The Exchanges

Q What does it mean to win the AI race? Like, why, why do we need to win that? And what, like, what would losing mean? How do we know we've won?

A Well, I'm, I'm suspect you folks agree. AI might be the most transformational economic cultural force of our lifetime. And I believe that if the country or the ecosystem Which winds up getting ahead is going to have these cyclic effects, right? Like you're going to, you know, you're going to power productivity. You're going to have drug discovery. You're going to, you know, discover, uh, new material sciences, new technologies, which then feed back into your infrastructure, feed back into your economy. And you're going to get this flywheel effect where who winds up getting ahead could wind up really accelerating ahead in kind of a classic network effect ecosystem way that all of us in Silicon Valley will understand. Now that is purely on the civilian economic Context. You can also imagine a military context, right? Think about everything from drones to autonomous weapons. I'm pretty sure it's not in our best interest to have another country have that same economy of scale and flywheel and race ahead of us. So that's the race. Now, one interesting question that we have been pondering, which we can get it to, is how do you actually measure what it means? How are we doing in the race? And one measure I've been playing around with, and maybe I want to get your take on, is I think Google just announced this morning, uh, that they inference one quadrillion tokens a month or a quarter…

AI assessment note: “What share of those tokens are being inferenced on American hardware on American models”

Q put together this plan were? What are the things that you worry about geopolitically? How do you think about AI and competition, big tech versus small tech? Like, it feels like there's a lot of threads in that, and it'd be great just to get a view of what are the main, the main issues that created this plan, and then it'd be great to talk through the plan itself.

A One of the catalytic moments which happened was the day before I started this job, I get a call, and this was the weekend DeepSecret came out, and there's actually some chatter that I've heard online that China timed it so it can come out right after the president got sworn in. And we were like, hey, we just want you to come in and brief a lot of people at the White House on Deep Seek because we were like, hey, what is this? Is it cheaper? Is it faster? Do they have some magic way of training these models which only cost a few million bucks and not, you know, hundreds of billions of dollars? What's going on? You folks might remember that narrative which existed that weekend. And so I got to go and, you know, me and David helped brief all of the White House leadership. But it was really a starting gun because I think that moment was profound because it immediately told us a few things. It told us that America doesn't have a huge lead. On AI. It actually has a very, very small lead. Uh, if you remember at the time, DeepSeq was the only reasoning model, which was not open AI. So I don't think Claude had come out of the reasoning model yet. I don't think, uh, Google had yet. It was the only non-open AI reasoning model. It was very high up in the leaderboards. Uh, it was a bit unclear as to what their cost claims were and how they had gotten there. I think we know a lot better.

AI assessment note: “It told us that America doesn't have a huge lead. On AI.”

Q of Manhattan, or, you know, it's the energy that a city uses at any point. Can you contextualize like how much capacity we really need to build and sort of like what the biggest bottleneck is? Like, is it, is it the grid? Is it sources? Is it workforce? Like, you know, when you want to solve this problem, like as a systems person, like what is the first problem?

A The first thing I would say is it is a system and this system was one that wasn't really battle tested for decades. Somebody showed me this number. I think the United States basically had like one to two percent of power usage growth for a very, very long period of time. And so you can imagine this whole system of everything from gas turbines, coal, renewable energy. There was a regulation of which kind of really stopped nuclear. And then you had these per state utility companies, which often didn't have the incentive to innovate. You basically kind of ran the state. You weren't really, you know, getting new demand or competition. You had a grid, which wasn't really pushed because again, you didn't need to. And, uh, and then you have essentially a patchwork of environmental laws, regulation, everything from water to emission to a whole other sort of things that I'm sure I'm forgetting. Right? So somebody kind of explains to me as kind of this tangled spaghetti mess of things, which again, Until two years ago was just fine because you and I were not dramatically using more energy than what we were doing 10 years ago. Now that obviously changed. The scaling laws arrived and everybody is trying to build new things. And I think the way we are trying to attack it is at every single step of the way, which is one is how do you make generation better? Second is how do you make sure we …

AI assessment note: “the way we are trying to attack it is at every single step”

Q Hey, Sriram, you used to work in social media for a long time, right? Like, this sounds a little bit familiar in terms of, like, is it a platform? Is it a publisher? What is the information consumption that most consumers have? Like, where does that analogy apply or break down?

A It's a good question. I think in some ways that's for the industry and the ecosystem to answer a bit. You're right. I spent a lot of time at Facebook, now Meta at Twitter. One of the things I saw when I was at Twitter was how easily you could inject cultural bias in your algorithms. I have so many stories about how if you pick the right kind of Twitter accounts, which then feed into the trending algorithm, which then feed into Twitter moments, and then with every journalist or editor will wake up. And next thing you know, it's like one of the news stories off the land and Buzzfeed will write a piece saying, People on the internet are talking about this. I saw this over and over again, and it left me this profound appreciation of how algorithms can shape culture. And, you know, one of the things I always say is Twitter or X is the memetic battleground upon which we fight a lot of these ideological battles. So when it comes to AI, I think it's probably going to be very similar. Like my kids use ChatGPT to answer everything, right? From history to geography to, you know, just kind of silly kids' questions. And you can easily imagine a world where people inject their own cultural biases into this. And, you know, in the EU, we have a few good examples. We have, you know, we have examples of the Pope being seen as a black person, misgendering someone being seen as worse than thermonu…

AI assessment note: “So when it comes to AI, I think it's probably going to be very similar.”

Q The other thing that I think is interesting from an infrastructure perspective is manufacturing capability and supply chain. And a subset of AI supply chain is dependent on China or other countries. Are there certain areas of supply chain that we should be repatriating back? Or how should we be thinking about more generally American manufacturing?

A I say that America needs not just engineers, but it needs people up and down the stack. It needs electricians, technicians. We need to get Get construction going and we need to get these jobs and this whole ecosystem back in, um, back in the US. So if you look at the action plan, there's a bunch of stuff in there about this. I think I mentioned two parts of the action plan, which is building and then innovation on cutting out red tape and open source. And the president also talked a little bit about, uh, copywriters today. The third piece of the app action plan, which I also think is a pretty dramatic switch away from how the Biden folks thought about it is around making sure the world uses our Standards on our technology. So just for context, and again, this is something, unless you are a policy one, wonk, you may not be super familiar with, uh, under the Biden era, there was something called the Biden diffusion rule, which basically was a 200 page document, which basically made it illegal for America to export GPUs. It was really hard for, uh, you know, if you're Jensen or if you're Lisa Su to really kind of get your GPUs out to other countries, even some of our allies who want to act, who are really enthusiastic about AI. And they want to help us out, but we're not actually giving them GPUs. So we listened to that order, and one of the things we talked about is how do we mak…

AI assessment note: “The third piece of the app action plan... is around making sure the world uses our Standards”

Q And I think, you know, you've gotten something like 90 different agency actions listed in this action plan. And how do you think about these things actually translating into industry, the economy, action by companies and other players? Like what, what are the mechanisms that you all have to sort of, uh, ensure that these things come together or happen? And if they don't come together, what's, what's plan B?

A Well, there is no plan B. We want to get this done. And I think one of the things of the Trump administration you will see is that the administration moves really, really fast, you know, which is why The first week we had a bunch of executive orders. Uh, look, we already work in all of it. Um, we had three executive orders signed yesterday, one for infrastructure, one for export, which kind of ties to a lot of things we talk about and one to stop ideology and wokeness and DEI. And I think you're going to see a lot more. We are already at work on, um, pretty much all of it. That is no plan B. We're gonna go get this done. And the other part I would say from yesterday is I've been inundated with just great response from the industry. A lot of folks that you and I know, uh, who are just really excited to see the government actually maybe understand AI and is actually happy to, uh, you know, make sure that American companies can go, uh, build American AI. So I think they're also very excited to go partner with us. So it's go, go, go. No time to waste. We're getting it done. There is no plan B.

AI assessment note: “Well, there is no plan B. We want to get this done.”