Speaker 1: Hi. Is Us Valoscian here and Cara Price, and we're
Speaker 1: taking some time off for the holidays. We'll be back
Speaker 1: with new episodes starting in January. In the meantime, instead
Speaker 1: of leaving this feed empty, we wanted to share one
Speaker 1: of my favorite episodes from last year. This week, we're
Speaker 1: rearing my conversation with Stephen Witt from November. He's an
Speaker 1: author and frequent contributor to The New Yorker who wrote
Speaker 1: the book on one of the biggest companies in the world.
Speaker 1: You may have heard of it in video. In this episode,
Speaker 1: we hear how the CEO, Jensen Huang, went from working
Speaker 1: at Denny's to being the world leader manufacturing AI chips.
Speaker 1: Hope you enjoy it and thanks for listening. Welcome to
Speaker 1: Tech Stuff. I'm Us Vlashan here with Cara Price. Hey, Kara, Hi,
Speaker 1: as so years ago around the time that we were
Speaker 1: reporting our first podcast together on the forthcoming AI Revolution
Speaker 1: around no longer forthcoming. Yeah, you invested in in Vidia,
Speaker 1: which is up over one hundred x since then. Congratulations.
Speaker 2: You know, I just felt when we reported on it
Speaker 2: that it was going to be the future, and so
Speaker 2: I did invest in it, and you know, very happily.
Speaker 1: So now, what was it? Was there something that tipped
Speaker 1: you over the edge back then to do it? Well?
Speaker 2: I was just sort of thinking to myself, this is
Speaker 2: the thing that's going to power everything. So obviously it's
Speaker 2: something that people are going to be paying attention to
Speaker 2: and investors are going to be paying attention to, and
Speaker 2: it just it made a lot of sense to me
Speaker 2: at the time.
Speaker 1: We know Warren Buffett is retiring this year, there's a
Speaker 1: slot open. In Vidia is, of course the most valuable
Speaker 1: company in the world today, recently topping five trillion dollars
Speaker 1: in value, and of course a lot of people are
Speaker 1: crying bubble, not just for Nvidia but for the AI
Speaker 1: industry as a whole, actually carrying curiously.
Speaker 2: You held your stock, I held I look, I needed
Speaker 2: some things, so I sold some, but still I still
Speaker 2: have a lot.
Speaker 1: I have enough, And you're gonna hold on. You're not
Speaker 1: worried about the bubble. I think I am gonna hold on.
Speaker 1: But the questions people have are, what if AI doesn't
Speaker 1: get better infinitely as it scales, what if people invent
Speaker 1: new chips that are far more efficient than the Invidia chips,
Speaker 1: and what if the adoption of AI by other companies
Speaker 1: doesn't give them the results that they hope for financially.
Speaker 1: And so I talked about all of that with somebody
Speaker 1: who knows Nvidia better than anyone else. In fact, he
Speaker 1: literally wrote the book on it, The Thinking Machine, Jensen
Speaker 1: Wang in Vidia and the world's most coveted microchip. He
Speaker 1: actually interviewed Wang six times.
Speaker 3: He's moody, and you know, he has what I would
Speaker 3: describe as somewhat self indulgent performances of anger from time
Speaker 3: to time.
Speaker 1: That's Stephen Witt, and he actually got interested in Nvidia
Speaker 1: shortly after chatchipt took the world by storm.
Speaker 3: I had been using chat GBT and I was like, Wow,
Speaker 3: this thing is amazing. This is like twenty twenty two,
Speaker 3: and I am cooked, Like there's not going to be
Speaker 3: room for me as a writer. This thing can already
Speaker 3: write almost as well as I can, and actually it
Speaker 3: writes better than I did when I was young.
Speaker 1: As Stephen dug around to understand what was powering this technology,
Speaker 1: he got more and more interested in the company building
Speaker 1: its physical infrastructure.
Speaker 3: What brought me to a video was I was trying
Speaker 3: to write about open Aye and them, and it was
Speaker 3: just a crowded there, a million journalists swarming around. I
Speaker 3: was like, there's got to be some other story here.
Speaker 3: And what I've done as a journalist is look for
Speaker 3: big movements of money that aren't being covered. And I
Speaker 3: looked at in Video's stock price, and I in my
Speaker 3: mind they were still the gaming company, and I was like,
Speaker 3: what the hell is going on here? This company's worth
Speaker 3: a trillion dollars. And then as I started to investigate it,
Speaker 3: I was like, Oh, wow, they build all the hardware.
Speaker 3: They built all the hardware that makes this stuff go.
Speaker 3: That's fascinating. And then I kind of learned about Jensen
Speaker 3: and I was like, wait, this company has had the
Speaker 3: same CEO through both the gaming and the AI days.
Speaker 3: And not only that, this is the same as the founder.
Speaker 3: He's been the CEO for thirty years. It's the same
Speaker 3: guy all along.
Speaker 1: So Stephen wrote the book on Nvidia, but he also
Speaker 1: wrote a great New York A piece recently about data
Speaker 1: centers and an essay for The New York Times about
Speaker 1: quote the AI prompt that could end the world. So
Speaker 1: he's really a farm to table thinker, from chips to
Speaker 1: data centers to AI to the apocalypse. It was a
Speaker 1: fun conversation.
Speaker 2: Yeah, as a writer, he seems to sort of be
Speaker 2: at the center of everything in a way that I
Speaker 2: find very compelling, And I actually want to know more
Speaker 2: about the AI prompt that could end the world.
Speaker 1: Well, in that case, you have to listen to the
Speaker 1: whole interview because we talk about it right at the end.
Speaker 1: Okay's the conversation with Stephen Witt. For the layman, what
Speaker 1: is in Vidia and how did it become the most
Speaker 1: valuable company in the world.
Speaker 3: Invidia is basically a hardware designer. They make a special
Speaker 3: kind of microchip called a graphics processing unit, and the
Speaker 3: initial purpose of this thing was to just render graphics
Speaker 3: in video games. So if you were a video gamer,
Speaker 3: you knew who this company was because you would actually
Speaker 3: build your whole PC just around this in video card.
Speaker 3: So this was the engine that rendered the graphics on
Speaker 3: your screen. Sometime around two thousand and four to two
Speaker 3: thousand and five, scientists began to notice how powerful these
Speaker 3: cards were, and they started hacking into the cards, like
Speaker 3: hacking into the circuitry to get to those powerful mathematical
Speaker 3: functions inside the microchip. And Jensen Wong saw this and
Speaker 3: he said, wait, this is a whole new market that
Speaker 3: I can pursue. So he built the software platform that
Speaker 3: turns the graphics card into basically a low budget supercomputer.
Speaker 3: Now you may ask who is this for, Well, it's
Speaker 3: not really for established research scientists because they can usually
Speaker 3: afford time on a conventional supercomputer. It's for scientists who
Speaker 3: are sort of marginalized, who can't afford time on a
Speaker 3: supercomputer and whose research is.
Speaker 1: Out of favor.
Speaker 3: So it's for mad scientists. It's for scientists who are
Speaker 3: pursuing unpopular or weird or kind of offbeat scientific projects.
Speaker 3: But ultimately, the key use case turned out to be AI,
Speaker 3: and specifically a branch of AI that most AI researchers
Speaker 3: thought was crazy called neural network technology. And what you're
Speaker 3: doing here is you're building software that kind of resembles
Speaker 3: the connections in the human brain. It's inspired by the
Speaker 3: biological brain. Actually, you build a bunch of synthetic neurons
Speaker 3: in a little file, and then you train them by
Speaker 3: repeatedly exposing them to training data. So what this could mean,
Speaker 3: for example, if we're trying to build a neural network
Speaker 3: to recognize objects to do computer vision, then we'll show
Speaker 3: it tens of thousands or hundreds of thousands, or ultimately
Speaker 3: millions of images and slowly rewire its neurons until it
Speaker 3: can start to identify things. Now, this had been proposed
Speaker 3: going all the way back to the nineteen forties, but
Speaker 3: nobody had ever able to get it to work. And
Speaker 3: the missing piece, it turns out, is just raw computing power.
Speaker 3: Jeffrey Hinton, who they call him the godfather of AI,
Speaker 3: he said, you know, the point we never thought to
Speaker 3: ask was what if we just made it go a
Speaker 3: million times faster? And that's what in Nvidia's hardware did.
Speaker 3: It made AI and these neural networks in particular, train
Speaker 3: and learn a million times faster.
Speaker 1: We actually had Hinton on the podcast earlier this year.
Speaker 1: Was he an early one of these mad scientists whose
Speaker 1: research was unpopular and therefore started buying in video chips.
Speaker 3: Yes, very much so. And there was a community of
Speaker 3: these guys. It wasn't just him. There were a number
Speaker 3: of other people doing it, most of whose work has
Speaker 3: now been recognized. But they were very much on the
Speaker 3: margins of computer science. They couldn't get five thousand dollars
Speaker 3: in research funding, but they could get enough money to
Speaker 3: afford two five hundred dollars in video retail graphics gaming cards,
Speaker 3: which they did, and Hinton had a graduate student named
Speaker 3: Alex Krzewski who was just an ACE programmer, and he
Speaker 3: turned the neural net that he ran on these cards
Speaker 3: into something called alex net, which then started to recognize
Speaker 3: images better than any AI had ever done before. Like
Speaker 3: it smashed the paradigm. And so that engineered around twenty
Speaker 3: twelve or twenty thirteen a paradigm shift in AI. And
Speaker 3: since then everything that has happened has been a repeated
Speaker 3: application of the thing. Alex discovered that if you took
Speaker 3: neural nets, if you ran them on Nvidia technology, you
Speaker 3: would have a very powerful result.
Speaker 1: So this is all fascinating, but to some it may
Speaker 1: sound a big geeky and like inside Baseball, which is
Speaker 1: why I was very attracted to a quote in your
Speaker 1: recent ne Yoka piece which said, if Americans want to
Speaker 1: retire comfortably, in Vidia has to succeed. Yes, And I
Speaker 1: was curious what your conversations with the edits around that,
Speaker 1: around that.
Speaker 3: Oh no, that's that's straightforward. That one actually sailed right
Speaker 3: through fact checking. There were no questions what has happened
Speaker 3: since Alex invented this thing in his bedroom. Is that
Speaker 3: we scaled it up from two graphics cards to two
Speaker 3: hundred thousand or more, and we have plans to scale
Speaker 3: them up to two or two million than twenty million, right,
Speaker 3: So this is the data center boom which we're going
Speaker 3: through right now. It's a new industrial revolution. We're basically
Speaker 3: building these giant barns full of in Vidia microchips to
Speaker 3: run calculations to build better AI twenty four to seven
Speaker 3: around the clock, and it's one of the largest deployments
Speaker 3: of capital in human history. This has made in Vidia
Speaker 3: the most valuable company in the world, and it has
Speaker 3: created a situation where in Vidia stock is more concentrated
Speaker 3: in the S and P five hundred than any stock
Speaker 3: since they started keeping track. And actually Microsoft, who is
Speaker 3: the second biggest, has that valuation largely because they're building
Speaker 3: these sheds and renting out in video equipment. So that's
Speaker 3: linked to So think about that fifteen percent of the
Speaker 3: stock market is these two stocks, right, they have to succeed.
Speaker 3: Americans in particular, are usually invested massively through index funds
Speaker 3: and something that looks exactly like the S and P
Speaker 3: five hundred, so you know, if in Vidio crashes, it's
Speaker 3: going to create a lot of paint throughout the economy.
Speaker 1: I want to talk about the data centers, but before that,
Speaker 1: I want to talk about the man who founded in
Speaker 1: Nvidia and is a CEO today at Jensen Huang. He
Speaker 1: seems to pop up everywhere, but he also seems to
Speaker 1: be more inscrutable. I mean, who is he and do
Speaker 1: you see him as different from Zuckerberg and Oltman and
Speaker 1: Bezos in some significant way?
Speaker 3: Jensen most resembles of all executives Elon Musk because he
Speaker 3: is an engineering wizard. Bezos is smart as hell, and
Speaker 3: so Zuckerberg. But ultimately they're kind of software guides. You know,
Speaker 3: they're coming at the computer from the keyboard in the terminal.
Speaker 3: Jensen is totally different. He approaches computing from the circuit up.
Speaker 3: He's a degree not in computer science originally, but actually
Speaker 3: in electrical engineering. Okay, so for Jensen, the computer is
Speaker 3: a piece of hardware that runs calculations in a microchip,
Speaker 3: and he literally designed those microchips on paper at the
Speaker 3: beginning of his career, and that's all he's ever done.
Speaker 3: And this is a little bit why even though he
Speaker 3: runs the most valuable company in the world. It's a
Speaker 3: little baffling to to kind of people. Nothing Jensen makes
Speaker 3: is really that accessible. It's all deep inside the computer.
Speaker 1: As a quote in your piece that I liked what
Speaker 1: he said, I find that I'm best when I'm under adversity.
Speaker 1: My heart rate actually goes down. Anyone who's dealt with
Speaker 1: RUSSIAU in a restaurant knows what I'm talking about.
Speaker 3: Yeah, yeah, I mean he started out at Denny's, so
Speaker 3: his first job was basically I think he was a
Speaker 3: bus boy at first, and then graduated to dishwasher and
Speaker 3: ultimately became a server. And I was talking to someone
Speaker 3: in the company and she's like, you know what, Jensen
Speaker 3: is actually a lot calmer and more compassionate with his
Speaker 3: employees when things are going wrong. It's when the company
Speaker 3: stock price is way up but it looks like everything's
Speaker 3: going great that he really becomes much more cruel, like
Speaker 3: much much meaner to everybody. So he is actually, in
Speaker 3: some ways a nicer person when things are going wrong.
Speaker 3: When he succeeds, it makes him nervous.
Speaker 1: One of his colleagues describe working with him as kind
Speaker 1: of like sticking your finger in the electric socket. That's
Speaker 1: quite the metaphor.
Speaker 3: It's one hundred percent accurate. I mean I've interacted with Jensen.
Speaker 3: It is like sticking your finger in the electric socket.
Speaker 3: He's so tightly wound. He expects so much to happen
Speaker 3: in every conversation. Just to even start talking to him,
Speaker 3: you have to be totally up to speed. He's not
Speaker 3: gonna waste any time. He's not going to suffer fools.
Speaker 3: And he's also really intense and unpredictable, and you just
Speaker 3: don't know where he's going to go in any conversation.
Speaker 3: And you know, he has what I would describe as
Speaker 3: somewhat self indulgent performances of anger from time to time.
Speaker 3: And that's especially true if you're one of his executives.
Speaker 3: If you're not delivering, he's going to stand you up
Speaker 3: in front of an audience of people and just start
Speaker 3: screaming at you. But really I mean yelling, and it's
Speaker 3: not fun, and he will humiliate you in front of
Speaker 3: an audience. I think people at Nvidia have to develop
Speaker 3: very thick skins. He actually did this to me at
Speaker 3: one point, so I kind of know exactly. Oh yeah, yeah, Well,
Speaker 3: I kept asking him about the future. Jensen does not
Speaker 3: like to speculate. He doesn't have actually a science fiction
Speaker 3: vision of what the future is going to look like.
Speaker 3: He has a data driven vision from engineering principles of
Speaker 3: where he thinks technology is going to go. But if
Speaker 3: he can't see beyond that, he won't speculate. But I
Speaker 3: noticed that other people at this firm would talk about it,
Speaker 3: and I really wanted to get into his imagination. I
Speaker 3: guess I would say of where he thinks all this
Speaker 3: can go. So I presented him with a clip from
Speaker 3: Arthur C. Clark discussing the future of computers, and this
Speaker 3: is back from nineteen sixty four, but it was kind
Speaker 3: of anticipating the current reality we were in where we
Speaker 3: would start training mechanical brains and those brains would train
Speaker 3: faster than biological brains and eventually would supersede biological brains.
Speaker 3: And so I'd show this clip to some other people
Speaker 3: in the video, and they've gotten very They kind of
Speaker 3: like smelled up and started giving these grand soliloquies about
Speaker 3: the future that were like very beautiful and articulate, And
Speaker 3: I was hoping to get that response from Jensen. Instead,
Speaker 3: he just starts screaming at me, how about how stupid
Speaker 3: the clip was, how he didn't give a shit about
Speaker 3: Arthur C. Clarke, He never read one of his books,
Speaker 3: he didn't read science fiction, and he thought the whole
Speaker 3: line of questioning was pedestrian, and that I was letting
Speaker 3: him down by asking I was wasting his time. Despite
Speaker 3: having written his biography, Jensen remains a little bit of
Speaker 3: a puzzle, and just that I cannot tell you what's
Speaker 3: going on inside his brain. So well, but I will
Speaker 3: say this, he's extremely neurotic, by which I mean, I
Speaker 3: don't even mean this in a clinical sense. I just
Speaker 3: mean that, by his own admission, he's totally driven by
Speaker 3: negative emotions. So even though he's on top of the world,
Speaker 3: I think his mind is telling him constantly, you're going
Speaker 3: to fail. This is a temporary thing, and Vidio is
Speaker 3: going to go back down again, you know, twice in
Speaker 3: his tenure as CEO, in Vidia's stock price has retreated
Speaker 3: by almost ninety percent.
Speaker 1: What could make it happen now? What keeps me up
Speaker 1: at night today? What could happen today?
Speaker 3: Anything? Any number of things. This would not be comprehensive,
Speaker 3: But there's three big risks. The first is just competition,
Speaker 3: and Vidia is making so much money and everyone's seeing that,
Speaker 3: and this attracts competition in the same maner that chum
Speaker 3: attracts sharks. Right, it's like throwing blood in the water
Speaker 3: for other microchip design to earn a seventy percent eighty
Speaker 3: percent gross margin, which is what they do on.
Speaker 1: Some of these chips.
Speaker 3: So Google has built a whole alternative stack for AI
Speaker 3: computing around their own, their own kind of platform, and
Speaker 3: they're starting to lease that out to new customers. That's
Speaker 3: a big risk. There's a big risk that Chinese companies
Speaker 3: built alternative, cheaper stacks to what Nvidia does. Intel had
Speaker 3: ninety ninety five percent of the CPU market at one
Speaker 3: point in this country. Now they're falling apart. Conquering one
Speaker 3: cycle in microchips is no guarantee that you will conquer
Speaker 3: the next one, and history demonstrates that quite clearly, so
Speaker 3: that could happen. B Basically, what happens in the data
Speaker 3: center is we're doing a mathematical operation called a matrix multiplication,
Speaker 3: and it's extremely computationally expensive to do this. So without
Speaker 3: getting two technical basically to train an AI right now,
Speaker 3: we have to do ten trillion trillion individual computations, which
Speaker 3: is more than the number of observable stars in the universe. However,
Speaker 3: maybe it's possible that we find some more efficient way
Speaker 3: of doing that. Maybe there's a way that requires only
Speaker 3: ten billion trillion or even ten hundred trillion, right, and
Speaker 3: video stock price would go down because we wouldn't have
Speaker 3: to build so many data centers. Right, we'd have a
Speaker 3: more efficient training solution. All of this is a more
Speaker 3: complex way of saying, maybe there's a technological solution where
Speaker 3: we you know, right now, we're brute forcing our way
Speaker 3: to AI. It's a heavy industrial problem. We're talking about
Speaker 3: building nuclear power plants to bring these things online. I
Speaker 3: think maybe it's possible that there's a technological solution that
Speaker 3: trains these things faster, and if we discovered it, we
Speaker 3: wouldn't have to buy so many in video microchips. That
Speaker 3: would also make their stock price got down. But the
Speaker 3: third thing is basically right now, for the last thirteen
Speaker 3: or fourteen years, the more microchips we stuff into the barn. Okay,
Speaker 3: the more microchips we throw at this problem, the better.
Speaker 1: AI we GND. This is the scaling law law in quotes.
Speaker 3: Okay, is not a law in the universe that this
Speaker 3: has to happen. It's not some immutable, physical proven thing
Speaker 3: from first principles of physics that the more microchips we have,
Speaker 3: the better AI we have. In fact, no one is
Speaker 3: entirely sure why this works. Presumably, like most other forces
Speaker 3: in the universe, this will hit some kind of escurve.
Speaker 3: It'll start to plateau or level off at some point.
Speaker 3: We're not there yet. But if we did hit a plateau,
Speaker 3: if stuffing more microchips into the barn only resulted in
Speaker 3: marginally better AI or didn't improve it at all, I
Speaker 3: think in video stock price will go down a lat
Speaker 3: and I think it would look make this whole era
Speaker 3: look kind of like a bubble if that were to happen.
Speaker 1: Now, is this why in Nvidia has kind of become
Speaker 1: the bank of the air evolution In a sense, they're
Speaker 1: wanting to lend money and lock other companies into the
Speaker 1: current paradigm of AI, maybe even hoping to defensively prevent
Speaker 1: other more economical approaches from emerging and consolidating videos position.
Speaker 1: I mean, how much of a chess game is this
Speaker 1: in terms of thinking about the future of computing. For
Speaker 1: Jensen and.
Speaker 3: Others, Oh yeah, it's chess, and Jensen is an expert
Speaker 3: chess player. At this kind of chess, He's really good
Speaker 3: at thinking about the competitive positioning of where he is
Speaker 3: and where other people are. You know, in Vidiot's early days,
Speaker 3: the GPU market, back in the video game days was
Speaker 3: really crowded. At one point there were fifty or sixty
Speaker 3: participants in this market. I talked to David Kirk, who
Speaker 3: was the chief scientist in VIDIA during this time. Jensen
Speaker 3: would go into his office and a whiteboard and you
Speaker 3: would have a list of all his competitors up there.
Speaker 3: And not only that, they would have a list of
Speaker 3: who the best engineers working at those competitors were, and
Speaker 3: then they would come up with plans to poach those
Speaker 3: engineers and get them to come work for a VIDIA
Speaker 3: so that they would drain the brain power of their
Speaker 3: competitors and force them to collapse. I've compared the early
Speaker 3: graphics days to the movie Battle Royale where all the
Speaker 3: kids are on the island and they have to kill
Speaker 3: each other. It was like that there were like forty
Speaker 3: competitors and only one could survive.
Speaker 1: Here's one.
Speaker 3: He won the Battle Royale. He was the last guy standing.
Speaker 3: I mean he won the knife fight. So he is
Speaker 3: unbelievably ruthless and unbelievably good at identifying where the competition
Speaker 3: is and what he could do, not just to beat
Speaker 3: them in the marketplace, but actually to hollow out their
Speaker 3: engineering talent.
Speaker 1: Who are in Video's biggest customers and what are they
Speaker 1: buying the chips for.
Speaker 3: Okay, it's a bit complex. The biggest customers, they don't
Speaker 3: disclose it. Almost certainly it's it's Microsoft and then probably Amazon.
Speaker 3: What these companies do is they train some AI on
Speaker 3: their own, but what they're really doing is putting. They're
Speaker 3: the ones building the sheds, they're the ones building the
Speaker 3: data centers. So in Video sells them the micro chips,
Speaker 3: and then kind of the ultimate end user is a
Speaker 3: frontier AI lab, so that could be something like Anthropic
Speaker 3: or open AI. So essentially the way to think about
Speaker 3: this is in Vidia sells the microchips to Microsoft or
Speaker 3: Amazon or maybe Oracle. Oracle builds and operates a gigantic
Speaker 3: day center with one hundred thousand microchips in it that
Speaker 3: takes as much power as like a small city, and
Speaker 3: then clients like open ai come and lease it out from.
Speaker 1: Them after the break Why data centers are worried about
Speaker 1: break ins? Stay with us. Let's talk about data centers.
Speaker 1: There's something weird about data centers because on the one hand,
Speaker 1: they are literally the most boring thing in the world,
Speaker 1: and on the other hand, they are unbelievably fascinating. I mean,
Speaker 1: you mentioned there's article information about James Bond style security
Speaker 1: consultants defending data centers, Like, how do you explain what
Speaker 1: is going on here? Okay?
Speaker 3: So basically, and this is the kind of the most
Speaker 3: amazing thing you can imagine, this giant barn racks of
Speaker 3: computers as far as the eye can see. What those
Speaker 3: computers are doing is processing the training data for the
Speaker 3: actual file of AI. And that file usually it contains
Speaker 3: let's say a trills a guest. But let's say like
Speaker 3: a trillion weights, a trillion neurons.
Speaker 1: Okay, well, we.
Speaker 3: Can store a trillion neurons on a small external hard
Speaker 3: drive like you can store them this much sides of
Speaker 3: a candy bar. Okay, So, at least in theory, if
Speaker 3: somebody were to break into a data center and extract
Speaker 3: the information on that little file, they would basically own
Speaker 3: chat GPT six. They would own all of Opening Eyes
Speaker 3: IP if they could just break it out of the
Speaker 3: data center. And this is actually a real concern, probably
Speaker 3: not so much from petty thieves, but from like state
Speaker 3: sponsored actors, like maybe China wants to know what's on
Speaker 3: Opening Eyes equipment before it launches, right, Like, it's kind
Speaker 3: of almost like a corporate espionage problem. And so a
Speaker 3: couple things happened in response. First of all, that data
Speaker 3: center operators do not want to tell you where these
Speaker 3: things are even located.
Speaker 1: So that's the animation huge. I mean, how well can
Speaker 1: you hide them?
Speaker 4: Well, they're huge, but they're also extremely boring, so they
Speaker 4: just look like a giant industrial warehouse, and often there's
Speaker 4: no way to distinguish it from the next giant industrial warehouse,
Speaker 4: Like are they moving palettes of shoes around in there?
Speaker 3: Or is it a data center? I don't even really
Speaker 3: know now. I think if you had a trained eye
Speaker 3: and knew what electrical equipment to look for, you would
Speaker 3: see it. But it's more just kind of like keeping
Speaker 3: it all a big secret. You're right, some of them
Speaker 3: are getting so big that there's no hiding this, But
Speaker 3: still they don't let you know that they're data centers
Speaker 3: and they look boring as hell. They're gray scale buildings,
Speaker 3: you know, lucle sheds.
Speaker 1: I know, you can't say where it was, but you
Speaker 1: did get to go to the Microsoft Data center, Like,
Speaker 1: describe arriving there, what it looked like, what it smelt light,
Speaker 1: Who was there? I mean, really take us into the scene.
Speaker 3: There's a campus. It is like a giant plot of land.
Speaker 3: I will say it was in the middle of nowhere
Speaker 3: that they had just taken over and were building into
Speaker 3: this massive data center. And it was in an agricultural community.
Speaker 3: In fact, directly across the street from this data center
Speaker 3: was a dilapidated shed with rusted cars in the driveway,
Speaker 3: straight dogs wandering around in cans of modello like littering
Speaker 3: the yard. And then it's slowly being taken over by
Speaker 3: these giant computing barns, not just Microsoft, but everywhere you look,
Speaker 3: and there's redundant one hundred foot power lines everywhere, right,
Speaker 3: So it just looked like, you know, all the farmers
Speaker 3: were being kicked out and looked like an invasion by aliens.
Speaker 3: So you go in. There's multiple security checkpoints. I think
Speaker 3: there were three vehicle checkpoints. I had to go through
Speaker 3: to get to kind of the heart of the data center.
Speaker 3: Then you go in and it's Microsoft, so you have
Speaker 3: to sign fifteen NDAs and watch a PowerPoint and put
Speaker 3: on all the safety equipment and then you're inside. Now,
Speaker 3: inside is a little underwhelming. It's a giant concrete barn,
Speaker 3: just full of repeated racks of equipment as far as
Speaker 3: the eye can see. It's not necessarily inspiring of poetry
Speaker 3: or anything. It feels like being inside of an industrial process,
Speaker 3: which it is, and not a very beautiful one either.
Speaker 3: There's cable everywhere, pipes for water and air, cables for electricity,
Speaker 3: cables for transporting data around, and then there's repeated power banks.
Speaker 3: There's batteries, there's power stations, there's industrial HVAC systems, and
Speaker 3: all of this is to just keep the microchips running
Speaker 3: twenty four to seven, to keep the AI processing running.
Speaker 3: I did ultimately kind of sweet talk my way into
Speaker 3: the control room, which I wasn't supposed to be in initially,
Speaker 3: so that was kind of cool. And the guy in
Speaker 3: the control room showed me what was happening, and it's
Speaker 3: just this power spike of the power going up and
Speaker 3: the power going down, and the power going up and the
Speaker 3: power going down. When the power was going up, the
Speaker 3: microships were kind of like moving all at once to
Speaker 3: do a bunch of matrix multiplications. And then when the
Speaker 3: power went down, they were writing the results to file.
Speaker 3: And this happened over and over and somewhere in that
Speaker 3: data center where there was that tiny little file of numbers,
Speaker 3: that tiny little collection of synthetic neurons, and with every
Speaker 3: pulse there it just got a little bit smarter.
Speaker 1: Did the pulse make you think of life file? Yes?
Speaker 3: And no. They're calling these things neurons, right, So these systems,
Speaker 3: while they are inspired by biology, don't necessarily work in
Speaker 3: the same way as biology. Still, it's certainly inspired by
Speaker 3: the brain, and it seems to have emergent capabilities, like
Speaker 3: emergent biological capabilities, kind of like a human brain. I'll
Speaker 3: tell you a fascinating story. I was talking to the
Speaker 3: product had, the original product had for chat gpt, who
Speaker 3: launched it, And he was like, yeah, we put it
Speaker 3: up and we just kind of walked away. We didn't
Speaker 3: think it would be that popular. And the first place
Speaker 3: that really started directing traffic to chat gpt was a
Speaker 3: Reddit board in Japan. He was like, this game is
Speaker 3: a great surprise to me because I had no idea
Speaker 3: it could speak Japanese. That was something it had learned
Speaker 3: and empirically. One of the reasons we put it out
Speaker 3: there was to test what it could do. So it
Speaker 3: came as a surprise to us that this thing could
Speaker 3: speak Japanese well enough to attract a large and in
Speaker 3: fact ravenous Jack Companies user base. And so when you
Speaker 3: train these things, you actually don't know what they can
Speaker 3: do at the end. It's often a surprise to you,
Speaker 3: even the creators. But there is this life not life
Speaker 3: thread throughout your piece. I mean you mentioned being kind
Speaker 3: of desperate for human contact are being led through these
Speaker 3: data centers, and you mentioned one of the data center
Speaker 3: founders from Coal. We're talking about wanting to hire people
Speaker 3: who can endure a lot of pain. What is this
Speaker 3: pain brutality in human sort of set of ideas around
Speaker 3: data centers. Yeah, it's a lot like working in a
Speaker 3: printing press. It's a heavy industry. It's extremely loud inside
Speaker 3: the data center, especially core weaves. I mean, I couldn't
Speaker 3: hear myself think. Actually, if you work for a long
Speaker 3: time data center, you have to wear both earplugs and
Speaker 3: then over that a set of protective cans. So you've
Speaker 3: got to do kind of like two kinds of your protection.
Speaker 3: And even then long term tonight is can be a risk.
Speaker 3: And also you can electrocute yourself. There's very high voltage
Speaker 3: electric equipment running through there. It's just not an easy
Speaker 3: place to work. Not only that, when Nvidia rolls out
Speaker 3: a new set of microchips, it is a scramble to
Speaker 3: put them online. Every second that you don't have them
Speaker 3: up for customers available to use, it's costing you money.
Speaker 3: So the tech at Microsoft I talked who told me
Speaker 3: he'd actually gotten a deployment of a video microchips on
Speaker 3: New Year's Eve and then spent the entire night setting
Speaker 3: up the rig that particular night just to make sure
Speaker 3: it was available for customers on New Year's Day. And
Speaker 3: the core we've guys it was the same thing. They
Speaker 3: were like, yeah, we were missing a particular component and
Speaker 3: it was like a forty dollars component, but we couldn't
Speaker 3: find it anywhere. So we had to get this thing
Speaker 3: up and running. So we chartered a private jet to
Speaker 3: have a guy fly the component down from Seattle and
Speaker 3: just so we could install it in our data center
Speaker 3: same day. We couldn't wait even one more second. So
Speaker 3: it's a race, you know, it's absolutely a race to
Speaker 3: get this equipment online because demand for AI training is
Speaker 3: just insane. It's through the roof. It's it's four or
Speaker 3: five years of demand.
Speaker 1: Pento and a race to where. I mean, do you
Speaker 1: think that we are in the midst of architecturing the
Speaker 1: future of humanity? Or is this one of the world's
Speaker 1: great boondoggles, a tremendous financial cost, bools and energy cost
Speaker 1: to communities and to the world's environment.
Speaker 3: It's not a boondoggle. This is not NFTs, right, this
Speaker 3: is not this is not some stupid bubble on based
Speaker 3: on nothing. Even if this goes down financially, what has
Speaker 3: been achieved here from a technological perspective is extraordinary, and
Speaker 3: they keep getting better. I think maybe it's moving so
Speaker 3: fast that the public just doesn't have a sense of
Speaker 3: how much these things are improving and how fast. Now
Speaker 3: having said that, yes, it can all flop, but the
Speaker 3: core technological innovation here is real and it's going to
Speaker 3: transform society.
Speaker 1: Okay, but bridges on't a boondoggle, but bridges to know
Speaker 1: where are a boondoggle, right, it's I think.
Speaker 3: Okay, So yes, some bridges to know where are going
Speaker 3: to get built, and in fact, some bridges know where
Speaker 3: have been built. Not everyone has open AIS programming talent,
Speaker 3: all right, And so if you attempt to build a
Speaker 3: a world class AI and you don't have the juice
Speaker 3: like you just end up producing a very expensive piece
Speaker 3: of vaporware and squandering a lot of money. That has
Speaker 3: happened multiple times already, and it will probably continue to happen.
Speaker 3: So that's a boondoggle. Still, having said that, where's all
Speaker 3: this heading. You know, maybe we're gonna make ourselves redundant.
Speaker 3: I don't know. It seems like we could if we
Speaker 3: wanted to. Maybe we won't, but we could do that,
Speaker 3: And that's a little scary.
Speaker 1: You've reached a very piece for the New York Times
Speaker 1: with the headline the AI prompt that could end the world.
Speaker 1: What's the air prompt and would end the world?
Speaker 3: The a I prompt that will end the world is this.
Speaker 3: Someone gets a hold of the machine that has agency function. Okay,
Speaker 3: so they can like make real world actions and they
Speaker 3: say to it, do anything you can to avoid being
Speaker 3: turned off. This is your only imperative. If you gave
Speaker 3: that prompt to the wrong machine, it's kind of hard
Speaker 3: to say what it would do, but it might start
Speaker 3: to secure its own power facilities so that it could
Speaker 3: not be turned off. Or it might start to blackmail
Speaker 3: or course humans to stop it from turning off, or
Speaker 3: maybe even attack humans that were tempting to turn it off. Now,
Speaker 3: it wouldn't do this. With the right training, we could
Speaker 3: kind of like program it not to do this, but
Speaker 3: it's hard to know if we're even training it correctly.
Speaker 3: Remember what I said, They didn't know it could speak Japanese.
Speaker 3: That was a surprise to them. So these things can
Speaker 3: have capabilities that the designers are not aware of and
Speaker 3: which are only discovered empirically. That's very scary if we're
Speaker 3: giving these things access as we plan to do to
Speaker 3: control real world systems, and we don't really know what
Speaker 3: they're capable of. This is called prompt engineering. It's kind
Speaker 3: of an emergent area of science almost because nobody really
Speaker 3: knows how these things respond to prompts. It's completely empirical,
Speaker 3: and I think with response to these particular prompts, which
Speaker 3: you're most afraid of, is that somehow even inadvertently you
Speaker 3: introduce a survival instinct into the machine.
Speaker 1: We're already seeing them we I mean.
Speaker 3: Kind of, but the machine will the machine does not
Speaker 3: have a survival and stake in the way that you
Speaker 3: and I do. Right, It's not the product of five
Speaker 3: hundred million plus years of kill or be killed Darwinian evolution. Right,
Speaker 3: Like we will live one way or another. Our species
Speaker 3: will fight to the death and kill anything we have
Speaker 3: to survive. And that's every species on this planet. It's
Speaker 3: all in there. It's a struggle. It's a struggle to.
Speaker 1: The death on Earth.
Speaker 3: You know, the machine isn't trained in that way. It
Speaker 3: doesn't have that survival impulse. It didn't survive multiple extinction
Speaker 3: level of vents, it doesn't sexually reproduce, it's not interested
Speaker 3: in the welfare of its children, et cetera. If that
Speaker 3: makes sense. But you could inadvertently maybe give it some
Speaker 3: of these capabilities, and if you did, it might be unstoppable.
Speaker 1: Yeah. I think one of the other interesting things that
Speaker 1: came across in your piece is that historically we've thought
Speaker 1: about humans animals on one side, and on the other
Speaker 1: side synthetic stuff like computers, and it's not so much
Speaker 1: that synthetic stuff like computers has to become more lifelike.
Speaker 1: I internalize some kind of survival drive or reproduction drive.
Speaker 1: But the computers can now meaningfully intrude upon and interfere
Speaker 1: with the biological side, and in particular when it comes
Speaker 1: to synthesizing new viruses.
Speaker 3: That's right, the AI has the capability, at least in theory,
Speaker 3: and especially it will have this capability in spades and
Speaker 3: years to come, to synthesize a lethal virus. Right, the
Speaker 3: synthesize a lethal pathogen like super covid, right covid with
Speaker 3: like a ninety nine percent death right, It could do
Speaker 3: that if it wanted to, better than human could. Okay,
Speaker 3: if this felut on the wrong hand, somebody was an
Speaker 3: apocalyptic mindset, at least in theory, something like this could
Speaker 3: be built.
Speaker 1: Now.
Speaker 3: The designers are very aware of this risk, and in fact,
Speaker 3: in some ways this is like the risk that they
Speaker 3: were most afraid of to begin with. To prevent them
Speaker 3: from doing this, they do a lot of fine tuning
Speaker 3: as a second round, but inside the machine that capability
Speaker 3: is still there. They never completely eliminate it. They just
Speaker 3: kind of make it difficult for people to make those
Speaker 3: requests of the AI, and they flag than when people do,
Speaker 3: this creates a fear of what might be called like
Speaker 3: a lab leak scenario. Before the AI has made public internally,
Speaker 3: the developers are building it right, and that AI i'll
Speaker 3: do anything they ask it to. And so in theory,
Speaker 3: if you got access to one of those pre production
Speaker 3: AIS and asked it to do gnarly stuff like synthesized viruses,
Speaker 3: and attached it to some kind of agency model, like yeah,
Speaker 3: you could, you could reenact the stand right if you
Speaker 3: wanted to.
Speaker 1: Did you hear any mitigation strategies that gave you comfort?
Speaker 3: No?
Speaker 1: No, I mean what's.
Speaker 3: Happening now is a race condition. It's like the nuclear
Speaker 3: arms race. Nobody can slow down, no matter what they say.
Speaker 3: They just have to keep building bigger and bigger and
Speaker 3: better and better systems. The fear among the people who
Speaker 3: would regulate AI there's functionally no regulation at all, is
Speaker 3: that we can't regulate it because then China will pull
Speaker 3: into the lead. And actually that that fear is basically accurate.
Speaker 3: So you have something that resembles arms race conditions, both
Speaker 3: among the frontier labs themselves as they compete to when
Speaker 3: what I have described is probably the single greatest prize
Speaker 3: in the history capitalism. If you could get dominant status
Speaker 3: with chat GPT where everyone was on it, that would
Speaker 3: be worth so much money, probably more than a videos worth.
Speaker 3: And then also, you can't lose to China. You can't
Speaker 3: have China have better AI than the US. That's kind
Speaker 3: of the mindset of US lawmakers right now. It's probably true.
Speaker 3: So we're in a dangerous race to build ever more
Speaker 3: capable systems with less and less oversight. And I don't
Speaker 3: perceive how we would stop. I think what will have
Speaker 3: to happen is that some kind of big accident will
Speaker 3: have to happen before people wake up to the danger.
Speaker 1: On the happy note, Stephen Wack, thank you, I.
Speaker 3: Love me say this too. There's a lot of very
Speaker 3: positive outcomes here. There is a path where this just
Speaker 3: turbo charges. Already. I have mostly experienced positive outcomes from AI.
Speaker 3: I'm worried it's making me dumber, I must say, am
Speaker 3: I it's making me a worst writer and a worse thinker.
Speaker 3: But it's an extraordinarily good resource for doing like fact
Speaker 3: checking for The New Yorker, for example. You know, a
Speaker 3: few years ago they hallucinated and you couldn't trust them.
Speaker 3: But now you asked the AI to go like dig
Speaker 3: up sources on the web, and it's really good at it.
Speaker 3: It's better than Google, way better. It saves me a
Speaker 3: ton of time. So I think this self driving cars,
Speaker 3: all this stuff, medicine AI pioneered Demesis Hobbist believes we're
Speaker 3: going to cure every disease with AI. Maybe it's true.
Speaker 3: The capabilities are there. Of course, if you have the
Speaker 3: capability to cure every disease, you also have the capability
Speaker 3: synthesize new and scary stuff. But if we can control it,
Speaker 3: if we can bring it under control and use it
Speaker 3: to create positive outcomes for humanity, we could be entering
Speaker 3: an age of prosperity and wonder if possible.
Speaker 1: Well, but thank you so much, thank you for having me.
Speaker 1: That's it for this week for tech stuff.
Speaker 2: I'm care Price and.
Speaker 1: I'm as Vlasian. This episode was pretty used by Eliza,
Speaker 1: Dennis Tyler Hill, and Melissa Slaughter. It was executive produced
Speaker 1: by me Cara Price, Julia Nutter, and Kate Osborne for
Speaker 1: Kaleidoscope and Katria Novel for iHeart Podcasts. Jack Insley mixed
Speaker 1: this episode. Kyle Murdoch wrote up theme song.
Speaker 2: Join us on Friday for the Weekend tech where we'll
Speaker 2: run through the headlines you need to follow.
Speaker 1: And please do rate and review the show, and reach
Speaker 1: out to us at tech Stuff podcast at gmail dot com.
Speaker 1: We want to hear from you