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François Chollet: Why Scaling Alone Isn’t Enough for AGI

François Chollet: Why Scaling Alone Isn’t Enough for AGI

21 segments available

François Chollet has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is—and how to build it from first principles. In this episode of Lightcone, he traces that path from his early work on deep learning to the creation of the ARC prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Kera 56:39 - Advice For How To Think About AI Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs

Segments Timeline

1
0:00 - 0:31
0:30 duration83 words

AGI by 2030?

"I think we're probably looking at AGI 2030 around the time uh that we're going to be releasing like maybe AR 6 or AR 7. You're not going to stop uh AI progress. I think I think it's too late for that...."

2
0:31 - 1:08
0:37 duration124 words

Introducing Ndea: A New Path Beyond Deep Learning

"Today we're lucky to be joined by France Chole, founder of the ARK Prize, a global competition to solve the ARC AGI benchmark. His latest project is NDIA, a lab exploring a new paradigm in frontier AI..."

3
1:08 - 1:30
0:21 duration69 words

A New ML Paradigm

">> right? So NDI is this new AGI research lab and we are trying some very different ideas and so our goal is basically to build this new branch of machine learning that will be much closer to optimal ..."

4
1:30 - 3:04
1:33 duration309 words

Replacing neural nets with compact symbolic programs

"where I got to 40,000 stars this morning >> on uh GStack. So it's like, oh, this is an open source project that now is one of the biggest ones and I have more than a 100 PRs from contributors to deal ..."

5
3:04 - 5:20
2:16 duration438 words

Why Ndea Isn’t Competing With Coding Agents

"we're replacing the parametric curve with a symbolic model that is meant to be as small as possible. It's like the simplest uh possible uh model to explain the data to model what's going on. uh and of..."

6
5:20 - 7:23
2:03 duration439 words

Why Everyone Might Be Wrong About Scaling LLMs

"optimality and so I'm trying to sort of like leaprog directly uh to optimality like to build to build the foundations of optimal AI today but in general you know our vision is very ambitious and I'm n..."

7
7:23 - 8:50
1:26 duration253 words

Why Coding Agents Suddenly Work So Well

"there's bugs compiles etc and mathematics as well where there all the theorems and proofs work out I guess it becomes more nebulous when you go couple degrees off where there fields that are not natur..."

8
8:50 - 10:50
1:59 duration368 words

The Limits of LLMs in Non-Verifiable Domains

"and so on. And so that means that uh the model was not just working from human pro annotations. It was actually trying its own things uh verifying the answer and uh and generating a lot lot more strin..."

9
10:50 - 13:30
2:39 duration525 words

What AGI Actually Means (And Why Most Definitions Are Wrong)

"potentially uh round to do. >> Do you think it's possible that we will accomplish the first definition of AGI, the automate most economically useful work before we accomplish your definition? >> Absol..."

10
13:30 - 14:02
0:32 duration96 words

Why Deep Learning Hits a Wall

"you know around that time around like 2015 2016 was that deep learning was extremely general that you could do everything with deep learning that you didn't need in anything else. It was training comp..."

11
14:02 - 18:21
4:19 duration778 words

ARC’s Origin Story

"help with uh reasoning problems and in particular uh first order logic problems uh uh theorem proving and so on. And I started finding that you could not really get cryion descent to encode uh uh sort..."

12
18:21 - 22:50
4:28 duration816 words

ARC Benchmarks Explained: From V1 to V3

"models so performance of of basel lamps on on v1 stayed very very low even though in the meantime you know we had scaled up these models by 50,000x right so it was really telling you that you know mor..."

13
22:50 - 27:03
4:13 duration773 words

The RL Loop Powering Coding Agents Today

"getting any smarter right now like at you know age 45. But you know I can learn how to do things and that's sort of what's happening with the models as of like late. >> Yeah, absolutely. When it comes..."

14
27:03 - 31:15
4:11 duration786 words

ARC-AGI V3: Measuring “Agentic Intelligence”

"ability to model its environment we're also looking at uh its exploration efficiency its ability to acquire goals on its own like goal setting and of course its ability to plan uh through the model of..."

15
31:15 - 35:31
4:16 duration738 words

Inside the ARC Game Studio

"involved uh in these games. >> It's like one of those uh IQ tests that are just pattern matching but now it has time series. >> Yeah. Uh it's not just time series it's interactive. must create your ow..."

16
35:31 - 44:01
8:30 duration1677 words

Could AGI Fit in 10,000 Lines of Code?

">> Yeah. Yeah. >> Do you ever think about it in terms of like well it would fit on a floppy disc? >> Well, okay. There there are two things to separate. There's the sort of like fluid intelligence eng..."

17
44:01 - 46:46
2:44 duration513 words

Building Ndea: From Idea to Compounding Research Stack

"four, five, six? Can you keep making it harder? >> Yeah. Yeah. I think there there will absolutely be ARK 4 and and AR five. I mean, we're currently planning ARK 5. Um the the point of the AKGI benchm..."

18
46:46 - 47:23
0:37 duration112 words

The Future of ARC: Benchmarks That Evolve With AI

"amount of comput and resources that we've thrown at uh deep learning and and gradient descent and and scaling that up if you had thrown the same amount of investment into almost anything else you woul..."

19
47:23 - 53:38
6:15 duration1223 words

Why There’s Still Huge Opportunity for New AI Paradigms

"top of the current stack with their slightly alternative like uh state space models for instance uh there's the the XLSM architecture like you you can basically you know current frontier is it's it's ..."

20
53:38 - 56:41
3:03 duration597 words

How to Build a Breakout Open Source Project - Lessons From Kera

"simple and intuitive. There was this big big focus on usability and this was inspired by scikitlearn like scikitlearn was sort of like the og uh machine learning library for python and what made it su..."

21
56:41 - 57:12
0:31 duration122 words

Advice For How To Think About AI

"this new development into an opportunity into into a tool they can use for themselves to improve their own lives. I think that's that's the right mindset because you know you're not going to stop uh A..."