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 had first of all declared that we were in one, and I think his speech was reminiscent of when President Kennedy declared that we were in a space race and had to win that race. I think since then what you've seen is that American companies have only innovated more. You're seeing all sorts of really incredible products being released all the time. I think that American AI models, chips, data centers only just keep getting better and better. So, I feel very good about the American position in this 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 dot-com situation. Remember where we had the whole fiber build-out in the late '90s and then we had a dot-com crash. The difference here is that in the late '90s and early 2000s, we had a problem known as dark fiber where you had this fiber build-out and then it didn't get 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. There's just been some releases in the last couple 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 build-out that we're seeing. So, I don't think it's going to stop anytime soon. Just last year, this infrastructure build-out added about 2% to the GDP growth rate, and that's what helped propel us to this 4 to 5% growth rate, and I think you're going to see something similar this year.
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. 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. I think we're pretty proud to say that we've made pretty good progress on all three.
Just focusing a little bit on the innovation one you're 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, but 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. 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, no, we want to be able to control our destiny when it comes to AI." What's most important when you look at that framework in terms of a federal oversight?
Yeah, I think in the executive order that the president signed in December directing us to kind of work through this proposal, he listed a few things that the states should continue to be able to pursue individually on their own. Legislation or rules around child safety was on that list. The rules around permitting of data centers and build-outs are continuing to be something that states should look at. So there were a few things that were enumerated, but that's the kind of stuff that I guess David and I are going to be working through. I don't know if you have any thoughts on that.
Yeah, I think the basic problem that we have is that, frankly, the states are going hog wild right now with regulation. There's over 1,200 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 since this technology is so new and the environment is so dynamic, 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 over-regulate the thing. But in any event, that's what we're seeing right now at the state level.
I think that the president's been very consistent that it would be better to have one rulebook, a single rulebook 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 in 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. As you know, it's very difficult to get a bill through Congress. You need 60 votes in the Senate, so it has to be bipartisan to a certain degree. But we're going to try and see if we can work to get that consensus.
And do you have any clarity on the timing on that in terms of support in Congress for a federal oversight, or do you see pushback there as well, depending on the state you're talking about?
Well, there's pushback in Congress to the idea of preemption without a federal standard. So in other words, you can't replace something with nothing. This is sort of the thing that we heard repeatedly, but I think there is 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 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 thwart the United States in the AI race if we just stop 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 their grid. They all saw standing up their own power generation as part of their build-out.
What Secretary Wright, our Secretary of Energy, has been doing is reformed 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: he said let the AI companies become power companies. Let them stand up their own power generation side-by-side with these new data centers, and the result of that is, A, we get this infrastructure, and B, 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. Mhm. And I think what we're seeing is that we need to share a good story about how ultimately this build-out is going to be net positive for American ratepayers. I think sometimes if you're in a small community and someone shows up to build a data center, you have to make it clear that ultimately this is something that's going to actually lower your rates long term. 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. 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, right, to build out the data centers. And there's also a worry that the banks will be left holding the bag for some of this because, again, the spending is too much. Your thoughts on that?
Well, I think there is obviously that concern. It's less I would say the banks—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 just make one other point 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 increase residential rates. It'll bring it down, and it'll do that in two ways. One is that the data centers can give or sell power back to the meter when they have excess, so that will help bring down rates. Second, there's a lot of fixed costs involved in power generation; it's not all variable. So when you're able to amortize those fixed costs over a greater supply, you bring down the meter rate for everybody. And so, there's huge economies of scale. 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 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 great policy change is made under this administration that the Biden administration had as a matter of policy made it such that you couldn't do this behind-the-meter energy generation. If you wanted to bring your own power, you couldn't; you had to be part of the larger grid. So, that rule has changed by Secretary Wright and by FERC to kind of allow this to happen. And ultimately, I agree with David. Once you have sort of greater scale in power generation, you'll be contributing back into the grid in a way that benefits ratepayers.
But let's go back to the uses and how AI is changing our lives. You mentioned earlier all of the uses and the impact 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. I think we started with AI chatbots like ChatGPT, and in a sense that was kind of like better web search. It was really great for research: you ask it questions, it gives you answers to anything. Then we saw models with chain of thought, and they could start to do deeper reasoning. Then we saw coding assistants. 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 this improvement of the quality of the coding assistants.
I think where that's going next is tools for knowledge workers. The same types of assistants that have been outputting code can now output any type of format, whether it's 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 software developers have been using AI to generate 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. I think that's one of the things we're seeing on the ground.
Separately, there's a bunch of things happening in industry verticals—different industries being impacted by AI. In healthcare, I think there's a tremendous opportunity to reduce 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 diagnoses. They've been able to put in their medical records into ChatGPT or other chat engines or 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.
Yeah, the one area I think a lot about is AI for science. And back to David's initial point about the progress we've seen in these frontier models: I think the very early ones started with just general knowledge. You have to go back and understand what was the data available for those model builders to start training their models. For the early ones, you could just scrape the internet and just kind of cram everything into a model and train it. That's where you had this first phase of large language models. The second one was coding. If you think about how do you get a really good coding model, you have to train it on existing code. 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. 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 formatted in a way that can easily be applied to a large language model training run. 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 that is in all types of different formats.
In our 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. Our national labs at the Department of Energy have been doing incredible research over the last 50, 60 years, and all of that is sitting and is ready to be used to be trained for these models. So my hope is that over the next year, we're going to see a lot more work in scientific discovery to be able to actually accelerate how quickly we can choose which experiments to run, run those experiments, go back and figure out what we did wrong, and run them again. 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. 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 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 and 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 new energy to space; having advanced material science is important. And the third is the one that everyone always cares about, which 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. Do you agree with that?
Well, with self-driving, yeah.
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 gone to the point where you're starting to see robotaxis now—Waymos and Tesla.
