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