Dario Amodei: belief

Transcript context

…al institutions, entities that are slow to change. But, the dynamic I see over and over again is yes, it takes a long time to move the ship. Yes. There’s a lot of resistance and lack of understanding. But, the thing that makes me feel that progress will in the end happen moderately fast, not incredibly fast, but moderately fast, is that you talk to … What I find is I find over and over again, again in large companies, even in governments which have been actually surprisingly forward leaning, you find two things that move things forward. One, you find a small fraction of people within a company, within a government, who really see the big picture, who see the whole scaling hypothesis, who understand where AI is going, or at least understand where it’s going within their industry. And there are a few people like that within the current US government who really see the whole picture. And those people see that this is the most important thing in the world until they agitate for it. And the thing they alone are not enough to succeed, because there are a small set of people within a large organization. But, as the technology starts to roll out, as it succeeds in some places in the folks who are most willing to adopt it, the specter of competition gives them a wind at their backs, because they can point within their large organization. They can say, “Look, these other guys are doing this.” One bank can say, “Look, this newfangled hedge fund is doing this thing. They’re going to eat our lunch.” In the US, we can say we’re afraid China’s going to get there before we are. And that combination, the specter of competition plus a few visionaries within these, the organizations that in many ways are sclerotic, you put those two things together and it actually makes something happen. It’s interesting. It’s a balanced fight between the two, because inertia is very powerful, but eventually over enough time, the innovative approach breaks through. And I’ve seen that happen. I’ve seen the arc of that over and over again, and it’s like the barriers are there, the barriers to progress, the complexity, not knowing how to use the model, how to deploy them are there. And for a bit it seems like they’re going to last forever, change doesn’t happen. But, then eventually change happens and always comes from a few people. I felt the same way when I was an advocate of the scaling hypothesis within the AI field itself and others didn’t get it. It felt like no one would ever get it. Then it felt like we had a secret almost no one ever had. And then, a couple years later, everyone has the secret. And so, I think that’s how it’s going to go with deployment AI in the world. The barriers are going to fall apart gradually and then all at once. d then, a couple years later, everyone has the secret. And so, I think that’s how it’s going to go with deployment AI in the world. The barriers are going to fall apart gradually and then all at once. And so, I think this is going to be more, and this is just an instinct. I could easily see how I’m wrong. I think it’s going to be more five or 10 years, as I say in the essay than it’s going to be 50 or 100 years. I also think it’s going to be five or 10 years more than it’s going to be five or 10 hours, because I’ve just seen how human systems work. And I think a lot of these people who write down these differential equations, who say AI is going to make more powerful AI, who can’t understand how it could possibly be the case that these things won’t change so fast. I think they don’t understand these things. So what to you is the timeline to where we achieve AGI, A.K.A. powerful AI, A.K.A. super useful AI?…

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