@swyx: every evals/analytics startup is going through a onetime generational upgrade into a continual learning platform in 202…

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Summary

The author predicts that evals/analytics startups will transition into continual learning platforms in 2026, with some failing and the tasteful ones succeeding.

every evals/analytics startup is going through a onetime generational upgrade into a continual learning platform in 2026 many will fail but as always the tasteful ones win
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Cached at: 06/01/26, 03:06 AM

every evals/analytics startup is going through a onetime generational upgrade into a continual learning platform in 2026

many will fail but as always the tasteful ones win

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@Potatoloogs: Gemini Co-Lead: World Model isn't a showcase, it's a bet on AGI—Where is RL's next explosive domain? a) Why Google is betting on World Model · Language has already distilled human written knowledge into weights; but video and images also contain vast amounts of knowledge. Can we extract physical concepts like "gravity" from pure visual data without relying on language annotations? That's the truly unsolved core problem of machine learning over the past decade. b) RL post-training: A greenfield, but with structural constraints. c) Memory and continual learning: The answer may not lie in weights. d) Can AI truly "innovate"? The capability Vinyals is most uncertain about. e) Advice for entrepreneurs.

X AI KOLs Timeline

Gemini co-lead Vinyals discusses World Model as key to AGI, argues that video data contains physical knowledge, RL post-training has huge potential but faces structural constraints, and is optimistic about non-parametric memory systems.