Speaker 1: Welcome to Stuff you should know, a production of iHeartRadio.
Speaker 2: Hey, and welcome to the podcast. I'm Josh, and there's
Speaker 2: Chuck and Jerry's here too, and we've got our pocket
Speaker 2: protectors and tape on the bridge of our glasses day
Speaker 2: and this is stuff you should know.
Speaker 1: Nice work. Did you just hear something?
Speaker 2: No, not weird. I heard you say nice work. Yeah,
Speaker 2: but then stop abruptly.
Speaker 1: Well I stopped abruptly because I thought I heard a
Speaker 1: little digital glitch.
Speaker 2: Oh no, I didn't hear anything.
Speaker 1: I might be losing my mind, then, I you know, I'm.
Speaker 2: Curious whether we end up editing this out or not.
Speaker 2: Any other podcast on the planet would edit that out
Speaker 2: without even thinking about it. But there's like a fifty
Speaker 2: percent chance that'll stay in with us.
Speaker 1: I mean, this is why we didn't get that Golden
Speaker 1: Globe nomination. That's right, it's kind of classic stuff you
Speaker 1: should know. On professionality.
Speaker 2: It's exactly right in the Italian accent, mispronunciations, there's a
Speaker 2: whole laundry list. Yeah, that's okay, Chuck, I think we're
Speaker 2: golden regardless.
Speaker 1: Agreed.
Speaker 2: So we're talking about data centers, which I had a
Speaker 2: very very rough idea about But actually no, I knew
Speaker 2: that they existed essentially and that they were becoming a
Speaker 2: problem with the rise of AI. Yes, that was about it.
Speaker 2: How about you are you are you d data center Feliac?
Speaker 1: No? You know me, I'm not super technology minded, so
Speaker 1: I don't know a lot about this stuff. I remember
Speaker 1: walking by our server room back in the day when
Speaker 1: we were at Pont City Market and seeing our colleague
Speaker 1: Izzy in there hard at word yeah. And when that
Speaker 1: door was unlocked and open, hearing the were of the
Speaker 1: of the servers and the cooling machines, and you know,
Speaker 1: that's on a smaller scale, that's a data center.
Speaker 2: Absolutely, one hundred percent, that's a data center. It was
Speaker 2: also so a great place to curl up and take
Speaker 2: a nap in.
Speaker 1: The middle of the day, like the warmth of the server.
Speaker 2: Yeah, and the word put right the word yeah, So
Speaker 2: uh yeah, that definitely counts as a data server. If
Speaker 2: you have like one of those little home networking setups
Speaker 2: in like a closet in your house, data center. Sure,
Speaker 2: technically the PC is a data center. Anywhere you can
Speaker 2: store and access data, that's technically a data center. And
Speaker 2: you're like, well, that's stupid. Why did you even say
Speaker 2: that Josh's quibbling, that's quotitian. Shut up and get on
Speaker 2: with data centers. Whoa, whoa, whoa. First of all, don't
Speaker 2: use the S word and then secondly word yes, Wait,
Speaker 2: what did I say? That's the keyword Quoteitian?
Speaker 1: Oh no, that's kW right.
Speaker 2: So the reason that I bring that up, though, Chuck,
Speaker 2: is because that technically is part of the progression of
Speaker 2: data centers. Yeah, it probably goes without saying, but it's
Speaker 2: evolved along with computing, and his computer's kind of gotten
Speaker 2: bigger and bigger. The need to store and access more
Speaker 2: data has gotten bigger so much so, Chuck that just
Speaker 2: wrap your head around this one. In twenty twenty four,
Speaker 2: just over a year ago. Yeah, we used one hundred
Speaker 2: and fifty zetabytes of data. That's what we consumed. And
Speaker 2: consuming is anything from making a video and uploading it
Speaker 2: to TikTok or putting a post up on Instagram. It's
Speaker 2: browsing a website, it's buying a song from iTunes, it's
Speaker 2: doing web analytics, it's buying something with your American Express card.
Speaker 2: All of that is data consumption. And we consumed one
Speaker 2: hundred and fifty zetabytes of data in twenty twenty four.
Speaker 1: Yeah, I don't even know how many big Max that is.
Speaker 1: I know you name dropped a lot of brands. It
Speaker 1: should be like the movies where every time you even
Speaker 1: just say, like buy something on your amex, the bank
Speaker 1: account grows by like ten dollars.
Speaker 2: I agree wholeheartedly. I agree that amex should do that. Amex,
Speaker 2: there's twenty bucks. I'll split it with you.
Speaker 1: Wow, you just bought lunch in nineteen ninety seven.
Speaker 2: That's right. So just real quick, as zeta byte, chuck
Speaker 2: is a trillion gigabytes, So we consumed one hundred and
Speaker 2: fifty trillion gigabytes. That was twenty twenty four.
Speaker 1: That's worldwide, right, yes, yeah.
Speaker 2: That's worldwide. In twenty ten, we consumed two zeta bytes. Jeez. Yeah.
Speaker 2: So it's growing exponentially, which means that data centers are
Speaker 2: growing exponentially. And now they're about to just blow up,
Speaker 2: like truffle up essentially from you know, this kind of
Speaker 2: calm like plateau that they'd reached. It's about to just
Speaker 2: go in hyper drive.
Speaker 1: Yeah, and actively is, and we're going we're gonna get
Speaker 1: to some startling statistics later on in the episode. But
Speaker 1: Kyle helped us out with this our writer over in
Speaker 1: the UK.
Speaker 2: He did a fantastic job.
Speaker 1: He did a really good job. And there's going to
Speaker 1: be some UK specific things in here because Kyle's always keen,
Speaker 1: as they say, to throw that stuff in there.
Speaker 2: Yeah, for sure, Kyle likes to pepper those in Yeah, of.
Speaker 1: Course, and he's not barred from doing so we allow it.
Speaker 1: So since Kyle, you know, is frequenter of the Wayback Machine,
Speaker 1: as are all the wonderful writers that we use, they
Speaker 1: all had the keys to the car. Essentially, he jumped
Speaker 1: in the Wayback Machine to sort of give us a
Speaker 1: little bit of a timeline on data centers and a
Speaker 1: bit on you know, mainframes and PCs.
Speaker 2: He also left all of his used tea bags in
Speaker 2: there too. I don't know if you noticed that.
Speaker 1: Oh it's fine, you know, you can throw those back
Speaker 1: in some hot water and they do just a little
Speaker 1: bit weaker tea.
Speaker 2: Well, if you put like five of them together, it's
Speaker 2: like one.
Speaker 1: Yeah, and Kyle, I mean that thing was full.
Speaker 2: Maybe really what he drinks a lot of tea, he
Speaker 2: really does.
Speaker 1: So if you want to talk about the earliest data
Speaker 1: centers that you could kind of call maybe a data center.
Speaker 1: They were you know, computers, they were electronic computers. Most
Speaker 1: of this stuff that we're going to talk about early
Speaker 1: on was military and and as you'll see, even the
Speaker 1: first when we talk about the UK one that was
Speaker 1: supposedly in that military, they even loaned it to the military,
Speaker 1: which was kind of interesting. But these things were built
Speaker 1: with you know, state of the art technology at the time,
Speaker 1: which meant vacuum tubes and you know, manual switches and
Speaker 1: plugs and things like that. And the first thing that
Speaker 1: we can really talk about as the first programmable electric
Speaker 1: digital computer was the Colossus. And as we'll see, Elon
Speaker 1: Musk has now stolen that for his own purposes that
Speaker 1: name probably because of this, I would imagine, but it
Speaker 1: was at Bletchley Park, of course, during World War Two,
Speaker 1: and they were trying to you know, crack into Hitler's
Speaker 1: messages at the time, and these things were huge and
Speaker 1: kind of to me. The thing that stood out about Colossus,
Speaker 1: which is a neat little factoid, is that where Colossus
Speaker 1: was at Bletchley Park at Block H, it is now
Speaker 1: the National Museum of Computing.
Speaker 2: I want to go to that so bad when we
Speaker 2: do that UK up tour next year. Oh, we got
Speaker 2: to go to that together.
Speaker 1: Okay, okay, are we doing that next year?
Speaker 2: That's what we were talking about. All right, we kind
Speaker 2: of already have promised it. We have to. Now we're
Speaker 2: locked in the punch.
Speaker 1: Why's my voice O high?
Speaker 2: Then? I don't know. You're practicing for the alph Yeah,
Speaker 2: that's right now.
Speaker 1: No, that that'd be a lot of fun. I'd love
Speaker 1: to go to that.
Speaker 2: So that was Colossus. Another one about the same time
Speaker 2: was the Aniac Electrical Numerical Integrator and Computer. So that's
Speaker 2: a quality acronym. Yeah, and it was the first general
Speaker 2: purpose electronic computer. And here's the thing. This is technically
Speaker 2: not data storage yet, it's data processing, right, But these
Speaker 2: things Colossus Aniac, you walked up to them and you said,
Speaker 2: what's the trajectory of this missile if I fire it
Speaker 2: from here? And Aniac would go peep pop, poop, poop,
Speaker 2: and then say, like, uh, whatever a trajectory is described
Speaker 2: in Sure or Colossus. You'd be like, what is Hilter
Speaker 2: saying here to Goebbels And the Colossus would say, Hilter
Speaker 2: is saying that he's a big fan of goebels work,
Speaker 2: but he's suspicious that the rest of the world doesn't
Speaker 2: like either of them. And that was it. After that,
Speaker 2: you'd be like, hey, what was the last answer, and
Speaker 2: they'd be like, what's an answer?
Speaker 1: Yeah, you gotta just tell me what's going on with
Speaker 1: this Hilter business.
Speaker 2: You don't remember from our art Mysteries of the Art World,
Speaker 2: how stuff works are to go? Oh, the title of
Speaker 2: that section was did Hilter do these pain? Oh?
Speaker 1: My god, that's a deep cut. I did not remember that.
Speaker 2: Yes, and I think it still says that on that
Speaker 2: oh babilistical Yeah, yeah, I can only hope it's got
Speaker 2: to be Hilter forever. All right.
Speaker 1: So we go into mainframes at this point, and this
Speaker 1: is like the nineteen fifties basically when companies could actually
Speaker 1: have their own computer. It wasn't just the military. These
Speaker 1: were the old punch card computers, and they were called mainframes.
Speaker 1: It wasn't made up for this. Mainframes were originally described
Speaker 1: We're describing like what you would house telecommunication equipment and
Speaker 1: maybe some other sciencey stuff, but it was referencing literally
Speaker 1: the cabinets that held this technology. And it became known
Speaker 1: as just you know, it kind of took over when
Speaker 1: the computer's world started using it as computer only.
Speaker 2: Yeah, but again, this is like you're a company and
Speaker 2: this is where you store and process all of your data,
Speaker 2: and it's in this one room, but it's not going
Speaker 2: anywhere else. It's not for anybody else, and you have
Speaker 2: to physically be in the room to get your answer,
Speaker 2: process whatever data you're looking for. When the PC came along,
Speaker 2: and then the Mac and Tosh came along, they took
Speaker 2: that thing and just made it very small so you
Speaker 2: could put it on all of your employee's desks. And
Speaker 2: now they had, like I was saying, before, their own
Speaker 2: little data center right there. So if you said, like, hey,
Speaker 2: what's the I need to know the Q four reports,
Speaker 2: they'd say, go to Debbie's desk. Debbie's is the one
Speaker 2: who's got that on her computer. And you would go
Speaker 2: over there and say, WI, what's the Q four report?
Speaker 2: And w would give it to you. Right, there was
Speaker 2: no connectivity, but you could still like do a lot
Speaker 2: more stuff than you could when you had a mainframe. Yeah,
Speaker 2: for sure, which makes mainframes feel like really outdated, but
Speaker 2: it turns out they're like totally still in use. Today.
Speaker 1: Oh yeah, absolutely, I do want to jump back in
Speaker 1: time a little bit, because I did. I promised talk
Speaker 1: of lending the military, oh basically your equipment, and that's
Speaker 1: what happened. In nineteen fifty one, there was a T
Speaker 1: shop chain in the UK. I don't know if it's
Speaker 1: still around, Lions Lyns, and they were the very first
Speaker 1: company in the world that used a mainframe. It was
Speaker 1: called the LEO, the l EO, and it was you know,
Speaker 1: like what you would think they've handled, like payroll and
Speaker 1: stock management and stuff like that, but there wasn't a
Speaker 1: lot for it to do at a T shop chain
Speaker 1: except for those couple of things. And so they calculated
Speaker 1: missile trajectories like you were talking about for the Ministry
Speaker 1: of Defense.
Speaker 2: And that actually kind of helped establish like a I
Speaker 2: guess a pay schedule. How people charged for data centers
Speaker 2: to come. Yeah, it was they like you would charge
Speaker 2: them for the time that they used it, or you
Speaker 2: could lease it for a month. And that really started
Speaker 2: to come around when IBM got in the game. They
Speaker 2: became like the main frame leader in the fifties, the
Speaker 2: early fifties, I think they had a unit that you
Speaker 2: could lease for sixteen thousand dollars per month. That's in
Speaker 2: nineteen fifty two money. And then as the things as
Speaker 2: like the processors got better and smaller and faster, that
Speaker 2: price came down dramatically. And then finally in the sixties
Speaker 2: they released the IBM six System three sixty, which not
Speaker 2: only got Apollo eleven to the moon and back, it
Speaker 2: is a It appears in an episode of mad Men apparently.
Speaker 1: Oh really, yeah, you usould have though, right.
Speaker 2: No, I never did. I just saw reference to it
Speaker 2: on the government and you knew it was the show. Yeah,
Speaker 2: but they like you should look up pictures of it.
Speaker 2: It's like those giant burnt orange cabinets with the real
Speaker 2: magnetic tape. It's just beautiful. They're cool to look at.
Speaker 1: Yeah, yeah, I remember. We've referenced the movie War Games
Speaker 1: from our childhood in the eighties a lot, and that
Speaker 1: the whopper for War Games was that was you know,
Speaker 1: at that age, to see the whopper and action and
Speaker 1: to see Matthew almost aid, Matthew Modine, Matthew Broderick hanging
Speaker 1: up his handheld telephone receiver onto a modem to talk
Speaker 1: to the school computer.
Speaker 2: It was mind blowing that, Yeah, that phone in the
Speaker 2: modem made just a really big impression on me.
Speaker 1: Yeah, and a cool sound.
Speaker 2: Peepoop boop.
Speaker 1: Should we take a break?
Speaker 2: Wait, let me talk about mainframes today though, because I
Speaker 2: just want to give a little I don't know if
Speaker 2: a shout out's the right term, but they are still
Speaker 2: around because they're so reliable, Because they're so secure. You
Speaker 2: can make it so that there's information on those things
Speaker 2: that you again have to be physically present in the
Speaker 2: room to access. Sure, you can put all sorts of
Speaker 2: different layers of security. So if you're like Visa, or
Speaker 2: you're a healthcare company, or you're the Census Bureau, you're
Speaker 2: probably still using a main frame because you're protecting information
Speaker 2: as tightly as you can. But those things are also
Speaker 2: super fast and can hold huge volumes of computation at once.
Speaker 1: Yeah, or a non Golden Globe nominated podcast.
Speaker 2: Yeah, we've got our own main frame. Yeah, we've got
Speaker 2: our IBM three sixty that's right.
Speaker 1: What year was that from again, the three.
Speaker 2: Sixty sixty four.
Speaker 1: Yeah, yeah, that's the one. I'll just making sure we
Speaker 1: didn't have the sixty five because that no, no, no,
Speaker 1: the sixty buggy. Yeah, yeah, all right, we can take
Speaker 1: that break now and we're gonna jump out of the
Speaker 1: wayback machine and venture into the modern world.
Speaker 2: Right after this.
Speaker 1: All right, so we are out of the way back machine.
Speaker 1: We're making that uh we're combining all those old tea
Speaker 1: bags and making some still somewhat weaker tea. Yeah, it's
Speaker 1: not too bad. It's a combination of Earl Gray and
Speaker 1: Camma meal, all kinds of fun stuff, but not too bad.
Speaker 1: And now we're going to talk a little bit about
Speaker 1: when things started to ramp up, because it kind of
Speaker 1: happened in fits and starts. And one of the biggest, uh,
Speaker 1: I guess would it be a fitter a start was
Speaker 1: the Internet. Because once the Internet came along, every business
Speaker 1: in the world started using it, and so all of
Speaker 1: a sudden you had to have a lot more data
Speaker 1: storage and bigger data centers and bigger server rooms in
Speaker 1: your companies, which was, you know, a pretty good thing
Speaker 1: at the time. After the dot com bus. There were
Speaker 1: a lot of casualties of that growth, but then things
Speaker 1: kind of you know, the ship kind of righted itself.
Speaker 2: Yeah, and because it was like accessible to basically every
Speaker 2: business now, like you didn't have to buy a mainframe
Speaker 2: you could lease space on someone else's main frame, like
Speaker 2: you're the Ministry of Defense or something, all of a sudden.
Speaker 2: So that led to this huge proliferation that gave the
Speaker 2: foundation for web commerce. E commerce, that's what they used
Speaker 2: to call it. That's an old timey term now, but
Speaker 2: it created the ability for e commerce to start and flourish.
Speaker 2: So this data center's scaling up to meet the needs
Speaker 2: of the Internet and then to kind of give people
Speaker 2: all sorts of new space and room to come up
Speaker 2: with new stuff. That's where the digital economy came from,
Speaker 2: right there.
Speaker 1: Yeah, all of a sudden, you could hop on webvan
Speaker 1: for an order a SACA groceries.
Speaker 2: I have a friend who was all in about that.
Speaker 1: Well man, yeah, yeah, I think I had a friend
Speaker 1: who was pretty heavily invested.
Speaker 2: It's but clearly it was ahead of its time. I mean, sure,
Speaker 2: let's see, it's a post Mates and.
Speaker 1: Well there's a lot of several of them now that
Speaker 1: have succeeded.
Speaker 2: Well name them, will get ten bucks each?
Speaker 1: Well, well you just got ten for post Mates, and
Speaker 1: you got to split that with me.
Speaker 2: You know, I will, all right.
Speaker 1: Cloud computing was the next big jump. When cloud computing
Speaker 1: came around the early two thousands, or do they call
Speaker 1: that the early oughts? I do, okay, I thought i'd
Speaker 1: heard that come from your mouth. But that is when
Speaker 1: you know, that was the real game changer because things
Speaker 1: were still I mean, when cloud computing came along, people
Speaker 1: thought of it if they didn't look too hard into it.
Speaker 1: They thought it was just you know, floating up in
Speaker 1: the ether somewhere. Yeah, it's it's still being stored on stuff,
Speaker 1: it's just not being stored locally. So all of a sudden,
Speaker 1: things were just going somewhere else for someone else to
Speaker 1: worry about all that storage. And more importantly, they could
Speaker 1: they could link everything together and store a lot of
Speaker 1: stuff from a bunch of different people.
Speaker 2: Right, So now you have data centers not just available
Speaker 2: to somebody like a huge bank or something like that,
Speaker 2: or the government in a shop then a bank, yeah,
Speaker 2: or a t shop, and then to e commerce businesses.
Speaker 2: Now it's available to you and me. So it's really
Speaker 2: hard to remember back because the world has changed so much.
Speaker 2: But Chuck, like two thousand and eight, two thousand and nine,
Speaker 2: they were given us like VPN, little things that like
Speaker 2: you could go home and work and like you would
Speaker 2: it would never work. I'd never understood how to make
Speaker 2: it work. Yeah, but that was like the very beginning
Speaker 2: of how you could take your work home with you
Speaker 2: and work from home and do things remotely like we
Speaker 2: can now. Like it's nothing, but this led to the
Speaker 2: rise of businesses like Dropbox. Right. So Dropbox goes to
Speaker 2: Amazon Web Services and say, hey, we want to buy
Speaker 2: a bunch of your cloud, right, which means that they're
Speaker 2: going to use a bunch of like different servers and
Speaker 2: different data centers all over the place. And then Dropbox
Speaker 2: turns around to you and says, hey, if you give
Speaker 2: me nineteen ninety five a month, you can have one
Speaker 2: terabyte of data. Right. You can consume one terabyte of data, right,
Speaker 2: and then hopefully you don't use all of that, so
Speaker 2: they don't have to pay Amazon Web Services for stuff
Speaker 2: they didn't use. But you're paying that nineteen ninety five
Speaker 2: a month rather whether you use that whole terabyte or not.
Speaker 2: It's a pretty smart business model. Would not exist at
Speaker 2: all if the cloud didn't exist.
Speaker 1: Yeah, as funny as you were talking about two thousand
Speaker 1: and eight and how quaint that is, now, that's what
Speaker 1: the year we started the show I know, I know,
Speaker 1: it's crazy to think about it really.
Speaker 2: Is, but imagine like working from home at that time,
Speaker 2: it was just didn't you could know.
Speaker 1: It was kind of great you went home and you homed.
Speaker 2: That's exactly right. That was a big difference I remember.
Speaker 1: But these data centers have now come together in such
Speaker 1: a big way now that they the largest ones are
Speaker 1: called hyper scale and they host more than five thousand servers,
Speaker 1: like servers, not individual persons. Data. It's like, it's incredible,
Speaker 1: how big is how big they've gotten Googles. And we'll
Speaker 1: go over some of the kind of square footage and
Speaker 1: then later talk about the elephant in the room, which
Speaker 1: is energy and water usage. But Google's first data center
Speaker 1: was built in two thousand and six, just but two
Speaker 1: years before stuff you should have launched. And this is
Speaker 1: an Oregon and they are still expanding that thing beyond
Speaker 1: one point three million square feet. Meanwhile, in China they're like,
Speaker 1: hold my tea, I guess, because China Telecom has a
Speaker 1: ten point seven million square foot data center in Inner Mongolia.
Speaker 2: It's two hundred and fifty acres.
Speaker 1: That is a warehouse full of worrying servers heating up
Speaker 1: and being cool.
Speaker 2: Yeah, which is a big problem for any data center,
Speaker 2: it turns out. But the whole expansion this jump starting
Speaker 2: in twenty seventeen thanks to cloud computing, because again, cloud
Speaker 2: computing just means all your stuff isn't on one server
Speaker 2: and one data center, it's broken up into pieces and
Speaker 2: spread all over the place. That's the cloud. That's basically it.
Speaker 2: Even though it's way more advanced and intricate than that.
Speaker 2: That's like all you really need to know for the
Speaker 2: purposes of this episode, right. It led to a huge jump,
Speaker 2: a huge need in data centers, and it also expanded
Speaker 2: all the stuff we can do now, and COVID actually
Speaker 2: gave it another bump, made building a data center very
Speaker 2: economically attractive thing to do if you had the money.
Speaker 2: Because remote working finally finally established itself as like, no,
Speaker 2: we're doing this, stop calling us back to the office, ye,
Speaker 2: which is what they're doing now. I know, I hope
Speaker 2: it doesn't work because I remember when all of that
Speaker 2: started and everybody was so nervous, like management was all
Speaker 2: so nervous that people were just going to totally like
Speaker 2: mess around and everything. It just it didn't happen. I
Speaker 2: don't know anybody who's even been like gotten a talking to,
Speaker 2: let alone, been fired for just messing around at home.
Speaker 2: As a matter of fact, like you were saying, it
Speaker 2: just makes you work more.
Speaker 1: Yeah, I mean, do you know how many times in
Speaker 1: our old offices I would see Jonathan Strickland just wandering
Speaker 1: aimlessly through the office chatting with people.
Speaker 2: Yes, I do, because he would chat with me a lot.
Speaker 2: He did.
Speaker 1: He did that in front of God and everybody, as
Speaker 1: they say, right there in the office. So I can't
Speaker 1: imagine what happened with him at home.
Speaker 2: Yeah, like it was a sorority mixer or something.
Speaker 1: We love Strickland. He's still around everyone. By the way,
Speaker 1: he retired from tech stuff, but he's still with a company.
Speaker 2: Which is great. We love Strickland all right.
Speaker 1: So now we're onto AI data centers and that was
Speaker 1: the I mean to call it a game changer. Seems
Speaker 1: quaint compared to the rise of cloud computing and everything,
Speaker 1: because it is off to the races in a way
Speaker 1: that seemingly cannot be stopped. The genie has left the bottle,
Speaker 1: as they say. Starting in twenty twenty two, when chat
Speaker 1: GPT was released by open Ai, all of a sudden,
Speaker 1: the need for these data centers became exponentially greater in
Speaker 1: size in the speed at which they need these things
Speaker 1: built because AI requires a ton of computing power to operate.
Speaker 2: So much so that they don't even use the standard
Speaker 2: what's called the compute machine. So compute is like all
Speaker 2: of the processing power, the networking, all of that stuff,
Speaker 2: and traditionally with a computer that's done on a CPU. Right,
Speaker 2: that's how all of this gets done, right, everything else
Speaker 2: is infrastructure. The CPU is doing all of the work.
Speaker 2: Those are so like they still work like. Most data
Speaker 2: centers are running on CPUs for AI, just not fast enough.
Speaker 2: They use GPUs graphic processing units, which are associated with
Speaker 2: video games for most people, right, you need a good
Speaker 2: graphics card to run your video game, I guess, but
Speaker 2: they the reason that for AI data centers that they
Speaker 2: use GPUs is because they're really good at parallel processing.
Speaker 2: They can run a bunch of different operations at once.
Speaker 2: So you're like, cool, you just throw a GPU in
Speaker 2: a data center and you can run an AI. No,
Speaker 2: you need hundreds of thousands of these things strung together,
Speaker 2: and instead of like a CPU running like a couple
Speaker 2: of servers or something like that. At data center. All
Speaker 2: of them are strung together to form one giant supercomputer
Speaker 2: that the AI operates on.
Speaker 1: Yeah, like chat GPT itself was trained on twenty thousand
Speaker 1: of these GPUs a GPU, you know, the sort of
Speaker 1: the biggest name in the game. There's a couple, but
Speaker 1: the biggest one obviously is the Navidia. But the Navidia
Speaker 1: H one hundred that is the standard right now. If
Speaker 1: you look this thing up, it it fits in your hand.
Speaker 1: It's not like some gigantic thing. H twenty thousand of
Speaker 1: them linked together or one hundred thousand of them linked together.
Speaker 1: Who knows how many, you know, hundreds of thousands are
Speaker 1: eventually going to be linked together to end the world.
Speaker 1: That's where all the power comes from, like you were saying,
Speaker 1: But it's you know, it's just a little rectangular handheld
Speaker 1: thing that's like, oh, that looks like something that maybe
Speaker 1: came out of a computer. And Navidia is what are
Speaker 1: their stock jumped over a couple of years, like nine
Speaker 1: hundred percent over twenty twenty three and twenty twenty four
Speaker 1: something like.
Speaker 2: That, Yeah, nine hundred percent increase.
Speaker 1: Yeah, and we'll talk about why all of this is
Speaker 1: super like scary and dangerous because it really is.
Speaker 2: Well, yeah, if you want a really good explanation of
Speaker 2: this about and like you said, how many GPUs you
Speaker 2: string together before we end the world? Nate Sores and
Speaker 2: Eliezer Yukowski In that book, I keep referencing that I
Speaker 2: think everybody should read. If anyone builds it, everyone dies.
Speaker 2: About the current state of AI. They talk about this
Speaker 2: in depth, but in a really understandable way. It's really fascinating.
Speaker 2: But that's essentially one of the things they say is
Speaker 2: like we keep stringing together tens and tens of thousands
Speaker 2: more GPUs, that just makes the supercomputer smarter and smarter
Speaker 2: and more capable. And eventually, what's going to happen. We're
Speaker 2: going to reach some point potentially where we just put
Speaker 2: that extra last GPU in there and all of a sudden,
Speaker 2: the balance is tipped and the thing becomes super intelligent.
Speaker 1: That's right. Also a time for me because you're always
Speaker 1: too shy to to plug the end of the world
Speaker 1: with Josh Clark, your fantastic limited series, of which AI
Speaker 1: is one of the central focuses or one of was
Speaker 1: it eight things?
Speaker 2: Ten? Well, there was ten episodes.
Speaker 1: Ten episodes, right, thanks baby? Well but one of the
Speaker 1: episodes was just like you talking about Jimmy Buffett Records.
Speaker 2: And that's right.
Speaker 1: You had to lighten the mood yep Should we take
Speaker 1: a break or should we keep going for a minute.
Speaker 1: Let's keep going for a minute, okay, because you talked
Speaker 1: about investment, and you know, if you have the money
Speaker 1: to open one of these things, and that's what these
Speaker 1: tech companies are doing, like to perhaps they're great peril.
Speaker 1: At some point we'll see. Microsoft has invested eighty eight
Speaker 1: billion dollars in data centers just in twenty twenty five.
Speaker 1: Amazon is pledged over the next fifteen years one hundred
Speaker 1: and fifty billion dollars. And Google and Meta together about
Speaker 1: you know, not working together, but they are expected to
Speaker 1: spend about seven hundred and fifty billion dollars just on
Speaker 1: equipment over the next two years. And Stanley Morgan says
Speaker 1: to Morgan, Stanley, what I say, Stanley Morgan, Yeah, I
Speaker 1: think we should leave that in there.
Speaker 2: Okay.
Speaker 1: Stanley says, Hey, you know, guys.
Speaker 2: Maybe you might like Stanley Kamma boar.
Speaker 1: Yeah, Stanley Kumo Morgan. Over five years between twenty five
Speaker 1: and twenty thirty, Morgan, Stanley says about three trillion dollars
Speaker 1: is going to be spent just on the data centers
Speaker 1: about I mean, half of which is the hardware and
Speaker 1: half of which is just building these things.
Speaker 2: Yeah, just in what the next four years. Yeah, So
Speaker 2: think about it. If you're in Nvidia and you're the
Speaker 2: industry leader for GPUs and everybody's like, we're gonna spend
Speaker 2: one point five trillion dollars on this on the infrastructure
Speaker 2: in the GPUs, you're looking pretty good down the.
Speaker 1: Road, Yeah, for sure. And you know they're doing this
Speaker 1: because there's a demand right now for use at least
Speaker 1: because things like OpenAI and other AI creators are using
Speaker 1: them like crazy. But these companies are also using them
Speaker 1: for their own AI research, right.
Speaker 2: Yeah. So like Xai has that Colossus machine that you
Speaker 2: were talking about earlier, which is two high hundred thousand
Speaker 2: GPUs strung together. I'm not sure if it's fully online yet, Tennessee. Yeah,
Speaker 2: And it's just for that. It's they're not doing any
Speaker 2: they're not calculating missile trajectories for the Ministry of Defense
Speaker 2: or anything like that. Like, it's just for that AI.
Speaker 2: And yeah, I think Meta is doing the same thing.
Speaker 2: Open ai I don't think is building their own because
Speaker 2: they're so in cahoots with Microsoft. I think they run
Speaker 2: their stuff on Microsoft's data centers. But yeah, yeah, if
Speaker 2: you have an AI essentially right now, which means like
Speaker 2: God and everybody, you probably have your own data center
Speaker 2: dedicated to it.
Speaker 1: Yeah. I And this isn't some some moral stand I'm
Speaker 1: taking by saying that I have never used AI. And
Speaker 1: trust me, I know that every part of my life
Speaker 1: is now touched by AI, so I am inadvertently using.
Speaker 2: It touched by an AI, that's right.
Speaker 1: But I've never I've never used like you know, chatbots
Speaker 1: or or large language models or inning like that, just
Speaker 1: mainly because I'm just I'm fine doing things like they
Speaker 1: are for now, and not in a luddite sort of way.
Speaker 1: I just everything's going along great for me in my
Speaker 1: job and how I live my life, so I just
Speaker 1: I don't have a need for it.
Speaker 2: I do the same thing. And I think also both
Speaker 2: of us are like if somebody else wants to do
Speaker 2: it the other way, that's fine. Like we're certainly not
Speaker 2: gonna criticize them or be crimudgeony about it or say
Speaker 2: that you know that's stupid.
Speaker 1: Right, But as you'll see. You know, and again this
Speaker 1: isn't yucking someone's yum, but everyone should know what they're
Speaker 1: a part of, and that's part of what the episode
Speaker 1: is about.
Speaker 2: That's right. Yeah, no, I totally do. Before we take
Speaker 2: a break. I think it's a small kind of side issue,
Speaker 2: but it's worth pointing out that it sucks because these
Speaker 2: Nvidia chips are so in demand from these massive companies.
Speaker 2: It has driven the price for just the average Nvidia
Speaker 2: graphics card sky high. So if you're a gamer and
Speaker 2: you're like trying to improve your system, like you pay
Speaker 2: way more than you used to for the same graphics
Speaker 2: card that you could have bought for like a quarter
Speaker 2: of the price, you know, a couple of years ago.
Speaker 1: Yeah, and I wasn't even looking like I didn't even
Speaker 1: know that you could just buy This is how little
Speaker 1: I know about all this before. This was like, could
Speaker 1: you just buy a Navidia Gpu? But I was just
Speaker 1: researching the size and like what do these things look like?
Speaker 1: And it, you know, it's one was on eBay for
Speaker 1: twenty thousand dollars and I was like, oh my god,
Speaker 1: I didn't Oh I didn't know that was the deal?
Speaker 2: Is that right?
Speaker 1: Yeah, and I don't know if that's accurate. I don't
Speaker 1: know anything about it, so I could easily be corrected
Speaker 1: on all this, but that's what the Internet told me.
Speaker 2: Okay, well the Internet never lies.
Speaker 1: Oh one thing before we break real quick, because we
Speaker 1: did promise a little UK specific stuff and I didn't
Speaker 1: want to short shrift our brit listeners or Kyle. The
Speaker 1: UK is right now like the third largest nation for
Speaker 1: data centers. The US is first, I think Germany a second,
Speaker 1: and they signed what was called the Tech Prosperity Deal
Speaker 1: with the giants, the tech giants of the United States,
Speaker 1: And right now Microsoft has announced a thirty billion dollar
Speaker 1: investment in UK data centers, and I think like one
Speaker 1: hundred new AI data centers are planned in the UK
Speaker 1: at this point moving forward.
Speaker 2: Yeah, and I saw there's at least one in Wales
Speaker 2: that's being smartly done. They took an old radiator factory
Speaker 2: plant campus and they're revitalizing that as AI data center.
Speaker 2: So it does sound like I get why the UK
Speaker 2: is doing it, but there's a lot of people in
Speaker 2: the UK and elsewhere who are like, these are not
Speaker 2: this is not a good investment for local governments or
Speaker 2: even national governments. There's a big problem with all this,
Speaker 2: Like there is a AI boom going on. Data centers
Speaker 2: are just one part of it. Like people are throwing
Speaker 2: money at AI like it's nineteen ninety nine, and a
Speaker 2: lot of people are like, there's another it's not a
Speaker 2: bubble this time, but it's a AI bubble. Yeah. One
Speaker 2: of the reasons why some people are pointing to it
Speaker 2: as an AI bubble is that it's just not clear
Speaker 2: how much money is going to be made from AI
Speaker 2: and when that's going to start. Yeah. I think the
Speaker 2: Financial Times called Open AI a money pit with a
Speaker 2: website on top. Yeah, not great, no, because people are
Speaker 2: just pumping money into this stuff, but they're not getting
Speaker 2: they're not seeing results from it, not yet. It's not
Speaker 2: necessarily a bad bet that AI is going to completely
Speaker 2: revolutionize the world and like revolutionize economies and going to
Speaker 2: make some people a lot of money, but there's just
Speaker 2: no clear path to it right now, which makes some
Speaker 2: people nervous.
Speaker 1: Yeah, there's about five percent, just five percent of pilot
Speaker 1: AI programs right now in business secure returns on their investments,
Speaker 1: you know, like they make the money.
Speaker 2: But Stanley Morgan is predicting revenues of a trillion dollars
Speaker 2: by twenty twenty eight, that's.
Speaker 1: What they're saying. I mean, we'll see Navidia. I mean,
Speaker 1: Kyle's also keen to point out that there's sort of
Speaker 1: a circular economy within all this going on. That's a
Speaker 1: little bit like troubling maybe because Navidia is investing in
Speaker 1: open AAI, but that depends on their purchase of those
Speaker 1: Navidia chips. So, you know, everyone from you know, just
Speaker 1: people who are smarter than us as far as the
Speaker 1: stuff goes, are warning people right down to the IMF,
Speaker 1: the International Monetary Fund are flashing the warning signs saying
Speaker 1: like this could be you know, it could make a
Speaker 1: trillion dollars by twenty twenty eight, or it could like
Speaker 1: wreck the global economy.
Speaker 2: Yeah for sure. Yeah, we have no idea, although I
Speaker 2: have seen people argue against it, that say like this
Speaker 2: is nothing like Yeah, a lot of these AI companies
Speaker 2: are probably overinflated, but it's nothing like it was with
Speaker 2: like the two thousand and eight meltdown or the dot
Speaker 2: com bubble, Like this is we're a lot we're a
Speaker 2: lot more seasoned, or investors are a lot more seasoned.
Speaker 2: Than they were before. The problem is one of the
Speaker 2: problems is that the financing is expected to come in
Speaker 2: large part from private credit, which is essentially an investment
Speaker 2: vehicle for investors to go lend money to say like
Speaker 2: companies that want to build data centers. Right, and this
Speaker 2: is largely unregulated. It's very shadowy. We don't know how
Speaker 2: many how much debt exists in the world on private
Speaker 2: credit because they don't have to report this stuff. And
Speaker 2: you know, as we learn from the two thousand and
Speaker 2: eight meltdown, when there's like a massive speculation among finances
Speaker 2: that involves debt's that can go really bad.
Speaker 1: Yeah for sure. And speaking of going bad, I guess
Speaker 1: we're at the sort of environmental piece of this whole thing.
Speaker 1: And this is what I was talking about when I
Speaker 1: said that, you know, people should just be aware of
Speaker 1: what they're taking part in. And again this is not
Speaker 1: to shame anybody who uses AI for their job or
Speaker 1: just to make funny fake videos, but but you know,
Speaker 1: everyone is sort of tied together to make this what
Speaker 1: it is. Who's using that stuff, And I get if
Speaker 1: someone says, like, hey, if I quit this thing, it's
Speaker 1: not going to make any difference. But that's sort of
Speaker 1: the the age old. Like, you know, if I don't
Speaker 1: recycle my ten can, my aluminum can, ten cans, my
Speaker 1: aluminum cans, then it's not gonna make that big of
Speaker 1: a difference. But the idea of everyone getting together to
Speaker 1: do something for the common good, that's where change happens,
Speaker 1: or where negative change happens. So as far as AI
Speaker 1: data centers go, the main you know, aside from just
Speaker 1: you know, the land use and everything else in the
Speaker 1: hardship on the local economies and towns in certain ways
Speaker 1: that we're going to get to, it's really just a
Speaker 1: sucubus of electricity and water usage. Yeah, psychobus is not
Speaker 1: the right word.
Speaker 2: No, but it makes sense. It's like a bunker down.
Speaker 1: Yeah, But I say psychibis to mean just like a
Speaker 1: bottomless pit. But I know that's not what it means,
Speaker 1: by the way.
Speaker 2: A giant sucking thing, right right, And it is. It's
Speaker 2: sucking tons of electricity and water up. Like some of
Speaker 2: these AI data plants use the same amount of electricity
Speaker 2: as a town of fifty thousand yeah, and about the
Speaker 2: same amount of water is a town of fifty thousand people.
Speaker 2: This is a data center we're talking about, and it's
Speaker 2: not even necessarily an AI data center. Just any hyper
Speaker 2: scale data center uses a ton of electricity and water.
Speaker 2: The reason it uses water is because all of these processors,
Speaker 2: the CPUs that are doing all this work, and just
Speaker 2: all of the networking that's going on with it, it's
Speaker 2: generating heat, and computing happens faster when it's cooler. So
Speaker 2: to keep the place cool they use evaporative cooling, where
Speaker 2: they funnel waste heat air through wet pads essentially, like
Speaker 2: they just buy old mattresses and doze them with water
Speaker 2: and then they run the heat through there and through
Speaker 2: evaporative cooling, it cools it off. It uses a little
Speaker 2: electricity than air cooling, but it uses water a lot
Speaker 2: of water.
Speaker 1: Yeah, I mean, I assume most people know this. But
Speaker 1: like your laptop has a tiny fan in it, Like
Speaker 1: every computer in the world has a little fan in
Speaker 1: it that cools it down. So when you've got all
Speaker 1: this stuff together, you know it's going to generate tons
Speaker 1: and tons of heat. That was the whirrying of the
Speaker 1: server room that you used to sleep in. Those were
Speaker 1: all fans, you know, And you know there's some other
Speaker 1: sounds coming but mostly those fans trying to cool everything down.
Speaker 1: We've got a lot of stats here that are pretty
Speaker 1: eye popping. But there are eleven roughly eleven thousand data
Speaker 1: centers around the world. Most of these are not AI obviously,
Speaker 1: but they're the most you know, robust sort of u
Speaker 1: users of the energy. But they use between one and
Speaker 1: one point five percent, which it doesn't sound like a lot,
Speaker 1: but of the entire world's electricity usage. I know on
Speaker 1: planet Earth goes to data centers right now, and in
Speaker 1: certain places like Ireland, data centers use about twenty percent
Speaker 1: of the country's electricity.
Speaker 2: Yeah, and if you dive into different places around like
Speaker 2: the world where data centers are like that's collectively, right,
Speaker 2: all of them in Iran and all of them in
Speaker 2: the world. If you kind of zoom into the towns
Speaker 2: where these things are located, there's well, there's something called
Speaker 2: Data Center Alley in northern Virginia outside of DC, where
Speaker 2: there's this huge concentration of large data centers, probably the
Speaker 2: biggest concentration in the world. Those data centers use about
Speaker 2: the same amount of electricity as sixty percent of all
Speaker 2: the households in the state of Virginia.
Speaker 1: Yeah, here's another one. By twenty thirty, they're predicting. This
Speaker 1: is Barclay's Bank is predicting that data center energy use
Speaker 1: in the United States would make up about thirteen percent
Speaker 1: of the entire electricity demand of the United States. And
Speaker 1: Meta has there. They all have silly names, but they're
Speaker 1: data centers called Hyperion. They're all you know, one was
Speaker 1: where are the where's that list? They're all these kind
Speaker 1: of sci fi sounding names.
Speaker 2: Yes, Stargate, Yeah, Jupiter, Prometheus. God, I'm sure all of
Speaker 2: those nerds are like, what do you mean silly?
Speaker 1: If I opened up a data center, I'd call it
Speaker 1: Old Bessie.
Speaker 2: Bessie's I hope so bad that somebody's listening to this
Speaker 2: and they open a massive hyper scale data center named
Speaker 2: Old Bessie.
Speaker 1: That would be great. But Meta's Hyperion data center will
Speaker 1: consume by the time it's finished, about five gigawatts. And
Speaker 1: if you're like, what's five gigawatts? That is about half
Speaker 1: of the peak load.
Speaker 2: Of all of New York City, the most that it
Speaker 2: can possibly it can possibly be demanded.
Speaker 1: Right, Yeah, the very toplope probably, I guess in New
Speaker 1: York City on the hottest day of the year, with
Speaker 1: all the lights on at night or something. Yeah, and
Speaker 1: that Rocketfeller tree just they just there's a summer version.
Speaker 2: That puts it over the edge that's right blackout. So
Speaker 2: you can imagine that when you're using all this electricity
Speaker 2: and using all this water, if you're starting to build
Speaker 2: these massive data centers, you're looking for places that have
Speaker 2: like cheap land, cheap electricity, and because electricity is often
Speaker 2: more expensive than water, they'll go to places. They'll build
Speaker 2: them in places that are like water scarce, that have
Speaker 2: cheap electricity. I'm the premise that, like, we're a massive
Speaker 2: multinational corporation, we can push around this little county and
Speaker 2: use up all of their water and what are they
Speaker 2: gonna do? Nothing?
Speaker 1: Yeah, And I mean that's literally happening. There's one right
Speaker 1: here in Georgia, in Newton County. It's a metadata center
Speaker 1: that's using tenth of the local water use. And like
Speaker 1: you said, water is a is a resource that isn't infinite.
Speaker 1: We've talked about the dangers in the future of like
Speaker 1: you know, perhaps the wars of the future will be
Speaker 1: fought over water, and this could get us there. I
Speaker 1: think in Phoenix, Arizona, you know, known for their abundant water.
Speaker 1: Meta and Microsoft use seven million gallons of water every
Speaker 1: single day for their data centers.
Speaker 2: Yeah, every day, you said.
Speaker 1: Every day, seven million gallons of water.
Speaker 2: That's insane. Yeah. And when I saw this, I was like, oh,
Speaker 2: here we go. In the UK, data centers used ten
Speaker 2: billion leaders of drinking water every year. L I. T R. E. S. Yeah,
Speaker 2: that's right. Uh.
Speaker 1: But you know you mentioned some of these towns. Not
Speaker 1: only are some there like using let's say ten percent
Speaker 1: of the local water here in Newton County in Virginia
Speaker 1: where data center Alley is, some of these places are
Speaker 1: like some of these towns are running out of water,
Speaker 1: Like they go to turn on their water and water
Speaker 1: doesn't come out because of this.
Speaker 2: Well. Plus also, like we talked about how gamers are
Speaker 2: getting the short end of the stick when it comes
Speaker 2: to buying graphic cards because they are in such high demand,
Speaker 2: same thing happens with electricity. So in addition to this
Speaker 2: data center coming to town and using up all your water,
Speaker 2: they're also jacking up your electricity prices because there's only
Speaker 2: so much that your local electrical company can produce, So
Speaker 2: because of supply and demand, your price is going to rise,
Speaker 2: and I guess around Data center Alley in Northern Virginia,
Speaker 2: electricity prices have increased two hundred and sixty seven percent
Speaker 2: since twenty twenty. And that also is affecting Maryland, which
Speaker 2: is getting little to no benefit from Data center Alley
Speaker 2: and is just helping pay the price for it. This
Speaker 2: is subsidization of these data centers, like they are subsidized
Speaker 2: in just about every single way you can imagine.
Speaker 1: Yeah, for sure. And if you say like, oh, well sure,
Speaker 1: but they create jobs, right, so that's great for the
Speaker 1: local economy. Kyle gives an example here of Northumberland, England.
Speaker 1: There's a ten billion pound data center there or I
Speaker 1: guess it's coming and you'd think, oh, great, that's that's
Speaker 1: going to employ probably like five thousand people, right, it's
Speaker 1: going to employ four hundred people with full time jobs.
Speaker 2: Yeah, a ten billion dollar or ten billion pound data
Speaker 2: center four hundred jobs. Because these things are so efficient
Speaker 2: and everything is just so advanced, they don't really need
Speaker 2: that many people to keep an eye on it. Right. Plus, Also,
Speaker 2: the money from that data center, if they're not going
Speaker 2: to spread it around the UK, it's going to flow
Speaker 2: right back to the US, to the parent company.
Speaker 1: Oh yeah, for sure. And you know, we also didn't
Speaker 1: point out that a lot of these these energy grids
Speaker 1: like are literally going to buckle under pressure at some point,
Speaker 1: like they're not built for this.
Speaker 2: Yes, and we're not so we're I know, it sounds
Speaker 2: like we're just like and this and that. How terrible
Speaker 2: are data centers like There's they're they're incredibly important and
Speaker 2: they support an amazing array of really great stuff, right
Speaker 2: and they they are the foundation that the next expansion
Speaker 2: of the digital economy and the world culture are going
Speaker 2: to grow on. Like, they're incredibly important, but they have
Speaker 2: a lot of problems with them that need to be addressed.
Speaker 2: They're not being addressed because every government from like the
Speaker 2: local city council up to the leaders of the free world,
Speaker 2: like are just giving these people whatever they want. That's
Speaker 2: what's going on now. There's no checks going on at all,
Speaker 2: right now, that's the problem.
Speaker 1: Yeah, And that's that's because the flow of money is
Speaker 1: so great at this point to a certain segment of
Speaker 1: the population. Only they're protecting their their own investment, you know,
Speaker 1: they're watching their own backsides.
Speaker 2: That's definitely I would say ninety nine percent of it.
Speaker 2: But I think there's also chuck, a little factor of
Speaker 2: like g whiz, Like these these titans of the AI
Speaker 2: industry are good at like razzle, dazzling elected officials into
Speaker 2: doing whatever they want by I think making them feel
Speaker 2: included in this new frontier. Essentially, I think there's a
Speaker 2: certain element of that.
Speaker 1: I think you're probably right. It's hey, maybe it'll all
Speaker 1: work out great.
Speaker 2: Sure, it probably will. It usually does astoundingly, it usually
Speaker 2: does work out well.
Speaker 1: True as far as the world hasn't ended.
Speaker 2: That's exactly what I mean. Yeah, yeah, yeah, So I
Speaker 2: think that's it. We said yeah like four or five
Speaker 2: times in secession. I think we accidentally triggered listener, ma'am.
Speaker 1: That's right. This relates to our history of the BBC episode,
Speaker 1: and this is from Erica, and Erica says, hey, guys,
Speaker 1: I really love the episode and left me reflecting on
Speaker 1: how I've come to understand the country through both the
Speaker 1: content the BBC produces and the people's reactions to the BBC.
Speaker 1: But more recently, my work as an academic has enabled
Speaker 1: me to be involved in creating programs for the BBC
Speaker 1: across TV, radio and online. Because there's one awesome fact
Speaker 1: about the BBC that wasn't included. For over fifty years,
Speaker 1: the BBC has partnered with the Open University OU, which
Speaker 1: specializes in accessible and distance education. The partnership started in
Speaker 1: the nineteen seventies to provide learning at scale, including facilitating
Speaker 1: university level lectures at night on public television. Today, the
Speaker 1: partnership facilitates access to academic consultants to co produce high
Speaker 1: quality and form content across platforms, including some of the
Speaker 1: David Attenborough.
Speaker 2: Nature stuff nice.
Speaker 1: Additionally, the Open University creates supplementary materials to enable people
Speaker 1: to continue their learning journey and explore topics in more detail. So,
Speaker 1: whether viewers or listeners realize it or not, this partnership
Speaker 1: enables the public to benefit from special US knowledge and
Speaker 1: accessible ways. And that is from Erica from the Open University,
Speaker 1: who is a professor of medical anthropology.
Speaker 2: Oh wow, that's an awesome Erica. You got to send
Speaker 2: us some topic ideas too, totally right up your alley
Speaker 2: and congratulations. That's pretty neat making stuff in conjunction with
Speaker 2: the BBC. That's gotta be a neat high water mark,
Speaker 2: you know, agreed, And I think, Chuck, I'm curious to
Speaker 2: see if we go look at our account, we'll see
Speaker 2: a little line item from open University and one from BBC.
Speaker 1: Well, it would be like seven pounds or something. I
Speaker 1: don't know the exchange rate, right.
Speaker 2: That sounds about right? All right, great, well, thanks again, Erica,
Speaker 2: and please do send us some medical anthropology ideas because
Speaker 2: that just sounds like it'll knock our socks off. And
Speaker 2: if you want to be like Erica and try to
Speaker 2: knock our socks off, good luck, you can send it
Speaker 2: off to us at stuff podcast at iHeartRadio dot com.
Speaker 2: Stuff you Should Know is a production of iHeartRadio. For
Speaker 2: more podcasts my heart Radio, visit the iHeartRadio app, Apple Podcasts,
Speaker 2: or wherever you listen to your favorite shows.