Not Your Father’s Data Center - NVIDIA's Role in AI, Energy, and the Data Center Evolution

Episode Date: October 8, 2025

In this episode, Raymond Hawkins, Chief Revenue Officer at Compass Datacenters, sits down with Mark Spieler, Senior Managing Director of Global Energy Industry at NVIDIA. Mark shares his unique background, from growing up in Minnesota and working at Cray Research and Silicon Graphics, to a 13-year tenure at Halliburton across commercial, finance, and mergers and acquisitions roles, before joining NVIDIA in 2019 to lead their global energy business.The conversation explores the rising impact of AI on energy consumption, positioning data centers as the new “AI factories”—manufacturing knowledge with data and electrons as raw materials. Mark discusses Nvidia’s evolution from gaming hardware to high-performance computing and AI platforms, unlocking efficiencies and new capabilities. Key topics include energy efficiency gains in AI workloads, the future of nuclear power and SMRs for data centers, grid optimization, and the transformative economic and social potential of AI. The episode offers deep insights into the intersection of technology, energy, and the data center industry’s future.Timestamped Overview00:00 Intro & Marc’s Background03:44 Corporate Transition to SGI08:26 Career Transition: Halliburton to NVIDIA12:04 From Gaming to AI Platform Leader16:03 AI Empowering Industries, Not Replacing17:47 AI Enhancing Legal Profession Tasks22:44 AI Cost Decline and Adoption Shift23:33 Data Center Consolidation and AI Efficiency27:53 NVIDIA’s Energy Solutions Collaboration30:36 Nuclear Energy: Safe and Underappreciated34:21 How NVIDIA is Innovating Energy Solutions

What people talk about is the fact that there's so much more being done now where we're training AI models and inferencing and therefore the demand for power is going up. But we saw this when we started creating steel, when we started creating, you know, cement, when we started creating a lot of things, there was an increased need for power, right? Manufacturing facilities around the world are going to require more and more power. And this is just another manufacturing facility that has an output. that drives GDP. Welcome to another edition of Not Your Father's Data Center. I am Raymond Hawkins, your host. As always, we are joined by guests who are smarter than I am and far more interesting.

Today, no exception. We have friends from the $4 trillion juggernaut Nvidia, Mark Spieler, they are their senior managing director of global energy industry. So all things power and talking about power for our friends at NVIDIA who are using just a little bit of energy to fire up those GPUs. Mark from Houston, how you doing? Great. Great. Amen. Thanks for having me. I'm the show. We're grateful that you came and talked with this. We want to hear about what you're doing with NVIDIA, but before we get down the NVIDIA and GPU and energy lane, let's back up to the early days of you and what makes you who you are, where were you born, where'd you grow up? Catch us up to, kind of up to the

beginning of your career. Tell us a little bit of background on you. We'd love that. Sure. So today I live in Houston, Texas, but I grew up in Minnesota. I grew up in the Twin Cities in Burdustville, Minnesota, and went to school down in southeast Minnesota and Winona. I went to Winona State University and thought this was great. There was a lot of bluffs. and you know it was it was a beautiful city and I did my undergrad down there in marketing tip of the hat to the warriors let's go one on a state yeah on a state warriors and uh did my undergrad there and my first masters there um but when I was uh going into between my junior and senior year I intern with a company called cray research Craig yeah right the super yeah the super

computer guys yeah so I I wavy worked for them for the summer and then actually did some additional work in the fall. Was Cray and Rochester? Where were they headquartered? We're actually headquartered in Chippewa Falls, Wisconsin. Chippewa Falls, okay, yeah. Right? So good to go up to Chippewa Falls.

Rochester's where the AS400 was. That was what was in Rochester, right? Is that what was there? Yeah, the IBM AS400 plant was there in Rochester, right? I was trying to think of what compute I knew was there. Okay. So, yeah, so worked for Cray as an intern, And then I stayed at Winona and did a master's and worked for the university for a while.

And during that time, Craig got bought by Silicon Graphics, SGI. Yeah. After I decided that I'd had enough of university life because I really thought that was where my career path was going to take me after I did my undergrad. I thought, I'm just going to stay and work at a university. I love the setting. I liked working with students, worked for the university for four years. And just thought, you know, this was where my calling was, did a master's and then professional development and leadership.

What were you doing for the school at one on the state? Student affairs. So I did residential life. I did auxiliary operations, which was like our food service operator, ran the Pepsi contract. Oh, cool stuff. You know, increased student life, right? And pots and got you.

Gotcha. And then just one day I said, you know what? I need to go back into the corporate world. I think there was room for more money in that space. And, you know, I said, well, I'm going to see what's out there and applied with Silicon Graphics. And within literally, I think two or three weeks, I had a job and was moving back up to the Twin Cities to work in inside sales at SGI. And so I started there in 2000 and they assigned me to a geography, Texas, Oklahoma, Louisiana, and Arkansas. So Tola.

So, yeah, so he got the Tola region. Tola was my new place. I was calling, coming down and visiting, and obviously Tola's big on a couple things, but energy is one-oh, oil and gas. Did those guys, do those early oil, I say early, but did the oil and gas guys use an SGI, were they doing, I forget what they call it, where they study what's under the ground and look for oil reserves, is that what they were doing with the SGI machines? Correct. Sub-surface understanding. So it was all ice-week processing and interpretation work. It was with Halliburton and Schlumberg-Jay software. Gotcha. So I partnered a lot with those software companies to sell full solutions. Their software are infrastructure solutions.

Worked a lot with some universities primarily that were focused on energy as well. But really an oil and gas focus. So I got to ask about two culture shocks, though. You go from Minnesota to Houston. There's a couple of massive culture shocks. First, the weather. Sure. Radically different.

you can't spell humidity in Minnesota for most of the year. Right. And you go to Houston, maybe the most humid place in America. Winter in Minnesota is seven months a year. Is that fair? Six and a half, seven months a year? Summer in Houston is nine months out of the year, maybe 10? Yeah.

Yeah. That's a big switch. Well, they asked me to come down in an interview in April. And so it was beautiful. So I thought, I'm in. And then four months later, I'm like, what the heck is this, right? Total bait and switch. Mark, this is so funny.

I told you earlier before we started recording the, I worked for a company in the Twin Cities. I went to interview in the Twin Cities in April. And all I brought were golf shirts. And I get off the plane, the rental car bus to the plane, and it's snowing. Right. I'm like, I'm from the deep south. What are you doing with snow in April? And they were like, this is totally normal.

Normal for that, right? Yeah, yeah. That's normal. That's for sure. But miss the change of seasons. Yeah, yeah. I don't miss the cold and the snow. So you've been in Houston since the early 2000s?

2002. They moved me down to cover oil and gas down for SGI down in Houston. I partnered very closely with Halliburton and Schlumberge as software partners. And after about four more years of working for SGI, I went to go work for Halliburton as in their software division running commercial and strategic alliances. And so that's when I really took a deeper dive in. to the oil field services, part of the business, and how oil companies ran their operations. I spent 13 years with Halliburton. Oh, wow.

That's a great run. Three years with global customer finance, and then five years with mergers and acquisitions. All right. They still are a great company. Traveled the world, saw many countries that most people will never have the opportunity to go to. Or even think about going to, yeah, yeah. Or want to, probably. I didn't say want to.

Probably want to. But I tell you, it was a fantastic experience. And it truly gave me an appreciation for what goes into producing the world's energy, right? It's not as simple as, you know, filling up your gas can or turning on the lights, right? There's a lot of men and women across the world that are doing really hard jobs to make sure that we have the energy required to run our lives. I'm not here to plug a TV show, but please tell me you've watched Landman. Oh, I have. I have.

I'm waiting for something to do. I don't get very real, but, you know, it's. It's a stylized version, but I think it captures West Texas and the oil patch out there nicely. Yeah. The business side doesn't nail, but it certainly paints the picture of what being in the oil patch is like out there in West Texas. Definitely parts of it. Yeah, yeah, yeah. I don't want to spend a lot of time in man camps.

That's not my speed, but I get it. My me either. Fortunately, I never had to, but I respect those that too. It's a tough way to make a living. It's a lifestyle, right? It's a lifestyle. But, yeah, so I spent 13 years at Halliburton, had a great run, and finished there after doing my MBA at Rice, and what a great experience.

And I did that with the intent of potentially staying on with Halliburton, a while and growing there, but the opportunity presented itself to go back into the tech space with Nvidia and run their global energy business, which was primarily oil and gas when I started, but now is very, very heavily power in utilities, as well as we've got mining companies that we're working closely with to produce what's required for energy. And then a lot of the large EPC companies who are building the next generation of large capital projects for sustainable energy or, you know, refinery and things like that. All right.

So a great technology background of anybody that was Cray and SGI, you got technology bona fides. That's nice. Then you switch over and do Halliburton for 13 years. That's about as good energy experience as you're ever going to get. And then you come back, what, late 2020, late 2019 to come back over to the tech business? Yep. It's July, I think July 7th of 2019. Okay, so six years at Nvidia. Yeah, you're just, yeah, all right. Gotcha. Well, that's not a bad six years to spend it in Vidia. I don't know, I know I joked while on our call, the $4 trillion man. I don't know what your market cap was six years ago, but it wasn't $4 trillion. I actually, I don't think it was a T. I think it was double digit billions. You were probably a $10 billion or $12 billion enterprise then?

No, I think we were maybe $100 billion. 100 billion. Okay. All right. So triple digit. Billet. Yeah. Yeah. It's been an incredible ride and very different culture from any company I've ever worked with or worked for or worked with. Right. It's a tremendous culture. Yeah. Yeah. I understand there's a very unique structure and the Jensen manage is in a very unique way. And boy, you guys are at the, it's interesting because you're the Nvidia energy guy in our business. You know, in the data center space, everyone's talking about, you know, AI, and of course the back end of that being your GPUs. And then the back end being that, well, man, there's a bunch of power. So we would just love to talk from invidious perspective about how you see global energy. It is in our business today, it's the number one conversation. The first question we get asked is, hey, where are you with the energy company? Where are you with the utility provider? How are they, what's your contract look like? What's their power ramp look like it's different you know and that's really only transpired in the last three years where

it's become the first qualifying question and you having spent six years in invidium with your energy background you're kind of at the center of all of that so let love to hear your thoughts as you guys continue to build more and more capable compute better and better GPUs and what it's doing from a power demand perspective look just that's kind of open-ended talk all you want mark go well you know it was interesting six years ago when I came into this role and I'd go and talk to executives, most of them didn't know who anybody even was, right? And unless they had a son or daughter that was a gamer, it just didn't know what we did. And still today, a lot of them believe we make chips, right? But we're-

Gaming consoles. Yeah, yeah. Yeah, yeah. And we do do that. We make great silicon, but we're a software company. We're a platform company. And that's why we're reflected in the market the way that we are, is we create platforms that other people can develop on top of. And it has become critical for lots of different technology to leverage AI, right? So we started as a gaming company and a visualization company. And then as people got to know the capabilities of our hardware, they decided, you know, we need to program this to do high performance computing and things in parallel, which made us a high-performance computing company, and therefore, seismic processing was a huge part

of our HPC workloads, right? And probably our biggest industry for a long time, to be honest. I didn't realize that. Because, yeah, you could process so much more seismic data running on hundreds or thousands of cores on a GPU than you could on 10 cores on a CPU. And then Jensen had the forethought to say, hey, you know, this AI thing, and it's been around a long time, but it's highly parallelized. How do we create the software stacks, right? It was probably in 2014, I think, that he introduced the first software stacks to basically start looking at

how do we use GPUs for AI. And it's taken off. And AI has become part of everyday life, right? My boys use it every day, right? It is their Google, right? And, you know, it's getting integrated into more and more things. And it's really the next industrial. revolution. So if you think about how building this knowledge base works, right? Because AI is really generative AI is knowledge. We're turning data centers. You guys are turning data centers into AI factories. And the input, you know, the feedstock in our, in our terms, the feedstock for this knowledge is two things. It's data and electrons. I got to pause on one second for you. I love the phrase AI factory. I know, was that it?

last, this just this past, and I forget what you call your big conference that kicks you guys do. GTC? GTC. Yeah. Is that the first time he used that phrase, AI factory? Was it this year's GTC or did it? Is he being used in using that previously? Because I love the phrase. Yeah. He's been using it, I think, for a couple years now. Okay. Yeah. Just love that. You know, if you envision these AI factories, right, they're no different to manufacturing facilities, right? You got a feedstock coming in. You run it through a factory, right? Or run it through servers, right? And then outside, on the other side comes a different product, right?

A byproduct, which is AI or knowledge, right? And so it's turning data into knowledge, but it's all dependent on electrons, right? You know, you've got to feed it power in order to do that. And while the data centers are consuming more and more power per unit, right? Call it per GPU, call it per server, per rack. the amount of knowledge that it's able to produce has gone up 100,000 X over the last 10 years, right, or more, depending on if you're training or inferencing. And so, you know, the great thing is it's becoming more and more energy efficient.

What people talk about is the fact that there's so much more being done now where we're training AI models and the inferencing and therefore the demand for power is going up. But we saw this when we started creating steel, when we started creating, you know, some meant when we started creating a lot of things, there was an increased need for power, right? Manufacturing facilities around the world are going to require more and more power. And this is just another manufacturing facility that has an output that drives GDP, right, and drives governments. And we see more and more sovereign AI opportunities popping up,

and we see individual companies developing their own AI models. And this is how companies are going to scale the work that they have without adding tremendous more cost to their operations, which will make things more affordable, will allow people who have studied and are working in these different industries to do more, right, than what they can do today. And, you know, I know there's always the conversation around, you know, replacing people and things like that, but I always use the analogy. It's like assuming that a telta later was going to replace mathematicians. Right? When you created the calculator and everybody saw, hey, now, anybody can do math, right? And so we don't need mathematicians. But really, it just allowed mathematicians to do much more complex math in a faster period of time. So now we could tell harder problems. And that's really what AI has been to do for people in every industry. Right. I mean, isn't that, though, been repeated time and time again in history when we come up with a new technology and we can go all the way back to the wheel, oh my gosh.

All the people who carry things are all going to lose their jobs. No, they're going to go do things that they can do that they didn't have to spend energy dragging or carrying that thing because now it rolls on the wheel. I mean, that has happened over and over and over and over again for millennia, right? That we now get to do a different set of work. We get to point our energy, I like your mathematician analysis, right? Our mathematicians are solving different problems now because they don't have to do the adding and subtracting and dividing that you had to do before. They don't have to burn that energy to do that. They can burn energy doing far more effective tasks.

And this is, and AI is the exact same thing. It's just doing it at a much higher, you know, knowledge base, a much higher value chain level. Hey, we're going to have lots of capability. So I'm going to give a personal example. My son's in his last year of law school. And there's this conversation around law school. Well, hey, is AI going to replace all of us? No, meaning all the lawyers, no, what it's going to do is it's going to take that drudgery level,

go research all of those cases, go analyze all of those briefs, that stuff that the first guys that are, you know, three or four years out of law school and having to do the heavy lifting of the firm, they're going to be doing more interesting things and more impactful things because you'll be able to get that out of an AI, you know, engine, right? And that's, you know, that's a great example of, hey, now my lawyer isn't going to spend 20 hours reading briefs, right? He's going to let a machine do it and give him a three-hour summary. That's absolutely right. We use the term at Invidia quite a bit, doing your life's work, right? There's very few people whose life work is to search for data, right? Right. And collect data. There are people that that is their life's work, but at the same point, most people that have a profession, they want to solve really hard problems or do things that are going to be impactful.

And so AI is going to allow them to focus more on those things and differentiated things than just looking for data and answers and outcomes, right? You know, and we even saw this transition from, you know, card catalogs to encyclopedias, right? And eventually, you know, things got put on microfiche. And then all of a sudden you had the Internet and Google and all that and search. And now you're going to be able to get answers and outcomes much faster than what you could in the past. And now you can put stuff together that allows you to really go and sell really hard problems. Now, Mark, most of our listeners are from the data center business and they are younger than you.

you and I, so I'm going to now have to explain card catalog. I'm going to have to explain microfish. Things are, you and I are very familiar with that my kids are going to call after this episode, go, Dad, what is a card catalog? And that's a good example. They didn't have to know how to use the Dewey Decimal System, right? My kids didn't, right? They had Google. Yeah, to your point. I like that phrase, doing your life's work, right? You know, never having to pull out a tiny drawer with cards and look for the book is, is a blessing. Yeah. Or even going to the library, right? You know, now

exactly right. They're on your phone. You're able to do things. And more and more, because of agentic corp clause, they're building stuff right into applications that everybody's using. And so things will become very efficient for people. And you know, now it's going to become, how do you strategically think? How do you prompt?

How do you take the expertise that you have in your field in music or prompt engineering in creating specific agents, right? And, you know, and having them become your own workforce, your own virtual workforce. And it's really exciting, right? We're going to solve some really hard problems in years to come. Yeah, we get to use people's, I love that. Life work, their highest, best capability gets focused on the toughest stuff, on the hardest questions. So this next question, it's going to feel a little bit like a setup. I'm going to put myself in the

boat with you, so it's not a setup. But our industry gets talked a lot about, you know, data centers you're using all this power, right? And, and of course, you guys, your chips are in the heart of what is growing my industry like crazy. And so we hear a lot, oh, my gosh, those GPUs are so hungry. Oh, data centers, you're using so much power. You, it's sort of the intersection of that energy production and electrons and the GPUs. Can you talk a little bit about what's a healthy way to think about that energy consumption? You alluded to it earlier, hey, we're getting a lot more, we're getting a lot more output for the same amount of energy use, but let's help us understand that a little bit more.

So the evolution has been great over the last 10 years, right? As far as the performance per watt, you know, and the amount of outcomes that we can get for the same amount of power, right? And like I said, I think in the training space, it's 2,000 backs. And in the inferencing space, it might be 100,000 X over the last 10 years as far as energy efficiency. Once again, it comes back to, yeah, we're using a lot more AI. We're using a lot more data centers. And I think the market understands that this is not going to slow down anytime soon,

that we're going to continue to evolve, right? And therefore, we're going to need more and more power in order to process that data, especially as the cost continue to come down, the power envelopes continue to get optimized, more use cases will be commercially viable than what was the case in the past, right? You know, TETGBT and OpenAI was a game changer, right, and opened up the door to a lot. But the technology in theory was, you know, neural networks are not new, right? I wrote a paper in college 30 years ago about that. And ultimately, the problem is, is it would have cost you,

call it hundreds of billions of dollars to build a system to do what they were able to do with Open AI. And now as it becomes more affection, it'll become cheaper and cheaper. And so you're going to see use cases now becoming more affordable, more affordable, cost-effective. And what people might choose not to use it for today, three to five years from now, they're going to bring those use cases on. And so we'll see more of it. But, you know, it's definitely a change. It's paired on shift. Yes, we need more power for DCs, but we need more power for a lot of stuff, right?

manufacturing and EVs and all kinds of other things that probably rank higher than data than AI but the thing is is we're consolidating these things into very large data centers and so I think it comes back to that consolidation and when you have a couple of hundred megawatts or you're going to gigawatt data center that's a lot of power in one area but in the past it could have been spread out to your laptop or my laptop running for 12 hours to write a paper that's now written in 20 seconds or 30 seconds with the right prompts, right? And so I'll give you another analogy, and I like to use analogies, but, you know, when you look at technologies like ways, right, and how we use GPS is now to get from one place to another

and redirect traffic, well, it takes computer, it takes energy to run those analytics and re-optimized and redirect and all of that stuff. But when we were young, our parents who went to work You know, they left, they watched the news, they left, and, you know, they wished their best for traffic. But if they, if there was an accident or something changed, they'd be idling in a car burning, you know, gas for an hour because they didn't know to get off and go down this street and move here or redirect traffic to keep traffic moving. And now, yes, we're burning some electrons to have real time traffic in the alerts and redirect. but we're saving a tremendous amount of resources and CO2 emissions and all that stuff by redirecting traffic using AI and technologies. So it's the same thing.

Yeah, I liked your phrase, do your life's work. We have a phrase at Compass. We talk about Make Lives Better. That's a great make lives better example. Not only is the car sitting there idling, but you're going nuts sitting there going nowhere where, you know, Ways can tell you, hey, if you hang a left here and go down three blocks and hang it right, you're going to. and get around all this and save yourself 14 minutes.

I mean, that is, that is, that is in making, that is making lives better with technology. And no one stops to think, hey, all that took some electrons. All that happened in a building somewhere and it delivered it to you in your hand, right? I just think that's a really good example of making lives better by using technology in a way that, you already, you already hit us with microfish and card catalog. I was waiting for you to say the word maps. because my kids my kids have no idea what a folding map is they have no concept that you used to have a binder that you went through and flip the pages or you had to fold it back up which by the way they never folded back the right way a second time yeah travel's totally different now with they are no one no one even knows what a map is right and and once again it makes life better like you said it it saves on energy right not maybe the same kind of energy right one might be gastille versus versus electrons but overall

all, it's better for the environment. It's better for everybody. And so I think it's going to be a philosophical change management thing that, you know, people and companies are going to have to look at as they start to get through this and understand just how much value that this creates. And overall, I truly believe that AI will solve more problems for energy than it'll create, especially when it comes to things like grid optimization, interconnection studies, getting more power onto the grid, how do we leverage more renewables and other aspects

into the grid, and get full utilization out of what we're doing and ultimately reduce costs to rate payers and everybody through better technology. That's real-time capable. Yeah, it's clearly another revolution, and it's disruptive by the nature of revolution is disruptive. Our grandkids are going to just think this is the way things always were. Yeah, this is how it's supposed to work. Of course, AI did all that me. What do you mean? I read 42 briefs. Are you crazy? No. I told the AI engine to go, here's the subject, go find the briefs, read them all and come back to me. That'll be completely normal and not the too distant future. And that's just one silly little use case. Well,

in the role of global, you know, the energy industry, what does Nvidia have you doing out there? Are you the liaison? What's the function of what you do at the crossroads of energy and Invidia. So I think I pointed out at the beginning. Invidia is a platform company, right? And so my job is to work with the ecosystem of organizations within the energy space to solve really hard problems related to energy. And my first choice is always to do that with software providers or solution providers

who can solve the problem once with us and then take that to multiple customers across the industry. So my team and I spend a lot of time working with solution providers, right? The big companies who are selling into utilities or selling into oil and gas to solve problems. And some of these companies want to build it themselves. And so we do have teams that help support them as they try to build technology. Nobody buys anything from Nvidia and just puts it to work, right? We don't, we're not an application company.

I was going to say, you're not the killer app. You're the engine for the killer app. Correct. Yeah. Yeah. Okay. Yeah. My whole team is focused on how do we figure out what are the biggest problems in the

industry? And then how do we go find the right partner or end customer that's interested in solving that problem? We go help them solve the problem and then we talk about it. And then go other people to adopt a similar technology, right? So we work with all the cloud providers. We work with all of the large global service companies, right? Accentures and E.WIs and Deloits and forth.

We work with the Stormberghs and Halliburton's, but also the GE Rinovus and the Siemens energies and those guys who are building software stacks for the industry. And then we work with end customers who basically want to create their own AI environment within their firewalls, right? That basically said, hey, we want to take what's available, but we also want to customize it with our proprietary confidential data. data so that we can provide better outcomes for our customers, whether it be an operational

outcome or whether it's call center response or, you know, all kinds of things. Customer support, weather prediction, right, and things like that, there's lots of things that we're working on to help solve problems across the industry. So as you've got all this interface with the people generating energy, delivering energy, I'd love to get your take as you help service providers, solution providers solve problems for the energy industry. Where do you think the nuclear space is headed for us today? Do you think how big a piece of the puzzle is that? How soon do you think it is?

Love to get your take as a guy that's got lots of energy in your background and lots of technology in your background. Where do you see nuclear going? Nuclear is a great resource, right? I think it's somewhat underappreciated because of just there's not a lot of it, right? compared to other forms of energy. And there's obviously been some major issues with nuclear over time. You know, I don't want to minimize, you know,

nuclear accidents, a terrible thing. But once again, it's been a small number of those compared to other types of issues that we have that hurt people and kill people. And so relatively speaking, it's a safe technology. And it's been around for a long time. But, you know, the permitting and like, licensing process and the ability to build and deploy nuclear, depending on the country,

is a very long process. And so I think we're going to start to see some changes in that, right? I hope in the U.S. I think we're seeing a lot of other countries moving much more aggressively with nuclear. I think there's a lot that we can do with AI and accelerated computing to not only simulate issues with different types of reactors, but to also do the weather study. and licensing and permitting to help demonstrate the safety and the efficiency of these technologies, especially SMRs and others. And so from that perspective,

I predict over the next, call it, five to ten years, a significant increase in the amount of people wanting to deploy nuclear, especially with data centers, right? Because it's firm power, right? That's... I was just to say that. That was what I was hoping you were going to say, right? It is high quality base load, right?

It is always on. And once you turn it on, it just runs, which is awesome. Yeah. No, it does. It's reliable. And you can get that through adding a bunch of batteries in front of renewables. And you can do it with gas turbines, right? And other things, but it's cost effective and it's reasonable.

And I think as soon as we start to see these SMR companies, and there's a lot of them out there, right? We talk with a lot of SMR companies. They're working fast, right? it's it's just a matter of how do we get them to scale, right? You know, building one SMR is hard from a commercial liability, 10, 20, 50. All of a sudden, you're going to start to see them. And because they're small enough and they're manufactured in a way that they're not one-offs, I think we're going to start to see some of these companies really accelerate the affordability over the next five years.

I think we've got to help them from a regulatory perspective, too, right? I mean, we need the generation online. And, you know, starting with a 20-year, you know, entitlement program is just, you know, permitting is just insane. We need the power in five years, not 15. No, you're absolutely right. The permitting and licensing is probably a long pole in the tent, right? You know, it's hard, right? You've got wind studies.

You've got all these other studies. You know, but we got lots of data, right? We got lots of, there's lots of wind monitoring devices around. And, you know, if you look at some of the stuff we've done with Earth 2, which is our climate simulator, we've done some great work at doing solar prediction models where we can, you know, increase the accuracy of predictions by, you know, anywhere from 10 to 25%. And I'm confident that we can help the NRC and others really make these studies much more efficient and reduce the time to power through licensing and permitting. Well, man, what a cool spot to be. The intersection of technology and energy, you at the,

maybe the coolest and most exciting tech company in the world right now at Nivitia and being in the middle of the energy space where, and at the end of the day, more energy is good for everybody, right? Energy is what, you know, raises lifestyles, takes people out of property, provides solutions, makes life better, you know, back to back to our desires to make lives better.

You can do that when you can have light, read at night and, you know, energy to power the hospitals and the cars and the transportation. So I'm a big believer that we need more energy because it makes people's lives better. It just does. And what you guys are doing in Nvidia to unlock, I like the way you said, it's making knowledge available to people in just unique ways that we've never had before. It's really, really awesome. Well, Mark, I'm super grateful to have had you come and chat with us for a little bit.

We're excited about the future in the data center business. We're grateful Envidia is such a hit because our buildings are full of them. And I tell people all the time, you talk about AI factories. We are warehouses for ones and zeros, but I like AI factory better. We are buildings full of data, trying to help people solve problems. And we're grateful for how you guys play a role in that for sure. Thank you, Raymond. It's been great talking with you.

And we appreciate everything you're doing. who help accelerate, you know, how people can leverage technology in AI, right? And, you know, you talked about how energy changes lives, but I tell you, I think AI is going to help a lot of countries get access to really smart teachers, teaching in their local languages, teaching the way. Knowledge changes lives too, amen, yeah. It's really going to change the way in which a lot of countries are able to develop the next generation of workforce and pull their people into a much better place.

yeah much greater standard living which is good for mankind awesome stuff mark thank you for hanging out with us um from one texan to another both transplanted texans we really appreciate you hanging out with us thank you raymond i look forward to the next one take care all right take care