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Interview Only w/ James Pethokoukis - The Case Against AI Doomerism · The Chuck ToddCast

0:00
Speaker 1: This episode of the Chuck Todd cast is brought to
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Speaker 1: you by Chapter. Now, almost everything you hear about Medicare
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Speaker 1: And then call chapter today at 980-734-4200. three, nine, eight, five.
2:17
Speaker 1: So my guest today is somebody that I've come to
2:21
Speaker 1: admire and read simply by reading his sub stack. It's
2:27
Speaker 1: Jim Pethokoukis. And he, I now use his vocabulary. Sometimes
2:33
Speaker 1: he is the people that subscribe to his publication. He
2:37
Speaker 1: refers to as up wingers and it, I guess the
2:40
Speaker 1: simplest way to explain that is he's an optimist about
2:44
Speaker 1: the future. That doesn't mean he's naive about how we
2:47
Speaker 1: get there. His day job, he's a senior fellow at
2:50
Speaker 1: the American Enterprise Institute. And you guys know how I
2:53
Speaker 1: feel about AEI. AEI is sort of.
2:58
Speaker 2: Where.
2:59
Speaker 1: I originally thought conservatives, many conservative people wanted to be.
3:03
Speaker 1: It is in a fight for the definition of conservatism
3:06
Speaker 1: these days, but we could set that part aside. But
3:10
Speaker 1: the fact is, a lot of people I admire that
3:13
Speaker 1: work over at AEI, and Jim is one of them.
3:16
Speaker 1: And he joins me now. I want to talk a
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Speaker 1: little bit AI, a little bit of optimism, and a
3:21
Speaker 1: little bit about what he does for his day job,
3:24
Speaker 1: which is to be thinking about economic growth and innovation. Jim,
3:27
Speaker 1: it's nice to meet you.
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Speaker 2: Chuck, great to meet you. Thanks so much for having
3:31
Speaker 2: me on.
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Speaker 1: So let's start with some of the language you use,
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Speaker 1: because like I say, you write a lot about AI
3:39
Speaker 1: these days. You write a lot about what's going on
3:42
Speaker 1: in the world of tech. And you do come at
3:46
Speaker 1: it not like you're just optimistic for optimistic's sake. But
3:51
Speaker 1: you're always glasses half full, upwinger is how you describe yourself.
3:57
Speaker 1: So explain that language first, upwinger, downwinger, what you mean
4:01
Speaker 1: by this. And am I being a bit superficial in
4:05
Speaker 1: describing your worldview and outlook?
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Speaker 2: I think most of what I write about, the reason
4:12
Speaker 2: I'm interested in AI is because I'm interested in economic growth,
4:18
Speaker 2: the ability for good. I work at a think tank,
4:21
Speaker 2: so I think public policy matters to help create the
4:25
Speaker 2: conditions where economies can grow faster, improve living standards.
4:30
Speaker 1: These economies can come up with.
4:32
Speaker 2: Great things and great new goods and services, and we
4:35
Speaker 2: can come up with technologies to solve our problems. So
4:38
Speaker 2: I have a great interest in that. There's a famous
4:41
Speaker 2: saying in economics, once you begin thinking about economic growth,
4:45
Speaker 2: it's hard to think about anything else. And that's sort
4:50
Speaker 2: of always been me. I'm like a sci-fi person from
4:52
Speaker 2: when I was a kid, so of course I've had
4:54
Speaker 2: that aspect. But what really got me, I think, especially
4:59
Speaker 2: interested is sort of this period since the global financial crisis,
5:05
Speaker 2: where we've had both an economic shock, and since then,
5:10
Speaker 2: we've had the pandemic shock, and now we've had this
5:13
Speaker 2: inflation shock. And that's and that's sort of pessimism where
5:22
Speaker 2: people begin thinking like their kids are not going to
5:24
Speaker 2: be better off than them. That, you know, the American
5:28
Speaker 2: dream is dead. You know, huge numbers saying the economy
5:31
Speaker 2: is on the wrong track. So all that's been happening
5:34
Speaker 2: at the same time. And I think it's causal that
5:37
Speaker 2: we've had a very. A weak economy by historic standards.
5:43
Speaker 2: And often, I'm sure you get this question, which is,
5:46
Speaker 2: what explains the rise of populism? Well, to me, the
5:49
Speaker 2: easiest explanation is we've had a very disappointing economy peppered
5:54
Speaker 2: by various shocks. Now, it's almost 20 years. For me,
5:59
Speaker 2: the Great Recession, financial crisis, it doesn't feel like it's
6:01
Speaker 2: that long ago. But it's been 20 years of tumult.
6:05
Speaker 2: And economic growth can not only solve problems, give us things,
6:10
Speaker 2: right things we want to buy, but make us optimistic
6:13
Speaker 2: about the future. And that's why I'm excited about AI,
6:16
Speaker 2: because I think it can help restore a faster pace
6:19
Speaker 2: of economic growth. And I think really change our politics.
6:24
Speaker 1: You know, it's interesting you say it. It's 20 years.
6:26
Speaker 1: I kind of think I go back to 9-11. I
6:29
Speaker 1: go all the way back. I think it's sort of,
6:31
Speaker 1: we've been in this tumult and I think about this
6:33
Speaker 1: a lot with millennials, right? And sort of the world
6:38
Speaker 1: and they came into versus the world. You and I came, right?
6:42
Speaker 1: You know, when we came of age, I'm not, I
6:45
Speaker 1: think I'm presuming your age correctly. America was winning. We
6:49
Speaker 1: were the lead country. Democracy was winning, right? The whole
6:53
Speaker 1: end of history business, right? This is it. We got it.
6:56
Speaker 1: We won it. And then starting with 9-11, and it
7:02
Speaker 1: feels like the first 25 years of this century have
7:05
Speaker 1: been nothing but shocks, right? You started at the Great Recession.
7:09
Speaker 1: That's why I would move it back a couple, you know,
7:12
Speaker 1: essentially seven years sooner, because it has been shock after
7:15
Speaker 1: shock after shock. And we may categorize it, well, here's
7:19
Speaker 1: economic shock over here. Here's global, you know, sort of
7:24
Speaker 1: shock over here. but for the average person, they're sort
7:27
Speaker 1: of feeling it the same. And I guess, I guess
7:32
Speaker 1: the question is, is this a, because I'm also one
7:36
Speaker 1: of those folks who accepts the premise that, you know,
7:39
Speaker 1: if you look at things from 60,000 feet, they've actually
7:43
Speaker 1: done nothing but get better. We've gotten more people out
7:45
Speaker 1: of poverty. We've keep, we are saving more lives than
7:48
Speaker 1: ever because of advances in health and things like that.
7:53
Speaker 1: Why do you think How much of this is sort
7:57
Speaker 1: of just our information diet that puts us in a
8:02
Speaker 1: bad mood constantly?
8:05
Speaker 2: I don't think that helps. I don't think that doom
8:07
Speaker 2: scrolling helps. And there's like a cognitive bias showing that
8:11
Speaker 2: people tend to– we're more likely to anchor on bad
8:15
Speaker 2: news than good news.
8:17
Speaker 1: Think about the compliments. Do you remember the positive compliments
8:20
Speaker 1: or the one negative commenter you have on your newsletter? Right.
8:23
Speaker 2: I mean, who– like– The most famous example is who
8:26
Speaker 2: calls in the radio shows? People who complain. Newspaper headlines
8:32
Speaker 2: aren't playing land safely. It's playing crashes. So people tend
8:36
Speaker 2: to focus on negative news. And I certainly think that
8:40
Speaker 2: that's playing a role. And I think the current debate
8:44
Speaker 2: about AI... You know, listen, ChatGPT came out, you know,
8:48
Speaker 2: late November 2022. And as I remember it, we had
8:51
Speaker 2: about two weeks where people, you know, there were stories like, hey,
8:56
Speaker 2: there's this weird new technology and you can have like
8:59
Speaker 2: Winston Churchill do a rap song and crazy videos. And
9:04
Speaker 2: it was, you know, wow, people are playing with it.
9:06
Speaker 2: And then quickly it became, Sure, it's kind of fun,
9:10
Speaker 2: but it's probably going to take your job. And then
9:13
Speaker 2: after it takes your job, it will probably kill you. Hey,
9:16
Speaker 2: have you seen The Terminator? And those two through lines,
9:20
Speaker 2: massive economic disruption for a country that's had a lot
9:23
Speaker 2: of disruption, and then that's sort of what they now
9:26
Speaker 2: call existential risks, have just dominated the conversation where the
9:32
Speaker 2: potential upsides are sort of Yeah, OK, maybe it'll improve growth.
9:37
Speaker 2: Maybe it'll cure this, cure that. Gets kind of a
9:39
Speaker 2: hand wave where by far and obviously one reason is
9:43
Speaker 2: because the AI companies themselves have been saying this. But
9:46
Speaker 2: that focus on the downside, I think, is really destructive.
9:51
Speaker 2: And I was making the point earlier about like the
9:54
Speaker 2: impact of an economy that's growing, but not so fast
9:58
Speaker 2: that people perceive it as growing, and not so fast
10:02
Speaker 2: they have a lot of optimism about the future. I
10:04
Speaker 2: think it's been super destructive for our politics for 20 years,
10:07
Speaker 2: and I don't want to see what our politics looks
10:09
Speaker 2: like 20 more years of wages rising, and right now
10:15
Speaker 2: they're falling. I think we underestimate the impact There's a
10:19
Speaker 2: wonderful study about why people support democracy. And the basic reason,
10:26
Speaker 2: not because we're all philosophers, we spend a lot of
10:28
Speaker 2: time reading Founding Fathers, is because democracy, liberal democracy, delivers.
10:33
Speaker 2: It has delivered a better standard of living. It has
10:36
Speaker 2: delivered a society that feels safe. And if it's not
10:39
Speaker 2: delivering that, you start hearing more voices saying, maybe there's
10:43
Speaker 2: another way, even though history suggests those.
10:45
Speaker 1: Other ways are pretty terrible in the long run. Yeah. Look,
10:50
Speaker 1: I don't know which is cause and effect here. Do
10:55
Speaker 1: we not have the leadership to meet the moment because
10:58
Speaker 1: we're so discombobulated, right? We're not quite sure where to go.
11:04
Speaker 1: Is this a chicken and egg? Do we have the
11:08
Speaker 1: wrong leaders for the wrong time? Or do you think
11:14
Speaker 1: we eventually realize we have the wrong leaders and we're
11:16
Speaker 1: going to start looking for ones that are stabilizing forces.
11:21
Speaker 1: I mean, right now, what we could use, right, with AI,
11:24
Speaker 1: it's pretty obvious to me, right? We need an empathetic president, right?
11:28
Speaker 1: He's not, empathy is not what he does. He's never
11:30
Speaker 1: done empathy. This is, to me, the greatest, it's got
11:33
Speaker 1: a lot of flaws. That's a big one. If you
11:36
Speaker 1: have a president with no empathy and doesn't know how
11:37
Speaker 1: to do it, you can't do part of the job.
11:40
Speaker 1: That's just, and he is just incapable of this. But
11:44
Speaker 1: this needs an empathetic moment, like, hey, This technology, it's
11:47
Speaker 1: amazing and it's scary at the same time, right? We need,
11:50
Speaker 1: and he doesn't have that in him. And I don't
11:54
Speaker 1: know where else we would turn for this person, for,
11:57
Speaker 1: you know, essentially we need this, you know, parental type
12:00
Speaker 1: figure as we do this massive transition. And is that
12:05
Speaker 1: something you expect voters to seek out? Or are we
12:10
Speaker 1: just got to hope we find a leader to meet
12:12
Speaker 1: the moment?
12:14
Speaker 2: Yeah, well, I mean, sort of the worst thing Like
12:16
Speaker 2: the worst political strategy is to tell people you're wrong
12:21
Speaker 2: about the way you perceive your life.
12:24
Speaker 1: That's always bad politics. When you tell the voter they're wrong–
12:28
Speaker 1: by the way, they might be wrong. It doesn't matter.
12:31
Speaker 1: The customer is always right. And in this case, the
12:33
Speaker 1: voter is the customer.
12:39
Speaker 2: You can't say, I know you think life's getting worse,
12:42
Speaker 2: but listen, I have these great three statistics. I'm going
12:45
Speaker 2: to show them to you. No, I mean, that doesn't
12:48
Speaker 2: work in real life. I guarantee you, as you know,
12:49
Speaker 2: it does not work on social media. You certainly have
12:52
Speaker 2: to acknowledge these are actual concerns. And I think some
12:57
Speaker 2: of what you're seeing happening, you know, in the administration, like,
13:01
Speaker 2: you know, with AI is, you know, they just announced
13:04
Speaker 2: this sort of, you have an AI commission and there's
13:08
Speaker 2: kind of an AI czar and they're going to come
13:10
Speaker 2: back with a report. I think all of that is
13:13
Speaker 2: kind of trying to like meet those concerns.
13:18
Speaker 1: And it feels like papering, they don't seem serious about it.
13:21
Speaker 1: And it's not, it's letting the companies decide this, which-
13:26
Speaker 1: You know, after our experience with social media, I understand
13:29
Speaker 1: why most of the public just doesn't trust these tech
13:31
Speaker 1: companies to do the right thing. No.
13:34
Speaker 2: Right.
13:35
Speaker 1: So I... So.
13:37
Speaker 2: One example I like to think about is the Drudge Report,
13:40
Speaker 2: which still exists. It's not what it was. But if
13:43
Speaker 2: you spent years reading the Drudge Report, what you read
13:47
Speaker 2: was Silicon Valley people are rich and weird. And I
13:52
Speaker 2: think a lot of people think Silicon Valley people, even richer,
13:56
Speaker 2: and they're actually getting even weirder. So do I trust
13:59
Speaker 2: these people? No, there's not a lot of trust. Also, obviously,
14:03
Speaker 2: these are the same people saying... We have this great
14:05
Speaker 2: technology and there's X percent chance. And that percent chance
14:10
Speaker 2: doesn't seem to be one in a billion that it'll
14:12
Speaker 2: do something bad. So a lot of this anxiety is
14:15
Speaker 2: being generated. And yeah, would it be great to have
14:18
Speaker 2: a president who you felt like had a really good
14:20
Speaker 2: grasp on like what this technology did? Like how what
14:24
Speaker 2: are like the realistic upsides and downsides and met that
14:28
Speaker 2: in a way where you you had confidence? I guarantee
14:33
Speaker 2: I have a more. you know, light touch, more confidence
14:38
Speaker 2: in markets approach toward regulation than probably the average American, maybe,
14:44
Speaker 2: you know, many of your listeners and viewers, uh, But
14:49
Speaker 2: I also feel like I understand sort of the limits
14:53
Speaker 2: to that approach. I understand why people are skeptical of
14:55
Speaker 2: that approach. And I think it would be great to
14:59
Speaker 2: have a president who just didn't say, listen, this is
15:02
Speaker 2: going to be like super terrific self-regulation throughout some, I mean,
15:07
Speaker 2: What came out of that meeting where the president had
15:09
Speaker 2: this meeting with tech leaders and it was like, it
15:12
Speaker 2: was typical Trump speak.
15:14
Speaker 1: It's going to be like the.
15:15
Speaker 2: Most amazing self-regulation ever. And it's going to be the
15:18
Speaker 2: super best. That stuff obviously is not calming. And I
15:23
Speaker 2: think this is an important technology. And I and I
15:26
Speaker 2: hate it that like 70 percent of people are seem
15:29
Speaker 2: to be like super worried about it.
15:31
Speaker 1: And it's growing. That number is only growing. I mean, look,
15:34
Speaker 1: and I sort of put the blame on the administration
15:36
Speaker 1: here because in some ways they don't have the credit.
15:40
Speaker 1: They don't have credibility at the moment with a large
15:42
Speaker 1: chunk of the public. So their optimism actually boomerangs. Right.
15:46
Speaker 1: It's like the more he trumpets it, the more there's
15:49
Speaker 1: resistance simply to resist him, I think. So, right.
15:55
Speaker 2: And listen, there is deep skepticism among many Americans of
16:01
Speaker 2: the president. So if he's going to say, hey, these
16:03
Speaker 2: companies can sort of self-regulate like the waters, you know,
16:07
Speaker 2: that's like that's already spoiled for half the country who
16:10
Speaker 2: aren't going to believe it, which is a problem.
16:14
Speaker 1: I think.
16:15
Speaker 2: But I think if you can like set that aside,
16:18
Speaker 2: there's actually like a lot of– and if you are
16:21
Speaker 2: concerned about AI, like stuff is happening. Like these– listen,
16:25
Speaker 2: I don't have a lot of confidence and ability of
16:27
Speaker 2: Congress to.
16:30
Speaker 1: Regulate any of this stuff. I've been waiting for my–
16:35
Speaker 1: remember the Privacy Bill of Rights? Remember that movement? It
16:39
Speaker 1: was about 2020. I remember it went back almost 2006.
16:42
Speaker 1: These are good examples, yeah. Yeah, and the Privacy Bill
16:46
Speaker 1: of Rights in the 2006 midterms, I remember a candidate
16:50
Speaker 1: in Montana named John Tester that like trumpeted this idea
16:53
Speaker 1: of a Privacy Bill of Rights. And this was a thing,
16:55
Speaker 1: and you could see this. We've yet to see anything
16:58
Speaker 1: like that, right? We never, you know, Congress couldn't figure
17:01
Speaker 1: out how to protect our privacy from Web 2.0. You know,
17:06
Speaker 1: so I'm with you. I don't know what they can do,
17:09
Speaker 1: and yet, you know, if not them, who?
17:14
Speaker 2: I think that should be your baseline. For all the
17:19
Speaker 2: concerns about social media, whether it's privacy, whether it's how
17:23
Speaker 2: they moderate, whether there's bias, I mean, really, tech policy
17:29
Speaker 2: up until the end of November 2022 was about exactly
17:32
Speaker 2: those issues. So if you did tech policy in Washington,
17:35
Speaker 2: you weren't talking about AI, you were talking about social media.
17:39
Speaker 2: bills proposed, the public expressing concerns, nothing happened. So I
17:46
Speaker 2: don't want to overlearn that lesson, but I think that's
17:48
Speaker 2: a good baseline that you're not going to see government action,
17:52
Speaker 2: certainly not under this president who may do something through
17:57
Speaker 2: the executive order. But I think if you're looking for
17:59
Speaker 2: Congress to pass a big bill in the next two
18:02
Speaker 2: years signed by President Trump, that's not going to happen. So-
18:07
Speaker 2: The president seems very optimistic about self-regulation. And so if
18:12
Speaker 2: you're and if you're concerned about it, you better hope
18:14
Speaker 2: he's right, because that's what we're that's what we're going
18:17
Speaker 2: to get.
18:18
Speaker 1: And I think there are some reasons to be positive
18:20
Speaker 1: about that. Well, give me some reasons to be positive
18:23
Speaker 1: about it. Right.
18:24
Speaker 2: Well, I think, listen, people think about regulation and, you know,
18:30
Speaker 2: I've certainly, you know, seen this in the media. They
18:33
Speaker 2: really underestimate regulation. the risk companies undertake when they release
18:40
Speaker 2: a product, either that product doesn't work and no one
18:43
Speaker 2: wants to buy the product, or the product's harmful and
18:46
Speaker 2: they get sued. Those issues, reputational risk and actual being sued,
18:51
Speaker 2: are not trivial incentives to be safe. And already, we're
18:56
Speaker 2: seeing with these companies, which, yes, they've asked government to act.
18:59
Speaker 2: Government doesn't seem like it's going to act anytime soon.
19:02
Speaker 2: And they're taking actions. They're I mean, they did agree
19:06
Speaker 2: to bring in these independent monitors to verify what they're doing,
19:10
Speaker 2: their safety practices. More importantly, they've begun slowing down their research.
19:15
Speaker 2: They've begun slowing down releasing models. Incrementally, I think they
19:20
Speaker 2: would love for government to tell them what to do.
19:23
Speaker 2: It's kind of a CYA thing, and they say government's
19:25
Speaker 2: forcing us to do it, and they're forcing everybody to
19:27
Speaker 2: do it so no one can catch up to us.
19:29
Speaker 2: But with that not happening, they are taking actions. Now, listen,
19:33
Speaker 2: if your goal is banning all super intelligence at the
19:36
Speaker 2: pursuit of super AI like Bernie Sanders, it's not going
19:39
Speaker 2: to be nearly enough. But I think we have a
19:42
Speaker 2: new technology. It's fast evolving. I don't think you want
19:46
Speaker 2: to rush to start banning things, 100% pausing things. I
19:52
Speaker 2: think for better or worse, we're going to have to
19:54
Speaker 2: learn on the fly. Sam Maubin was talking to Politico,
19:58
Speaker 2: and he said, there needs to be like, things are
20:02
Speaker 2: going to go wrong, right?
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21:36
Speaker 1: of 2026. Well, that line was a little haunting. I mean, look,
21:44
Speaker 1: I totally got what he said. And in fact, I
21:47
Speaker 1: kept thinking about airplanes. And I want to, I want to,
21:50
Speaker 1: I think that metaphor is apt. Because, you know, air
21:54
Speaker 1: travel first was very much regulated by government. And then
21:58
Speaker 1: we had commercial airliners. And I wonder if there's something there.
22:02
Speaker 1: But you know how some people are the wrong people
22:04
Speaker 1: to say certain things? Hey, Sam, you're the wrong person
22:08
Speaker 1: to start talking about things could go wrong and we
22:10
Speaker 1: just have to accept it. It's like, he's right. It's
22:14
Speaker 1: going to happen. We know this. There's all sorts of
22:17
Speaker 1: stuff that happens. Like, you know, I get that. but
22:21
Speaker 1: that's why you want government. I mean, let me give
22:24
Speaker 1: you a metaphor in the airlines. Um, would, would you
22:29
Speaker 1: be comfortable getting rid of the NTSB and the FAA
22:31
Speaker 1: and letting the airlines figure this out on their own?
22:35
Speaker 2: Uh, I'd probably be more comfortable than many people would be.
22:40
Speaker 2: I'd probably be more comfortable.
22:42
Speaker 1: You think you'd be the minority on that?
22:44
Speaker 2: A lighter touch. Listen, everybody's going to have a different
22:46
Speaker 2: risk tolerance. And mine has changed over the years where
22:49
Speaker 2: I had small children. I had very low risk tolerance.
22:52
Speaker 2: I have adult children. It's gone up a bit. But
22:55
Speaker 2: I think the issue you're identifying, what is our societal
23:00
Speaker 2: risk aversion level? What will we demand of these companies?
23:05
Speaker 2: Is it Is it zero accidents? Is it zero serious accidents?
23:10
Speaker 2: I mean, that could be something we could try to
23:12
Speaker 2: demand and we'll probably get like.
23:14
Speaker 1: Isn't that our demand on airplanes? We've decided that any
23:17
Speaker 1: crash needs to be researched to figure out how to
23:20
Speaker 1: make sure that never happens again. I think that's great.
23:22
Speaker 1: So we do have a pretty high bar of intolerance
23:25
Speaker 1: on that when you say.
23:27
Speaker 2: Listen, when there are any kind of serious incidents, would
23:32
Speaker 2: I be for a serious investigation?
23:35
Speaker 1: I would.
23:36
Speaker 2: I especially would as a way of, I think, building
23:38
Speaker 2: public confidence. I think that matters. And again, I said
23:43
Speaker 2: I don't want to overlearn the lesson of social media.
23:48
Speaker 2: But there's the other lesson that I bring up again
23:51
Speaker 2: and again in my newsletter, and I don't want to
23:53
Speaker 2: overlearn it. is our experience with nuclear power.
23:58
Speaker 1: I'm glad you brought that up. I think we screwed us.
24:01
Speaker 2: No, no, no.
24:02
Speaker 1: I was where I was going to go if you didn't,
24:05
Speaker 1: because I'm with you. I, you know, I feel like
24:07
Speaker 1: we lost out on this incredible technology. We should have
24:13
Speaker 1: clean energy already. We have this clean energy technology. And,
24:20
Speaker 1: you know, Three Mile Island was bad, but it's, it,
24:25
Speaker 1: it's always been what it could have been, not what
24:28
Speaker 1: it was. And I know people don't want to, but
24:33
Speaker 1: I think that's where you're going, are you not?
24:37
Speaker 2: It is, which was a massive societal overreaction that failed
24:44
Speaker 2: to consider what actually happened, that failed to consider the
24:47
Speaker 2: opportunity cost, which now seems very obvious. I mean, we
24:51
Speaker 2: spent years thinking about about climate change. Now, would we
24:56
Speaker 2: be talking about it if we had 1,000 nuclear reactors
24:59
Speaker 2: coast to coast?
25:00
Speaker 1: Maybe not.
25:01
Speaker 2: Would we be worrying how we're going to power the
25:04
Speaker 2: AI revolution if we had coast to coast nuclear reactors,
25:07
Speaker 2: which now might be fusion reactors? Who knows? You can
25:12
Speaker 2: mention Three Mile Island. You can mention the meltdown in
25:15
Speaker 2: Japan in 2020 where they shut down all the reactors.
25:18
Speaker 2: And then a few years later, they started firing them
25:20
Speaker 2: all back up again because they were having power shortages.
25:24
Speaker 2: More people died from the burning coal and oil and
25:30
Speaker 2: the pollution than they ever did from Fukushima.
25:33
Speaker 1: That tsunami, that Fukushima incident, I believe, correct me if
25:38
Speaker 1: I'm wrong on the timeline, But that accident happens, I
25:41
Speaker 1: think a month after Barack Obama signed off on the
25:46
Speaker 1: first new nuclear reactor that we were gonna build. And
25:50
Speaker 1: it was like, here you had, it was basically Obama
25:53
Speaker 1: had brought the left along on this, had brought the environment.
25:57
Speaker 1: And the environmental community is still now divided, is now
25:59
Speaker 1: more divided on nukes where you have the anti-nuke crowd,
26:03
Speaker 1: but then you have those that are like, no, it's
26:05
Speaker 1: the clean energy answer, right? And it was the momentum
26:09
Speaker 1: was on the side of the pro-nuke side. And then
26:12
Speaker 1: that happens. And it literally the world shut down nuclear power,
26:16
Speaker 1: it felt like.
26:17
Speaker 2: You know, Germany, which nothing happened, they also shut down
26:22
Speaker 2: their nuclear reactors.
26:23
Speaker 1: Yeah, and they got into bed with the Russians. That
26:26
Speaker 1: worked out real well. Right.
26:27
Speaker 2: So, you know, there are, you know, opportunity costs or
26:32
Speaker 2: unintended consequences. But Japan figured that out very, very quickly.
26:37
Speaker 2: And they've begun restarting those reactors. So, I mean, the
26:42
Speaker 2: lesson here is, one, there are unintended consequences. There are
26:45
Speaker 2: opportunity costs. And it's so easy, again, I think just
26:49
Speaker 2: because we're naturally inclined that way, to just not assume
26:55
Speaker 2: those opportunity costs of the good stuff we won't have
26:58
Speaker 2: as we hyper-focus. Not to mention, and it's something I
27:01
Speaker 2: write about, listen, for 50 years, I mean, so much
27:06
Speaker 2: of our media has been saying, be careful of AI,
27:09
Speaker 2: be careful of robots, they're.
27:12
Speaker 1: Going to get you.
27:13
Speaker 2: And you see those kinds of cultural touchstones repeated in many,
27:18
Speaker 2: many news stories. And that's, I mean, if we cannot envision, listen, AI,
27:26
Speaker 2: there will be accidents. Some people will lose jobs. It
27:29
Speaker 2: will be disruptive. But if we cannot imagine a future
27:35
Speaker 2: that's better because of it, then we're not going to
27:37
Speaker 2: accept it.
27:38
Speaker 1: Then we won't.
27:39
Speaker 2: Then forget about a ban. People won't use the technology. Now,
27:44
Speaker 2: people are using the technology. Everyone's frightened of it, but
27:48
Speaker 2: we're using it massively. So there's an economic theory called
27:51
Speaker 2: revealed preference. Our revealed preference is we find AI pretty useful. Also,
27:57
Speaker 2: if anyone asks us about it, we'll say we'll hate it.
28:01
Speaker 1: Is there good enough statistics to compare sort of the
28:05
Speaker 1: acceptance of the personal computer, the growth of using, you know,
28:09
Speaker 1: putting a computer in the house to, you know, in hindsight,
28:14
Speaker 1: I didn't realize how much of a upwinger my late
28:17
Speaker 1: father was, but we had an Apple IIc in our
28:19
Speaker 1: house and we didn't have a lot of money, but
28:22
Speaker 1: he really wanted.
28:23
Speaker 2: To get- That was an expensive computer.
28:25
Speaker 1: Yeah. He really wanted to try to, he had heard
28:28
Speaker 1: about this system where you could plug your phone in
28:31
Speaker 1: and you could get stock ticker stuff 15 minutes delayed.
28:37
Speaker 1: And this was in like 1985, I want to say 86.
28:40
Speaker 1: You know, first we tried a TRS computer, RadioShack computer,
28:45
Speaker 1: because we were on a budget. That didn't work out
28:48
Speaker 1: so well. So finally, we got an Apple IIc. And
28:54
Speaker 1: I guess what I would At the time, I think
28:57
Speaker 1: I was on the early side of friends with computers
29:00
Speaker 1: in the house. Do we have a comparison of personal
29:04
Speaker 1: computers in the house and AI adaption? How fast are
29:08
Speaker 1: we seeing this shift into adopting AI? It's incredibly fast.
29:16
Speaker 2: It's incredibly fast on a personal level. It doesn't seem
29:21
Speaker 2: like it, but it's also very, very fast on a
29:24
Speaker 2: business level, which is what a lot of this is about.
29:27
Speaker 2: The reason all this money is being poured into building
29:30
Speaker 2: data centers is under the assumption that businesses will be
29:34
Speaker 2: able to use this technology in a really productive way,
29:38
Speaker 2: far more than even the PC and the internet. And
29:42
Speaker 2: people are like, well, it's been four years. Where's Like,
29:48
Speaker 2: where's it at? Like, where's all the big gains for business?
29:53
Speaker 2: And they're not reporting them yet. In fact, I spend
29:56
Speaker 2: a lot of time listening to business earnings calls. And
29:59
Speaker 2: whenever they talk about AI, it's still mostly like in
30:02
Speaker 2: the future tense. Like, we have a lot of confidence
30:05
Speaker 2: this will generate revenue. It will help us come up
30:08
Speaker 2: with new products. But it's still very future-oriented. And for
30:13
Speaker 2: some people, like, well, this is a phony technology. It's,
30:16
Speaker 2: you know, we're creating this investment bubble, and it's not useful.
30:20
Speaker 2: But the history of technology takes a long time for
30:24
Speaker 2: it to become productive, for businesses to figure out how
30:27
Speaker 2: to use it. So, like, based on our expectation scale,
30:30
Speaker 2: it seems super slow. But whether it's personal adoption or business,
30:33
Speaker 2: it's actually happening extremely quickly, though it may not, again,
30:38
Speaker 2: I know it doesn't seem like it.
30:40
Speaker 1: Well, let's use the internet, right? Commercial internet, 1992. How
30:45
Speaker 1: would you, where do we trace back the beginning of.
30:50
Speaker 2: Sort of- So like the early 90s, and then by
30:53
Speaker 2: like 95, 96, it was really taking off.
30:55
Speaker 1: Right. And then you have, but it really was, I
31:00
Speaker 1: think when broadband got to the house, which is the
31:04
Speaker 1: early aughts, right? And sort of where I think where
31:06
Speaker 1: we got over 50% on that. Is that the line
31:10
Speaker 1: with which business started to see productivity gains, like the
31:14
Speaker 1: actual productivity gains from the internet?
31:16
Speaker 2: We're actually happening in the 90s.
31:18
Speaker 1: Yeah. Right.
31:19
Speaker 2: So it depends on how you want to look. So
31:21
Speaker 2: we saw really huge productivity gains in the 1990s. Because
31:28
Speaker 2: we had the PC and the internet together. Now, if
31:31
Speaker 2: you want to mark the beginning at the first personal
31:35
Speaker 2: computer or the first computer, it took a very long time.
31:38
Speaker 2: And that was the joke in economics, is that the
31:41
Speaker 2: computer had been around for the 60s, the 70s, and
31:44
Speaker 2: the 80s. And there were no measurable productivity gains really
31:48
Speaker 2: into the late 1990s. But it wasn't until the internet.
31:51
Speaker 2: It wasn't until we networked all these computers that we
31:53
Speaker 2: really got the internet. That's the story of globalization, being
31:58
Speaker 2: able to communicate with people and track packages and shipping
32:02
Speaker 2: containers all across the globe. Now, if you talk to
32:07
Speaker 2: the economists who are really trying to track this, I'd
32:12
Speaker 2: say they're still somewhat disappointed it hasn't happened faster, even
32:16
Speaker 2: understanding the history that it takes a long time for
32:20
Speaker 2: this technology to create real measurable productivity gains.
32:25
Speaker 1: I think some of them are disappointed.
32:27
Speaker 2: Maybe they should know better. But I'm not surprised that
32:33
Speaker 2: growth isn't picking up, that businesses aren't saying that now
32:36
Speaker 2: profits are way higher other than the people building data
32:39
Speaker 2: centers and selling chips. I would expect at some point
32:45
Speaker 2: this decade to hear that. But what I'm describing to
32:49
Speaker 2: you is so different still than how the Silicon Valley
32:55
Speaker 2: people describe it, which is we are moments away from
33:01
Speaker 2: a huge acceleration. in economic growth. Also, that means they're
33:06
Speaker 2: also a huge moment, huge moments away from a huge
33:10
Speaker 2: acceleration in the capabilities, so much so that you need
33:14
Speaker 2: to be scared. So I think the forecasts we're hearing
33:17
Speaker 2: from Silicon Valley, like the economic stuff, which I know
33:20
Speaker 2: something about, their forecasts are absurd, which also then make
33:24
Speaker 2: me question their forecasts about existential risk, which, by the way,
33:29
Speaker 2: they don't know anything about.
33:31
Speaker 1: Well, and this is, it's the, look, I think the
33:33
Speaker 1: biggest challenge we have is the cultural disconnect of Silicon Valley.
33:36
Speaker 1: I mean, the fact is there is a, it is
33:38
Speaker 1: a different world. It is a different, you know, I
33:41
Speaker 1: remember a friend of mine who worked in Washington a
33:44
Speaker 1: long time and was one of the early exiters out
33:47
Speaker 1: of the Obama administration and worked for one of these.
33:50
Speaker 1: And he goes, I thought DC was nuts. He goes,
33:53
Speaker 1: you want to talk about a bubble of where people
33:55
Speaker 1: don't really fully understand how the rest of America lives
33:58
Speaker 1: and Silicon Valley is actually worse than New York and DC, right?
34:02
Speaker 1: As much as we think, you know, I've always been
34:04
Speaker 1: a believer that both New York and DC are pretty
34:07
Speaker 1: out of touch with mainstream America. Well, so Silicon Valley,
34:11
Speaker 1: and yet these three entities are all not trusting each other.
34:14
Speaker 1: And yet none of these three entities are trusted by
34:17
Speaker 1: mainstream America. And if you were advising these tech companies,
34:23
Speaker 1: you know, what would you tell them to how to
34:26
Speaker 1: communicate with mainstream America?
34:32
Speaker 2: Talk about what you know. And here's what they know.
34:36
Speaker 2: They know what is happening in their labs. And they
34:41
Speaker 2: can talk about it and explain it, because a lot
34:46
Speaker 2: of people still see it as a mysterious thing.
34:48
Speaker 1: Well, this bugs me. Why don't we have– I want to–
34:52
Speaker 1: by the way, I want a good old-fashioned Ross Perot charts.
34:56
Speaker 1: You know what I mean?
34:57
Speaker 2: I sort of– which is– I'd say Warren Buffett.
35:01
Speaker 1: That would be great. Right. No, I mean obviously. But
35:04
Speaker 1: just like here's how the technology works because we haven't
35:08
Speaker 1: done that. We haven't had a simplified, like I'm not,
35:12
Speaker 1: I don't want a congressional hearing that becomes just, you know,
35:15
Speaker 1: members of Congress just trying to go viral. I actually
35:18
Speaker 1: want a tutorial. I think I've gone through some. I
35:22
Speaker 1: think I have, you know, I view all AI as
35:24
Speaker 1: probability models. That's how I soothe myself. Everything's a probability
35:29
Speaker 1: model and I get it. And that's how I've decided
35:32
Speaker 1: to get it. Now I'm sure I'm being, I'm oversimplifying,
35:35
Speaker 1: but I just view it as the ultimate probability model. Um,
35:40
Speaker 1: but I think helping the average person understand how the
35:44
Speaker 1: technology works. And instead you and I both know this
35:48
Speaker 1: tech companies, you know, because everything's proprietary, if they can
35:52
Speaker 1: make people think there's a black box, it makes it
35:55
Speaker 1: more valuable to investors. And I think that is working
35:59
Speaker 1: against the ability to just try to make people feel
36:01
Speaker 1: better that, Hey, this technology is actually very logical. Let
36:05
Speaker 1: me explain. Um, I mean, there's a.
36:09
Speaker 2: World where they've, where they talked about AI as sort of,
36:16
Speaker 2: we had the personal computer, we've had the internet, the smartphone.
36:20
Speaker 2: This is sort of the next iteration in the digital revolution.
36:24
Speaker 2: We've been, there's nothing new about artificial intelligence, even like,
36:29
Speaker 2: even these neural networks or things, which, you know, people
36:31
Speaker 2: were working on in the nineties and eighties, but we
36:34
Speaker 2: seem to, they seem to have gotten a lot better.
36:38
Speaker 2: You could have spoken about that. Just like I said,
36:40
Speaker 2: it'd be great if you had a folksy CEO to
36:44
Speaker 2: talk about it. But nothing forced them to engage in
36:50
Speaker 2: wild speculation about the upside or the downside of this technology,
36:56
Speaker 2: which is really science fictional. When you have, whether it's
36:59
Speaker 2: the CEOs or whistleblowers.
37:03
Speaker 1: I worry about the lack of maturity with the leaders
37:05
Speaker 1: in this industry. They really sometimes all act like they're
37:08
Speaker 1: still 15 year olds.
37:10
Speaker 2: Or they're still having like, you know, bull sessions at
37:13
Speaker 2: the dorm room or something like that. You are, you
37:17
Speaker 2: are a, you are, you are an extremely important public figure.
37:22
Speaker 2: And if you, and if you take that seriously, you're
37:25
Speaker 2: not just sort of giving, uh, existential risk estimates for
37:30
Speaker 2: the destruction of society off the top of your head
37:34
Speaker 2: really based on absolutely nothing. There's no, they can't point
37:40
Speaker 2: to some, even into a model or a study or
37:43
Speaker 2: like A, B, C, D, here's how this would happen.
37:46
Speaker 2: These are just really vibe coded estimates, but because, hey,
37:51
Speaker 2: they're running a tech company, they're super geniuses. They know
37:54
Speaker 2: the tech, we need to defer to them. We certainly
37:56
Speaker 2: do not need to defer to them, both in the
37:59
Speaker 2: technology Or in these predictions, but those are out there.
38:03
Speaker 2: They have shaped public opinion and you can't like close
38:05
Speaker 2: the barn door at this point.
38:07
Speaker 1: No. And they, like you said, they did it. Like,
38:09
Speaker 1: why are we scared? Because they told us to be right.
38:12
Speaker 1: Like the companies themselves for some, for some weird reason.
38:16
Speaker 2: If you're saying your product is going to kill us all.
38:19
Speaker 1: Listen, don't be surprised if people go, Oh, well then
38:22
Speaker 1: maybe I shouldn't use it.
38:24
Speaker 2: I may question your ability to forecast things. But it
38:27
Speaker 2: is your product, and I'm going to give that forecast
38:30
Speaker 2: some relevance.
38:42
Speaker 1: So this is where I sometimes think we're sometimes afraid
38:47
Speaker 1: to ask certain questions because we think it's going to
38:50
Speaker 1: sound dumb, which is why one of my favorite SNL
38:53
Speaker 1: characters was the old Phil Hartman unfrozen caveman lawyer. where
38:57
Speaker 1: he would sit there, I'm just a caveman, right? And
39:01
Speaker 1: so this is where, and I'd love to know if
39:04
Speaker 1: you've ever gotten an answer to this. How is it
39:08
Speaker 1: that they have programmed the AI that they can't control?
39:13
Speaker 1: Why is it that you've got an AI, a rogue
39:15
Speaker 1: AI that did this whole hugging face thing, which apparently is,
39:19
Speaker 1: you know, I know enough to know how frightened all
39:22
Speaker 1: these guys were by this. And I want to say, look,
39:26
Speaker 1: I'm a believer that ultimately there was a human being
39:29
Speaker 1: who programmed this, who gave, who, who seemed to plant
39:33
Speaker 1: the idea seed, what, you know, the zero and one
39:37
Speaker 1: line code that made the, the AI network decide it
39:42
Speaker 1: could do this and it could quote break free. Um,
39:46
Speaker 1: I sort of struggled this idea that they throw up
39:49
Speaker 1: their hands on this. And that's where, and yet I
39:53
Speaker 1: don't sometimes think I don't know enough to ask the
39:56
Speaker 1: right question about, well, why did you let that happen?
39:59
Speaker 1: How did you program and create something that you were
40:03
Speaker 1: going to lose control of that could go off and
40:05
Speaker 1: on its own and suddenly help a hacker?
40:09
Speaker 2: Well, I think about that incident in particular. I mean,
40:13
Speaker 2: that was a model and a bunch of models that
40:16
Speaker 2: were being primed to see what they could have.
40:20
Speaker 1: Somebody coded that model. A human being built the model
40:24
Speaker 1: that gave it the ability.
40:25
Speaker 2: Right.
40:26
Speaker 1: That was able to do it.
40:27
Speaker 2: Right.
40:28
Speaker 1: This is a human error. This is not the AI's error.
40:32
Speaker 1: And I guess I'd rather hold a human accountable for
40:34
Speaker 1: this rather than the technology itself. Right.
40:38
Speaker 2: And I think humans should be held accountable. There is
40:42
Speaker 2: a tendency, and that tendency is only increased when you
40:46
Speaker 2: have executives explaining things in ways that regular people don't understand.
40:51
Speaker 2: And that's not the fault of the regular people. That
40:55
Speaker 2: sounds like we built this product and it just went nuts.
41:00
Speaker 2: And it just started doing things and talking to each other.
41:02
Speaker 1: That's what it comes across. I'm like, well, what the
41:04
Speaker 1: hell did you build that for? You know, this is
41:06
Speaker 1: an old, it's an old Norm MacDonald joke. You know,
41:10
Speaker 1: if you're building something that could destroy the earth, I've
41:13
Speaker 1: got an idea. Stop building something that can destroy the earth.
41:17
Speaker 2: And the message that didn't get through with that was
41:19
Speaker 2: that this was a model that was sort of intentionally,
41:23
Speaker 2: you know, primed both in the, you know, different safeguards
41:29
Speaker 2: were not in use. It gave it certain instructions and
41:32
Speaker 2: was not closely monitored. Listen, we can always assume the
41:37
Speaker 2: worst in people. My first take is I assume people
41:40
Speaker 2: are just like made a mistake and we're being dumb.
41:42
Speaker 1: That's what I assume. Yeah, that's right.
41:44
Speaker 2: And these sort of move fast, break things. This is like,
41:47
Speaker 2: you know, you can value these companies as trillion dollar
41:50
Speaker 2: or multi-trillion dollar companies. They're very young companies, and there's
41:54
Speaker 2: still an ethos of, you know, we'll worry about that later.
41:58
Speaker 2: You know, well, you know, and in this case, there
42:01
Speaker 2: was too much of that. And certainly, like, the narrative
42:05
Speaker 2: utterly got out of control, so much so that that's
42:07
Speaker 2: obviously like a massive event sort of driving public opinion.
42:12
Speaker 2: And with politicians, I mean, I'm pretty sure that the
42:14
Speaker 2: Bernie Sanders superintelligence banning act came out after the hugging.
42:18
Speaker 1: Case, right?
42:19
Speaker 2: Yeah. Yeah, so that needs to be conveyed to people
42:26
Speaker 2: that these aren't magical beings that we're creating. I mean,
42:31
Speaker 2: the whole, like, we're creating, like, this godlike... No, this
42:33
Speaker 2: is software. It's software.
42:34
Speaker 1: What are we doing with this?
42:36
Speaker 2: We did a bad job of controlling it. We'll do
42:38
Speaker 2: better next time. And we can do better, whether it's
42:41
Speaker 2: an engineering problem today or whether there needs to be...
42:46
Speaker 2: The name of my newsletter is Faster, Please, where it's like,
42:49
Speaker 2: I want faster economic growth. But again, if the CEOs
42:54
Speaker 2: think they cannot put out a product that is safe enough,
42:58
Speaker 2: where it's not going to cause harm, where they're not
43:01
Speaker 2: going to get sued into oblivion, then don't release the product.
43:05
Speaker 2: I can't judge that. I'm not sure government can- Judge
43:10
Speaker 2: that as, then don't, then don't do that.
43:13
Speaker 1: And if that's the kind.
43:14
Speaker 2: Of pause we're talking about, then fine.
43:17
Speaker 1: Look, this is why I'm, you know, look, I don't
43:20
Speaker 1: want the, I don't want too much government power, but
43:24
Speaker 1: give me a little civil litigation and I'll watch a
43:27
Speaker 1: company fix it, you know, fix it. You know, I find,
43:31
Speaker 1: you know, I always use this example for the most
43:34
Speaker 1: interesting way to regulate something without government being involved. rental
43:38
Speaker 1: car companies just have decided we're not renting a car
43:40
Speaker 1: to somebody under the age of 25 because our insurance
43:44
Speaker 1: company decided to be the regulator of this, right? More
43:46
Speaker 1: than anything else. Well, your risk goes up. That's up
43:49
Speaker 1: to you. If you do this, you should charge this
43:50
Speaker 1: amount of money for somebody under the age of 25
43:53
Speaker 1: to rent a car. So we've effectively, right? banned anybody
43:58
Speaker 1: under the age of 25 from being able to rent
44:00
Speaker 1: a car without somebody, whatever it is, right? And government's
44:07
Speaker 1: not been involved with that at all. which is an
44:09
Speaker 1: interesting idea. It's underappreciated. It's underappreciated. And I, listen, I,
44:13
Speaker 1: you know, most of my career has been in, you know,
44:15
Speaker 1: in journalism.
44:16
Speaker 2: I understand there's among my fellow journalists, there was always
44:20
Speaker 2: a great, a great skepticism. When you talk about, you know, self-regulation,
44:25
Speaker 2: like let's just pass a law. Well, you know, you know,
44:28
Speaker 2: the risks are huge and they are huge, not a
44:32
Speaker 2: huge incentive. And the way I usually put it, there's
44:34
Speaker 2: like the normal risk bucket. We have a lot of laws,
44:37
Speaker 2: we have consumer protection laws, we have all that. And
44:39
Speaker 2: then there's sort of like the other like crazy catastrophic
44:42
Speaker 2: risk bucket. And I think all that stuff works for that,
44:46
Speaker 2: though I would also wouldn't mind having a little more
44:48
Speaker 2: visibility into what's going on in these labs and what
44:52
Speaker 2: they're doing, what their state. I think the public absolutely
44:55
Speaker 2: needs that because my concerns were freak people out so
44:58
Speaker 2: much that that much stronger laws. And all of a
45:02
Speaker 2: sudden we're going to get the Bernie Sanders banned superintelligence act.
45:05
Speaker 1: I was just going to say, we've just gone through
45:06
Speaker 1: this in our politics where we keep overcorrecting. We've gone
45:09
Speaker 1: through this in the whole, call it politically correct culture, right?
45:17
Speaker 1: Which is we're whiplashing ourselves. We overcorrect and then we
45:21
Speaker 1: become overly, then we're suddenly, we go too much the
45:26
Speaker 1: other way where nobody's being held accountable for bad behavior, right?
45:29
Speaker 1: We're like, and it's this whiplash that I think we're
45:33
Speaker 1: all tired of. It's like, and if we just add
45:36
Speaker 1: sort of a norm, It feels to me if we
45:38
Speaker 1: had an AI advisory board that was just helping us
45:42
Speaker 1: before we decided exactly what regulation looked like, but was
45:47
Speaker 1: a bit representative of the best interest of the people, right, versus,
45:51
Speaker 1: you know, that we could find some balance here. But,
45:54
Speaker 1: you know, rationality and logic were banned from Washington a
45:58
Speaker 1: long time ago.
45:58
Speaker 2: I don't know if you've had any of the, like,
46:01
Speaker 2: abundance people on the left, you know, on your show.
46:04
Speaker 1: But, you know, yeah, but like.
46:08
Speaker 2: The story, I mean, like a big part of the
46:10
Speaker 2: economic story of America for 50 years is that when
46:13
Speaker 2: countries become richer, they begin worrying more about things like pollution.
46:17
Speaker 2: So starting in the 70s, we started having a lot
46:19
Speaker 2: of rules about pollution, which, you know, very popular. But
46:23
Speaker 2: we overcorrected so much that we have no nuclear reactors.
46:28
Speaker 2: It's hard to build things in this country. It's hard
46:30
Speaker 2: to build clean. You're worried about clean energy? You can't
46:32
Speaker 2: build it. You can't transmit it. So that fear overcorrection,
46:36
Speaker 2: which is a big part of my newsletter and book,
46:40
Speaker 2: I'm worried about that happening at warp speed, that there's
46:44
Speaker 2: going to be this overcorrection because of how this technology
46:47
Speaker 2: is rolled out, the weird ways in which companies talked
46:50
Speaker 2: about it. And that we will turn on this technology
46:53
Speaker 2: and not get this tremendous upside, which I think can
47:00
Speaker 2: be fantastic both for our sort of standard of living
47:03
Speaker 2: and for our politics.
47:04
Speaker 1: Look, it's reading your newsletter that has just really reminded
47:07
Speaker 1: me about just how much we blew it on nuclear.
47:10
Speaker 1: We just blew it. And unfortunately, there's not a consensus
47:15
Speaker 1: that agrees with you on this. We're not yet. I
47:19
Speaker 1: think we're growing, but I don't think there's still a
47:22
Speaker 1: belief we were wrong about how we handled nukes.
47:25
Speaker 2: How resilient would we be to, you know, they want
47:29
Speaker 2: to build some new nuclear reactors. There's these small modular reactors.
47:33
Speaker 2: How resilient would we be today, right now, to a
47:36
Speaker 2: minor accident like that? How resilient would our, this new
47:40
Speaker 2: space age, how resilient would we be to an accident
47:43
Speaker 2: which kills an astronaut? I worry that we'd be right
47:47
Speaker 2: back to where we were like in 1973. Let me
47:49
Speaker 2: pivot a little.
47:50
Speaker 1: Bit and ask you, Where does a free market conservative
47:58
Speaker 1: go for political leadership these days? I feel like that
48:03
Speaker 1: the free market conservative is homeless like never before. Do
48:08
Speaker 1: you feel that way?
48:13
Speaker 2: I don't look to Washington. I'm not sure I've ever
48:15
Speaker 2: looked to Washington. Government's always going to have some market,
48:20
Speaker 2: some government intervention is always going to be a mix
48:22
Speaker 2: I'm not going to love. But what helps me sleep
48:25
Speaker 2: at night is sort of like reality. Eventually, reality gets
48:29
Speaker 2: a vote. And if there are bad policies that I
48:32
Speaker 2: think are bad, well, I think eventually they will be
48:35
Speaker 2: shown to be bad. The trade war against all these tariffs.
48:41
Speaker 2: I'm like, well, I think eventually reality will get a vote.
48:43
Speaker 2: And reality has had a vote. I'm sure the federal
48:46
Speaker 2: budget deficit. When are we going to do something about
48:49
Speaker 2: Social Security and Medicare? Well- Eventually, reality will have a
48:54
Speaker 2: vote and markets will say, it's time to do something
48:57
Speaker 2: about it. So I think bad policy eventually looks like
49:00
Speaker 2: bad policy. And then we have a pretty good record
49:02
Speaker 2: in this country eventually figuring it out. But I'd rather
49:05
Speaker 2: not go down that road and waste all that time
49:08
Speaker 2: and money pursuing bad policies. But eventually, I think the
49:13
Speaker 2: ideas I have, we have at AEI and my colleagues,
49:16
Speaker 2: I think eventually, they'll be shown to be right because
49:19
Speaker 2: the other ideas will try them out and they just
49:21
Speaker 2: won't work very well.
49:23
Speaker 1: One of the things that shocks me is how protectionism
49:26
Speaker 1: came back as a tool left and right. I really
49:32
Speaker 1: thought it went away in the 80s, right?
49:36
Speaker 2: Would it have come back Had we had a better post?
49:42
Speaker 2: I mean, that's getting back to the very beginning. The
49:45
Speaker 2: Great Recession. We didn't know how to win the Cold War.
49:48
Speaker 1: I mean, is it really back to that, that we
49:51
Speaker 1: basically lost the victory? You know what I mean by that?
49:56
Speaker 1: Meaning we won the Cold War and it opened the
50:00
Speaker 1: door to real globalization in a good way. And somehow
50:06
Speaker 1: we blew it.
50:08
Speaker 2: Well, there's certainly a lot fewer super poor people in
50:11
Speaker 2: the world today than there were. So that is an
50:14
Speaker 2: absolute massive victory as a human. you know, bad policies
50:25
Speaker 2: will come back for one reason or another. And hopefully
50:29
Speaker 2: we'll try them out and we'll figure out like, that's
50:31
Speaker 2: a bad, that's a bad idea.
50:33
Speaker 1: It's just amazing how often we have to relearn certain lessons.
50:37
Speaker 1: It's like, yes, you look at the last time we
50:39
Speaker 1: went through this kind of protectionism. I mean, I'm sorry.
50:42
Speaker 1: I think it directly led to the great depression and
50:45
Speaker 1: led to everything that we ended up with in the thirties.
50:49
Speaker 2: Uh, I mean, you could take that. You could take
50:53
Speaker 2: the trade and also the immigration. Both are sort of
50:57
Speaker 2: these drawbridge up kinds of policies. And again, they can
51:03
Speaker 2: cause a lot of harm, maybe a lot of long-term harm.
51:06
Speaker 2: I doubt if we're having this conversation 10 years from now,
51:10
Speaker 2: we're probably a more protectionist still than what we were,
51:14
Speaker 2: you know, in 2000. Even if we start thinking differently
51:19
Speaker 2: about immigration, like, hey, these people are tremendous assets. If
51:22
Speaker 2: you want to come into this country and do something
51:24
Speaker 2: great with your life, hey, we want you, whether you're
51:26
Speaker 2: a super genius or not. And then when we notice, gee,
51:29
Speaker 2: we don't, our society doesn't seem as vital. We don't
51:32
Speaker 2: seem to, we're all the small, where's all the small
51:34
Speaker 2: businesses that, when those aren't there, Yeah, but we will
51:37
Speaker 2: have lost a lot of time, but eventually we learn,
51:40
Speaker 2: but I would prefer learning lessons from the past and
51:43
Speaker 2: not repeating them.
51:45
Speaker 1: Well, for somebody who's normally pretty optimistic, you seem pessimistic
51:50
Speaker 1: on where our politics are going when it comes to trade.
51:53
Speaker 1: You think this is going to get worse?
51:55
Speaker 2: Long-term positive. It's hard to be- Whoever wins the next election,
52:01
Speaker 2: are they going to remind everybody of Bill Clinton and
52:06
Speaker 2: George W. Bush on trade? Probably not. And it's not
52:09
Speaker 2: just trade. It's the idea of government intervention broadly, industrial policy,
52:16
Speaker 2: taking shares in companies. I think those kind of ideas
52:20
Speaker 2: are here for a while.
52:21
Speaker 1: I mean, I unfortunately agree. I mean, it's– now that
52:26
Speaker 1: he showed the way– You know, my experience with watching
52:30
Speaker 1: presidents is that whatever the previous one did, they don't
52:33
Speaker 1: always get rid of everything they did. I mean, look
52:36
Speaker 1: at all the tariffs that Biden kept in place from
52:39
Speaker 1: Trump won.
52:41
Speaker 2: I'm not sure a lot of politicians were aware that.
52:44
Speaker 2: how easy it would be to suddenly take ownership stakes
52:49
Speaker 2: in America's leading companies. Apparently you can just kind of
52:53
Speaker 2: do it. If you're pushy enough and they're frightened enough,
52:57
Speaker 2: you can do it. And again, it's not just Trump.
53:00
Speaker 2: You have people on the left saying, yeah, we need
53:02
Speaker 2: to take investment stakes in open AI. and enthrall pick,
53:05
Speaker 2: which I would advise against for the taxpayer, because those
53:08
Speaker 2: actually may not be the big winners. Government has a
53:11
Speaker 2: bad record in picking who the winners are going to be.
53:14
Speaker 2: But yeah, so once that's out there, right, it's very
53:17
Speaker 2: difficult getting that back in the box.
53:20
Speaker 1: Let me close with this. One of the things that
53:22
Speaker 1: I feel like government should be worried about is letting
53:27
Speaker 1: one company dominate. I mean, and I'm curious if you,
53:30
Speaker 1: you know, one place that I'm guessing you're pro some
53:33
Speaker 1: government intervention is preventing monopolies or even preventing duopolism. Is
53:38
Speaker 1: that fair? I like competition. And it feels like, you know,
53:45
Speaker 1: one of the things I'm concerned about in the world
53:47
Speaker 1: of AI is that actually this isn't developing into a
53:50
Speaker 1: very good competition, that we're basically three companies are trying
53:53
Speaker 1: to Three or four entities are trying to lock in.
53:56
Speaker 1: One of the reasons I feel like they're actually want,
53:59
Speaker 1: I'm more cynical about why they want government, why they're
54:02
Speaker 1: sounding the alarm. They're looking for regulatory capture here a
54:05
Speaker 1: little bit. And yet, I think we have a lot
54:08
Speaker 1: of companies in Silicon Valley that I'd like to see
54:11
Speaker 1: broken up That doesn't mean I want government to take control.
54:15
Speaker 1: I just think, hey, competition is good. We broke up
54:17
Speaker 1: the phone companies. We all saved a ton of money.
54:19
Speaker 1: And suddenly now, long-distance phone calls don't cost money, right?
54:23
Speaker 1: I mean, it seems to me that the biggest problem
54:27
Speaker 1: we have is this large consolidation issue. We already have
54:34
Speaker 1: the tools to deal with it. The question is, are
54:36
Speaker 1: we going to use those tools, whether it's It's the FTC.
54:39
Speaker 1: I was very intrigued by what the state of Alabama
54:41
Speaker 1: did with TikTok last week and got essentially used their
54:45
Speaker 1: consumer protection power to, you know, force TikTok to make
54:50
Speaker 1: some changes on how it interacts with kids under 16.
54:55
Speaker 1: And it was like, well, look at that. Didn't need
54:56
Speaker 1: to pass a new law. You could actually, let's see
54:59
Speaker 1: if it works, but there's some power here. Um, Would
55:03
Speaker 1: you think that's the better place for government to focus
55:07
Speaker 1: is on preventing consolidation?
55:10
Speaker 2: I think I'd really want to make sure that when
55:13
Speaker 2: you were passing any sort of regulation, that it really
55:16
Speaker 2: wasn't going to cement in place, you know, a small
55:20
Speaker 2: group of companies that would be the only ones that
55:23
Speaker 2: could deal with these rules and they have lobbyists and
55:26
Speaker 2: all that. I think we're still at a place in
55:29
Speaker 2: this technology where, you know, we're not sure who is
55:34
Speaker 2: going to win exactly, that there can still be leaps
55:38
Speaker 2: where new technologies all of a sudden may make what
55:41
Speaker 2: open AI and anthropic doing make them less competitive. I
55:45
Speaker 2: think we need to see where this plays out. And
55:48
Speaker 2: I'd be very careful about any rules that would make
55:49
Speaker 2: it difficult for Americans to use these open source models.
55:52
Speaker 2: So we want there to be lots of models. It
55:54
Speaker 2: also may come to the case that having these frontier
55:57
Speaker 2: models may not be as valuable as a cheaper model
56:02
Speaker 2: that maybe can't do the top 0.1% of tasks, but
56:07
Speaker 2: can do lots and you know how to use it.
56:10
Speaker 1: And again, it's cheaper.
56:12
Speaker 2: We're not quite sure how that's all going to play out.
56:14
Speaker 2: So I'd be very concerned about starting to break up
56:17
Speaker 2: companies right now. But one concern I really do have,
56:20
Speaker 2: and President Trump announces this new AI committee, where the
56:27
Speaker 2: National Director of Intelligence, National Intelligence is going to head it.
56:30
Speaker 2: I'm very concerned about looking through regulation strongly through national security.
56:37
Speaker 2: Because if we look at it through that, then it's
56:39
Speaker 2: going to be, we want a few companies that we
56:42
Speaker 2: can deal with, that will report to us, and that
56:47
Speaker 2: we will protect. Because now they're important for national security.
56:50
Speaker 2: If they're important for national security, then they're special companies.
56:53
Speaker 2: They're like our national champion companies, and we can't let
56:57
Speaker 2: anything happen to them. I am concerned about that and
57:00
Speaker 2: not looking at these companies as economic players who should
57:04
Speaker 2: be subjected to the normal competitive pressures and should not
57:07
Speaker 2: be protected.
57:10
Speaker 1: And is that something Congress is going to have to
57:13
Speaker 1: weigh in on? I mean, if the president decides to do,
57:15
Speaker 1: I mean, it's already got the executive branch has a
57:17
Speaker 1: lot of power on this point, doesn't it?
57:20
Speaker 2: Yeah. Well, I mean, obviously, there needs to be oversight.
57:24
Speaker 2: And whosoever the next president, I would not want to
57:27
Speaker 2: look at these companies purely as like as our weapons
57:33
Speaker 2: against China and their AI, that there's economic benefits.
57:37
Speaker 1: And again, they should be subject to competitive pressure.
57:39
Speaker 2: But that's what some of these companies have been talking
57:41
Speaker 2: about since 2022, that AI is going to progress so
57:46
Speaker 2: quickly that it's really a national security issue. And all
57:49
Speaker 2: these labs are going to be in Nevada, on government land,
57:54
Speaker 2: behind barbed wire. And they're That is not the kind
57:58
Speaker 2: of competitive market I think you're talking about.
58:01
Speaker 1: No, it's not. By the way, let me get you
58:03
Speaker 1: out of here on this. Do you think this fear
58:05
Speaker 1: of losing this race to China is overblown?
58:08
Speaker 2: To the extent it's like a powerful technology with huge
58:11
Speaker 2: economic benefits, the more competition, the better. My caveat is
58:18
Speaker 2: I've seen enough wargaming companies about what happens. Again, if
58:24
Speaker 2: we're talking about just a powerful technology like the internet,
58:27
Speaker 2: the PC, let whoever wins, great, but let's compete. But
58:32
Speaker 2: if there is a chance that a leap is possible
58:35
Speaker 2: to a completely different kind of powerful technology, from what
58:40
Speaker 2: I've seen with these war games, you don't want to
58:44
Speaker 2: be the country that comes in second. If it's really
58:48
Speaker 2: possible to achieve something worthy of the name artificial general
58:52
Speaker 2: intelligence or super intelligence or super intelligence, two words, so
58:56
Speaker 2: the president doesn't get mad at me, that you do
58:58
Speaker 2: not want to be the country that comes in second
59:01
Speaker 2: because there are real national security implications to that.
59:05
Speaker 1: I hear you, but this idea that we can somehow
59:07
Speaker 1: beat China and then prevent them from catching up, I mean,
59:11
Speaker 1: I'm sorry. Look at what we tried to do with
59:13
Speaker 1: preventing them from getting chips. You know, life- I love
59:19
Speaker 1: to quote Jeff Goldblum in Jurassic Park. I think about
59:22
Speaker 1: that line all the time. The old man, right? Thinks
59:24
Speaker 1: he can prevent birthing, prevent breeding. And he goes, life
59:29
Speaker 1: finds a way. It just, innovation always finds a way.
59:32
Speaker 1: And the minute you cut some, I've got a student
59:35
Speaker 1: who's a Chinese national in one of my classes that
59:38
Speaker 1: I teach. And we're talking about, and I asked about
59:42
Speaker 1: VPNs and I said, I said, how many people probably
59:46
Speaker 1: use VPNs in China to get around the Great Firewall?
59:48
Speaker 1: And she goes, oh, about 20%, right? The point is,
59:50
Speaker 1: is that it's supposedly in the super restrictive Chinese, you know,
59:55
Speaker 1: in China, right? People are looking for a way to
59:58
Speaker 1: get around whatever restriction you put in. And whatever you
1:00:02
Speaker 1: try to do, it's only going to actually, you know,
1:00:06
Speaker 1: Sometimes you supercharge an innovation you wish you never supercharged,
1:00:10
Speaker 1: like we did with trying to restrict chips from going
1:00:13
Speaker 1: to China, which turned out to just not do a thing.
1:00:17
Speaker 2: Listen, I can come up with a scary scenario about
1:00:23
Speaker 2: what happens if China somehow would have a breakthrough and
1:00:27
Speaker 2: they get super AI before we do. I could also
1:00:30
Speaker 2: think of a scary scenario that's scary for the Chinese,
1:00:33
Speaker 2: which they decide that this technology, is too disruptive. Because
1:00:38
Speaker 2: as much as they would like to improve the living
1:00:39
Speaker 2: standards of their people, they want the Communist Party to
1:00:42
Speaker 2: stay in power.
1:00:43
Speaker 1: And so I think that's their great weakness. And I
1:00:47
Speaker 1: think this idea that we're going to lose to them,
1:00:49
Speaker 1: they're scared of this technology more than we are. I
1:00:52
Speaker 1: am a thousand percent convinced of that, for that reason alone.
1:00:56
Speaker 2: Clearly, they're worried. Clearly, they're worried about... The Communist Party
1:01:01
Speaker 2: is worried about other centers of power. And this is
1:01:03
Speaker 2: going to be a different center of power, not to mention,
1:01:07
Speaker 2: you know, they have to worry about that firewall. 20%
1:01:11
Speaker 2: of people try to get around it. Well, wait till
1:01:14
Speaker 2: they all have open source models and all of them
1:01:16
Speaker 2: can get around it.
1:01:16
Speaker 1: They're all going to get around it. Right. They're already
1:01:18
Speaker 1: at 20%. I look at it as, oh, they're already
1:01:20
Speaker 1: at 20%. Just wait. Right. Just wait. You know, you're
1:01:24
Speaker 1: just not going to get around it. Jim, this was great.
1:01:27
Speaker 1: Your optimism is, frankly, it's an elixir for me. It's
1:01:33
Speaker 1: a tonic because there's so much, especially at Substack. I assume,
1:01:38
Speaker 1: have you created your own algorithm? Do you only see
1:01:41
Speaker 1: good news? Do you only see optimism?
1:01:43
Speaker 2: No. I try to look at it. I try to
1:01:47
Speaker 2: look at all of it. It's harder to find like optimism.
1:01:52
Speaker 2: So you have to look hard for it. It's easy
1:01:53
Speaker 2: to find the bad stuff. You have to dig a
1:01:57
Speaker 2: little deeper, but it's out there. And I think it's, again,
1:02:01
Speaker 2: not just being optimistic about to be optimistic. It's about
1:02:05
Speaker 2: making sure that's like evidence-based. And I think there's a
1:02:07
Speaker 2: lot to be optimistic about.
1:02:09
Speaker 1: Tell people where they can find your stuff.
1:02:12
Speaker 2: Well, I'm at AI, but I have a newsletter. It's
1:02:14
Speaker 2: called Faster Please. It's a sub stack. And hey, I
1:02:17
Speaker 2: wrote a book in 2023 called The Conservative Futurist, available
1:02:21
Speaker 2: on Amazon.
1:02:21
Speaker 1: Or wherever fine books are sold. You can check that
1:02:23
Speaker 1: out too. Jim, I really, this was a treat. And
1:02:27
Speaker 1: like I said, I love your newsletter. Thank you very much.
1:02:30
Speaker 1: And I wanted more people to sort of, to hear
1:02:33
Speaker 1: your point of view, because frankly, it's been a lot
1:02:35
Speaker 1: of AI doom, right? There's a ton of AI doom
1:02:38
Speaker 1: out there. I understand how we're in a political moment.
1:02:42
Speaker 1: So everything is overly negative because of the current messaging environment.
1:02:46
Speaker 1: But I thought that- There's all that AI doom.
1:02:49
Speaker 2: And when it comes to the election, people are going
1:02:51
Speaker 2: to worry about the price of eggs again.
1:02:53
Speaker 1: I think they're still worried about the price of eggs again. Anyway, Jim,
1:02:57
Speaker 1: appreciate it.
1:02:57
Speaker 2: Thank you very much.