François Chollet
Creator of the Keras deep-learning library and the ARC-AGI benchmark.
François Chollet did not write this page.
We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.
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Our reading
Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
Their wordsAutomating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
- 2 days earlier
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Our reading
Breakthroughs belong to the few who are obsessed with the process, not just the idea of the outcome
Their wordsThe breakthroughs belong to the few who are obsessed with the process, not just the idea of the outcome
- 1 day earlier
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Their wordsThe ideal outcome for science isn't a couple of labs using proprietary models and millions of dollars to crack trophy problems, it's to have millions of AI-aided human scientists who follow their curiosity and become way more ambitious
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Their wordsYou can't solve real-world physical tasks through autoregressive sampling alone.
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Our reading
Actual autonomous robotics requires engineering causality and discrete reasoning back into the loop.
Their wordsIf we want actual autonomous robotics we have to engineer causality and discrete reasoning back into the loop.
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Their wordsFor this reason, intelligence is practically never the bottleneck to solving impactful problems.
- 1 day earlier
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Their wordsIsolated chats are suboptimal in team environments because they lack the context of what the team already discussed and decided.
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Their wordsIf an agent saves an engineer 2 hours to implement a feature but collectively costs the team 3 hours in alignment and review, the ROI is negative.
- 3 days earlier
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Our reading
The shift from a transductive paradigm to an inductive paradigm is the big story of 2025 and 2026.
Their wordsHow AI shifted from a transductive paradigm (token-by-token answer completion) to an inductive paradigm (on-the-fly reasoning chain synthesis), in one chart. This is the big story of 2025 and 2026.
- 1 day earlier
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Their wordsThis is why science as a system requires exponential resources (input) to produce linear impact (output).
- 1 day earlier
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Their wordsBut super-exponential injection of *external* resources is not sustainable in the long-term.
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Their wordsTo achieve sustained exponential progress, the AI industry will need to achieve matching exponential profits -- it will need to close the resource loop.
- 4 days earlier
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Their wordsHumanity has not yet created a conscious computer program, and there are no signs we are close to doing so.
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Our reading
Their wordsLikewise a static input-output program that has *none* of the properties we associate with conscious beings (e.g. information integration, interoception, temporal binding, embodiment, etc.) has no more reason to be presumed conscious than a rock has to be presumed alive.
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Their wordscreating artificial consciousness should *not* be a goal of AI research. Understanding consciousness, sure, synthesizing it, no.
- 1 day earlier
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Their wordsThe notion that current AI models are sentient and can suffer, combined with the foolish idea that suffering can be mathematically quantified and weighted between humans and non-humans, could lead us down an incredibly dark and dystopian path.
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Their wordsDo not confuse competent information processing and sentience, they are wholly unrelated.
- 5 days earlier
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Their wordsThe purpose of the AI industry should be to produce tools that, in the human hand, will improve human prosperity and welfare. It should not be to create a "successor species" to the human race.
- 3 days earlier
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Their wordsI think the "difficulty" of software engineering is essentially constant no matter what abstraction level you move to, because human cognition adapts to new tools until it can fully utilize itself.
- 1 day earlier
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Their wordsAt some point in evolutionary history, matter learned to feel.
- 3 days earlier
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Our reading
People who built their understanding through code-centric workflows need new workflows to do so.
Their wordsIf code-centric workflows were how you developed that understanding, then you need new workflows.
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Their wordsIf code was previously your source of truth for managing understanding, then you need a new source of truth artifact.
- 7 days earlier
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Their wordsPeople will almost always align their beliefs and their interests.
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Their wordsYou can be sincere *and* self-interested, and that's usually the default.
- 1 day earlier
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Their wordsBut you cannot say "yes this is likely to end humanity in your lifetime, and we're going to completely wing it." That is simply not rational.
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Their wordsThe only way to avoid extreme power concentration is to ensure we have multiple independent providers of frontier AI models, including open-source options.
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Their wordsOne of the most worrying risks linked to frontier AI is extreme power concentration.
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Our reading
Current models are unsafe because they are not smart enough, not because they are too smart.
LLMsAI alignmentreinforcement learning
Their wordsCurrent models are unsafe not because they're too smart, but because they take goals too literally or take nonsensical shortcuts to achieve these goals, i.e. they're RL-fried. They lack common sense. They don't do the right thing in the face of ambiguity. Basically, they're not smart enough.
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Their wordsIf there really is a high chance of AI leading to the extinction of humanity within years/decades, then the only rational stance towards safety monitoring and research pacing should be stringent, top-down government involvement and universally ratified international treaties.
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Their wordsIn the near term (definitely not in the long term), more capable models should mean safer models (maybe paradoxically).