Francois Chollet quote, Jun 11, 2024

Creator of ARC-AGI, co-founder ARC Prize · Jun 11, 2024 · Interview, Dwarkesh Podcast: Why the biggest AI models can't solve simple puzzles

Now LLMs have sucked the oxygen out of the room. Everyone is just doing LLMs. I see LLMs as more of an off-ramp on the path to AGI actually.

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Words found on the source page on Sep 25, 2026.

More from Francois Chollet

Date	Quote	Context	Source
Dec 20, 2024	Passing ARC-AGI does not equate to achieving AGI, and, as a matter of fact, I don't think o3 is AGI yet. o3 still fails on some very easy tasks, indicating fundamental differences with human intelligence.Dec 20, 2024· Post	PostOpenAI o3 Breakthrough High Score on ARC-AGI-Pub	Source
Dec 20, 2024	This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs.Dec 20, 2024· Post	PostOpenAI o3 Breakthrough High Score on ARC-AGI-Pub	Source
Dec 20, 2024	You'll know AGI is here when the exercise of creating tasks that are easy for regular humans but hard for AI becomes simply impossible.Dec 20, 2024· Post	PostOpenAI o3 Breakthrough High Score on ARC-AGI-Pub	Source
Jun 11, 2024	I’m pretty skeptical that we’re going to see an LLM do 80% in a year.Jun 11, 2024· Interview	InterviewDwarkesh Podcast: Why the biggest AI models can't solve simple puzzles	Source
Jun 11, 2024	OpenAI basically set back progress towards AGI by quite a few years, probably like 5-10 years.Jun 11, 2024· Interview	InterviewDwarkesh Podcast: Why the biggest AI models can't solve simple puzzles	Source
Jun 11, 2024	The way LLMs work is that they’re basically this big interpolative memory.Jun 11, 2024· Interview	InterviewDwarkesh Podcast: Why the biggest AI models can't solve simple puzzles	Source

Date	Quote	Context	Source
Sep 24, 2026	An AI with more real layers can actually be “smarter” in the conventional sense of the term.Scott AlexanderSep 24, 2026· Post	PostThe Specter Of Neuralese	Source
Sep 24, 2026	For 100 steps in a row, the AI can shoot delicate subtle ideas from layer to layer at the speed of light. Then there’s one step where it has to encode them into twenty-six glyphs invented by Phoenician turquoise miners in 1800 BC. Then it has to re-encode the Phoenician glyphs into delicate subtle lightspeed ideas before it can do anything else.Scott AlexanderSep 24, 2026· Post	PostThe Specter Of Neuralese	Source
Sep 24, 2026	The science of reading these thoughts is a subfield of AI interpretability, which is still in its infancy.Scott AlexanderSep 24, 2026· Post	PostThe Specter Of Neuralese	Source
Sep 23, 2026	This trains the AIs to be focused on task success, which naturally risks including things like reward-hacking, cheating, and single-minded pursuit of stated goals at the expense of ethical injunctions.Scott AlexanderSep 23, 2026· Post	PostMysteries Of AI Generalization	Source
Sep 19, 2026	It is important to not confuse massive steps in inference-time scaling, a dynamic which should be fairly predictable, with being the outputs of RSI, which is highly uncertain.Nathan LambertSep 19, 2026· Post	PostWhy I still haven't bought into true RSI	Source
Sep 17, 2026	And actually the models today are far beyond what was possible even six months ago.Noam BrownSep 17, 2026· Interview	InterviewDwarkesh Podcast: Agent swarms, alignment, and recursive self-improvement	Source

About this quote

Said by: Francois Chollet, Creator of ARC-AGI, co-founder ARC Prize
Where: Dwarkesh Podcast: Why the biggest AI models can't solve simple puzzles
Topics: AGI, Research
Company: ARC Prize
Source: dwarkesh.com/p/francois-chollet

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