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