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Welcome to Stuff You Should Know, a production of iHeartRadio.
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.
And this is Stuff You Should Know.
Nice work.
Did you just hear something?
No.
Oh, weird.
I heard you say nice work and then stop abruptly.
Well, I stopped abruptly because I thought I heard a little digital glitch.
Oh, no, I didn't hear anything.
I might be losing my mind then.
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.
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 golden regardless.
Agreed.
So we're talking about data centers, which I had a very, very rough idea about.
But actually no, I knew that they existed essentially and that they were becoming a problem with the rise of AI.
Yes.
But that was about it.
How about you?
Are you a data center-philiac?
Right.
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 Ponce City Market and seeing our colleague Izzy in there hard at work.
Yeah.
And when that door was unlocked and open, hearing the whir of the servers and the cooling machines and
You know, that's a, on a smaller scale, that's 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.
The warmth of the server.
Yeah.
And the whirr puts you right to sleep.
Yeah.
So, yeah, that definitely counts as a data server.
If you have like one of those little home networking setups in like a closet in your house data center.
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.
Or the Q word.
Yes.
Wait, what did I say that's the Q word?
Quotidian.
Oh.
You're like, no, that's KW.
Right.
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 store and access more data has gotten bigger.
So much so, Chuck, that just wrap your head around this one.
In 2024... Mm-hmm. 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 of data in 2024.
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.
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.
Yeah.
So just real quick, a zettabyte, Chuck, is a trillion gigabytes.
Okay, wow.
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.
Wow.
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 this kind of calm plateau that they'd reached.
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 Kyle helped us out with this, 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, because 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.
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.
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.
It really was.
He drinks a lot of tea.
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 not military.
They even loaned it to the military, which was kind of interesting.
Yeah.
But these things were built with state-of-the-art technology at the time, which meant vacuum tubes and 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.
I want to go to that so bad.
When we do that UK-Europe tour next year, we got to go to that together, okay?
Okay.
Are we doing that next year?
That's what we were talking about.
We kind of already have promised it.
We have to now.
We're locked in the punch.
Why is my voice so high then?
I don't know.
You practicing for the Alps?
Yeah, that's right.
No, 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.
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.
Right.
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
Sure.
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?
Yeah, you got to just tell me what's going on with this Hilter business.
You don't remember from our Art Mysteries of the Art World that How Stuff Works article.
The title of that section was Did Hilter Do These Paintings?
Oh, my God.
That's a deep cut.
I did not remember that.
Yes.
And I think it still says that on that listicle.
Yeah.
Yeah, I can only hope.
It's got to be Hilter forever.
Yeah.
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 were describing like what you would house telecommunication equipment and maybe some other science-y stuff.
But it was referencing literally the cabinets that held this technology together.
And it became known as just you know.
It kind of took over when the computer world started using it as computer only.
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?
Mm-hmm.
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 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, Lions, 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 L-E-O.
And it was, you know, like what you would think.
They've 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.
Exactly.
And that actually kind of helped establish like a –
I guess a pay schedule or 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 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?
But they like you should look up pictures of it.
It's like those giant burnt orange cabinets with real to real magnetic tape.
It's just beautiful.
They're cool to look at.
Yeah.
Yeah, I remember we've referenced the movie War Games from our childhood in the 80s a lot and the Whopper from War Games.
That was, you know, at that age, to see the Whopper in action and to see Matthew I 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.
Yeah, that phone in the modem made just a really big impression on me.
Yeah, and a cool sound.
Beep, bop, boop, boop.
Should we take a break?
Wait, let me talk about mainframes today though, because I just want to give a little – I don't know if a shout-out is 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.
Sure.
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.
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.
What year was that from again, the 360?
64.
Yeah, yeah, that's the one.
I was just making sure we didn't have the 65 because that was notoriously buggy.
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.
Beep, bop, boop, boop.
Hi, I'm Dr. Priyanka Wally.
And I'm Hari Kundabolu.
It's a new year, and on the podcast Health Stuff, we're resetting the way we talk about our health.
Which means being honest about what we know, what we don't know, and how messy it can all be.
I like to sleep in late and sleep early.
Is there a chronotype for that, or am I just depressed?
We talk to experts who share real experiences and insight.
You just really need to find where it is that you can have an impact in your own life and just start doing that.
We break down the topics you want to know more about.
Sleep, stress, mental health, and how the world around us affects our overall health.
We talk about all the ways to keep your body and mind inside and out healthy.
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Health stuff is about learning, laughing, and feeling a little less alone.
Listen on the iHeart Radio app, Apple Podcasts, or wherever you get your podcasts.
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.
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.
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 resources, data storage and bigger data centers and bigger server rooms in your companies, which was a pretty good thing at the time.
After the dot-com bust there were a lot of casualties of that growth, but then 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 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 center's 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.
All of a sudden you could hop on Webvan and Or order a sack of groceries.
Mm-hmm.
I have a friend who was all in about that.
WebM?
Yeah.
Yeah, I think I had a friend who was pretty heavily invested in WebM.
But clearly it was ahead of its time.
I mean, let's see.
It's Postmates.
Well, there's several of them now that have succeeded.
Well, name them.
We'll get $10 each.
Wow.
Well, you just got 10 for Postmates.
You got to split that with me, you know.
I will.
All right.
Cloud computing was the next big jump when cloud computing came around the early 2000s.
Or did they call that the early aughts?
I do.
Okay.
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.
Yeah.
It's still being stored on stuff.
It's just not being stored locally.
Yeah.
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 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 a British tea shop.
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?
Yeah, they were giving us like VPN, little things that like you could go home and work and like it would never work.
I'd never understood how to make it work.
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 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 different servers and different data centers all over the place.
And then Dropbox turns around to you and says hey, if you give me 1995 a month, you can have one terabyte of data.
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 $19.95 a month whether you use that whole terabyte or not.
It's a pretty smart business model.
It would not exist at all if the cloud didn't exist.
Yeah, it's funny as you were talking about 2008 and how quaint that is now.
That's the year we started this show.
I know, I know.
Isn't that crazy to think about?
It really is.
But imagine like working from home at that time.
It was just, you 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.
But these data centers have now come together in such a big way now that the largest ones are called hyperscale.
And they host more than 5,000 servers, like servers.
Not individual person's data.
It's like it's incredible 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 about 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 107 million square foot data center in Inner Mongolia.
It's 250 acres.
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 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 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 a 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 hope it doesn't work because I remember 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.
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 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.
Right.
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.
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 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.
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?
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.
Yeah, like ChatGPT itself was trained on 20,000 of these GPUs.
A GPU, you know, 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.
That's where all the power comes from, like you were saying.
But 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 are their stock jumped over a couple of years, like 900 over 2023 and 2024, something like that.
Yeah, 900% increase.
Yeah.
And 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.
If anyone builds it, everyone dies about the current state of A.I.?
?
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 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, 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 what was it?
Eight things.
Ten.
Well, there was ten episodes.
Ten episodes, right.
Thanks, baby.
Well, but one of the episodes was just like you talking about Jamie Buffett records.
That's right.
He 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 in data centers just in 2025.
Amazon has pledged over the next 15 years, $150 billion.
And Google and Meta together.
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... Morgan Stanley.
What did I say?
Stanley Morgan?
Yeah.
I think we should leave that in there.
Okay.
Stanley says, hey, you know, guys.
Maybe you meant like Stanley comma Morgan.
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.
Yeah.
Just in what, the next four years?
Yeah.
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 15 trillion on this, on the infrastructure and the GPUs, you're looking pretty good down the road.
Yeah, for sure.
And you know 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.
Right, yeah.
So, like XAI, has that Colossus machine that you were talking about earlier, which is 200000 GPUs strung together.
I'm not sure if it's fully online yet.
In Memphis, Tennessee.
Yeah, and it's just for that.
They're not calculating missile trajectories for the Ministry of Defense or anything like that.
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 because they're so in cahoots with Microsoft.
I think they run their stuff on Microsoft's data centers.
But yeah, if you have an AI essentially right now, which means like God and everybody You probably have your own data center dedicated to it.
Yeah.
And this isn't 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 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.
Right.
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.
Yeah, no, I totally do.
Before we take a break.
I think 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.
Yeah.
And I wasn't even looking like I didn't even know that you could just buy this.
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 twenty thousand dollars.
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 know anything about it, so I could easily be corrected on all this, but that's what the internet told me.
Okay.
Well, the internet never lies.
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 the 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 100 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 factory plant campus and they're revitalizing that as an 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 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 an AI bubble.
One of the reasons why some people are pointing to it as an 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 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.
But there's just no clear path to it right now, which makes some people nervous.
Yeah, there's about 5 just 5 of pilot AI programs right now in business.
Secure returns on their investments.
You know, like they make the money.
But Stanley Morgan is predicting revenues of a trillion dollars by 2028.
That's what they're saying.
I mean, we'll see.
NVIDIA.
I mean Kyle's also keen to point out that there's sort of a circular economy within all this going on.
That's a little bit troubling, maybe because NVIDIA is investing in open AI, 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 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.
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 before.
With, like the 2008 meltdown or the dot-com bubble, like this is 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 much debt exists in the world on private credit, because 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, that can go really bad.
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.
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.
But, you know, 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 going to make any difference.
But that's sort of the...
The age-old.
Like you know, if I don't recycle 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.
Or where negative change happens.
So as far as AI data centers go, the main—
You know, aside from just you know, 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.
Yeah.
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 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.
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.
The reason it uses water is because all of these processors, these CPUs that are doing all this work, and just all of the networking that's going on with it, 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.
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.
That was the whirring of the server room that you used to sleep in.
Those were all fans, you know, and 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 11000 data centers around the world.
Most of these are not AI obviously, but they're the most you know robust sort of users of the energy.
But they use between 1 and 15 percent 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.
Well, there's something called Data Center Alley in northern Virginia, outside of DC, 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.
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'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, I hope, so bad that somebody's listening to this and they open a massive hyperscale data center named Old Bessie.
That would be great.
But Meta's Hyperion data center will consume, by the time it's finished, about five gigawatts.
And if you're like, what's five gigawatts?
That is about half of the peak load of all of New York City.
The most that it can possibly be demanded, right?
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.
Yeah.
And that Rockefeller tree, they just did the summer version.
That puts it over the edge.
That's right.
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, 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, in Newton County.
It's a metadata center that's using 10 of the local water use.
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.
Yeah.
Every day, you said.
Every day, 7 million gallons of water.
That's insane.
Yeah.
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.
But, you know, you mentioned some of these towns.
Not only are some of they're like using, let's say, 10 percent 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 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 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 percent since 2020.
And that also is affecting Maryland, which is getting little to no benefit from Data Center Alley and is just helping pay the price for it.
This is subsidization. of these data centers.
Like they are subsidized in just about every single way you can imagine.
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 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.
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.
And you know we also didn't point out that a lot of 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 I know it sounds like we're just like, and this, and that.
How terrible are data centers?
Like, 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.
And that's.
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 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 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 frontier.
Essentially,
I think there's a certain element of that.
I think you're probably right.
It's, hey, maybe it'll all work out great.
Sure, it probably will.
It usually does.
Astoundingly, it usually does work out.
Well, true, as far as the world hasn't ended.
That's exactly what I mean.
Yeah, yeah.
Yeah.
So 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.
This relates to our History of the BBC episode, and this is from Erica.
And Erica says, Hey guys, 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.
Nice.
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 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.
That's pretty neat.
Making stuff in conjunction with the BBC.
That's got to be a neat high watermark, you know.
Agreed.
And I think Chuck, I'm curious to see, if we go look at our account, if we'll see a little line item from Open University and one from BBC.
Well, it would be like seven pounds or something.
I don't know the exchange rate right now.
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 at stuffpodcasts at iheartradio.com.
Stuff You Should Know is a production of iHeart Radio.
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I'm John Polk.
For years I was the poster boy of the conversion therapy movement the ex-gay who married an ex-lesbian and traveled the world telling my story of how I changed my sexuality from gay to straight.
You might have heard my story.
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