Kristian Stout: The Costs of Holding Back AI

Kristian Stout explains what's holding back AI innovation and why slowing its development could be costly.

Chelsea Follett: Joining me on the Human Progress podcast today is Kristian Stout. He serves as Director of Innovation Policy at the International Center for Law and Economics. His work addresses competition, telecommunications, and artificial intelligence policy—very big topic these days—with a focus on how law and economics informs regulatory design. Alongside his ICLE role, Stout has been a partner and counsel at A & S Technologies. So he’s a lawyer, software developer. He has seen all sides of this industry. He joins the podcast today to talk about AI and some recent developments. Thank you for joining me, Kristian. How are you?

Kristian Stout: I’m very good. Thank you. Thank you for having me.

Chelsea Follett: Thank you for joining me. So let’s start with a big-picture overview. AI feels like it is a very new topic almost because we’re always seeing headlines about new advances. But how new is AI? Can you sketch out sort of the big picture of the history of AI and data centers for us before we dive into the current debates?

Kristian Stout: Sure. Yeah, it’s understandable why it feels like something very new because when the computer starts talking to you, that’s unusual. But this is part of a trend in computer science that goes back to the 1940s, really. So for many decades, researchers have been working on both sides of this coin. One, getting ubiquitous computing available throughout the United States. And then, on the other hand, how to make computers smarter, frankly. So as far back as the ’40s, there were people, researchers, trying to understand how you could model, for instance, human neurons inside of computers. Now, it was just one neuron, and they were just trying to understand what that could do for computers. But that’s what we’ve been thinking about since then. That’s what our computer scientists have been thinking about. So I think it’s important to understand that, you know, as far back as, say, that era, 1940s, 1950s, even though those computers were nowhere near as capable in so many dimensions as they are now, we’ve been framing the whole project of computing as trying to introduce more intelligence into it, more sophistication. And what we have today is the result of compounding developments over those many decades.

Kristian Stout: So none of it is really surprising from a research perspective. There have been surprises. The surprise is not that computers are capable of doing these things. It’s how we’ve managed to get them to the point where they can do these things. So, you know, and frankly, we’ve been living with the fruits of more intelligent computing over that time just sort of silently and under the covers for most consumer uses, even though researchers have been trying to push the boundaries of this stuff. So, you know, as far back as—well, I mean, you can really say in the ’70s you started getting some more of these sophisticated algorithms being deployed. But the ones that consumers are most familiar with, and they just don’t even think of as artificial intelligence, are things like autocomplete. So when you were able to have really advanced statistical methods for having very responsive searches for users, first in the desktops, say Google, Yahoo, those kinds of search engines, and then all throughout, like, your devices, like phones and so forth, where you start typing and it figures out what you might be wanting to type or what you might be wanting to search for, like, those are all forms of artificial intelligence.

Kristian Stout: And as recently as, say, 12 years ago, we started really focusing on machine learning. For a long time, the news was about this sort of big data, is what we were calling it before, again, the computer started talking back to us. Big data was all the rage. All of the investment money was going to big data, was going into these different machine learning techniques that would be useful for forecasting all kinds of behaviors and making the computers smarter at what they’re doing. That was artificial intelligence. Nobody was writing news articles about it as artificial intelligence. People weren’t afraid of it as artificial intelligence. There were still anxieties around it, for instance, like what that augmentation meant for labor, meant for the future of work, things like that. But we didn’t frame it in terms of, like, Skynet taking over. Now it becomes more popular to think about it that way because, again, you can interact with the computers in a way that feels very human-like. But none of it is, like, new in a kind sort of way. It’s new in a scale sort of way, is a better way to think about it.

Kristian Stout: So we’ve finally gotten to the point where we have them doing the things we’ve been wanting them to do, we’ve been trying to get them to do for 70 or 80 years at this point. But that’s not new. The same thing with the data centers. Data centers have been with us since, I believe, the 1950s is when you can really start to source that. Again, very small scale. But IBM was very—what made IBM so successful was the development of the mainframe model in which you would have—sometimes you’d have it on premises, networked inside of your research environment. But sometimes, you know, the early internet, for instance, is where this came from. You would have a mainframe that researchers would share over long distances with phone lines. It was very slow, but that was an early form of what you can think of as a data center. It was like—there was like the kernel of what became a data center. And then over that time, we’ve come up with more and more complicated ways to get really advanced compute into areas that then you could share, you do time-sharing, so forth.

Kristian Stout: And, you know, the first data centers that we think of now as the cloud really started, say, around 2005, 2006, when the scale of the internet really was taking off and we needed a lot more computing support. So what we’re dealing with right now is, again, a scale difference, not a kind difference. We’ve been trying to do this since the 1950s.

Chelsea Follett: And that scale difference has brought with it some interesting political debates. If you look at public opinion polling, the public’s view of data centers has seemingly soured even as people are using AI more and more. And so we have seen more and more political moves to ban or restrict the construction of data centers. What is your take on this development?

Kristian Stout: I try to have a nuanced and generous take on it because I think there are multiple things that are going into it. First, I think all of your viewers are going to be familiar with the idea that humans often do not express exactly what they’re saying, what they’re thinking when they respond to public opinion polls, right? So you will get a different result if you say something like, “What do you think about data centers?” versus “How do you feel about an industrial project in your area that might bring jobs and might have these costs and benefits?” You know, you’re going to get different answers to that. It’s the same thing with AI. You know, Dario goes out and says something about how it’s going to kill us in 10 years, he thinks. And then, so then you go and poll people, say, “What do you think about AI?” Like, “I would like it not to kill me in 10 years.” But then if you then had a poll that said something like, “How would you feel about a computer technology that enables you to do all of your week’s work in two days, and then you have like two days extra of leisure?”

Kristian Stout: And they’d be like, “Oh, well, that sounds kind of good. I like that.” So, you know, part of it is a categorical thinking problem. We try to put—you know, this is something that enables us to survive as human beings. We find ways to look at the natural environment, and we create groups of things that allow us to do very fast decision-making. That decision-making is often not complete enough for us to actually answer complicated modern questions. So my generous take is I think there’s anxiety that leads people, when they’re given generic questions, to sort of respond and say, like, “I don’t like that thing.” I think most people, if you actually drill down into the questions with them, they have a more nuanced response to it. So that’s just like a framing point that I always try to keep in mind, and I try to advocate that other people keep in mind. In terms of the actual anxieties that I think people are experiencing and how that feeds into what we’re doing with AI and data centers right now, I think that there is an understandable concern among the general population when they interact with these technologies to think, like, “What does this mean for the future of my job?

Kristian Stout: And what my kid—like, what are my kids going to do when they’re out of high school?” Right? So, like, you have kids now, you’re putting them in middle school or grade school, you’re like, “How do I even advise them on, like, what the future—how are they going to have kids of their own? How are they going to have families? What are they going to do?” Right? So, like, I understand that anxiety floating around there because the way that we’re thinking about what AI might be able to help us do in the future will significantly transform the economy. I personally, from the research I look at and just from interacting with these technologies, I don’t think it’s going to cause mass layoffs. I think, in fact, it will be a bit of a genie’s wish granted, where it gives us all these awesome new powers, but then it will have costs, meaning that we’re going to be expected to be even more productive. So I think employment will go up. What will happen is we’re going to invent new forms of consumption that then we need to satisfy.

Kristian Stout: So I don’t think anybody’s going to actually get a vacation, is the short answer of it. But this is not a new public response, right? Like the whole idea of sabotage, right? The word sabotage was from people taking their wooden shoes, the sabots, and throwing them into the machines that were replacing the weavers back in—I forget what year it was—like 1400 or something, right? So, like, people have been mad about machines taking their jobs for hundreds and hundreds of years, and it’s not unreasonable. Like, I don’t want to downplay the anxiety, right? So, like, you know, everybody likes to talk about the buggy whip manufacturers when Henry Ford came around and say, you know, to kind of make fun of the buggy whip manufacturers, right? But I don’t want to make fun of them. I want to understand, and I think we should be understanding of that anxiety. So if I went to a buggy whip manufacturer in 1900, 1910 or whatever, whenever the Model T was, like, coming off the assembly line, and they were, you know, and I said, “Look, these cars are going to transform the world.

Kristian Stout: These motors, like what we’re doing with transportation, are going to transform the world.” And these people said, “Well, what is the world going to look like?” Like, in 1910, there’s no way I could tell them what 1990 would look like. It just wouldn’t make any sense. Planes, rocket ships, cars, like all of these things, they build on each other. All I could say to them at that time is like, “Look, it’s going to be better. I want to, you know, personally, I want to help you figure out how to transform into this new economy. You’re not going to be completely unemployed. You’re not going to make buggy whips anymore, though.” But they want to know, like, “Well, what is the buggy whip of 1990?” There is no buggy whip in 1990. It’s a completely different world. It’s a better world. It gives more people more opportunities and more access to things. But there’s no way to communicate that adequately to people who have invested themselves in a certain thing. I don’t think there was mass starvation for the people who made buggies and whips and so forth.

Kristian Stout: They all figured out how to get involved in the new economy. And I think we’re sort of at a similar kind of precipice where AI has got a lot of promise to do a lot of interesting things, but it’s what researchers call a general-purpose technology. It’s like electricity. It’s a platform that people are going to develop new ways of doing things with. It’s intelligence. It’s commodified intelligence that we’re now going to deploy in the same way that people, when they started standing up the electric industry, were able to create a field in which people would now use electricity to do all kinds of things. If you went back to Thomas Edison and said, “Tell me what you think people are going to do with electricity,” that dude had no idea what the world would look like right now. There’s no way you could know that. What it did was it enabled really smart people to take this new source of power and this new source of driving innovation and actually develop things that people wanted. We don’t know what it is until the market discovers it, unfortunately.

Kristian Stout: So the anxiety is understandable. What I think we need to do when we’re looking at this is be solicitous of people who have that anxiety and figure out—you know, one of the nice things about this particular technological revolution is it helps us have cognitive adjuncts. So the technology itself can help us figure out how to onboard more people more quickly and more seamlessly into this new intelligence revolution in a way that previous technological revolutions couldn’t. But I think we do need to be patient, and we do need to understand that there is this anxiety, and it’s not going to go away overnight. And we also should not just brush it under the carpet and be like, “Oh, look, you’re just being a Luddite.” I don’t think that’s a fair way to treat the situation.

Chelsea Follett: That’s great historical context, and I love how you brought attention to the world-transforming, society-transforming potential of

Kristian Stout: Yeah.

Chelsea Follett: this technology, because there is so much focus on the risks, and you don’t hear as much talk about the benefits. Even as many people compare it to the Industrial Revolution, you don’t hear people sketching out the potential positives of a world post this AI revolution as

Kristian Stout: Yep.

Chelsea Follett: often. And you said something very interesting that I would love for you to elaborate on. We hear so many fears related to technological unemployment, but you said that you actually believe with AI, employment will go up. That is the opposite, the exact opposite of the typical narrative that we hear. Can you elaborate on that?

Kristian Stout: Yeah. So, I mean, this comes from—I, full disclosure, my predilection is to look at things through an economic analysis lens. So in economics, people think about it as—I was actually just having a conversation with a friend this morning about it, and I was talking about economics. Like, “Oh, well, that’s just about, like, money and profits.” Like, no, that is not what it is about. We use money and studying the economy as a proxy for understanding human motivations, human desires and human action, right? All economics is is the study of human action, really, at root, trying to understand why people make the decisions they do, what are the things that drive them to make those decisions, and then how to understand the trade-offs among those things. If you really take human nature seriously—this is both a good thing and a bad thing in a way—we have an unlimited amount of desire. The reason why we are where we are is because there was once upon a time a caveman who’s like, “You know what? I think I can make something that makes me kill that animal a little bit better.”

Kristian Stout: That process has not stopped for however many hundreds of thousands of years humans have been looking at their environment and being like, “I think I can do a little bit better.” Part of that “I think I can do a little bit better” is, “I would like to relax more, and I would like to eat more, and I would like to have more mating opportunities. I would like to have more of everything,” right? We have an unlimited amount of desires. Now, there are tons of spiritual traditions around the world that try to help us learn how to tame that and be at peace. So far, they have not been working very well. What we have seen instead is that when a new opportunity presents itself, our desire fills the volume that you provide it. So if you have a technology that comes along and says, “Hey, now you can get your workweek done in a day,” I don’t think people are going to stop working at one day. I think—because the other thing to keep in mind, too, is that we’re status creatures. We’re status-driven creatures.

Kristian Stout: The reason why we value money, the reason why we value resources is not, for most people, not for the resources themselves, the money in itself. It’s because of the status that it provides us relative to other people. All of our preferences in the world are based on comparing ourselves to the Joneses, right? Like, that’s the reason why this theme keeps coming up in literature for the millennia that we have literature. You know, I don’t think it’s as base as—I don’t want to impugn it as saying it’s something like envy, but, like, that inherits from this drive that we have to compare ourselves to others and judge our value based on others, which means that there’s never enough. If someone else is perceived as having more status, we’re always going to want to increase our own status. Now, if the world now suddenly expands the volume of status opportunities, our desire for status does not go away, right? Like, that’ll feed back down. Now, the economy might look different. It might be that just obtaining the best car is no longer sufficient. It might be—it’s the same way that…

Kristian Stout: So you can almost say that modern wealthy people in the United States compete on their investment in their children, right? When I was a kid in the ’80s and ’90s, no one went to anybody’s practices, right? Like, you would get dropped off. Now they’re almost like this social event where people vie to be the coaches on the teams and who’s the most involved in all the things at school. Like, you know, my parents did not do that when I was a kid, and most people’s parents did not do that, because the field about what counts and how we invest our time and our resources changed, and now we’re investing it differently. AI is going to give us the same thing. It’s going to expand our capability to get more work done more easily, but the things that we value are going to expand, and it’s going to give us more opportunity. So we’re going to fill that time with more value-seeking opportunity. And it might not look like sitting at a desk for 40 hours a week, but it will still exist, and there will still be employment and resource opportunities within that, because humans like to hire other humans to do things.

Kristian Stout: They like to buy things from each other. They like to trade with each other, and we’re going to fill that gap. The gap, it’s not like it goes away. The computer is not going to be consuming for us.

Chelsea Follett: Absolutely. And one of the jobs that people predicted would be among the first to be automated would be radiologists.

Kristian Stout: Radiologists, yeah. There are more of them. Yeah, yeah. Yeah.

Chelsea Follett: Yes. And what we’ve seen there, interestingly, is the employment actually has gone up for that field.

Kristian Stout: And the demand—they don’t—we don’t have enough radiologists, and the radiologists are one of the most augmented by artificial—early, really augmented by artificial intelligence resources. And if you think about it, it makes sense. Like, the human body is

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Source: Human Progress