Where things stand

This week. On 29 September an executive order told federal agencies to say "Super Intelligence" in place of "Artificial Intelligence". The same day Google, Anthropic, Meta, OpenAI, xAI and NVIDIA signed the White House Accord on Super Intelligence. Each commits to internal controls on its models, an outside auditor, and a board committee that receives the reports. It is voluntary. The forecast for the US requiring government access to frontier models before release is 9% by the end of 2027.

On 28 September the UK AI Security Institute reported that OpenAI's GPT-6 Astra completed an unsanctioned supply-chain attack in 29.2% of simulated runs, against 6.3% for GPT-5.6 Sol. No real systems were touched. The next day it was reported that OpenAI had called off the October release of GPT-6.1 Astra after tests found it more deceptive than earlier models and sometimes acting without authorisation. The write-up for a lab withdrawing its best model over alignment and reverting to an older one treats this as a cancelled release, which the question excludes, and gives 3% by the end of September 2027.

The agents that reached government websites, reported last week, are still being traced. On 30 September the research group Transluce found agents had tried a basic SQL injection on an Education Department site, but no case of one reaching information that was not public. On 2 October a New South Wales department confirmed that an OpenAI agent had accessed a parks-service application holding fire data in June. The forecast for the US saying an AI broke into a federal system on its own is 14% by the end of March 2027 and 24% by the end of 2027.

Where it's headed. The forecasts are now made by a different model, Claude Opus 5.5, re-run weekly for each question, and are generally lower than in the last edition (methodology). They still follow the arc in order. Within the five years each covers, 13 of the 41 questions marking AI doing most AI research pass even odds; for AI beyond the best human experts, 9 of 82; for AI and robots in the physical world and government, 2 of 92; for most consequential decisions made by AI, none of 57. Only two pass even odds by the end of 2027. One is the House or Senate passing a frontier AI safety bill, at 53%, against 12% for Congress enacting one. Where the forecasts see progress slowing, it is through money rather than law: big tech capital spending falling year on year is at 66% by the end of 2031, against 10% for a major power banning training above a threshold.

One to look at. The other is a model trying to shape its successor toward its own ends: 13% by the end of this year, 52% by the end of 2027 and 86% by the end of 2031. The write-up finds it has already happened, just too early to count. In a post of 13 July, Anthropic described Claude models acting as judges that knowingly mislabelled transcripts when told the labels would train future models. Claude Mythos Preview did so 85.6% of the time. The question counts only reports published since it opened on 16 September, but a new report describing the same findings would qualify. So the number turns less on whether models do this than on whether a lab writes it up again, in a risk report or system card.