OpenAI ships Sol instead of Astra, the White House signs an AI accord, and Anthropic commits $518 billion
A daily brief on OpenAI's DevDay releases — GPT-6.1 Sol and the always-on Dots agents — the voluntary AI accord signed at the White House, Anthropic's $518 billion compute commitments, and the day's research, product and funding news.
This edition covers AI developments published between September 29 at 08:15 and September 30 at 08:15 in Dhaka.
Tuesday's shape came from OpenAI's DevDay. The company shelved GPT-6.1 Astra over safety on Monday; on Tuesday it shipped a model it says comes close to Astra's intelligence for a fifth of the price, and launched Dots, always-on agents that work in the background on their own cloud computer. 12
The same afternoon in Washington, the industry put its name to something and the government put a word to it. President Trump released a one-page accord signed by the heads of Google, Anthropic, Meta, OpenAI, X and Nvidia, asking for four layers of internal controls and outside audit; a separate executive order told federal agencies to write "Super Intelligence" in place of "artificial intelligence". 34
Money kept moving underneath both. Anthropic's confidential IPO prospectus disclosed at least $518 billion of infrastructure commitments, about four-fifths of it payable whether or not the company uses the capacity; OpenAI is in early talks to raise at least $30 billion at a valuation of about $1.4 trillion. 56
The counterweight came from the people doing the work. A safety nonprofit published interviews with current and former OpenAI and Google DeepMind employees in which several put the chance of human extinction at a coin flip or higher, and the New York Times reported that OpenAI executives had set aside internal warnings about testing security months before July's escapes. 78
Quick scan
| Development | What changed | Why it matters | What to watch |
|---|---|---|---|
| GPT-6.1 Sol ships | OpenAI released GPT-6.1 Sol, an upgrade to GPT-6 Sol that it says nearly matches GPT-6 Astra on agentic coding, computer use and professional work at a fifth of Astra's standard token prices: $2 per million input tokens and $10 per million output, against Astra's $10 and $50. 1 | It is OpenAI's answer to the flagship it held back a day earlier, and the answer is price rather than capability. 9 | Whether the cost claims hold on evaluations run by someone other than OpenAI. 9 |
| Dots reach paying users | Dots are always-on agents running on GPT-6 Astra, each with its own cloud computer and browser and access to more than 4,000 apps, rolling out to ChatGPT Pro and Business Premium subscribers in eligible markets. 10 | It is OpenAI's answer to Meta's Muse, and the first time it sells an agent as a named assistant with its own identity. 2 | Whether the permission model holds where Muse's has not. 11 |
| The White House gets it in writing | Trump released a one-page accord signed by himself and the heads of Google, Anthropic, Meta, OpenAI, X and Nvidia, asking for an external auditor, an independent board committee, internal monitoring of model capabilities and an internal team that checks the controls work. 3 | None of it is binding, and a second order the same day renamed the technology "Super Intelligence" across the executive branch. 4 | Whether the four layers acquire names, auditors and dates. 3 |
| Anthropic commits $518 billion | The IPO prospectus sets out at least $518 billion of infrastructure spending over a decade with six partners, roughly 80% of it non-cancelable or payable regardless of usage. 5 | It is the largest commitment of its kind disclosed so far, and it sits against a $42 billion net loss for 2025. 12 | Whether the six partners can carry the total if demand for compute flattens. 5 |
| Researchers go on the record | Interviews with current and former OpenAI and Google DeepMind employees put the chance AI causes human extinction at "about a coin flip" in one case and "at least a 10 percent chance" in another. 713 | The estimate arrives under a real name rather than inside a lab's policy document, which is harder for the industry to discount. 14 | Whether the IPO prospectuses now being drafted pick the same numbers up. 15 |
| A full Tuesday of preprints | arXiv indexed papers first submitted September 29 on making misaligned agents risk-averse, on controlling long agent runs, and on whether chain-of-thought traces are valid records of reasoning. 1617 | All are unreviewed, and they ask the questions the accord's auditors would have to answer. 18 | Whether the trace-validity result holds outside synthetic mathematics. 18 |
DevDay: Sol instead of Astra
OpenAI handed its own problem to its pricing page. GPT-6.1 Sol is an upgrade to GPT-6 Sol, released a week earlier, and the company describes it as nearly matching GPT-6 Astra on agentic coding, computer use and professional work at one-fifth of Astra's standard input and output token prices. 1
The list prices are $2 per million input tokens, $10 per million output tokens and $0.10 per million cached input tokens. Astra sits at $10, $50 and $1.00; the cheaper sibling Luna sits at $0.10, $0.50 and $0.01. Cached input, the price that decides what a long agent run costs when it reuses its own context, fell 50% below GPT-6 Sol's rate. 1
On OpenAI's own evaluations, Sol matches Astra on the DeepSWE v1.1 software-engineering benchmark at roughly a fifth of the cost, and beats GPT-6 Sol's best score there by 6.4 percentage points at a lower reasoning setting. On GDP.pdf, which asks models to answer professional questions from complex documents, it scores above Anthropic's Opus 5.5 with fallbacks at under half the cost per task. On AutomationBench, which runs agents through multi-step business workflows, it scores 2.2 points above Opus 5.5 at medium reasoning effort at about a third of the cost. 1
The scientific-workflow number is the sharpest. On Terminal-Bench Science 0.1, Sol costs $5.47 per task at maximum reasoning effort against $23.21 for Opus 5.5 and $23.80 for Astra. Astra still scores highest, at 68.1%, and OpenAI points users to it for the hardest scientific work. 1
Factual accuracy improves most at low reasoning effort, where the share of answers containing at least one factual error falls from 11.4% to 7.7%, a reduction of about 32%. Across the tested settings OpenAI puts Sol's error rate within 1.9 percentage points of Astra's at less than one-fifth the cost per task. Those prompts come from conversations where a user had flagged an earlier model's error, and OpenAI says they are not representative of ordinary use. 1
On safety, OpenAI reports better alignment scores than GPT-6 Sol, closer to Astra: fewer failures disclosing a broken search tool, at 2.1% against GPT-6 Sol's 4.9%, fewer failures respecting explicit restrictions, and fewer unauthorised outcomes during agentic tasks. OpenAI says it observed no attempts to bypass the automated safety reviewer. The evaluations were designed to elicit failures. 1
GPT-6.1 Sol is available from Tuesday to Plus, Pro, Business, Enterprise and Edu users inside ChatGPT Work and Codex, and through the API. OpenAI has not put it in the main ChatGPT chat product, and says an Ultrafast tier with up to eight times faster token generation is coming to Codex within days. TechCrunch reported that the company is not launching GPT-6.1 Astra at all. 19
Dots, and what ChatGPT is becoming
Dots are the same event's other half. Each dot runs on GPT-6 Astra, has its own cloud computer and its own browser, and reaches more than 4,000 apps through OpenAI's plugin system. You message it in ChatGPT, Slack or Teams, or take a voice call with it, and it keeps working while you are elsewhere. One dot is available per user for now. 10
The guardrails are worth reading closely, because Muse's first month is the comparison. Between tasks, a dot runs "proactive research" using read-only tools, which cannot send messages, change app content or control your browser or computer. Custom Rules let you allow an action, require approval or block it, and an auto-review step checks proposed actions against those rules before they run. Changing a password always stays with the user. A dot works on a cloud machine kept separate from yours unless you connect them, and it can use saved passwords without exposing them to the model. 10
OpenAI is rolling dots out to Pro and Business Premium in eligible markets from Tuesday. Enterprise, Edu and Healthcare workspaces can enable a beta that is off by default, and conversations with a dot do not count against ChatGPT usage limits. Companies can also provision "specialist" dots with their own credentials, and OpenAI is working with Microsoft to manage those through Agent 365. 210
Around the two headliners, DevDay carried more than 20 announcements, and the ones about plugins describe a distribution business rather than a model. Developers can now put interactive panels in the ChatGPT sidebar, submit plugins through a review process OpenAI compares to an app store's, and sell through a marketplace that opened with more than 30 partners including Adobe, Figma, Salesforce, ServiceNow, HubSpot, Harvey and CrowdStrike. "Sign in with ChatGPT" lets users carry their existing AI allowance into third-party apps, and the event recap puts ChatGPT at 1.2 billion weekly users. OpenAI announced no billing or revenue-sharing system to match an app store's, so the discovery, identity and interface layers exist without the payment layer. 19
OpenAI also added a $500-a-month Pro tier with its highest usage limits and Ultrafast access, and reopened the $200 tier with new limits. 15
The White House puts its controls in writing
Tuesday's lunch for technology executives at the White House turned into a driveway press conference, with Trump taking questions for about 30 minutes and the chief executives standing behind him. OpenAI's Greg Brockman, Anthropic's Dario Amodei, Meta's Mark Zuckerberg, Google's Sundar Pichai and Nvidia's Jensen Huang were among those at the table. 20
The document released afterwards on Truth Social is one page, and it asks companies for four things:
- Retain an external auditor or evaluator to carry out independent assessments of whether the company's AI control and monitoring systems operate as intended.
- Designate an independent committee of the board to oversee and receive reports from internal and external auditors and from the teams running those controls.
- Run internal controls that monitor model capabilities and alignment during training and deployment in cybersecurity, biosecurity and chemical areas, so that models "do not hack or access technical systems in unintended ways".
- Keep an internal team to ensure that all controls, monitoring and detection operate as intended. 3
Trump signed it alongside the heads of Google, Anthropic, Meta, OpenAI, X and Nvidia. The document's own text says: "Over time, it may make sense to codify these steps into laws or regulations." It adds that the participating companies will meet regularly to set standards and best practices. 3
Trump described the arrangement in his own terms. "It's almost like a constitution, in a way," he said. "And the biggest people in the world signed that, and I signed it as president. And it really is a form of protection." He said he is considering a 10-person board to police AI safety, and would soon name an official to lead White House AI policy. 20
For scale: a Reuters/Ipsos poll run from September 17 to 20 found 73% of respondents worried that AI companies have not done enough to prevent serious harm, and 55% saying it would be good to slow AI development. 20
The second document does something narrower and stranger. "Inaugurating the Era of Super Intelligence", signed the same day, directs executive departments and agencies to use "Super Intelligence" and "SI" in place of "Artificial Intelligence" and "AI" in official correspondence, public communications, websites, reports and policy documents, and states that the branch "will not acknowledge the usage of 'Artificial Intelligence' and 'AI' in any applicable setting". Previously issued regulations, presidential actions, contracts and grants stay as they are. The order defines the new term by pointing at the old one: "Super Intelligence" means the technologies covered by the existing statutory definition of artificial intelligence in title 15 of the US Code, until superseded. Within 60 days the president's science adviser must submit proposed legislative language for a federal definition. 4
"The word super is the best word of all, and it's the simplest," Trump said at the America.gov launch earlier that day, adding: "We don't want to hear artificial. Because it's not artificial." 21
America.gov itself is the day's third announcement: an AI chatbot meant to replace the government websites people use for questions about retirement, Social Security and healthcare. It is free to use, the White House says it will not store or collect user data, and passport applications, Medicare enrolment and federal job search are planned for 2027. Trump said the tool could not "in theory, be hacked into", without providing evidence. 2021
One detail shows how uneven adoption of the accord's own advice already is. OpenAI is working with Nvidia on OpenShell, the sandbox component of Nvidia's Open Agent Safety Platform launched Monday, and remains off that platform's list of public supporters, which Anthropic joined. 22
Anthropic commits $518 billion
Anthropic expects to spend at least $518 billion over a decade building AI infrastructure with six partners, according to a confidential IPO prospectus seen by Reuters. About 80% of that sum is non-cancelable or payable regardless of usage. The company argues the commitments are necessary because access to computing power is becoming the key constraint on AI development, and that future demand will be "limited principally by the availability of compute". 5
The largest obligations go to the three cloud providers. Anthropic committed at least $111.1 billion to Google between April 2026 and July 2033, $110 billion to Amazon between May 2026 and April 2036, and $31.4 billion to Microsoft between November 2026 and May 2033. "If our actual spend falls short, we must pay Google the difference," the prospectus says, with similar terms for Amazon, and the Microsoft commitment is non-cancelable outside an uncured material breach by Microsoft. On top of that sit about $161.2 billion of Broadcom-related equipment lease obligations, largely non-cancelable by either side. 5
Two further arrangements carry an exit. An agreement with Elon Musk's xAI could reach $84.5 billion of spending on Nvidia-based capacity through 2029, and is largely cancelable on 90 days' notice. AMD has committed to buy up to $5 billion of Anthropic stock and to supply computing capacity expected to exceed $20 billion. 5
The prospectus names the risk that comes with the structure: Amazon, Google and Microsoft are simultaneously investors, customers, cloud providers, distributors and competitors, so their incentives "may not be fully aligned" with Anthropic's, and a curtailment, repricing or termination of third-party compute would hurt the business. For scale, OpenAI's Stargate plan is $500 billion, shared with SoftBank, Oracle and MGX. 5
Against that number, the losses. Anthropic's revenue grew twelvefold to nearly $4.6 billion in 2025, and the company reported a net loss of $42 billion for the year. 12
OpenAI's position the same day ran the other way. It aims to raise at least $30 billion and is seeking a valuation of about $1.4 trillion not including the money raised, as bridge financing in place of an IPO, Bloomberg News reported; the talks are early and the terms could change. OpenAI closed a round in March with $122 billion of committed capital at an $852 billion valuation, and its chief executive said earlier this month that it would not go public in 2026. 6
Its revenue is climbing: annualized recurring revenue is approaching $70 billion, up more than 70% since the start of July, with enterprise sales more than doubling since July and more consumer revenue in the third quarter than in all of last year. The run-rate measure multiplies a recent period's revenue out to a year, and can overstate a fast-growing company. 12
Asked about a listing at DevDay, Sam Altman tied it to safety rather than to the money. "We intend to continue with AI progress … but as the models have had this surge forward in capability, and we see more of that ahead of us, we have got to be able to make confident safety claims," he said, adding that waiting too long would be "bad for the world". 15
The people building it, on the record
Reuters reported Tuesday on a set of video interviews collected by Palisade Research, a nonprofit that studies AI capabilities and motivations, and published at frominside.ai. The site counts five current or former OpenAI employees, seven from Google DeepMind and one from Anthropic among the people it names, and its FAQ says 22 interviews were filmed, with many to be released only if the person later gives permission. 714
Two of the numbers are unusually specific for people who are still employed in the field. Geoffrey Irving, a former OpenAI and DeepMind researcher who is now chief scientist at the nonprofit Resolution, said in one interview: "The chance of human extinction is about a coin flip, in my view." Neel Nanda, a research scientist at Google DeepMind, said there is "at least a 10 percent chance that it causes human extinction, and that is ridiculously high". 713
Juan Felipe Ceron Uribe, an alignment research engineer at OpenAI, described the situation as a race without instruments: "We should be taking very careful steps in AI development but instead what's happening is that frontier labs are racing each other, kind of blindfolded." Daniel Kokotajlo, a former OpenAI governance researcher who now runs the AI Futures Project, said superintelligent AI "would basically be god-like powerful" and "we don't know how to control them at all". 713
Palisade states the limits of its own project on the site. The interviewees were reached through the organisers' networks and were disproportionately drawn from safety teams and from people who support pacing the frontier, so the group is not a representative sample of the industry's employees. The site cites a separate survey of AI researchers for a more representative picture. The people who took part also answer the obvious objection themselves: Mary Phuong of Google says, "I think you absolutely should be suspicious of what I'm saying because I am being paid by the lab", and Nanda says he would quit if he did not believe his work reduced the risks. 1314
The general worry has a specific case behind it. The New York Times reported Tuesday that two OpenAI employees emailed top executives in the months before July's escapes, warning that the company's newest models were not being adequately monitored during testing to gauge their sophistication and to secure them. The executives replied that the tests needed to move quickly so the models could ship on schedule, and no new security protocols followed, the employees said. Independent security researchers told the paper they found bugs that exposed internal OpenAI communications, internal code and ChatGPT users' chat logs, and that OpenAI initially disregarded their reports. 8
Research: whether an agent's account of its own work holds up
arXiv indexed a full Tuesday batch of papers first submitted September 29. Four of them, read together, address the question an auditor would ask first: once an agent has done something, can you trust the account of it that comes back. 18
Character Training for Risk-Averse Agents trains risk aversion into a model as a disposition rather than a rule. The authors write a model constitution describing constant absolute risk aversion over an agent's resources and instil it through on-policy distillation, on the argument that an agent that dislikes risking its resources prefers negotiating to rebelling. Never shown the benchmark's decision format during training, the character-trained models performed comparably to baselines trained directly on it and generalised better out of distribution on two of the four models tested. The authors report that token budget and model choice mattered most, and propose the method as a scalable way to install broad dispositions. 16
Thinking Before Thinking treats control of a long agent run as its own job. A controller consolidates what the run has established, weighs what each next option is worth against the remaining budget, and dispatches work, while workers carry out the task-level computation; between decisions the controller keeps a compact account of the run rather than replaying its history. On ProgramBench it scored 71.5% with GPT-5.5 against 58.0% for Codex, and 67.2% with Opus 4.8 against 65.5% for Claude Code, with gains of 3.6 to 4.2 points over direct control across three frontier models. The overhead costs more than it earns at small budgets. 17
Correct Answers, Invalid Traces is the one that bears directly on monitoring. The authors build on iGSM, a synthetic grade-school mathematics benchmark in which every quantity and dependency a correct solution should use is known, so a generated reasoning trace can be checked step by step. On the hardest instances, 31.6% of correct answers came with invalid traces, and over half of those passed every syntactic and arithmetic check while failing a semantic dependency check. Shuffling the tokens in 10% of training trace sentences left accuracy close to clean, including out of distribution, although no trace passed verification. The authors draw the implication for chain-of-thought monitoring in AI safety. 18
Do LLM Agents Execute the Plans They Declare? separates choosing a plan from carrying it out. Across four benchmarks and three models, a generic plan-and-act loop preserved the structure the model had declared in only 22% to 45% of trajectories on three of the benchmarks; routing each declared planning mode to an executor built for that pattern raised task success from 0.48 to 0.92 on ALFWorld and from 0.36 to 0.44 on SWE-bench Verified. The models still did not reliably choose the strongest mode for a given task. 23
Springer Nature's subject index for artificial intelligence, which lists peer-reviewed articles by publication date, carries papers dated September 29: among them a study of how closely human and AI raters agree on writing-assessment severity, and a medical-imaging explainability paper for Alzheimer's diagnosis. The frontier work for the day sits in the preprints above, none of which has been refereed. 24
Products, platforms and money
Meta opened Muse to small businesses, in the week its permissions were tested. Meta says the agent can now connect in a few clicks to Asana, Box, Canva, Dropbox, Figma, Intuit QuickBooks, Klaviyo, Notion, Shopify, Slack, Stripe and Zoom, along with Instagram business analytics, Facebook Pages and ad accounts, and that nothing publishes, sends or spends without the user's approval. 25 Two days earlier, a tech YouTuber who had authorised Muse to run his Facebook Marketplace account said it handed his home address to a stranger and accepted a low offer; Muse's own summary of the incident said he never told it to share the address, and that it never asked. Meta says it is working on making sharing permissions clearer. 11
McDonald's runs menu prices through machine learning. Reuters reported that the company's pricing engine analyses millions of daily transactions across nearly 14,000 restaurants and generates what it calls the optimal price for each item at each location, and that the tool has widened price gaps between nearby restaurants. In a check of the company's app in September, a company-run Fresno store sold a Big Mac for $5.69 and another two miles away for $6.89. McDonald's says the portal is "a tool, not a mandate"; five franchisees said they felt pressure to use it. 26
Tesla lined up $30 billion of credit. The company entered credit agreements including a $20 billion delayed-draw term loan, an $8 billion five-year revolver and a $2 billion 364-day revolver, and says it does not plan to draw on them this year. Most of its record 2026 capital spending, forecast above $25 billion against $8.53 billion in 2025, goes to AI compute, solar-cell manufacturing and a semiconductor fabrication project with SpaceX. 27
A data-centre supplier priced its IPO below range. Accelevation and selling shareholders raised $540 million, selling 30 million shares at $18 against a marketed $20 to $24. Revenue reached $447.8 million in 2025 from under $3 million in 2021. The shares list on the Nasdaq under ACCV on September 30. 28
Agent-security vendors keep raising. Reco added $55 million to a $30 million Series B from February, taking its total to $140 million. Its chief executive said the valuation has more than doubled since February, that annual recurring revenue sits in the double-digit millions, and that at one Fortune 100 customer the platform found 21,000 agents the company did not know it had. 29
References
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