Progress toward automating AI research and development is likely to stall in the next few years.
Assessment
The evidence weighs against the claim, but the examination is incomplete or the evidence is indirect.
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.
Full reasoning: the evidence and decisions behind this verdict
Reading of the claim: "stall" is taken as progress toward automating AI R&D largely flattening (not merely slowing from an accelerated pace), within roughly 2027 to 2029; "likely" as more probable than not.
Prior: forecasts of imminent AI plateaus have repeatedly failed since 2020 (scaling-wall claims of 2024 were followed by reasoning-model and agent gains). S-curves do eventually bend, but the timing of the bend is the hard part, and input build-out is committed for several years.
Evidence against (argument from unbent capability trends): - Anthropic Institute, "When AI builds itself" (www.anthropic.com/institute/recursive-self-improvement): time horizons doubling about every four months; SWE-bench and CORE-Bench saturated within roughly two years; >80% of merged code Claude-authored as of May 2026; explicit statement that the stall scenario is not likely because "We have not yet seen that curve bend." Interested source; internal figures are evidence not shown, but public benchmark figures are checkable. - METR Time Horizon 1.1 (Jan 2026) revised the post-2024 doubling to ~3 months; METR (as quoted by Imas and Garicano) estimated the Oct 2026 pace at ~2x the 2024-2025 pace, range 1.3x to 3x. - AI Futures Project's Q3 2026 update moved its timelines earlier, toward 2027.
Evidence for (argument from diminishing returns and constrained inputs): - Imas and Garicano (Nov 13 2026, aleximas.substack.com/p/are-we-approaching-an-intelligence): capability gains per dollar fell about two-thirds and per employee-year about two-fifths; a break around Oct 2025 looks like a one-time step from coding agents rather than continuing acceleration; progress must eventually slow to what chips, power and capital allow. They do not date a stall or claim one is imminent. - Projections (Epoch-style compute forecasts, AI Futures Model) of compute and labour growth slowing around 2028-2030. - Possible bottleneck in research taste/judgment (acknowledged by Anthropic). - Possible exogenous shocks (compute or power supply, financing).
Weighing: the for-argument establishes that a slowdown in growth rate is likely eventually and possibly by ~2030, but a stall within a few years requires both input growth to falter soon and no offsetting AI contribution to research; the latter is already partly visible. Credence ~0.2 that progress stalls in the next few years. Status doubtful rather than contradicted because the claim is a forecast and the evidence is indirect; "stall" is vague, which also limits confidence.
Decomposition
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We include this scenario for completeness, but we don’t believe it’s likely. Every capability we can measure, including those that feel “squishier,” like quality of code and success on open-ended tasks, has so far followed the same curve. We have not yet seen that curve bend.
Scenario: the trend stalls (S-curve, compute/energy constraints, or need for new architecture).
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Hence capability gains per dollar decreased by two-thirds, while gains per technical employee-year fell by about two-fifths. ... if maintaining growth in capabilities requires ever-increasing research effort, eventually the gains in capabilities will necessarily have to slow down.
Argues progress is slowing relative to inputs and must eventually slow in calendar time, without dating the slowdown to the next few years or calling it a stall.
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Created by extractor · Nov 13, 2026. Every judgment on this page is accompanied by a reasoning trace.