Data Centers: Can't Live With Em, Can't Live Without Em | STUFF YOU SHOULD KNOW

Welcome to Stuff You Should Know, a. production of iHeart Radio. Hey, and welcome to the podcast. I'm. Josh and there's Chuck and Jerry's here, too, and we've got our pocket protectors. and tape on the bridge of our glasses. Hey. And this is Stuff You Should Know. Nice work. Did you just hear something? No. Oh, weird. >> I heard you say nice work Yeah, but then.

>> stop abruptly. >> Well, I stopped abruptly cuz I thought I heard. a little. digital glitch. Oh, no, I didn't hear anything. I might be losing my mind, then. I I You know, I'm curious whether we end. up editing this out or not. Any other. podcast on the planet would edit that. out without even thinking about it, but. there's like a 50% chance it'll stay in. with us. Uh I mean, this is why we. didn't get that Golden Globe nomination. That's right. >> This kind of classic Stuff You Should. Know unprofessionality. >> That's exactly right. That and the.

Italian accent, the mispronunciations, there's a whole laundry list. Yeah. That's okay, Chuck. I think we're. we're golden regardless. Agreed. So, uh we're talking about data centers, which I had a very, very rough idea. about, but actually no, I I knew that. they existed essentially and that they. were becoming a problem with um the rise. of AI. Yes. >> But that was about it. How about you? Are you Are you data uh data center. philiac?

Uh no, you know me. I'm not I'm not. super technology-minded, so I don't know. a lot about this stuff. Uh I remember. walking by. our server room back in the day when we. were at Ponce City Market and seeing uh. our colleague Izzy in there hard at. work. Yeah. And when that door was. unlocked and open, hearing the whir. Uh-huh. of the of the servers and the. cooling machines, and. you know, that that's a on a smaller. scale, that's a a data center. 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. I. like the the warmth of the server. Yeah, and and the whir put you right to sleep. >> yeah. So, uh yeah, that definitely counts as a. data server. If you have like one of. those um little home networking setups. in 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'd 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, >> Yes. I Wait, what did I say that's the Q. word? Quotidian. Oh. You're like, "No, that's KW." You're. right. Um so, the reason that I bring that up. though, Chuck, is because that. technically is part of the progression. of data centers. >> Yeah. Probably goes without saying, but. it's evolved along with computing, and. as computing's kind of gotten bigger and.

bigger, the need to to store and access. more data has gotten bigger. So much so, Chuck, that just wrap your head around. this one. In 2024, Mhm. 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. Yeah. It's um 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 uh. data in 2024. Yeah, I don't even know what how many. Big Macs that is. Uh I know you. name-dropped a lot of brands. Mhm. 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. I agree wholeheartedly. I agree that Amex should do that. Amex.

There's 20 bucks. I'll split it with. you. >> Wow, you just bought lunch in 1997. >> That's right. Uh so, just real quick, a zettabyte, Chuck, is a trillion gigabytes. And we. So, we consumed 150 trillion gigabytes. Wow. That was 2024. That's worldwide, right? >> Yes. Yeah, that's worldwide. In 2010, we. consumed two zettabytes. Jeez. 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. Yeah, and actively is, and we're going to we're going to get to. some startling statistics later on in. the episode, but uh Kyle helped us out. with this, our our writer over in the. UK. He did a fantastic job. He did a. really good job, and there's going to be. some UK-specific things in here cuz. Kyle's always keen, as they say, to.

throw that stuff in there. Yeah, for. sure. Kyle likes to pepper those in. Yeah, of course. Uh. and he's not barred from doing so. We. allow it. [Laughter]. So, uh since Kyle, you know, is. frequenter of the Wayback Machine, as. are all the uh wonderful writers that we. use, Mhm. they all have the keys to the. car, essentially. Uh 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. He also.

left all of his used tea bags in there, too. I don't know if you noticed. >> Oh, it's fine. You know, you can throw. those back in some hot water and they do. just a little bit weaker tea. Well, if. you put like five of them together, it's. like one. Yeah. And Kyle I mean, that. thing was full. I know. It really was. He drinks a lot of tea. He really does. Uh so, if you want to talk about the. earliest data centers that you could. kind of call maybe a data a data center, uh they were, you know, computers. They. were electronic computers. Um most of. this stuff that we're going to talk. about early on was military in use. Mhm.

Uh and as you'll see, even the first um. when we talk about the UK one that was. supposedly not military, they even. loaned it to the military, which was. kind of interesting. Uh 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. Uh and as we'll see, Elon Musk has now. stolen that for his own purposes, that.

name. Uh probably because of this, I would. imagine, but uh it was at Bletchley. Park, of course, during World War II. And they were trying to, you know, crack into Hitler's uh 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. I want to go to. that so bad. When we do that that UK. Europe tour next year, Ooh. we we got to.

go to that together, okay? Okay. Are we. doing that next year? That's what we were talking about. All. right. We kind of already half promised. it. We have to now. We're locked in the. punch. Why's my voice so high, then? I. don't know. You practicing for the Alps? >> Uh yeah, that's right. No, that would. that'd be a lot of fun. I'd love to go. to that. So, that was Colossus. Another one about. the same time was the ENIAC, electrical. numerical integrator and computer. So, that's a quality acronym. Yeah.

Um 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, um. "What's the trajectory of this missile. if I fire it from here?" And ENIAC would. go beep boop boop boop, and then say. like. whatever a trajectory is described in. Sure. Or Colossus, you'd be like, "What. is Hilter saying here to Goebbels?" And.

they Colossus would say, "Hilter is. saying. um 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.". Uh 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?". Yeah, you got to just tell me what's. going on with this Hilter business. You. don't remember from our um. our Mysteries of the Art World that How. Stuff Works article? Oh, did it say. >> of that section was, "Did Hilter do.

these paintings?". >> That's a That's a deep cut. I did not. remember that. Yes, and I think it's. still says that on that um that article. Yeah. >> Yeah, I can only hope. It's got to be. Hilter forever. All right, so uh we go into mainframes. at this point, and this is uh like the. 1950s, basically, when companies. um could actually have their own. computer. It wasn't just the military. Uh they These were the old punch card. computers, and they were called. mainframes. It wasn't made up for the. term. Uh mainframes were originally.

described uh or describing like what you. would house telecommunication equipment. and maybe some other science-y stuff. Mhm. Uh but it was referencing literally. the cabinets that held this technology, and it became known as just, you know, it kind of took over it when the. computer world started using it as. computer only. Yeah, but again, this is. um 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. um. uh 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. Yeah, for sure. Which makes mainframes. feel like really outdated, but it turns. out they're like totally still in use. today. Oh, yeah, absolutely. I do want. to jump back in time a little bit. because I did I promised talk of uh. lending the military uh 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. Uh 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 uh management and stuff like. that. But uh there wasn't a lot for it. to do uh 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. Exactly. And that actually kind.

of helped establish like a um. uh. I guess a pay schedule how people. charged for data centers to come. >> Right. Yeah, yeah. It was you you they. 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. um 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 um not only got Apollo 11 to the. moon and back. Um it is a uh it appears in an episode. of Mad Men, apparently. Oh, really? Yeah. You didn't see that though, right? No, I. never did. I just saw a reference to it.

on the internet. And you knew it was a. show. Yeah. But they like you should look up. pictures of it. It's like those giant. burnt orange cabinets with real the real. magnetic tape. It's just. they're cool to look at, yeah. Yeah, I. remember we've referenced the movie. WarGames from our childhood in the '80s. a lot. And the the Whopper from WarGames. was that was. you know, at that age to see uh the. Whopper in action and to see Matthew uh. almost said Matthew Modine, Matthew um. Broderick. uh hanging up his handheld telephone.

receiver onto a modem. to talk to the school computer, it was. mind-blowing. The the yeah, that phone. and the modem made just a really big. impression on me. Yeah, and a cool. sound. It did. Beep boop boop boop. Uh should we take a break? Uh wait, let me talk about mainframes. today though cuz 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 um 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. Yeah, or a. non-Golden Globe nominated podcast.

Yeah, we've got our own mainframe. Yeah. We've got our IBM 360. That's right. Uh. what year was that from again, the 360? '64. Yeah, yeah, that's the one. I'm just making sure we didn't have the. '65 cuz that was. >> No, no, no, the '64. yeah. >> Uh all right, we can take that break now. and we're going to uh jump out of the. Wayback Machine and venture into the. modern world right after this.

[Music]. [Music]. All right. So, we are out of the Wayback. Machine. We're making that uh we're. combining all those old tea bags and.

making some still somewhat weaker tea. It really works though. Yeah, it's not. too bad. It's a combination of Earl Grey. and chamomile, all kinds of fun stuff. But. not too bad. Uh and now we're going to. talk a little bit about when things. started to ramp up cuz it kind of. happened in fits and starts. And one of. the biggest uh I guess would it be a fit. or a start was the internet. Cuz 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 uh.

data storage and uh bigger data centers. and bigger server rooms in your. companies. Uh which was, you know, a a. pretty good thing at the time. Uh after. the dot-com bust, there were a lot of. casualties of that growth. Uh but then. things kind of, you know, the ship kind. of righted itself. 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 the um. foundation for um. web commerce, e-commerce. That's what. they they used to call it. That's. old-timey term now. But it it created. the ability for e-commerce to start and. flourish. So, it. it 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. Mhm. That's where the digital. economy came from right there. Yeah, yeah, all of a sudden you could hop on. Webvan and or.

order a sack of groceries. Mhm. I have a friend who. was all in about that. And yeah. Yeah, I. had I think I had a friend who would was. pretty heavily invested in Webvan. It's but clearly it was ahead of its. time. I mean, it's. uh let's see. It's a Postmates. and. >> a lot several of them now that have. succeeded. Well, name them. We'll get 10. bucks each. What? [Laughter]. Well, you just got 10 for Postmates. You.

got to split that with me, you know. I. will. All right. Uh cloud computing was the next big jump. when cloud computing came around in the. early 2000s. Or do they call that the. early aughts? I do. Okay. I 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. uh or if they didn't look too hard into. it, they thought it was just, you know, floating up in the ether somewhere. It's.

it's still being stored on stuff. It's. just not being stored locally. Um 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. Right. So, now you have data centers not. just available to somebody like a huge. bank or something like that. Or the. government initially than a bank, yeah, or a tea shop. Um 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 that 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 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. $19.95 a month, uh you can have 1 TB of. data." Right. You can consume 1 TB 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 $19.95 a month rather. whether you use that whole TB or not. It's a pretty smart business model. Would not exist at all if the cloud. didn't didn't exist. Yeah, what's funny. is you were talking about 2008 and how. quaint that is now. That's the year we. started the show. I know. I know. Crazy. to think about. It really is. But. imagine like like working from home at. that time. It was just it didn't.

>> You couldn't. >> No. It was kind of great. You went home. and you homed. That's exactly right. That was a big difference. Yeah, I. remember. Uh 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 it how big. they've gotten. Google's and we'll go. over some of the kind of square footage. and then later talk about uh the. elephant in the room, which is energy.

and water usage. But Google's uh first. data center was built in 2006. Uh just. about 2 years before Stuff You Should. Know launched. And this is in Oregon. And they are still expanding that thing. beyond 1.3 million square feet. Meanwhile, in China they're like uh hold. my tea, I guess. Because China Telecom has a 10.7 million. square foot data center. in Inner Mongolia. It's 250 acres.

That that is a warehouse full of. whirring servers heating up and being. cooled. Yeah, which is a big problem for. any data center it turns out. But the the whole 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 and 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 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. Yeah. 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.

Yeah, which is what they're doing now. I. know. I know. I 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 for just. messing around at home. As a matter of. fact like you were saying it just makes. you work more. 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? Yes, I do because he would chat with me. a lot. He did. He did that in. 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. Yeah, like it was a sorority mixer. or something. We love Strickland. He's still around. everyone by the way. He he retired from. tech stuff but he's still with the. company which is great. We love. Strickland. All right, so now we're on to AI data. centers and that was the.

I mean to call it a game changer is is. 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 chat GPT 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. Mhm. Because AI requires a ton of.

computing power to operate. So much so that they don't even use the. standard. 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? Mhm. That's how. that's how all of this gets done, right? Everything else is infrastructure. The. CPU's doing all of the work. Those are so like they still work. They.

like most data centers are running on. CPUs for AI just not fast enough. Yeah. They use GPUs graphic processing units. which are associated with video games. for most people, right? You need a good. graphics card to to run your video game. I guess. Mhm. But. 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 data. center all of them are strung together. to form one giant supercomputer. that the AI operates on. Yeah, like chat GPT itself was trained. on 20,000 of these GPUs. Mhm. A GPU you. know the sort of the 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 it fits in. your hand. It's not like some gigantic. thing. Mhm. 20,000 of them linked. together or 100,000 of of them linked. together who knows how many. you know hundreds of thousands or. eventually going to be linked together. to to end the world. Right. That's where. all the power comes from like you were. saying but it's you know it's just a.

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 jump over a couple. of years like 900% over 2023 and 2024? Something like that? Yeah, 900%. increase. Yeah, we'll talk about why all of this. is super like scary and dangerous. because it really is. 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 Yudkowsky in. that book I keep referencing that I. think everybody should read. Mhm. builds. it everyone dies about the current state. of AI. They 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. That's right. Also a time for me cuz 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? 10. Well, there was 10 episodes. 10.

episodes, right? Thanks, baby. Well, but one of the episodes was just. like you talking about Jimmy Buffett. records and That's right. You had to lighten the mood. Yep. Should we take a break or should we keep. going for a minute? Let's keep going for. a minute. >> Okay. Because you talked about. investment and you know if you had the. money to open one of these things and. that's. what these tech companies are doing like. to. perhaps their great peril at some point. We'll see. Microsoft has invested 88. billion dollars in data centers.

just in 2025. Amazon has pledged over. the next 15 years 150 billion dollars. And Google and Meta. together about you know not working. together but they're expected to spend. about 750 billion dollars just on. equipment over the next two years. >> Mhm. And Stanley Morgan says Morgan. Stanley. What I say Stanley Morgan? >> Yeah. I think we should leave that in there. Okay. Stanley says. Hey, you know guys Maybe you might like. Stanley comma Morgan. Yeah, Stanley.

comma Morgan. Over 5 years between 2025 and 2030. Morgan Stanley says about 3 trillion. dollars. 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. Yeah. Just in what the next four years? Yeah. 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 dollars on.

on this on the infrastructure and the. GPUs. You you're looking pretty good. down the road. Yeah, for sure. And you know they're. doing this because there is 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. 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 yet. In Memphis, Tennessee. 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's. doing the same thing. OpenAI I don't. think is building their own cuz 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 a your own data center. dedicated to it. 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. Touched by an AI. That's. right. But I've never I've never used like. you know chatbots or or. large language models or anything like.

that just mainly because I'm just I'm. fine. doing things like they are for now and. not in a 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. 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 going to. criticize them or be curmudgeony about. it or say that you know that's stupid. 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. That's right. You know? Yeah, I. know I totally do. Before we take a. break I think it's it's. small kind of side issue but it's worth. pointing out that it sucks. Um 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. Yeah, and. I wasn't even looking like I 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 um GPU? But I was just. researching the size and like what do. these things look like? And it, you. know, it's 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. >> Is that right? Yeah, and I don't know if that's. accurate. I don't I don't know anything. about it, so I could easily be corrected. on all this, but uh that's what the. internet told me. Okay. Well, the. internet never lies. Oh, one thing before we break real quick. cuz we did promise a little UK specific. stuff and I don't want to shortchange. our Brit listeners or Kyle. Uh. 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 the giants, uh. the tech giants of the United States. And right now uh 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. Yeah, and I saw. there's at least one in Wales that's. being smartly done. They like took an. old radiator um factory plant campus and. they're they're revitalizing that as a.

AI data center. So, that it does sound. like I get why the UK's 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 um.

this time, but it's a AI bubble. Yeah. Um 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's going to be made from AI and. when that's going to start. Yeah. I. think the Financial Times called OpenAI. a money pit with a website on top. Yeah, not great. >> 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. Um but there's just no clear path to it. right now, which makes some people. nervous. Yeah, there's about um 5% just. 5% of pilot AI programs right now in. business uh secure returns on their. investment. So, you know, like they make. their money. But Stanley Morgan is.

predicting uh revenues of a trillion. dollars by 2028. That's what they're. saying. I mean, we'll see. Um Nvidia, I. mean, Kyle's also keen to point out that. the uh there's sort of a a 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 are flashing the warning signs. saying like, this could be, you know, it. it could make a trillion dollars by 2028. or it could like wreck the global. economy. Yeah, for sure. Um. Yeah, I 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 um with like the 2008 meltdown or.

the dot-com bubble. Like this is We're a. lot We're a lot more seasoned. Our. 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 like how much debt exists. in the world on private credit cuz they. don't have to report this stuff. And you know, as we learned from the. 2008 meltdown, when there's like a. massive speculation among finances that. involves debt, um that's that that can. go really bad. Yeah, for sure. Um. And 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, you know, 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. Um. but but, you know, everyone is sort of. tied together to make this what it is. Who's using that stuff? And and I get if. someone says like, hey, if I quit this. thing, it's not going to make any. difference. Um but that's sort of the. the age-old like, you know, if I don't. recycle my tin can my aluminum can tin. cans.

uh my aluminum cans, then it's not going. to 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. Right. Uh or where. negative change happens. So, uh as far as uh AI data centers go, the. main um. you know, aside from just, you know, the. land use and everything else and the the. hardship on the local economies and. towns in certain ways that we're going. to get to, it's really. uh. just a a succubus of electricity and.

water usage. Succubus is not the right word. No, but. it makes sense. It's like a bunker down. Yeah, but I say succubus to mean just. like a bottomless pit, but I don't know. that's not what it means, 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. Yeah. And about the same amount of water. as a town of 50,000 people. This is a.

data center we're talking about. And and. it's not even necessarily an AI data. center, just any hyper-scale 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 is. generating heat and computing happens. faster when it's cooler. So, to keep the. place cool, they use evaporative cooling. where they funnel um.

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. it it through evaporative cooling, it. cools it off. It's It uses a little less electricity. than um air cooling, but it uses water, a lot of water. 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 going to. generate tons and tons of heat. Uh that. was the the whirring of the server room. that you used to sleep in. Uh those were all fans, you know? And. you know, there's some other sounds. coming but mostly it was the those fans. trying to cool everything down. Um we. got a lot of stats here that are pretty. eye-popping, but uh there are 11 roughly. 11,000 data centers around the world. Um. most of these are not AI, obviously, but. they're the most, you know, uh robust. sort of users of the energy, but uh they.

use between 1 and 1.5% which it 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. 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 there's um well, there's something. called Data Center Alley in Northern. Virginia uh outside of DC um where. there's just this huge concentration of. large data centers. Uh 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. Yeah. Uh here's another one. By 2030, they're. predicting This is Barclays Bank is.

predicting that uh data center energy. use in the United States. would make up about 13% of the entire. electricity demand of the United States. >> Mhm. And uh Meta has their They all have. silly names, but their data center's. called Hyperion. They're all, you know, one was. uh where are the Where's that list? They're all these kind of. sci-fi sounding names. Yeah, Stargate. Yeah. Jupiter, Prometheus. Oh god. I'm. sure all of those nerds are like, what. do you mean, silly?

If I opened up a data center, I'd call. it Old Bessie. Old Bessie is That's an I hope so bad. that somebody's listening to this and. they open a massive hyper-scale data. center named Old Bessie. That would be great. But Meta's Hyperion. data center will consume, by the time. it's finished, about 5 GW. And if you're. like, "What's 5 GW?" Um that is about uh. half of the peak load of all of New York. City. The most that it can possibly that. can possibly be demanded, right?

>> Yeah, the the the very top load probably. I guess New York City uh on the hottest. day of the year uh. with all the lights on at night or. something. Yeah. And that Rockefeller tree just They just. This is a summer version. >> That puts it over the over the edge. That's right. >> Blackout. So, you can imagine that when. you're using all this electricity and. using all this water, um if you're. starting to build these massive data. centers, you're looking for places that. have like cheap land, Yeah. >> 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 like 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. Yeah, and I mean that's literally. happening. There's one right here in. Georgia, Newton County. It's a Meta data. center that's using 10% of the local.

water use. And. like you said, water is a 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. Um. I think in Phoenix, Arizona, you know, known for their abundant. water, uh Meta and Microsoft uh use 7 million. gallons of water every single day for. their data centers. >> Yeah. Every day, you said. Every day, 7 million gallons of water.

>> That's insane. Yeah. 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. L I T R E S. >> Yeah, that's right. Uh but, you know, you mentioned some of these towns. Not. only are some of these 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. Oh, plus also, like we talked about how. gamers are getting um getting uh 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 um Data Center Alley in. Northern Virginia, electricity prices. have increased 267%. Wow. >> since 2020. And that also is affecting. um Maryland, which is getting little to. no benefit from Data Center Alley and is. just helping pay the price for it. This. is subsidy subsidization. of these data centers. Like they are. subsidized in just about every single. way you can imagine. Yeah, for sure. Uh.

and if you say like, "Oh, well, sure, but they create jobs, right? So, that's. great for the local economy.". >> Mhm. Um Kyle gives an example here of uh. Northumberland, England. There's a 10. billion-pound data center there. Uh or I guess it's it's coming. And. you'd think, "Oh, great. That's That's. going to employ probably like 5,000. people, right?" Uh it's going to employ. 400 people with full-time jobs. Yeah. A. 10 billion-dollar 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. Oh, yeah, for sure. Uh and you know, we also didn't point. out that a lot of these uh. um these energy grids like are literally. going to buckle under pressure at some. point. Like they're not built for this. Yes. And we're not So, we're I know it.

sounds like we're just like, "And this, and that. How terrible are data. centers?" W- Like there's They're. They're incredibly important, and they. support um an amazing amazing array of. really great stuff, right? And they they. are the 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 um leaders of the. free world Mhm. uh 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, and that's that's because. the flow of money is so great uh at this. point to a certain segment of the. population only. Um they're protecting their their own. investment, you know. They're They're.

watching their own backsides. 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. these titans of the the um 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. I think you're probably right. It's. uh hey, maybe it'll all work out great.

>> Sure, it probably will. It usually does. Astoundingly, it usually does work out. Uh well, true as far as the world hasn't. ended. That's exactly what I mean. Yeah, yeah. Yeah. So, uh I think that's it. We. said yeah like four or five times in. succession. I think we accidentally. triggered listener mail. That's right. Uh this relates to our. history of the BBC episode, and this is. from Erica. And Erica says, "Hey guys, um I really loved the episode. Left me.

reflecting on how I've come to. understand the country through both the. content the BBC produces and the. 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. Uh the partnership started in the 1970s. to provide learning at scale, including. facilitating university-level lectures.

at night on public television. Neat. >> Today, the partnership facilitates uh. access to academic consultants to. co-produce high-quality informed content. across platforms, uh including some of. the David Attenborough nature stuff. >> Nice. Uh additionally, the Open. University creates supplementary. materials to enable people to continue. their learning journey and explore. topics in more detail. Uh so, whether viewers or listeners. realize it or not, this partnership. enables the public to benefit from. specialist knowledge in accessible ways.

And that is from Erica from the Open. University, who is a professor of. medical anthropology. Oh, wow. That's an. awesome Erica, you got to send us some. topic ideas, too. Totally. Right up your. alley. >> And congratulations. It's pretty neat. making stuff in conjunction with the. BBC. That's got to be a neat high-water. mark, you know? Agreed. And I think, Chuck, I'm curious to see if we go look. at our account, we'll we'll see a little. line item from Open University and one.

from BBC. Uh well, it would be like 7 pounds or. something. I don't know the exchange. rate right now. >> about right. All right. Um 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 at. stuffpodcast@iheartradio.com. [Music]. Stuff You Should Know is a production of.

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