Chollet on AI: Pattern Sponge and the Unbounded Scaling in Verifiable Domains
fchollet · x · 2026-08-28
Francois Chollet offers a perspective that current AI acts as a "sponge for patterns," absorbing and operationalizing patterns it is exposed to. He argues that once the complete space of patterns in a domain can be programmatically enumerated, saturating the domain becomes purely a matter of computational resources. Consequently, in verifiable domains, model capability scaling should remain unbounded, as models improve by absorbing more of the infinite computational universe.
Related event: Chollet: Today's AI Has Superhuman Skill, Not Intelligence(5 posts)→
More from AGI Musings
- GitCoin founder on Nick Land's technocapital: capitalism as a self-assembling machine intelligence — khademinori · 2026-10-16
- AI-driven job losses could turn $10T consumer debt into a $1T banking hole — churchkey · 2026-10-16
- Into the Unknown: an AI agent benchmark of 15 open research problems from US national lab scientists — Jeande_d · 2026-10-16
- Soon your AI will meet their AI — and humans without agents can't compete — danfaggella · 2026-10-16
- Analyst: in a year, models 2-3x better across the board at 10x lower cost, token use up 10-20x — DeryaTR_ · 2026-10-16
- Researcher argues RSI is an empty term: iteration, not recursive self-improvement — gerardsans · 2026-10-16