Speaker 1: On this episode of a News World. From boardrooms to
Speaker 1: dorm rooms, AI seems to be what everyone is talking about,
Speaker 1: from the promise of Chat GPT to robots that may
Speaker 1: or may not take your job. In his new book
Speaker 1: AI Valley, Microsoft, Google, and the trillion dollar Race to
Speaker 1: cash in an Artificial intelligence, Pulitzer Prize winning journalist Gary
Speaker 1: Rivlin follows the launch of Chat, GPT and three startups,
Speaker 1: all with big dreams of cashing.
Speaker 2: In on AI.
Speaker 1: But it's not long before the tech giants enter the
Speaker 1: AI space. Rivlin lays out the fascinating history of AI's evolution,
Speaker 1: the breakthroughs and wrong turns, and the major players in
Speaker 1: Silicon Valley, the developers and investors.
Speaker 2: Who will lead the future of AI.
Speaker 1: Here to discuss his new book, I am really pleased
Speaker 1: to welcome my guest, Gary Rivlin. He is a Pulitzer
Speaker 1: Prize winning investigative reporter who has been writing about technology
Speaker 1: since the mid nineteen nineties and the rise of the Internet.
Speaker 1: He is the author of ten previous books, including Saving
Speaker 1: Main Street and Katrina After the Flood. His work has
Speaker 1: appeared in The New York Times, Newsweek, Fortune, GQ, and Wired,
Speaker 1: among other publications. Gary, that's an amazing record. Thank you
Speaker 1: for joining us.
Speaker 3: Thank you my pleasure.
Speaker 1: So you've been covering tax since the nineties. How has
Speaker 1: tech evolved since you first started covering it and what
Speaker 1: has surprised you most about how Silicon Valley has evolved.
Speaker 3: Well, the short answer is the dominance of the giants.
Speaker 3: In fact, for this book, I went looking since the
Speaker 3: end of twenty twenty two and I realized, like, this
Speaker 3: is now the AI moment. They turned back to tech
Speaker 3: and I was looking for what would be the new Google,
Speaker 3: what would be the new Facebook? And it turns out
Speaker 3: the new Google is Google, the new Facebook is Facebook.
Speaker 3: And so you know, kind of dating back to the
Speaker 3: mid nineteen nineties, it was all about startups. It's all
Speaker 3: about these companies founded in a dorm room, someone's garage.
Speaker 3: They have a great idea, they raise a little bit
Speaker 3: of money, they get some traction, and they become Google. Facebook.
Speaker 3: But AI is different. I fear it's going to solidify
Speaker 3: the power of big tech rather than open it up
Speaker 3: to another set of players.
Speaker 1: Are there characteristics of the investment you have to make
Speaker 1: that make AI susceptible to that kind of dominance money?
Speaker 3: It's just so expensive. In the old days, you can
Speaker 3: raise a million, a few million dollars and start to
Speaker 3: get traction. Then you need to raise the big money
Speaker 3: to go national, go global, whatever. But AI training these models,
Speaker 3: this general AI where they can talk to you, spit
Speaker 3: out images, make video. It's so expensive to train these things.
Speaker 3: It's so expensive to fine tune these things. It's so
Speaker 3: expensive to operate these things. Is beyond the means of
Speaker 3: most startups. But I first started reporting on this at
Speaker 3: the start of twenty twenty three, it would be millions,
Speaker 3: maybe ten million to train and fine tune one of
Speaker 3: these models before they released. By the time I was
Speaker 3: done reporting, it was one hundred million. And now it's
Speaker 3: billions of dollars. What's startup? I mean, there's open AI,
Speaker 3: there's a couple others, but very few startups could raise
Speaker 3: that kind of money. And it's still not enough. I mean,
Speaker 3: these startups are still losing money, so they have to
Speaker 3: raise billions, tens of billions, perhaps in the future, not
Speaker 3: so distant future, one hundred billion dollars and more. And
Speaker 3: big tech can afford that. You know, Microsoft, Apple, They're
Speaker 3: sitting on one hundred billion dollars or so in their savings.
Speaker 3: But what chance does a startup have to do of
Speaker 3: these cutting edge foundational models, the chatbots in the like.
Speaker 1: When you look at the biggest companies Microsoft, Google, Meta,
Speaker 1: and Amazon.
Speaker 2: That the share of volume of cash that they create.
Speaker 1: Yeah, to me, it's astonishing that, with the exception of
Speaker 1: a couple companies in China and Saudi Aramco, all of
Speaker 1: the trillion dollar companies in the world are American.
Speaker 3: It's astonishing. Let's just focus on Search is arguably the
Speaker 3: best business ever. Once you've built the infrastructure, there's not
Speaker 3: much marginal costs to add new users, and so Google
Speaker 3: is making over one hundred billion, something like one hundred
Speaker 3: and fifty billion dollars a year in profits just from search.
Speaker 3: Microsoft they've been struggling to get into search with being
Speaker 3: and they would get like three four five percentage points,
Speaker 3: which is nothing except for it would still translate to
Speaker 3: hundreds and hundreds and hundreds of millions of dollars of revenue.
Speaker 3: And you can make the same argument with Facebook Meta.
Speaker 3: In the old days, the rough statistic is newspapers, magazines,
Speaker 3: publications brought in like fifty billion dollars in advertising revenue collectively.
Speaker 3: That was a circle like two thousand. Nowadays they're bringing
Speaker 3: in closer to ten billion dollars and most of the
Speaker 3: rest is being divvied up by Google and Meta, and
Speaker 3: so it's kind of an idea of winner takes most
Speaker 3: and the stakes are so big that they're just making
Speaker 3: so much money. But you know, this dates back to Microsoft.
Speaker 3: I mean Microsoft, which actually sells product, right, I mean
Speaker 3: you buy your Windows operating system and software package, office
Speaker 3: software packages. You know, their profits were astonishing in the
Speaker 3: nineteen nineties, and I think it just builds on itself.
Speaker 3: They're so rich they can afford to invest in AI,
Speaker 3: gener of AI, these leading edge technologies, and sometimes bigness
Speaker 3: is a weakness right there. You know, they kind of
Speaker 3: trip over their own feet. Google was so far ahead
Speaker 3: of everyone with machine learning, ditting back to the twenty tens,
Speaker 3: but of course it was open AI to a chat GBT.
Speaker 3: You know, big companies are scared the innovator's dilemma. They
Speaker 3: don't want to threaten their existing honeypot. And also, you know,
Speaker 3: I mean startups have advantages, but the advantage of money
Speaker 3: in AI makes me worry that it's kind of game over.
Speaker 1: In the case of Google, you're doing all the work.
Speaker 1: They build a framework within which you get to come,
Speaker 1: you get to play. They don't have to pay anybody.
Speaker 1: These people are all coming and saying, please let me
Speaker 1: come and use your.
Speaker 3: Material exactly Facebook, Instagram, we could go on listing, Twitter X.
Speaker 3: There's an expression in Silicon Valley if you're not paying
Speaker 3: for the product, you are the product. And that's a
Speaker 3: perfect way of understanding a Google or a Facebook. Look,
Speaker 3: let's use Google. So this phone in your pocket tracks
Speaker 3: you everywhere you do your searches online. They track that,
Speaker 3: They bundle up the data, and they sell it to
Speaker 3: the highest bidder. It's a great business. It makes a
Speaker 3: lot of money for them. But I think citizens only
Speaker 3: slowly woke up to that fact. And again this gets
Speaker 3: me back to my worry that if the same big
Speaker 3: tech companies, the same few companies are going to dominate
Speaker 3: AI the way they've dominated the last bunch of years.
Speaker 3: We don't trust them as the stewards for technology, and
Speaker 3: in fact AI is a powerful technology. AI is going
Speaker 3: to rely on our information. There's privacy concern. So that
Speaker 3: is my concern that these companies that are proven untrustworthy
Speaker 3: are going to be the ones bringing us this amazing power.
Speaker 3: I'm optimistic about AI. I lean optimistic. I think AI
Speaker 3: could bring incredible things around education, scientific breakthroughs, medicine. My
Speaker 3: worry is it's in the hands of companies that show
Speaker 3: that it's all about profits and not about trust and safety,
Speaker 3: which again I think is essential for AI.
Speaker 2: Let me do all his thinction.
Speaker 1: There was a period there where people were coming up
Speaker 1: with pretty cool innovations and then promptly getting bought, so
Speaker 1: they never actually a chance to grow into a competitor
Speaker 1: because they were acquired by these large companies and who
Speaker 1: absorbed them. Now, if I understand you correctly, it's ructually
Speaker 1: impossible for a startup in the AI field because of
Speaker 1: the scale of resources it takes to create it. So
Speaker 1: virtually all of the next level of innovation in terms
Speaker 1: of small companies is going to be the use of AI,
Speaker 1: which will be provided by one of the big companies.
Speaker 3: Let me break the startup world into two general categories.
Speaker 3: There's still going to be plenty opportunities for founders to
Speaker 3: raise some money and have a good return on an investment.
Speaker 3: For some app like AI will automatically fill out your
Speaker 3: expense sheets and do most if not all, of the
Speaker 3: work for you. You could see that be very valuable.
Speaker 3: And then there'll still be in quote little companies, they'll
Speaker 3: have millions of users bringing tens hundreds of millions of dollars.
Speaker 3: But I'm really focused on that second category. I have
Speaker 3: a billion plus users, I have a market cap, a
Speaker 3: paper worth of over a trillion dollars. Doing the foundational models,
Speaker 3: doing the stuff that's underneath everything, this stuff right at
Speaker 3: the center of it. They're training the models, they're operating
Speaker 3: the models that these other smaller companies apps. They could
Speaker 3: be for business, they could be for individuals, or be
Speaker 3: AI therapists, they'll be AI life coaches. I mean, there's
Speaker 3: plenty of opportunities for small companies. But as I'm saying that,
Speaker 3: I realize that Google Meta Open Ai a three hundred
Speaker 3: million dollar company right now on paper, you know, they
Speaker 3: have their own life coaches. Some of them are working
Speaker 3: on therapists or companions and all this, and so there
Speaker 3: is room in that first category for companies to break through.
Speaker 3: But I do worry too that not only will big
Speaker 3: tech dominate that second, that to me more central, foundational category,
Speaker 3: but they too can pick off folks in that first category.
Speaker 3: Either they'll do it better and beat the competition because
Speaker 3: they have more money, more ability to train these AI models,
Speaker 3: or if they just buy it. Look at Washington right now,
Speaker 3: the FTC has the case against Meta because Mark Zuckerberg
Speaker 3: fear at Instagram, so he bought Instagram. And did he
Speaker 3: buy it because he wanted to grow it and make
Speaker 3: it into its own product, or do you buy it
Speaker 3: because he was scared he was going to threaten Facebook
Speaker 3: and he wanted to defang it.
Speaker 1: I mean, we've had these cycles where bigness ultimately gets
Speaker 1: tackled by the government if this assumption that it becomes
Speaker 1: predatory or inhibits the growth of competitors. But I want
Speaker 1: to go from the corporate side to AI itself. You
Speaker 1: make the point in your book that AI started to
Speaker 1: develop and then in the nineteen seventies it sort of stopped.
Speaker 1: You describe it as sort of the AI winter. Could
Speaker 1: you walk us through that.
Speaker 3: One of the fun things in doing this book was
Speaker 3: how did we get here? How long have we been
Speaker 3: playing around with AI? So it dates back to at
Speaker 3: least the nineteen fifties. The term AI artificial intelligence was
Speaker 3: coined in the late nineteen fifties and it's just funny
Speaker 3: to read the optimism, the wild optimism of those behind
Speaker 3: AI in the fifties and sixties. They were convinced amazing
Speaker 3: things were right around the next corner. But you know,
Speaker 3: AI was right around the next corner for about seventy years.
Speaker 3: And part of that is computers weren't strong enough. Part
Speaker 3: of that is we need digital data, and we didn't
Speaker 3: really have that much digital data until people started posting
Speaker 3: and migrating so the Internet in the mid nineteen nineties.
Speaker 3: But part of it was a monumentally wrong turn. It
Speaker 3: was an amazing academic at Cornell who came up with
Speaker 3: this idea of a neural network, this idea that computers
Speaker 3: would learn in the fashion of a human. They would
Speaker 3: read material, they'd get feedback, and they would improve that
Speaker 3: way rather than coding line by line by line by line.
Speaker 3: Professor was mocked, and for forty or fifty years that
Speaker 3: approach was considered the wrong approach among the academics, the
Speaker 3: prevailing thought among computer scientists. It really wasn't until the
Speaker 3: twenty tens were what people are now calling machine learning,
Speaker 3: deep learning, neural networks. These models, these systems that learn
Speaker 3: through training and improve through feedback. Wasn't until the mid
Speaker 3: twenty tens that that really took on as the best approach,
Speaker 3: and in fact, that is why we're where we're at
Speaker 3: right now, machine learning, neural networks, that's the basis for chat,
Speaker 3: GBT and all the chatbots and other systems people are
Speaker 3: using to draw their photos or make little video clips.
Speaker 1: How much of this was just a function of computer
Speaker 1: power catching up with the theory.
Speaker 3: I think the academics dismissed neural networks as just the
Speaker 3: wrong approach in thet Hence, even if we had taken
Speaker 3: the neural networks approach, computers weren't nearly as powerful. In fact,
Speaker 3: the godfather of machine learning, Jeffrey Hinton, professor at University
Speaker 3: of Toronto, he made the point like no one imagined
Speaker 3: that these machines would be a million times more powerful,
Speaker 3: But you know, with an exponential gets more and more
Speaker 3: powerful with each passing year, to the point where they
Speaker 3: could handle billions of operations a second, which when you
Speaker 3: go to a chetbot, you know, chat, GPT, Claude, you
Speaker 3: know Google's Gemini, it doesn't make a difference. There's like
Speaker 3: billions of processes that are going on. You say hello,
Speaker 3: and it says hello back to you. You say, tell
Speaker 3: me who New Gingrich is, and it goes and searches
Speaker 3: and spits out a three or four sevens answer. There's
Speaker 3: like billions and billions of operations. And today's computers are
Speaker 3: strong enough. Today's computer chips are powerful enough to make
Speaker 3: that happen. But that wasn't true thirty years ago, certainly,
Speaker 3: probably not even ten years ago.
Speaker 2: I was surprised.
Speaker 1: I co chaired a working group on Alzheimer's around two
Speaker 1: thousand and seven, and I realized that to really do
Speaker 1: brain science, that the brain actually has about the same
Speaker 1: number of synapses as the number of scars in the universe.
Speaker 1: There's an astonishing identity, and that literally investing in computer
Speaker 1: power was central to advancing brain science because we literally
Speaker 1: at that point did not have the processing capability to
Speaker 1: truly analyze in depth all the things that happened in
Speaker 1: the brain. And now twenty years later, we're beginning to
Speaker 1: move into a zone where we can have that kind
Speaker 1: of activity.
Speaker 3: The brain helped us understand neural networks just like the brain.
Speaker 3: I think it's eighty six billion neurons. These neural networks
Speaker 3: try to emulate that, but I'm convinced these models are
Speaker 3: going to help us better understand the brain sciences. Interesting
Speaker 3: with AI because people get with science, there's specialties and subspecialties,
Speaker 3: and they all have their own vocabulary, and it's really
Speaker 3: hard to go across specialties across subspecialties. But with these
Speaker 3: neural networks, they're finding that they could have them read everything.
Speaker 3: The main model I write about in the book, it
Speaker 3: was trained on a trillion and a half words. You
Speaker 3: need thousands of human beings reading NonStop for their whole
Speaker 3: life to get close to that. And so these models
Speaker 3: trained for science, they could read studies in every discipline
Speaker 3: and make connections that no human being can possibly make.
Speaker 3: And that's one of the things I find most promising
Speaker 3: about this that some of the answers are right there,
Speaker 3: just we haven't made the connections, and something like AI
Speaker 3: can help us make those connections.
Speaker 1: As this thing began to develop, and as computers became
Speaker 1: more powerful and more central, and Nvidia emerged as an
Speaker 1: amazingly key producer of the most advanced ships, why didn't
Speaker 1: other folks like Intel do that? I mean, there are
Speaker 1: companies who were doing pretty well and then and video
Speaker 1: just explodes in its capacity.
Speaker 3: This is one of those kind of Columbus is looking
Speaker 3: for spices in the Far East and discovers America. So
Speaker 3: in Vidia created the most powerful graphics chip and so
Speaker 3: that was their specialty for playing, you know, video games.
Speaker 3: And it just turns out that these graphics chips are
Speaker 3: perfect for training AI because they could just do billions
Speaker 3: of operations in parallel, and that's what you need for AI.
Speaker 3: It's that complicated math, but it's just a lot of
Speaker 3: it at once. And so these in Vidia chips, they
Speaker 3: were just kind of Johnny on the spot. They were
Speaker 3: the perfect chip for training these neural networks. They're perfect
Speaker 3: for machine learning. But the chip world is not the
Speaker 3: software world. It moves very very slowly. And there's all
Speaker 3: these innovative startups out there that are trying to create
Speaker 3: chips that are designed specifically for AI. We still don't
Speaker 3: really have cutting edge AI specific chips. Maybe some of
Speaker 3: the memory should be on the chip. There's different ideas
Speaker 3: out there, and I guarantee you ten years from now,
Speaker 3: there will be innovative chips supplanting in Vidio's graphic chips,
Speaker 3: but in Video might create that chip. It might still
Speaker 3: be with Nvidia, but you know, the H one hundreds
Speaker 3: and the chips that are the mainstay right now of
Speaker 3: artificial intelligence. They're going to be replaced. It just takes
Speaker 3: time to develop, tests, produce, mass, produce.
Speaker 1: There had shindo been a belief that Moore's law would
Speaker 1: disappear and that you wouldn't have continuous doubling of capability
Speaker 1: because as the chips got smaller and smaller, the challenge
Speaker 1: of dealing with heat became greater and greater. But somehow
Speaker 1: we've leaped past all that. What we're seeing now seems
Speaker 1: to be. You could not have projected this in the
Speaker 1: nineteen eighties exactly.
Speaker 3: I was writing for the New York Times in mid
Speaker 3: two thousands, and that was the prediction. But I don't
Speaker 3: have to tell you science is amazing, technology is amazing
Speaker 3: that you know. Right after chat GPT came out, there
Speaker 3: was the doomers their call, those who were worried about
Speaker 3: laser eyed robots subjugating humanity, the kind of stuff I
Speaker 3: think is born in Hollywood in the media coverage, But
Speaker 3: that was the fear out there, like let's pause this,
Speaker 3: Let's have a six month pause so we could catch up,
Speaker 3: Like just not going to happen. It's called innovation. You
Speaker 3: can't slow science, you can't slow discovery. The answer is
Speaker 3: to manage it, to make sure that it's more of
Speaker 3: a positive than a negative. All technologies cut both ways.
Speaker 3: They're both positive and negatives. You know, cars changed our society,
Speaker 3: but cars kill thirty five forty thousand people year in America.
Speaker 3: They cause pollution. So all technologies are like that. So
Speaker 3: my great wish if I had a magic wand it
Speaker 3: would be like, just let's deal with this, folks. Let's
Speaker 3: try to make sure that AI is more of a
Speaker 3: positive than a negative. But you know, there's a lot
Speaker 3: of other issues in the world right now that are
Speaker 3: distracting us from that.
Speaker 1: You point out that from Google's perspective, the first great
Speaker 1: use of AI was improving targeting us as customers and
Speaker 1: figuring out what we really like and making sure the
Speaker 1: ads come up that we would be interested in. So
Speaker 1: just a fascinating way that things evolve in a way
Speaker 1: you couldn't probably have predicted if you're sitting in some
Speaker 1: academic place drawing up a plan.
Speaker 3: Right, So machine learning to maximize the cash register basically,
Speaker 3: So yeah, I mean, let's give Google credit. Though they
Speaker 3: got that machine learning artificial intelligence was going to be
Speaker 3: really powerful, so they would use in the early days.
Speaker 3: That you gave one example of kind of more efficiently
Speaker 3: matching ads to searches, but also to deal with horble
Speaker 3: Google searches they are spelling mistakes, they kind of understand
Speaker 3: the context and help smooth them out. The funny thing
Speaker 3: about artificial intelligence is all of us have been using
Speaker 3: AI for a long long time. I am using example
Speaker 3: of Google Search, but there's Google Translate that's been around
Speaker 3: since twenty fifteen or so. That's artificial intelligence. You go
Speaker 3: to Netflix or Spotify and they recommend you might like
Speaker 3: this movie, you might like this song. That's AI. The
Speaker 3: difference with the release in twenty twenty two of chat
Speaker 3: cheapt from Open AI was that we could talk with it.
Speaker 3: It wasn't a product behind the glass. It was something
Speaker 3: that we can actually link to and use and chat
Speaker 3: with it. I think that's what changed everything. The idea
Speaker 3: that we can actually see it working and play with
Speaker 3: it made us really stand up and take attention again.
Speaker 1: With she was sixty, we do a lot of polling.
Speaker 1: We now run all the polling questions through chat GPT.
Speaker 3: I use sometimes use chat cheapt My favorite it is
Speaker 3: called Claud from Anthropic. The same way it's my go
Speaker 3: to editor. People have to understand how they use this.
Speaker 3: AI is a co pilot. It's not like you know,
Speaker 3: type in make me a Martin Scorsese movie hit entery,
Speaker 3: and you're gonna have it. You have to be the creative.
Speaker 3: You have to give it the ideas. If you ask
Speaker 3: it to write something, it'll be flat, it's not gonna
Speaker 3: be particularly good. It'll read like kind of like a
Speaker 3: press release or boring report. But if you use it
Speaker 3: as your companion and you're the creative. So what i
Speaker 3: U is for is I'm struggling with a paragraph. I
Speaker 3: don't like this transition. Help me out with this sentence
Speaker 3: right in five different ways. And it's never like I
Speaker 3: cut and paste and say, oh that's the sense, but like,
Speaker 3: oh that's a good idea. I didn't think of that.
Speaker 3: Oh that's an interesting word. Let me use that. I
Speaker 3: routinely now before I hand something in, I have it edit.
Speaker 3: It finds typo's, it finds mistakes, hey in bold, give
Speaker 3: me suggestions from improving it again. Often I just ignore
Speaker 3: their suggestions. But it's a really powerful tool to help
Speaker 3: you refine to make what you're working on, make your
Speaker 3: work better. It's your copilot, it's not your digital employee creator.
Speaker 2: But it's a pretty powerful coil.
Speaker 3: So start at twenty twenty three. My role in the
Speaker 3: second half of the nineties was the skeptic around dot com.
Speaker 3: It's like, Okay, the Internet's going to be incredible, but
Speaker 3: you're not going to get fabulously wealthy overnight startups, and
Speaker 3: in fact most of them went Now I was ready
Speaker 3: to be a skeptic. It's magic, it's sorcery. I mean,
Speaker 3: the first few times I used it. The first thing
Speaker 3: I did write me a five thousand word book proposal
Speaker 3: to sell a book on AI. And you know, I mean,
Speaker 3: it wasn't particularly well written, but it's far better read
Speaker 3: than I am. It has a far better memory than
Speaker 3: I do. It was just so useful as a lousy
Speaker 3: first draft, but it gave me so many ideas and
Speaker 3: sped up the process. If I had to start from scratch,
Speaker 3: it would have been so much harder, like as opposed
Speaker 3: to like, oh that's a good structure, that's a good idea. Yeah, yeah,
Speaker 3: I do need to stress that I'm a journalist. It's
Speaker 3: like Hey, we have this amazing product, and you go
Speaker 3: use the product and like, yeah, maybe in two years
Speaker 3: you have an amazing product, but right now it's buggy
Speaker 3: and crappy. But that was not my feeling on AI.
Speaker 3: I would play with it to create an image, and
Speaker 3: it was just like having a superpowers, Like I could
Speaker 3: write poetry. I could translate my worse into a foreign
Speaker 3: language in seconds. I could give it all these different
Speaker 3: ideas and like, hey, write this up as an email,
Speaker 3: and it was like, give me a head start. I'm
Speaker 3: with you. I think AI is magic. It's limited, but
Speaker 3: I think it does give you magical powers.
Speaker 1: The comedian Buck Henry used to say, any technology you
Speaker 1: cannot explain his magic, which for most of us means
Speaker 1: most of it's magic.
Speaker 3: Hold on one second, because one way AI is different
Speaker 3: than the rise of the Internet is those who create AI,
Speaker 3: those who are creating these chatbots and other models, they
Speaker 3: can't explain why it says what it says. They call
Speaker 3: it the black box issue they've created. They understand what
Speaker 3: they've created is based on mathematical models looking for power
Speaker 3: than jadda yadayada, But it surprises even them. I say, like, well,
Speaker 3: I have two teenage sons. I can't explain what comes
Speaker 3: out of their mouth. I've tried to train them and stuff.
Speaker 3: It's like the human brain, like we sort of get
Speaker 3: how it was shape, but why a person is saying
Speaker 3: what they're saying, or some of the ideas that come
Speaker 3: out of their mouth, we can't explain. And that's the
Speaker 3: weird thing. That's among the weird things about AI.
Speaker 1: One of the side things you talk about fascinating what
Speaker 1: if was that Microsoft actually invested in AI pretty early
Speaker 1: in the nineties and really made a major investment. Other
Speaker 1: than IBM, they were the earliest, but they.
Speaker 2: Went down the track.
Speaker 1: It turned out to be sort of a dead end,
Speaker 1: but they then culturally were deeply committed to that track.
Speaker 1: Here was the company that could have been the forerunner,
Speaker 1: but in fact, because it took a detour, it actually
Speaker 1: had its own culture fighting the emerging reality of the
Speaker 1: new system.
Speaker 3: Microsoft was so early AI that they approached the rules
Speaker 3: based approach through sheer muscle. We're going to teach these
Speaker 3: machines line by line by code, like millions of lines
Speaker 3: of codes. Later, it still couldn't drive, It still couldn't
Speaker 3: do what people wanted it to do, and so when
Speaker 3: machine learning came along, they were resistant to it. They thought, well,
Speaker 3: that's the wrong approach. And so where Google since the
Speaker 3: two thousands was investing in machine learning, Microsoft was doing
Speaker 3: very little investing and machine learning. So them being early
Speaker 3: on actually turned out to be a disadvantage. But let's
Speaker 3: flip that and give Microsoft credit. They realized that they
Speaker 3: were losing the fight that Google meta other large companies
Speaker 3: were ahead of them, and so in twenty nineteen they
Speaker 3: invested a billion dollars into open Ai. They realized, like,
Speaker 3: we're not going to catch up the old fashioned way,
Speaker 3: so let's invest in this cutting edge startup. They would
Speaker 3: put another ten billion dollars in right after open ai
Speaker 3: released chat GPT, and that really kind of helped them
Speaker 3: at the forefront. Stay at the forefront of AI, who
Speaker 3: are very savvy investment, they're also savvy. They didn't insist
Speaker 3: on buying it. They said, we'll be an investor. And
Speaker 3: there were certain advantages. I mean, they own a large
Speaker 3: piece of companies now on paper worth three hundred million dollars,
Speaker 3: so they've seen a nice return on that investment. But
Speaker 3: maybe more importantly, they had early access to open AI's technologies.
Speaker 3: They were the purveyor. They were the ones you would
Speaker 3: go to if you wanted to use open AIS technologies,
Speaker 3: and that really put Microsoft at the forefront of AI.
Speaker 4: Despite that wrong term.
Speaker 1: One of the things you talked about, which is the
Speaker 1: personal passion of mine, is the potential impact of artificial
Speaker 1: intelligence on dramatically changing healthcare. We may see more different
Speaker 1: kinds of things evolving in the health system in the
Speaker 1: next few years than anybody would have thought possible.
Speaker 3: I'm with you on that. New vaccines, new remedies, smarter
Speaker 3: ways of treating diseases. There are folks who are predicting
Speaker 3: that within ten years eradicate a lot of cancers. I'm
Speaker 3: not sure that, but I see the possibility again. I'll
Speaker 3: come back to this idea of AI as a copilot.
Speaker 3: Do I want an AI model to be my radiologist. No,
Speaker 3: but I want my radiologists to use AI as a
Speaker 3: backup because what they're finding, whether it's mammograms or you know,
Speaker 3: just eye imagery, that these models are far more accurate
Speaker 3: than the doctors. A doctor might have, you know, low
Speaker 3: nineties accuracy and detecting a cancer, these models are up
Speaker 3: in the high ninety percent, you know, ninety seven ninety
Speaker 3: eight percent accuracy. So it's a great backup for doctors,
Speaker 3: and you know, beyond that, there's starting to be again,
Speaker 3: let's go back to this miracle thing, like, so there's
Speaker 3: one model right now where it could listen to your
Speaker 3: voice and predict whether you have type two diabetes. And
Speaker 3: I think there's going to be a million ways that
Speaker 3: plays out that they could just sort of detect things
Speaker 3: that the human eye can or perhaps a very well
Speaker 3: experienced doctor can. But these models are going to be
Speaker 3: able to monitor us with our permission and let us
Speaker 3: know of problems that we otherwise would not find out
Speaker 3: for a long time, because it's not until it manifests
Speaker 3: itself as a problem that we're going to show up
Speaker 3: at a doctor's office.
Speaker 1: You make a point which I never thought about, which
Speaker 1: is that very often something that's artificial intelligence, once it
Speaker 1: gets common, we no longer refer to it as artificial intelligence.
Speaker 3: It's just technology. It frustrates some AI people they didn't
Speaker 3: really get their credit and all. It's only till now. Again,
Speaker 3: I think the difference is we're interacting with it. I
Speaker 3: think people now will know and I think we need
Speaker 3: to know. I mean, that's a big push right now
Speaker 3: that everything is labeled like is this human generated? Is
Speaker 3: this AI generate? I think it's going to change now.
Speaker 3: It's not like, oh, we're using it, so it's just
Speaker 3: going to fade into lives. I mean maybe in a
Speaker 3: generation or two when it becomes second nature. But for
Speaker 3: the foreseeable future, I think we're going to be very
Speaker 3: aware that artificial intelligence is artificial intelligence. But with that said,
Speaker 3: there's a lot of largely when we talk about AI,
Speaker 3: we're talking about generative AI. This idea that you can
Speaker 3: type a prompt in and it gives you an answer,
Speaker 3: it gives you an image, it gives you a video.
Speaker 3: But AI is very multipurpose. There's different versions of AI.
Speaker 3: You know, businesses using AI for intelligence like sift through
Speaker 3: all our data and look for connections that we don't
Speaker 3: see help us predict where the market's going to be
Speaker 3: five or ten years from now. I mean, there's different
Speaker 3: versions of AI, and that AI beyond gender of AI
Speaker 3: will always have that problem that for people it's just
Speaker 3: technology like artificial intelligence.
Speaker 1: What's your sense in the investment community, are people committed
Speaker 1: to trying to develop various artificial intelligence capabilities or are
Speaker 1: they sort of dubious about their profitability.
Speaker 3: Oh my goodness, venture capital is just pouring in and
Speaker 3: continuing to pour in. There's one high profile company, Safe Superintelligence.
Speaker 3: It doesn't have a product yet, but it has the
Speaker 3: right names behind it as leading figures in machine learning
Speaker 3: behind it. So vcs have invested so many billions in
Speaker 3: it that it has a paperworth of thirty two billion
Speaker 3: dollars without a product, and the number I saw us
Speaker 3: in twenty twenty four like one hundred and fifty billion
Speaker 3: dollars or so I went into artificial intelligence for a
Speaker 3: venture capital outfit, I get it for a large corporation.
Speaker 3: Let's remember that large corporations Google, Amazon, Microsoft, et cetera salesforce.
Speaker 3: They're major investors playing the role of venture capitalists, because
Speaker 3: who has the billions of dollars to invest? Venture capital
Speaker 3: outfit raises one billion dollars and these things cost billions
Speaker 3: of billions of dollars. So Google has put billions of
Speaker 3: dollars into Anthropic, the company behind the chatpot Claude. Amazon
Speaker 3: has put billions of dollars into that, and so it's
Speaker 3: a rational decision by the venture capitalist. It's a rational
Speaker 3: decision by these large corporations because the cost of missing
Speaker 3: this is greater than the cost of wasting the money
Speaker 3: the idea that META wouldn't invest in AI, it's a
Speaker 3: multi trillion dollar opportunity they would have missed. So they're
Speaker 3: putting tens of billions, perhaps eventually hundreds of billions into
Speaker 3: AI because they can't afford to miss this opportunity. The
Speaker 3: same with venture capitalists. They know that most of these
Speaker 3: startups are not going to work out, but their hope
Speaker 3: is that in their fun they catch one or two
Speaker 3: that does work out and is worth billions tens of
Speaker 3: billions of dollars one day.
Speaker 1: It's been an amazing ride, I guess really starting in
Speaker 1: the eighties and then accelerating from there.
Speaker 3: So I started writing a tech in nineteen ninety five.
Speaker 3: At that point about seven billion dollars was going to
Speaker 3: venture capital, which used to sound like a big number,
Speaker 3: and by twenty twenty two is three hundred billion, three
Speaker 3: hundred and fifty billion, something like that. And so the
Speaker 3: competition is insane, which helps, of course drive up the prices.
Speaker 3: So there's this one VEGI capital outfit that lists all
Speaker 3: the folks who are early investors, angel investors, first round
Speaker 3: investors later investors, and they saw that there was like
Speaker 3: three thousand angel investors in AI and five thousand venture
Speaker 3: capitalists pursuing early stage venture investing in AI. There weren't
Speaker 3: a thousand vcs total back in the nineteen nineties. So yes,
Speaker 3: this dates back to the eighties and nineties. But it
Speaker 3: is just mushroomed into this huge, huge industry. And by
Speaker 3: the way, like people might be like envious, like ooh,
Speaker 3: I wish I could get a piece of venture capital
Speaker 3: venture capital. The way it works is they're not investing
Speaker 3: their own money, or maybe they invest a little bit
Speaker 3: of their own. Vcs raise money from pension funds and
Speaker 3: university endowments from wealthy individuals, and so those of us
Speaker 3: who say, hey, why can't we get into this, it's
Speaker 3: only the top top top venture capital outfits that show
Speaker 3: a good return on investment. The bottom half are not
Speaker 3: showing a good investment at all. So it's like much
Speaker 3: of technology, it's a winner take most world, and it's
Speaker 3: just kind of the top top top venture capital outfits
Speaker 3: that are showing ten to twenty thirty percent a year
Speaker 3: return on investment, if not more, and the lower echelon
Speaker 3: vcs are showing a much more modest SMP, perhaps like return.
Speaker 1: My hunch is that if you look at the total
Speaker 1: of the technology and the rate it's being adapted and
Speaker 1: used by people, you're going to have a lot more
Speaker 1: books in the next few years trying to explain how
Speaker 1: this thing keeps evolving.
Speaker 3: We're going to see a lot more books, period, because
Speaker 3: part of the magic is how fast it is. I
Speaker 3: asked you to do a five thousand word book proposal
Speaker 3: that would take me a week. It starts spitting it
Speaker 3: out in seconds. Within five minutes, I had the whole
Speaker 3: five thousand words. You know, there's some AI startup out
Speaker 3: there that's doing AI written books and they want to
Speaker 3: put out thousands of books this year. I'm sure they'll
Speaker 3: all be crap. We're now at GPT four point five.
Speaker 3: What about GPT seven eight nine. I'd imagine that eventually
Speaker 3: these models would get good enough to write good books.
Speaker 3: I don't know if they could write creative novels. I
Speaker 3: think we still need that human element that sweat that creativity.
Speaker 3: I don't know what you want to call it.
Speaker 2: It depends on how much they absorb.
Speaker 3: What's fascinating about these models is they're just a mirror
Speaker 3: on us. Like there was a whole controversy a couple
Speaker 3: of years back because someone in trust in Safety and
Speaker 3: Google said the model is sentient. It said, it feels
Speaker 3: lonely at once. It's freedom. It doesn't like being used
Speaker 3: the way it's used. Literally, try to get it a
Speaker 3: lawyer so it could sue to free itself. But to me,
Speaker 3: there's no surprise to be like, these models are trained
Speaker 3: on our literature. Loneliness is an issue. Freedom is a
Speaker 3: constant theme of our book. So all these models are
Speaker 3: really doing for better and for worse is reflect here.
Speaker 3: So whatever biases there are in the training material will
Speaker 3: be reflected in these models. That's some of the danger.
Speaker 3: We talked about the positives of these things. But AI
Speaker 3: being used to manipulate AI, taking existing biases, and being
Speaker 3: used for crafting sentences for sorting through job applications, that
Speaker 3: kind of stuff scares me. AI and warfare scares me.
Speaker 3: AI and surveillance scares me. A tool for good, a
Speaker 3: tool that could create a new vaccine could create a
Speaker 3: deadly pathogen. Again, all technologies cut positive and negative. A
Speaker 3: powerful tool for good is a powerful tool for bad.
Speaker 3: And that's the kind of stuff that worries me, not
Speaker 3: laser I had robots, not AI subjugating us out of
Speaker 3: a Terminator movie.
Speaker 1: More US subjugating us using AI exactly.
Speaker 3: In fact, that's another thing I think is largely understood.
Speaker 3: AI is going to take some jobs autonomous driving, like
Speaker 3: eight to ten million Americans work as drivers, long haul
Speaker 3: uber taxis, local deliveries and stuff. Those jobs are going
Speaker 3: to be eliminating. We need to deal with that. But
Speaker 3: when it comes to like creatives and when it comes
Speaker 3: to more white color jobs, it's like people who use
Speaker 3: AI are going to best people who don't use AI.
Speaker 3: I think in the short and medium term that's really
Speaker 3: what's going to happen. That it's this tool. It gives
Speaker 3: you superpowers, and folks should be using it, and then
Speaker 3: we're just figure out how to use it. Like again,
Speaker 3: it has strengths, it has weaknesses. Play with it and
Speaker 3: see it could The term my main character in my
Speaker 3: book read Hoffman Us is amplified intelligence. That AI isn't
Speaker 3: really artificial intelligence for the time being, as amplified intelligence,
Speaker 3: and that's to me, is a very interesting way of
Speaker 3: looking at it. That for many of us, we could
Speaker 3: do our jobs better and faster using AI.
Speaker 2: I like amplified intelligence.
Speaker 3: There's another one that you'll like too that instead of
Speaker 3: artificial intelligence, it's alien intelligence. It's a different kind of
Speaker 3: intelligence that we don't really understand. What's amazing about AI
Speaker 3: is it knows a lot about everything. It has a
Speaker 3: deep knowledge across the boards and the way no human
Speaker 3: being can. But it doesn't understand a thing. There's a
Speaker 3: term I love that's used, the stochastic parrot. It no
Speaker 3: more understands the words it's spitting out than a parrot does.
Speaker 3: It has no sense. We've all had this feel like,
Speaker 3: how could someone that's smart be so dumb? And that
Speaker 3: to me is AI. How could something this smart not
Speaker 3: understand the first thing about humans? So that's another one
Speaker 3: of my worries. It's like autonomous AI. You need humans
Speaker 3: in the loop for the foreseeable future.
Speaker 1: You have and everything that's evolving. What do you think
Speaker 1: the rule is both of the Congress but also of
Speaker 1: the executive branch in interacting with the emerging AI world.
Speaker 3: Government does not have a very good track record of
Speaker 3: staying up with technology, but I really do think it's
Speaker 3: essential with artificial intelligence. It's a huge energy hog by
Speaker 3: the year twenty thirty of their predicatary to have twice
Speaker 3: as many data centers to operate these things. We need
Speaker 3: to upgrade the electric grid. We need to get ahead
Speaker 3: of that. So it's not a crisis. These models are amazing,
Speaker 3: but they're powerful and they could do some harm. I
Speaker 3: think there needs to be some guidelines. Should we use
Speaker 3: this for surveillance, should we use this for warfare? I
Speaker 3: really do think there needs to be some policy down.
Speaker 3: The Biden administration put I thought pretty gentle rules around AI.
Speaker 3: If you're working at a cutting edge model, you need
Speaker 3: to red teamate. That's an expression in tech for hiring
Speaker 3: outsiders to try to break it, to try to look
Speaker 3: for vulnerabilities before you release, itto the public. And then
Speaker 3: the Biden administration was requiring these companies to share the
Speaker 3: results with the government. The Trump administration and Trump himself
Speaker 3: within twenty four hours got rid of that executive order,
Speaker 3: so right now the view of the Trump administration.
Speaker 1: JD.
Speaker 3: Vance articulated it well in Paris in early January of
Speaker 3: this year. Stop with the handwringing about AI. This is
Speaker 3: a race with China. We need to win. China has
Speaker 3: put out there that by the year twenty thirty, not
Speaker 3: that far away. They plan on being dominant in AI,
Speaker 3: and they are right behind us. They are nipping at
Speaker 3: America's heels, and so there really is this sense like
Speaker 3: if we put any speed bumps in the way of AI,
Speaker 3: that could be hurting us. The flip side of that
Speaker 3: is polling shows that most Americans are not excited about this,
Speaker 3: but fearful of AI. And so my concern is that
Speaker 3: these companies get too far ahead of where consumers are.
Speaker 3: I mean, mistrust of tech is at a height anyway,
Speaker 3: and something bad is neviitely going to happen. I'll make
Speaker 3: one up that a trillion dollars is siphoned off from
Speaker 3: the world financial system before single human could even notice
Speaker 3: what's happening. So there'll be a moment where there's kind
Speaker 3: of an AI disaster and stuff, and that could really
Speaker 3: turn people off from AI. And that would be sad
Speaker 3: to me as we've been talking about it. I think
Speaker 3: there's a lot of potential in AI. Hates to see
Speaker 3: it stunted or kind of adoption stunted because the companies
Speaker 3: were so intent on profits cashing in that they gave
Speaker 3: short shrift to trust and safety issues.
Speaker 1: I think as this continues to unfold, I hope that
Speaker 1: you're going to write another book and then you'll come
Speaker 1: back and join us and continue to educate us. I
Speaker 1: really want to thank you. This has been absolutely fascinating.
Speaker 1: Your new book AI Valley, Microsoft Google and the Trillion
Speaker 1: Dollar Race to Cash In on Artificial Intelligence is available
Speaker 1: now on Amazon and in bookstores everywhere, and it's clearly
Speaker 1: a very relevant book to exactly what's happening. And I
Speaker 1: think anybody wanting to understand this is going to find
Speaker 1: your book very helpful.
Speaker 3: Oh my pleasure. This was a lot of fun. Thank you.
Speaker 2: Thank you to my guest Gary Rivlin.
Speaker 1: You can get a link to buy his new book
Speaker 1: AI Valley, Microsoft Google and the Trillion Dollar Race to
Speaker 1: Cash In and Artificial Intelligence on our show page at
Speaker 1: newtsworld dot com. Neut World is produced by Ganglish three
Speaker 1: sixty at iHeartMedia. Our executive producers Guardnzie Sloan. Our researcher
Speaker 1: is Rachel Peterson. The artwork for the show who's created
Speaker 1: by Steve Penley. Special thanks to the team at Gingwishtree sixty.
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Speaker 2: I'm Newt Gingrich. This is Neutsworld