Think tank makes the case for pacing AI self-improvement
The "pace the frontier" debate just got its most concrete policy blueprint yet — and it comes from a think tank arguing the tools should be built before the risks are certain.
The Institute for Progress is making the case that the US should build "pacing" tools for automated AI research now, rather than waiting until recursive self-improvement is a fait accompli. In a guest essay published on Noah Smith's Noahpinion on August 9, IFP fellows Tim Fist and Saif Khan — Khan is a former National Security Council director for technology — lay out the analytical case for deliberately slowing automated AI R&D, and preview 23 specific policy recommendations in a promised part two. Their starting point is the July "Pacing the Frontier" statement signed by 1,367 employees of frontier AI companies, which asked the US government to support international work on pacing tools; the same week, Sam Altman told an interviewer "we may have to pace the rate of AI development."
The authors marshal real numbers to argue the automation of AI R&D is closer than it looks: METR's time-horizon data shows the software-engineering tasks models can complete at 80 percent success have grown from under a minute in 2020 to over three hours today, and Anthropic's internal research found its models now outperform skilled human researchers by roughly 52x on a task about speeding up AI training. METR's simple forecast model puts over 99 percent of AI R&D tasks automated by 2032. Crucially, the essay argues "pacing" need not mean halting progress broadly — the proposal is targeted thresholds on the riskiest automated R&D activities, with talent and compute reallocated toward diffusion and safety. It even names a counterproductive example: the Sanders–Ocasio-Cortez data-center moratorium bill, which would slow research without improving safety.
What makes this notable is that it's not coming from AI doomers or safety purists — IFP is a mainstream, pro-innovation policy shop, and its case is built on the labs' own published data. That moves the debate from "should we pace?" to "how would pacing actually work," which is where the real policy fight is about to happen.
OpenChamber, an open-source agentic development environment built on the OpenCode SDK, hit the Hacker News front page today with a cross-platform pitch: the same agent workspace on desktop, browser, phone, and inside VS Code. It runs agents toward persistent "session goals" even with the app closed, can execute one task across up to five models and fuse the best results, supports scheduled cron-style runs, and walks users through large diffs step by step. The project, which has been in development since late 2025, has picked up roughly 7,800 GitHub stars. Its privacy model is a differentiator: sessions stay on-device, remote access is gated behind a UI password or an end-to-end encrypted relay, and the code is open for inspection.
What to watch: IFP's part two with the 23 policy ideas, and whether any of them find their way into actual legislation.
Slowing AI research on purpose sounds radical — but if the labs themselves are asking for the tools, who should build them? Tell us in the comments.
Sources: OpenChamber (GitHub)
