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François Chollet: AGI is likely by 2030–early 2030s, based on current progress and investment across LLMs, plus side bets that might work out By the time ARC-AGI reaches version 6 or 7, we'll probably have AGI

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ARC-AGI is redefining how to measure progress on the path to AGI - focusing on reasoning, generalization, and adaptability instead of memorization or scale. At NeurIPS 2025, YC's Diana sat down with ARC Prize President Greg Kamradt to find out why most AI benchmarks fail, how ARC-AGI reveals the limits of today’s models, and why measuring intelligence may be harder than building it. 00:11 — What ARC Prize is and why it exists 00:38 — François Chollet’s definition of AGI 01:48 — What ARC-AGI Actually Tests 02:25 — When LLMs Failed the ARC Benchmark 03:38 — ARC-AGI Becomes the Standard 04:49 — False Positives in AI Progress 06:06 — The Evolution of ARC-AGI 08:55 — Measuring Intelligence beyond just accuracy 10:25 — What happens if a model solves ARC-AGI?

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.Elon Musk "I think we'll hit AGI in 2026", and he is confident that "AI will exceed the intelligence of all humans combined by 2030". AGI this year, ASI ~2030. We are in for a wild ride.

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🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

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François Chollet (François Chollet) on the ARC Prize and how we get to AGI. At AI Startup School in San Francisco. 00:00 - The Falling Cost of Compute 00:57 - Deep-Learning’s Scaling Era & Benchmarks 01:59 - The ARC Benchmark 03:02 - The 2024 Shift to Test-Time Adaptation 05:01 - What Is Intelligence? 07:12 - Why Benchmarks Matter (and Mislead) 08:57 - ARC 1 Exposes Scaling Limits 10:58 - ARC 2: Compositional Reasoning Arrives 12:55 - Humans vs. Models on ARC2 14:58 - Previewing ARC3 & Interactive Agency 17:00 - Kaleidoscopic Hypothesis and Abstractions 22:00 - Type 1 vs. Type 2 Abstractions 26:00 - Discrete Program Search & Inventive AI 29:00 - Fusing Intuition with Symbolic Reasoning 32:00 - Building AGI Through Meta-Learning Systems

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Ray Kurzweil: AGI 2029 After Ray Kurzweil recently confirmed his predictions in the MIT magazine, here is an excerpt from his latest Ted Talk, where he once again predicts AGI for 2029 based on his projections. Ray Kurzweil assumes that humans and machines will merge in the 2030s and that shortly thereafter, AI and human brains will be connected via the cloud. What sounds like science fiction is becoming increasingly realistic thanks to current developments.

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Ray Kurzweil: AGI 2029 After Ray Kurzweil recently confirmed his predictions in the MIT magazine, here is an excerpt from his latest Ted Talk, where he once again predicts AGI for 2029 based on his projections. Ray Kurzweil assumes that humans and machines will merge in the 2030s and that shortly thereafter, AI and human brains will be connected via the cloud. What sounds like science fiction is becoming increasingly realistic thanks to current developments.

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François Chollet (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 the Lightcone Podcast, 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 Keras 56:39 - Advice For How To Think About AI

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"You can't just think your way into an advantage." Balaji Srinivasan on why AGI may be embodied: "Every drone, every self-driving car, every humanoid pulls in all that [sensory] data. They actually can have a world model." "The very Abrahamic western concept of a single AGI that will tell us what to do might have been plausible three years ago—I don't think it's really that plausible now." "AGI is gated by the physical world." Balaji on Network State Podcast

"You can't just think your way into an advantage." Balaji Srinivasan on why AGI may be embodied: "Every drone, every self-driving car, every humanoid pulls in all that [sensory] data. They actually can have a world model." "The very Abrahamic western concept of a single AGI that will tell us what to do might have been plausible three years ago—I don't think it's really that plausible now." "AGI is gated by the physical world." Balaji on Network State Podcast

a16z

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ELON: AGI IS LIKELY TO EMERGE FROM OUR AUTONOMOUS CARS AND HUMANOIDS “We're not actually specifically focused on AGI. I'm simply saying that AGI seems likely to be an emergent property of what we're doing, because we're creating all these autonomous cars and autonomous humanoids that are actually a truly gigantic data stream that's coming in and being processed. It's by far the most amount of real-world data, and data you can't get by just searching the Internet, because you have to be out there in the world and interacting with people and interacting with the roads. Earth is a big place, and reality is messy and complicated. If you've got tens or hundreds of millions of autonomous vehicles and maybe even a comparable number of humanoids, maybe more than that, on the humanoid front, well, that's just the most amount of data. And if that video is being processed, it just seems likely that the cars will definitely get way better than human drivers, and the humanoid robots will become increasingly indistinguishable from humans, perhaps. And so then, like you said, you have this emergent property of AGI.” Source: Tesla AI Day, September 2022

ELON: AGI IS LIKELY TO EMERGE FROM OUR AUTONOMOUS CARS AND HUMANOIDS “We're not actually specifically focused on AGI. I'm simply saying that AGI seems likely to be an emergent property of what we're doing, because we're creating all these autonomous cars and autonomous humanoids that are actually a truly gigantic data stream that's coming in and being processed. It's by far the most amount of real-world data, and data you can't get by just searching the Internet, because you have to be out there in the world and interacting with people and interacting with the roads. Earth is a big place, and reality is messy and complicated. If you've got tens or hundreds of millions of autonomous vehicles and maybe even a comparable number of humanoids, maybe more than that, on the humanoid front, well, that's just the most amount of data. And if that video is being processed, it just seems likely that the cars will definitely get way better than human drivers, and the humanoid robots will become increasingly indistinguishable from humans, perhaps. And so then, like you said, you have this emergent property of AGI.” Source: Tesla AI Day, September 2022

Mario Nawfal

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Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities

Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities

Rob Wiblin

34,618 görüntüleme • 1 yıl önce

$AGI timelines are very bimodal. It's either by 2030 or bust. AI progress over the last decade has been driven by scaling training compute of frontier systems (3.55x a year, 160x over 4 years). This simply cannot continue beyond this decade, whether you look at chips, power, even fraction of raw GDP used on training. After 2030, AI progress has to mostly come from algorithmic progress. But even there the low hanging fruit will be plucked (at least under the deep learning paradigm). So the yearly probability of AGI craters. And we're plausibly looking at 2040+ timelines. I discuss this dynamic with Sholto Douglas and Trenton Bricken:$

AGI timelines are very bimodal. It's either by 2030 or bust. AI progress over the last decade has been driven by scaling training compute of frontier systems (3.55x a year, 160x over 4 years). This simply cannot continue beyond this decade, whether you look at chips, power, even fraction of raw GDP used on training. After 2030, AI progress has to mostly come from algorithmic progress. But even there the low hanging fruit will be plucked (at least under the deep learning paradigm). So the yearly probability of AGI craters. And we're plausibly looking at 2040+ timelines. I discuss this dynamic with Sholto Douglas and Trenton Bricken:

Dwarkesh Patel

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Leopold explains his approach to not blowing up (2024) "Obviously, not blowing up is task number one and two or whatever. I think this investment firm is going to just be betting on AGI and superintelligence before the decade is out, making the bets you would make if you took that seriously." "I think if that's wrong, the firm is not going to do that well." "The thing you have to be able to resist is, you know, one or a couple or a few individual calls. It's like AI stagnates for a year because of the data wall, or you got the call wrong on when revenue would go up." "You have to get timing right. I do think in general that the sequence of bets on the way to AGI is actually pretty critical, and something people underrate."

Leopold explains his approach to not blowing up (2024) "Obviously, not blowing up is task number one and two or whatever. I think this investment firm is going to just be betting on AGI and superintelligence before the decade is out, making the bets you would make if you took that seriously." "I think if that's wrong, the firm is not going to do that well." "The thing you have to be able to resist is, you know, one or a couple or a few individual calls. It's like AI stagnates for a year because of the data wall, or you got the call wrong on when revenue would go up." "You have to get timing right. I do think in general that the sequence of bets on the way to AGI is actually pretty critical, and something people underrate."

dnap

2,327,691 görüntüleme • 22 gün önce

ELON: AGI IS COMING FAST, AND IT WON’T STOP AT HUMAN LEVELS Elon says the timeline isn’t speculative anymore. What once sounded distant now looks imminent. He’s been consistent on this point for years, and his confidence hasn’t softened. The bigger shift, he argues, comes right after AGI, when intelligence stops being additive and starts compounding. “I think we’ll hit AGI next year, in ’26… Yeah, I’ve said that for a while… I’m confident by 2030 AI will exceed the intelligence of all humans combined.” Source: Moonshots Peter H. Diamandis, MD

ELON: AGI IS COMING FAST, AND IT WON’T STOP AT HUMAN LEVELS Elon says the timeline isn’t speculative anymore. What once sounded distant now looks imminent. He’s been consistent on this point for years, and his confidence hasn’t softened. The bigger shift, he argues, comes right after AGI, when intelligence stops being additive and starts compounding. “I think we’ll hit AGI next year, in ’26… Yeah, I’ve said that for a while… I’m confident by 2030 AI will exceed the intelligence of all humans combined.” Source: Moonshots Peter H. Diamandis, MD

Mario Nawfal

72,477 görüntüleme • 7 ay önce

Elon Musk on how AGI might actually emerge: “We’re not actually specifically focused on AGI. I’m simply saying that AGI seems likely to be an emergent property of what we’re doing” The reason is the massive real-world data Tesla is generating from autonomous cars and humanoid robots This is not internet data. This is real-world interaction driving on messy roads, navigating unpredictable environments, and interacting with people With tens or hundreds of millions of autonomous vehicles and potentially even more humanoids, the amount of real-world video and data being processed will be enormous From this, cars will become far better than human drivers, and humanoid robots will become increasingly indistinguishable from humans AGI might not come from a lab trying to build it directly. It may simply emerge from building useful robots at massive scale

Elon Musk on how AGI might actually emerge: “We’re not actually specifically focused on AGI. I’m simply saying that AGI seems likely to be an emergent property of what we’re doing” The reason is the massive real-world data Tesla is generating from autonomous cars and humanoid robots This is not internet data. This is real-world interaction driving on messy roads, navigating unpredictable environments, and interacting with people With tens or hundreds of millions of autonomous vehicles and potentially even more humanoids, the amount of real-world video and data being processed will be enormous From this, cars will become far better than human drivers, and humanoid robots will become increasingly indistinguishable from humans AGI might not come from a lab trying to build it directly. It may simply emerge from building useful robots at massive scale

X Freeze

21,256 görüntüleme • 2 ay önce

OpenAI Staff-member and MIT Professor Alexander Madry: "By the end of 2026, we might be able to declare "AGI has been achieved" tl;dr: "By the end of 2026, AI will begin to significantly permeate various sectors of the economy" "The scientific breakthroughs needed for AGI have already been achieved" "By the end of 2026, we might be able to declare "AGI has been achieved, by the end of 2026, AI will begin to significantly permeate various sectors of the economy." "What is happening now is a transformation comparable to the Industrial Revolution" It comes from an interview with a Polish YouTuber. I turned on the subtitles and tried to cut the relevant scenes. h/t as always the legendary Jimmy Apples 🍎/acc

OpenAI Staff-member and MIT Professor Alexander Madry: "By the end of 2026, we might be able to declare "AGI has been achieved" tl;dr: "By the end of 2026, AI will begin to significantly permeate various sectors of the economy" "The scientific breakthroughs needed for AGI have already been achieved" "By the end of 2026, we might be able to declare "AGI has been achieved, by the end of 2026, AI will begin to significantly permeate various sectors of the economy." "What is happening now is a transformation comparable to the Industrial Revolution" It comes from an interview with a Polish YouTuber. I turned on the subtitles and tried to cut the relevant scenes. h/t as always the legendary Jimmy Apples 🍎/acc

Chubby♨️

124,063 görüntüleme • 10 ay önce

Demis Hassabis's new interview: "Society needs to hear that because we don't have long to prepare for what that means. We are standing in the foothills of the singularity now. ..which is AGI. I believe that we are only a few years away from that, maybe around 2030, plus or minus a year. " ~ Demis Hassabis, Co-Founder and CEO of Google DeepMind It is going to be enormously profound, I think. The future, in my view, is still to be written. But these next few years are going to be very critical as to which way that will go, and how we collectively want that to look.” --- IMO, The real disruption is not whether AGI arrives exactly in 2030, plus or minus a year, but whether institutions can adapt, as in post-AGI world, technology will change much faster than human systems can respond. Schools still train people for stable professions, companies still organize work around human bottlenecks, and governments still regulate after harm becomes visible. AGI, if it arrives anywhere near the frontier-lab timelines, compresses that lag into a dangerous gap. ---- From "Stanford Graduate School of Business" YouTube channel, (link in comment)

Demis Hassabis's new interview: "Society needs to hear that because we don't have long to prepare for what that means. We are standing in the foothills of the singularity now. ..which is AGI. I believe that we are only a few years away from that, maybe around 2030, plus or minus a year. " ~ Demis Hassabis, Co-Founder and CEO of Google DeepMind It is going to be enormously profound, I think. The future, in my view, is still to be written. But these next few years are going to be very critical as to which way that will go, and how we collectively want that to look.” --- IMO, The real disruption is not whether AGI arrives exactly in 2030, plus or minus a year, but whether institutions can adapt, as in post-AGI world, technology will change much faster than human systems can respond. Schools still train people for stable professions, companies still organize work around human bottlenecks, and governments still regulate after harm becomes visible. AGI, if it arrives anywhere near the frontier-lab timelines, compresses that lag into a dangerous gap. ---- From "Stanford Graduate School of Business" YouTube channel, (link in comment)

Rohan Paul

70,642 görüntüleme • 2 ay önce

Elon Musk: "I think we'll hit AGI next year, in '26" "I'm confident by 2030, AI will exceed the intelligence of all humans combined" And that's way pessimistic. If you hit AGI next year and that date is in flux but from that date to self-improvements that are on the order of 1,000 to 10,000x just algorithmic improvements... is very short So why is nobody talking about this right now? on 𝕏 they are..... everyday basically I'll tell you something that most people in the AI community don't yet understand Almost no one understands this. The intelligence density potential is vastly greater than what we're currently experiencing

Elon Musk: "I think we'll hit AGI next year, in '26" "I'm confident by 2030, AI will exceed the intelligence of all humans combined" And that's way pessimistic. If you hit AGI next year and that date is in flux but from that date to self-improvements that are on the order of 1,000 to 10,000x just algorithmic improvements... is very short So why is nobody talking about this right now? on 𝕏 they are..... everyday basically I'll tell you something that most people in the AI community don't yet understand Almost no one understands this. The intelligence density potential is vastly greater than what we're currently experiencing

X Freeze

33,585 görüntüleme • 7 ay önce