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Science Explained/Brief
A new paper coauthored by Parker, Tom, and seven other economists presents simple models of how AI may accelerate AI R&D. It focuses on the strength of feedback effects and whether they could cause self-sustaining acceleration.
The paper does not claim that recursive self-improvement is happening. Instead, it quantifies feedback effects and identifies the most uncertain link: how increased model capabilities would speed up algorithmic progress. The authors say they cannot rule out substantial acceleration, but list bottlenecks—data, compute, experiments—that could cause it to fizzle out. The work is a modeling exercise, not an empirical finding.
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Stated by metr.org
This is a conceptual modeling paper, not evidence that acceleration is occurring or will occur.
What the reporting says: The paper walks through simple models of how AI may accelerate AI R&D, that the most uncertain relationship is how an increase in model capabilities would increase the rate of algorithmic progress, and that the authors cannot rule out a substantial acceleration.
Found through monitored feeds: METR. A feed address is how the desk finds a story, not the evidence for it — the sources listed above are the pages the facts were taken from.
How this story was checked. Written from the 1 page listed above, stored 15 September 2026; claims checked against that stored text on 15 September 2026.