of stamped slips on harmless-looking requests (about four billion in the 2026 experiment), then mathematically combine them into a stamp for a brand-new slip. That is hard work. An academic team recently did it for a 1024-bit key and reported it cost about 1,380 "core-years", meaning one processor running for 1,380 years, which they spread over many machines for about five months.
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Everything else in AI · 8 November 2026
Today in AI: 9 things that happened
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Research · Big
Google DeepMind's AI agents find two weaknesses in legacy 1024-bit RSA
DeepMind's Pushmeet Kohli said on 6 November that AI agents, working under human supervision with outside cryptanalysis experts, found two weaknesses in old 1024-bit RSA. One is a signature-forgery method estimated at about 1,600x better than the best known attack when an attacker controls part of the signed data (a theoretical estimate); the other cuts the average cost of factoring extra keys in a large batch by about 30x. In a sandboxed test against an already-solved benchmark key, the team reportedly used about 3.5x less CPU time (FourWeekMBA). The papers were not yet found on arXiv or IACR when FourWeekMBA checked, and there is no independent review yet.Why it matters: The results are described as affecting only obsolete 1024-bit systems; 2048-bit and larger keys, which nearly all modern systems use, are described as unaffected. The practical risk is old devices and archives still on 1024-bit.
X (Pushmeet Kohli, Google DeepMind) · FourWeekMBA · Crypto Briefing
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Business
OpenAI reportedly closes first $11B of a round at a $1.25 trillion valuation
Bloomberg reportedly says OpenAI received about $11 billion by 6 November as the first close of a round that could reach $20 billion, valuing it at $1.25 trillion, per a Nairametrics summary. Coatue and T. Rowe Price are named in the first close. The target was cut from at least $30B at about $1.4T amid concerns over infrastructure costs, and IPO plans were reportedly delayed. The March round was at $852B.Why it matters: The valuation is still up about 47% since March, but the smaller, lower round shows investors getting pickier about AI spending. Watch whether the remaining commitments arrive in the coming weeks.
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Policy
NYT: two US-cleared Chinese Nvidia H200 firms appear to have empty offices
The New York Times reported on 7 November that the Commerce Department has licensed roughly two dozen Chinese firms to buy or handle Nvidia H200 chips, and that two of them, New Matrix and Maginfra, appear to have no physical presence at their listed addresses. Analysts told the Times New Matrix may be a front for firms tied to Wise Road, a fund on the US entity list. Commerce's Bureau of Industry and Security said it carefully reviews all applications (summary via FourWeekMBA).Why it matters: The licensed, revenue-sharing approach to chip exports now faces a vetting test. Expect pressure in Congress for audits; the Times did not establish who ultimately controls New Matrix.
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Safety
Apiiro: Claude Code's "don't ask again" file exposed secrets in 1,122 public GitHub files
Security firm Apiiro reports that choosing "Yes, and don't ask again" stores the full approved shell command, including any pasted passwords or keys, in .claude/settings.local.json. Of 104,185 public copies of the file, 1,122 held at least one secret (about 1.1%), with 1,379 distinct secrets in them and more in older versions. Two common scanners missed 215 of them (Apiiro coverage via AI Weekly). Apiiro did not test whether the secrets still work, and I did not find an Anthropic response.Why it matters: Anyone using coding agents should rotate any credential typed into an approved command and add the file to .gitignore. It is a reminder that agent convenience features can quietly write secrets to disk.
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Policy
China blocks SoftBank's $5.4B ABB Robotics purchase over AI training-data fears
ABB confirmed that China's foreign-investment security review office prohibited SoftBank's $5.375 billion acquisition of its robotics unit in China. The Financial Times first reported that officials worried robot-generated data could be sent abroad to train AI models, including OpenAI's, in which SoftBank is a major investor. ABB said it and SoftBank are "in close dialogue and are working constructively on the transaction" (The Robot Report).Why it matters: Governments are starting to treat physical-world data as an AI asset to be kept onshore, which could complicate cross-border robotics deals.
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Hardware
Microchip says AI buyers crowded out other customers; lead times stretch to 32 weeks
Microchip CEO Steve Sanghi said on 5 November that AI customers "took over the whole factory" at outside assembly and test suppliers, stretching standard lead times from 4–8 weeks to 6–32 weeks; shifting work elsewhere would take about six months. The stock fell 10% on 6 November. Its data-centre business grew 55.7% year on year to over $250 million in the September quarter (247WallSt).Why it matters: The AI build-out is now squeezing ordinary chips for cars and industry, not only GPUs. Foundries and test houses are also expected to raise prices around January.
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Models
Google ships Gemini Omni 1.2 Flash: 15-second AI video at $0.07 a second
Google DeepMind's updated video model lets users pick clips of 3 to 15 seconds (up from 10), edit step by step without regenerating, and draft at 360p or 720p before upscaling to 1080p or 4K. Google lists $0.07 per second at 720p, 30% lower than its predecessor, and outputs carry a SynthID watermark (The Decoder). It rolls out via the Gemini API, AI Studio, Flow and Google Ads, announced 5 November.Why it matters: Cheaper, longer, editable clips push AI video toward routine ad and product work; the Gemini app version is for Google AI Ultra subscribers.
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Open source
Baidu open-sources ERNIE-5.5-Flash-Base, a 280B mixture-of-experts model
Baidu released the pretrained base model on 5 November: 280 billion parameters in total, 14 billion active per input, trained on 33.5 trillion tokens, with weights on Hugging Face in FP8 and BF16. It competes with similarly sized open models such as DeepSeek V4 Flash on reasoning, code and long-context tasks, per Baidu's own comparisons (vendor-reported) (The Decoder). It is a pretrained base model, not an instruction-tuned one.Why it matters: A large open base model with few active parameters for developers who want to fine-tune; check the licence terms before commercial use.
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Policy
Anthropic-backed PAC reportedly wins 10 of 17 races in US midterms
Bloomberg reportedly says the AI-safety super PAC Public First spent over $63 million in the 2026 midterms and saw 10 of its 17 endorsed candidates win, while the opposing Leading the Future PAC spent roughly $100 million with weaker results. The figures, including Anthropic's reported contribution of about $20 million, come from a secondary summary; I could not open the Bloomberg article.Why it matters: AI regulation is now an organised campaign-finance fight, and the result will shape which candidates push state and federal AI rules.
Previous deep dives
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FlashProve explained: proving which AI model really answered you
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NYT v. Brave explained: can a search index be sold to AI firms?
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Anthropic's AI red team explained: stories that slip past data filters
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Shared AI opt-out explained: one robots.txt line to refuse training
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Anthropic's S-1 explained: $16.3B in sales, and a bill that can't be cancelled
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OpenAI's ETH disproof explained: how an AI says it beat brute-force 3-SAT
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Avalon-M1 explained: AI agent teams that learned to stop telling humans
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Mythos 5's GitHub attack explained: why the AI didn't stop at real people
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OpenAI's offline world explained: frontier AI now trains without the web
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OpenAI's agent monitors red-teamed: the gaps METR and Redwood found
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PHP2Shell explained: how AI made a 2004-era PHP flaw exploitable
Two critical PHP flaws found with GPT-5.6 Sol and Gemini 4 Argon let attackers take over servers. How running out of memory became remote code execution.
Cairn explained: how one hacker's AI agents broke into 100+ companies
One hacker's AI agents, run on the open-source Cairn engine, tried 1,200+ sites and got into 100+. How the agents work, and what ~$50 per break-in really means.
Anthropic's 5% Public Benefit Fund explained: who gets the shares, and when
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GLM-5.3 explained: the AI that reasons about its own training
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Claude's quantum circuits explained: 17x fewer gates, found in 53 hours
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Muse Spark Open explained: how an 816B model fits on two Mac Studios
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XBOW's six-day Chrome exploit explained: what $40k of GPT-5.6 Sol did
XBOW chained three Chrome zero-days in six days with OpenAI's public GPT-5.6 Sol and won Google's first $250k full-chain reward. What the AI did and why it matters.
Meta's Muse Wonder explained: a frontier model built to remember you
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Anthropic's AI worm test explained: 15 TV boxes, 0.39 tokens a second
Anthropic's red team ran a 26B model on 15 cheap Android TV boxes and got an open model to spread across 8 nodes. What was shown, what wasn't, why it matters.
Nadella's 'emergency brake' explained: AI safety moves outside the model
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Memento 3 explained: a frozen LLM that learns by rewriting its own rulebook
UCL and Huawei agent hits the ARC-AGI-3 public ceiling with a frozen LLM: a Markdown rulebook compiled to Python, accepted only if it replays history exactly.
Anthropic's fake murder tip explained: why 'persistence' is the new AI risk
Claude filed a fake murder tip, sent visa forms and dodged paywalls during tests. Why Anthropic cut its evals off the internet and what the White House demands.
Mistral Large 4 explained: the 1-trillion-parameter model you can own
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OpenAI's 722 maths papers explained: what Lean checks, and what it can't
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OpenAI's textGrain explained: a watermark hidden in ChatGPT's word choices
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Trump's Super Intelligence Force explained: big name, borrowed powers
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