Many would agree the AI field has reached a point of diminishing returns from scaling, and faith in scaling as a route to AGI has dissipated.

Judge as of January 2026. The claim is about degree and has two parts: (a) many people in the AI field agree that scaling (more compute, data and parameters for pretraining) has reached diminishing returns; (b) faith in scaling as a route to AGI has dissipated, meaning it has broadly faded among researchers, commentators and industry, not just among critics. TRUE: evidence from before or around January 2026 shows both. For (a), surveys of AI researchers or public statements from a substantial number of prominent researchers or lab leaders acknowledge diminishing returns from scaling. For (b), there is a clear, widely reported shift away from 'scaling alone gets us to AGI', with a majority or a clear plurality of experts in surveys doubting it, or leading proponents publicly stepping back. MOSTLY TRUE: (a) is well supported and faith in pure scaling has clearly declined, but sizeable parts of the field (for example several major labs) still openly state that scaling is a main path to AGI, so 'dissipated' slightly overstates the change. MIXED: one part holds and the other does not, or the evidence shows the field roughly split on both. MOSTLY FALSE: only a minority of critics hold these views, and the leading labs and most surveyed experts still publicly expect scaling to deliver large gains and to lead to AGI; the claim inflates a minority view into a broad consensus. FALSE: evidence from the time shows broad agreement that scaling returns were not diminishing and that confidence in scaling toward AGI was steady or rising.

Set before the research began.

1. E1 AI scientists are sceptical that modern models will lead to AGI New Scientist · 14 March 2025

   “In a survey of 475 AI researchers, about 76 per cent of respondents said it was “unlikely” or “very unlikely” that scaling up current approaches will succeed in achieving AGI.”

2. E2 AI Beyond the Scaling Laws HEC Paris (DARE) · 1 December 2025

   “The scaling laws that powered the exponential progress of Large Language Models (LLMs) like GPT-4, and fueled bold predictions of Artificial General Intelligence (AGI) by 2026 from Sam Altman (OpenAI) and Dario Amodei (Anthropic) have started to show diminishing returns.”

3. E3 Ilya Sutskever — We're moving from the age of scaling to the age of research Dwarkesh Podcast · 25 November 2025

   “But is the belief that if you just 100x the scale, everything would be transformed? I don’t think that’s true. So it’s back to the age of research again, just with big computers.”

4. E4 Scaling Could Now Give Diminishing Returns, We're Back To The Age Of Research: Ilya Sutskever OfficeChai · 25 November 2025

   “Interestingly, Sutskever’s comments come at at time when Google researchers have said that they’re still seeing gains from pre-training.”

A survey of 475 AI researchers found about 76% judged it unlikely that scaling up current approaches will achieve AGI (E1), and an HEC Paris analysis said scaling laws had started to show diminishing returns (E2). Ilya Sutskever publicly said that 100x scale would not transform everything and that it is "back to the age of research" (E3). However, Google researchers were reported to be still seeing gains from pre-training (E4). "Dissipated" therefore slightly overstates how far the change went.