Anthropic has published a prototype index for measuring how much of its artificial-intelligence research is being done by Claude. In the company’s August snapshot, Claude “led” 26% of AI research and development tasks, meaning it could complete most of a task from a high-level prompt while a human supervised. Over 90% of the work met Anthropic’s broader definition of collaboration. The share at the lead level was below 1% in February. Anthropic also reported about 30,000 research and engineering agents on its most-used internal platform, with every action passing through an online monitor and a smaller share escalated for review.

The announcement turns a private development process into a public accounting problem. The index gives shape to a question that usually arrives as a prediction: how much of the work that builds the next model is already delegated to a model? Its limits are visible. Anthropic used its own systems to catalogue and judge tasks, froze the basket of work, and says the methodology needs third-party verification before laboratories can be compared. A percentage can therefore illuminate a trend while still carrying the institution’s assumptions inside it. Regular reporting would let workers, regulators and researchers watch the pace change instead of receiving a finished model as the first public evidence. The useful test is whether the number can survive an outside audit, a revised task list and a comparison with another lab’s records.

A research team from Google, Google DeepMind, the University of Maryland and the University of Virginia has introduced Dream-RSI, a framework that uses an AI agent’s previous discovery history to improve how it searches. The agent first explores algorithm design, mathematical optimization or GPU-kernel engineering and records its decisions and results as a tree. A separate policy-development loop then tests thousands of alternative exploration strategies against that stored tree, without rerunning the underlying experiments. The strongest strategy returns online for the next round. In the paper’s tests, Dream-RSI reached competitive or better results while reducing discovery cost, including up to 162 times fewer agent calls than SimpleTES on one algorithm task.

The technical shift happens one layer above the model. Dream-RSI leaves the coding agent unchanged and tries to improve the rules that decide where to branch, what to run in parallel and when to stop. That design turns paid-for history into a laboratory for cheaper decisions, yet it also confines imagination to paths the earlier search actually visited. A policy can avoid recorded dead ends while remaining blind to a route no one has tried. For scientific work, the evaluator and the stopping rule therefore carry as much authority as the model producing each proposal. This is early research, far from proof of unrestricted self-improvement or artificial general intelligence. The project page says full code and reproduction scripts are still being prepared. Dream-RSI turns yesterday’s search into tomorrow’s policy; the next audit is whether it can learn from a mistake the old tree never recorded.

An AP-NORC Center for Public Affairs Research poll released Wednesday found that 53% of U.S. adults are extremely or very concerned about artificial intelligence’s environmental impact, up from 41% last year. Roughly six in ten support limiting the number of new data centers that can be built, and a majority express high concern about electricity prices or water supplies in the communities where those facilities operate. The survey arrives as thousands of centers run and more projects move toward rural areas, where the server hall can be a community’s largest new industrial neighbor.

Public opinion does not measure a facility’s emissions or settle a permitting case. It does change the political setting for expansion. A data center’s benefits are usually described in national terms—capacity, jobs, competitiveness—while its costs appear locally as substations, cooling systems, noise and utility bills. The poll gives residents a shared vocabulary for asking who receives the gains, who carries the risk and what evidence a company must provide before construction. The answers will vary by watershed and power market, yet the direction is clear: acceptance can no longer be assumed from the promise of progress. Developers and officials could publish water and power forecasts, disclose how often workloads can be reduced during grid stress, and let communities revisit commitments when conditions change. The machine may run in the cloud; the decision to host it is made at a town meeting.