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François Chollet (tweeted 2026-05-09) argues that agentic coding is essentially a…

2026-05-09 · 22:04 UTC ·@fchollet ·1 min read

Brief

François Chollet (2026-05-09) frames agentic coding as machine learning: engineers set an objective and constraints (spec + tests), coding agents optimize iteratively, producing a deployable blackbox codebase analogous to a neural network. He warns this brings ML problems—overfitting, Clever Hans, data leakage, concept drift—and asks what high-level tooling (a 'Keras') will let humans steer such training.

Why it matters

François Chollet (tweeted 2026-05-09) argues that agentic coding is essentially a form of machine learning: the engineer defines an optimization goal plus constraints (the spec and its tests) and autonomous coding agents iterate until that goal is reached.

Key details

  • He claims the generated codebase should be treated as a blackbox artifact—deployed without inspecting internal logic—so classic ML failure modes (overfitting to the spec, Clever Hans shortcuts, data leakage, concept drift) will become core risks; he asks what will serve as the 'Keras' (high-level abstractions) for steering codebase 'training'.
Source evidence

Agentic coding is a form of machine learning. Generated code is best treated as a blackbox artifact whose behavior and generalization should be managed via empirical evaluation, like with any ML model.

François Chollet (@fchollet)

Sufficiently advanced agentic coding is essentially machine learning: the engineer sets up the optimization goal as well as some constraints on the search space (the spec and its tests), then an optimization process (coding agents) iterates until the goal is reached.

The result is a blackbox model (the generated codebase): an artifact that performs the task, that you deploy without ever inspecting its internal logic, just as we ignore individual weights in a neural network.

This implies that all classic issues encountered in ML will soon become problems for agentic coding: overfitting to the spec, Clever Hans shortcuts that don't generalize outside the tests, data leakage, concept drift, etc.

I would also ask: what will be the Keras of agentic coding? What will be the optimal set of high-level abstractions that allow humans to steer codebase 'training' with minimal cognitive overhead?

— https://nitter.net/fchollet/status/2024519439140737442#m