Jun 22, 2026 · 1 hr 21 min · 10 segments
This week, we are bringing you a conversation, recorded live at the Times Center in Manhattan last Thursday, between Jack Clark, co-founder of Anthropic, head of the Anthropic Institute and the man…
Sam KimbrelHostUm, the, the frame with which I come to this conversation is that when we look at AI progress, there's going to be faster and more profound AI progress and diffusion in the coming years than there has been in recent years.
And that's for a very specific technical reason which relates to the singularity, which is this concept called, you know, recursive self-improvement.
Who here has heard of this concept? Okay, so I'll give a, I'll give a quick explanation.
So recursive self-improvement is what happens if we build AI systems that rather than just assisting researchers in their own creation, which is the case today, we build systems that might be able to invent their successor, come up with the ideas that helps Claude 10 build Claude 11 without any researchers involved, only you give it the, the care and feeding of compute and data.
We think there's a, there's a chance this happens this decade, and if I had to put a year on it, it would be in 2028 or so.
We'd enter some regime where suddenly the progress of AI and science more broadly would be defined not only by humans, but by humans and something else, which has never really happened before.
And I think the world after that can become increasingly hard to think about, so sometimes people call that the, the singularity.
There are, there are vast intimidating questions ahead for all of us if this actually comes true, and the kinds of choices which we're gonna be able to make as society involve making choices about stuff that seems really unintuitive.
One choice is if we have systems with this capability, we may be able to choose which parts of science we want to, we want to intentionally speed up solely by allocating these automated scientists to them.
Today, we have a scarce amount of human scientists operating at the frontiers of all of these fields, and we can't simply suddenly decide to produce 1,000 more scientists in any given field.
If we want to do that, it takes us 20 to 30 years of intentional policy and societal work.
If we could do it with the application of compute, we'd have strange choices ahead, like which parts of science do we want to progress most quickly? Do we want to pick genres of science? Do we want them all to progress equally? These are real choices we'll get to make, and it will turn out into the choice of where do you allocate compute, which is running this AI system.
The second choice is, is how do we wrestle with these really, really challenging questions of individual sovereignty and liberty versus, like, collective safety and control? 'Cause what we have already today are AI systems that have within themselves capabilities that previously we could only access if we hired incredibly skilled humans.
And we also have capabilities that were previously really only the domain of, of nation states or, or other, other kind of non-state actors like hacking capabilities or surveillance capabilities.
And where we set this dial about what do we let individuals access and what do we decide needs to be somehow gated and what do we decide needs to be controlled, has huge implications for our own ability to act in the world and how societies will work.
And again, these are choices that we haven't had to make before, and they're, they're sort of choices that fall out of the technology.
Um, the, the frame with which I come to this conversation is that when we look at AI progress, there's going to be faster and more profound AI progress and diffusion in the coming years than there has been in recent years.
And that's for a very specific technical reason which relates to the singularity, which is this concept called, you know, recursive self-improvement.
Who here has heard of this concept? Okay, so I'll give a, I'll give a quick explanation.
So recursive self-improvement is what happens if we build AI systems that rather than just assisting researchers in their own creation, which is the case today, we build systems that might be able to invent their successor, come up with the ideas that helps Claude 10 build Claude 11 without any researchers involved, only you give it the, the care and feeding of compute and data.
We think there's a, there's a chance this happens this decade, and if I had to put a year on it, it would be in 2028 or so.
We'd enter some regime where suddenly the progress of AI and science more broadly would be defined not only by humans, but by humans and something else, which has never really happened before.
And I think the world after that can become increasingly hard to think about, so sometimes people call that the, the singularity.
There are, there are vast intimidating questions ahead for all of us if this actually comes true, and the kinds of choices which we're gonna be able to make as society involve making choices about stuff that seems really unintuitive.
One choice is if we have systems with this capability, we may be able to choose which parts of science we want to, we want to intentionally speed up solely by allocating these automated scientists to them.
Today, we have a scarce amount of human scientists operating at the frontiers of all of these fields, and we can't simply suddenly decide to produce 1,000 more scientists in any given field.
If we want to do that, it takes us 20 to 30 years of intentional policy and societal work.
If we could do it with the application of compute, we'd have strange choices ahead, like which parts of science do we want to progress most quickly? Do we want to pick genres of science? Do we want them all to progress equally? These are real choices we'll get to make, and it will turn out into the choice of where do you allocate compute, which is running this AI system.
The second choice is, is how do we wrestle with these really, really challenging questions of individual sovereignty and liberty versus, like, collective safety and control? 'Cause what we have already today are AI systems that have within themselves capabilities that previously we could only access if we hired incredibly skilled humans.
And we also have capabilities that were previously really only the domain of, of nation states or, or other, other kind of non-state actors like hacking capabilities or surveillance capabilities.
And where we set this dial about what do we let individuals access and what do we decide needs to be somehow gated and what do we decide needs to be controlled, has huge implications for our own ability to act in the world and how societies will work.
And again, these are choices that we haven't had to make before, and they're, they're sort of choices that fall out of the technology.
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