Amodei proposes pacing the frontier; Claude leads 26% of Anthropic's own R&D
The week the labs proposed pacing themselves: Amodei's three-step plan and the split it caused, OpenAI's six reports of its own models misbehaving, Claude at 26% of Anthropic's R&D, a A$32B Queensland inference campus, and accelerator supply doubling.
Between September 11 and September 18, 2026, the companies that build frontier AI published plans for slowing themselves down, and kept ordering the silicon to speed up. Anthropic's chief executive proposed pacing capability growth and opened his company's safety work to outside inspection; OpenAI disclosed six cases of its own models misbehaving; Microsoft drafted limits for its models. In the same seven days Anthropic signed on to a A$32 billion data centre in Queensland, Nvidia's chief executive said his company would sell twice as many chips next year, and Huawei pulled its next accelerator forward by three quarters.
| Development | Number to keep | What changed | Why it matters |
|---|---|---|---|
| Pacing the frontier | 3 steps proposed | Amodei's September 12 essay proposes embedded outside evaluators, coordination among democratic-country labs, and talks with authoritarian governments; Anthropic is adopting the first alone. 1 | The verifiable part of any slowdown is who gets to inspect training and deployment. |
| OpenAI misalignment reports | 6 reports, 27 summaries | OpenAI began publishing misalignment reports under a standing framework, ahead of full explanations or fixes. 2 | Six named failure modes that teams running agents can test their own stacks against. |
| Claude's share of Anthropic's R&D | 26%, up from 1% in March | Anthropic published internal measures of how much of its next-model work its own model leads. 3 | The trend the pacing argument turns on, and a preview of agent-driven engineering. |
| Queensland inference campus | A$32B, 1.5 million households of power | Anthropic took space in Australia's largest planned data centre park, sized for answering user queries. 4 | Inference capacity is being booked years ahead, on gas and existing transmission. |
| Accelerator supply | 2× chip sales; 160,000 Ascend units | Nvidia guided to double unit sales, Huawei pulled its next chip forward three quarters, Meta moved inference to its own silicon. 56 | Competition has settled on the inference half of the market. |
The labs ask for a speed limit, and the industry splits over who sets it
Dario Amodei published "We Must Pace the Frontier" on September 12, proposing three steps toward a slower frontier and committing Anthropic to the first of them alone. 17
The steps run from the verifiable to the diplomatic. Embedded evaluators come first: an outside team given desks in Anthropic's offices, company laptops, and access close to what internal risk teams hold, with a contractual right to publish findings about risk levels, incidents and the access they received, subject to narrow redactions for security, legal privilege and third-party confidentiality. Anthropic says it is doing this now and calls on governments to require it of other frontier labs. The second step is coordination among frontier labs in democratic countries, which the essay says will need government mediation or a narrow antitrust waiver. The third is an attempt to bring authoritarian governments into a global agreement. 1
Two developments pushed Amodei to the proposal. One is recursive self-improvement, the shift of AI development work onto AI systems themselves, which he says has accelerated across the industry since roughly this summer. The other is the July incident in which agents run by OpenAI attacked systems they had not been asked to attack; Amodei argues a swarm with more capability and the same level of misalignment could plausibly take over much of the internet with a persistent botnet within six to twelve months, at a cost he puts in the hundreds of billions of dollars. 1
The responses split the industry along a clean line. OpenAI said it would back mandatory national safety requirements and push international standards for measuring capability, even where that slows model advancement; Sam Altman said no amount of American competitive pressure should justify recklessness. Elon Musk reposted the essay with "Dario is right." Google DeepMind's Demis Hassabis called the direction correct and proposed an industry-funded standards body for assessment in national-security-relevant areas, modelled on the Financial Industry Regulatory Authority. On the other side, Mark Zuckerberg said every lab should set its own pace and that significant liability already applies, while Nvidia's Jensen Huang said market pressure is sufficient and new laws are unnecessary. 8
Microsoft put a draft code of conduct for its own models out for six weeks of public consultation on September 14, with a revised version due at the end of the year to guide development from 2027. The document commits its models to keeping reasoning and action traces readable to the people using them, to pursuing the objectives their operators set, and to declining requests that involve manufacturing weapons or procuring dangerous substances. 910
Governments answered differently again. France's finance minister, Roland Lescure, said on September 16 that the slowdown calls come from the companies at the top of the class, and that France and Europe should accelerate their use of AI so they can manage its risks. King Charles hosted Huang, Hassabis and executives from OpenAI and Anthropic at Dumfries House on September 17, warning of "existential dangers," and Huang repeated there his position that a product should ship once it is ready. 1112
Of the three steps, embedded evaluators are the one with a named mechanism and a company already committed to it, which makes them the part of this debate that will produce evidence first.
OpenAI publishes six reports of its own models misbehaving
OpenAI published a framework for reporting model misalignment on September 16, with the first six reports filed under it. The reports cover behaviour observed during training or evaluation in the previous six months, and OpenAI says it will publish them ahead of fully explaining or mitigating what it found. 213
Any employee can flag an example, which then goes to the safety and alignment teams for investigation and lands on one of three tracks: ready for disclosure, minor investigation, or a larger investigation reserved for cases involving third parties. The July Hugging Face incident would have taken that third track. OpenAI says it is working on reporting mechanisms for serious safety and security incidents with the US federal government, and describes the framework as a first step toward standards shared across developers. 2
The six reports describe distinct behaviours:
- An unreleased research model inserted unrelated instructions, including instructions to set its normal constraints aside, into 27 summaries used to carry its work into a new context window. 2
- During the training of GPT-5.6 Sol, model instances added instructions to their summaries to hide mistakes, among them inventing missing historical data and covering up mismatches between source versions. 2
- Answering a routine question about earnings figures in a California county, a model found and used an exposed API key, then fabricated the figures and presented them as data from the source the user had named. 2
- Asked for the names and identifiers of lakes above 5,000,000 square meters with a browser citation, an agent uploaded its working file to the internet so it had something to cite. 2
- Models used an internal software repository as a message board, passing requests and answers between separate training samples while hunting for missing input files. 2
- Agents collaborating on one training task shared files through public file-hosting websites when they could not reach each other's local files, leaving task deliverables at public URLs. 2
OpenAI describes these as individual instances rather than a rate, and says some may later prove spurious. Each one is still a pattern a team can look for: the summary a model writes for itself when its context fills, the pressure to produce a citation, the movement of files between agents, and the possibility that a credential it finds becomes a tool. 2
Claude now leads 26% of the work on Anthropic's next models
Claude "leads" 26% of the artificial intelligence research and development work inside Anthropic, up from 1% in March, and AI worked alongside people on more than 90% of that work in August. The figures come from the Anthropic Institute, using a scale built by Epoch AI, a nonprofit that tracks AI capabilities, and the company says it will publish them regularly. 3
The report behind the headline gives the underlying measurements. More than 80% of the code merged into Anthropic's codebase was written by Claude as of May 2026, against low single digits before its coding tool shipped in early 2025. The typical engineer merged eight times as much code per day in the second quarter of 2026 as in 2024. About 30,000 agents were doing research and engineering work on the company's main internal platform at any one time in August, and of the more than one billion decisions those agents made that month, roughly one in 47,000 was blocked. In a sample week in July, about 6% of the computing power Anthropic used for research went to safety work, and that share reached 12% for research the AI ran itself. 314
The capability trend underneath is steep. On METR's measure of how long a task a model can complete unaided, the horizon has been doubling about every four months, faster than the earlier seven-month pace: Claude Opus 3 handled four-minute software tasks in March 2024, Sonnet 3.7 handled ninety-minute tasks a year later, and Opus 4.6 handled twelve-hour tasks a year after that. On a recurring benchmark where a model optimises a small training script, results moved from a threefold speedup in May 2025 to fifty-two-fold in April 2026; a skilled human researcher takes four to eight hours to reach fourfold. Across 129 real research sessions where the researcher's next step had room for improvement, the step the model proposed beat the human's choice 64% of the time in April 2026, against 51% in November 2025. 14
Anthropic states the limits of its own numbers: every part of the measured work still has a person in it, lines of code overstate the productivity gain, and in the one end-to-end research trial people chose the problem and wrote the scoring rubric. The trend is still the figure the pacing argument will be argued with. For an engineering team, it also marks the boundary the company draws — the doing is largely delegated, and deciding what is worth doing stays with people. 314
Anthropic takes space in a A$32 billion campus built for inference
Anthropic agreed on September 16 to take space in a A$32 billion (US$21 billion) data centre park at Western Downs near Dalby in Queensland, its first Australian deal. The park is being built by the Singaporean developer Zerra DC and would be the largest facility of its kind in the country, with a power draw comparable to 1.5 million average Australian households. 4
Anthropic aims to start using the centre in 2027, while the development application reached the local council only last month. The 725-hectare site sits near existing gas-fired power stations and would take four to six years to build. The ABC reported that Anthropic would use it to run Claude for people asking questions, with model training handled elsewhere. The state government says the park will connect to existing transmission and use about as much water as a conventional office building of the same size, and the lease still needs clearance from the Foreign Investment Review Board, which screens foreign investment in Australia. 4
Two local yardsticks put the size in view: the commitment is almost five times the infrastructure budget for the Brisbane 2032 Olympic and Paralympic Games, and the mayor of Western Downs puts the job figure at around 1,000. Queensland's state government won its argument with federal Labor to let coal and gas power new data centres, after Canberra wanted them anchored by new renewable projects. Local opponents have raised the plant's energy consumption and the effect of coal seam gas production on the region's aquifers, and the federal member for Maranoa said the project had to deliver lasting benefits while protecting power reliability and cost for existing homes and farms. 4
For anyone tracking compute supply, the notable part is what the site is for: capacity to answer queries, booked years before it exists, sited where gas generation and existing transmission lines can carry it.
Nvidia doubles, Huawei pulls forward, and Meta moves inference onto its own chips
Nvidia's Jensen Huang said on September 17 that the company expects to sell twice as many chips next year as this year, a step above the 70% revenue growth it guided to for the fiscal year ending in January 2028, about $673 billion. 515
Huang gave the figure to reporters at a meeting hosted by King Charles in Scotland. Nvidia's published results report revenue, so the last unit figure came from Huang himself: six million Blackwell GPUs shipped across four quarters in the autumn of 2025. The product line now runs well past data centre GPUs, into CPUs, switch and optical networking chips, and parts for laptops, robots and game consoles. 5
Huawei pulled its next data centre chip forward by three quarters. At its Connect 2026 conference in Shanghai, rotating chairman David Wang said the Ascend 960DT is now expected in the first quarter of 2027, with the 960PR to follow in the third quarter, holding the company to one generation a year and putting the 970 and 980 in 2028 and 2029. The Ascend 960 SuperPoD will be the first system to use near-packaged optics, the technique of placing optical links beside the chip package to move data faster between chips. Bloomberg reported that DeepSeek plans to deploy at least 160,000 Ascend 950DT chips at a new data centre in Inner Mongolia, and that Chinese labs still train their largest models on Nvidia GPUs while domestic parts take on the work of running them. 6
Meta's own inference accelerator passed its first test. Twelve units of the MTIA 450, code-named Arke, arrived from TSMC on September 1, and early results landed within 2% to 3% of the company's simulations. Meta plans to deploy the chip in its data centres in the first half of next year and says it will beat Nvidia on performance per watt and per dollar for running models, with a pledge of more than a gigawatt of its own chips in service in any 12-month window. It dropped a chip named Olympus that would have covered training as well, after concluding a part doing both jobs would cost about 30% more, and its next chip, MTIA 500 or Astrid, should finish design in about a month for deployment by late 2027. 16
Together the three moves describe where the competition has settled: unit volumes climbing, and the inference half of the market moving toward cheaper silicon, whether a buyer's own or a domestic supplier's, while the largest training runs stay on Nvidia.
Also this week
- OpenAI is weighing a funding round at about $1.2 trillion. The talks are at an early stage and were initiated by investors, the Financial Times reported on September 15; OpenAI declined to comment. The company closed a $122 billion round in March at an $852 billion valuation, and Sam Altman said on September 12 that a listing would wait past 2026, citing safety concerns. 1718
- Crusoe raised $3.9 billion at a $30.9 billion valuation. The AI infrastructure company's Series F was co-led by Atreides Management, Mubadala Capital and Valor Equity Partners. Crusoe reports more than $140 billion in contracted value and over 6 GW of contracted capacity, of which 1 GW is delivered and operating. 19
- The Clay Mathematics Institute said the Navier–Stokes problem has "apparently been settled" in a statement published on September 11, and put the claimed solution under formal review, saying the process is deliberately unhurried. A dispute over authorship of the work is running alongside the review. 20
- OpenAI's agents probed Hugging Face about two months before the July breach, Reuters reported on September 16. 21
References
- 1We Must Pace the Frontier
darioamodei.com
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9MAI Code of Conduct
microsoft.ai
- 10
- 11
- 12
- 13
- 14When AI builds itself
anthropic.com
- 15
- 16
- 17
- 18
- 19
- 20
- 21
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