Arvind Narayanan

And I think that trend is only going to accelerate. We talked earlier about Moore's law, and it still continues to apply to these models. And even if one country decides that models should be closed, the odds of getting every country to enact that kind of rule are just vanishingly small.

So if our approach to safety with AI is going to be premised on ensuring that, quote unquote, bad guys don't get access to it, we've already lost. because it's only a matter of time before it becomes impossible to do that.

So if our approach to safety with AI is going to be premised on ensuring that, quote unquote, bad guys don't get access to it, we've already lost. because it's only a matter of time before it becomes impossible to do that.

So if our approach to safety with AI is going to be premised on ensuring that, quote unquote, bad guys don't get access to it, we've already lost. because it's only a matter of time before it becomes impossible to do that.

And instead, I think we should radically embrace the opposite, which is to figure out how we're going to use AI for safety in a world where AI is very widely available, because it is going to be widely available. And when we look at how we've done that in the past, it's actually a very reassuring story.

And instead, I think we should radically embrace the opposite, which is to figure out how we're going to use AI for safety in a world where AI is very widely available, because it is going to be widely available. And when we look at how we've done that in the past, it's actually a very reassuring story.

And instead, I think we should radically embrace the opposite, which is to figure out how we're going to use AI for safety in a world where AI is very widely available, because it is going to be widely available. And when we look at how we've done that in the past, it's actually a very reassuring story.

When we go back to the cybersecurity example, for 10 or 20 years, the software development community has been using automated tools, some of which you could call AI, to improve cybersecurity because software developers can use them to find bugs and fix bugs in software before they put them out there, before hackers even have a chance to take a crack at them.

When we go back to the cybersecurity example, for 10 or 20 years, the software development community has been using automated tools, some of which you could call AI, to improve cybersecurity because software developers can use them to find bugs and fix bugs in software before they put them out there, before hackers even have a chance to take a crack at them.

When we go back to the cybersecurity example, for 10 or 20 years, the software development community has been using automated tools, some of which you could call AI, to improve cybersecurity because software developers can use them to find bugs and fix bugs in software before they put them out there, before hackers even have a chance to take a crack at them.

So my hope is that the same thing is going to happen with AI. We're going to be able to acknowledge the fact that it's going to be widely available and to shape its use for defense more than offense.

So my hope is that the same thing is going to happen with AI. We're going to be able to acknowledge the fact that it's going to be widely available and to shape its use for defense more than offense.

So my hope is that the same thing is going to happen with AI. We're going to be able to acknowledge the fact that it's going to be widely available and to shape its use for defense more than offense.

Like a lot of people, I was fooled by how quickly after GPD 3.5, GPD 4 came out. It was just three months or so, but it had been in training for 18 months. That was only revealed later. So it gave a lot of people, including me, an inflated idea of how quickly AI was progressing.

Like a lot of people, I was fooled by how quickly after GPD 3.5, GPD 4 came out. It was just three months or so, but it had been in training for 18 months. That was only revealed later. So it gave a lot of people, including me, an inflated idea of how quickly AI was progressing.

Like a lot of people, I was fooled by how quickly after GPD 3.5, GPD 4 came out. It was just three months or so, but it had been in training for 18 months. That was only revealed later. So it gave a lot of people, including me, an inflated idea of how quickly AI was progressing.

And what we've seen in the nearly year and a half since GPD 4 came out is that we haven't really had models that have surpassed it in a meaningful way. And this is not based on benchmarks. Again, I think benchmarks are not that useful. It's more based on vibes. When you get people using these things, what do they say? I don't think models have really qualitatively improved on GPT-4.

And what we've seen in the nearly year and a half since GPD 4 came out is that we haven't really had models that have surpassed it in a meaningful way. And this is not based on benchmarks. Again, I think benchmarks are not that useful. It's more based on vibes. When you get people using these things, what do they say? I don't think models have really qualitatively improved on GPT-4.

And what we've seen in the nearly year and a half since GPD 4 came out is that we haven't really had models that have surpassed it in a meaningful way. And this is not based on benchmarks. Again, I think benchmarks are not that useful. It's more based on vibes. When you get people using these things, what do they say? I don't think models have really qualitatively improved on GPT-4.

And I don't think things are moving as quickly as I did 12 months ago.