Inside America's AI Strategy: Infrastructure, Regulation, and Global Competition

Great to see everyone. And I'm thrilled to be able to talk about the issue of the day, and that is artificial intelligence and AI in our world. David. Michael, I'd love you to talk about where we are right now in terms of the pursuit to be the number one lead AI country. How are we doing, David?

0:00

I think we're doing great, Maria. Last year, President Trump gave a major AI policy speech, this is in July, and he declared that the United States had to win the air race. He, he had, first of all, declared that we were in one. And I think his speech was reminiscent of when President Kennedy declared that we were in a space race and had to win that race. I think since then, what you've seen is that American companies have only innovated more. You're seeing all sorts of really incredible products being released all the time. I think that American AI models, chips, data centers only just keep getting better and better. So I feel very good about the American position in this ara. Certainly we have some very competent and formidable competitors. China obviously has a lot of very smart people working in this area. But I do think that just what you see from American companies in Silicon Valley right now is really incredible.

0:21

And yet there are still so many questions about all of the spending underway to build this out with regard to data centers. And of course, the question keeps coming up, are we spending too much? Will we get the return on investment? How do you see that?

1:20

I think that we will. I think that the reason why you're seeing this huge infrastructure build out is because the demand is ultimately there. I know a lot of people worry about whether this could be like a.com situation. Remember where we had the whole fiber build out in the late 90s, then we had a dot com crash. The difference here is that in the late 90s and early 2000s, we had a problem known as dark fiber, where you had this fiber build out and then it didn't get get used. There's no such thing as a dark gpu. Right now. Every GPU that's being put in a data center is getting used and it's being used to generate tokens and that's to power this new generation of AI chatbots or coding assistants. And there's just been some releases in the last couple of months on the coding front that, you know, it's, if you're following what develop software developers are saying, they're saying it's mind blowing, it's completely revolutionizing their industry. So demand for tokens just increases and that increases the demand for this data center build out that we're seeing. So I don't think it's going to stop anytime soon. And just last year this infrastructure buildout added about 2% to the GDP growth rate. And I think that's what helped propel us to this 4 to 5% growth rate. And I think you're going to see something similar this year.

1:34

Well, it is certainly leading growth, Michael, and I'm so happy to be able to get this conversation going with both of you who are really leading this. David, thank you. And Michael, thank you. Same questions for you, Michael. Assess where we are right now on.

2:48

As yeah, I think just a reminder for the group, for those who haven't been tracking as closely as we do every day, the plan really had essentially three pillars and it talked about how one, how can the US continue to out innovate our competitors? Two, how can we drive the infrastructure bill that we need to support this AI revolution? And three, how do we actually share with the world or export our great American technology? And for each of those three pillars, there was quite a lot of actions that the federal government has taken to drive that forward. And I think we're pretty proud to say that we've made, I think, pretty good progress on all three. Just focusing a little bit on the innovation one you're talking about earlier. I think the, the, the, the, the core insight that we've always had about how you drive this innovation is you have to have a regulatory environment that allows this technology to be developed and ultimately commercialized in the United States. And the US has done a, a great job compared to the rest of the world on sort of setting that up and creating a framework that works, but we could always do better and improve it. And the President in his, his speech in July talked a lot about this issue of a patchwork of state regulations and how can we ensure that there aren't 50 different rules around AI. And what's most important about this debate, which I think a lot of people sometimes don't sometimes miss, is the patchwork is actually most detrimental to early stage young companies and entrepreneurs. If you want to develop a new AI technology, if you want to build something on top of one of our great frontier models, having to figure out how to navigate 50 different rules across 50 different states creates a lot of friction. And ultimately the big guys are the ones that can succeed in that environment the best. So we're spending a lot of time trying to think about how can you create a legislative proposal that can actually deliver on a sensible national framework to solve to solve this regulatory issue.

3:01

So what would you say then, Michael, are the basic frameworks that are sort of must have in that kind of federal oversight? Because some states did push back in the US and say, no, no, we want to be able to control our destiny when it comes to AI. What's most important when you look at that framework in terms of a federal oversight?

4:49

Yeah, I think in the executive order the President signed in, in December directing us to kind of work through this proposal, he listed a few things that the state should continue to be able to pursue individually on their own. Legislation or rules around child safety was on that list. The rules around permitting of data centers and build outs are continuing to be something that states should, should look at. So there are a few things that were enumerated, but that's the kind of stuff that I guess Dave and I are going to be working through. I don't know if you have any thoughts on, on that.

5:12

Yeah, I mean, I think the, the basic problem that we have is that, I mean frankly, the states are going hog wild right now with regulation. There's over 1200 bills going through state legislatures right now. I think it's very much a knee jerk reaction. I know there's a lot of fears and concerns about AI, but it seems like for every hypothetical concern, there's multiple state bills now to try and regulate that thing before we really know how it's going to play out. And I think it would be better to, I think since this technology is so new and the environment is so dynamic, I think it'd be better to spend a little bit more time studying how AI is actually being used and what risks are actually materializing before you overregulate the thing. But in any event, that, that's the, what we're seeing right now at the state level. And, and, and I think that the President's been very consistent that it would be better to have a single, have one rule book, a single rulebook at the federal level. Lightweight federal standard. I think this problem's only going to get more acute over time because again, you, you, as you have 50 different states running in 50 different directions, the patchwork problem only gets more significant. So in any event, this is something that we're going to work, I think, closely together on this year, which is to see if we can get enough consensus on a federal framework to enact a law. Only Congress can ultimately preempt the states. We understand that. And you know, as you know, it's very difficult to get a bill through Congress. You need 60 votes in the Senate. So that's be bipartisan to, to a certain degree. So. But we're going to try and see if we can work to get that consensus.

5:39

Yeah. And do you have any clarity on the timing on that in terms of support in Congress for a federal oversight, or do you see pushback there as well, depending on the state you're talking about?

7:13

Well, there's pushback in Congress to the idea of preemption without a federal standard. So in other words, you can't replace something with nothing. This is sort of the thing that we heard repeatedly. But I think there is a quite a bit of interest in both the House and the Senate towards having again, some sort of lightweight federal standard. But we're still in the early stages of those conversations and we're going to see what we can try and get done this year.

7:25

Meanwhile, you've got some people pushing back after wanting to see the innovation and growth of data centers. Now they're saying, not in my backyard. What about that? Is that an issue?

7:48

Yeah, I mean, we got a letter recently from Bernie Sanders saying stop all data centers, all data center development. And, you know, if we do that, we will lose the AI race. I mean, you do need this infrastructure. Other countries are building out this infrastructure China's building out. I think they're spinning up a new nuclear power plant or coal plant, new energy every single week. And a lot of that is going to power their data center. So it would fundamentally, I think, cripple the United States in the AI race if we just stopped building data centers altogether. At the same time, there are concerns about affordability, about whether consumers would have to pay a higher electrical rate because of data centers. President Trump's been really clear that consumers should not have to pay higher rates for electricity because of data centers. You saw just last week, Microsoft stepped up and made a pledge that it will that its data centers will not cause residential rates to increase. I think you'll likely see other tech companies stepping up and making similar commitments. And in fact, when I've talked to the hyperscalers and when I've talked to the AI companies, it was never their plan to draw off their grid. They all saw standing up their own power generation as part of their build out. And what Secretary Wright, our Secretary of Energy has been doing is trying to is reform the regulations that actually make it more difficult for these AI data centers to stand up their own power behind the meter. So that basically is our vision is let. And I should say this is President Trump's vision really, since the beginning of the administration is he said, let the AI companies become power companies. Let them stand up their own power generation as they built, you know, side by side with these new data centers. And the, the result of that is, you know, a, we get this infrastructure, B, residential rates don't go up.

7:58

Yeah. Because Michael, this, this race has fast become, it's moved from an air race to a power race.

9:47

And I think what we're seeing is that we need to share a good story about how ultimately this build out is going to be net positive for American ratepayers. And I think sometimes if, you know, if you're in a small community and someone shows up to build a data center, I mean, you have to make it clear that ultimately this something's going to actually lower your, your rates long term. And, and the President put out a truth last Monday where he was, as David said, very clear that, you know, if you're going to build a data center, you have to pay your own way for it. And Microsoft has stepped up and our hope is that many others will do the same.

9:56

But some companies, because they don't have the cash right now, are borrowing money. Right. To build out the data centers. And there's also a worry that the banks will be left holding the bag for some of this because again, the spending is too much. Your thoughts on that?

10:28

Well, I think there is obviously that concern. I mean, you know, I think it's, it's less. I would say the banks are more. You see Oracle making a huge investment. You see Blackstone making huge investments. Real estate companies, ultimately, I think these are very savvy market players, very deep pocketed companies. And they're doing this because they see an ROI there at the end of the rainbow. Just make one other point about just the data center. So just on electricity, I actually think that if we allow the data centers to stand up their own power generation, it will actually bring down rates. Not only will it not increase residential rates, it'll bring it down. And it'll do that in two ways. One is that the data centers can give or sell power back to the meter when they have excess. So that will help bring down rates. Second, there's a lot of fixed costs involved in power generation. It's not all variable. So when you're able to amortize those fixed costs over a greater supply, you bring down the meter rate for everybody. And so there's huge economies of scale. So the more scale you get in electricity, like most other things, the price comes down. That's. So it's actually a good thing that the, that we have this build out going on because it will ultimately reduce prices for consumers. But we do have to make sure that these new data centers aren't just plugging into the grid and using, they have to be contributing back.

10:45

And I think what a great policy change has made under this administration. The Biden administration had, as a matter of policy, had made it such that you couldn't do this behind the meter energy generation. If you wanted to bring your own power, you couldn't, you had to be part of the larger grid. So I think that rule has changed by Secretary Wright and by FERC to kind of allow this to happen. And ultimately I agree with David. I think once you have sort of greater scale in the power generation, you'll be contributing back into the grid in a way that benefits ratepayers.

12:12

But let's go back to the uses and how AI is changing our lives. You mentioned earlier all of the uses and the impact the AI is having. What do you see as the most important use and where AI is being deployed and implemented best right now?

12:40

Well, it's interesting. I think there's been an evolution. So I think we started with AI chatbots like ChatGPT and in a sense that was kind of like better web search. It was really great for research. You ask IT questions and give you answers to anything. Then we saw models add chain of thought and they could start to do deeper reasoning. Then we saw coding assistance. And I think over the past few months there's been a real breakthrough. If you talk to people, software developers, it really seems like there's been a major shift in just improvement in the quality of the coding assistance. And I think where that's going next is tools for knowledge workers. So the same types of assistance that have been outputting code can now output any type of format. So whether it's like Excel models, PowerPoints, websites, you name it, knowledge workers are now going to be able to generate all these different types of things the same way that coders have been, that software developers have been using. AI generate code. I think that's one of the big things you're going to see in 2026 is again this productivity boom for knowledge workers. So I think that's like one of the things you're seeing on the ground. And then separately there's a bunch of things happening in industry verticals, so different industries being impacted by AI. So in healthcare, I think there's a tremendous opportunity to improve or to reduce sort of administrative bureaucracy to improve this Processing of paperwork that happens also to use AI and medical and scientific research to help find new cures. You're already seeing users tell all sorts of stories about diagnoses. They've been able to put in their medical records into ChatGPT or other chat engine or chat bots and get, like, remarkable results. They've been able to finally figure out what was, you know, what, what was wrong with them. And they've been able to take that to a doctor. You have doctors using it too. So medical, I think, is a really interesting area, but there's a whole bunch of these examples of different industries are now being impacted.

12:58

The one area I think a lot about is AI for science. And back to David's initial point about the progress we've seen in these frontier models. I think the very early ones sort of started with just general knowledge, and you have to go back and understand why. And the question was, what was the data available for those model builders to start training their models? And for the early ones, you could just scrape the Internet and just kind of cram everything to a model and train it. And, and that's where you kind of had this, this first phase of, of large language models. And the second one was coding. And if you think about how do you get a really good coding model, you again, you have to train it, you have to train it on existing code. And that's again, something that is, you know, relatively easier to, to acquire than other types of data. And you saw great progress and jumps in, in the coding models. I think the, the third big sort of shift that hasn't really been touched on yet, which the government itself is trying to do a good push on, is the AI for science question. And why it's so challenging for scientific discovery to like, tie in with the way that LMS are traditionally trained, is that the science data is extraordinarily fragmented and it's not done in a way or formatted in a way that can easily be applied to a large language model. Sort of like training run. And if you think about scientific discovery, it's spread out across so many different disciplines. You have chemistry data, you have math data, you have material science data, and all of that is all types of different formats. And our effort in administration, we launched something called the Genesis mission, which is our attempt to sort of make these big, bold leaps in AI for scientific discovery. And our national labs at the Department of Energy have been doing incredible research over the last 50, 60 years. And all of that is sitting and is ready to be used to be trained for these models. So my hope is that over the next year we're going to see a lot more work in scientific discovery to be able to actually accelerate how quickly we can choose which experiments to run, run those experiments, go back and figure out what we did wrong and run them again. And, and this ties in with lots of interesting ideas that people have around some of these AI labs where you essentially have, you can put in the thesis or the hypothesis and ultimately these labs can do lab experiment itself and move forward. So that's kind of the dream that I have that ultimately we as a country can, can almost double our, our R and D output over the next 10 years because of AI.

15:08

So, so what kind of breakthroughs would you expect or would you like to see?

17:24

Yeah, I think there are. The ones that I think can make a big impact are first, the, the, the, the experimentation and training runs around fusion are extraordinarily computation heavy and they themselves, if we can, if we can have a faster feedback loop on how we do these simulations for fusion, we can move the timelines in for fusion. So that could be a big, a big step. Material science is also a very, very big area where you want to be able to test all types of, of different molecules and interact with each other. This is important for all the big things we're trying to do in space. Whether it's our lunar base or getting to Mars or bringing nuclear energy to space. Having advanced material science is important. And the third is one that everyone always cares about is, is health care and therapeutics. How can you more quickly be able to identify the best molecules to solve a particular, particular health challenge? And how do you more quickly iterate to a point where you can move to a, to a clinical trial and.

17:29

On an everyday level, I mean, you also have the auto sector, I think, as a big beneficiary here. I think that's one area that seems to be spending a lot on this as well. Do you agree with that?

18:27

Well, I mean with like self driving or. Yeah, I mean self driving for sure is going to be huge. It feels like we've had some sort of new inflection point there where the quality has gone to the point where you're starting to see Robo taxis now, Waymos and Tesla.

18:37

What, what about an AI assistant? I mean, is that going to be something that is sort of commonplace? Someone said to me the other day that, oh, in China we're doing things so much differently because you're using AI for research as, as you said, but we're using it as I have my AI assistant and you know, they're paying my bills and cleaning my house and buying my wife a birthday present and, and doing everything for me, I think.

18:54

So I think that'll happen probably this year. So the product that just came out recently that everyone's kind of going crazy over is the latest iteration of Claude code, which is powered by Anthropic's Opus 4.5 model, which seems to be a real breakthrough in encoding. And so again this is, you know, the software developers are really impressed with it, but inside of Claude Co they introduce a new tab called Cowork where again you can, as a non coder or as someone who is looking for to create output other than code, you can now use it to basically create all sorts of other kinds of outputs. Like I mentioned, you can do spreadsheets or PowerPoints, things like that. And you can have it, you can point it to your file drive and it can look at the work you've already done. So if there's a particular type of format for a PowerPoint you like, you just point it to the work you've already done and say, I want to do, you know, a new, you know, presentation, but using this style, but on this topic. And it'll actually emulate, you know, your style and the work, your format, the work you've already done. And people are very impressed with this. And you can also point it at your email and have it analyze your email, pull things out of it. So right now it's very task based. You the user have to prompt it for each task. But you can see there the beginning of a personal digital assistant where you connect it to your file drive, to your email, to all of your data sources and it can start to do tasks for you. And again it understands the format and the style that you like to produce work in. So it feels to me like we just need one more layer of abstraction on top of a tool like that and you'll have your own personal digital assistant. And you know, there'll be like a voice interface. You ever seen the movie her? You know, with Joaquin Phoenix and I think Scarlett Johansson is just the voice, but you know, he's telling her what, what to do through an earpiece. I mean we're very close to something like that. I mean, I'm not saying that the AI is going to become sentient or whatever, but no, we're like, I think in 2026 you could see that these types of tools again started as coding assistants, but now they become personal digital assistants. That could definitely happen this year.

19:20

Michael, what don't people understand about AI? What do you think is most important for us to understand about the innovation underway right now with science and AI?

21:47

I think some people, I think it's easy to underestimate the, the long term impact this is going to have across so many industries and domains. I think very much, you know, it's easy to quickly think about AI as a, as just a sophisticated chatbot because that's what most people interact with every day and that's what they touch and feel. But I think that to me, I think the long term impacts, and not to keep harping on the science, I think there's a real fundamental shift happening in the velocity and pace that we can test and, and evaluate and execute scientific discovery and endeavors. And I think, I think that's going to have huge repercussions for the way that we as a country innovate, broadly.

21:57

Speaking, years ahead, which is why we're watching what China is doing. Let's talk a bit about China and where it is relative to the United States. Are we winning? Is it about chips? What's the race specifically really about?

22:38

Well, I think that in general we're ahead of China. There's different layers of the stack. So you've got the models, then you've got the chips and then you've got the chip making equipment. So you go down the stack. I would say that the deeper in the stack that you go, the greater the American advantage. I think on models, most people would say that our models are maybe six months ahead or so, plus or minus the Chinese models. You look at chips maybe two years ahead, you go to the semiconductor manufacturing equipment, it could be like five years. So the US does have significant, significant advantages. There's only maybe a couple of areas where I think China has, has an advantage. One is on energy production. If you look at their grid, their grid has roughly doubled in the last 10 years, whereas ours has only grown by about 2 to 3%. Energy production in the US has been a relatively sleepy industry for AI came along and a lot of that had to do with regulations and the antipathy of the previous administration towards energy production. Obviously, President Trump had a very different view on this. I think he was prescient on this issue. You go back 10 years and he was talking about, we got a drill, baby drill. And I think he understood that energy growth was the precondition for economic growth and it's definitely the precondition for this AI infrastructure growth. So this is an area where again, we have to basically expand our energy production. And so I think that is an area where we need to catch up. The other area where I would say, I don't know if I would call this an advantage exactly, but you could argue that China, China has the edge in what is, what's being called AI optimism. So there was a polling done by Stanford across countries, and they asked the citizens of all these different countries, do you feel that the benefits of AI will be more beneficial or more harmful? And if you thought that overall be more beneficial than harmful, they call that AI optimism. Well, in China, AI optimism was 83%. So 83% of the population feels that it's going to be more beneficial than harmful. That number in the United states is only 39%. So for some reason, people in China are more optimistic about AI than in the United States. And you generally see this, that Asian countries are very high on AI optimism, in the Western countries are lower. And I think it's a interesting or open question about why this is. I think there's a few possible explanations for it. I think that, first of all, the media tends to focus on the doom and gloom stories with AI, the fear, the fears. And we can talk about some of those fears and whether we think they're real. But I think the media has a lot to do with it. I think that the way that Hollywood has portrayed AI over the decades, you know, with whether it's the Terminator or 2001, has portrayed this dystopian view of the future. And I think that plays into people's thinking. And then frankly, I would say that part of the fault lies with our tech leaders who haven't necessarily done a great job describing the benefits of AI. In fact, when they're talking about AI eliminating 50% of knowledge workers, that doesn't sound like a very utopian scenario. That sounds dystopian to most people. And so I do think that unintentionally, some of our tech leaders have played into this AI pessimism. And the reason why I think this could be a disadvantage for the United States is because again, it's feeding into this regulatory frenzy. We're seeing again, 1200 bills at the state level. And right now, I think we are winning this AI race. We're ahead in all the key dimensions, chips, models and so on, but we could shoot ourselves in the foot. You know, if we end up over regulating this thing to death, we could actually cost ourselves this AI race. So I do worry about this question of AI optimism.

22:50

Right.

27:10

It's a Great point. And what would happen if the US Is not number one in this, Michael?

27:11

Yeah, I think we. We need to be, and that's why we put the plan out. I think, you know, when I think about the China question and about the sort of larger question of how do we win the race, what always, what I always like to think about is this question of adoption. And I think sometimes there is this overemphasis on the leaderboard. It's like, which frontier model is number one on some sort of. Sort of metric? And the reality is we're neck and neck, and as David said, we're probably ahead, you know, six to 12 months in our frontier models. But I think what we have seen over. Over time and over history is that you don't necessarily need to have the very best model or very best piece of technology in the world for it to proliferate globally. And, and a lot of us who were part of the first Trump administration saw this very firsthand with the telecom wars of that era of what Huawei was able to do globally. And at the time when, when Huawei first started their sort of global export push, they certainly were not the very best technology in the world. They were current, they were certainly, you know, you know, subpar compared to Ericsson and Nokia. Yet they were good enough, and they were subsidized enough such that they became sort of the default telecom system for a lot of the world. And we've learned a lot of lessons from that, and we take very seriously when it comes to AI, we know there is ambition for the Chinese to export their models and have them be the models that are powering all these different use cases across. Across the Global south and across the rest of the world. That's why the President launched something called the American AI Export Program. And our mission, and I think we're in a very lucky position here compared to what we're dealing with with Huawei is, as David said, we are dominant in almost every part of the stack. We have the very best models. We have the various applications. We have the very best chips. So we are in a position of power now. And it's up to us as a country that technology, with the world, with all of our partners and allies, make sure that any developer anywhere in the world that wants to build a new application using AI is using, is fine tuning an American model on top of an American chip. And that isn't. That isn't a hard reality to see. That is something that I think we can very easily do just because we have the Very best tech. That's a program that we launched last, late last year and we're doing a big push this year to get that, get that out the door.

27:15

It's an important point that you make in terms of exporting AI to the rest of the world. Is it true that China is telling its companies don't use American chips, don't use American AI right now?

29:20

It seems so. I mean, China is developing its own models. Obviously about a year ago you had the Deep Seq moment where you had a powerful model released by Deep Seq. And I think that kind of put Chinese AI on the map in a way. I think people in the west didn't realize in a way how good China was at producing models. And there was a little bit of complacency towards our relative position. People weren't really talking about the global competition. Two years ago. It wasn't really discussed at all. I remember when the Biden administration created this 100 page Biden executive order regulating AI. No one was talking about whether this might slow, whether all this regulation would slow us down vis a vis China. It wasn't even part of the conversation. Then Deepseek launched and I think we did realize we're in a global competition and we have to win and that's why we have to actually be quite careful about how we regulate this and not make sure we're not over regulating it. But I think, you know, China definitely wants to compete. There have been some stories recently, I think Bloomberg and Reuters reported that they actually are not allowing Nvidia chips into their country. And the reason for that, we think, is that they want to indigenize chip production. They want to stand up Huawei as their national champion. And effectively they're creating a market subsidy for Huawei by keeping out the competition. So they're protecting their market to stand up Huawei. And I think their plan would be to have Huawei dominate chips in China first and then use that to scale up and then try to take over the rest of the world. Chip production is a scale up business. So if they can dominate the Chinese market first, that gives them a powerful platform to then proliferate to the rest of the world.

29:33

So where are we in that, Michael? I mean, first you all came up with the AI action plan, then came up with another plan in terms of exporting AI to the rest of the world. What can you tell us in terms of where we are in that?

31:17

Yeah, so the progress is moving on that. We closed a request for information from the Commerce Department late last year which went out to industry and said, hey, if we want to export the American AI stack, what should we be thinking about? How should we be designing these packages that we share with the world? Commerce is now ingesting that, that information. There'll be a request for proposals that comes out very shortly. And that's where we actually want companies to come together to form consortia and say like look, this is what a package looks like. And I think what, you know, what, what people need to sort of, what I always try to remind people is that the, the, the, the buyers of AI around the world vary quite dramatically in their level of sophistication. So in the US if you're a very sort of, you know, if you're a Fortune 50 company and you want to deploy AI, you know, you have a pretty sophisticated sort of CIO or CTO shop you are thinking very carefully about like which cloud you want to buy, which potential model you want to use, do you want to fine tune it on your own data? Do you want to build your own application? You know what applications you go out and see. You can like test various things you like, go to all these third parties and evaluate which is best. And it's a very sort of complicated mix of how you end up creating something that's optimum for your particular company. For a lot of countries around the world that are aspiring to, to use AI for their people or to support the services, whether it be health care or you know, tax collection or whatever it may be, you know, they don't have a, a, you know, billion dollar IT budget. You know, they're just trying to figure out what is a tool that I can use in my country to deliver the benefits of AI to my people. So we think very carefully around how can we craft solutions which, you know, turnkey could be one way to put it, or how do you provide a solution that can easily be deployed in a country? And what's often, you know, what often sort of gets caught up in this debate is this question of, you know, how many chips is the US going to be sending around the world? And what I always try to remind people is that, you know, outside of the us, China and maybe a few other countries, most countries around the world do not have the capital or the aspiration to do large scale training runs or development of their own frontier models. There are very few countries around the world that are going to build sort of colossus style training centers. Most countries around the world need smaller data centers, just have inference related chips that can drive and, and do the, you know, do the inference on, on the particular runs that the government wants to have. So I think what we're working very hard to do is, is, is create sort of these, these, these TurnKey, manageably sized AI solutions that then we can partner with a lot of our export finance organizations like Development Finance Corporation or the Export Import bank to make the export of that particular stack much more appealing and, and commercially viable in countries that are not extraordinarily deep pocketed. So we're going to be in India next month for the India Impact Summit. This is sort of the largest global gathering for, for AI folks and we're going to be sharing a lot more on the progress of this, of this program there.

31:28

You want to weigh in?

34:24

Well, I would just, just to build on that, I think people sometimes ask, you know, how do you, how will you know if, if you've won the race, you know, with, with China, with, with other countries? And I think there's a very simple answer to that, which is market share. You know, if five years we look around the world and we see that it's American chips and models are being used everywhere, well, that means we won. But if in five years we look around the world and it's Huawei chips and deep SEQ models, then that would be very bad, right? That'd be a bad sign. That means that we lost. So I do think that the proliferation or diffusion of American technology is really critical to winning this AI race. We know from Silicon Valley that the companies that end up becoming huge are the ones that create ecosystems. It's the, you know, you, as a technology company, you want to have the most apps in your app store, you want to have the most developers writing on top of your API. You want to be a platform company. And so in all these technology races, biggest ecosystem wins. And we want to have the. That's basically why I think this program is so important is we want to create the biggest ecosystem. Now this is not only about benefiting the US because in order to have a successful ecosystem, you have to create value for your partners. And that's really important. Like Michael's saying, not every country is going to be on the cutting edge of developing its own chips or developing its own frontier models, but they can use these tools to derive value, to apply them to their businesses, to their economies, to extract value and be part of this technological revolution. So I think that we have to think in this, with this partner mindset, and I do think that this type of mindset is actually very common to Silicon Valley. Like I mentioned, I think every great technology company thinks in terms of how do we get the most people on top of our tech stack. But it is a form of thinking that's pretty alien to the bureaucracy in Washington, which has much more of a command and control type of mindset. And when President Trump came into office, just give a couple examples of this. The regulations that were sitting on our desk, that had just been handed down by our predecessors, again, we had this hundred page Biden executive order on AI that was all this new regulation. And there was a 200 page was called the Biden diffusion rule, which was 200 pages of regulations on the export of semiconductors. So we were turning the AI industry models and chips into a highly regulated industry. That was basically the direction that Washington was going in. And the first thing President Trump did his first week in office was rescind all of those initial regulations, which I think was absolutely critical. The thing that really makes Silicon Valley special is this concept of permissionless innovation. Since Hewlett and Packard started 85 years ago, started building Silicon Valley, it's the idea has always been that just a couple of founders, kind of a great idea, start their company. They get some angel investors to write a check for seed capital. Those investors think they're probably going to lose their money, but they figure there's a shot. So it could be the two guys in a garage or it could be the college dropout in the dorm room. And they don't need to go to Washington to get permission for their idea. It's permissionless innovation. That's what has made Silicon Valley the crown jewel of the world. It's why so many of the, I think heads of state who are here are always asking, how do we create our own Silicon Valley? That was not the direction we were on when President Trump came into office. The new 300 pages of regulations concerning AI the Biden administration left us with would have changed this environment of permissionless innovation to an environment of you have to go to Washington to get approval for your idea. And I think that President Trump really corrected that. And since then we've been implementing his AI action plan, which is all about pro innovation, pro infrastructure, pro energy and pro export. So it's been, I think, a total change. And I think just in the past year, you've seen the results of that.

34:25

And I think one thing to add there, part of the international agenda that we have on AI is one obviously, let's do the export. But the other piece is trying to share with all of our partners and allies, how you can actually create a regulatory environment that allows this technology to succeed. And here we are in Europe and I think many of us that sort of have tried to work with technology companies in Europe have hit sort of a lot of roadblocks and a lot of stumbles and no matter the drug report came out and he can say that there's a lot of issues, but things don't ever seem to really change. And I think all of that, the, the, the way that our regulatory structure is, is designed in the US and the way that the entrepreneurial syrup thrives in the US is something that we try to share with countries all around the world. And I think the, the, the general knee jerk reaction for most policymakers around the world is one that moves to a corner that is obsessed with the precautionary principle. This concept that every time something new comes out, the role of the policymaker is to sort of like sit in a room and whiteboard everything that could go wrong and then design regulations to make sure those wrong things, these hypothetical wrong things don't happen. When in reality what we do in the US we try to do is sit in a room and whiteboard what rules can create to actually unlock innovation. What are the ones we should remove to allow more innovation to happen. And I think that mindset is something that we constantly try to share at all these international fora. The US has, you know, there has been an ab test on what regulatory structure works and what succeeds. You know, we've seen how the, how, how Europe has approached this in the last 20 years and we've seen what the US has done. So I think the recipe is kind of obvious but, but sometimes we have to just keep repeating it to, to our counterparts.

38:33

And I love the Draghi report because it was so clearly identifying companies that are in Europe that, you know, like Novo Nordisk is like 3 to 50 billion or a $400 billion company and in America we' trillion dollar companies. Nvidia hitting $5 trillion. So, so what is the path to innovation?

40:19

Well, I think part of it is, and I think this is the difference between maybe the American mindset and the European mindset towards this is that ultimately the innovation in the United States comes from the private sector. It comes from the entrepreneurs, the founders, the innovators, the geniuses with an idea. And I think that the government sees its role, at least when it's thinking properly about this, as being an enabler and is just setting the rules of the road and maybe putting in some guardrails but basically it's letting the entrepreneurs cook and that's how you get innovation. And now, I don't want to bash our European hosts too much, but when the EU thought talks about AI leadership, they're talking about the regulators and they think their value add is, well, we're going to, we're going to show the whole world the regulatory model for AI. So it's kind of a bad case of main character syndrome, where, you know, where like, the regulators think they're the main characters in this. No, look, the regulators are the supporting players. The main characters always have to be the entrepreneurs. It's got to be the innovators. That's how you unlock innovation. And when you start to see yourself, I mean, the regulators and the policymakers as the main characters, that's not a great recipe for innovation.

40:43

And I think just a minor point on the AI stuff in Europe, that the EU AI act, which has been so detrimental to the AI ecosystem here in Europe, was passed before ChatGPT was even invented. And that shows the challenge here. You're believing that you can solve some kind of problem or some. You're solving something, but end of the day, innovation is moving so much more quickly and ultimately that that rule makes no sense now in a world of frontier models, large language models, and they have to sort of edit it.

42:04

So let me push back before we go and ask you to identify any risks or threats or downside risks in all of this. What should we be worried about, if anything, with regard to AI usage?

42:33

Well, I think there are Orwellian scenarios of AI that I think we should be concerned about. And again, I tend to think that those scenarios were described by George Orwell, not by James Cameron and the Terminator and specifically its misuse of AI by government. I do think that AI could be used as a tool to surveil to censorship, to even potentially brainwash the population. This is why the administration has taken such a firm stance against what is called Woke AI, which I almost think that that name maybe trivializes the magnitude of the problem we're talking about. We're talking about AI having a political bias built into it. And the bias can be so subtle that people don't even necessarily notice over time, but it has a huge impact on what people are allowed to learn and think and know and what children learn. And so I think it's very important that we try to make sure that AI was politically unbiased in this regard. One of the things that we were so concerned about with that Biden executive order on AI that we were sended in the first week is that it had 20 pages of language on DEI and it was promoting this idea that AI models need to build in a DEI layer. Well, this is how you ended up with the black George Washington story where the first version of Gemini came out and it was basically rewriting history to serve a current political agenda of dei. And that was, in a way, that case of bias was so ludicrous that everyone kind of laughed at it. But it gives you a sense of what could happen if you start to build the bias into AI. And that same so called trust and safety apparatus that was starting to be built into social media sites as a way to censor and de platform and shadow ban. You could see that being built into AI models as a way to control the public discourse in a very serious way. And I think that President Trump again just put a total halt to that, rescinded that. But it was also, we also. President Trump signed an executive order saying that the federal government would not ProCure politically biased AI. So look, on a First Amendment basis, if an AI company wants its AI to be biased in some direction, they probably have a First Amendment right to do that. But we have, as the federal government have the discretion not to buy that software. And we said that we won't. So I feel very good that during President Trump's term in office for the next three years, this idea of Orwellian AI is not going to be a problem. But I do worry that at some point in the future, if you had a different regime in Washington, if the federal government started to pressure AI companies to build in this political bias, that would be a very serious threat, I think, to, to our freedoms.

42:48

It's a, it's a great point to make. Before we wrap up real quick on jobs, can either of you explain what Elon Musk is, is saying about the impact of AI Said we're not going to need to work. You know, the, the AI AI is going to do it all. I just, I'm trying to understand what he's saying that just go, we're going to go on holiday, jobs are going away and AI is going to do everything.

45:58

Well, Elon's a friend of mine and I'll disagree with him slightly on this, but his comment about the job loss obviously is what gets all the headlines. But at the same time he's saying that. He's also saying that in this future there's going to be so much abundance that everyone's going to have what they want and there's not going to be any money. So people leave out that part of the story and they just report. Elon says everyone's going to lose their jobs. No, we're talking about a radically different future. It could be the future it's kind of described in Star Trek, where there is no money because we have everything. Look, I think that Elon is directionally correct about the future. I think we are heading towards a world of much greater abundance. Rising living standards for everybody, greater productivity. I think that will lead to rising wages. I don't think it's going to put everyone out of work. I don't think that's going to happen. But again, the timelines matter a lot. And getting to a world with no money is not something that's gonna happen in the next five years.

46:19

And of course, Michael, this is helping us in terms of longevity and living longer, right? In terms of the impact on science, totally.

47:23

I think generally the. The abundance story extends itself well into. Into, you know, healthcare and everywhere else that. And. And just quality of life. So good things ahead.

47:32

I think we'll leave it there. Michael Kratzios and David Sacks, thanks so much.

47:42

Thank you. It.

47:46