Arvind Narayanan

One is, in a lot of cases where I had thought crypto or blockchain was going to be the solution, I realized that that was not the case. While there is potential for crypto to help the world's unbanked, the tech is not the real bottleneck there. And the other part of it was just a philosophical aspect of this community.

One is, in a lot of cases where I had thought crypto or blockchain was going to be the solution, I realized that that was not the case. While there is potential for crypto to help the world's unbanked, the tech is not the real bottleneck there. And the other part of it was just a philosophical aspect of this community.

I do believe that many of our institutions are in need of reform or maybe decentralization, whatever it is. And that includes academia, by the way, so many reforms so badly needed. And in an ideal world, we would have this, you know, hard but important conversation about how do you fix our institutions. But instead, these students have been sold on blockchain and they want to replace their

I do believe that many of our institutions are in need of reform or maybe decentralization, whatever it is. And that includes academia, by the way, so many reforms so badly needed. And in an ideal world, we would have this, you know, hard but important conversation about how do you fix our institutions. But instead, these students have been sold on blockchain and they want to replace their

I do believe that many of our institutions are in need of reform or maybe decentralization, whatever it is. And that includes academia, by the way, so many reforms so badly needed. And in an ideal world, we would have this, you know, hard but important conversation about how do you fix our institutions. But instead, these students have been sold on blockchain and they want to replace their

these institutions with a script. And that just didn't seem like the right approach to me. So both from a technical perspective, and from a philosophical perspective, I really soured on it. While there are harms around AI, I think it has been a net positive for society. I can't say the same thing about Bitcoin. Are we in an AI hype cycle right now? I think that's possible.

these institutions with a script. And that just didn't seem like the right approach to me. So both from a technical perspective, and from a philosophical perspective, I really soured on it. While there are harms around AI, I think it has been a net positive for society. I can't say the same thing about Bitcoin. Are we in an AI hype cycle right now? I think that's possible.

these institutions with a script. And that just didn't seem like the right approach to me. So both from a technical perspective, and from a philosophical perspective, I really soured on it. While there are harms around AI, I think it has been a net positive for society. I can't say the same thing about Bitcoin. Are we in an AI hype cycle right now? I think that's possible.

Generative AI companies specifically made some serious mistakes in the last year or two about how they went about things. What mistakes did they make, Harvind? So when ChatGPT was released, people found, you know, a thousand new applications for it, right? That OpenAI application. might not have anticipated. And that was great.

Generative AI companies specifically made some serious mistakes in the last year or two about how they went about things. What mistakes did they make, Harvind? So when ChatGPT was released, people found, you know, a thousand new applications for it, right? That OpenAI application. might not have anticipated. And that was great.

Generative AI companies specifically made some serious mistakes in the last year or two about how they went about things. What mistakes did they make, Harvind? So when ChatGPT was released, people found, you know, a thousand new applications for it, right? That OpenAI application. might not have anticipated. And that was great.

But I think developers, AI developers, took the wrong lesson from this. They thought that AI is so powerful and so special that you can just put these models out there and people will figure out what to do with them. They didn't think about actually building products, making things that people want, finding product market fit, and all those things that are so basic in tech.

But I think developers, AI developers, took the wrong lesson from this. They thought that AI is so powerful and so special that you can just put these models out there and people will figure out what to do with them. They didn't think about actually building products, making things that people want, finding product market fit, and all those things that are so basic in tech.

But I think developers, AI developers, took the wrong lesson from this. They thought that AI is so powerful and so special that you can just put these models out there and people will figure out what to do with them. They didn't think about actually building products, making things that people want, finding product market fit, and all those things that are so basic in tech.

But somehow, AI companies deluded themselves into thinking that the normal rules don't apply here.

But somehow, AI companies deluded themselves into thinking that the normal rules don't apply here.

But somehow, AI companies deluded themselves into thinking that the normal rules don't apply here.

So if we look at what's happened historically, the way in which compute has improved model performance is with companies building bigger models. In my view, at least the biggest thing that changed between GPT-3.5 and GPT-4 was the size of the model. And it was also trained with more data, presumably, although they haven't made the details of that public and more compute and so forth.

So if we look at what's happened historically, the way in which compute has improved model performance is with companies building bigger models. In my view, at least the biggest thing that changed between GPT-3.5 and GPT-4 was the size of the model. And it was also trained with more data, presumably, although they haven't made the details of that public and more compute and so forth.

So if we look at what's happened historically, the way in which compute has improved model performance is with companies building bigger models. In my view, at least the biggest thing that changed between GPT-3.5 and GPT-4 was the size of the model. And it was also trained with more data, presumably, although they haven't made the details of that public and more compute and so forth.