The Exchanges

Q To what extent do you think enterprises today are willing to let passive AI products into their enterprises to observe, to learn, to test? And is there really that willingness, do you think?

A I think it's, it's, it's gotta be more than passive observation. It's got, you have to actually deploy AI to be able to, uh, get to certain types of learning, and I think that's gonna be very slow, and I think the, uh, a good analogy is self-driving cars, of which we had prototypes, you know, two or three decades ago, but for, for these things to actually be deployed, you have to roll it out on slightly larger and larger scales while you collect data While you make sure you get to the next nine of reliability, you know, four nines of reliability to five nines of reliability, so it's that very slow rollout process. It's a very slow feedback loop, and I think that's going to happen with a lot of AI deployment in organizations as well.

AI assessment note: “I think it's, it's, it's gotta be more than passive observation.”

Q kind of move a layer deeper to the companies building the products and the leaders leading those companies. You've got Zach and Demis who are saying that AGI is further out than we think. And then you have Sam Altman and you have, uh, Dario and Elon in some cases saying it's sooner than we think. What are your reflections and analysis on company leader predictions on AI, on AGI?

A So let's talk for a second about what AGI is. Different people mean different things by it and so often talk past each other. Um, the definition that we consider most relevant is AI that is capable of automating most economically valuable tasks. So it's a very pragmatic definition. It doesn't care about, you know, whether it's conscious, doesn't think like a person, Those questions are, uh, frankly not that interesting to us, but also harder to predict or reason about. Uh, and so by this definition, you know, of automating most economically valuable tasks, if we did have AGI, that would truly be a profound thing in our society. Okay, so now for the CEO predictions. I think one thing that's helpful to keep in mind is that there have been these predictions of imminent AGI since the earliest days of AI for more than a half century. Alan Turing, when the first, uh, computers were built or about to be built, people thought, you know, the two main things we need for AI are hardware and software. We've done the hard part, the hardware, and now there's just one thing left, the easy part, the software. Uh, but of course now we know how hard that is, so I think historically what we've seen is it's kind of like climbing a mountain. Wherever you are, it looks like there's just kind of one step to go, but when you climb up a little bit further, the complexity reveals itself, and so we've se…

AI assessment note: “I wouldn't put too much stock into these overconfident”

Q If you, uh, Suggesting, as you said at the beginning about kind of your work on policy, you have US regulators and European regulators. What would you put forward as the most proactive and effective policy for US and European regulation around AI and models?

A So in a sense, AI regulation is a misnomer. Let me give you an example from just this morning. The FTC, uh, has been worried about, uh, the Federal Trade Commission in the US, uh, Uh, you know, which is, um, an antitrust and consumer protection authority has been worried about, uh, people writing fake reviews for their products, and this has, of course, been a problem for many years. It's become a lot easier to do that with AI. So now, someone who thinks about this in terms of AI regulation might say, oh, you know, regulators have to ensure that AI companies don't allow their products to be used for generating fake reviews. And I think this is a losing proposition. Like, how would an AI model know whether something is a fake review or a real, real review, right? It just depends on who's, uh, writing their review. But instead, you know, that's not the approach that the FTC took. They recognized correctly that it's a problem whether AI is generating the fake review or people are. So what they actually banned is fake reviews, right? And so what is often thought of as AI regulation is better understood as regulating certain harmful activities, whether or not AI is Used as a tool for doing those harmful activities. So I think, you know, 80% of what gets called AI regulation is better seen this way.

AI assessment note: “regulating certain harmful activities, whether or not AI is Used as a tool”

Q To what extent do you think enterprises today are willing to let passive AI products into their enterprises to observe, to learn, to test? And is there really that willingness, do you think?

A I think it's, it's, it's gotta be more than passive observation. It's got, you have to actually deploy AI to be able to, uh, get to certain types of learning, and I think that's gonna be very slow, and I think the, uh, a good analogy is self-driving cars, of which we had prototypes, you know, two or three decades ago, but for, for these things to actually be deployed, you have to roll it out on slightly larger and larger scales while you collect data While you make sure you get to the next nine of reliability, you know, four nines of reliability to five nines of reliability, so it's that very slow rollout process. It's a very slow feedback loop, and I think that's going to happen with a lot of AI deployment in organizations as well.

AI assessment note: “I think it's, it's, it's gotta be more than passive observation.”

Q performance. A lot of people say, well, there's a lot of data that we haven't mined yet, which the obvious example that many have suggested is kind of YouTube, which has obviously I think a hundred and fifty billion hours of video. Um, and then secondarily to that, synthetic data, the creation of artificial data that hasn't, that isn't exist in existence yet. To what extent are those effective pushbacks?

A So there are, uh, there are a lot of sources that haven't been mined yet, but when we start to look at the volume of that data, how many tokens is that? I think, uh, the picture is a little bit different. A hundred and fifty billion hours of video sounds, you know, really impressive. Uh, but when you put that video through a speech recognizer and actually extracts the text tokens out of it and deduplicated and so forth, it's actually not that much. It's an order of magnitude smaller than, uh, what some of the largest models today have already been trained with. Now, training on video itself instead of text extracted from the video, uh, I think that could be, uh, that could lead to some new capabilities, but not in the same fundamental way That we've had before where you have the emergence of new capabilities, uh, right? Models being able to do things, uh, that, uh, people just weren't anticipating. So, like, the kind of shock that the AI community had when I think back in the day, I think it was GPT-II was trained primarily on English text, and they had actually tried to filter out text in other languages to keep it clean, but a tiny amount of text from other languages had gotten into it, and it turned out that that was enough for the model Uh, to pick up a reasonable level of competence for conversing in various other languages. So these are the kinds of emergence capabilities…

AI assessment note: “when you put that video through a speech recognizer... it's actually not that much”

Q kind of move a layer deeper to the companies building the products and the leaders leading those companies. You've got Zach and Demis who are saying that AGI is further out than we think. And then you have Sam Altman and you have, uh, Dario and Elon in some cases saying it's sooner than we think. What are your reflections and analysis on company leader predictions on AI, on AGI?

A So let's talk for a second about what AGI is. Different people mean different things by it and so often talk past each other. Um, the definition that we consider most relevant is AI that is capable of automating most economically valuable tasks. So it's a very pragmatic definition. It doesn't care about, you know, whether it's conscious, doesn't think like a person, Those questions are, uh, frankly not that interesting to us, but also harder to predict or reason about. Uh, and so by this definition, you know, of automating most economically valuable tasks, if we did have AGI, that would truly be a profound thing in our society. Okay, so now for the CEO predictions. I think one thing that's helpful to keep in mind is that there have been these predictions of imminent AGI since the earliest days of AI for more than a half century. Alan Turing, when the first, uh, computers were built or about to be built, people thought, you know, the two main things we need for AI are hardware and software. We've done the hard part, the hardware, and now there's just one thing left, the easy part, the software. Uh, but of course now we know how hard that is, so I think historically what we've seen is it's kind of like climbing a mountain. Wherever you are, it looks like there's just kind of one step to go, but when you climb up a little bit further, the complexity reveals itself, and so we've se…

AI assessment note: “I wouldn't put too much stock into these overconfident”

Q have a tutor in your pocket. Yeah, I get you, but we do also have your videos that we can watch at home. Like a tutor has personal relationships. It's one-to-one where I want to impress you, Arvind, and I have that personal desire to fulfill, you know, abilities, potentials that doesn't have. How do you think AI impacts the future of education, one-on-one tuition, and that up-leveling of students?

A I think there's, Uh, you know, different populations of students. I think, you know, there's a small subset of learners who are very self-motivated, will learn very well, even if there's no, uh, you know, physical tutor, uh, whether it's at the, uh, uh, the primary school level, or it's at the college level, or at the expert level. I think those, there are those kinds of learners at, uh, at all different levels. And then there's the vast majority of learners for whom the social aspect of learning is really the most critical thing. And if you take that away, um, they're just not going to be able to learn very well. And I think this is often forgotten, especially because in the AI developer community, there are a lot of these, uh, self-taught learners. I'm among them, right? I just paid zero attention throughout school and college and everything that I know literally is stuff that I taught myself. So I grew up in India. The education system wasn't very great there. Uh, our geography teacher thought that India was in the southern hemisphere. True story. Right, right. So again, I, I literally mean it when I say everything that I know I taught myself. Um, and so, you know, you have a lot of AI developers who are thinking of themselves as the typical learner, and they're not. And I think for someone like me, AI is on a daily basis, uh, an incredible, uh, tool for, for learning. I use…

AI assessment note: “for the vast majority of learners for whom the social aspect of learning is really the most critical”

Q performance. A lot of people say, well, there's a lot of data that we haven't mined yet, which the obvious example that many have suggested is kind of YouTube, which has obviously I think a hundred and fifty billion hours of video. Um, and then secondarily to that, synthetic data, the creation of artificial data that hasn't, that isn't exist in existence yet. To what extent are those effective pushbacks?

A So there are, uh, there are a lot of sources that haven't been mined yet, but when we start to look at the volume of that data, how many tokens is that? I think, uh, the picture is a little bit different. A hundred and fifty billion hours of video sounds, you know, really impressive. Uh, but when you put that video through a speech recognizer and actually extracts the text tokens out of it and deduplicated and so forth, it's actually not that much. It's an order of magnitude smaller than, uh, what some of the largest models today have already been trained with. Now, training on video itself instead of text extracted from the video, uh, I think that could be, uh, that could lead to some new capabilities, but not in the same fundamental way That we've had before where you have the emergence of new capabilities, uh, right? Models being able to do things, uh, that, uh, people just weren't anticipating. So, like, the kind of shock that the AI community had when I think back in the day, I think it was GPT-II was trained primarily on English text, and they had actually tried to filter out text in other languages to keep it clean, but a tiny amount of text from other languages had gotten into it, and it turned out that that was enough for the model Uh, to pick up a reasonable level of competence for conversing in various other languages. So these are the kinds of emergence capabilities…

AI assessment note: “when you put that video through a speech recognizer... it's actually not that much”

Q I'd just love to start before we dive in deep on infrastructure. How does the AI hype today compared to Bitcoin hype? How is it the same, and how is it different?

A So I spent years of my time on this. I really believed that Decentralization, uh, could have tremendous societal impacts. And that was the angle that really mattered to me, right? How is this going to make society better? It was not the money angle. But by around 2018, I had started to get really disillusioned. And that was because of a couple of main things. One is, uh, in a lot of cases where I thought, where I had thought crypto or blockchain was going to be the solution, I realized that that was not the case. So for instance, Uh, you know, while there is potential for crypto to help the world's unbanked, uh, the tech is not the real bottleneck there. And the other part of it was just a philosophical aspect of this community. Uh, you know, I, 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 these institutions with a script. And, uh, 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, uh, around AI, I think it has been a net…

AI assessment note: “I think it has been a net positive for society. I can't say the same”