Dario Amodei

It's turned into these flame wars on Twitter and nothing good is going to come of that.

Yeah, so I was at OpenAI for roughly five years. For the last, I think it was a couple of years, I was vice president of research there. Probably myself and Ilya Sutskiver were the ones who really kind of set the research direction.

around 2016 or 2017, I first started to really believe in or at least confirm my belief in the scaling hypothesis when Ilya famously said to me, the thing you need to understand about these models is they just want to learn. The models just want to learn. And again, sometimes there are these one sentences, these Zen cones that you hear them and you're like, ah, That explains everything.

That explains like a thousand things that I've seen. And then I, you know, ever after I had this visualization in my head of like, you optimize the models in the right way, you point the models in the right way. They just want to learn. They just want to solve the problem regardless of what the problem is.

Get out of their way. Yeah. Don't impose your own ideas about how they should learn. And, you know, this was the same thing as Rich Sutton put out in The Bitter Lesson or Gern put out in The Scaling Hypothesis. You know, I think generally the dynamic was, you know, I got this kind of inspiration from Ilya and from others, folks like Alec Radford, who did the original GPT-1. And then...

Ran really hard with it, me and my collaborators on GPT-2, GPT-3, RL from human feedback, which was an attempt to kind of deal with the early safety and durability, things like debate and amplification, heavy on interpretability. So again, the combination of safety plus scaling, probably 2018, 2019, 2020, those were kind of the years when myself and my collaborators probably –

You know, many of whom became co-founders of Anthropic kind of really had a vision and like drove the direction. Why'd you leave?

Yeah. So, look, I'm going to put things this way. And, you know, I think it ties to the race to the top. Right. Which is, you know, in my time at OpenAI, what I come to see as I'd come to appreciate the scaling hypothesis and as I come to appreciate kind of the importance of safety along with the scaling hypothesis. The first one, I think, you know, OpenAI was getting on board with.

The second one, in a way, had always been part of OpenAI's messaging. But, you know, over many years of the time that I spent there, I think I had a particular vision of how these, how we should handle these things, how we should be brought out in the world, the kind of principles that the organization should have.

And look, I mean, there were like many, many discussions about like, you know, should the org do, should the company do this? Should the company do that? Like there's a bunch of misinformation out there. People say like, we left because we didn't like the deal with Microsoft. False.

Although, you know, it was like a lot of discussion, a lot of questions about exactly how we do the deal with Microsoft. We left because we didn't like commercialization. That's not true. We built GPT-3, which was the model that was commercialized. I was involved in commercialization. It's more, again, about how do you do it? Like... civilization is going down this path to very powerful AI.

What's the way to do it that is cautious, straightforward, honest, that builds trust in the organization and in individuals? How do we get from here to there? And how do we have a real vision for how to get it right? How can safety not just be something we say because it helps with recruiting?

And, you know, I think at the end of the day, if you have a vision for that, forget about anyone else's vision. I don't want to talk about anyone else's vision. If you have a vision for how to do it, you should go off and you should do that vision. It is incredibly unproductive to try and argue with someone else's vision. You might think they're not doing it the right way.

You might think they're dishonest. Who knows? Maybe you're right. Maybe you're not. Um, uh, but, uh, what, what you should do is you should take some people you trust and you should go off together and you should make your vision happen. And if your vision is compelling, if you can make it appeal to people, some, you know,

If you can make a company that's a place people want to join, that engages in practices that people think are reasonable while managing to maintain its position in the ecosystem at the same time, if you do that, people will copy it.

And the fact that you are doing it, especially the fact that you're doing it better than they are, causes them to change their behavior in a much more compelling way than if they're your boss and you're arguing with them. I don't know how to be any more specific about it than that, but I think it's generally very unproductive to try and get someone else's vision to look like your vision.

It's much more productive to go off and do a clean experiment and say, this is our vision. This is how we're going to do things. Your choice is you can ignore us, you can reject what we're doing, or you can start to become more like us, and imitation is the sincerest form of flattery.

And, you know, that plays out in the behavior of customers, that pays out in the behavior of the public, that plays out in the behavior of where people choose to work. And again, again, at the end, it's not about one company winning or another company winning if...

If we or another company are engaging in some practice that, you know, people people find genuinely appealing and I want it to be in substance, not just not just in appearance. And, you know, I think I think researchers are sophisticated and they look at substance. and then other companies start copying that practice and they win because they copied that practice, that's great. That's success.

That's like the race to the top. It doesn't matter who wins in the end, as long as everyone is copying everyone else's good practices, right? One way I think of it is like, the thing we're all afraid of is the race to the bottom, right? And the race to the bottom doesn't matter who wins because we all lose, right?