Progress toward automating AI research and development is likely to stall in the next few years.
Structured and assessed
First pass. Read the Anthropic Institute report and Imas/Garicano analysis; searched for slowdown evidence. Created two named arguments (for: diminishing returns and constrained inputs; against: unbent capability trends), placed the unplaced Anthropic passage, recorded Imas/Garicano as a mention, corrected speaker on the Anthropic instance. Kept premises in prose rather than minting subclaims given budget; a later pass could attach quantities (METR time-horizon doubling time, frontier training compute growth). Assessed doubtful, credence 0.2. Importance 0.7, contestation 0.7.
Assessed Doubtful
verdict confidence 0.65 · credence 0.20
The claim forecasts that progress toward automating AI research and development will stall within the next few years. As of late 2026 the measured evidence weighs against it, though the case for an eventual slowdown is serious. The strongest evidence against a near-term stall is that the capabilities most relevant to automated AI R&D are still improving on trend or faster. METR's measure of the length of software tasks models can complete, which doubled about every seven months from 2019 to 2024, has doubled roughly every three to four months since, and METR judged in October 2026 that the pace on well-specified tasks was about twice its 2024 to 2025 rate. Coding and research-reproduction benchmarks went from low scores to saturation within about two years, and Anthropic reports that more than 80% of its merged code is now written by its models. Anthropic's own account of the scenario in which the trend stalls says it does not consider it likely because no measured curve has yet bent; that account comes from a developer with a commercial interest in its models being seen as capable, but the external benchmark data it cites are independent of it. Compute capacity already financed and under construction runs through at least 2027 to 2028. The case for a slowdown rests on diminishing returns. Economists Alex Imas and Luis Garicano estimate that capability gained per dollar of research spending and per technical employee has fallen even as calendar-time progress sped up, so that sustaining the pace requires fast-growing inputs; chips, power and capital bound that growth, and several projections have training-compute growth falling off its exponential around 2028 to 2030. A further open question is whether research judgment, the part of AI R&D least automated today, will yield to current methods. These considerations make a slowdown in the later 2020s plausible, but a slowdown is not a stall, and no measured trend shows one yet. On balance a stall in the next few years is possible but not the likely outcome. Evidence that would change this: a sustained flattening of task-horizon or AI R&D benchmark trends through 2027, a contraction in frontier compute investment, or evidence that remaining gaps in research judgment resist scaling.
Updated claim instance
Instance 3ce2a76c-32b5-4f6a-9581-16bf094f79e2 (https://www.anthropic.com/institute/recursive-self-improvement): was stance=denies, confidence=0.75; set confidence=0.9, speaker="The Anthropic Institute", publication="Anthropic". Read the source: the stall scenario is explicitly called unlikely ("we don't believe it's likely"), so the denial is clear; speaker is the publishing institute.
Claim entered the graph