This is Dana Perkins and you're listening to Switched on the BNAF podcast. Today we bring you a recording from our BNAF summit in San Francisco, which took place on the fourth and fifth of February. The panel was titled Data Dynamics. Now certainly a hot topic in energy circles has been the growth in data centers and how AI has led to a rise in demand for power. In order to meet this, big tech companies are looking for solutions and firms like Google are signing power purchase agreements in an array of technologies like nuclear, long duration energy storage, and even geothermal. While these data centers are undeniably energy intensive, efficiencies can be found when it comes to reducing load requirements. This is seen with the recent release of Deepseek's AI model in January twenty twenty five. On today's show, the panelists discuss the increasing capital expenditure on data centers, as well as what has been driving the decision making of big tech companies when it comes to this spend and how potential bottlenecks might slow them down. The panelists include Stephen Carlini, Chief Advocate Data Centers and AI at Schneider Electric, Will Conkling, head of Data Center Energy for the America's in Amia at Google, Kleiber Costa, chief commercial officer for AS Corporation, and Darwesh Singh, the founder and CEO of Bold Graphics. The panel was moderated by Mark Daily, BNF's head of Technology and Innovation. For more information about BNF Summit's taking place around the world, as well as our upcoming event in New York on the twenty ninth and thirtieth of April, an to view recordings from this and other previous events, head to about dot BNF dot com Forward Slash Summit. Right now, let's hear from our panel regarding power demand and data centers.

This is Dana Perkins and you're listening to Switched on the BNAF podcast. Today we bring you a recording from our BNAF summit in San Francisco, which took place on the fourth and fifth of February. The panel was titled Data Dynamics. Now certainly a hot topic in energy circles has been the growth in data centers and how AI has led to a rise in demand for power. In order to meet this, big tech companies are looking for solutions and firms like Google are signing power purchase agreements in an array of technologies like nuclear, long duration energy storage, and even geothermal. While these data centers are undeniably energy intensive, efficiencies can be found when it comes to reducing load requirements. This is seen with the recent release of Deepseek's AI model in January twenty twenty five. On today's show, the panelists discuss the increasing capital expenditure on data centers, as well as what has been driving the decision making of big tech companies when it comes to this spend and how potential bottlenecks might slow them down. The panelists include Stephen Carlini, Chief Advocate Data Centers and AI at Schneider Electric, Will Conkling, head of Data Center Energy for the America's in Amia at Google, Kleiber Costa, chief commercial officer for AS Corporation, and Darwesh Singh, the founder and CEO of Bold Graphics. The panel was moderated by Mark Daily, BNF's head of Technology and Innovation. For more information about BNF Summit's taking place around the world, as well as our upcoming event in New York on the twenty ninth and thirtieth of April, an to view recordings from this and other previous events, head to about dot BNF dot com Forward Slash Summit. Right now, let's hear from our panel regarding power demand and data centers.

Thank you everyone very much for joining us here today. We've heard an awful lot about the energy transition, like at all be enough events, and I imagine a lot of events that everyone here goes to. It's very energy focused group, transport focused group, but there's lots of big transitions happening in the global economy. We're here today to talk about the intersection of the energy transition with one of the other really big transformations in the economy, maybe the biggest one of our lifetime. If you believe some people. So we're here to talk about data centers, energy demand, artificial intelligence. I'm joined here today by Stephen Carlini, Chief Advocate Data Centers and AI at Schneider Electric, Will Conkling, head of Data Center Energy America's Animea at Google, Clever Costa, chief commercial Officer at the AEES Corporation, and Darbush Sing, Founder and CEO of Bolt Graphics. So the first thing that I want to talk about is really why has there been so much interest in data centers this year? Like, let's just start from the beginning. What's causing out go to you first, Star Resh, and if everyone can actually just introduce themselves and kind of their view on the industry in their first answer, that'd be great.

Thank you everyone very much for joining us here today. We've heard an awful lot about the energy transition, like at all be enough events, and I imagine a lot of events that everyone here goes to. It's very energy focused group, transport focused group, but there's lots of big transitions happening in the global economy. We're here today to talk about the intersection of the energy transition with one of the other really big transformations in the economy, maybe the biggest one of our lifetime. If you believe some people. So we're here to talk about data centers, energy demand, artificial intelligence. I'm joined here today by Stephen Carlini, Chief Advocate Data Centers and AI at Schneider Electric, Will Conkling, head of Data Center Energy America's Animea at Google, Clever Costa, chief commercial Officer at the AEES Corporation, and Darbush Sing, Founder and CEO of Bolt Graphics. So the first thing that I want to talk about is really why has there been so much interest in data centers this year? Like, let's just start from the beginning. What's causing out go to you first, Star Resh, and if everyone can actually just introduce themselves and kind of their view on the industry in their first answer, that'd be great.

Yeah. Thanks Mark.

Yeah. Thanks Mark.

I'm dar Wesh Funer Ceobo Graphics. We are a semi conductory startup focusing on GPUs. My background is in building data centers, so I share a lot of the pains at the panel and in the industry in general. Mark, I think to your question, compute requirements have always been increasing. This is I think it's not new to anyone. We have phones now that are more powerful than PlayStation fours from twelve years ago. What is new, though, is the demand for ten years in the future computing power right now. And a lot of these AI companies, whether they're training models, whether they're building data centers to host these models, or whether they're making phones that can run inference on these things, they want it right now. And so I think that creates a lot of interest, a lot of hype. It also creates an opportunity for new players in the market to come in, whether they're DC builders that can build data centers in six months instead of four years, small modular nuclear reactor companies that are building these and seven years instead of twenty five years. So I think it's just like a timeline. Let's shift all this left. I want to do right now and what does that really enable?

I'm dar Wesh Funer Ceobo Graphics. We are a semi conductory startup focusing on GPUs. My background is in building data centers, so I share a lot of the pains at the panel and in the industry in general. Mark, I think to your question, compute requirements have always been increasing. This is I think it's not new to anyone. We have phones now that are more powerful than PlayStation fours from twelve years ago. What is new, though, is the demand for ten years in the future computing power right now. And a lot of these AI companies, whether they're training models, whether they're building data centers to host these models, or whether they're making phones that can run inference on these things, they want it right now. And so I think that creates a lot of interest, a lot of hype. It also creates an opportunity for new players in the market to come in, whether they're DC builders that can build data centers in six months instead of four years, small modular nuclear reactor companies that are building these and seven years instead of twenty five years. So I think it's just like a timeline. Let's shift all this left. I want to do right now and what does that really enable?

Okay, and so Stephen, could you give us a primer on how Schneider Electric relates to this conversation and when did your job change in this kind of this new hype cycle that we're going through.

Okay, and so Stephen, could you give us a primer on how Schneider Electric relates to this conversation and when did your job change in this kind of this new hype cycle that we're going through.

Yeah, Kinder Electric, if you're not familiar with this largest power and cooling solution provider for data centers in the world, and as you said in an earlier panel, you know AI is not new AI has been around for a while. We've been talking to a lot of the hyperscalers. We have staff of people signed to each hyperscaler, each large COLO, each large enterprise accounts. And we you know, we noticed five or six years ago, you know, these data centers can't be built overnight. And five or six years ago, a lot of the hyper scalers were coming to us, not only talking to us about higher densities, but higher densities and scales that we've never heard of. You know, twenty twenty megawat data centers. Back then it was a big data center. They're talking one hundred, one hundred and fifty three hundred megawat campuses back then, and we're like, wow, this is this is a big change. So the densities, you know, started to really change when Nvidia came out with you know, the A one hundreds and the.

Yeah, Kinder Electric, if you're not familiar with this largest power and cooling solution provider for data centers in the world, and as you said in an earlier panel, you know AI is not new AI has been around for a while. We've been talking to a lot of the hyperscalers. We have staff of people signed to each hyperscaler, each large COLO, each large enterprise accounts. And we you know, we noticed five or six years ago, you know, these data centers can't be built overnight. And five or six years ago, a lot of the hyper scalers were coming to us, not only talking to us about higher densities, but higher densities and scales that we've never heard of. You know, twenty twenty megawat data centers. Back then it was a big data center. They're talking one hundred, one hundred and fifty three hundred megawat campuses back then, and we're like, wow, this is this is a big change. So the densities, you know, started to really change when Nvidia came out with you know, the A one hundreds and the.

A one hundreds.

A one hundreds.

It was about three years ago and and the A one hundreds were kind of the first you know, at scale deployments for a lot of the hyperscalers, and a lot of those were air cool they were twenty five kilowats parac Then you saw the grace hoppers, which were last year, which were thirty six kilowats are acting seventy two kilowats parak and now we have the Blackwells. The black Wells are one hundred and thirty two kilowats per rack, which is really pushing the limit of what we can get powered to these racks and cooling to these racks. Next, they're working on Ruben, which is the next generation. They're already we're already working on the designs for that at two hundred and forty kilowats per rack. So it's just exponentially. For years, it was just two socket x eighty six pizza box servers in these data centers, and we were doing ten kilo wats per rack. And in the last four or five years, it's just exponentially, you know, increased, and not just increased in small scale, but it's a large scale that we talked about.

It was about three years ago and and the A one hundreds were kind of the first you know, at scale deployments for a lot of the hyperscalers, and a lot of those were air cool they were twenty five kilowats parac Then you saw the grace hoppers, which were last year, which were thirty six kilowats are acting seventy two kilowats parak and now we have the Blackwells. The black Wells are one hundred and thirty two kilowats per rack, which is really pushing the limit of what we can get powered to these racks and cooling to these racks. Next, they're working on Ruben, which is the next generation. They're already we're already working on the designs for that at two hundred and forty kilowats per rack. So it's just exponentially. For years, it was just two socket x eighty six pizza box servers in these data centers, and we were doing ten kilo wats per rack. And in the last four or five years, it's just exponentially, you know, increased, and not just increased in small scale, but it's a large scale that we talked about.

Okay, so will you're you're coming from Google, the company that started the all it was Google's R and D Live a transformer and Google's obviously been a huge energy procurement company for years and years though and when did when did this translate into changes in your job? The recent technology advances.

Okay, so will you're you're coming from Google, the company that started the all it was Google's R and D Live a transformer and Google's obviously been a huge energy procurement company for years and years though and when did when did this translate into changes in your job? The recent technology advances.

Yeah, so the story for me is like I've been at Google just over ten years in some way to performed buying energy for data centers and buying renewal energy for data centers, and working with utilities and working energy supply. And you know, Google has been an AI first company since about like twenty sixteen, twenty seventeen. Our CEO has been saying that for a long time, right, And we've been infusing AI and machine learning into our products since then in various various ways that you know, we probably don't notice as users, but it's been there. And then in the last you know, eighteen months or so, it see the emergence of you know, sort of more consumer facing pure mL and AI products you know before so, like i'd say, mid twenty twenty three was kind of a watershed moment for us, but we saw the green suits of this before that, when you know, the grid started to see demand growth, you know, generally across a number of sectors and industries, right from more manufacturing and more batteries and manufacturing and more car manufacturing and more s reshoring of a lot of stuff back to the US, and that started to manifest itself, right, and utilities coming to us and saying, you know, they have to buil out their transmission system in ways that they hadn't anticipated in order to continue to serve load. And that's sort of you know, power for the course as business as usual for us. You couple that sort of like growth of going from twenty years ago or sorry for the last twenty years twenty twenty three of zero point five percent annual growth on the grid to to three four now four and a half five percent annual growth from the grid, and then you have an emergence of a new sort of large you know, data center demand with machine learning chips to what Steve has talked about with with nvidious chips and then our own internal tens of processing units. We saw in mid twenty twenty three the sort of crossover point from you know, being able to sort of generally source power where when we needed it on a timescal that made sense to us, to an acceleration of demand to what Darbush just said around you know, needing it sooner, and then a push out of lead times on some of the grid buildout and some of and serving that demand by utilities, and it's a it was kind of a collision of technology sort of product development curves and infrastructure timelines that don't always mesh well. And we've been kind of been in that that soup ever since. And it's I like to say, it's been one of the more dynamic times in my career. And I don't thpect I'll see anything like I would again, So.

Yeah, so the story for me is like I've been at Google just over ten years in some way to performed buying energy for data centers and buying renewal energy for data centers, and working with utilities and working energy supply. And you know, Google has been an AI first company since about like twenty sixteen, twenty seventeen. Our CEO has been saying that for a long time, right, And we've been infusing AI and machine learning into our products since then in various various ways that you know, we probably don't notice as users, but it's been there. And then in the last you know, eighteen months or so, it see the emergence of you know, sort of more consumer facing pure mL and AI products you know before so, like i'd say, mid twenty twenty three was kind of a watershed moment for us, but we saw the green suits of this before that, when you know, the grid started to see demand growth, you know, generally across a number of sectors and industries, right from more manufacturing and more batteries and manufacturing and more car manufacturing and more s reshoring of a lot of stuff back to the US, and that started to manifest itself, right, and utilities coming to us and saying, you know, they have to buil out their transmission system in ways that they hadn't anticipated in order to continue to serve load. And that's sort of you know, power for the course as business as usual for us. You couple that sort of like growth of going from twenty years ago or sorry for the last twenty years twenty twenty three of zero point five percent annual growth on the grid to to three four now four and a half five percent annual growth from the grid, and then you have an emergence of a new sort of large you know, data center demand with machine learning chips to what Steve has talked about with with nvidious chips and then our own internal tens of processing units. We saw in mid twenty twenty three the sort of crossover point from you know, being able to sort of generally source power where when we needed it on a timescal that made sense to us, to an acceleration of demand to what Darbush just said around you know, needing it sooner, and then a push out of lead times on some of the grid buildout and some of and serving that demand by utilities, and it's a it was kind of a collision of technology sort of product development curves and infrastructure timelines that don't always mesh well. And we've been kind of been in that that soup ever since. And it's I like to say, it's been one of the more dynamic times in my career. And I don't thpect I'll see anything like I would again, So.

And clever, let's get the energy company perspective on this. When did you start noticing a big change from what was happening.

And clever, let's get the energy company perspective on this. When did you start noticing a big change from what was happening.

Look, I think if you hear my my my colleagues here on the panel, it has been about a lot of changes over a very short period of time, a lot of volatility. I would say, you know, I think, I think you'll see, uh, these these massive growth over the past few years projections and then one day opened the papers and there's deep, deep seek there changing everything and you don't know what is true what is not. So everybody's trying to figure figure that out. Look the industry, I'm with AES corporation. I've been at AS for for about seven years there, but I've been in the energy business for about twenty five years. I started my career right when air Ron was the greatest thing on earth. Everybody wanted to work for and run not not after that and Ron, we all know what happened. And then UH, there's the boom and bust on the gas cycle. Hainesville Economics showed up with Shell Gas UH. A lot of companies also went bankrupt, and volatility in the in the energy sector disappeared for a long period of time, with very small growth over the past five years or maybe a little a little more, as Will said, is explosive demand. I think it's been one of the most dynamics periods of times of my career as well.

Look, I think if you hear my my my colleagues here on the panel, it has been about a lot of changes over a very short period of time, a lot of volatility. I would say, you know, I think, I think you'll see, uh, these these massive growth over the past few years projections and then one day opened the papers and there's deep, deep seek there changing everything and you don't know what is true what is not. So everybody's trying to figure figure that out. Look the industry, I'm with AES corporation. I've been at AS for for about seven years there, but I've been in the energy business for about twenty five years. I started my career right when air Ron was the greatest thing on earth. Everybody wanted to work for and run not not after that and Ron, we all know what happened. And then UH, there's the boom and bust on the gas cycle. Hainesville Economics showed up with Shell Gas UH. A lot of companies also went bankrupt, and volatility in the in the energy sector disappeared for a long period of time, with very small growth over the past five years or maybe a little a little more, as Will said, is explosive demand. I think it's been one of the most dynamics periods of times of my career as well.

But I guess the point here.

But I guess the point here.

Is that the energy the industry is not UH is very familiar with challenges and how to overcome those challenges. So I think my job really changed when when we moved from being providers of projects of or or or or or technology to partnering with some of these hyperscalers, some of these large data center companies and start putting together solutions with them. Not only at AS, not only we're one of the largest according to bn EF. Actually we're the largest provider of renewable energy to corporates over the past three years, so we know how that works. But we also own utility companies. We own utilities in Ohio and Indiana, and we work very closely with those hyper scalers and data center companies to meet their needs at the utility level as well.

Is that the energy the industry is not UH is very familiar with challenges and how to overcome those challenges. So I think my job really changed when when we moved from being providers of projects of or or or or or technology to partnering with some of these hyperscalers, some of these large data center companies and start putting together solutions with them. Not only at AS, not only we're one of the largest according to bn EF. Actually we're the largest provider of renewable energy to corporates over the past three years, so we know how that works. But we also own utility companies. We own utilities in Ohio and Indiana, and we work very closely with those hyper scalers and data center companies to meet their needs at the utility level as well.

But again, I think the biggest takeaway.

But again, I think the biggest takeaway.

It feels very very uncertain in terms of demand projections. We're going to talk about that later. But whatever projections you look, however you slice and dice there. The growth is here, is real, and I think the industry is everybody said to meet those challenges and meet solutions for that growth.

It feels very very uncertain in terms of demand projections. We're going to talk about that later. But whatever projections you look, however you slice and dice there. The growth is here, is real, and I think the industry is everybody said to meet those challenges and meet solutions for that growth.

So this growth is real, we need to deal with it. What's been a big question that I've been asked actually a lot in my role is does this look different than the data center growth that came before? With the idea being data centers have been cited close to populations so that latency is low and your Netflix loads really quick. But that might not be the case for a new AI applications where actually training doesn't need to take place close to people. Maybe it'll take place in a far away place. Starsh what's your sense of this kind of regional dynamic.

So this growth is real, we need to deal with it. What's been a big question that I've been asked actually a lot in my role is does this look different than the data center growth that came before? With the idea being data centers have been cited close to populations so that latency is low and your Netflix loads really quick. But that might not be the case for a new AI applications where actually training doesn't need to take place close to people. Maybe it'll take place in a far away place. Starsh what's your sense of this kind of regional dynamic.

Yeah, definitely, AI is changing the requirements for where you build data centers, where you can source power and also the conversation. Like I think four years ago, if I had a conversation with someone about sourcing like hydrogen power for a data center, be laughed at.

Yeah, definitely, AI is changing the requirements for where you build data centers, where you can source power and also the conversation. Like I think four years ago, if I had a conversation with someone about sourcing like hydrogen power for a data center, be laughed at.

And now I'd be laughed at, but like a lot less.

And now I'd be laughed at, but like a lot less.

I think I'd be pointed towards other areas maybe in this direction.

I think I'd be pointed towards other areas maybe in this direction.

But definitely.

But definitely.

The workload that that's running in the data center does impact really heavily what those requirements are where I can put that data center. And now with training and with let's say training is a batch workload, all right, HPEC supercomputing is also a batch workload that can also be an maybe choose Jensen's common It can be an Antarctica somewhere, right, it doesn't be very close to me. So yeah, that definitely does change, But I don't think it changes the economics that much because you still have to deliver a lot of power to it. You still need a really fat gigabit multi hunter gigabit network link, and these requirements like haven't really changed, I think, to be honest, Like, if you're going to build a lot of data centers, you're already going in the direction of, hey, I need to build these further and further away from large metropolitan areas because I can't get power anyway.

The workload that that's running in the data center does impact really heavily what those requirements are where I can put that data center. And now with training and with let's say training is a batch workload, all right, HPEC supercomputing is also a batch workload that can also be an maybe choose Jensen's common It can be an Antarctica somewhere, right, it doesn't be very close to me. So yeah, that definitely does change, But I don't think it changes the economics that much because you still have to deliver a lot of power to it. You still need a really fat gigabit multi hunter gigabit network link, and these requirements like haven't really changed, I think, to be honest, Like, if you're going to build a lot of data centers, you're already going in the direction of, hey, I need to build these further and further away from large metropolitan areas because I can't get power anyway.

So this is a problem like three four years ago, it's just worse now.

So this is a problem like three four years ago, it's just worse now.

Well, actually, interested in you, you're the energy part of the equation at Google. Can you give us a bit of information? How does that factor into decision making rom where to put data centers? Is it it's decided where it goes and then we need to find energy or is it part of the decision making process?

Well, actually, interested in you, you're the energy part of the equation at Google. Can you give us a bit of information? How does that factor into decision making rom where to put data centers? Is it it's decided where it goes and then we need to find energy or is it part of the decision making process?

Is that the first thing you decide on?

Is that the first thing you decide on?

Yes? And yes it you know historically so before you know, before the growth and the grid that I talked about kind of started. You know, it's not quite true, but you could almost like throw a dart on a map and you know, if you were somewhere within a reasonable distance of a metropolitan area, you could probably find power and a utility would at least build something for you in a couple of years, right, And and that was generally okay. You know, today, available grid and available generation on that grid are are more scarce, at least for the you know, short to medium term, right, And so you have to be sort of smarter and better at picking locations that have available power in the time scale you're looking for and and and sort of act quick to go and and reserve it and use it, right. So, so yet, yes, it's a it's a more important factor for us than maybe it was in the past. But it doesn't I think divers to your last point there. It's like if you go in the middle of nowhere, like there isn't power, there isn't fiber, there isn't there aren't people to build things, right, There's just these things require a certain amount of infrastructure and civilization around them to support them, right, and no one wants to live next to it. You have to have people that work there all the time, right like so so so there's there's that factor. And and then our products also like don't want to be in the middle of nowhere if they can help it. Because if you build a building for a data center, and and and so I'll back up. There's there's a few different ways that or its fuveent things that happen in an mL data center, right or a data center. One, you could be training at Google at least an internal model too. You could have a customer paying you to be able to train their model, right, or Three, you could be serving a model or serve doing inference and serving AI to customers, right. Only that first thing really is that flexible because our customers still want to be like within spitting distance of their of their footprint on the cloud. And then for inference, we still want to be having little latency service to our to our consumers, right. And so the internal training, yes, it's more flexible. But if you build a data in the middle of nowhere just for that and then you finish that job, you actually have a Strandard asset, right. And so if you think about efficiency of capital and how you want to be able to reuse your capital and recycle it, you actually don't necessarily always want to be just going far afield to to you know, the antarcticas or the deserts. We're might to be power, but no people or no no users or no customers. So so we tend to still have to follow where our products want to be. And and then within those you know regions or those uber regions we have to we have to go then you know, find power availability.

Yes? And yes it you know historically so before you know, before the growth and the grid that I talked about kind of started. You know, it's not quite true, but you could almost like throw a dart on a map and you know, if you were somewhere within a reasonable distance of a metropolitan area, you could probably find power and a utility would at least build something for you in a couple of years, right, And and that was generally okay. You know, today, available grid and available generation on that grid are are more scarce, at least for the you know, short to medium term, right, And so you have to be sort of smarter and better at picking locations that have available power in the time scale you're looking for and and and sort of act quick to go and and reserve it and use it, right. So, so yet, yes, it's a it's a more important factor for us than maybe it was in the past. But it doesn't I think divers to your last point there. It's like if you go in the middle of nowhere, like there isn't power, there isn't fiber, there isn't there aren't people to build things, right, There's just these things require a certain amount of infrastructure and civilization around them to support them, right, and no one wants to live next to it. You have to have people that work there all the time, right like so so so there's there's that factor. And and then our products also like don't want to be in the middle of nowhere if they can help it. Because if you build a building for a data center, and and and so I'll back up. There's there's a few different ways that or its fuveent things that happen in an mL data center, right or a data center. One, you could be training at Google at least an internal model too. You could have a customer paying you to be able to train their model, right, or Three, you could be serving a model or serve doing inference and serving AI to customers, right. Only that first thing really is that flexible because our customers still want to be like within spitting distance of their of their footprint on the cloud. And then for inference, we still want to be having little latency service to our to our consumers, right. And so the internal training, yes, it's more flexible. But if you build a data in the middle of nowhere just for that and then you finish that job, you actually have a Strandard asset, right. And so if you think about efficiency of capital and how you want to be able to reuse your capital and recycle it, you actually don't necessarily always want to be just going far afield to to you know, the antarcticas or the deserts. We're might to be power, but no people or no no users or no customers. So so we tend to still have to follow where our products want to be. And and then within those you know regions or those uber regions we have to we have to go then you know, find power availability.

So okay, great, and Stephen, you you have great insight into the entire kind of data center supply chain in your ola Schneider, I'm interested, do you have any kind of sense of how much of this new data build, data center build that we're seeing is specifically I related. Is that even something that makes sense? Is there an AI data center versus something else or is it just bits?

So okay, great, and Stephen, you you have great insight into the entire kind of data center supply chain in your ola Schneider, I'm interested, do you have any kind of sense of how much of this new data build, data center build that we're seeing is specifically I related. Is that even something that makes sense? Is there an AI data center versus something else or is it just bits?

Absolutely, the you know, the servers are completely different, and you know, the power and the cooling is different. And if you look at a data center, it's an AI data center today, say it's a ten megawaut data center. You know, five or six years ago, there'll be a thousand IT racks and the data hall would be huge. Now the data hall has seven d I t racks and you have all these chillers outside, you know, to support the cooling that so it's much different. It's not these Amazon warehouse type data centers.

Absolutely, the you know, the servers are completely different, and you know, the power and the cooling is different. And if you look at a data center, it's an AI data center today, say it's a ten megawaut data center. You know, five or six years ago, there'll be a thousand IT racks and the data hall would be huge. Now the data hall has seven d I t racks and you have all these chillers outside, you know, to support the cooling that so it's much different. It's not these Amazon warehouse type data centers.

More.

More.

There's smaller, more confined in the IT rooms, a lot of power and cooling going through it. It's not a place that you know, it used to be walk around and you know your place, servers and everyone had a good time, but not anymore. It's a it's a business. But you know, as we were saying, you know, the you know, putting an asset where there's where there's power, and everybody in the world right now is saying that the data centers are going to go where.

There's smaller, more confined in the IT rooms, a lot of power and cooling going through it. It's not a place that you know, it used to be walk around and you know your place, servers and everyone had a good time, but not anymore. It's a it's a business. But you know, as we were saying, you know, the you know, putting an asset where there's where there's power, and everybody in the world right now is saying that the data centers are going to go where.

The power is.

The power is.

But but what we're what we're seeing and the hyper scales are all doing this as you just said, is they're they're building these training clusters that are close to where people are with the intention of using those to make money. There's not a lot of money being made, you know, training a model, and the question is how many more of these models are we going to need, so the money is going to be made and deploying them in the field. And we're seeing a big shift and this, in my opinion, is kind of the year of infernts and and we're starting to see you know a lot of these training clusters that were originally deployed just to train are now doing infrints, either full time or part time. And the other thing that we're seeing is you know, with infrints close to the users, optimized for different applications. We're not seeing those yet because AI is still developing. We're still at the beginning of this. We don't know, you know, what it's going to take to do an AI agent and a genta AI. You know what's that going to take. It's going to be multi modal how much processing, how much how much of the IT stack is going to be needed and where, and as we start inputting more and more video. You right now everything's text to text, you know, multimodal, it's going to be video, tech, text, image, it's going to be all these different things. So we can't optimize the data centers close to the users yet for inference, we're still going to have to do those in data centers. I think that's going to be the case for a few years.

But but what we're what we're seeing and the hyper scales are all doing this as you just said, is they're they're building these training clusters that are close to where people are with the intention of using those to make money. There's not a lot of money being made, you know, training a model, and the question is how many more of these models are we going to need, so the money is going to be made and deploying them in the field. And we're seeing a big shift and this, in my opinion, is kind of the year of infernts and and we're starting to see you know a lot of these training clusters that were originally deployed just to train are now doing infrints, either full time or part time. And the other thing that we're seeing is you know, with infrints close to the users, optimized for different applications. We're not seeing those yet because AI is still developing. We're still at the beginning of this. We don't know, you know, what it's going to take to do an AI agent and a genta AI. You know what's that going to take. It's going to be multi modal how much processing, how much how much of the IT stack is going to be needed and where, and as we start inputting more and more video. You right now everything's text to text, you know, multimodal, it's going to be video, tech, text, image, it's going to be all these different things. So we can't optimize the data centers close to the users yet for inference, we're still going to have to do those in data centers. I think that's going to be the case for a few years.

Okay, so things not really changing too dramatically. It's just build close to the users, like always.

Okay, so things not really changing too dramatically. It's just build close to the users, like always.

So clever.

So clever.

Actually, something I want to ask you about. I'm from Ireland and so when I hear data center, I think, oh my god, it's destroying the power system. There's like moratorium and new data centers there because it's such a large share of the power system. And there's a couple of reasons in Europe where something like this has happened, and now there's been conversations about this. This is going to happen in the United States. Kind of seems like that's calm down in the last few months. But interested to hear your thoughts on how big a challenge this will be for your business.

Actually, something I want to ask you about. I'm from Ireland and so when I hear data center, I think, oh my god, it's destroying the power system. There's like moratorium and new data centers there because it's such a large share of the power system. And there's a couple of reasons in Europe where something like this has happened, and now there's been conversations about this. This is going to happen in the United States. Kind of seems like that's calm down in the last few months. But interested to hear your thoughts on how big a challenge this will be for your business.

It's hard to talk about that without politicizing things. But I think, look, there will be parts of the country where there's there would be some resistance uh to data center deployment. We are seeing some of that in the Southeast and other and other parts. I think I think it's the real answer to that is all going to depend on the solutions that we bring to to that data center load growth.

It's hard to talk about that without politicizing things. But I think, look, there will be parts of the country where there's there would be some resistance uh to data center deployment. We are seeing some of that in the Southeast and other and other parts. I think I think it's the real answer to that is all going to depend on the solutions that we bring to to that data center load growth.

When you say we, do you mean as or do you mean we.

When you say we, do you mean as or do you mean we.

The providers of energy and together with the data center operators and the and the hyper skaters. I think a lot of all we talked about here is that there was a there was a time not long ago, there was this idea that not because of the l LAM training phase, a lot of data centers, large data centers will co locate with generation in places where there's as Will was saying, there's no load, that there's no infrastructure, there's no fiber offs so you have to make up for the lack of all that with low cost of power. But when you when you when when you look at the amount of capital that in you to deploy in those data centers, you don't want to run the risk of being stranded after the training phase of the of the large language model uh ends, so you want to use that for something else. So we talked a lot about this that here you end up going back to where the traditional data center markets are, and in some of those markets we are seeing local resistance.

The providers of energy and together with the data center operators and the and the hyper skaters. I think a lot of all we talked about here is that there was a there was a time not long ago, there was this idea that not because of the l LAM training phase, a lot of data centers, large data centers will co locate with generation in places where there's as Will was saying, there's no load, that there's no infrastructure, there's no fiber offs so you have to make up for the lack of all that with low cost of power. But when you when you when when you look at the amount of capital that in you to deploy in those data centers, you don't want to run the risk of being stranded after the training phase of the of the large language model uh ends, so you want to use that for something else. So we talked a lot about this that here you end up going back to where the traditional data center markets are, and in some of those markets we are seeing local resistance.

Uh.

Uh.

We're also seeing local resistance to the development of power plants to supply those those data centers. The famous NIM business not in my backyard. So I think that is a challenge that that the industry, both the energy industry and the data center industry, has to overcome. It is a real challenge, uh. And I think it's one that will be will be met with deployment of transmission, because I think that there's more flexibility where you can deploy data centers than there will be on where you deploy the generation, whether it's gas or or renewable. I hopefully we don't get to new coal to supply to supply this demand. And the bottomneck here is actually transmission to get to from from from the generational sources to the to the data centers.

We're also seeing local resistance to the development of power plants to supply those those data centers. The famous NIM business not in my backyard. So I think that is a challenge that that the industry, both the energy industry and the data center industry, has to overcome. It is a real challenge, uh. And I think it's one that will be will be met with deployment of transmission, because I think that there's more flexibility where you can deploy data centers than there will be on where you deploy the generation, whether it's gas or or renewable. I hopefully we don't get to new coal to supply to supply this demand. And the bottomneck here is actually transmission to get to from from from the generational sources to the to the data centers.

Okay, and we'll actually going to ask you about Google has been quite active on trying to develop new sources of clean energy before actually this whole AI drive became a thing, but you signed a couple of pretty first of a kind PPAs in the last couple of years. It'd be interesting to hear how progress in that is developing.

Okay, and we'll actually going to ask you about Google has been quite active on trying to develop new sources of clean energy before actually this whole AI drive became a thing, but you signed a couple of pretty first of a kind PPAs in the last couple of years. It'd be interesting to hear how progress in that is developing.

Yeah.

Yeah.

Sure, So the history of you know, energy consumption and energy generation Google is a long one. But the short story is this, since I've been there last you know, ten and a half yars or so, are our energy consumption globally has grown between twenty twenty five percent a year. We're now approaching thirty plus maybe more tearrawad hours of energy you know, consumed every year. That's doubling every you know, five years or so. And we've also signed you know, you know, twenty plus gigawatts of renewable energy generation you know, contracts to with folks like A Yes and others to to help supply energy to our facilities. We we maintain a strong climent to our to our clean energy goals, and we have an hourly carbon free energy goal a BYT twenty thirty that we continue to chase and uh chase very vigorously and and to that end, you know, we The story there is you can get to about seventy to eighty percent carbon free and energy supplies you know, through wind and solar and batteries and sort of like the mix of things there. But that last twenty to twenty five percent, that last mile is is harder because you have to start thinking about capacity and baseload and reliability and this sort of stuff. And so we spent the last few years thinking about and working on, you know, sort of what are those next gen technologies after wind and solar that are going to be carbon free and start to supply you know, up to that that nearly one hundred percent carbon free energy. And for US, it's things like nuclear power, it's things like launderation storage, it's potentially hydrogen, don't look too hard. It's potentially like carbon capture and storage and geothermal and we've done a couple of these in the last couple of years. We did a deal in Nevada between US and a company called Ferbo, who's a geothermal developer. They're using our cloud technology to optimize how they drill wells and operate their wells and operate their plants to then build advanced geothermal plants to sell that power to Novada Energy, the utility with whom We designed a tariff for a new rate that the regulator is approving that's gonna a sign or a lot allocate the costs of that geothermal above and beyond sort of business as usual to us, the customer right so that we don't so that we get the product we want, the grid gets a clean baseload generation source, and other right pairers don't don't pay the cost. So that's what we're really proud of. And that's a model, you know, that sort of rate structure and that you can slot different technologies into is a model we're working on with a lot of other utilities across the US. And then the other is a partnership we recently as signed with a small modular reactor technology provider called Chiros Power. Chiros is developing Gen four small monulor reactors. They have a pilot planned for twenty twenty nine for which we're going to be a customer in the Tennessee Valley. And in addition to being a customer for the pilot to help get that commercialized and off the ground, we committed to being a customer for their next five reactors. And what we get out of that is, you know, some confidence of having access to power, assuming that everything goes well with our technology, but also the ability to sort of partner with them to cite those next reactors in places that you know, hopefully make make sense for us and our loads and our data centers to then meet that hourly carbon free energy goal in places we have growing load. And so we expect in the twenty thirties to be deploying small modulor reactors onto the grid. It's not going to be like a you know, Antarctica small modul the actor of data center behind the meter microgrid thing. That's not the plan. It's really meant to be a grid participant and putting capacity on the grid to supply our needs. And so Bi you said about that as well.

Sure, So the history of you know, energy consumption and energy generation Google is a long one. But the short story is this, since I've been there last you know, ten and a half yars or so, are our energy consumption globally has grown between twenty twenty five percent a year. We're now approaching thirty plus maybe more tearrawad hours of energy you know, consumed every year. That's doubling every you know, five years or so. And we've also signed you know, you know, twenty plus gigawatts of renewable energy generation you know, contracts to with folks like A Yes and others to to help supply energy to our facilities. We we maintain a strong climent to our to our clean energy goals, and we have an hourly carbon free energy goal a BYT twenty thirty that we continue to chase and uh chase very vigorously and and to that end, you know, we The story there is you can get to about seventy to eighty percent carbon free and energy supplies you know, through wind and solar and batteries and sort of like the mix of things there. But that last twenty to twenty five percent, that last mile is is harder because you have to start thinking about capacity and baseload and reliability and this sort of stuff. And so we spent the last few years thinking about and working on, you know, sort of what are those next gen technologies after wind and solar that are going to be carbon free and start to supply you know, up to that that nearly one hundred percent carbon free energy. And for US, it's things like nuclear power, it's things like launderation storage, it's potentially hydrogen, don't look too hard. It's potentially like carbon capture and storage and geothermal and we've done a couple of these in the last couple of years. We did a deal in Nevada between US and a company called Ferbo, who's a geothermal developer. They're using our cloud technology to optimize how they drill wells and operate their wells and operate their plants to then build advanced geothermal plants to sell that power to Novada Energy, the utility with whom We designed a tariff for a new rate that the regulator is approving that's gonna a sign or a lot allocate the costs of that geothermal above and beyond sort of business as usual to us, the customer right so that we don't so that we get the product we want, the grid gets a clean baseload generation source, and other right pairers don't don't pay the cost. So that's what we're really proud of. And that's a model, you know, that sort of rate structure and that you can slot different technologies into is a model we're working on with a lot of other utilities across the US. And then the other is a partnership we recently as signed with a small modular reactor technology provider called Chiros Power. Chiros is developing Gen four small monulor reactors. They have a pilot planned for twenty twenty nine for which we're going to be a customer in the Tennessee Valley. And in addition to being a customer for the pilot to help get that commercialized and off the ground, we committed to being a customer for their next five reactors. And what we get out of that is, you know, some confidence of having access to power, assuming that everything goes well with our technology, but also the ability to sort of partner with them to cite those next reactors in places that you know, hopefully make make sense for us and our loads and our data centers to then meet that hourly carbon free energy goal in places we have growing load. And so we expect in the twenty thirties to be deploying small modulor reactors onto the grid. It's not going to be like a you know, Antarctica small modul the actor of data center behind the meter microgrid thing. That's not the plan. It's really meant to be a grid participant and putting capacity on the grid to supply our needs. And so Bi you said about that as well.

Yeah, okay, great.

Yeah, okay, great.

So something someone alluded to earlier was the topic of energy efficiency and the word deep seek, which hads.

So something someone alluded to earlier was the topic of energy efficiency and the word deep seek, which hads.

Up in the room.

Up in the room.

Does everyone know like what I'm referring to when I talk about deep seek? Yes, okay, great, darsh I'm gonna go to you here because we're having a conversation whether this this something that everyone saw coming. Not necessarily the idea that like deep seek was going to release a model and this would be the exact market reaction, but that there was going to be big gains in energy efficiency improvements. This has obviously been the history of data centers for ages that energy efficiency has improved. So like, why were people so surprised by this? And what's the future of energy efficiency in artificial intelligence? Simple question if.

Does everyone know like what I'm referring to when I talk about deep seek? Yes, okay, great, darsh I'm gonna go to you here because we're having a conversation whether this this something that everyone saw coming. Not necessarily the idea that like deep seek was going to release a model and this would be the exact market reaction, but that there was going to be big gains in energy efficiency improvements. This has obviously been the history of data centers for ages that energy efficiency has improved. So like, why were people so surprised by this? And what's the future of energy efficiency in artificial intelligence? Simple question if.

You could just do great question.

You could just do great question.

I think technology comes in waves where like you make really good hardware and then you try to extract performance out of the hardware as much you can. When you reach the limit of what you can do in software space, you go back and make better hardware. And ideally that's like a very quick trend of like, hey, I spent six months, let's say one to two years making hardware, one to two years making software, and then I find the holes in the hardware and I make it better. Right, So I think, what I think, it's just more mostly timing like this happened now, I think, yeah, this is the expectation is that, hey, I can only buy a certain number of Vida GPUs. What can I do with this?

I think technology comes in waves where like you make really good hardware and then you try to extract performance out of the hardware as much you can. When you reach the limit of what you can do in software space, you go back and make better hardware. And ideally that's like a very quick trend of like, hey, I spent six months, let's say one to two years making hardware, one to two years making software, and then I find the holes in the hardware and I make it better. Right, So I think, what I think, it's just more mostly timing like this happened now, I think, yeah, this is the expectation is that, hey, I can only buy a certain number of Vida GPUs. What can I do with this?

Now?

Now?

Let me hire highly specialized PTX programmers that don't write Kuda code. They right level below that because Kuda doesn't solve the problem that I needed to solve. It's too abstracted, it too, it's too power hungry, right, so I don't get I don't get enough control over the hardware, so I need to go to a lower level. You keep going down that stack. You get down to hardware, then you go down, you go to TSMC, right, then you go down you get to like minerals and things like that.

Let me hire highly specialized PTX programmers that don't write Kuda code. They right level below that because Kuda doesn't solve the problem that I needed to solve. It's too abstracted, it too, it's too power hungry, right, so I don't get I don't get enough control over the hardware, so I need to go to a lower level. You keep going down that stack. You get down to hardware, then you go down, you go to TSMC, right, then you go down you get to like minerals and things like that.

So you can keep going down that stack.

So you can keep going down that stack.

But definitely, I think what Deep Sea proved and if you guys with the pre the paper, the last page, there's suggestions on how to improve in Vida GPUs, which is really interesting because this is a redesigning them how to use them. Yeah, the micro architecture of the Vida GPUs is not optimal for what deep SEEQ wants basically, which is interesting, right because that's the that's the cycle we're going to go through and so not related. But you know, the gp that we're designing solves those problems. We did our own benchmarks three years ago and we found out some of the problems that Deep Sea found out as well. So there are ways to improve hardware honestly, like the vendor should do you know, benchmarking and research and make the GPUs better themselves. But yeah, it does require some interactivity with and end user that's like, hey, I want this to be better. Here's like four things you can do to make it better. And that will continue happening, right. People will make new GPUs, new accelerators, people will make co package optics, they'll do all sorts of fancy stuff and people will use it. But like, I'd actually don't like the way this is running. It's actually too slow for my use case. Can you fix this thing? So I think this is like normal, but I think there's so much focus on the volume of GPU ship that I think people forgot that there's still optimization room. There's a lot of headroom and optimizing software for that.

But definitely, I think what Deep Sea proved and if you guys with the pre the paper, the last page, there's suggestions on how to improve in Vida GPUs, which is really interesting because this is a redesigning them how to use them. Yeah, the micro architecture of the Vida GPUs is not optimal for what deep SEEQ wants basically, which is interesting, right because that's the that's the cycle we're going to go through and so not related. But you know, the gp that we're designing solves those problems. We did our own benchmarks three years ago and we found out some of the problems that Deep Sea found out as well. So there are ways to improve hardware honestly, like the vendor should do you know, benchmarking and research and make the GPUs better themselves. But yeah, it does require some interactivity with and end user that's like, hey, I want this to be better. Here's like four things you can do to make it better. And that will continue happening, right. People will make new GPUs, new accelerators, people will make co package optics, they'll do all sorts of fancy stuff and people will use it. But like, I'd actually don't like the way this is running. It's actually too slow for my use case. Can you fix this thing? So I think this is like normal, but I think there's so much focus on the volume of GPU ship that I think people forgot that there's still optimization room. There's a lot of headroom and optimizing software for that.

Okay, how do you how does that kind of coordination work between the software developers and a video like is there? Do they have channels to work on this together? Doing video have their own internal research teams? Yeah, it's called hardware software code design. Some companies do it much better than others. We do it the best. Yeah, we do a good job.

Okay, how do you how does that kind of coordination work between the software developers and a video like is there? Do they have channels to work on this together? Doing video have their own internal research teams? Yeah, it's called hardware software code design. Some companies do it much better than others. We do it the best. Yeah, we do a good job.

But yeah, no, they have teams internally that are building fundational models at video train them on supercomputers, AI clusters and then they're finding these things. But I think it's interest that, Like your next question is, well, why didn't they find this out, you know last year when they made the hardware, Well, it's the same thing. Why is black WU one hundred three to two kilowats per wack instead of one hundred and twenty five? So there are like there are fuzzy zones where you can miss things, and I'm sure I'll miss things and it happens. But I think the magnitude of that coming out so aggressively saying hey, we don't need this much computing power. And also there are things in the GPU at the micro architecture level in the silicon that I don't like that I want you to change.

But yeah, no, they have teams internally that are building fundational models at video train them on supercomputers, AI clusters and then they're finding these things. But I think it's interest that, Like your next question is, well, why didn't they find this out, you know last year when they made the hardware, Well, it's the same thing. Why is black WU one hundred three to two kilowats per wack instead of one hundred and twenty five? So there are like there are fuzzy zones where you can miss things, and I'm sure I'll miss things and it happens. But I think the magnitude of that coming out so aggressively saying hey, we don't need this much computing power. And also there are things in the GPU at the micro architecture level in the silicon that I don't like that I want you to change.

Is is a step shift because.

Is is a step shift because.

Now you're expecting every customer to go down to that level. And I think that's the race now as to how optimized can you get with one water one hundred watts or one giga water or whatever?

Now you're expecting every customer to go down to that level. And I think that's the race now as to how optimized can you get with one water one hundred watts or one giga water or whatever?

Do you have a kind of benchmark in your own mind internally of this is the energy standard that like a query and chat GPT was a year ago. How much more energy efficient can we get? That? Is it orders of magnitude or.

Do you have a kind of benchmark in your own mind internally of this is the energy standard that like a query and chat GPT was a year ago. How much more energy efficient can we get? That? Is it orders of magnitude or.

Orders of magnitude orders of magnitude.

Orders of magnitude orders of magnitude.

Absolutely, yeah, I think this conversation is good because everyone in the panels like it. Will we're delivering power, that's great, that's a problem. But also like I'm a chip guy, like we should make better chips that consume less power. Perhaps maybe there's like a push and pulled balance there of you know, like keep using a good job. Right, that's orders of magnitude less, less power consumption, more efficient than and envita GPU. It is the main specific in that sense, but it does solve the problem and it's more efficient. So you will see like a chip startups, AI startups, quote package optic startups, all these all these companies competing and being able to deliver much orders a magnitude better efficiency.

Absolutely, yeah, I think this conversation is good because everyone in the panels like it. Will we're delivering power, that's great, that's a problem. But also like I'm a chip guy, like we should make better chips that consume less power. Perhaps maybe there's like a push and pulled balance there of you know, like keep using a good job. Right, that's orders of magnitude less, less power consumption, more efficient than and envita GPU. It is the main specific in that sense, but it does solve the problem and it's more efficient. So you will see like a chip startups, AI startups, quote package optic startups, all these all these companies competing and being able to deliver much orders a magnitude better efficiency.

That's what we're doing.

That's what we're doing.

Okay, So for the three other panelists, I'm got to ask you the same question is did you see this coming? Well? Actually, sorry, The first question is how do you operate and like you need to make these big decisions about what to build and what to allocate resource to under this level of uncertainty around energy efficiency improvements. But then I guess the second part of the question is does everyone who works in the industry kind of assume there's gonna be these energy efficiency imrovements you're kind of making decisions around that.

Okay, So for the three other panelists, I'm got to ask you the same question is did you see this coming? Well? Actually, sorry, The first question is how do you operate and like you need to make these big decisions about what to build and what to allocate resource to under this level of uncertainty around energy efficiency improvements. But then I guess the second part of the question is does everyone who works in the industry kind of assume there's gonna be these energy efficiency imrovements you're kind of making decisions around that.

So ho to you first student, I think everyone expected more efficiencies to be to be gained in the transformers and the algorithms and and but you know, I think he had a panelist earlier that said, you know, all the all the models that have been trained have been trained on the public data, and there's all this other, you know, private data that's actually going to be you know, more beneficial. But all I can say about about that is that it was this is probably the most confusing topic, all the experts that are weighing in and saying completely different things. But we haven't seen any reaction, negative reaction from of our from our customers as far as you know.

So ho to you first student, I think everyone expected more efficiencies to be to be gained in the transformers and the algorithms and and but you know, I think he had a panelist earlier that said, you know, all the all the models that have been trained have been trained on the public data, and there's all this other, you know, private data that's actually going to be you know, more beneficial. But all I can say about about that is that it was this is probably the most confusing topic, all the experts that are weighing in and saying completely different things. But we haven't seen any reaction, negative reaction from of our from our customers as far as you know.

Orders or or forecasts. It's all. It's all, you know, business as usual. There's been like no effect at all.

Orders or or forecasts. It's all. It's all, you know, business as usual. There's been like no effect at all.

Yeah, well, yeah, I think you know, Google and look, training models and and and uh, software and hardware are by no means my own expertise, and so so i'm I'm I don't have a lot to offer here except for I think we always expect deficiency gains. I think we welcome them. I think we're seeing the same sort of efficiency gains and our own you know, model building and model training and uh and uh, you know that it hasn't to See's point, it hasn't really changed our look on on our on our business planning because training is only part of you know, the AI story and the machine learning story. The serving and the inferences is A is also a big part of it. And and you know, don't forget that Google does myriad other things and data centers right, and so data center, so mL and A I are are but a portion of our of our forecast and our outlook, and so so we remain sort of on a stalle path.

Yeah, well, yeah, I think you know, Google and look, training models and and and uh, software and hardware are by no means my own expertise, and so so i'm I'm I don't have a lot to offer here except for I think we always expect deficiency gains. I think we welcome them. I think we're seeing the same sort of efficiency gains and our own you know, model building and model training and uh and uh, you know that it hasn't to See's point, it hasn't really changed our look on on our on our business planning because training is only part of you know, the AI story and the machine learning story. The serving and the inferences is A is also a big part of it. And and you know, don't forget that Google does myriad other things and data centers right, and so data center, so mL and A I are are but a portion of our of our forecast and our outlook, and so so we remain sort of on a stalle path.

Yeah, I think the same thing here. I haven't seen any real change perhaps with the with the small exception of investors over reaction to to headlines, but but the fundamentals of the business remain very strong. Let's not forget here that a lot of what we're trying to solve for in the power markets is also replacement of aging infrastructure in the US, especially no cold generation gas generation. So efficiency gains are definitely welcome because what that's going to do is to make sure that we don't overbuild. The worst thing that could happen here is if the market starts overbuild, overbuilding generation, transmission, distribution, and rate payers get left with massive bills and the demand doesn't show up, right, that would be a cycle of boom and bus.

Yeah, I think the same thing here. I haven't seen any real change perhaps with the with the small exception of investors over reaction to to headlines, but but the fundamentals of the business remain very strong. Let's not forget here that a lot of what we're trying to solve for in the power markets is also replacement of aging infrastructure in the US, especially no cold generation gas generation. So efficiency gains are definitely welcome because what that's going to do is to make sure that we don't overbuild. The worst thing that could happen here is if the market starts overbuild, overbuilding generation, transmission, distribution, and rate payers get left with massive bills and the demand doesn't show up, right, that would be a cycle of boom and bus.

Right.

Right.

So we believe in competitive markets. We believe in market efficiency. And I think if if the efficiency is on the on, the on, the on the computing power, also if that brings demand back to a more reasonable level. But no matter what the projections are, the numbers are staggering. The projections for demand for new generation and new and new infrastructure for transmission are staggering. So we just hope that that that we don't overbuild. I guess that's right.

So we believe in competitive markets. We believe in market efficiency. And I think if if the efficiency is on the on, the on, the on the computing power, also if that brings demand back to a more reasonable level. But no matter what the projections are, the numbers are staggering. The projections for demand for new generation and new and new infrastructure for transmission are staggering. So we just hope that that that we don't overbuild. I guess that's right.

Okay.

Okay.

So I have one quick fire question which you're only allowed to answer one number two by twenty thirty. What percentage of US power demand do you think will come from data centers?

So I have one quick fire question which you're only allowed to answer one number two by twenty thirty. What percentage of US power demand do you think will come from data centers?

Not no explanation, just a number.

Not no explanation, just a number.

There's a networking session after seven and a half, well SIXI.

There's a networking session after seven and a half, well SIXI.

Ish, yeah, I think I'm a little perhaps a little more more bullish than that. I think it would be like somewhere between eight and ten percent.

Ish, yeah, I think I'm a little perhaps a little more more bullish than that. I think it would be like somewhere between eight and ten percent.

Okay, especially pretty clustered around a certain range. But thank you very much. This is really informantive panel. I think we learned a lot about the idea that the data center industry is going to grow. We're very all, very confident on that, but actually maybe won't change quite as much in terms of its geographic structure or capital investment cycled. So thank you very much for joining me. Please join me and giving my Palels a round of applause.

Okay, especially pretty clustered around a certain range. But thank you very much. This is really informantive panel. I think we learned a lot about the idea that the data center industry is going to grow. We're very all, very confident on that, but actually maybe won't change quite as much in terms of its geographic structure or capital investment cycled. So thank you very much for joining me. Please join me and giving my Palels a round of applause.

Today's episode of Switched On was produced by Cam Gray with production assistance from Kamala Shelling. Bloomberg NIF is a service provided by Bloomberg Finance LP and its affiliates. This recording does not constitute, nor should it be construed as investment in vice, investment recommendations, or a recommendation as to an investment or other strategy. Bloomberg ANIF should not be considered as information sufficient upon which to base an investment decision. Neither Bloomberg Finance LP nor any of its affiliates makes any representation or warranty as to the accuracy or completeness of the information contained in this recording, and any liability as a result of this recording is expressly disclaimed,

Today's episode of Switched On was produced by Cam Gray with production assistance from Kamala Shelling. Bloomberg NIF is a service provided by Bloomberg Finance LP and its affiliates. This recording does not constitute, nor should it be construed as investment in vice, investment recommendations, or a recommendation as to an investment or other strategy. Bloomberg ANIF should not be considered as information sufficient upon which to base an investment decision. Neither Bloomberg Finance LP nor any of its affiliates makes any representation or warranty as to the accuracy or completeness of the information contained in this recording, and any liability as a result of this recording is expressly disclaimed,