#250 – Toby Ord on where AGI timelines go wrong
episodePreviously titled “Where AGI timelines go wrong | Toby Ord, Oxford University” — renamed by the publisher on Aug 20, 2026
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Why does Toby Ord think recursive self‑improvement is unlikely to cause an intelligence explosion?
Today, I'm speaking with Toby Ord, Senior Researcher at Oxford University's AI Governance Initiative and the author of The Existential Risk and the Future of Humanity. Welcome back to the show, Toby. I think it's your fifth appearance. It's great to be back. It feels like today, everyone is talking about recursive self-improvement, or at least like everyone in my circles is talking about AI or recursive self-improvement. Do you think that RSI will lead to an intelligence explosion or like a massive takeoff in AI capabilities?
No, at least I think that it's unlikely. However, the chance that it might happen, I think is credible. And this possibility really is one of the biggest issues in AI at the moment. So why do you think it probably won't work or won't pack much of a punch? Yeah, there's a couple of reasons. One is that I think that AI research involves a lot more than just programming and a lot more than just running the kinds of simple experiments that have been automated so far. So I think that there's a pretty reasonable chance that some major breakthroughs are still needed and that the AI assistance isn't going to be able to provide that.
What if AIs do get better such that they can more comprehensively replace the work that staff at the companies are doing?
Well, there's a lot of different types of work at a company. And I think that this includes, say, cleaning and catering, also programming and other aspects of software engineering, and then kind of running some simple experiments. But it also includes, I think, genuine strategic decision-making, and also AI research. That is not the case of programming where you know what you want and then you type it in, but the case if you don't know what you want, you don't know what changes to these basic architectures will lead to solving some of these longstanding issues and really letting letting these systems take off. And there's not much evidence at the moment that AI systems can do that kind of thing.
They might be able to really speed up the programming, but even if they made programming instantaneous, I think that that may not actually change the timelines all that much. So they really would need to be able to help with these harder aspects of research.
Is that the crux of the issue? If they could replace human researchers, like the full suite of things that the staff are doing, do you think then we would get an intelligence explosion?
I think it's quite plausible. There's still some other issues. So when you think of an intelligence explosion, you tend to think that this rate of progress over time is kind of bending upwards in some way. But most people think that then it will eventually kind of plateau off. And so it will start to bend back down as you reach some kind of limit, or maybe the kind of increasing complexity of the system starts to overwhelm your ability to improve it.
How do I.J. Good’s and Yudkowsky’s ideas shape the modern view of recursive self‑improvement?
And so most people think it will bend back down to flatten off. And so the real question is before that, is there a phase where it's bending upwards or is it just the phase that's flattening off? And I don't think that it's clear which one of those it is, even if it could replace all of the human skills.
What are the most distinctive aspects of recursive self-improvement that make it different than humans doing the work?
Yeah. So there's an interestingly long history of talking about recursive self-improvement. Quite a few decades of talking about it, followed by now we're in the situation where a lot of people are suggesting that they're doing it. And so this started famously with I.J. Goode, who talked about this possibility of an ultra-intelligent machine. And what I think a lot of people forget about his story of recursive self-improvement, although he didn't use that term, he used the term intelligence explosion. He was thinking of an AI system, once you have an ultra-intelligent machine, one that's more outside the human range, is kind of beyond anything that a human can do. And in fact, it's beyond what the entire research community can do.
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Chapters
8 chapters
1
Why does Toby Ord think recursive self‑improvement is unlikely to cause an intelligence explosion?
0:00–2:48
2
How do I.J. Good’s and Yudkowsky’s ideas shape the modern view of recursive self‑improvement?
2:48–11:32
3
What are the main risks of recursive self‑improvement and how might a moratorium address them?
11:32–26:34
4
How could verification and international agreements make a moratorium on dangerous AI research feasible?
26:34–1:25:49
5
Why do current AI models struggle with sample efficiency and how might large‑scale data pooling change that?
1:25:49–1:35:38
6
What are the privacy and workplace‑culture implications of companies installing constant monitoring cameras on employees?
1:35:38–2:13:27
7
How did an AI system disprove the unit‑distance conjecture and why is that mathematically significant?
2:13:27–2:24:18
8
Why does Toby Ord advocate for ‘broad timelines’ and how should individuals and governments plan for multiple possible AI futures?
2:24:18–2:45:58
Speakers
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