A (2:06)

No, you know me, I'm not super technology minded, so I don't know a lot about this stuff. I remember walking by our server room back in the day when we were at Pont City Market and seeing our colleague Izzy in there hard at work.

C (2:20)

Yeah.

A (2:21)

And when that door was unlocked and open, hearing the whir of the servers and the cooling machines and you know, that's on a smaller scale. That's a Data Center.

C (2:31)

Absolutely. 100% that's a data center. It was also a great place to curl up and take a nap in the middle of the day.

A (2:38)

The warmth of the server.

C (2:40)

Yeah, and the were put Your sleep. Yeah. So yeah, that definitely counts as a data server if you have like one of those little home networking setups and like a closet in your house. Data center, sure. Technically the PC is a data center. Anywhere you can store and access data, that's technically a data center. And you're like, well, that's stupid. Why did you even say that, Josh? That's quibbling. That's quotidian. Shut up and get on with data centers. Whoa, whoa, whoa. First of all, don't use the S word. And then secondly, the keyword. Yes. Wait, what did I say? That's the keyword.

A (3:17)

Quotidian. Oh, no, that's kw. Right.

C (3:23)

So the reason that I bring that up though, Chuck, is because that technically is part of the progression of data centers. Yeah, it probably goes without saying, but it's evolved along with computing. And as computing's kind of gotten bigger and bigger, the need to store and access more data has gotten bigger. So much so, Chuck, that. Just wrap your head around this one. In 2024, just over a year ago, yeah, we used 150 zettabytes of data. That's what we consumed. And consuming is anything from making a video and uploading it to TikTok or putting a post up on Instagram. It's browsing a website, it's buying a song from itunes, it's doing web analytics. It's buying something with your American Express card. All of that is data consumption. And we consumed 150 zettabytes of data in 2024.

A (4:18)

Yeah. I don't even know how many Big Macs that is. I know you name dropped a lot of brands. It should be like the movies where every time you even just say, like, buy something on your Amex, the bank account grows by like $10.

C (4:31)

I agree wholeheartedly. I agree that Amex should do that. Amex. There's 20 bucks. I'll split it with you.

A (4:39)

Wow. You just bought lunch in 1997.

C (4:42)

That's right. So just real quick, a zettabyte, Chuck, is a trillion gigabytes. Okay. Wow. So we consumed 150 trillion gigabytes. Wow. That was 2024.

A (4:53)

That's worldwide, right?

C (4:55)

Yes. Yeah, that's worldwide. In 2010, we consumed two zettabytes.

A (4:59)

Geez.

C (5:00)

Yeah. So it's growing exponentially, which means that data centers are growing exponentially. And now they're about to just blow up like truffle up essentially from, from, you know, this kind of calm, like, plateau that they'd reach. It's about to just Go in hyperdrive.

A (5:19)

Yeah, and actively is. And we're going to. We're going to get to some startling statistics later on in the episode. But Kyle helped us out with this. Our writer over in the uk, he.

C (5:29)

Did a fantastic job.

A (5:30)

He did a really good job. And there's going to be some UK specific things in here because Kyle's always keen, as they say, to throw that stuff in there.

C (5:37)

Yeah, for sure. Kyle likes to pepper those in.

A (5:39)

Yeah, of course. And he's not barred from doing so. We allow it. So since Kyle, you know, is frequenter of the Wayback Machine, as are all the wonderful writers that we use, they all have the keys to the car, essentially. He jumped in the Wayback Machine to sort of give us a little bit of a timeline on data centers and a bit on, you know, mainframes and PCs.

C (6:04)

He also left all of his used tea bags in there, too. I don't know if you noticed.

A (6:08)

Oh, it's fine. You know, you can throw those back in some hot water and they do just a little bit weaker tea.

C (6:12)

Well, if you put like five of them together, it's like one.

A (6:15)

Yeah. And Kyle, I mean, that thing was full.

C (6:18)

It really was. He drinks a lot of tea.

A (6:20)

He really does. So if you want to talk about the earliest data centers that you could kind of call maybe a data center, they were, you know, computers. They were electronic computers. Most of this stuff that we're going to talk about early on was military in use. And as you'll see, even the first when we talk about the UK one that was supposedly in that military, they even loaned it to the military, which was kind of interesting. But these things were built with, you know, state of the art technology at the time, which meant vacuum tubes and, you know, manual switches and plugs and things like that. And the first thing that we can really talk about as the first programmable electric digital computer was the Colossus. And as we'll see, Elon Musk has now stolen that for his own purposes. That name probably because of this, I would imagine. But it was at Bletchley park, of course, during World War II. And they were trying to, you know, crack into Hitler's messages at the time. And these things were huge. And kind of, to me, the thing that stood out about Colossus, which is a neat little factoid, is that where Colossus was at Bletchley park, at Block H? It is now the National Museum of Computing.

C (7:30)

I want to go to that so bad when we do that UK Europe tour next year. Ooh, we got to go to that together. Okay.

A (7:39)

Okay. Are we doing that next year?

C (7:40)

I thought we were talking about. All right. We kind of already half promised it. We have to. Now we're locked in the punch.

A (7:46)

Why is my voice so high, then?

C (7:48)

I don't know. You practicing for the Alps?

A (7:52)

Yeah, that's right. No, that'd be a lot of fun. I'd love to go to that.

C (7:55)

So that was Colossus. Another one about the same time was the eniac. Electrical Numerical Integrator and Computer. So that's a quality acronym.

A (8:04)

Yeah.

C (8:06)

And it was the first general purpose electronic computer. And here's the thing. This is technically not data storage yet. It's data processing. But these things, Colossus, eniac, you walked up to them, and you said, what's the trajectory of this missile if I fire it from here? And ENIAC would go beep, bop, boop, boop. And then say, like, whatever a trajectory is described in.

A (8:31)

Sure.

C (8:31)

Or Colossus, you'd be like, what is Hilter saying here to Goebbels? And Colossus would say, hilter is saying that he's a big fan of Goebbels work, but he's suspicious that the rest of the world doesn't like either of them. And that was it. After that, you'd be like, hey, what was the last answer? And they'd be like, what's an answer?

A (8:52)

Yeah, you gotta just tell me what's going on with this Hilter business.

C (8:56)

You don't remember from our art mysteries of the art world that how stuff works are to go, oh, did the title of that section was, Did Hilter do these paintings?

A (9:06)

Oh, my God. That's a. That's a deep cut. I did not remember that.

C (9:10)

Yes, And I think. I think it still says that on that. Yeah. Yeah.

A (9:15)

I can only hope.

C (9:16)

It's gotta be Hilter forever.

A (9:18)

All right, so we go into mainframes at this point, and this is like the 1950s, basically, when companies could actually have their own computer. It wasn't just the military. These were the old punch card computers, and they were called mainframes. It wasn't made up for this term. Mainframes were originally described or describing, like, what you would house telecommunication equipment, and maybe some other sciencey stuff. But it was referencing, literally, the cabinets that held this technology, and it became known as just, you know, it kind of took over when the computer world started using it as computer only.

C (9:53)

Yeah. But again, this is, like, you're a company, and this is where you store and process all of your data and it's in this one room, but it's not going anywhere else. It's not for anybody else. And you have to physically be in the room to get your answer. Process whatever data you're looking for. When the PC came along and then the Macintosh came along, they took that thing and just made it very small so you could put it on all of your employees desks. And now they had, like I was saying before, their own little data center right there. So if you said like, hey, what's the. I need to know. The Q4 reports, they'd say, go to Debbie's desk. Debbie's the one who's got that on her computer. And you would go over there and say, Debbie, what's the Q4 report? And Debbie would give it to you. Right. There was no connectivity, but you could still like do a lot more stuff than you could when you had a mainframe.

A (10:48)

Yeah, for sure.

C (10:49)

Which makes mainframes feel like really outdated. But it turns out they're like totally still in use today.

A (10:54)

Oh yeah, absolutely. I do want to jump back in time a little bit because I promised talk of lending the military basically your equipment, and that's what happened. In 1951 there was a tea shop chain in the UK. I don't know if it's still around Lyons L Y O N S. And they were the very first company in the world that used a mainframe. It was called the leo. The leo. And it was, you know, like what you would think. They handled like payroll and stock management and stuff like that. But there wasn't a lot for it to do at a tea shop chain except for those couple of things. And so they calculated missile trajectories like you were talking about for the Ministry of Defense.

C (11:34)

Exactly. And that actually kind of helped establish like a, I guess a pay schedule. How people charged for data centers to come. It was like you would charge them for the time that they used it or you could lease it for a month. And that really started to come around when IBM got in the game. They became like the mainframe leader in the 50s, the early 50s, I think they had a unit that you could lease for $16,000 per month. That's in 1952 money. And then as the things as like the processors got better and smaller and faster, that price came down dramatically. And then finally in the 60s, they released the IBM System 360, which not only got Apollo 11 to the moon and back, it is a. It appears in an episode of Mad Men, apparently.

A (12:29)

Oh, really? Yeah, you didn't see that though, right?

C (12:32)

No, I never did. I just saw a reference to it on the Internet.

A (12:36)

And you knew it was a show.

C (12:38)

Yeah, but they like, you should look up pictures of it. It's like those giant burnt orange cabinets with reel to reel magnetic tape. It's just beautiful. They're cool to look at. Yeah, yeah.

A (12:48)

I remember we've referenced the movie War Games from our childhood in the 80s a lot. And the Whopper from War Games was, that was, you know, at that age to see the Whopper in action and to see Matthew Almost said Matthew Modine, Matthew Broderick hanging up his handheld telephone receiver onto a modem to talk to the school computer. It was mind blowing.

C (13:12)

Yeah, that phone in the modem made just a really big impression on me.

A (13:16)

Yeah. And a cool sound. Should we take a break?

C (13:22)

Wait, let me talk about mainframes today though, because I just want to give a little. I don't know if a shout out's the right term, but they are still around because they're so reliable, because they're so secure. You can make it so that there's information on those things that you again have to be physically present in the room to access. You can put all sorts of different layers of security. So if you're like Visa or you're a healthcare company or you're the Census Bureau, you're probably still using a mainframe because you're protecting information as tightly as you can. But those things are also super fast and can hold huge volumes of computation at once.

A (14:02)

Yeah. Or a non Golden Globe nominated podcast.

C (14:06)

Yeah, we've got our own mainframe. Yeah, we've got our IBM 360.

A (14:10)

That's right. What year was that from again?

C (14:13)

The 360 64.

A (14:15)

Yeah, yeah, that's the one. I was just making sure we didn't have the 65 because that was.

C (14:19)

No, no, no. The 64.

A (14:20)

Notoriously buggy.

C (14:21)

Yeah, yeah.

A (14:22)

All right, we can take that break now and we're going to jump out of the wayback machine and venture into the modern world right after this.

B (14:48)

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A (15:29)

All right, so we are out of the Wayback machine. We're making that. We're combining all those old tea bags and making some still somewhat weaker tea.

C (15:37)

It really works though.

A (15:38)

Yeah, it's not too bad. It's a combination of Earl Grey and chamomile. All kinds of fun stuff, but not too bad. And now we're going to talk a little bit about when things started to ramp up because it kind of happened in fits and starts. And one of the biggest, I guess, would it be a fit or a start was the Internet. Because once the Internet came along, every business in the world started using it. And so all of a sudden you had to have a lot more data storage and bigger data centers and bigger server rooms in your companies, which was, you know, a pretty good thing at the time. After the dot com bust, there were a lot of casualties of that growth. But then things kind of, you know, the ship kind of righted itself.

C (16:20)

Yeah. And because it was like accessible to basically every business now, like you didn't have to buy a mainframe. You could, you could lease space on someone else's mainframe, like you're the Ministry of Defense or something all of a sudden. So that led to this huge proliferation that gave the foundation for web commerce. E commerce, that's what they used to call it. That's an old timey term now. But it created the ability for E commerce to start and flourish. So this data centers scaling up to meet the needs of the Internet and then to kind of give people all sorts of new space and room to come up with new stuff. That's where the digital economy came from. Right there. Yeah.

A (17:03)

All of a sudden you could hop on webvan and order a sack of groceries.

C (17:08)

I have a friend who was all in about that.

A (17:14)

Yeah, Yeah, I think I had a friend who was pretty heavily invested in webman.

C (17:20)

But clearly it was ahead of its time. I mean, sure, let's see, it's postmates.

A (17:26)

And well, there's a lot, several of them now that have succeeded.

C (17:29)

Well, name them. We'll get 10 bucks each.

A (17:33)

Well, you just got 10 for Postmates and you got to split that with.

C (17:36)

Me, you know, I will.

A (17:37)

All right. Cloud computing was the next big jump. When cloud computing came around in the early 2000s, or did they call that the early aughts?

C (17:46)

I do okay.

A (17:49)

I thought I'd heard that come from your mouth. But that is when you know that was the real game changer because things were still, I mean, when cloud computing came along, people thought of it. If they didn't look too hard into it, they thought it was just, you know, floating up in the ether somewhere.

C (18:04)

Yeah, it's.

A (18:04)

It's still being stored on stuff. It's just not being stored locally. So all of a sudden things were just going somewhere else for someone else to worry about all that storage. And more importantly, they could. They could link everything together and store a lot of stuff from a bunch of different people.

C (18:21)

Right. So now you have data centers. Not just available to somebody like a huge bank or something like that, or the government initially, then a bank. Yeah. Or a tea shop and then to e commerce businesses. Now it's available to you and me. So it's really hard to remember back because the world has changed so much. But Chuck, like 2008, 2009, they were giving us like VPN little things that like, you could go home and work and like, you would. It would never work. I never understood how to make it work. Yeah, but that was like the very beginning of how you could take your work home with you and work from home and do things remotely. Like, we can now, like, it's nothing. But this led to the rise of businesses like, like Dropbox. Right. So Dropbox goes to Amazon Web Services and, and say, hey, we want to buy a bunch of your cloud. Right? Which means that they're going to use a bunch of, like, different servers and different data centers all over the place. And then Dropbox turns around to you and says, hey, if you give me 1995amonth, you can have one terabyte of data.

A (19:29)

Right.

C (19:30)

You can consume one terabyte of data. Right. And then hopefully you don't use all of that. So they don't have to pay Amazon Web Services for stuff they didn't use. But you're paying that 1995amonth whether you use that whole terabyte or not. It's a pretty smart business model. Would not exist at all if the cloud didn't exist.

A (19:52)

Yeah. It's funny, as you were talking about 2008 and how quaint that is now. That's the year we started the show.

C (19:57)

I know, I know.

A (19:58)

Isn't that crazy to think about?

C (19:59)

It really is. But imagine, like working from home at that time. It was just. You didn't. You couldn't.

A (20:06)

It was kind of great. You went home and you homed.

C (20:09)

That's exactly right. That was a big difference. Yeah, I remember.

A (20:12)

But these data centers have now come together in such a big way now that they. The largest ones are called hyperscale, and they host more than 5,000 servers. Like servers, not. Not individual person's data. It's like, it's incredible how big is how big they've gotten. Google's. And we'll go over some of the kind of square footage and then later talk about the elephant in the room, which is energy and water usage. But Google's first data center was built in 2006, just but two years before stuff you should know launched. And this was in Oregon. And they are still expanding that thing beyond 1.3 million square feet. Meanwhile, in China, they're like, hold my tea, I guess. Because China Telecom has a 10.7 million square foot data center in Inner Mongolia.

C (21:02)

It's 250 acres.

A (21:06)

That is a warehouse full of whirring servers heating up and being cooled.

C (21:12)

Yeah. Which is a big problem for any data center, it turns out. But the whole expansion, this jump starting in 2017, thanks to Cloud computing, because again, cloud computing just means all your stuff isn't on one server in one data center. It's broken up into pieces and spread all over the place. That's the cloud. That's basically it. Even though it's way more advanced and intricate than that. That's like all you really need to know for the purposes of this episode. Right. It led to a huge jump, a huge need in data centers. And it also expanded all the stuff we can do now. And Covid actually gave it another bump, made building a data center very economically attractive thing to do if you had the money. Because remote working finally, finally established itself as like, no, we're doing this. Stop calling us back to the office.

A (22:04)

Yeah. Which is what they're doing now.

C (22:06)

I know, I know. I hope it doesn't work because I remember when, when all of that started and everybody was so nervous, like management was all so nervous that people were just going to totally, like, mess around and everything. It just, it didn't happen. I don't know anybody who's even been like, gotten a talking to, let alone been fired for just messing around at home. As a matter of fact, like you were saying, it just makes you work more.

A (22:32)

Yeah. I mean, do you know how many times in our old offices I would see Jonathan Strickland just wandering aimlessly through the office chatting with people?

C (22:39)

Yes, I do. Because he would chat with me a lot. He did.

A (22:42)

He did that in front of God and everybody, as they say, right there in the office. So I can't imagine what happened with him at home.

C (22:49)

Yeah. Like it was a sorority mixer or something.

A (22:54)

We love Strickland. He's still around everyone, by the way. He retired from tech stuff, but he's still with the company, which is great.

C (22:59)

We love Strickland.

A (23:01)

All right, so now we're on to AI data centers. And that was the. I mean, to call it a game changer seems quaint compared to the rise of cloud computing and everything, because it is off to the races in a way that seemingly cannot be stopped. The genie has left the bottle, as they say. Starting in 2022, when ChatGPT was released by OpenAI, all of a sudden, the need for these data centers became exponentially greater in size, in the speed at which they need these things built. Because AI requires a ton of computing power to operate.

C (23:41)

So much so that they don't even use the standard com, what's called the compute machines. So compute is like all of the processing power, the networking, all of that stuff. And traditionally with a computer, that's done on a cpu. Right. That's how. That's how all of this gets done. Right. Everything else is infrastructure. The CPU is doing all of the work. Those are so, like, they still work. Most data centers are running on CPUs for AI, just not fast enough. They use GPUs, graphic processing units, which are associated with video games for most people. Right. You need a good graphics card to run your video game, I guess. But they. The reason that for AI data centers that they use GPUs, is because they're really good at parallel processing. They can run a bunch of different operations at once. So you're like, cool, you just throw a GPU in a data center and you can run an AI. No, you need hundreds of thousands of these things strung together. And instead of like a CPU running like a couple of servers or something like that at a data center, all of them are strung together to form one giant supercomputer that the AI operates on.

A (24:56)

Yeah. Like ChatGPT itself was trained on 20,000 of these GPUs. A GPU, you know, the sort of the biggest name in the game, there's a couple, but the biggest one, obviously is the Nvidia. But the Nvidia H100, that is the standard, right? Now, if you look this thing up, it fits in your hand. It's not like some gigantic thing, 20,000 of them linked together or 100,000 of them linked together. Who knows how many hundreds of thousands are eventually going to be linked together to end the world.

C (25:30)

Right.

A (25:31)

That's where all the power comes from, like you were saying. But it's, you know, it's just a little rectangular handheld thing that's like, oh, that looks like something that maybe came out of a computer. And Nvidia is what did their stock jumped over a couple of years, like 900% over 2023 and 2024, something like that.

C (25:52)

Yeah. 900% increase.

A (25:55)

Yeah. And we'll talk about why all of this is super, like, scary and dangerous, because it really is.

C (26:01)

Well, yeah, if you want a really good explanation of this about. And like you said, how many GPUs you string together before we end the world. Nate Soares and Eliezer Yudkowski in that book I keep referencing that I think everybody should read, if anyone builds it, everyone dies. About the current state of AI. They talk about this in depth, but in a really understandable way. It's really fascinating. But that's essentially one of the things they say is like we keep stringing together tens and tens of thousands more GPUs. That just makes the supercomputer smarter and smarter and more capable. And eventually what's going to happen, we're going to reach some point potentially where we just put that extra last GPU in there and all of a sudden the balance is tipped and the thing becomes super intelligent.

A (26:48)

That's right. Also a time for me, because you're always too shy to. To plug the End of the World with Josh Clark, your fantastic limited series of which AI is one of the central focuses or one of. Was it eight things?

C (27:02)

10. Well, there was 10 episodes.

A (27:04)

10 episodes, right.

C (27:05)

Thanks, baby.

A (27:07)

Well, but one of the episodes was just like you talking about Jamie Buffett records and.

C (27:10)

That's right.

A (27:12)

He had to lighten the mood.

C (27:13)

Yep.

A (27:15)

Should we take a break or should we keep going for a minute? Let's keep going for a minute.

C (27:19)

Okay.

A (27:19)

Because you talked about investment, and if you had the money to open one of these things, and that's what these tech companies are doing, perhaps their great peril at some point. We'll see. Microsoft has invested $88 billion in data centers just in 2025. Amazon has pledged over the next 15 years, $150 billion. And Google and Meta together are about, you know, not working together, but they are expected to spend about $750 billion just on equipment over the next two years. And Stanley Morgan says.

C (27:54)

Morgan Stanley.

A (27:55)

What did I say? Stanley Morgan. Yeah, I think we should leave that in there.

C (27:58)

Okay.

A (28:00)

Stanley says, hey, you know, guys, maybe.

C (28:03)

You meant like Stanley comma Morgan.

A (28:05)

Yeah, Stanley comma Morgan. Over five years between 2025 and 2030, Morgan Stanley says about $3 trillion is going to be spent just on the data centers about. I mean, half of which is the hardware and half of which is Just building these things.

C (28:22)

Yeah, Just in, what, the next four years.

A (28:26)

Yeah.

C (28:27)

So think about it. If you're Nvidia and you're the industry leader for GPUs, and everybody's like, we're going to spend $1.5 trillion on. On this, on the infrastructure and the GPUs, you're looking pretty good down the road.

A (28:41)

Yeah, for sure. And they're doing this because there's a demand right now for use, at least, because things like OpenAI and other AI creators are using them like crazy, but these companies are also using them for their own AI research.

C (28:55)

Right? Yeah. So like, XAI has that colossus machine that you were talking about earlier, which is 200,000 GPUs strung together. I'm not sure if it's fully online.

A (29:07)

Yet in Memphis, Tennessee.

C (29:10)

Yeah. And it's just for that, it's. They're not doing any. They're not calculating missile trajectories for the Ministry of Defense or anything like that. Like, it's just for that AI. And, yeah, I think Meta is doing the same thing. OpenAI, I don't think is building their own because they're so in. In cahoots with Microsoft. I think they run their stuff on Microsoft's data centers. But, yeah, if you have a AI, essentially right now, which means like God and everybody, you probably have your own data center dedicated to it.

A (29:41)

Yeah, I. And this isn't some. Some moral stand I'm taking by saying that I have never used AI, and trust me, I know that every part of my life is now touched by AI, so I am inadvertently using it.

C (29:54)

Touched by an AI. That's right.

A (29:57)

But I've never used like, you know, chatbots or large language models or anything like that. Just mainly because I'm fine doing things like they are for now and not in a Luddite sort of way. I just. Everything's going along great for me and my job and how I live my life, so I just. I don't have a need for it.

C (30:17)

I do the same thing. And I think also both of us are like, if somebody else wants to do it the other way, that's fine. Like, we're certainly not gonna criticize them or be curmudgeony about it or say that, you know, that's stupid.

A (30:31)

Right. But as you'll see, you know. And again, this isn't yucking someone's yum, but everyone should know what they're a part of, and that's part of what the episode is about.

C (30:41)

That's right. You know. Yeah, no, I totally do. Before we take a break, I think it's. It's a small kind of side issue, but it's worth pointing out that it sucks. Because these Nvidia chips are so in demand from these massive companies, it has driven the price for just the average Nvidia graphics card sky high. So if you're a gamer and you're, like, trying to improve your system, like, you pay way more than you used to for the same graphics card that you could have bought for like, a quarter of the price, you know, a couple years ago.

A (31:19)

Yeah. And I wasn't even looking. Like, I didn't even know that you could just buy. This is how little I know about all this. Before this was like, could you just buy a Nvidia gpu? But I was just researching the size and, like, what do these things look like? And, you know, one was on eBay for $20,000. And I was like, oh, my God. I didn't. I didn't know that was the deal.

C (31:38)

Is that right?

A (31:39)

Yeah. And I don't know if that's accurate. I don't know anything about it. So I could easily be corrected on all this, but that's what the Internet told me.

C (31:47)

Okay, well, the Internet never lies.

A (31:51)

Oh, one thing before we break real quick, because we did promise a little UK specific stuff, and I don't want to short shrift our Brit listeners or Kyle. The UK is right now, like, the third largest nation for data centers. The US Is first. I think Germany is second. And they signed what was called a tech prosperity deal with the giants. The tech giants of the United States. And right now, Microsoft has announced a $30 billion investment in UK data centers. And I think, like, a hundred new AI data centers are planned in the UK at this point moving forward.

C (32:24)

Yeah. And I saw there's at least one in Wales. That's being smartly done. They, like, took an old radiator factory plant campus, and they're revitalizing that as a AI data center. So it does sound like I get why the UK is doing it, but there's a lot of people in the UK and elsewhere who are like, these are not. This is not a good investment for local governments or even national governments. There's a big problem with all this. Like, there is a AI boom going on. Data centers are just one part of it. Like, people are throwing money at AI like it's 1999. And a lot of people are like, there's another. It's not a dot com bubble this time, but it's a AI bubble.

A (33:07)

Yeah.

C (33:08)

One of the reasons why some people are pointing to it as a AI bubble is that there's not, it's just not clear how much money is going to be made from AI and when that's going to start. Yeah, I think the financial times call OpenAI a money pit with a website on top.

A (33:27)

Yeah. Not great.

C (33:28)

No, because people are just pumping money into this stuff, but they're not getting, they're not seeing results from it. Not yet. It's not necessarily a bad bet that AI is going to completely revolutionize the world and like revolutionize economies and going to make some people a lot of money, but there's just no clear path to it right now, which makes some people nervous.

A (33:51)

Yeah, there's about 5%, just 5% of pilot AI programs right now in business. Secure returns on their investment. You know, like they make them money.

C (34:03)

But Stanley Morgan is predicting revenues of a trillion dollars by 2028.

A (34:09)

That's what they're saying. I mean, we'll see. Nvidia, I mean, Kyle's also keen to point out that the, there's sort of a circular economy within all this going on that's a little bit like, troubling maybe because Nvidia is investing in OpenAI, but that depends on their purchase of those Nvidia chips. So, you know, everyone from, you know, just people who are smarter than us as far as this stuff goes, are warning people, right down to the, the imf, the International Monetary Fund are flashing the warning signs saying like, this could be, you know, it could make a trillion dollars by 2028 or it could like wreck the global economy.

C (34:47)

Yeah, for sure. Yeah. We have no idea. Although I have seen people argue against it that say like, this is nothing like. Yeah, a lot of these AI companies are probably overinflated, but it's nothing like it was with like the 2008 meltdown or the dot com bubble. Like this is. We're a lot, we're a lot more seasoned or investors are a lot more seasoned than they were before. The problem is, one of the problems is that the financing is expected to come in large part from private credit, which is essentially an investment vehicle for investors to go lend money to. Say like companies that want to build data centers. Right. And this is largely unregulated, it's very shadowy. We don't know how many, how much debt exists in the world on private credit because they don't have to report this stuff. And as we Learned from the 2008 meltdown, when there's a massive speculation among finances that involves debt that can go really bad.

A (35:58)

Yeah, for sure. And speaking of going bad, I guess we're at the sort of environmental piece of this whole thing. And this is what I was talking about when I said that people should just be aware of what they're taking part in. And again, this is not to shame anybody who uses AI for their job or just to make funny fake videos. But everyone is sort of tied together to make this what it is. Who's using that stuff. And I get if someone says like, hey, if I quit this thing, it's not gonna make any difference, but that's sort of the age old, like, you know, if I don't recycle my aluminum can, tin cans, my aluminum cans, then it's not gonna make that big of a difference. But the idea of everyone getting together to do something for the common good, that's where change happens or where negative change happens. So as far as AI data centers go, the main, you know, aside from just the land use and everything else and the hardship on the local economies and towns in certain ways that we're going to get to, it's really just a succubus of electricity and water usage.

C (37:08)

Yeah.

A (37:09)

Succubus is not the right word.

C (37:10)

No, but it makes sense. It's like bunker down.

A (37:14)

Yeah, but I say succubus to mean just like a bottomless pit. But I know that's not what it means.

C (37:19)

By the way, a giant sucking thing. Right, Right. And it is, it's sucking tons of electricity and water up. Like some of these AI data plants use the same amount of electricity as a town of 50,000.

A (37:32)

Yeah.

C (37:32)

And about the same amount of water as a town of 50,000 people. This is a data center we're talking about. And it's not even necessarily an AI data center. Just any hyperscale data center uses a ton of electricity and water. Reason it uses water is because all of these processors, the CPUs that are doing all this work and just all of the networking that's going on with it's generating heat and computing happens faster when it's cooler. So to keep the place cool, they use evaporative cooling, where they funnel waste heat air through wet pads, essentially. Like they just buy old mattresses and dose them with water and then they run the heat through there and through evaporative cooling. It cools it off. It uses a little less electricity than air cooling, but it uses water. A lot of water.

A (38:28)

Yeah. I mean, I assume most people know this, but like your Laptop has a tiny fan in it. Like every computer in the world has a little fan in it that cools it down. So when you've got all this stuff together, you know it's gonna generate tons and tons of heat. That was the whirring of the server room that you used to sleep in. Those were all fans, you know. And, you know, there's some other sounds coming, but mostly it was those fans trying to cool everything down. We got a lot of stats here that are pretty eye popping, but There are roughly 11,000 data centers around the world. Most of these are not AI obviously, but they're the most robust sort of users of the energy. But they use between 1 and 1.5%, which doesn't sound like a lot, but of the entire world's electricity usage, I know on planet Earth goes to data centers right now. And in certain places, like Ireland, data centers use about 20% of the country's electricity.

C (39:23)

Yeah. And if you dive into different places around like the world where data centers are like, that's collectively right. All of them in Ireland, all of them in the world. If you kind of zoom into the towns where these things are located, there's, well, there's something called Data Center Alley in Northern Virginia outside of D.C. where there's just this huge concentration of large data centers, probably the biggest concentration in the world. Those data centers use about the same amount of electricity as 60% of all the households in the state of Virginia.

A (39:59)

Yeah. Here's another one. By 2030, they're predicting, this is. Barclays bank is predicting that data center energy use in the United States would make up about 13% of the entire electricity demand of the United States. And Meta has their. They all have silly names, but their data center is called Hyperion. They're all, you know, one was, where are the. Where's that list? They're all these kind of sci fi sounding names.

C (40:27)

Yeah. Stargate.

A (40:29)

Yeah.

C (40:30)

Jupiter. Prometheus. Oh God. I'm sure all of those nerds are like, what do you mean, silly?

A (40:35)

If I opened up a data center, I'd call it Old Bessie.

C (40:38)

Well, Bessie is, I hope, so bad that somebody's listening to this and they open a massive hyperscale data center named Old Bessie.

A (40:48)

That would be great. But Meta's Hyperion data center will consume by the time it's finished, about 5 gigawatts. And if you're like, what's 5 gigawatts? That is about half of the peak load of all of New York City.

C (41:01)

The most that it can possibly. That can possibly be demanded. Right?

A (41:04)

Yeah. The very top load probably, I guess New York City on the hottest day of the year with all the lights on at night or something.

C (41:12)

Yeah.

A (41:14)

And that Rockefeller tree just, they just summer version.

C (41:18)

That puts it over the. Over the edge.

A (41:20)

That's right.

C (41:20)

Blackout. So you can imagine that when you're using all this electricity and using all this water, if you're starting to build these massive data centers, you're looking for places that have like, cheap land, cheap electricity. And because electricity is often more expensive than water, they'll go to places, they'll build them in places that are like, water scarce, that have cheap electricity. On the premise that we're a massive multinational corporation, we can push around this little county and use up all of their water, and what are they going to do? Nothing.

A (41:57)

Yeah. And I mean, that's literally happening. There's one right here in Georgia, in Newton County. It's a metadata center that's using 10% of the local water use. And like you said, water is a resource that isn't infinite. We've talked about the dangers in the future of like, you know, perhaps the wars of the future will be fought over water, and this could get us there. I think in Phoenix, Arizona, you know, known for their abundant water, Meta and Microsoft use 7 million gallons of water every single day for their data centers.

C (42:29)

Yep, every day. You said every day.

A (42:33)

7 million gallons of water.

C (42:34)

That's insane.

A (42:36)

Yeah.

C (42:36)

And when I saw this, I was like, oh, here we go. In the UK, data centers use 10 billion liters of drinking water every year.

A (42:48)

L I T R E S. Yeah, that's right. But you know, you mentioned some of these towns, not only are some of the, they're like using, let's say, 10% of the local water. Here in Newton county in Virginia, where Data Center Alley is, some of these places are like, some of these towns are running out of water. Like they go to turn on their water and water doesn't come out because of this.

C (43:09)

Oh, plus also, like we talked about how gamers are getting, getting the short end of the stick when it comes to buying graphic cards because they are in such high demand. Same thing happens with electricity. So in addition to this data center coming to town and using up all your water, they're also jacking up your electricity prices because there's only so much that your local electric electrical company can produce. So because of supply and demand, your price is going to rise. And I guess around Data Center Alley in Northern Virginia, electricity prices have increased 267% since 2020. And that also is affecting Maryland which is getting little to no benefit from data center ally and is just helping pay the price for it. The this is subsidization of these data centers. Like they are subsidized in just about every single way you can imagine.

A (44:03)

Yeah, for sure. And if you say like, oh well sure, but they create jobs. Right. So that's great for the local economy. Kyle gives an example here of Northumberland, England. There's a 10 billion pound data center there, or I guess it's coming and you'd think, oh great, that's going to employ probably like 5,000 people. Right. It's going to employ 400 people with full time jobs.

C (44:27)

Yeah. A $10 billion or 10 billion pound data center. 400 jobs. Because these things are so efficient and everything is just so advanced. They don't really need that many people to keep an eye on it. Right. Plus also the money from that data center, they're not going to spread it around the uk. It's going to flow right back to the us to the parent company.

A (44:50)

Oh yeah, for sure. And we also didn't point out that a lot of these energy grids are literally going to buckle under pressure at some point. They're not built for this.

C (45:02)

Yes. And we're not. So I know it sounds like we're just like and this and that. How terrible are data centers? They're incredibly important and they support an amazing array of really great stuff. Right. And they are the foundation that the next expansion of the digital economy and the world culture are going to grow on. Like they're incredibly important but they have a lot of problems with them that need to be addressed. They're not being addressed because every government from like the local city council up to the leaders of the free world, like are just giving these people whatever they want. That's what's going on now. There's no checks going on at all right now. That's the problem. Yeah.

A (45:52)

And that's because the flow of money is so great at this point to a certain segment of the population only. They're protecting their own investment. You know, they're watching their own backsides.

C (46:06)

That's definitely, I would say 99% of it. But I think there's also, Chuck, a little factor of like gee whiz, like these titans of the AI industry are good at like razzle dazzling elected officials into doing whatever they want by I think making them feel included in this new like frontier essentially. I think there's a certain element of that.

A (46:31)

I think you're probably right. It's, hey, maybe it'll all work out great.

C (46:35)

Sure, it probably will. It usually does. Astoundingly, it usually does work out well.

A (46:42)

True. As far as the world hasn't ended.

C (46:44)

That's exactly what I mean. Yeah, yeah, yeah. So I think that's it. We said yeah like four or five times in secession. I think we accidentally triggered listener mail.

A (46:55)

That's right. This relates to our history of the BBC episode. And this is from Erica. And Erica says, hey guys, I really love the episode. Left me reflecting on how I've come to understand the country through both the content the BBC produces and the people people's reactions to the BBC. But more recently, my work as an academic has enabled me to be involved in creating programs for the BBC across tv, radio and online. Because there's one awesome fact about the BBC that wasn't included. For over 50 years, the BBC has partnered with the Open University OU, which specializes in accessible and distance education. The partnership started in the 1970s to provide learning at scale, including facilitating university level lectures at night on public television. Today, the partnership facilitates access to academic consultants to co produce high quality informed content across platforms, including some of the David Attenborough nature stuff.

C (47:49)

Nice.

A (47:50)

Additionally, the Open University creates supplementary materials to enable people to continue their learning journey and explore topics in more detail. So whether viewers or listeners realize it or not, this partnership enables the public to benefit from specialist knowledge and accessible ways. And that is from Erica from the Open University, who is a professor of medical anthropology.

C (48:13)

Oh wow, that's an awesome. Erica, you gotta send us some topic ideas too.

A (48:18)

Totally right up your alley.

C (48:20)

And congratulations. That's pretty neat. Making stuff in conjunction with the BBC, that's gotta be a neat high watermark, you know.

A (48:27)

Agreed.

C (48:27)

And I think, Chuck, I'm curious to see if we we go look at our account, we'll see a little line item from Open University and one from BBC.

A (48:38)

Well, what would be like £7 or something? I don't know, the exchange rate, Right?

C (48:41)

That sounds about right. All right, great. Well, thanks again Erica. And please do send us some medical anthropology ideas because that just sounds like it'll knock our socks off. And if you want to be like Erica and try to knock our socks off, good luck. You can send it off to us@stuffpodcastheartradio.com.

B (49:03)

Stuff youf Should Know is a production of iHeartRadio. For more podcasts, my heart radio, visit the iHeartRadio app, Apple Podcasts or wherever you listen to your favorite shows. Revitalize your bathroom with big savings at Lowe's. Get up to 40% off select vanities and free delivery during our bath savings event. Plus get up to 40% off select shower heads. No matter what style you're looking for, we've got you covered. Shop for your bathroom refresh at Lowes. We help you save while supplies last selection varies by location.

A (49:48)

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