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François Chollet (tweeted 2026-08-06) revised his 2023–early-2024 views…

2026-08-06 · 18:26 UTC ·@fchollet ·0 min read

Brief

François Chollet updated his 2023–early‑2024 positions (tweeted 2026‑08‑06), arguing that while LLMs constitute substantial foundational progress, they did not advance us toward AGI. He insists scaling deep learning will continue to yield gains but is not the route to AGI, and that current deep‑learning methods remain under‑deployed, leaving large untapped value.

Why it matters

François Chollet (tweeted 2026-08-06) revised his 2023–early-2024 views, asserting that LLMs represent considerable progress as a foundation but did not move us closer to AGI.

Key details

  • He lists four claims to hold simultaneously: (1) scaling up deep learning will keep paying off, (2) scaling alone isn't the path to AGI, (3) we aren't particularly close to AGI, and (4) LLMs did not represent a step closer.
  • He emphasizes that current deep‑learning techniques are far from fully deployed and that 'a huge amount of value remains to be created' with existing technology.
Source evidence

These are the 2023 and early 2024 views I updated based on new evidence. LLMs did in fact represent considerable progress, as a foundation.

François Chollet (@fchollet)

These are all true simultaneously:

  1. Scaling up deep learning will keep paying off (unlock more applications, or higher performance on existing ones).
  2. Scaling up deep learning isn't the path to AGI.
  3. We aren't particularly close to AGI, and LLMs did not represent a step closer.
  4. We're not anywhere near full deployment of existing deep learning techniques. A huge amount of value remains to be created with the tech we already have.

— https://nitter.net/fchollet/status/1785727866690547829#m