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Trend 1: Move more logic into neural models for tasks with densely sampled…

2026-08-06 · 15:09 UTC ·@fchollet ·1 min read

Brief

François Chollet argues AI's trajectory is shaped by two persistent trends: pushing logic into neural models when training data is dense (the move to end‑to‑end learning circa 2013–2016 and recent model‑embedded harness features) and using those neural models inside neurosymbolic architectures to gain power and generality (AlphaGo in 2016, Waymo in the early 2020s, and modern TTA LRMs and coding agents); he predicts this dual trend will continue.

Why it matters

Trend 1: Move more logic into neural models for tasks with densely sampled training data — exemplified by the shift from pre‑DL feature engineering to end‑to‑end learning circa 2013–2016, and the recent tendency to bake more harness functionality directly into models.

Key details

  • Trend 2: Build more powerful and generalizable systems by embedding neural models in neurosymbolic architectures — examples include AlphaGo (2016) instead of an end‑to‑end Go player, Waymo's neurosymbolic stack (early 2020s) instead of a single end‑to‑end driving model, and current TTA LRMs and coding‑agent harnesses; Chollet asserts this dual trend will continue because neurosymbolic systems can always do more than the neural model alone.
Source evidence

An accurate characterization of the arc of AI is that it is shaped by two trends:

  1. Moving more and more logic to a neural model for tasks where training data can be densely sampled (e.g. the shift from pre-DL feature engineering to end-to-end learning circa 2013-2016, and more recently the trend of baking more and more harness functionality directly into models over time)

  2. Achieving more and more powerful / generalizable systems by leveraging those neural models in sophisticated neurosymbolic architectures, e.g. AlphaGo instead of an end-to-end Go-player model (2016), the Waymo neurosymbolic architecture instead of a single end-to-end vehicle control model (early 2020s), TTA LRMs and coding agent harnesses instead of plain LLM inference (now).

As far as I can tell, this dual trend will keep going. You can always do more with a neurosymbolic system than with just the neural model inside it.