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. Um David, Michael, I'd love you to talk about What where we are right now in terms of the pursuit to be the number one uh lead AI country. How are we doing, David?

I think we're doing great. Um Maria last year uh President Trump gave a major AI policy speech this is in July and he declared that the United States had to win the AI race. Uh he he had dec first of all declared that We were in one. Uh, 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 um American uh uh AI models, chips, um data centers only just keep um getting better and better. Um so I feel very good about the American position in this AI race. Certainly we have some very Uh you know, competent.

uh uh and formidable competitors. Um China obviously has a lot of very smart people working in this area. But I do think that uh uh just um what you see from uh American companies in Silicon Valley right now is really incredible. And yet there are still so many questions about all of the spending underway uh to build this out w 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?

I I think that we will. Um, I think that the reason why you're seeing this huge infrastructure build out is because the demand is ultimately there. I I know a lot of people worry and uh about whether this could be like a dot com situation. Remember where we had the whole fiber build out in the late nineties and then we had a dot com crash.

The difference here is that uh in the late nineties and early two thousands, we had a a problem known as dark fiber, where you had this fiber build out and then it didn't 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 uh and it's being used to generate tokens and that's to power the this new generation of AI chatbots or coding assistants.

Uh, 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 inc 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 build out added about uh two percent to the GDP growth rate. And I th and that's what helped propel us to the You're four to five percent.

growth rate and I think you're going to see something similar this year. 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 question is for you, Michael. Assess where we are right now on AI. The the 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 um this 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.

Um, and I think I think we're pretty proud to say that we've made, I think, pretty good progress on on all three. Um, just focusing a little bit on the innovation one you're talking about earlier, I think the The the the the core um 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

um this issue of a patchwork of state regulations and how can we ensure that there aren't fifty different rules around AI. And and what's most what's most important about this debate, which I 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 fifty different rules across fifty different states creates a lot of friction. And ultimately the big guys are the ones that can succeed in in that environment the best.

Um so we're spending a lot of time trying to think about how can you create a legislative proposal that can actually um deliver on um a sensible national framework uh to solve to solve this regulatory issue. So so what would you say then, Michael, are the basic frameworks that are uh sort of must have in in that kind of federal

oversight because some states did push back in the US and say, no, 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 i in terms of um a federal oversight?

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 um the state should continue to be able to pursue individually on their own. Um legislation or rules around child safety was on that list.

Um 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 gonna be working through. I don't know if you have any thoughts on on that.

Yeah, I mean I I I think the 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 twelve hundred bills going through state legislatures right now. I think it's very much a a knee-jerk r reaction. I know there's a lot of fears and concerns about AI.

But it seems like for every hypothetical concern, there's um multiple state bills now to try and regulate that thing before we really know how it's gonna play out. And um 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 um 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 rule book at the federal level, lightweight federal standard.

Uh I think this problem's only gonna get more acute over time because again you you as you have fifty different states running in fifty different directions, uh the patchwork problem only gets um more significant. So in any event, this is something that we're gonna work I 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.

Um and uh you know, as you know, it's very difficult to get a bill through Congress. You need sixty votes in the Senate, so that's to be bipartisan to a to a certain degree. So but we're gonna try and see if we can um work to get that consensus. Yeah. um support in Congress for a federal oversight or do you see pushback there as well, depending on the state you're talking about?

Well, there's pushback in Congress to the idea of preemption without a a federal standard. So in other words, you can't replace something with nothing. This is sort of the the thing that we heard. Uh 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 stage of those conversations and we're gonna see what we can try and get done this year.

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? Yeah, I mean we got a letter recently.

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. Uh other countries are building out this infrastructure. China's building out, I think they're um spinning up a a new uh a nuclear power plant or um or coal plant, new energy every single week and a lot of that is going to power their data centers. So

It would fundamentally, I think, cripple uh the United States in the I AI race if we just stopped building data centers altogether. At the same time, there are concerns about affordability, about um whether consumers would have to pay a higher electrical rate. because of data centers. Uh 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 the grid. They all are uh saw standing up their own power generation as part of their build out. Um And what Secretary Wright, the uh our Secretary of Energy has been doing is trying to

um i i is 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 is our vision. is let and 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 um the result of that is, you know, A, we got this infrastructure, B, residential rates don't go up. Yeah, because Michael, the this this race has fast become uh it's moves from an AI race to a power race. Mm-hmm. Mm-hmm. And and I think what we're seeing is that um we we need to share a good story about how ultimately this build out is gonna be net positive for American ratepayers.

And I think sometimes if, you know, in a 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 is something that's gonna actually lower your your rates long term. Um

And and the president put out a tw truth last Monday where he was as as David said very clear that you know if you're gonna build a data center, you have to pay your own way for it. And um Microsoft has stepped up and our our hope is that many others will do the same. But but some companies um

because they don't have the cash right now are borrowing money, right, to 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 i is too much. Your thoughts on that?

Well, I think there there there is obviously that concern. I mean, you you you know, I I think it's it's um it's less I would say the banks are more you see Oracle making a huge investment, you see uh Blackstone making huge investments, real estate companies.

Um 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 the rainbow. Um can I just make one other point about just the the data center? So Um just on electricity, uh 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 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 what so it's actually a good thing that the um 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.

And I think what a a great policy change was made under this administration, the the Biden administration had, as a matter of policy, had made it such that you couldn't do this behind the meter energy generation. Um and if you wanted to bring your own power you couldn't. You had to be part of the larger grid. So

I think um that rule has has changed uh uh by by Secretary Wright and by FERC to kinda allow this to happen and ultimately I agree with David. I think once you have sort of greater scale in in the power generation, you'll be contributing back into the grid in a way that that benefits ratepayers.

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

Well it's interesting. I think it there's been an evolution. So I think we started with, you know, AI chatbots like ChatGPT. And in a sense, that was kind of like better web search. Um, it was really great for research. We ask it questions and give you answers to anything. Then we saw um we saw models add chain of thought and they could start to do, you know, deeper reasoning.

Then we saw coding assistance. This is real and 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, you know, a major shift and and dis improvement in the quality of the coding assistance. And I think where that's going next is um tools for knowledge workers. So the same types of assistants 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 gonna be able to generate uh all these different types of things the same way that coders have been gen that software developers have been using AI to generate code. I think that's one of the big things you're gonna see in twenty twenty six is again this this um

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 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 uh to improve um or to to reduce sort of administrative bureaucracy.

to uh to improve this um processing of paperwork that happens, also to use uh AI and medical and scientific research to help find new uh cures. You're already seeing users tell all sorts of stories about

uh diagnoses. They've been able to put in their medical records into ChatGPT or your other chat engine or chat bots and get like remarkable results. They've been able to you know, finally figure out what was you know, what w 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 um examples of different different industries are now being

impacted. Do you have the ones? The w the one area I think a lot about is is AI for science. And and back to to 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 like 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 trade it, you have to train it on existing code. And that's again something that is you know, r 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 uh 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 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 um 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 is all types of different formats. And our effort in administration, um, we launched something called the Genesis Mission, which are is our attempt to sort of make these big bold leaps in AI for scientific discovery and our national labs as the Department of Energy are have been doing incredible research over the last, you know, fifty, sixty years.

And all of that has is sitting and is ready to be used to be trained for for for these models. So my hope is that over the next year we're gonna see a lot more work in this 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 the 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 that ultimately we as a country can can almost double our our R and D output over the next ten years because of AI. So so what kind of breakthroughs um Would you expect or would you

Like to see. Yeah, I I think they're um the ones that I think can make a a big impact are uh first the the the the experimentation and training runs around fusion um are extraordinarily computation heavy. And they themselves, if we can if we can have a a a a faster feedback loop on how we do these 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 a 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 the one that everyone always cares about is is healthcare and and therapeutics. How can you more quickly be able to identify the 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 on a everyday level, I mean you also have the auto sector, I think. Uh 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? Well, I mean with like self driving or

I mean, self driving for sure is gonna be huge. It feels like we've hit some sort of new inflection point there where the quality's gotten to the point where you're starting to see robotaxis now, Waymo's and Tough slaw. Um What what about an AI assistant? I mean, is that going to be something that is sort of commonplace? I mean someone said to me the other day that, oh, in China we're doing things so much differently.

because you're using a AI for research as as you said, but we're using it as I have my AI assistant and I'm um y you know, they're paying my bills and cleaning my house and buying my wife a a birthday present and and doing everything for me. I I think so. I think that'll happen probably this year. So the the the product that just came out recently that uh everyone's kinda going crazy over is the latest iteration of flawed code.

uh which is uh powered by uh anthropic's uh opus four point five model, which seems to be a real breakthrough in in encoding. And so again, this is the you know, the software developers are really impr impressed with it, but In inside of Claude Co. they had they introduced a new tab called uh cowork. where again you can a as a non coder

uh or as someone who is looking for um to create output other than code, you can now use it to uh to basically create all sorts of other kinds of output. So like I mentioned, you can do uh 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 um, you know, presentation, but using this style, but on this topic. And it'll actually emulate you know, your style and and the work, your format, the work you've already done. And um 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 it right now it's very task-based. You 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 r 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 um, you know, there'll be like a voice interface. You ever seen the movie Her? You know, with uh Walking Phoenix and um I think Scarlett Johansson is just the voice. But uh, 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

you know, the AI is gonna become sentient or whatever. But um but no, we're like I think in twenty twenty six you could see th th th these types of of tools again started as coding assistants, but now they become Personal digital assistance. That could definitely happen this year. Michael, what what don't people understand about AI? What what do you think is most important for us to understand about the innovation underway right now with science and and AI?

I I I think some people I I think it's easy to underestimate the the long term impact this is gonna have across so many industries and and domains. Um I think very much You know, it it's easy to to to quickly think about AI as a as just a sophisticated chat bot because that's what most people interact with every day and and that's what they they they touch and feel.

Um but I think the t to me I think the long term impacts and not to keep harping on the science, I think there is a there's a a real fundamental shift happening in the velocity and pace that we can test. and uh and evaluate and execute scientific discovery and endeavors. And I think

I think that's gonna have huge repercussions for the way that we as a country innovate, broadly speaking, in the 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? Well, uh I I I think that in general we're ahead uh of China. There's different layers of the stack. So you've got the the the models, then you've got the chips and you know, then you've got the chip making equipment. You know, so you you go down the stack

I would say that the deeper in the in the stack that you go, the greater the American advantage. Um, I think on models most people would say that we're pr our models are maybe six months ahead or so, plus or minus of the Chinese models. You look at chips. Maybe two years ahead. You go to the a semiconductor manufacturing equipment, it could be like five years. So the US does have significant significant advantages. There there's only maybe a couple of areas where I think China

has has an advantage. Um one is on energy production. If you look at the their grid uh their grid has roughly doubled in the last ten years, whereas ours has only grown by about two to three percent. Energy production in the US has been a relatively sleepy industry before 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

pression on this issue. You go back ten years and he was talking about we gotta drill baby drill. And um and I think he understood that energy growth was the precondition for economic growth and is definitely the precond precondition for this uh AI infrastructure growth. So

This is an area where again again we have to basically expand our energy production. Um and I and and and and so I think that is an area where we need to catch up. The other area where I would say You know, I I don't know if I would call this an advantage exactly, but if you i but you could argue that

China i China has the edge in what is what's being um called AI optimism. So there was a a polling done by Stanford across countries and they asked the citizens of all these different countries Uh, do you feel that the benefits of AI will be more beneficial or more harmful? And if if you thought that it that overall be more beneficial than harmful, they call that AI optimism. Well, in China, AI optimism was eighty three percent. So eighty three percent of the population feels

That's being more beneficial than harmful. That number in the United States is only thirty nine percent. So for some reason People in China are more optimistic about AI than in the United States. And you generally s you generally see this, that uh Asian countries are very high on AI optimism and 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 I think that um first of all the the media tends to focus on the doom and gloom stories with with AI. The fear. The fears. Um and we can talk about some of those fears and uh and and how you know whether we think they're they're real. Um but I think the media has a lot to do with it. I think that

the the way that Hollywood has portrayed AI over the decades, you know, with whether it's the Terminator or two thousand one, uh, has you is 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 the fault lies with are tech leaders who haven't necessarily done a great job describing the benefits of AI. In fact, when they're talking about, you know, AI eliminating fifty percent of knowledge workers,

That doesn't sound like a, you know, 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 um AI pessimism. And the reason why I think this could be a disadvantage to the United States is because again, it's feeding into this regulatory frenzy we're seeing. Again, twelve hundred bills at the state level.

And I right now I think, you know, 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 to death. we could actually cost ourselves this AI race. So I do worry about this question of AI optimism. Right. It's a great point. And ha uh what would happen if the US is not number one in this, Michael?

Yeah, I I I think we we need to be and that's why we put put the plan out. I think, you know, when when I think about the the China question and about the the sort of larger question of how do we win the AI race, what always what I always like to think about is this question of adoption. And I think Sometimes there's this overemphasis on the leaderboard. It's like which frontier model is number one on some sort sort of metric.

In the reality is we're neck and neck, and as David said, we probably had, you know, six to twelve months on our frontier models. But I think what we have seen over over time and over history is that um you don't necessarily need to have the very best model or very best piece of technology in the world for it to perforate globally. 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 their sort of global export push, um, they certainly were not the very best technology in the world. They were current they were certainly, you know you know, subpar compared to to Erickson and Nokia, yet they were good enough and they were subsidized enough. such that they became sort of the the default telecom um system for a lot of the world.

And we've learned a lot of lessons from that and we take that 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. Um, 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 very best 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 to share 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 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 um we launched last late last year and we're gonna be doing a big push this year to get that get that out the door. 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?

It it s it seems so. Um I mean China's developing its own models. Obviously about a year ago you had the Deep Seek moment where you you had a powerful model released by Deep Seek and I think that kind of put Chinese uh 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.

uh towards our relative position. I people weren't really talking about the global competition two years ago. It wasn't really discussed at all. Uh I remember when, you know, the Biden administration created this, you know, hundred page Biden executive order regulating AI.

No one was talking about whether this might slow the whether all this regulation would slow us down vis a vis China. It wasn't even part of the conversation. Then Deep Seek 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

overregulating it. But I think, you know, China definitely it wants to compete. Um there have been s uh some stories recently. I think uh 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 if 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's a scale up business.

So, you know, if they can dominate the Chinese market first, that gives them a powerful platform to then proliferate to the rest of the world. So so where are we in that state? Michael. You all came up with the AI action plan, then came up with another plan in terms of a exporting AI to the rest of the world. What can you tell us in terms of

where we are in that. Yeah, so the the progress is is moving on that. We um 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 um you know What what people need to sort what I always try to remind people is that the the the the buyers of AI around the world

um, 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 fifty company and you want to deploy AI,

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 application should 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 healthcare or um, you know, tax collection or whatever it may be.

Um, 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, turnkeys m could be one way to put it, or how do you 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 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 gonna 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 and do the you know, d do the inference on on the particular um 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 uh commercially viable in countries that are not extraordinarily deep pocketed. So we're going to be in India next month for the India AI Impact Summit. This is sort of the largest global gathering for AI folks.

Um and we're gonna be sharing a lot more on on the progress of this uh of this program there. You wanna weigh in? Well, I would just just to build on that, I think People sometimes ask, you know, how how do you how will you know if if you've won the AI race, you know, with 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 seek 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 you 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 wanna be a platform company. And so in all these technology races, biggest ecosystem wins. And we want to have the b that so 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 gonna 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, you know, we have to think in this with this partner mindset.

And I do think that th this 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. Yep. And when President Trump came into office, just give a couple of 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 two hundred page uh was called the Biden diffusion rule, which was two hundred pages of regulations uh occ uh on the export of semiconductors. So we were turning the the AI industry models and chips into a highly regulated industry. That was

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 unnecessary regulations, which I think was absolutely critical. You know, the thing that really makes Silicon Valley special is this concept of permissionless innovation.

You know, since um Hewland and Packard started 85 years ago, started building Silicon Valley, it's it the idea's always been that just a couple of founders kind of a great idea, start their company, they get some angel investors. to write, you know, a check for, you know, seed capital. Those investors think they're probably gonna lose their money, but they figure there's a shot.

And, you know, and it's so it's it could be the two guys in our 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, right? It's permissionless innovation. That's what's uh uh 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 three hundred pages of regulations concerning AI the Biden administration left us with would have changed this um 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, you know, his AI action plan, uh, which is all about, you know, pro innovation, pro infrastructure, pro energy and pro export. So it's been, I think, a total change. And I think e just in the past year you've seen the results of that. And I think w one thing to to add there, um, part of the the international uh agenda that we have on AI is one, obviously let's 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 us technology to succeed. And here we are in Europe and I think many of us that sort of have

you know, tried to work with technology companies in Europe have have hit sort of a lot of roadblocks and a lot of stumbles. And no matter, you know, the drug report can came out and and he can say that there's a lot of issues, but things don't ever seem to seem to really change. And

I think all of that that the the the the way that our regulatory structure is is designed in the US and the way that the entrepreneurial spirit thrives in the US is something that we try to share with countries all around the world and I think the the the general um

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 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, what 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 A B test.

on what regulatory structure works and what succeeds. You know, we've seen how the you the how how Europe has approached this in the last twenty years and we've seen what the US has done. So I think the the recipe is kind of obvious, but but sometimes we have to just keep repeating it to to our counterparts. And I love the Draghi report because i it was so clearly uh identifying companies that

or in Europe that, you know, like no Nor Novo Nordusk is like three hundred and fifty billion or a four hundred billion dollar company. And in America we've had companies of trillion dollar companies, NVIDIA hitting five trillion dollars. So so what is the path to innovation? Well, I I think part of it is and I I 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, um and m 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 you know, the when when the uh when the EU th talks about AI leadership They're talking about the regulators and they think their value added is well, we're gonna we're gonna show the whole world the regulatory model for AI. So It's kind of a bad case of uh main character syndrome.

Where uh, 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 gotta be the innovators. That's how you unlock innovation. When the When you start to see yourself, I mean the the regulators and the policymakers as the as the main characters, that's not a great recipe for innovation.

And I think just just a minor point on the on the AI stuff in Europe that you know the EU AI Act, which has been so detrimental to to the AI ecosystem here here in Europe. was passed before Chat GPT was even invented. And that shows the challenge here. You're you're you're believing that you can solve some kind of problem or some you're solving something. But at the end of the day, innovation is moving so much more quickly.

And ultimately that that rule makes no sense now in a world of of frontier models, large language models, and they have to sort of edit it. So let me push back uh 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? Well, I I think there are Orwellian scenarios uh of AI that

I think we should be concerned about. And again, I I tend to think that th those scenarios were described by George Orwell, not by, you know, 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 um surveil, to censor, 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 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. Um, and it c 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, you know, children learn.

And so I think it's very important that we try to make sure that AI was politically unbiased. Um, there just in this regard. One of the things that we were so concerned about with that by an executive order on AI that we were sended in the first week is that it had twenty pages of language on DEI.

And it was promoting this idea that AI models need to build in a DEI layer. Well, you know, this is how you ended up with, you know, the the the Black George Washton uh, you know, story where that the first version of of uh Gemini came out and it was, you know, it was basically rewriting history. to serve a current political agenda of DEI. And um, you know, that that was in a way that that that case of bias was so ludicrous that everyone kinda laughed at it.

But it gives you a sense of what could happen if you start to build the the bias into AI. And you know, that same you know, so-called trust and safety apparatus that was starting to be built into social media sites as a way to censor and deplatform and shadow ban. You could see that being built into AI models as a way to control uh the the public discourse i in a very serious way. And I think that Your President Trump again just put a total

halt to that, you know, rescinded that. But it was also we also um 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've said that we won't. So I feel very good that during President Trump's uh term in office for the next three years. this this idea of Orwellian AI is not gonna be a problem. But I do worry that at some point in the future, if you had a different regime in Washington, you know, 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. 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 gonna need to work You know, the the AI uh AI is gonna do it all. I mean I'm I just I'm trying to understand what he's saying that we just go we're gonna go on holiday, um jobs are going away and AI is gonna do everything.

Well E Elon's a friend of mine and um I I'll I'll uh I'll uh I I'll disagree with him slightly on this, but um but but let me just I i i w the his comment about the 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 gonna be so much abundance that everyone's gonna have what they want and there's not gonna be any money.

So people people leave out that part of the story and they just report Elon says everyone's gonna 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. you know, where like there is no money because we have everything. Look, I I think that You know, Elon is directionally correct about the future. I think we are heading toward to war uh 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 gonna put everyone out out of work. I don't think that's gonna happen. Uh but again the timelines matter a lot and you know, getting to a world with no money is not something that's gonna happen in the next five years. And and of course, Michael, this is helping us um in terms of longevity.

and living longer, right? I in terms of the impact on science. Totally. I I think generally th th 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, I think. We'll leave it there. Michael Kratzios and David Sachs. Thanks so much. Thank you.