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Speaker 1: Welcome to Stuff you should know, a production of iHeartRadio.

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Speaker 2: Hey, and welcome to the podcast. I'm Josh, and there's

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Speaker 2: Chuck and Jerry's here too, and we've got our pocket

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Speaker 2: protectors and tape on the bridge of our glasses day

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Speaker 2: and this is stuff you should know.

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Speaker 1: Nice work. Did you just hear something?

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Speaker 2: No, not weird. I heard you say nice work. Yeah,

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Speaker 2: but then stop abruptly.

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Speaker 1: Well I stopped abruptly because I thought I heard a

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Speaker 1: little digital glitch.

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Speaker 2: Oh no, I didn't hear anything.

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Speaker 1: I might be losing my mind, then, I you know, I'm.

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Speaker 2: Curious whether we end up editing this out or not.

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Speaker 2: Any other podcast on the planet would edit that out

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Speaker 2: without even thinking about it. But there's like a fifty

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Speaker 2: percent chance that'll stay in with us.

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Speaker 1: I mean, this is why we didn't get that Golden

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Speaker 1: Globe nomination. That's right, it's kind of classic stuff you

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Speaker 1: should know. On professionality.

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Speaker 2: It's exactly right in the Italian accent, mispronunciations, there's a

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Speaker 2: whole laundry list. Yeah, that's okay, Chuck, I think we're

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Speaker 2: golden regardless.

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Speaker 1: Agreed.

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Speaker 2: So we're talking about data centers, which I had a

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Speaker 2: very very rough idea about But actually no, I knew

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Speaker 2: that they existed essentially and that they were becoming a

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Speaker 2: problem with the rise of AI. Yes, that was about it.

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Speaker 2: How about you are you are you d data center Feliac?

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Speaker 1: No? You know me, I'm not super technology minded, so

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Speaker 1: I don't know a lot about this stuff. I remember

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Speaker 1: walking by our server room back in the day when

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Speaker 1: we were at Pont City Market and seeing our colleague

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Speaker 1: Izzy in there hard at word yeah. And when that

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Speaker 1: door was unlocked and open, hearing the were of the

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Speaker 1: of the servers and the cooling machines, and you know,

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Speaker 1: that's on a smaller scale, that's a data center.

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Speaker 2: Absolutely, one hundred percent, that's a data center. It was

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Speaker 2: also so a great place to curl up and take

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Speaker 2: a nap in.

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Speaker 1: The middle of the day, like the warmth of the server.

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Speaker 2: Yeah, and the word put right the word yeah, So

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Speaker 2: uh yeah, that definitely counts as a data server. If

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Speaker 2: you have like one of those little home networking setups

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Speaker 2: in like a closet in your house, data center. Sure,

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Speaker 2: technically the PC is a data center. Anywhere you can

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Speaker 2: store and access data, that's technically a data center. And

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Speaker 2: you're like, well, that's stupid. Why did you even say

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Speaker 2: that Josh's quibbling, that's quotitian. Shut up and get on

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Speaker 2: with data centers. Whoa, whoa, whoa. First of all, don't

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Speaker 2: use the S word and then secondly word yes, Wait,

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Speaker 2: what did I say? That's the keyword Quoteitian?

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Speaker 1: Oh no, that's kW right.

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Speaker 2: So the reason that I bring that up, though, Chuck,

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Speaker 2: is because that technically is part of the progression of

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Speaker 2: data centers. Yeah, it probably goes without saying, but it's

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Speaker 2: evolved along with computing, and his computer's kind of gotten

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Speaker 2: bigger and bigger. The need to store and access more

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Speaker 2: data has gotten bigger so much so, Chuck that just

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Speaker 2: wrap your head around this one. In twenty twenty four,

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Speaker 2: just over a year ago. Yeah, we used one hundred

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Speaker 2: and fifty zetabytes of data. That's what we consumed. And

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Speaker 2: consuming is anything from making a video and uploading it

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Speaker 2: to TikTok or putting a post up on Instagram. It's

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Speaker 2: browsing a website, it's buying a song from iTunes, it's

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Speaker 2: doing web analytics, it's buying something with your American Express card.

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Speaker 2: All of that is data consumption. And we consumed one

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Speaker 2: hundred and fifty zetabytes of data in twenty twenty four.

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Speaker 1: Yeah, I don't even know how many big Max that is.

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Speaker 1: I know you name dropped a lot of brands. It

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Speaker 1: should be like the movies where every time you even

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Speaker 1: just say, like buy something on your amex, the bank

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Speaker 1: account grows by like ten dollars.

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Speaker 2: I agree wholeheartedly. I agree that amex should do that. Amex,

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Speaker 2: there's twenty bucks. I'll split it with you.

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Speaker 1: Wow, you just bought lunch in nineteen ninety seven.

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Speaker 2: That's right. So just real quick, as zeta byte, chuck

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Speaker 2: is a trillion gigabytes, So we consumed one hundred and

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Speaker 2: fifty trillion gigabytes. That was twenty twenty four.

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Speaker 1: That's worldwide, right, yes, yeah.

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Speaker 2: That's worldwide. In twenty ten, we consumed two zeta bytes. Jeez. Yeah.

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Speaker 2: So it's growing exponentially, which means that data centers are

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Speaker 2: growing exponentially. And now they're about to just blow up,

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Speaker 2: like truffle up essentially from you know, this kind of

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Speaker 2: calm like plateau that they'd reached. It's about to just

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Speaker 2: go in hyper drive.

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Speaker 1: Yeah, and actively is, and we're going we're gonna get

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Speaker 1: to some startling statistics later on in the episode. But

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Speaker 1: Kyle helped us out with this our writer over in

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Speaker 1: the UK.

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Speaker 2: He did a fantastic job.

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Speaker 1: He did a really good job. And there's going to

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Speaker 1: be some UK specific things in here because Kyle's always keen,

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Speaker 1: as they say, to throw that stuff in there.

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Speaker 2: Yeah, for sure, Kyle likes to pepper those in Yeah, of.

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Speaker 1: Course, and he's not barred from doing so we allow it.

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Speaker 1: So since Kyle, you know, is frequenter of the Wayback Machine,

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Speaker 1: as are all the wonderful writers that we use, they

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Speaker 1: all had the keys to the car. Essentially, he jumped

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Speaker 1: in the Wayback Machine to sort of give us a

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Speaker 1: little bit of a timeline on data centers and a

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Speaker 1: bit on you know, mainframes and PCs.

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Speaker 2: He also left all of his used tea bags in

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Speaker 2: there too. I don't know if you noticed that.

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Speaker 1: Oh it's fine, you know, you can throw those back

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Speaker 1: in some hot water and they do just a little

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Speaker 1: bit weaker tea.

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Speaker 2: Well, if you put like five of them together, it's

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Speaker 2: like one.

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Speaker 1: Yeah, and Kyle, I mean that thing was full.

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Speaker 2: Maybe really what he drinks a lot of tea, he

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Speaker 2: really does.

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Speaker 1: So if you want to talk about the earliest data

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Speaker 1: centers that you could kind of call maybe a data center.

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Speaker 1: They were you know, computers, they were electronic computers. Most

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Speaker 1: of this stuff that we're going to talk about early

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Speaker 1: on was military and and as you'll see, even the

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Speaker 1: first when we talk about the UK one that was

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Speaker 1: supposedly in that military, they even loaned it to the military,

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Speaker 1: which was kind of interesting. But these things were built

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Speaker 1: with you know, state of the art technology at the time,

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Speaker 1: which meant vacuum tubes and you know, manual switches and

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Speaker 1: plugs and things like that. And the first thing that

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Speaker 1: we can really talk about as the first programmable electric

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Speaker 1: digital computer was the Colossus. And as we'll see, Elon

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Speaker 1: Musk has now stolen that for his own purposes that

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Speaker 1: name probably because of this, I would imagine, but it

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Speaker 1: was at Bletchley Park, of course, during World War Two,

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Speaker 1: and they were trying to you know, crack into Hitler's

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Speaker 1: messages at the time, and these things were huge and

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Speaker 1: kind of to me. The thing that stood out about Colossus,

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Speaker 1: which is a neat little factoid, is that where Colossus

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Speaker 1: was at Bletchley Park at Block H, it is now

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Speaker 1: the National Museum of Computing.

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Speaker 2: I want to go to that so bad when we

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Speaker 2: do that UK up tour next year. Oh, we got

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Speaker 2: to go to that together.

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Speaker 1: Okay, okay, are we doing that next year?

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Speaker 2: That's what we were talking about. All right, we kind

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Speaker 2: of already have promised it. We have to. Now we're

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Speaker 2: locked in the punch.

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Speaker 1: Why's my voice O high?

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Speaker 2: Then? I don't know. You're practicing for the alph Yeah,

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Speaker 2: that's right now.

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Speaker 1: No, that that'd be a lot of fun. I'd love

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Speaker 1: to go to that.

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Speaker 2: So that was Colossus. Another one about the same time

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Speaker 2: was the Aniac Electrical Numerical Integrator and Computer. So that's

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Speaker 2: a quality acronym. Yeah, and it was the first general

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Speaker 2: purpose electronic computer. And here's the thing. This is technically

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Speaker 2: not data storage yet, it's data processing, right, But these

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Speaker 2: things Colossus Aniac, you walked up to them and you said,

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Speaker 2: what's the trajectory of this missile if I fire it

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Speaker 2: from here? And Aniac would go peep pop, poop, poop,

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Speaker 2: and then say, like, uh, whatever a trajectory is described

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Speaker 2: in Sure or Colossus. You'd be like, what is Hilter

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Speaker 2: saying here to Goebbels And the Colossus would say, Hilter

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Speaker 2: is saying that he's a big fan of goebels work,

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Speaker 2: but he's suspicious that the rest of the world doesn't

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Speaker 2: like either of them. And that was it. After that,

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Speaker 2: you'd be like, hey, what was the last answer, and

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Speaker 2: they'd be like, what's an answer?

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Speaker 1: Yeah, you gotta just tell me what's going on with

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Speaker 1: this Hilter business.

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Speaker 2: You don't remember from our art Mysteries of the Art World,

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Speaker 2: how stuff works are to go? Oh, the title of

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Speaker 2: that section was did Hilter do these pain? Oh?

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Speaker 1: My god, that's a deep cut. I did not remember that.

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Speaker 2: Yes, and I think it still says that on that

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Speaker 2: oh babilistical Yeah, yeah, I can only hope it's got

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Speaker 2: to be Hilter forever. All right.

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Speaker 1: So we go into mainframes at this point, and this

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Speaker 1: is like the nineteen fifties basically when companies could actually

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Speaker 1: have their own computer. It wasn't just the military. These

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Speaker 1: were the old punch card computers, and they were called mainframes.

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Speaker 1: It wasn't made up for this. Mainframes were originally described

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Speaker 1: We're describing like what you would house telecommunication equipment and

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Speaker 1: maybe some other sciencey stuff, but it was referencing literally

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Speaker 1: the cabinets that held this technology. And it became known

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Speaker 1: as just you know, it kind of took over when

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Speaker 1: the computer's world started using it as computer only.

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Speaker 2: Yeah, but again, this is like you're a company and

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Speaker 2: this is where you store and process all of your data,

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Speaker 2: and it's in this one room, but it's not going

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Speaker 2: anywhere else. It's not for anybody else, and you have

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Speaker 2: to physically be in the room to get your answer,

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Speaker 2: process whatever data you're looking for. When the PC came along,

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Speaker 2: and then the Mac and Tosh came along, they took

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Speaker 2: that thing and just made it very small so you

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Speaker 2: could put it on all of your employee's desks. And

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Speaker 2: now they had, like I was saying, before, their own

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Speaker 2: little data center right there. So if you said, like, hey,

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Speaker 2: what's the I need to know the Q four reports,

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Speaker 2: they'd say, go to Debbie's desk. Debbie's is the one

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Speaker 2: who's got that on her computer. And you would go

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Speaker 2: over there and say, WI, what's the Q four report?

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Speaker 2: And w would give it to you. Right, there was

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Speaker 2: no connectivity, but you could still like do a lot

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Speaker 2: more stuff than you could when you had a mainframe. Yeah,

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Speaker 2: for sure, which makes mainframes feel like really outdated, but

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Speaker 2: it turns out they're like totally still in use. Today.

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Speaker 1: Oh yeah, absolutely, I do want to jump back in

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Speaker 1: time a little bit, because I did. I promised talk

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Speaker 1: of lending the military, oh basically your equipment, and that's

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Speaker 1: what happened. In nineteen fifty one, there was a T

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Speaker 1: shop chain in the UK. I don't know if it's

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Speaker 1: still around, Lions Lyns, and they were the very first

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Speaker 1: company in the world that used a mainframe. It was

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Speaker 1: called the LEO, the l EO, and it was you know,

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Speaker 1: like what you would think they've handled, like payroll and

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Speaker 1: stock management and stuff like that, but there wasn't a

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Speaker 1: lot for it to do at a T shop chain

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Speaker 1: except for those couple of things. And so they calculated

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Speaker 1: missile trajectories like you were talking about for the Ministry

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Speaker 1: of Defense.

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Speaker 2: And that actually kind of helped establish like a I

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Speaker 2: guess a pay schedule. How people charged for data centers

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Speaker 2: to come. Yeah, it was they like you would charge

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Speaker 2: them for the time that they used it, or you

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Speaker 2: could lease it for a month. And that really started

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Speaker 2: to come around when IBM got in the game. They

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Speaker 2: became like the main frame leader in the fifties, the

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Speaker 2: early fifties, I think they had a unit that you

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Speaker 2: could lease for sixteen thousand dollars per month. That's in

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Speaker 2: nineteen fifty two money. And then as the things as

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Speaker 2: like the processors got better and smaller and faster, that

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Speaker 2: price came down dramatically. And then finally in the sixties

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Speaker 2: they released the IBM six System three sixty, which not

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Speaker 2: only got Apollo eleven to the moon and back, it

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Speaker 2: is a It appears in an episode of mad Men apparently.

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Speaker 1: Oh really, yeah, you usould have though, right.

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Speaker 2: No, I never did. I just saw reference to it

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Speaker 2: on the government and you knew it was the show. Yeah,

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Speaker 2: but they like you should look up pictures of it.

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Speaker 2: It's like those giant burnt orange cabinets with the real

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Speaker 2: magnetic tape. It's just beautiful. They're cool to look at.

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Speaker 1: Yeah, yeah, I remember. We've referenced the movie War Games

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Speaker 1: from our childhood in the eighties a lot, and that

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Speaker 1: the whopper for War Games was that was you know,

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Speaker 1: at that age, to see the whopper and action and

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Speaker 1: to see Matthew almost aid, Matthew Modine, Matthew Broderick hanging

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Speaker 1: up his handheld telephone receiver onto a modem to talk

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Speaker 1: to the school computer.

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Speaker 2: It was mind blowing that, Yeah, that phone in the

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Speaker 2: modem made just a really big impression on me.

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Speaker 1: Yeah, and a cool sound.

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Speaker 2: Peepoop boop.

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Speaker 1: Should we take a break?

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Speaker 2: Wait, let me talk about mainframes today though, because I

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Speaker 2: just want to give a little I don't know if

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Speaker 2: a shout out's the right term, but they are still

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Speaker 2: around because they're so reliable, Because they're so secure. You

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Speaker 2: can make it so that there's information on those things

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Speaker 2: that you again have to be physically present in the

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Speaker 2: room to access. Sure, you can put all sorts of

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Speaker 2: different layers of security. So if you're like Visa, or

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Speaker 2: you're a healthcare company, or you're the Census Bureau, you're

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Speaker 2: probably still using a main frame because you're protecting information

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Speaker 2: as tightly as you can. But those things are also

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Speaker 2: super fast and can hold huge volumes of computation at once.

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Speaker 1: Yeah, or a non Golden Globe nominated podcast.

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Speaker 2: Yeah, we've got our own main frame. Yeah, we've got

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Speaker 2: our IBM three sixty that's right.

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Speaker 1: What year was that from again, the three.

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Speaker 2: Sixty sixty four.

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Speaker 1: Yeah, yeah, that's the one. I'll just making sure we

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Speaker 1: didn't have the sixty five because that no, no, no,

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Speaker 1: the sixty buggy. Yeah, yeah, all right, we can take

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Speaker 1: that break now and we're gonna jump out of the

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Speaker 1: wayback machine and venture into the modern world.

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Speaker 2: Right after this.

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Speaker 1: All right, so we are out of the way back machine.

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Speaker 1: We're making that uh we're combining all those old tea

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Speaker 1: bags and making some still somewhat weaker tea. Yeah, it's

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Speaker 1: not too bad. It's a combination of Earl Gray and

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Speaker 1: Camma meal, all kinds of fun stuff, but not too bad.

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Speaker 1: And now we're going to talk a little bit about

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Speaker 1: when things started to ramp up, because it kind of

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Speaker 1: happened in fits and starts. And one of the biggest, uh,

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Speaker 1: I guess would it be a fitter a start was

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Speaker 1: the Internet. Because once the Internet came along, every business

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Speaker 1: in the world started using it, and so all of

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Speaker 1: a sudden you had to have a lot more data

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Speaker 1: storage and bigger data centers and bigger server rooms in

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Speaker 1: your companies, which was, you know, a pretty good thing

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Speaker 1: at the time. After the dot com bus. There were

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Speaker 1: a lot of casualties of that growth, but then things

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Speaker 1: kind of you know, the ship kind of righted itself.

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Speaker 2: Yeah, and because it was like accessible to basically every

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Speaker 2: business now, like you didn't have to buy a mainframe

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Speaker 2: you could lease space on someone else's main frame, like

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Speaker 2: you're the Ministry of Defense or something, all of a sudden.

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Speaker 2: So that led to this huge proliferation that gave the

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Speaker 2: foundation for web commerce. E commerce, that's what they used

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Speaker 2: to call it. That's an old timey term now, but

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Speaker 2: it created the ability for e commerce to start and flourish.

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Speaker 2: So this data center's scaling up to meet the needs

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Speaker 2: of the Internet and then to kind of give people

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Speaker 2: all sorts of new space and room to come up

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Speaker 2: with new stuff. That's where the digital economy came from,

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Speaker 2: right there.

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Speaker 1: Yeah, all of a sudden, you could hop on webvan

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Speaker 1: for an order a SACA groceries.

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Speaker 2: I have a friend who was all in about that.

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Speaker 1: Well man, yeah, yeah, I think I had a friend

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Speaker 1: who was pretty heavily invested.

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Speaker 2: It's but clearly it was ahead of its time. I mean, sure,

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Speaker 2: let's see, it's a post Mates and.

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Speaker 1: Well there's a lot of several of them now that

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Speaker 1: have succeeded.

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Speaker 2: Well name them, will get ten bucks each?

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Speaker 1: Well, well you just got ten for post Mates, and

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Speaker 1: you got to split that with me.

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Speaker 2: You know, I will, all right.

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Speaker 1: Cloud computing was the next big jump. When cloud computing

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Speaker 1: came around the early two thousands, or do they call

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Speaker 1: that the early oughts? I do, okay, I thought i'd

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Speaker 1: heard that come from your mouth. But that is when

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Speaker 1: you know, that was the real game changer because things

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Speaker 1: were still I mean, when cloud computing came along, people

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Speaker 1: thought of it if they didn't look too hard into it.

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Speaker 1: They thought it was just you know, floating up in

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Speaker 1: the ether somewhere. Yeah, it's it's still being stored on stuff,

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Speaker 1: it's just not being stored locally. So all of a sudden,

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Speaker 1: things were just going somewhere else for someone else to

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Speaker 1: worry about all that storage. And more importantly, they could

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Speaker 1: they could link everything together and store a lot of

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Speaker 1: stuff from a bunch of different people.

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Speaker 2: Right, So now you have data centers not just available

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Speaker 2: to somebody like a huge bank or something like that,

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Speaker 2: or the government in a shop then a bank, yeah,

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Speaker 2: or a t shop, and then to e commerce businesses.

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Speaker 2: Now it's available to you and me. So it's really

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Speaker 2: hard to remember back because the world has changed so much.

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Speaker 2: But Chuck, like two thousand and eight, two thousand and nine,

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Speaker 2: they were given us like VPN, little things that like

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Speaker 2: you could go home and work and like you would

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Speaker 2: it would never work. I'd never understood how to make

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Speaker 2: it work. Yeah, but that was like the very beginning

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Speaker 2: of how you could take your work home with you

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Speaker 2: and work from home and do things remotely like we

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Speaker 2: can now. Like it's nothing, but this led to the

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Speaker 2: rise of businesses like Dropbox. Right. So Dropbox goes to

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Speaker 2: Amazon Web Services and say, hey, we want to buy

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Speaker 2: a bunch of your cloud, right, which means that they're

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Speaker 2: going to use a bunch of like different servers and

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Speaker 2: different data centers all over the place. And then Dropbox

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Speaker 2: turns around to you and says, hey, if you give

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Speaker 2: me nineteen ninety five a month, you can have one

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Speaker 2: terabyte of data. Right. You can consume one terabyte of data, right,

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Speaker 2: and then hopefully you don't use all of that, so

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Speaker 2: they don't have to pay Amazon Web Services for stuff

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Speaker 2: they didn't use. But you're paying that nineteen ninety five

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Speaker 2: a month rather whether you use that whole terabyte or not.

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Speaker 2: It's a pretty smart business model. Would not exist at

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Speaker 2: all if the cloud didn't exist.

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Speaker 1: Yeah, as funny as you were talking about two thousand

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Speaker 1: and eight and how quaint that is, now, that's what

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Speaker 1: the year we started the show I know, I know,

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Speaker 1: it's crazy to think about it really.

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Speaker 2: Is, but imagine like working from home at that time,

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Speaker 2: it was just didn't you could know.

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Speaker 1: It was kind of great you went home and you homed.

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Speaker 2: That's exactly right. That was a big difference I remember.

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Speaker 1: But these data centers have now come together in such

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Speaker 1: a big way now that they the largest ones are

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Speaker 1: called hyper scale and they host more than five thousand servers,

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Speaker 1: like servers, not individual persons. Data. It's like, it's incredible,

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Speaker 1: how big is how big they've gotten Googles. And we'll

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Speaker 1: go over some of the kind of square footage and

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Speaker 1: then later talk about the elephant in the room, which

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Speaker 1: is energy and water usage. But Google's first data center

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Speaker 1: was built in two thousand and six, just but two

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Speaker 1: years before stuff you should have launched. And this is

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Speaker 1: an Oregon and they are still expanding that thing beyond

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Speaker 1: one point three million square feet. Meanwhile, in China they're like,

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Speaker 1: hold my tea, I guess, because China Telecom has a

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Speaker 1: ten point seven million square foot data center in Inner Mongolia.

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Speaker 2: It's two hundred and fifty acres.

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Speaker 1: That is a warehouse full of worrying servers heating up

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Speaker 1: and being cool.

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Speaker 2: Yeah, which is a big problem for any data center,

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Speaker 2: it turns out. But the whole expansion this jump starting

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Speaker 2: in twenty seventeen thanks to cloud computing, because again, cloud

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Speaker 2: computing just means all your stuff isn't on one server

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Speaker 2: and one data center, it's broken up into pieces and

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Speaker 2: spread all over the place. That's the cloud. That's basically it.

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Speaker 2: Even though it's way more advanced and intricate than that.

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Speaker 2: That's like all you really need to know for the

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Speaker 2: purposes of this episode, right. It led to a huge jump,

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Speaker 2: a huge need in data centers, and it also expanded

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Speaker 2: all the stuff we can do now, and COVID actually

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Speaker 2: gave it another bump, made building a data center very

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Speaker 2: economically attractive thing to do if you had the money.

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Speaker 2: Because remote working finally finally established itself as like, no,

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Speaker 2: we're doing this, stop calling us back to the office, ye,

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Speaker 2: which is what they're doing now. I know, I hope

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Speaker 2: it doesn't work because I remember when all of that

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Speaker 2: started and everybody was so nervous, like management was all

399
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Speaker 2: so nervous that people were just going to totally like

400
00:21:12,520 --> 00:21:15,840
Speaker 2: mess around and everything. It just it didn't happen. I

401
00:21:15,880 --> 00:21:19,439
Speaker 2: don't know anybody who's even been like gotten a talking to,

402
00:21:19,760 --> 00:21:23,280
Speaker 2: let alone, been fired for just messing around at home.

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Speaker 2: As a matter of fact, like you were saying, it

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Speaker 2: just makes you work more.

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Speaker 1: Yeah, I mean, do you know how many times in

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Speaker 1: our old offices I would see Jonathan Strickland just wandering

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Speaker 1: aimlessly through the office chatting with people.

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Speaker 2: Yes, I do, because he would chat with me a lot.

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Speaker 2: He did.

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Speaker 1: He did that in front of God and everybody, as

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Speaker 1: they say, right there in the office. So I can't

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Speaker 1: imagine what happened with him at home.

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Speaker 2: Yeah, like it was a sorority mixer or something.

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Speaker 1: We love Strickland. He's still around everyone. By the way,

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Speaker 1: he retired from tech stuff, but he's still with a company.

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Speaker 2: Which is great. We love Strickland all right.

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Speaker 1: So now we're onto AI data centers and that was

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Speaker 1: the I mean to call it a game changer. Seems

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Speaker 1: quaint compared to the rise of cloud computing and everything,

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Speaker 1: because it is off to the races in a way

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Speaker 1: that seemingly cannot be stopped. The genie has left the bottle,

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Speaker 1: as they say. Starting in twenty twenty two, when chat

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Speaker 1: GPT was released by open Ai, all of a sudden,

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Speaker 1: the need for these data centers became exponentially greater in

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Speaker 1: size in the speed at which they need these things

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Speaker 1: built because AI requires a ton of computing power to operate.

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Speaker 2: So much so that they don't even use the standard

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Speaker 2: what's called the compute machine. So compute is like all

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Speaker 2: of the processing power, the networking, all of that stuff,

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Speaker 2: and traditionally with a computer that's done on a CPU. Right,

431
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Speaker 2: that's how all of this gets done, right, everything else

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Speaker 2: is infrastructure. The CPU is doing all of the work.

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Speaker 2: Those are so like they still work like. Most data

434
00:23:01,960 --> 00:23:05,840
Speaker 2: centers are running on CPUs for AI, just not fast enough.

435
00:23:06,800 --> 00:23:11,159
Speaker 2: They use GPUs graphic processing units, which are associated with

436
00:23:11,720 --> 00:23:14,040
Speaker 2: video games for most people, right, you need a good

437
00:23:14,080 --> 00:23:19,240
Speaker 2: graphics card to run your video game, I guess, but

438
00:23:20,640 --> 00:23:23,600
Speaker 2: they the reason that for AI data centers that they

439
00:23:23,680 --> 00:23:26,640
Speaker 2: use GPUs is because they're really good at parallel processing.

440
00:23:26,680 --> 00:23:28,840
Speaker 2: They can run a bunch of different operations at once.

441
00:23:29,320 --> 00:23:31,399
Speaker 2: So you're like, cool, you just throw a GPU in

442
00:23:31,400 --> 00:23:33,720
Speaker 2: a data center and you can run an AI. No,

443
00:23:34,200 --> 00:23:38,080
Speaker 2: you need hundreds of thousands of these things strung together,

444
00:23:38,720 --> 00:23:41,760
Speaker 2: and instead of like a CPU running like a couple

445
00:23:41,800 --> 00:23:45,200
Speaker 2: of servers or something like that. At data center. All

446
00:23:45,240 --> 00:23:48,959
Speaker 2: of them are strung together to form one giant supercomputer

447
00:23:49,600 --> 00:23:51,320
Speaker 2: that the AI operates on.

448
00:23:52,000 --> 00:23:55,600
Speaker 1: Yeah, like chat GPT itself was trained on twenty thousand

449
00:23:55,720 --> 00:24:00,320
Speaker 1: of these GPUs a GPU, you know, the sort of

450
00:24:00,320 --> 00:24:02,639
Speaker 1: the biggest name in the game. There's a couple, but

451
00:24:02,680 --> 00:24:06,320
Speaker 1: the biggest one obviously is the Navidia. But the Navidia

452
00:24:06,440 --> 00:24:09,840
Speaker 1: H one hundred that is the standard right now. If

453
00:24:09,880 --> 00:24:13,000
Speaker 1: you look this thing up, it it fits in your hand.

454
00:24:13,480 --> 00:24:16,960
Speaker 1: It's not like some gigantic thing. H twenty thousand of

455
00:24:17,000 --> 00:24:19,960
Speaker 1: them linked together or one hundred thousand of them linked together.

456
00:24:20,040 --> 00:24:22,760
Speaker 1: Who knows how many, you know, hundreds of thousands are

457
00:24:22,760 --> 00:24:26,640
Speaker 1: eventually going to be linked together to end the world.

458
00:24:26,680 --> 00:24:28,960
Speaker 1: That's where all the power comes from, like you were saying,

459
00:24:29,000 --> 00:24:33,800
Speaker 1: But it's you know, it's just a little rectangular handheld

460
00:24:33,800 --> 00:24:36,000
Speaker 1: thing that's like, oh, that looks like something that maybe

461
00:24:36,000 --> 00:24:40,639
Speaker 1: came out of a computer. And Navidia is what are

462
00:24:40,640 --> 00:24:43,240
Speaker 1: their stock jumped over a couple of years, like nine

463
00:24:43,320 --> 00:24:46,320
Speaker 1: hundred percent over twenty twenty three and twenty twenty four

464
00:24:46,640 --> 00:24:47,200
Speaker 1: something like.

465
00:24:47,119 --> 00:24:50,040
Speaker 2: That, Yeah, nine hundred percent increase.

466
00:24:51,240 --> 00:24:53,720
Speaker 1: Yeah, and we'll talk about why all of this is

467
00:24:54,160 --> 00:24:56,879
Speaker 1: super like scary and dangerous because it really is.

468
00:24:57,119 --> 00:24:59,199
Speaker 2: Well, yeah, if you want a really good explanation of

469
00:24:59,240 --> 00:25:01,879
Speaker 2: this about and like you said, how many GPUs you

470
00:25:01,920 --> 00:25:05,800
Speaker 2: string together before we end the world? Nate Sores and

471
00:25:06,119 --> 00:25:08,840
Speaker 2: Eliezer Yukowski In that book, I keep referencing that I

472
00:25:08,880 --> 00:25:12,159
Speaker 2: think everybody should read. If anyone builds it, everyone dies.

473
00:25:12,359 --> 00:25:15,879
Speaker 2: About the current state of AI. They talk about this

474
00:25:16,119 --> 00:25:20,160
Speaker 2: in depth, but in a really understandable way. It's really fascinating.

475
00:25:21,119 --> 00:25:23,399
Speaker 2: But that's essentially one of the things they say is

476
00:25:23,440 --> 00:25:26,760
Speaker 2: like we keep stringing together tens and tens of thousands

477
00:25:26,760 --> 00:25:31,600
Speaker 2: more GPUs, that just makes the supercomputer smarter and smarter

478
00:25:31,760 --> 00:25:35,200
Speaker 2: and more capable. And eventually, what's going to happen. We're

479
00:25:35,200 --> 00:25:38,520
Speaker 2: going to reach some point potentially where we just put

480
00:25:38,520 --> 00:25:41,040
Speaker 2: that extra last GPU in there and all of a sudden,

481
00:25:41,040 --> 00:25:43,600
Speaker 2: the balance is tipped and the thing becomes super intelligent.

482
00:25:44,119 --> 00:25:46,960
Speaker 1: That's right. Also a time for me because you're always

483
00:25:47,000 --> 00:25:48,800
Speaker 1: too shy to to plug the end of the world

484
00:25:48,800 --> 00:25:53,040
Speaker 1: with Josh Clark, your fantastic limited series, of which AI

485
00:25:53,359 --> 00:25:55,600
Speaker 1: is one of the central focuses or one of was

486
00:25:55,640 --> 00:25:56,320
Speaker 1: it eight things?

487
00:25:57,560 --> 00:26:00,320
Speaker 2: Ten? Well, there was ten episodes.

488
00:26:00,040 --> 00:26:03,240
Speaker 1: Ten episodes, right, thanks baby? Well but one of the

489
00:26:03,240 --> 00:26:06,440
Speaker 1: episodes was just like you talking about Jimmy Buffett Records.

490
00:26:06,119 --> 00:26:06,920
Speaker 2: And that's right.

491
00:26:07,960 --> 00:26:11,680
Speaker 1: You had to lighten the mood yep Should we take

492
00:26:11,720 --> 00:26:13,600
Speaker 1: a break or should we keep going for a minute.

493
00:26:13,640 --> 00:26:15,960
Speaker 1: Let's keep going for a minute, okay, because you talked

494
00:26:16,000 --> 00:26:18,000
Speaker 1: about investment, and you know, if you have the money

495
00:26:18,000 --> 00:26:20,639
Speaker 1: to open one of these things, and that's what these

496
00:26:20,720 --> 00:26:24,679
Speaker 1: tech companies are doing, like to perhaps they're great peril.

497
00:26:24,680 --> 00:26:27,800
Speaker 1: At some point we'll see. Microsoft has invested eighty eight

498
00:26:27,800 --> 00:26:31,480
Speaker 1: billion dollars in data centers just in twenty twenty five.

499
00:26:32,160 --> 00:26:34,720
Speaker 1: Amazon is pledged over the next fifteen years one hundred

500
00:26:34,760 --> 00:26:39,040
Speaker 1: and fifty billion dollars. And Google and Meta together about

501
00:26:39,200 --> 00:26:41,280
Speaker 1: you know, not working together, but they are expected to

502
00:26:41,280 --> 00:26:44,439
Speaker 1: spend about seven hundred and fifty billion dollars just on

503
00:26:44,760 --> 00:26:49,320
Speaker 1: equipment over the next two years. And Stanley Morgan says

504
00:26:49,920 --> 00:26:53,119
Speaker 1: to Morgan, Stanley, what I say, Stanley Morgan, Yeah, I

505
00:26:53,119 --> 00:26:54,200
Speaker 1: think we should leave that in there.

506
00:26:54,320 --> 00:26:54,800
Speaker 2: Okay.

507
00:26:55,440 --> 00:26:58,160
Speaker 1: Stanley says, Hey, you know, guys.

508
00:26:58,160 --> 00:26:59,920
Speaker 2: Maybe you might like Stanley Kamma boar.

509
00:27:00,680 --> 00:27:04,560
Speaker 1: Yeah, Stanley Kumo Morgan. Over five years between twenty five

510
00:27:04,640 --> 00:27:09,240
Speaker 1: and twenty thirty, Morgan, Stanley says about three trillion dollars

511
00:27:10,040 --> 00:27:12,479
Speaker 1: is going to be spent just on the data centers

512
00:27:13,080 --> 00:27:15,760
Speaker 1: about I mean, half of which is the hardware and

513
00:27:15,880 --> 00:27:17,520
Speaker 1: half of which is just building these things.

514
00:27:17,680 --> 00:27:22,720
Speaker 2: Yeah, just in what the next four years. Yeah, So

515
00:27:22,880 --> 00:27:24,760
Speaker 2: think about it. If you're in Nvidia and you're the

516
00:27:24,840 --> 00:27:27,840
Speaker 2: industry leader for GPUs and everybody's like, we're gonna spend

517
00:27:27,880 --> 00:27:31,920
Speaker 2: one point five trillion dollars on this on the infrastructure

518
00:27:32,000 --> 00:27:35,320
Speaker 2: in the GPUs, you're looking pretty good down the.

519
00:27:35,359 --> 00:27:38,440
Speaker 1: Road, Yeah, for sure. And you know they're doing this

520
00:27:38,560 --> 00:27:41,160
Speaker 1: because there's a demand right now for use at least

521
00:27:41,280 --> 00:27:45,360
Speaker 1: because things like OpenAI and other AI creators are using

522
00:27:45,400 --> 00:27:48,480
Speaker 1: them like crazy. But these companies are also using them

523
00:27:48,520 --> 00:27:51,120
Speaker 1: for their own AI research, right.

524
00:27:51,359 --> 00:27:56,879
Speaker 2: Yeah. So like Xai has that Colossus machine that you

525
00:27:56,960 --> 00:28:00,639
Speaker 2: were talking about earlier, which is two high hundred thousand

526
00:28:00,720 --> 00:28:05,880
Speaker 2: GPUs strung together. I'm not sure if it's fully online yet, Tennessee. Yeah,

527
00:28:06,119 --> 00:28:08,760
Speaker 2: And it's just for that. It's they're not doing any

528
00:28:08,800 --> 00:28:13,040
Speaker 2: they're not calculating missile trajectories for the Ministry of Defense

529
00:28:13,119 --> 00:28:16,000
Speaker 2: or anything like that. Like, it's just for that AI.

530
00:28:16,600 --> 00:28:18,520
Speaker 2: And yeah, I think Meta is doing the same thing.

531
00:28:19,119 --> 00:28:22,000
Speaker 2: Open ai I don't think is building their own because

532
00:28:22,040 --> 00:28:25,600
Speaker 2: they're so in cahoots with Microsoft. I think they run

533
00:28:25,640 --> 00:28:28,600
Speaker 2: their stuff on Microsoft's data centers. But yeah, yeah, if

534
00:28:28,640 --> 00:28:31,280
Speaker 2: you have an AI essentially right now, which means like

535
00:28:31,400 --> 00:28:35,240
Speaker 2: God and everybody, you probably have your own data center

536
00:28:35,359 --> 00:28:36,119
Speaker 2: dedicated to it.

537
00:28:36,800 --> 00:28:41,120
Speaker 1: Yeah. I And this isn't some some moral stand I'm

538
00:28:41,160 --> 00:28:43,960
Speaker 1: taking by saying that I have never used AI. And

539
00:28:44,520 --> 00:28:46,960
Speaker 1: trust me, I know that every part of my life

540
00:28:47,040 --> 00:28:49,720
Speaker 1: is now touched by AI, so I am inadvertently using.

541
00:28:49,560 --> 00:28:51,760
Speaker 2: It touched by an AI, that's right.

542
00:28:53,080 --> 00:28:56,840
Speaker 1: But I've never I've never used like you know, chatbots

543
00:28:56,920 --> 00:29:00,840
Speaker 1: or or large language models or inning like that, just

544
00:29:01,320 --> 00:29:04,719
Speaker 1: mainly because I'm just I'm fine doing things like they

545
00:29:04,760 --> 00:29:07,400
Speaker 1: are for now, and not in a luddite sort of way.

546
00:29:07,560 --> 00:29:10,440
Speaker 1: I just everything's going along great for me in my

547
00:29:10,560 --> 00:29:12,080
Speaker 1: job and how I live my life, so I just

548
00:29:12,160 --> 00:29:13,160
Speaker 1: I don't have a need for it.

549
00:29:13,520 --> 00:29:15,440
Speaker 2: I do the same thing. And I think also both

550
00:29:15,520 --> 00:29:18,240
Speaker 2: of us are like if somebody else wants to do

551
00:29:18,360 --> 00:29:20,600
Speaker 2: it the other way, that's fine. Like we're certainly not

552
00:29:20,680 --> 00:29:24,400
Speaker 2: gonna criticize them or be crimudgeony about it or say

553
00:29:24,480 --> 00:29:26,640
Speaker 2: that you know that's stupid.

554
00:29:27,440 --> 00:29:31,240
Speaker 1: Right, But as you'll see. You know, and again this

555
00:29:31,400 --> 00:29:35,000
Speaker 1: isn't yucking someone's yum, but everyone should know what they're

556
00:29:35,000 --> 00:29:36,840
Speaker 1: a part of, and that's part of what the episode

557
00:29:36,880 --> 00:29:37,200
Speaker 1: is about.

558
00:29:37,360 --> 00:29:41,200
Speaker 2: That's right. Yeah, no, I totally do. Before we take

559
00:29:41,240 --> 00:29:45,480
Speaker 2: a break. I think it's a small kind of side issue,

560
00:29:45,600 --> 00:29:49,160
Speaker 2: but it's worth pointing out that it sucks because these

561
00:29:49,280 --> 00:29:53,480
Speaker 2: Nvidia chips are so in demand from these massive companies.

562
00:29:53,800 --> 00:29:57,360
Speaker 2: It has driven the price for just the average Nvidia

563
00:29:57,480 --> 00:30:01,240
Speaker 2: graphics card sky high. So if you're a gamer and

564
00:30:01,360 --> 00:30:04,680
Speaker 2: you're like trying to improve your system, like you pay

565
00:30:05,360 --> 00:30:08,600
Speaker 2: way more than you used to for the same graphics

566
00:30:08,680 --> 00:30:11,600
Speaker 2: card that you could have bought for like a quarter

567
00:30:11,760 --> 00:30:14,200
Speaker 2: of the price, you know, a couple of years ago.

568
00:30:14,760 --> 00:30:17,280
Speaker 1: Yeah, and I wasn't even looking like I didn't even

569
00:30:17,320 --> 00:30:18,800
Speaker 1: know that you could just buy This is how little

570
00:30:18,840 --> 00:30:21,000
Speaker 1: I know about all this before. This was like, could

571
00:30:21,080 --> 00:30:24,760
Speaker 1: you just buy a Navidia Gpu? But I was just

572
00:30:24,840 --> 00:30:26,840
Speaker 1: researching the size and like what do these things look like?

573
00:30:27,360 --> 00:30:29,360
Speaker 1: And it, you know, it's one was on eBay for

574
00:30:29,480 --> 00:30:31,600
Speaker 1: twenty thousand dollars and I was like, oh my god,

575
00:30:31,760 --> 00:30:33,480
Speaker 1: I didn't Oh I didn't know that was the deal?

576
00:30:33,680 --> 00:30:34,160
Speaker 2: Is that right?

577
00:30:35,200 --> 00:30:36,920
Speaker 1: Yeah, and I don't know if that's accurate. I don't

578
00:30:37,280 --> 00:30:39,800
Speaker 1: know anything about it, so I could easily be corrected

579
00:30:39,840 --> 00:30:43,040
Speaker 1: on all this, but that's what the Internet told me.

580
00:30:43,240 --> 00:30:45,400
Speaker 2: Okay, well the Internet never lies.

581
00:30:46,960 --> 00:30:48,600
Speaker 1: Oh one thing before we break real quick, because we

582
00:30:48,640 --> 00:30:50,760
Speaker 1: did promise a little UK specific stuff and I didn't

583
00:30:50,760 --> 00:30:55,080
Speaker 1: want to short shrift our brit listeners or Kyle. The

584
00:30:55,280 --> 00:30:58,200
Speaker 1: UK is right now like the third largest nation for

585
00:30:58,320 --> 00:31:00,880
Speaker 1: data centers. The US is first, I think Germany a second,

586
00:31:01,680 --> 00:31:04,120
Speaker 1: and they signed what was called the Tech Prosperity Deal

587
00:31:04,200 --> 00:31:07,240
Speaker 1: with the giants, the tech giants of the United States,

588
00:31:07,920 --> 00:31:11,600
Speaker 1: And right now Microsoft has announced a thirty billion dollar

589
00:31:11,680 --> 00:31:15,520
Speaker 1: investment in UK data centers, and I think like one

590
00:31:15,600 --> 00:31:18,400
Speaker 1: hundred new AI data centers are planned in the UK

591
00:31:18,520 --> 00:31:19,600
Speaker 1: at this point moving forward.

592
00:31:19,920 --> 00:31:21,840
Speaker 2: Yeah, and I saw there's at least one in Wales

593
00:31:21,920 --> 00:31:26,720
Speaker 2: that's being smartly done. They took an old radiator factory

594
00:31:26,880 --> 00:31:31,920
Speaker 2: plant campus and they're revitalizing that as AI data center.

595
00:31:32,040 --> 00:31:35,160
Speaker 2: So it does sound like I get why the UK

596
00:31:35,360 --> 00:31:37,360
Speaker 2: is doing it, but there's a lot of people in

597
00:31:37,440 --> 00:31:39,280
Speaker 2: the UK and elsewhere who are like, these are not

598
00:31:39,520 --> 00:31:42,920
Speaker 2: this is not a good investment for local governments or

599
00:31:42,960 --> 00:31:46,240
Speaker 2: even national governments. There's a big problem with all this,

600
00:31:46,480 --> 00:31:50,280
Speaker 2: Like there is a AI boom going on. Data centers

601
00:31:50,320 --> 00:31:52,800
Speaker 2: are just one part of it. Like people are throwing

602
00:31:52,920 --> 00:31:57,360
Speaker 2: money at AI like it's nineteen ninety nine, and a

603
00:31:57,400 --> 00:31:59,760
Speaker 2: lot of people are like, there's another it's not a

604
00:32:00,400 --> 00:32:04,360
Speaker 2: bubble this time, but it's a AI bubble. Yeah. One

605
00:32:04,400 --> 00:32:06,840
Speaker 2: of the reasons why some people are pointing to it

606
00:32:06,920 --> 00:32:11,560
Speaker 2: as an AI bubble is that it's just not clear

607
00:32:12,000 --> 00:32:14,400
Speaker 2: how much money is going to be made from AI

608
00:32:14,920 --> 00:32:18,800
Speaker 2: and when that's going to start. Yeah. I think the

609
00:32:18,920 --> 00:32:21,760
Speaker 2: Financial Times called Open AI a money pit with a

610
00:32:21,840 --> 00:32:25,400
Speaker 2: website on top. Yeah, not great, no, because people are

611
00:32:25,480 --> 00:32:27,760
Speaker 2: just pumping money into this stuff, but they're not getting

612
00:32:28,440 --> 00:32:32,360
Speaker 2: they're not seeing results from it, not yet. It's not

613
00:32:32,480 --> 00:32:35,400
Speaker 2: necessarily a bad bet that AI is going to completely

614
00:32:35,480 --> 00:32:38,960
Speaker 2: revolutionize the world and like revolutionize economies and going to

615
00:32:39,040 --> 00:32:42,880
Speaker 2: make some people a lot of money, but there's just

616
00:32:43,000 --> 00:32:45,240
Speaker 2: no clear path to it right now, which makes some

617
00:32:45,320 --> 00:32:46,120
Speaker 2: people nervous.

618
00:32:46,800 --> 00:32:50,840
Speaker 1: Yeah, there's about five percent, just five percent of pilot

619
00:32:50,920 --> 00:32:56,480
Speaker 1: AI programs right now in business secure returns on their investments,

620
00:32:56,800 --> 00:32:58,080
Speaker 1: you know, like they make the money.

621
00:32:58,520 --> 00:33:03,360
Speaker 2: But Stanley Morgan is predicting revenues of a trillion dollars

622
00:33:03,400 --> 00:33:05,480
Speaker 2: by twenty twenty eight, that's.

623
00:33:05,320 --> 00:33:08,440
Speaker 1: What they're saying. I mean, we'll see Navidia. I mean,

624
00:33:08,560 --> 00:33:11,280
Speaker 1: Kyle's also keen to point out that there's sort of

625
00:33:11,680 --> 00:33:14,400
Speaker 1: a circular economy within all this going on. That's a

626
00:33:14,440 --> 00:33:17,920
Speaker 1: little bit like troubling maybe because Navidia is investing in

627
00:33:18,040 --> 00:33:21,520
Speaker 1: open AAI, but that depends on their purchase of those

628
00:33:21,600 --> 00:33:26,160
Speaker 1: Navidia chips. So, you know, everyone from you know, just

629
00:33:26,360 --> 00:33:28,600
Speaker 1: people who are smarter than us as far as the

630
00:33:28,640 --> 00:33:31,560
Speaker 1: stuff goes, are warning people right down to the IMF,

631
00:33:31,680 --> 00:33:36,160
Speaker 1: the International Monetary Fund are flashing the warning signs saying

632
00:33:36,280 --> 00:33:39,120
Speaker 1: like this could be you know, it could make a

633
00:33:39,200 --> 00:33:41,440
Speaker 1: trillion dollars by twenty twenty eight, or it could like

634
00:33:41,520 --> 00:33:42,680
Speaker 1: wreck the global economy.

635
00:33:43,040 --> 00:33:49,000
Speaker 2: Yeah for sure. Yeah, we have no idea, although I

636
00:33:49,080 --> 00:33:52,040
Speaker 2: have seen people argue against it, that say like this

637
00:33:52,240 --> 00:33:55,120
Speaker 2: is nothing like Yeah, a lot of these AI companies

638
00:33:55,160 --> 00:34:00,560
Speaker 2: are probably overinflated, but it's nothing like it was with

639
00:34:01,000 --> 00:34:03,560
Speaker 2: like the two thousand and eight meltdown or the dot

640
00:34:03,640 --> 00:34:07,120
Speaker 2: com bubble, Like this is we're a lot we're a

641
00:34:07,160 --> 00:34:09,560
Speaker 2: lot more seasoned, or investors are a lot more seasoned.

642
00:34:09,600 --> 00:34:12,000
Speaker 2: Than they were before. The problem is one of the

643
00:34:12,080 --> 00:34:16,600
Speaker 2: problems is that the financing is expected to come in

644
00:34:16,800 --> 00:34:21,360
Speaker 2: large part from private credit, which is essentially an investment

645
00:34:21,480 --> 00:34:25,400
Speaker 2: vehicle for investors to go lend money to say like

646
00:34:25,600 --> 00:34:28,960
Speaker 2: companies that want to build data centers. Right, and this

647
00:34:29,239 --> 00:34:33,520
Speaker 2: is largely unregulated. It's very shadowy. We don't know how

648
00:34:33,600 --> 00:34:37,600
Speaker 2: many how much debt exists in the world on private

649
00:34:37,680 --> 00:34:40,320
Speaker 2: credit because they don't have to report this stuff. And

650
00:34:41,040 --> 00:34:42,759
Speaker 2: you know, as we learn from the two thousand and

651
00:34:42,760 --> 00:34:48,040
Speaker 2: eight meltdown, when there's like a massive speculation among finances

652
00:34:48,400 --> 00:34:52,759
Speaker 2: that involves debt's that can go really bad.

653
00:34:53,440 --> 00:34:57,239
Speaker 1: Yeah for sure. And speaking of going bad, I guess

654
00:34:57,320 --> 00:34:59,560
Speaker 1: we're at the sort of environmental piece of this whole thing.

655
00:35:00,280 --> 00:35:01,719
Speaker 1: And this is what I was talking about when I

656
00:35:01,760 --> 00:35:03,600
Speaker 1: said that, you know, people should just be aware of

657
00:35:03,680 --> 00:35:05,880
Speaker 1: what they're taking part in. And again this is not

658
00:35:05,960 --> 00:35:09,120
Speaker 1: to shame anybody who uses AI for their job or

659
00:35:09,320 --> 00:35:13,320
Speaker 1: just to make funny fake videos, but but you know,

660
00:35:13,960 --> 00:35:16,320
Speaker 1: everyone is sort of tied together to make this what

661
00:35:16,440 --> 00:35:19,400
Speaker 1: it is. Who's using that stuff, And I get if

662
00:35:19,400 --> 00:35:21,000
Speaker 1: someone says, like, hey, if I quit this thing, it's

663
00:35:21,040 --> 00:35:23,920
Speaker 1: not going to make any difference. But that's sort of

664
00:35:23,960 --> 00:35:26,879
Speaker 1: the the age old. Like, you know, if I don't

665
00:35:26,920 --> 00:35:30,560
Speaker 1: recycle my ten can, my aluminum can, ten cans, my

666
00:35:30,640 --> 00:35:33,000
Speaker 1: aluminum cans, then it's not gonna make that big of

667
00:35:33,080 --> 00:35:36,080
Speaker 1: a difference. But the idea of everyone getting together to

668
00:35:36,160 --> 00:35:38,680
Speaker 1: do something for the common good, that's where change happens,

669
00:35:39,840 --> 00:35:44,880
Speaker 1: or where negative change happens. So as far as AI

670
00:35:45,040 --> 00:35:48,759
Speaker 1: data centers go, the main you know, aside from just

671
00:35:49,000 --> 00:35:51,080
Speaker 1: you know, the land use and everything else in the

672
00:35:52,120 --> 00:35:55,640
Speaker 1: hardship on the local economies and towns in certain ways

673
00:35:55,680 --> 00:35:59,120
Speaker 1: that we're going to get to, it's really just a

674
00:35:59,320 --> 00:36:05,480
Speaker 1: sucubus of electricity and water usage. Yeah, psychobus is not

675
00:36:05,560 --> 00:36:06,320
Speaker 1: the right word.

676
00:36:06,360 --> 00:36:08,560
Speaker 2: No, but it makes sense. It's like a bunker down.

677
00:36:10,160 --> 00:36:12,520
Speaker 1: Yeah, But I say psychibis to mean just like a

678
00:36:12,640 --> 00:36:14,839
Speaker 1: bottomless pit. But I know that's not what it means,

679
00:36:14,880 --> 00:36:15,400
Speaker 1: by the way.

680
00:36:15,360 --> 00:36:18,839
Speaker 2: A giant sucking thing, right right, And it is. It's

681
00:36:18,840 --> 00:36:21,440
Speaker 2: sucking tons of electricity and water up. Like some of

682
00:36:21,520 --> 00:36:25,640
Speaker 2: these AI data plants use the same amount of electricity

683
00:36:25,680 --> 00:36:28,719
Speaker 2: as a town of fifty thousand yeah, and about the

684
00:36:28,800 --> 00:36:31,479
Speaker 2: same amount of water is a town of fifty thousand people.

685
00:36:31,520 --> 00:36:34,440
Speaker 2: This is a data center we're talking about, and it's

686
00:36:34,480 --> 00:36:37,760
Speaker 2: not even necessarily an AI data center. Just any hyper

687
00:36:37,840 --> 00:36:42,879
Speaker 2: scale data center uses a ton of electricity and water.

688
00:36:43,200 --> 00:36:47,200
Speaker 2: The reason it uses water is because all of these processors,

689
00:36:47,320 --> 00:36:50,160
Speaker 2: the CPUs that are doing all this work, and just

690
00:36:50,280 --> 00:36:52,400
Speaker 2: all of the networking that's going on with it, it's

691
00:36:52,520 --> 00:36:56,960
Speaker 2: generating heat, and computing happens faster when it's cooler. So

692
00:36:57,120 --> 00:36:59,919
Speaker 2: to keep the place cool they use evaporative cooling, where

693
00:37:00,120 --> 00:37:06,520
Speaker 2: they funnel waste heat air through wet pads essentially, like

694
00:37:06,800 --> 00:37:09,680
Speaker 2: they just buy old mattresses and doze them with water

695
00:37:10,120 --> 00:37:12,359
Speaker 2: and then they run the heat through there and through

696
00:37:12,400 --> 00:37:16,600
Speaker 2: evaporative cooling, it cools it off. It uses a little

697
00:37:17,040 --> 00:37:22,279
Speaker 2: electricity than air cooling, but it uses water a lot

698
00:37:22,400 --> 00:37:22,840
Speaker 2: of water.

699
00:37:23,600 --> 00:37:26,040
Speaker 1: Yeah, I mean, I assume most people know this. But

700
00:37:26,280 --> 00:37:28,560
Speaker 1: like your laptop has a tiny fan in it, Like

701
00:37:28,960 --> 00:37:31,000
Speaker 1: every computer in the world has a little fan in

702
00:37:31,080 --> 00:37:33,719
Speaker 1: it that cools it down. So when you've got all

703
00:37:33,760 --> 00:37:35,799
Speaker 1: this stuff together, you know it's going to generate tons

704
00:37:35,840 --> 00:37:38,480
Speaker 1: and tons of heat. That was the whirrying of the

705
00:37:38,520 --> 00:37:41,839
Speaker 1: server room that you used to sleep in. Those were

706
00:37:41,960 --> 00:37:44,399
Speaker 1: all fans, you know, And you know there's some other

707
00:37:44,520 --> 00:37:47,399
Speaker 1: sounds coming but mostly those fans trying to cool everything down.

708
00:37:48,280 --> 00:37:49,919
Speaker 1: We've got a lot of stats here that are pretty

709
00:37:49,920 --> 00:37:53,520
Speaker 1: eye popping. But there are eleven roughly eleven thousand data

710
00:37:53,520 --> 00:37:56,920
Speaker 1: centers around the world. Most of these are not AI obviously,

711
00:37:57,000 --> 00:37:59,920
Speaker 1: but they're the most you know, robust sort of u

712
00:38:00,040 --> 00:38:03,000
Speaker 1: users of the energy. But they use between one and

713
00:38:03,160 --> 00:38:05,759
Speaker 1: one point five percent, which it doesn't sound like a lot,

714
00:38:06,200 --> 00:38:11,360
Speaker 1: but of the entire world's electricity usage. I know on

715
00:38:11,520 --> 00:38:13,560
Speaker 1: planet Earth goes to data centers right now, and in

716
00:38:13,640 --> 00:38:17,840
Speaker 1: certain places like Ireland, data centers use about twenty percent

717
00:38:17,960 --> 00:38:19,120
Speaker 1: of the country's electricity.

718
00:38:19,280 --> 00:38:22,360
Speaker 2: Yeah, and if you dive into different places around like

719
00:38:22,760 --> 00:38:25,920
Speaker 2: the world where data centers are like that's collectively, right,

720
00:38:26,280 --> 00:38:27,719
Speaker 2: all of them in Iran and all of them in

721
00:38:27,800 --> 00:38:30,280
Speaker 2: the world. If you kind of zoom into the towns

722
00:38:30,800 --> 00:38:35,279
Speaker 2: where these things are located, there's well, there's something called

723
00:38:35,400 --> 00:38:40,560
Speaker 2: Data Center Alley in northern Virginia outside of DC, where

724
00:38:40,760 --> 00:38:45,680
Speaker 2: there's this huge concentration of large data centers, probably the

725
00:38:45,719 --> 00:38:49,319
Speaker 2: biggest concentration in the world. Those data centers use about

726
00:38:49,360 --> 00:38:52,799
Speaker 2: the same amount of electricity as sixty percent of all

727
00:38:52,840 --> 00:38:54,880
Speaker 2: the households in the state of Virginia.

728
00:38:55,280 --> 00:38:58,920
Speaker 1: Yeah, here's another one. By twenty thirty, they're predicting. This

729
00:38:59,040 --> 00:39:03,080
Speaker 1: is Barclay's Bank is predicting that data center energy use

730
00:39:03,120 --> 00:39:06,360
Speaker 1: in the United States would make up about thirteen percent

731
00:39:06,480 --> 00:39:10,400
Speaker 1: of the entire electricity demand of the United States. And

732
00:39:11,360 --> 00:39:13,680
Speaker 1: Meta has there. They all have silly names, but they're

733
00:39:14,000 --> 00:39:17,520
Speaker 1: data centers called Hyperion. They're all you know, one was

734
00:39:18,200 --> 00:39:20,560
Speaker 1: where are the where's that list? They're all these kind

735
00:39:20,600 --> 00:39:23,080
Speaker 1: of sci fi sounding names.

736
00:39:23,480 --> 00:39:28,560
Speaker 2: Yes, Stargate, Yeah, Jupiter, Prometheus. God, I'm sure all of

737
00:39:28,560 --> 00:39:30,360
Speaker 2: those nerds are like, what do you mean silly?

738
00:39:31,120 --> 00:39:32,759
Speaker 1: If I opened up a data center, I'd call it

739
00:39:32,840 --> 00:39:33,400
Speaker 1: Old Bessie.

740
00:39:34,480 --> 00:39:38,920
Speaker 2: Bessie's I hope so bad that somebody's listening to this

741
00:39:39,040 --> 00:39:42,319
Speaker 2: and they open a massive hyper scale data center named

742
00:39:42,360 --> 00:39:42,920
Speaker 2: Old Bessie.

743
00:39:43,680 --> 00:39:46,719
Speaker 1: That would be great. But Meta's Hyperion data center will

744
00:39:46,800 --> 00:39:49,680
Speaker 1: consume by the time it's finished, about five gigawatts. And

745
00:39:49,719 --> 00:39:54,440
Speaker 1: if you're like, what's five gigawatts? That is about half

746
00:39:54,600 --> 00:39:55,560
Speaker 1: of the peak load.

747
00:39:55,440 --> 00:39:57,440
Speaker 2: Of all of New York City, the most that it

748
00:39:57,480 --> 00:40:00,240
Speaker 2: can possibly it can possibly be demanded.

749
00:40:00,120 --> 00:40:02,480
Speaker 1: Right, Yeah, the very toplope probably, I guess in New

750
00:40:02,560 --> 00:40:06,400
Speaker 1: York City on the hottest day of the year, with

751
00:40:06,640 --> 00:40:10,120
Speaker 1: all the lights on at night or something. Yeah, and

752
00:40:10,200 --> 00:40:13,560
Speaker 1: that Rocketfeller tree just they just there's a summer version.

753
00:40:13,400 --> 00:40:17,160
Speaker 2: That puts it over the edge that's right blackout. So

754
00:40:17,320 --> 00:40:20,640
Speaker 2: you can imagine that when you're using all this electricity

755
00:40:20,800 --> 00:40:24,080
Speaker 2: and using all this water, if you're starting to build

756
00:40:24,120 --> 00:40:26,920
Speaker 2: these massive data centers, you're looking for places that have

757
00:40:27,120 --> 00:40:32,080
Speaker 2: like cheap land, cheap electricity, and because electricity is often

758
00:40:32,320 --> 00:40:36,200
Speaker 2: more expensive than water, they'll go to places. They'll build

759
00:40:36,239 --> 00:40:39,239
Speaker 2: them in places that are like water scarce, that have

760
00:40:39,560 --> 00:40:43,720
Speaker 2: cheap electricity. I'm the premise that, like, we're a massive

761
00:40:43,960 --> 00:40:48,960
Speaker 2: multinational corporation, we can push around this little county and

762
00:40:49,320 --> 00:40:51,000
Speaker 2: use up all of their water and what are they

763
00:40:51,040 --> 00:40:51,920
Speaker 2: gonna do? Nothing?

764
00:40:52,840 --> 00:40:55,560
Speaker 1: Yeah, And I mean that's literally happening. There's one right

765
00:40:55,600 --> 00:40:58,520
Speaker 1: here in Georgia, in Newton County. It's a metadata center

766
00:40:58,920 --> 00:41:03,480
Speaker 1: that's using tenth of the local water use. And like

767
00:41:03,600 --> 00:41:06,200
Speaker 1: you said, water is a is a resource that isn't infinite.

768
00:41:06,239 --> 00:41:08,360
Speaker 1: We've talked about the dangers in the future of like

769
00:41:08,760 --> 00:41:10,400
Speaker 1: you know, perhaps the wars of the future will be

770
00:41:10,480 --> 00:41:14,080
Speaker 1: fought over water, and this could get us there. I

771
00:41:14,160 --> 00:41:18,200
Speaker 1: think in Phoenix, Arizona, you know, known for their abundant water.

772
00:41:19,239 --> 00:41:23,280
Speaker 1: Meta and Microsoft use seven million gallons of water every

773
00:41:23,480 --> 00:41:25,160
Speaker 1: single day for their data centers.

774
00:41:25,239 --> 00:41:27,640
Speaker 2: Yeah, every day, you said.

775
00:41:28,120 --> 00:41:30,160
Speaker 1: Every day, seven million gallons of water.

776
00:41:30,320 --> 00:41:33,839
Speaker 2: That's insane. Yeah. And when I saw this, I was like, oh,

777
00:41:33,960 --> 00:41:37,600
Speaker 2: here we go. In the UK, data centers used ten

778
00:41:37,880 --> 00:41:45,040
Speaker 2: billion leaders of drinking water every year. L I. T R. E. S. Yeah,

779
00:41:45,080 --> 00:41:46,680
Speaker 2: that's right. Uh.

780
00:41:46,960 --> 00:41:50,120
Speaker 1: But you know you mentioned some of these towns. Not

781
00:41:50,239 --> 00:41:52,919
Speaker 1: only are some there like using let's say ten percent

782
00:41:52,960 --> 00:41:56,320
Speaker 1: of the local water here in Newton County in Virginia

783
00:41:56,640 --> 00:41:59,359
Speaker 1: where data center Alley is, some of these places are

784
00:41:59,520 --> 00:42:01,319
Speaker 1: like some of these towns are running out of water,

785
00:42:01,480 --> 00:42:03,359
Speaker 1: Like they go to turn on their water and water

786
00:42:03,480 --> 00:42:04,640
Speaker 1: doesn't come out because of this.

787
00:42:04,920 --> 00:42:07,480
Speaker 2: Well. Plus also, like we talked about how gamers are

788
00:42:07,520 --> 00:42:11,560
Speaker 2: getting the short end of the stick when it comes

789
00:42:11,600 --> 00:42:14,880
Speaker 2: to buying graphic cards because they are in such high demand,

790
00:42:15,200 --> 00:42:18,120
Speaker 2: same thing happens with electricity. So in addition to this

791
00:42:18,320 --> 00:42:20,600
Speaker 2: data center coming to town and using up all your water,

792
00:42:20,920 --> 00:42:24,239
Speaker 2: they're also jacking up your electricity prices because there's only

793
00:42:24,360 --> 00:42:28,360
Speaker 2: so much that your local electrical company can produce, So

794
00:42:28,480 --> 00:42:31,399
Speaker 2: because of supply and demand, your price is going to rise,

795
00:42:31,440 --> 00:42:35,640
Speaker 2: and I guess around Data center Alley in Northern Virginia,

796
00:42:36,000 --> 00:42:39,600
Speaker 2: electricity prices have increased two hundred and sixty seven percent

797
00:42:40,280 --> 00:42:45,359
Speaker 2: since twenty twenty. And that also is affecting Maryland, which

798
00:42:45,440 --> 00:42:48,280
Speaker 2: is getting little to no benefit from Data center Alley

799
00:42:48,440 --> 00:42:50,960
Speaker 2: and is just helping pay the price for it. This

800
00:42:51,120 --> 00:42:56,359
Speaker 2: is subsidization of these data centers, like they are subsidized

801
00:42:56,400 --> 00:42:58,719
Speaker 2: in just about every single way you can imagine.

802
00:42:59,239 --> 00:43:02,239
Speaker 1: Yeah, for sure. And if you say like, oh, well sure,

803
00:43:02,280 --> 00:43:04,520
Speaker 1: but they create jobs, right, so that's great for the

804
00:43:04,560 --> 00:43:09,720
Speaker 1: local economy. Kyle gives an example here of Northumberland, England.

805
00:43:10,120 --> 00:43:13,640
Speaker 1: There's a ten billion pound data center there or I

806
00:43:13,680 --> 00:43:16,719
Speaker 1: guess it's coming and you'd think, oh, great, that's that's

807
00:43:16,760 --> 00:43:19,560
Speaker 1: going to employ probably like five thousand people, right, it's

808
00:43:19,600 --> 00:43:22,240
Speaker 1: going to employ four hundred people with full time jobs.

809
00:43:22,600 --> 00:43:25,919
Speaker 2: Yeah, a ten billion dollar or ten billion pound data

810
00:43:25,960 --> 00:43:29,120
Speaker 2: center four hundred jobs. Because these things are so efficient

811
00:43:29,520 --> 00:43:33,040
Speaker 2: and everything is just so advanced, they don't really need

812
00:43:33,120 --> 00:43:36,359
Speaker 2: that many people to keep an eye on it. Right. Plus, Also,

813
00:43:37,400 --> 00:43:40,000
Speaker 2: the money from that data center, if they're not going

814
00:43:40,040 --> 00:43:42,439
Speaker 2: to spread it around the UK, it's going to flow

815
00:43:42,680 --> 00:43:45,200
Speaker 2: right back to the US, to the parent company.

816
00:43:46,200 --> 00:43:49,359
Speaker 1: Oh yeah, for sure. And you know, we also didn't

817
00:43:49,440 --> 00:43:52,960
Speaker 1: point out that a lot of these these energy grids

818
00:43:53,400 --> 00:43:56,319
Speaker 1: like are literally going to buckle under pressure at some point,

819
00:43:56,440 --> 00:43:57,480
Speaker 1: like they're not built for this.

820
00:43:57,840 --> 00:44:00,720
Speaker 2: Yes, and we're not so we're I know, it sounds

821
00:44:00,760 --> 00:44:03,800
Speaker 2: like we're just like and this and that. How terrible

822
00:44:03,840 --> 00:44:08,360
Speaker 2: are data centers like There's they're they're incredibly important and

823
00:44:08,520 --> 00:44:12,759
Speaker 2: they support an amazing array of really great stuff, right

824
00:44:13,160 --> 00:44:17,520
Speaker 2: and they they are the foundation that the next expansion

825
00:44:17,680 --> 00:44:20,879
Speaker 2: of the digital economy and the world culture are going

826
00:44:20,960 --> 00:44:23,759
Speaker 2: to grow on. Like, they're incredibly important, but they have

827
00:44:24,080 --> 00:44:27,000
Speaker 2: a lot of problems with them that need to be addressed.

828
00:44:27,280 --> 00:44:31,160
Speaker 2: They're not being addressed because every government from like the

829
00:44:31,320 --> 00:44:36,120
Speaker 2: local city council up to the leaders of the free world,

830
00:44:37,560 --> 00:44:41,200
Speaker 2: like are just giving these people whatever they want. That's

831
00:44:41,280 --> 00:44:45,279
Speaker 2: what's going on now. There's no checks going on at all,

832
00:44:45,440 --> 00:44:46,880
Speaker 2: right now, that's the problem.

833
00:44:47,520 --> 00:44:50,120
Speaker 1: Yeah, And that's that's because the flow of money is

834
00:44:50,239 --> 00:44:54,440
Speaker 1: so great at this point to a certain segment of

835
00:44:54,480 --> 00:45:00,120
Speaker 1: the population. Only they're protecting their their own investment, you know,

836
00:45:00,200 --> 00:45:01,719
Speaker 1: they're watching their own backsides.

837
00:45:01,840 --> 00:45:04,040
Speaker 2: That's definitely I would say ninety nine percent of it.

838
00:45:04,440 --> 00:45:06,840
Speaker 2: But I think there's also chuck, a little factor of

839
00:45:06,960 --> 00:45:11,920
Speaker 2: like g whiz, Like these these titans of the AI

840
00:45:12,120 --> 00:45:17,680
Speaker 2: industry are good at like razzle, dazzling elected officials into

841
00:45:17,760 --> 00:45:20,200
Speaker 2: doing whatever they want by I think making them feel

842
00:45:20,200 --> 00:45:24,920
Speaker 2: included in this new frontier. Essentially, I think there's a

843
00:45:25,080 --> 00:45:25,960
Speaker 2: certain element of that.

844
00:45:26,880 --> 00:45:30,080
Speaker 1: I think you're probably right. It's hey, maybe it'll all

845
00:45:30,120 --> 00:45:30,680
Speaker 1: work out great.

846
00:45:30,840 --> 00:45:35,080
Speaker 2: Sure, it probably will. It usually does astoundingly, it usually

847
00:45:35,200 --> 00:45:37,239
Speaker 2: does work out well.

848
00:45:37,520 --> 00:45:39,440
Speaker 1: True as far as the world hasn't ended.

849
00:45:39,760 --> 00:45:43,480
Speaker 2: That's exactly what I mean. Yeah, yeah, yeah, So I

850
00:45:43,520 --> 00:45:45,359
Speaker 2: think that's it. We said yeah like four or five

851
00:45:45,440 --> 00:45:48,560
Speaker 2: times in secession. I think we accidentally triggered listener, ma'am.

852
00:45:51,280 --> 00:45:54,960
Speaker 1: That's right. This relates to our history of the BBC episode,

853
00:45:55,120 --> 00:45:58,600
Speaker 1: and this is from Erica, and Erica says, hey, guys,

854
00:45:59,000 --> 00:46:01,320
Speaker 1: I really love the episode and left me reflecting on

855
00:46:01,400 --> 00:46:03,320
Speaker 1: how I've come to understand the country through both the

856
00:46:03,400 --> 00:46:06,800
Speaker 1: content the BBC produces and the people's reactions to the BBC.

857
00:46:07,000 --> 00:46:09,720
Speaker 1: But more recently, my work as an academic has enabled

858
00:46:09,760 --> 00:46:12,040
Speaker 1: me to be involved in creating programs for the BBC

859
00:46:12,600 --> 00:46:15,520
Speaker 1: across TV, radio and online. Because there's one awesome fact

860
00:46:16,000 --> 00:46:18,960
Speaker 1: about the BBC that wasn't included. For over fifty years,

861
00:46:19,000 --> 00:46:22,239
Speaker 1: the BBC has partnered with the Open University OU, which

862
00:46:22,239 --> 00:46:26,680
Speaker 1: specializes in accessible and distance education. The partnership started in

863
00:46:26,719 --> 00:46:30,359
Speaker 1: the nineteen seventies to provide learning at scale, including facilitating

864
00:46:30,480 --> 00:46:33,360
Speaker 1: university level lectures at night on public television. Today, the

865
00:46:33,400 --> 00:46:38,279
Speaker 1: partnership facilitates access to academic consultants to co produce high

866
00:46:38,360 --> 00:46:43,080
Speaker 1: quality and form content across platforms, including some of the

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Speaker 1: David Attenborough.

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Speaker 2: Nature stuff nice.

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Speaker 1: Additionally, the Open University creates supplementary materials to enable people

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Speaker 1: to continue their learning journey and explore topics in more detail. So,

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Speaker 1: whether viewers or listeners realize it or not, this partnership

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Speaker 1: enables the public to benefit from special US knowledge and

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Speaker 1: accessible ways. And that is from Erica from the Open University,

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Speaker 1: who is a professor of medical anthropology.

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Speaker 2: Oh wow, that's an awesome Erica. You got to send

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Speaker 2: us some topic ideas too, totally right up your alley

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Speaker 2: and congratulations. That's pretty neat making stuff in conjunction with

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Speaker 2: the BBC. That's gotta be a neat high water mark,

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Speaker 2: you know, agreed, And I think, Chuck, I'm curious to

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Speaker 2: see if we go look at our account, we'll see

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Speaker 2: a little line item from open University and one from BBC.

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Speaker 1: Well, it would be like seven pounds or something. I

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Speaker 1: don't know the exchange rate, right.

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Speaker 2: That sounds about right? All right, great, well, thanks again, Erica,

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Speaker 2: and please do send us some medical anthropology ideas because

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Speaker 2: that just sounds like it'll knock our socks off. And

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Speaker 2: if you want to be like Erica and try to

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Speaker 2: knock our socks off, good luck, you can send it

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Speaker 2: off to us at stuff podcast at iHeartRadio dot com.

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Speaker 2: Stuff you Should Know is a production of iHeartRadio. For

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Speaker 2: more podcasts my heart Radio, visit the iHeartRadio app, Apple Podcasts,

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Speaker 2: or wherever you listen to your favorite shows.