主持人: 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?

David: I think we're doing great. Um Maria, last year uh President Trump gave a major AI policy speech. is in July and he declared that the United States had to win the AI race. Uh he he had first of all declared that we were in one. Uh and I think his speech was reminiscent of when President Kenny 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. And so I feel very good about the American position in this AI race. Certainly we have some very uh you know competent uh and formidable competitors. Um China obviously has a lot of very smart people working in this area. But I do think that uh just 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 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?

Michael: I I think that we will. Um I think that the reason why you're seeing this huge infrastructure buildout is because the demand is ultimately there. I I know a lot of people worry and about whether this could be like a dot situation. And remember where we had the whole fiber build out in the late '9s and then we had a doc crash. The difference here is that uh in the late '9s and early 2000s we had a a problem known as dark fiber where you had this fiber buildout 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 chat bots or coding assistants. uh and there's just been some releases in the last couple 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 buildout 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 uh 2% to the GDP growth rate and I and that's what helped propel us to this you know four to 5% % growth rate and I think you're going to see something similar this year.

主持人: Well, it is certainly leading growth, Michael. Um, 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 AI.

Michael: I I think just a reminder for the group for those who haven't been tracking as closely as we do every day. 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 build 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.

Michael: Um just focusing a little bit on the innovation one you were 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 great job compared to the rest of the world on sort of setting that up and creating a framework that works. But we can 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 50 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 50 different rules across 50 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 to solve to solve this regulatory issue.

主持人: 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, 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 um a federal oversight?

Michael: 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 buildouts 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.

David: Yeah, I mean I 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,200 bills going through state legislatores 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 um multiple state bills now to try and regulate that thing before we really know how it's going to 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 is 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 um 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 cramp the states. We understand that. Um, and you know, as you know, it's very difficult to get a bill through Congress. You need 60 votes in the Senate. So, has to be bipartisan to to a certain degree. So, but we're going to try and see if we can um work to get that consensus.

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

David: Well, there's push back in Congress to the idea of preeemption without 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 uh 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.

主持人: 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?

David: 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. Uh other countries are building out this infrastructure. China's building out I think they're um spinning up a a new uh 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 uh 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 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 buildout. Um and what Secretary Wright, the our Secretary of Energy has been doing is trying to um 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 get this infrastructure b residential rates don't go up. Yeah, because Michael, this this race has fast become it's moves from an AI race to a power race.

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

主持人: 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 is too much. your thoughts on that?

David: Well, I think there there is obviously that concern. I mean, 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 companies, and they're doing this because they see an ROI there at the end of the the rainbow. Um can I 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 uh that we have this buildout 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.

Michael: And I think what a great policy change has made under this administration, the the B 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 FK to kind of 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 rateayers.

主持人: 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 AI is having. What do you see as the most important use and where AI is being deployed and implemented best right now?

Michael: Well, it's interesting. I think it there's been an evolution. So I think we started with you know AI chat bots like ChatGPT and in a sense that was kind of like better web search. Um it was really great for research asking 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 really 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 in in just improvement in the quality of the coding assistants. 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 going to be able to generate all these different types of things the same way that coders have been gen 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 just this um this productivity boom for knowledge workers.

Michael: So I think that's like one of the things you're seeing on the ground. And then separately there's 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 diagnosis. They've been able to put in their medical records into chat GBT or you know 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 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.

Michael: The 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 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 into 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 relative atively easier to to acquire than other types of data and you saw great progress and jumps in in the coding models.

Michael: 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 at the Department of Energy are have been doing incredible research over the last you know 50 60 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 going to 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&D output over the next 10 years because of AI.

主持人: So so what kind of breakthroughs um would you expect or would you like to see?

Michael: Yeah, I I think there um the ones that I think can make a big impact are uh first the the the the experimentation and training runs around fusion um are extraordinarily computationheavy and they themselves if we can if we can have 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 how 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 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

主持人: and on a 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 you agree with that

Michael: well I mean with like self-driving or

主持人: I mean self-driving for sure is going to 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 robo taxis now Whimo and Tesla. Um

David: what what about an AI assistant? I mean, is that going to be something that is sort of common place? I 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 I'm um 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 I think so. I think that'll happen probably this year. So the the product that just came out recently that everyone's kind of going crazy over is the latest iteration of flawed code uh which is uh powered by uh anthropics uh Opus 4.5 model which seems to be a real breakthrough in in in coding and so again this is you know the software developers are really imp impressed with it but in inside of cloud code they had they introduced a new tab called co-work again you can 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 outputs. 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 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 taskbased. 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 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

David: 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 going to become sensient or whatever, but um but no, we're like I think in 2026, you could see that that these types of of tools again started as coding assistants, but now they become personal digital assistants. That could definitely happen this year.

主持人: 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?

Michael: I I think some people I I think it's easy to underestimate the the long-term impact this is going to have across so many industries and and domains. Um I think very much, you know, it's easy to to to quickly think about AI as a as just a sophisticated chatbot 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 that 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 going to have huge repercussions for the way that we as a country innovate broadly speaking in the years ahead

主持人: 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?

David: Well, I I I think that in general we're ahead 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 chipm equipment, you know. So, you go down the stack. I would say that the deeper in in the stack that you go, the greater the American advantage. Um I think on models most people would say that we're our models are maybe 6 months ahead or so plus or minus of the Chinese models. You look at chips maybe 2 years ahead. You go to the semiconductor manufacturing equipment it could be like 5 years. So the US does have sign significant advantages there. There's only maybe a couple of areas where I think China has has an advantage.

David: Um one is on energy production. And if you look at the 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 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 preient on this issue. you go back 10 years and he was talking about we got a drill baby drill and um and I think he understood that energy growth was the precondition for economic growth and it's definitely the pre precondition for this uh AI infrastructure growth. So this is an area where again we have to basically expand our energy production um and I and and and so I think that is an area where we need to catch up.

David: 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 but you could argue that 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 83%. So 83% of the population feels that it's being 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 you generally see this that uh 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 I think that um first of all, the the media tends to focus on the doom and gloom stories with with AI,

主持人: the fear,

David: 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 2001, uh has you has portrayed this dystopian view of the future. And I think that plays into fuel's thinking. And then frankly, I would say that part of the 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, you know, AI eliminating 50% 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 for the United States is because again it's feeding into this regulatory frenzy we're seeing again 1,200 bills at the state level. And 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 overregulating this thing to to death, we could actually cost ourselves this AI race. So, I do worry about this question of AI optimism,

主持人: right? It's a great point. And how what would happen if the US is not number one in this, Michael?

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 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 and the reality is we're neck and neck and as David said we're probably had you know six to 12 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 Ericen and Nokia. that they were good enough and they were subsidized enough such that they became sort of 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.

Michael: When it comes to AI, we know there's 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 various applications. We have the very best chips. So, we are in a position of power now and is 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 doing a big push this year to get that get that out the door.

主持人: 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?

David: It it it seems so. Um I mean China is developing its own models. Obviously about a year ago you had the Deepseek 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 you in a way how good China was at producing models and there was a little bit of complacency uh towards our relative position. 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 100page Biden executive order regulating AI. No one was talking about whether this might slow whether all this regulation would slow us down. Visa v China 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 overregulating it. But I think you know China definitely wants to compete. Um there have been 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 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, 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.

主持人: So, 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?

Michael: 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.

Michael: So in the US, if you're a very sort of, you know, if you're a Fortune50 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 you go 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 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 turn keys 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.

Michael: 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 largecale 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 that just have inference related chips that can drive and and and do the you know 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 manageablysized 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 commercially viable in countries that are not extraordinarily deeped. Um so we're going to be in India next month for the India um AI impact summit. Um, this is sort of the largest global gathering for for AI folks. Um, and we're going to be sharing a lot more on on the progress of this uh of this program there.

主持人: You want to weigh in?

David: Well, I just just to build on that. I think people sometimes ask, you know, h 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 5 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 5 years we look around the world and it's Huawei chips and Deepseek models, then that would be very bad, right? That would 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.

David: 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 want to be a platform company. And so in all these technology races, biggest ecosystem wins. And we want to have the 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 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 you know we have to think in this with this partner mindset and I do think that 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.

David: 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 100page Biden executive order on AI that was all this new regulation and there was a 200page uh was called the Biden diffusion rule, which was 200 pages of regulations 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 rescend all of those unust regulations which I think was absolutely critical.

David: You know the thing that really makes Silicon Valley special is this concept of permissionless innovation. you know, since um Hulin and Packard started 85 years ago, started building Silicon Valley, it's it 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, you know, a check for, you know, seed capital. Those investors think they're probably going to lose their money, but they figure there's a shot. and you know and it's so it could be the two guys in their 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 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 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 just in the past year you've seen the results of that

Michael: and I think what 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 regatory environment that allows this 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 reporting 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 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 policy makers around the world is one that moves to a corner that is obsessed with the precautionary principle.

Michael: this concept that every time something new comes out, the role of the policy maker 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, what we try to do is sit in a room and whiteboard what rules we 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 the recipe is kind of obvious but but sometimes we have to just keep repeating it to to our counterparts. And I love the Draghy report because it was so clearly uh identifying companies that are in Europe that you know like no Novo Nordus is like 350 billion or $400 billion company and in America we've had companies of trillion dollar companies Nvidia hitting**$5 trillion**. So so what is the path to innovation?

David: Well, I 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 as just setting the rules of the road. um and maybe putting in some guard rails, 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 host too much, but you know the when when the uh when the EU talks about AI leadership, they're talking about the regulators and they think their value ad 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 u 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 got to be the innovators. That's how you unlock innovation. When the when you start to see yourself, I mean, the the regulators and the policy makers 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 EUAI 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 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.

主持人: 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?

David: 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 those scenarios were described by George Orwell, not by, you know, James Cameron and the Terminator. And specifically, it's 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 it's 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. Um, and it 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.

David: Um there just 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, you know, this is how you ended up with, you know, the the the black George Washington 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 kind of 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 shadowban. You could see that being built into AI models as a way to control uh the the public discourse in in a very serious way. And I think that, you know, President Trump again just put a total halt to that, you know, rescended 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 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, 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.

主持人: 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 I just I'm trying to understand what he's saying that it's we're going to go on holiday. Um jobs are going away and AI is going to do everything.

David: Well, Elvon's a friend of mine and um I'll I'll uh I I'll disagree with him slightly on this, but um but but let me just 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 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 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, 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 toward 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 going to put everyone out of work. I don't think that's going to happen. Uh but again, the timelines matter a lot. And you know, getting to a world with no money is not something that's going to happen in the next 5 years.

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

Michael: Totally. I think generally the the abundance story extends itself well into into, you know, health care and everywhere else that and and just quality of life. So, good things ahead.

主持人: I think we'll leave it there. Michael Katzios and David Saxs, thanks so much.

David: Thank you.