Great to see everyone and I'm thrilled to be able to talk about the issue of the day and that is artificial intelligence and AI in our world. David Michael, I'd love you to talk about where we are right now in terms of the pursuit to be the number one lead AI country. How are we doing, David?
I think we're doing great. Maria, last year, President Trump gave a major AI policy speech. This is in July, and he declared that the United States had to win the AI race. He first of all declared that we were in one. And I think his speech was reminiscent of when President Kennedy declared that we were in a space race and had to win that race. I think since then, what you've seen is that American companies have only innovated more. You're seeing all sorts of really incredible products being released all the time. I think that American AI models, chips, data centers only just keep getting better and better. So I feel very good about the American position in this AI race. Certainly we have some very competent and formidable competitors. China obviously has a lot of very smart people working in this area. But I do think that just what you see from American companies in Silicon Valley right now is really incredible.
And yet there are still so many questions about all of the spending underway to build this out with regard to data centers. And of course the question keeps coming up, are we spending too much? Will we get the return on investment? How do you see that?
I think that we will. I think that the reason why you're seeing this huge infrastructure build out is because the demand is ultimately there. I know a lot of people worry about whether this could be like a dotcom situation. Remember where we had the whole fiber build out in the late 90s and then we had a dotcom crash. The difference here is that in the late 90s and early 2000s we had a problem known as dark fiber where you had this fiber 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 and it's being used to generate tokens and that's to power this new generation of AI chatbots or coding assistants. And there's just been some releases in the last couple of months on the coding front that if you're following what 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 build out added about 2% to the GDP growth rate. And I think 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.
Well, it is certainly leading growth, Michael. And I'm so happy to be able to get this conversation going with both of you who are really leading this. David, thank you. And Michael, thank you. Same questions for you, Michael. Assess where we are right now on AI.
I think just a reminder for the group for those who haven't been tracking as closely as we do every day. The plan really had essentially three pillars and it talked about how one, how can the US continue to out innovate our competitors, two, how can we drive the infrastructure build that we need to support this AI revolution, and three, how do we actually share with the world or export our great American technology. And for each of those three pillars there was quite a lot of actions that the federal government has taken to drive that forward. And I think we're pretty proud to say that we've made pretty good progress on all three. Just focusing a little on the innovation one you were talking about earlier. I think the core insight that we've always had about how you drive this innovation is you have to have a regulatory environment that allows this technology to be developed and ultimately commercialized in the United States. And the US has done a great job compared to the rest of the world on sort of setting that up and creating a framework that works. We can always do better and improve it. And the president in his speech in July talked a lot about this issue of a patchwork of state regulations and how can we ensure that there aren't 50 different rules around AI. And what's most important about this debate which I think a lot of people sometimes miss is the patchwork is actually most detrimental to early stage young companies and entrepreneurs. If you want to develop a new AI technology, if you want to build something on top of one of our great frontier models, having to figure out how to navigate 50 different rules across 50 different states creates a lot of friction and ultimately the big guys are the ones that can succeed in that environment the best. So we're spending a lot of time trying to think about how can you create a legislative proposal that can actually deliver on a sensible national framework to solve this regulatory issue.
So what would you say then, Michael, are the basic frameworks that are sort of must-have in that kind of federal oversight? Because some states did push back in the US and say, 'No, no, we want to be able to control our destiny when it comes to AI.' What's most important when you look at that framework in terms of federal oversight?
Yeah, I think in the executive order the president signed in December directing us to kind of work through this proposal, he listed a few things that the state should continue to be able to pursue individually on their own. Legislation or rules around child safety was on that list. The rules around permitting of data centers and buildouts are continuing to be something that states should look at. So there are a few things that were enumerated, but that's the kind of stuff that I guess Dave and I are going to be working through. I don't know if you have any thoughts on that.
Yeah, I mean I think the basic problem that we have is that, frankly, the states are going hog wild right now with regulation. There's over 200 bills going through state legislatures right now. I think it's very much a knee-jerk reaction. I know there's a lot of fears and concerns about AI, but it seems like for every hypothetical concern, there's multiple state bills now to try and regulate that thing before we really know how it's going to play out. And I think it would be better to, 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's what we're seeing right now at the state level. And I think that the president's been very consistent that it would be better to have a single rule book at the federal level, a lightweight federal standard. I think this problem is only going to get more acute over time because again, as you have 50 different states running in 50 different directions, the patchwork problem only gets more significant. So, at any event, this is something that we're going to work closely together on this year, which is to see if we can get enough consensus on a federal framework to enact a law. Only Congress can ultimately preempt the states. We understand that. And as you know, it's very difficult to get a bill through Congress. You need 60 votes in the Senate. It has to be bipartisan to a certain degree. So, but we're going to try and see if we can work to get that consensus.
Yeah. And do you have any clarity on the timing on that in terms of support in Congress for a federal oversight or do you see push back there as well depending on the state you're talking about?
Well, there's push back in Congress to the idea of preemption without a federal standard. So, in other words, you can't replace something with nothing. This is sort of the thing that we heard repeatedly. But I think there is 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.
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 from Bernie Sanders saying stop all data centers, all data center development. And you know if we do that we will lose the AI race. I mean you do need this infrastructure. Other countries are building out this infrastructure. China's building out, I think they're spinning up a new nuclear power plant or coal plant, new energy every single week and a lot of that is going to power their data centers. So it would fundamentally hurt the United States in the AI race if we just stopped building data centers altogether. At the same time, there are concerns about affordability, about whether consumers would have to pay a higher electrical rate because of data centers. President Trump's been really clear that consumers should not have to pay higher rates for electricity because of data centers. You saw just last week Microsoft stepped up and made a pledge that 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 saw standing up their own power generation as part of their buildout. And what Secretary Wright, our Secretary of Energy, has been doing is trying to reform the regulations that actually make it more difficult for these AI data centers to stand up their own power behind the meter. So that basically is our vision, 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 build side by side with these new data centers. And the result of that is, you know, we get this infrastructure, residential rates don't go up.
Yeah, because Michael, this race has fast become, it's moved from an AI race to a power race.
Mhm. And I think what we're seeing is that we need to share a good story about how ultimately this buildout is going to be net positive for American ratepayers. And I think sometimes if you're in a small community and someone shows up to build the data center, you have to make it clear that ultimately it's something that's going to actually lower your rates long term. And the president put out a truth last Monday where he was, as David said, very clear that if you're going to build a data center you have to pay your own way for it. And Microsoft has stepped up and our hope is that many others will do the same.
But some companies, because they don't have the cash right now, are borrowing money to build out the data centers and there's also a worry that the banks will be left holding the bag for some of this because again the spending is too much. Your thoughts on that?
Well, I think there is obviously that concern. I mean, I think it's less the banks and more you see Oracle making a huge investment. You see Blackstone making huge investments, real estate companies. Ultimately, I think these are very savvy market players, very deep pocketed companies, and they're doing this because they see an ROI there at the end of the rainbow. Can I make one other point about just the data center. So, just on electricity, I actually think that if we allow the data centers to stand up their own power generation, it will actually bring down rates. Not only will it not increase residential rates, it'll bring it down. And it'll do that in two ways. One is that the data centers can give or sell power back to the meter when they have excess. So, that will help bring down rates. Second, there's a lot of fixed costs involved in power generation. It's not all variable. So when you're able to amortize those fixed costs over a greater supply, you bring down the metered 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. So it's actually a good thing 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.
And I think what a great policy change was made under this administration. The Biden administration had, as a matter of policy, made it such that you couldn't do this behind the meter energy generation. And if you wanted to bring your own power, you couldn't. You had to be part of the larger grid. So I think that rule has changed by Secretary Wright and by FERC to kind of allow this to happen. And ultimately I agree with David. I think once you have sort of greater scale in the power generation, you'll be contributing back into the grid in a way that benefits ratepayers.
Let's go back to the uses and how AI is changing our lives. You mentioned earlier all of the uses and the impact that 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 there's been an evolution. So I think we started with AI chatbots like ChatGPT and in a sense that was kind of like better web search. It was really great for research, could ask it questions and give you answers to anything. Then we saw models add chain of thought and they could start to do deeper reasoning. Then we saw coding assistance. This is really, and I think over the past few months there's been a real breakthrough. If you talk to software developers, it really seems like there's been a major shift in just improvement in the quality of the coding assistance. And I think where that's going next is tools for knowledge workers. So the same types of 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 generating code. I think that's one of the big things you're going to see in 2026 is again just this productivity boom for knowledge workers. So I think that's one of the things you're seeing on the ground. And then separately there's a bunch of things happening in industry verticals. So different industries being impacted by AI. So in healthcare I think there's a tremendous opportunity to improve or to reduce sort of administrative bureaucracy, to improve this processing of paperwork that happens. Also to use AI in medical and scientific research to help find new cures. You're already seeing users tell all sorts of stories about diagnosis. They've been able to put in their medical records into ChatGPT or other chatbots and get remarkable results. They've been able to finally figure out what was wrong with them and they've been able to take that to a doctor. You have doctors using it, too. So medical I think is a really interesting area but there's a whole bunch of these examples of different industries that are now being impacted.
The one area I think a lot about is AI for science. And back to David's initial point about the progress we've seen these frontier models. I think the very early ones sort of started with just general knowledge and you have to go back and understand why. And the question was what was the data available for those model builders to start training their models. And for the early ones you could just scrape the internet and just kind of cram everything into a model and train it. And that's where you kind of had this first phase 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 have to train it on existing code and that's again something that is relatively easier to acquire than other types of data. And you saw great progress and jumps in the coding models. I think the third big sort of shift that hasn't really been touched on yet, which the government itself is trying to do a good push on, is the AI for science question. And why it's so challenging for scientific discovery to tie in with the way that LLMs are traditionally trained is that the science data is extraordinarily fragmented and it's not done in a way or formatted in a way that can easily be applied to a large language model sort of 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 in all types of different formats. And our effort in the administration, we launched something called the Genesis mission, which is our attempt to sort of make these big bold leaps in AI for scientific discovery. And our national labs at the Department of Energy have been doing incredible research over the last, you know, 50, 60 years. And all of that is sitting and is ready to be used to be trained for these models. So my hope is that over the next year we're going to see a lot more work in scientific discovery to be able to actually accelerate how quickly we can choose which experiments to run, run those experiments, go back and figure out what we did wrong and run them again. And this ties in with lots of interesting ideas that people have around some of these AI labs where you essentially can put in the thesis or the hypothesis and ultimately these labs can do the lab experiment itself and move forward. So that's kind of the dream that I have that ultimately we as a country can almost double our R&D output over the next 10 years because of AI.
So what kind of breakthroughs would you expect or would you like to see?
Yeah, I think the ones that I think can make a big impact are first the experimentation and training runs around fusion are extraordinarily computation heavy. And if we can have a faster feedback loop on how we do these simulations for fusion, we can move the timelines in for fusion. So that could be a big step. Material science is also a very big area where you want to be able to test all types of different molecules, how they 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 healthcare and therapeutics. How can you more quickly be able to identify the best molecules to solve a particular health challenge? And how do you more quickly iterate to a point where you can move to a clinical trial.
And on an everyday level, I mean you also have the auto sector I think as a big beneficiary here. I think that's one area that seems to be spending a lot on this as well. You agree with that?
