Marc Andreessen: How Risk Taking, Innovation & Artificial Intelligence Transform Human Experience

00:00:00.000 | - Welcome to the Huberman Lab Podcast,

00:00:02.280 | where we discuss science and science-based tools

00:00:04.880 | for everyday life.

00:00:05.900 | I'm Andrew Huberman,

00:00:10.240 | and I'm a professor of neurobiology and ophthalmology

00:00:13.320 | at Stanford School of Medicine.

00:00:15.340 | Today, my guest is Marc Andreessen.

00:00:17.720 | Marc Andreessen is a software engineer

00:00:19.760 | and an investor in technology companies.

00:00:22.240 | He co-founded and developed Mosaic,

00:00:24.440 | which was one of the first widely used web browsers.

00:00:27.160 | He also co-founded and developed Netscape,

00:00:30.040 | which was one of the earliest widespread used web browsers.

00:00:33.340 | And he co-founded and is a general partner

00:00:35.520 | at Andreessen Horowitz,

00:00:37.300 | one of the most successful Silicon Valley

00:00:39.320 | venture capital firms.

00:00:40.900 | All of that is to say that Marc Andreessen

00:00:43.400 | is one of the most successful innovators and investors ever.

00:00:47.400 | I was extremely excited to record this episode with Marc

00:00:49.640 | for several reasons.

00:00:50.920 | First of all, he himself is an incredible innovator.

00:00:53.960 | Second of all, he has an uncanny ability

00:00:56.300 | to spot the innovators of the future.

00:00:58.640 | And third, Marc has shown over and over again

00:01:00.860 | the ability to understand how technologies

00:01:03.280 | not yet even developed are going to impact

00:01:05.820 | the way that humans interact at large.

00:01:08.240 | Our conversation starts off by discussing

00:01:10.500 | what makes for an exceptional innovator,

00:01:12.880 | as well as what sorts of environmental conditions

00:01:15.320 | make for exceptional innovation and creativity

00:01:17.760 | more generally.

00:01:18.820 | In that context, we talk about risk-taking,

00:01:21.240 | not just in terms of risk-taking in one's profession,

00:01:24.020 | but about how some people, not all,

00:01:26.040 | but how some people who are risk-takers and innovators

00:01:29.020 | in the context of their work,

00:01:30.640 | also seem to take a lot of risks in their personal life

00:01:33.780 | and some of the consequences that can bring.

00:01:36.220 | Then we discuss some of the most transformative technologies

00:01:39.060 | that are now emerging,

00:01:40.440 | such as novel approaches to developing clean energy,

00:01:43.540 | as well as AI or artificial intelligence.

00:01:46.600 | With respect to AI, Marc shares his views

00:01:49.180 | as to why AI is likely to greatly improve human experience.

00:01:53.460 | And we discussed the multiple roles

00:01:54.780 | that AI is very likely to have

00:01:56.560 | in all of our lives in the near future.

00:01:58.800 | Marc explains how not too long from now,

00:02:00.960 | all of us are very likely to have AI assistance.

00:02:04.400 | For instance, assistance that give us

00:02:06.180 | highly informed health advice,

00:02:08.180 | highly informed psychological advice.

00:02:10.240 | Indeed, it is very likely that all of us

00:02:12.580 | will soon have AI assistance that govern

00:02:14.720 | most if not all of our daily decisions.

00:02:17.360 | And Marc explains how if done correctly,

00:02:20.080 | this can be a tremendously positive addition to our life.

00:02:23.440 | In doing so, Marc provides a stark counterargument

00:02:26.660 | for those that argue that AI

00:02:28.540 | is going to diminish human experience.

00:02:30.860 | So if you're hearing about and/or concerned about

00:02:33.240 | the ways that AI is likely to destroy us,

00:02:36.060 | today you are going to hear about the many different ways

00:02:38.820 | that AI technologies now in development

00:02:41.140 | are likely to enhance our human experience at every level.

00:02:44.660 | What you'll soon find is that while today's discussion

00:02:46.680 | does center around technology and technology development,

00:02:49.880 | it is really a discussion about human beings

00:02:52.240 | and human psychology.

00:02:53.680 | So whether you have an interest

00:02:54.840 | in technology development and/or AI,

00:02:57.480 | I'm certain that you'll find today's discussion

00:02:59.560 | to be an important and highly lucid view

00:03:02.080 | into what will soon be the future that we all live in.

00:03:05.320 | Before we begin, I'd like to emphasize that this podcast

00:03:07.920 | is separate from my teaching and research roles at Stanford.

00:03:10.440 | It is, however, part of my desire and effort

00:03:12.500 | to bring zero cost to consumer information about science

00:03:14.960 | and science-related tools to the general public.

00:03:17.520 | In keeping with that theme,

00:03:18.600 | I'd like to thank the sponsors of today's podcast.

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00:03:32.980 | The electrolytes and hydration are absolutely key

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00:06:02.260 | And now for my discussion with Marc Andreessen.

00:06:05.400 | Marc, welcome.

00:06:06.420 | - Hey, thank you.

00:06:07.820 | - Delighted to have you here and have so many questions

00:06:10.040 | for you about innovation, AI, your view of the landscape

00:06:14.220 | of tech and humanity in general.

00:06:17.340 | I want to start off by talking about innovation

00:06:20.660 | from three different perspectives.

00:06:23.300 | There's the inner game, so to speak,

00:06:25.660 | or the psychology of the innovator or innovators,

00:06:29.340 | things like their propensity for engaging in conflict

00:06:33.140 | or not, their propensity for having a dream or a vision,

00:06:37.060 | and in particular, their innovation as it relates

00:06:40.500 | to some psychological trait or expression.

00:06:45.140 | So we'll get to that in a moment.

00:06:46.640 | The second component that I'm curious about

00:06:48.820 | is the outer landscape around innovators,

00:06:51.220 | who they place themselves with,

00:06:53.900 | the sorts of choices that they make,

00:06:55.800 | and also the sorts of personal relationships

00:06:58.020 | that they might have or not have.

00:06:59.660 | And then the last component is this notion

00:07:02.860 | of the larger landscape that they happen

00:07:04.460 | to find themselves in.

00:07:05.400 | What time in history?

00:07:06.560 | What's the geography?

00:07:09.020 | Bay Area, New York, Dubai, et cetera.

00:07:12.420 | So to start off, is there a common trait of innovators

00:07:17.420 | that you think is absolutely essential as a seed

00:07:22.700 | to creating things that are really impactful?

00:07:26.180 | - Yeah, so I'm not a psychologist,

00:07:28.040 | but I've picked up some of the concepts,

00:07:30.060 | some of the terms.

00:07:31.140 | And so it was a great moment of delight in my life

00:07:33.940 | when I learned about the big five personality traits.

00:07:36.020 | 'Cause I was like, aha, there's a way

00:07:37.540 | to actually describe the answer to this question

00:07:39.540 | in at least reasonably scientific terms.

00:07:41.740 | And so I think what you're looking for

00:07:43.540 | when you're talking about real innovators,

00:07:45.460 | people who actually do really creative breakthrough work,

00:07:47.180 | I think you're talking about a couple things.

00:07:48.380 | So one is very high in what's called trait openness,

00:07:51.740 | which is one of the big five,

00:07:53.620 | which is basically just like flat out open to new ideas.

00:07:57.120 | And of course, the nature of trait openness

00:07:59.420 | is trait openness means you're not just open to new ideas

00:08:01.380 | in one category, you're open to many different kinds

00:08:03.160 | of new ideas.

00:08:04.000 | And so we might talk about the fact

00:08:05.420 | that a lot of innovators also are very creative people

00:08:07.820 | in other aspects of their lives,

00:08:09.660 | even outside of their specific creative domain.

00:08:13.020 | So that's important.

00:08:13.860 | But of course, just being open is not sufficient.

00:08:15.620 | 'Cause if you're just open,

00:08:16.460 | you could just be curious and explore,

00:08:18.060 | and spend your entire life reading

00:08:19.100 | and talking to people and never actually create something.

00:08:21.700 | So you also need a couple other things.

00:08:23.760 | You need a high level of conscientiousness,

00:08:25.620 | which is another one of the big five.

00:08:26.800 | You need somebody who's really willing to apply themselves.

00:08:29.540 | And in our world, typically over a period of many years,

00:08:32.140 | to be able to accomplish something great.

00:08:34.180 | They typically work very hard.

00:08:36.580 | That often gets obscured 'cause the stories

00:08:38.620 | that end up getting told about these people are,

00:08:40.380 | it's just like there's this kid, and he just had this idea,

00:08:42.220 | and it was like a stroke of genius,

00:08:43.400 | and it was like a moment in time,

00:08:44.620 | and it's just like, oh, he was so lucky.

00:08:46.460 | And it's like, no, for most of these people,

00:08:48.380 | it's years and years and years of applied effort.

00:08:51.200 | And so you need somebody with an extreme,

00:08:53.740 | basically, willingness to defer gratification

00:08:55.740 | and really apply themselves to a specific thing

00:08:58.200 | for a long time.

00:08:59.340 | And of course, this is why there aren't very many

00:09:01.460 | of these people, is there aren't many people

00:09:02.680 | who are high in openness and high in conscientiousness

00:09:04.860 | 'cause to a certain extent, they're opposed, right, traits.

00:09:07.820 | And so you need somebody who has both of those.

00:09:10.240 | Third is you need somebody high in disagreeableness,

00:09:12.940 | which is the third of the big five.

00:09:15.100 | So you need somebody who's just like basically ornery, right,

00:09:18.360 | because if they're not ornery,

00:09:19.700 | then they'll be talked out of their ideas

00:09:21.280 | by people who will be like, oh, well, you know,

00:09:22.820 | 'cause the reaction most people have to ideas is,

00:09:24.660 | oh, that's dumb.

00:09:25.780 | And so somebody who's too agreeable

00:09:27.640 | will be easily dissuaded to not pursue,

00:09:29.840 | you know, not pulling the thread anymore.

00:09:31.880 | So you need somebody highly disagreeable.

00:09:33.140 | Again, the nature of disagreeableness

00:09:35.140 | is they tend to be disagreeable about everything, right?

00:09:37.940 | So they tend to be these very kind of like kind of classic,

00:09:40.420 | you know, kind of renegade characters.

00:09:42.620 | And then there's just a table stakes component,

00:09:44.420 | which is they just also need to be high IQ, right?

00:09:46.380 | They just need to be really smart

00:09:47.980 | because it's just it's hard to innovate in any category

00:09:50.340 | if you can't synthesize large amounts of information quickly.

00:09:53.300 | And so those are four like basically like high spikes,

00:09:57.180 | you know, very rare traits

00:09:58.520 | that basically have to come together.

00:10:01.740 | You could probably also say they probably at some point

00:10:04.020 | need to be relatively low in neuroticism,

00:10:06.140 | which is another of the big five

00:10:07.260 | 'cause if they're too neurotic,

00:10:08.180 | they probably can't handle the stress, right?

00:10:09.900 | So it's kind of this dial in there.

00:10:11.720 | And then of course, if you're into like this sort of science

00:10:14.940 | of the big five, basically, you know,

00:10:16.180 | these are all people who are on like the far outlying

00:10:19.060 | kind of point on the normal distribution

00:10:20.820 | across all these traits.

00:10:22.140 | And then that just gets you to, I think,

00:10:23.840 | the sort of hardest topic of all around this whole concept

00:10:26.940 | which is just there are very few of these people.

00:10:29.540 | - Do you think they're born with these traits?

00:10:31.860 | - Yeah, well, so they're born with the traits

00:10:34.000 | and then of course the traits are not,

00:10:35.420 | you know, genetics are not destiny.

00:10:36.500 | And so the traits are not deterministic

00:10:38.980 | in the sense of that, you know,

00:10:39.820 | just 'cause they have those personality traits

00:10:41.460 | doesn't mean they're gonna deliver great creativity,

00:10:43.880 | but like they need to have those properties

00:10:46.660 | because otherwise they're just not either

00:10:47.700 | gonna be able to do the work

00:10:48.680 | or they're not gonna enjoy it, right?

00:10:50.460 | I mean, look, a lot of these people

00:10:51.880 | are highly capable, competent people.

00:10:53.900 | You know, it's very easy for them to get like high paying jobs

00:10:57.100 | in traditional institutions and, you know,

00:10:58.780 | get lots of, you know, traditional awards

00:11:00.420 | and, you know, end up with big paychecks.

00:11:01.860 | And, you know, there's a lot of people at, you know,

00:11:03.880 | big institutions that we, you know, you and I know well

00:11:06.300 | and I deal with many of these

00:11:07.820 | where people get paid a lot of money

00:11:08.900 | and they get a lot of respect and they go for 20 years

00:11:11.040 | and it's great and they never create anything new, right?

00:11:13.660 | And so there's a lot of--

00:11:14.500 | - Administrators.

00:11:15.340 | - Yeah, well, a lot of them, yeah,

00:11:16.500 | a lot of them end up in administrative jobs.

00:11:18.620 | And that's fine, that's good.

00:11:20.360 | The world needs, you know, the world needs that also, right?

00:11:22.380 | The innovators can't run everything 'cause everything,

00:11:24.540 | you know, the rate of change would be too high.

00:11:26.280 | Society, I think, probably wouldn't be able to handle it.

00:11:27.920 | So you need some people who are on the other side

00:11:30.120 | who are gonna kind of keep the lights on

00:11:31.360 | and keep things running.

00:11:33.080 | But there is this decision that people have to make,

00:11:35.280 | which is, okay, if I have the sort of latent capability

00:11:37.920 | to do this, is this actually what I want

00:11:39.720 | to spend my life doing?

00:11:40.800 | And do I wanna go through the stress and the pain

00:11:43.160 | and the trauma, right, and the anxiety, right,

00:11:45.760 | and the risk of failure, right?

00:11:47.360 | And so do I really want to?

00:11:48.480 | Once in a while, you run into somebody who's just like,

00:11:50.460 | can't do it any other way, like they just have to.

00:11:53.400 | - Who's an example of that?

00:11:54.240 | - I mean, Elon's the, you know,

00:11:55.840 | the paramount example of our time.

00:11:57.480 | And I bring him up in part

00:11:59.040 | because he's such an obvious example,

00:12:00.400 | but in part because he's talked about this in interviews

00:12:03.740 | where he basically says like, he's like,

00:12:05.240 | I can't turn it off.

00:12:06.080 | Like, the idea's come, I have to pursue them, right?

00:12:10.080 | It's why he's like running five companies at the same time

00:12:12.000 | and like working on a sixth, right?

00:12:14.240 | It's just like, he can't turn it off.

00:12:16.680 | You know, look, there's a lot of other people

00:12:17.960 | who probably have the capability to do it,

00:12:20.120 | who ended up talking themselves into

00:12:22.120 | or, you know, whatever events conspired

00:12:23.880 | to put them in a position where they did something else.

00:12:26.560 | You know, obviously there are people who try to be creative

00:12:28.200 | who just don't have the capability.

00:12:29.960 | And so there's some Venn diagram there

00:12:31.860 | of determinism through traits, but also choices in life.

00:12:35.960 | And then also of course, the situation in which they're born,

00:12:38.340 | the context within which they grow up, the culture, right?

00:12:41.480 | What their parents expect of them and so forth.

00:12:43.760 | And so you have to, you know,

00:12:44.720 | you kind of get all the way through this.

00:12:46.360 | You have to thread all these nadles kind of at the same time.

00:12:49.560 | - Do you think there are folks out there

00:12:51.120 | that meet these criteria who are disagreeable,

00:12:54.640 | but that can feign agreeableness?

00:12:57.400 | You know, they can, for those just listening,

00:13:00.240 | Mark just raised his right hand.

00:13:01.840 | In other words, that can sort of,

00:13:04.920 | phrase that comes to mind,

00:13:07.000 | maybe because I can relate to a little bit,

00:13:08.600 | they sneak up through the system,

00:13:11.240 | meaning they behave ethically

00:13:13.040 | as it relates to the requirements of the system.

00:13:14.760 | They're not breaking laws or breaking rules.

00:13:16.580 | In fact, quite the opposite.

00:13:17.640 | They're paying attention to the rules

00:13:19.120 | and following the rules until they get to a place

00:13:21.560 | where being disagreeable feels less threatening

00:13:26.020 | to their overall sense of security.

00:13:27.760 | - Yeah, I mean, look, the really highly competent people

00:13:30.600 | don't have to break laws, right?

00:13:31.720 | Like it's, there was this myth, you know,

00:13:36.400 | that sort of happened around the movie "The Godfather."

00:13:38.060 | And then there was this character Meyer Lansky, you know,

00:13:39.800 | who's like ran basically the mafia, you know,

00:13:41.280 | 50, 60, 70 years ago.

00:13:42.500 | And there was this great line of like,

00:13:44.080 | well, Meyer Lansky had only like applied himself

00:13:45.720 | to running General Motors.

00:13:46.560 | He would have been the best CEO of all time.

00:13:48.400 | It's like, no, not really, right?

00:13:49.620 | Like the people who are like great

00:13:51.200 | at running the big companies,

00:13:52.040 | they don't have to be mob bosses.

00:13:54.000 | They don't have to like break laws.

00:13:55.040 | They can, you know, they can do, they can work.

00:13:57.280 | They're smart and sophisticated enough

00:13:58.840 | to be able to work inside the system.

00:14:00.560 | You know, they don't need to take the easy out.

00:14:01.980 | So I don't think there's any implication

00:14:03.560 | that they have to, you know,

00:14:04.400 | that they have to break laws.

00:14:05.600 | That said, they have to break norms, right?

00:14:07.460 | And specifically the thing,

00:14:09.600 | this is probably the thing that gets missed the most,

00:14:11.200 | 'cause the process of innovating,

00:14:14.040 | the process of creating something new,

00:14:15.180 | like once it works, like the stories get retconned,

00:14:18.600 | as they say in comic books.

00:14:20.560 | So the stories get adapted

00:14:21.920 | to where it's like it was inevitable all along.

00:14:23.640 | You know, everybody always knew that this was a good idea.

00:14:25.680 | You know, the person has won all these awards.

00:14:27.640 | Society embraced them.

00:14:29.040 | And invariably, if you were with them

00:14:31.480 | when they were actually doing the work,

00:14:33.200 | or if you actually get a couple drinks into them

00:14:34.960 | and talk about it, it'd be like,

00:14:35.980 | no, that's not how it happened at all.

00:14:37.600 | They faced a wall of skepticism,

00:14:39.500 | just like a wall of basically social,

00:14:41.720 | you know, essentially denial.

00:14:42.780 | No, this is not gonna work.

00:14:43.960 | No, I'm not gonna join your lab.

00:14:45.380 | No, I'm not gonna come work for your company.

00:14:47.200 | No, I'm not gonna buy your product, right?

00:14:48.940 | No, I'm not gonna meet with you.

00:14:50.440 | And so they get just like tremendous social resistance.

00:14:54.200 | So they're not getting positive feedback

00:14:56.180 | from their social network

00:14:57.840 | the way that more agreeable people need to have, right?

00:15:00.520 | And this is why agreeableness is a problem for innovation.

00:15:03.100 | If you're agreeable,

00:15:03.940 | you're gonna listen to the people around you.

00:15:05.520 | They're gonna tell you that new ideas are stupid, right?

00:15:08.120 | End of story.

00:15:08.960 | You're not gonna proceed.

00:15:10.440 | And so I would put it more on

00:15:11.680 | like they need to be able to deal with,

00:15:13.480 | they need to be able to deal with social discomfort

00:15:15.840 | to the level of ostracism,

00:15:17.600 | or at some point they're gonna get shaken out

00:15:19.360 | and they're just gonna quit.

00:15:20.660 | - Do you think that people that meet these criteria

00:15:22.940 | do best by banding with others

00:15:24.780 | that meet these criteria early?

00:15:26.860 | Or is it important that they form this deep sense of self,

00:15:31.160 | like the ability to cry oneself to sleep at night

00:15:34.500 | or, you know, lie in the fetal position

00:15:36.440 | worrying that things aren't going to work out

00:15:38.280 | and then still get up the next morning

00:15:39.960 | and get right back out there?

00:15:41.440 | - Right.

00:15:42.280 | So Sean Parker has the best line by the way on this.

00:15:45.080 | He says, you know, being an entrepreneur, being a creator

00:15:47.400 | is like, you know, getting punched in the face,

00:15:49.040 | like over and over again.

00:15:50.000 | He said, eventually you start to like

00:15:51.120 | the taste of your own blood.

00:15:53.140 | And I love that line 'cause it makes everybody

00:15:54.740 | like massively uncomfortable, right?

00:15:56.420 | But it gives you a sense of like how basically painful

00:15:58.720 | the process is.

00:15:59.560 | If you talk to any entrepreneur, you know,

00:16:01.480 | who's been through it about that,

00:16:02.520 | they're like, oh yeah, that's exactly what it's like.

00:16:04.600 | So there is this, there is a big individual component to it.

00:16:07.960 | But look, it can be very lonely, right?

00:16:10.360 | And especially, you know, very hard I think to do this

00:16:13.120 | if nobody around you is trying to do anything

00:16:15.020 | even remotely similar, right?

00:16:16.020 | And if you're getting just universally negative responses,

00:16:18.200 | like, you know, very few people I think have,

00:16:20.160 | very few people have an ego strength

00:16:21.380 | to be able to survive that for years.

00:16:23.480 | So I do think there's a huge advantage.

00:16:25.160 | And this is why you do see clusters.

00:16:26.800 | There's a huge advantage to clustering, right?

00:16:28.580 | And so you have, and you know, throughout history,

00:16:30.800 | you've had this clustering effect, right?

00:16:32.060 | You had, you know, clustering of the great artists

00:16:33.640 | and sculptors in Renaissance Florence, you know,

00:16:35.680 | you have the clustering of the philosophers of Greece,

00:16:37.780 | you have the clustering of tech people in Silicon Valley,

00:16:39.960 | you have the clustering of creative, you know, arts,

00:16:41.720 | movie TV people in Los Angeles, right?

00:16:44.120 | And so forth and so on, you know, for, you know,

00:16:46.400 | there's always a scene, right?

00:16:47.460 | There's always like a nexus and a place

00:16:49.740 | where people come together, you know,

00:16:51.660 | for these kinds of things.

00:16:53.100 | So generally speaking, like, if somebody

00:16:54.700 | wants to work in tech, innovate in tech,

00:16:56.100 | they're going to be much better off being around a lot of people

00:16:58.280 | who are trying to do that kind of thing

00:17:00.180 | than they are in a place where nobody else is doing it.

00:17:02.900 | Having said that, the clustering has, it can have downsides.

00:17:05.600 | It can have side effects.

00:17:06.760 | And you put any group of people together

00:17:08.420 | and you do start to get groupthink,

00:17:09.820 | even among people who are individually very disagreeable.

00:17:12.980 | And so these same clusters where you

00:17:15.040 | get these very idiosyncratic people,

00:17:16.700 | they do have fads and trends just like every place else,

00:17:19.540 | right?

00:17:20.020 | And so they get wrapped up in their own social dynamics.

00:17:23.840 | And the good news is the social dynamic in those places

00:17:26.180 | is usually very forward looking, right?

00:17:28.180 | And so it's usually like, you know, it's, I don't know,

00:17:30.660 | it's like a herd of iconoclasts looking for the next big thing,

00:17:33.380 | right?

00:17:33.860 | So iconoclasts looking for the next big thing, that's good.

00:17:36.280 | The herd part, right?

00:17:37.500 | That's what you've got to be careful of.

00:17:38.580 | So even when you're in one of these environments,

00:17:40.120 | you have to be careful that you're not getting sucked

00:17:41.620 | into the groupthink too much.

00:17:43.020 | When you say groupthink, do you mean excessive friction

00:17:45.380 | due to pressure testing each other's ideas to the point

00:17:47.660 | where things just don't move forward?

00:17:49.260 | Or are you talking about groupthink

00:17:50.880 | where people start to form a consensus or the self-belief

00:17:54.540 | that, gosh, we are so strong because we are so different?

00:17:59.600 | Can we better define groupthink?

00:18:01.060 | It's actually less either one of--

00:18:02.860 | those things both happen.

00:18:04.020 | Those are good.

00:18:05.060 | Those are good.

00:18:05.980 | The part of groupthink I'm talking about

00:18:07.780 | is just like we all basically zero in.

00:18:09.900 | We just end up zeroing in on the same ideas, right?

00:18:12.300 | In Hollywood, there's this classic thing.

00:18:14.060 | It's like there are years where all of a sudden there's

00:18:16.620 | a lot of volcano movies.

00:18:18.260 | It's like, why are there all these volcano movies?

00:18:20.300 | And it's just like, I don't know.

00:18:21.100 | There was just something in the gestalt, right?

00:18:22.660 | There was just something in the air.

00:18:23.940 | You know, look, Silicon Valley has this.

00:18:25.940 | There are moments in time where you'll have these--

00:18:28.380 | it's like the old thing.

00:18:29.700 | What's the difference between a fad and a trend, right?

00:18:31.940 | A fad is the trend that doesn't last, right?

00:18:33.820 | And so Silicon Valley is subject to fads, both fads and trends,

00:18:37.500 | just like anyplace else.

00:18:39.100 | In other words, you take smart, disagreeable people,

00:18:41.140 | you cluster them together, they will act like a herd, right?

00:18:43.820 | They will end up thinking the same things,

00:18:45.580 | unless they try very hard not to.

00:18:48.140 | You've talked about these personality traits

00:18:50.220 | of great innovators before.

00:18:53.220 | And we're talking about them now.

00:18:55.220 | You invest in innovators.

00:18:56.820 | You try and identify them.

00:18:58.140 | And you are one.

00:18:59.100 | So you can recognize these traits.

00:19:00.980 | Here, I'm making the presumption that you have these traits.

00:19:03.180 | Indeed, you do.

00:19:04.820 | We'll just get that out of the way.

00:19:08.100 | Have you observed people trying to feign these traits?

00:19:12.540 | And are there any specific questions

00:19:14.540 | or behaviors that are a giveaway

00:19:17.780 | that they're pretending to be the young Steve Jobs

00:19:22.380 | or that they're pretending to be the young Henry Ford?

00:19:25.780 | Pick your list of other names

00:19:28.500 | that qualify as authentic, legitimate innovators.

00:19:32.380 | We won't name names of people

00:19:33.740 | who have tried to disguise themselves as true innovators,

00:19:36.060 | but what are some of the litmus tests?

00:19:40.380 | And I realize here that we don't want you to give these away

00:19:43.260 | to the point where they lose their potency.

00:19:45.420 | But if you could share a few of those.

00:19:47.100 | - Yeah, no, that's good.

00:19:47.940 | We're actually a pretty open book on this.

00:19:49.380 | So yeah, so first of all, yes.

00:19:51.940 | So there are people who definitely try to come in

00:19:53.580 | and basically present as being something that they're not.

00:19:55.420 | And they've read all the books.

00:19:56.820 | They will have listened to this interview, right?

00:19:58.260 | They study everything, and they construct a facade,

00:20:01.540 | and they come in and present as something they're not.

00:20:03.500 | I would say the amount of that varies

00:20:05.260 | exactly correlated to the NASDAQ, right?

00:20:09.220 | And so when stock prices are super low,

00:20:12.500 | you actually get the opposite.

00:20:13.340 | When stock prices are super low,

00:20:14.420 | people get too demoralized,

00:20:15.500 | and people who should be doing it basically give up

00:20:17.220 | 'cause they just think that whatever the industry's over,

00:20:19.460 | the trend is over, whatever, it's all hopeless.

00:20:21.140 | And so you get this flushing thing.

00:20:22.420 | So nobody ever shows up at a stock market low

00:20:24.940 | and says, "I'm the new next big thing,"

00:20:27.260 | and doesn't really want to do it

00:20:30.780 | because there are higher status.

00:20:32.780 | The kinds of people who do the thing

00:20:34.260 | that you're talking about,

00:20:35.100 | they're fundamentally oriented for social status.

00:20:36.820 | They're trying to get the social status

00:20:39.380 | without actually the substance,

00:20:41.380 | and there are always other places to go get social status.

00:20:44.020 | So after 2000, the joke was,

00:20:46.900 | so when I got to Silicon Valley in '93, '94,

00:20:50.740 | the valley was dead.

00:20:51.620 | We can talk about that.

00:20:52.660 | By '98, it was roaring,

00:20:54.020 | and you had a lot of these people showing up

00:20:55.820 | who were, you basically had a lot of people

00:20:57.900 | showing up with some kind of stories.

00:20:59.860 | 2000 market crash.

00:21:00.700 | By 2001, the joke was that there were these terms,

00:21:04.580 | B2C and B2B,

00:21:06.220 | and in 1998, B2C meant business to consumer,

00:21:09.820 | and B2B meant business to business,

00:21:11.420 | which is two different kinds of business models

00:21:12.940 | for internet companies.

00:21:13.780 | By 2001, B2B meant back to banking,

00:21:17.780 | B2C meant back to consulting,

00:21:19.220 | which is the high status people,

00:21:21.860 | the people oriented to status who showed up to be in tech

00:21:24.500 | were like, "Yeah, screw it.

00:21:25.660 | "This is over.

00:21:26.500 | "Stick a fork in it.

00:21:27.340 | "I'm gonna go back to Goldman Sachs

00:21:28.740 | "or go back to McKinsey where I can be high status,"

00:21:31.940 | and so you get this flushing kind of effect

00:21:34.540 | that happens in a downturn.

00:21:36.620 | That said, in a big upswing,

00:21:38.900 | yeah, you get a lot of people showing up

00:21:41.380 | with a lot of kind of, let's say, public persona

00:21:44.740 | without the substance to back it up.

00:21:46.940 | So the way we stress that,

00:21:48.340 | and you can actually say exactly how we test for this,

00:21:50.260 | which because the test exactly addresses the issue

00:21:53.860 | in a way that is impossible to fake,

00:21:55.820 | and it's actually the same way homicide detectives

00:21:58.820 | try to find out if you've actually,

00:22:01.140 | if you're innocent or whether you've killed somebody,

00:22:02.300 | it's the same tactic.

00:22:04.540 | Which is you ask increasingly detailed questions, right?

00:22:08.500 | And so the way a homicide cop does this

00:22:11.060 | is what were you doing last night?

00:22:12.500 | Oh, I was at a movie.

00:22:13.740 | Well, which movie?

00:22:14.660 | Oh, which theater?

00:22:17.660 | Okay, which seat did you sit in?

00:22:19.300 | Okay, what was the end of the movie, right?

00:22:22.980 | And you ask increasingly detailed questions

00:22:24.740 | and people have trouble,

00:22:25.580 | at some point, people have trouble making up

00:22:27.380 | and things just fuzz into just kind of obvious bullshit.

00:22:29.580 | And basically, fake founders

00:22:31.100 | basically have the same problem.

00:22:32.580 | They're able to relay a conceptual theory

00:22:34.980 | of what they're doing that they've kind of engineered.

00:22:37.260 | But as they get into the details, it just fuzzes out.

00:22:39.980 | Whereas the true people that you want to back

00:22:43.020 | that can do it, basically what you find is

00:22:44.700 | they've spent five or 10 or 20 years

00:22:46.460 | obsessing on the details of whatever it is

00:22:48.340 | they're about to do.

00:22:49.180 | And they're so deep in the details

00:22:50.660 | that they know so much more about it than you ever will.

00:22:52.980 | And in fact, the best possible reaction

00:22:54.620 | is when they get mad, right?

00:22:56.380 | Which is also what the homicide cops say, right?

00:22:58.500 | Which you actually want the emotional response of like,

00:23:01.780 | "I can't believe that you're asking me questions

00:23:03.600 | this detailed and specific and picky."

00:23:06.220 | And they kind of figure out what you're doing

00:23:07.860 | and then they get upset.

00:23:09.180 | Like that's good.

00:23:10.620 | That's perfect, right?

00:23:11.620 | But then they have to prove it themselves

00:23:14.340 | in the sense of like they have to be able

00:23:15.660 | to answer the questions in great detail.

00:23:18.420 | - Do you think that people that are able

00:23:19.600 | to answer those questions in great detail

00:23:21.580 | have actually taken the time to systematically think

00:23:24.300 | through the if-ands of all the possible implications

00:23:28.740 | of what they're going to do

00:23:29.580 | and they have a specific vision in mind

00:23:31.140 | of how things need to turn out or will turn out?

00:23:34.740 | Or do you think that they have a vision

00:23:37.660 | and it's a no matter what it will work out

00:23:40.400 | because the world will sort of bend around it?

00:23:42.780 | I mean, in other words,

00:23:43.640 | do you think that they place their vision in context

00:23:46.540 | or they simply have a vision

00:23:47.900 | and they have that tunnel vision of that thing

00:23:50.580 | and that's going to be it.

00:23:51.740 | Let's use you for an example with Netscape.

00:23:54.460 | I mean, that's how I first came to know your name.

00:23:58.340 | When you were conceiving Netscape, did you think,

00:24:01.900 | okay, there's this search engine and this browser

00:24:05.620 | and it's going to be this thing that looks this way

00:24:08.460 | and works this way and feels this way.

00:24:10.760 | Did you think that and also think about, you know,

00:24:15.280 | that there was going to be a gallery of other search engines

00:24:18.140 | and it would fit into that landscape of other search engines

00:24:20.540 | or were you just projecting your vision of this thing

00:24:23.100 | as this unique and special brainchild?

00:24:27.420 | So I'm going to give the general answer

00:24:28.660 | and then we can talk about the specific example.

00:24:30.220 | So the general answer is with entrepreneurship,

00:24:32.780 | creativity, innovation is what economists call

00:24:34.860 | decision-making under uncertainty, right?

00:24:37.020 | And so in both parts, it's important, decision-making,

00:24:38.860 | like you're going to make a ton of decisions

00:24:40.020 | because you have to decide what to do, what not to do

00:24:41.620 | and then uncertainty, which is like,

00:24:43.580 | the world's a complicated place, right?

00:24:45.500 | And in mathematical terms,

00:24:46.900 | the world is a complex adaptive system with feedback loops

00:24:49.300 | and like, it's really, I mean, it's extreme, you know.

00:24:53.380 | Isaac Asimov wrote, you know, in his novels,

00:24:55.380 | he wrote about this field called psychohistory

00:24:57.340 | which is the idea that there's like a supercomputer

00:24:59.060 | that can predict the future of like human affairs, right?

00:25:01.100 | And it's like, we don't have that.

00:25:03.260 | - Not yet.

00:25:04.100 | - Not yet, not yet.

00:25:04.940 | - We'll get to that later.

00:25:06.620 | - We certainly don't have that yet.

00:25:08.660 | And so you're just dealing, you know,

00:25:10.460 | military commanders call this the fog of war, right?

00:25:13.220 | You're just dealing with a situation

00:25:14.860 | where the number of variables are just off the charts.

00:25:17.260 | It's all these other people, right?

00:25:19.020 | Who are inherently unpredictable,

00:25:20.260 | making all these decisions in different directions.

00:25:22.140 | And then the whole system is combinatorial,

00:25:23.820 | which is these people are colliding with each other,

00:25:25.260 | influencing their decisions.

00:25:26.660 | And so, I mean, look, the most straightforward kind of way

00:25:29.940 | to think about this is it's just, it's amazing.

00:25:32.260 | Like anybody who believes in economic central planning,

00:25:33.940 | it always blows my mind 'cause it's just like,

00:25:35.340 | it's just like try opening a restaurant.

00:25:37.540 | Like try just opening a restaurant on the corner down here

00:25:40.660 | and like 50/50 odds the restaurant's gonna work.

00:25:43.260 | And like all you have to do to run a restaurant

00:25:44.940 | is like have a thing and serve food and like,

00:25:47.020 | and it's like most restaurants fail, right?

00:25:48.460 | And so, and restaurant people who run restaurants

00:25:49.900 | are like pretty smart.

00:25:50.740 | Like they're, you know, they usually think

00:25:52.060 | about these things very hard and they all wanna succeed.

00:25:54.220 | And it's hard to do that.

00:25:55.420 | And so to start a tech company

00:25:56.700 | or to start an artistic movement or to fight a war,

00:26:00.300 | like you're just going into this,

00:26:01.820 | like basically about conceptual battleground

00:26:03.740 | or military terms, real battleground

00:26:05.660 | where there's just like incredible levels of complexity,

00:26:08.300 | branching future paths.

00:26:10.100 | And so there's nothing, it's, you know,

00:26:11.700 | there's nothing predictable.

00:26:12.540 | And so what we look for is basically the sort of drop,

00:26:16.460 | the really good innovators, they've got a drive

00:26:18.700 | to basically be able to cope with that and deal with that.

00:26:20.820 | And they basically do that in two steps.

00:26:22.540 | So one is they try to pre-plan as much as they possibly can.

00:26:25.340 | And we call that the process of navigating,

00:26:27.580 | we call the idea maze, right?

00:26:29.020 | And so the idea maze basically is I've got this general idea

00:26:31.700 | and it might be the internet's gonna work

00:26:32.980 | or search or whatever.

00:26:34.220 | And then it's like, okay, in their head,

00:26:35.940 | they have thought through of like,

00:26:37.260 | okay, if I do it this way, that way, this third way,

00:26:39.300 | here's what will happen, then I have to do that,

00:26:40.900 | then I have to do this,

00:26:41.740 | then I have to bring in somebody to do that.

00:26:43.140 | Here's the technical challenge I'm gonna hit.

00:26:44.900 | And they've got in their heads as best anybody could,

00:26:47.900 | they've got as complete as sort of a map

00:26:50.060 | of possible futures as they could possibly have.

00:26:51.940 | And this is where I say,

00:26:53.340 | when you ask them increasingly detailed questions,

00:26:54.860 | that's what you're trying to kind of get them

00:26:56.260 | to kind of chart out is, okay,

00:26:57.940 | how far ahead have you thought

00:26:59.620 | and how much are you anticipating

00:27:01.220 | all of the different twists and turns

00:27:02.500 | that this is gonna take?

00:27:03.900 | Okay, so then they start on day one.

00:27:05.380 | And then of course, what happens is,

00:27:06.700 | now they're in it, now they're in the fog of war, right?

00:27:09.300 | They're in future uncertainty.

00:27:10.340 | And now that idea maze is maybe not helpful practically,

00:27:13.120 | but now they're gonna be basically constructing

00:27:14.780 | on the fly day by day as they learn

00:27:16.420 | and discover new things.

00:27:17.820 | And as the world changes around them.

00:27:19.060 | And of course, it's a feedback loop

00:27:20.180 | 'cause they're gonna change, if their thing starts to work,

00:27:22.020 | it's gonna change the world.

00:27:22.940 | And then the fact the world is changing

00:27:24.220 | is gonna cause their plan to change as well.

00:27:27.460 | And so, yeah, the great ones basically,

00:27:29.980 | they course correct,

00:27:31.740 | the great ones course correct every single day.

00:27:33.860 | They take stock of what they've learned.

00:27:36.340 | They modified the plan.

00:27:38.100 | The great ones tend to think in terms of hypotheses, right?

00:27:40.460 | It's a little bit like a scientific sort of mentality,

00:27:42.580 | which is they tend to think, okay, I'm gonna try this.

00:27:45.660 | I'm gonna go into the world,

00:27:46.500 | I'm gonna announce that I'm doing this for sure.

00:27:48.780 | I'm gonna say like, this is my plan.

00:27:50.660 | And I'm gonna tell all my employees that.

00:27:51.820 | I'm gonna tell all my investors that.

00:27:52.900 | I'm gonna put a stake in there, that's my plan.

00:27:54.060 | And then I'm gonna try it, right?

00:27:55.740 | And even though I sound like I have complete certainty,

00:27:57.700 | I know that I need to test

00:27:58.900 | to find out whether it's gonna work.

00:28:00.380 | And if it's not, then I have to go back

00:28:02.060 | to all those same people and I have to say,

00:28:03.420 | well, actually, we're not going left, we're going right.

00:28:05.740 | And they have to run that loop thousands of times, right?

00:28:09.060 | And they had to get through the other side.

00:28:10.260 | And this led to the creation of this great term pivot,

00:28:13.220 | which has been very helpful in our industry

00:28:14.660 | 'cause the word when I was young,

00:28:16.540 | the word we used was fuck up and pivot

00:28:18.900 | sounds like so much better.

00:28:20.500 | It sounds like so much more professional.

00:28:22.260 | But yeah, you like make mistakes.

00:28:24.540 | It's just too complicated to understand.

00:28:26.460 | You course correct, you adjust, you evolve.

00:28:29.100 | Often these things, at least in business,

00:28:31.180 | the businesses that end up working really well

00:28:32.780 | tend to be different than the original plan.

00:28:35.100 | But that's part of the process of a really smart founder

00:28:38.380 | basically working their way through reality, right?

00:28:40.900 | As they're executing their plan.

00:28:42.860 | - The way you're describing this has parallels

00:28:44.820 | to a lot of models in biology

00:28:46.420 | and the practice of science, random walks,

00:28:49.100 | but that aren't truly random,

00:28:50.460 | pseudo random walks in biology, et cetera.

00:28:52.780 | But one thing that is becoming clear

00:28:55.460 | from the way you're describing this is that

00:28:57.740 | I could imagine a great risk to early success.

00:29:01.560 | So for instance, somebody develops a product,

00:29:03.460 | people are excited by it.

00:29:05.380 | They start to implement that product,

00:29:06.640 | but then the landscape changes

00:29:08.420 | and they don't learn how to pivot

00:29:10.720 | to use the less profane version of it, right?

00:29:13.860 | They don't learn how to do that.

00:29:15.300 | In other words, the, and I think of everything these days,

00:29:18.700 | or most everything in terms of reward schedules

00:29:21.040 | and dopamine reward schedules,

00:29:22.800 | 'cause that is the universal currency of reward.

00:29:25.340 | And so when you talk about the Sean Parker quote

00:29:28.100 | of learning to enjoy the taste of one's own blood,

00:29:31.300 | that is very different

00:29:32.700 | than learning to enjoy the taste of success, right?

00:29:35.620 | It's about internalizing success

00:29:38.180 | as a process of being self-determined

00:29:40.740 | and less agreeable, et cetera.

00:29:43.140 | In other words, building up of those five traits

00:29:46.360 | becomes the source of dopamine, perhaps,

00:29:48.780 | in a way that's highly adaptive.

00:29:50.200 | So on the outside, we just see the product, the end product,

00:29:52.880 | the iPhone, the MacBook, the Netscape, et cetera.

00:29:55.820 | But I have to presume, and I'm not a psychologist,

00:29:59.420 | but I have done neurophysiology

00:30:00.840 | and I've studied the dopamine system enough to know that

00:30:04.500 | what's being rewarded in the context

00:30:06.660 | of what you're describing

00:30:08.060 | sounds to be a reinforcement of those five traits,

00:30:10.740 | rather than, oh, it's going to be this particular product,

00:30:14.040 | or the company's going to look this way,

00:30:15.300 | or the logo is going to be this or that.

00:30:16.940 | That all seems like the peripheral to what's really going on,

00:30:21.940 | that great innovators are really in the process

00:30:24.620 | of establishing neural circuitry

00:30:26.980 | that is all about reinforcing the me

00:30:30.100 | and the process of being me.

00:30:32.220 | - Yeah, yeah, so this goes to, yeah,

00:30:34.460 | so this is like extrinsic versus intrinsic motivation.

00:30:36.700 | So the Steve Jobs kind of Zen version of this, right,

00:30:39.100 | or the sort of hippie version of this was,

00:30:40.580 | the journey is the reward, right?

00:30:42.380 | He always told his employees that.

00:30:44.460 | It's like, look, everybody thinks in terms

00:30:46.140 | of these big public markers, like the stock price

00:30:47.940 | or the IPO or the product launch or whatever.

00:30:49.740 | He's like, no, it's actually the process itself

00:30:52.700 | is the point, right?

00:30:54.240 | And to your point, if you have that mentality,

00:30:56.580 | then that's an intrinsic motivation,

00:30:58.160 | not an extrinsic motivation.

00:30:59.700 | And so that's the kind of intrinsic motivation

00:31:01.140 | that can keep you going for a long time.

00:31:03.620 | Another way to think about it

00:31:04.480 | is competing against yourself, right?

00:31:05.900 | It's like, can I get better at doing this, right?

00:31:08.820 | And can I prove to myself that I can get better?

00:31:11.220 | There's also a big social component to this,

00:31:13.140 | and this is one of the reasons why Silicon Valley

00:31:15.060 | punches so far above its weight as a place.

00:31:17.580 | There's a psychological component,

00:31:18.660 | which also goes to the comparison set.

00:31:21.140 | So a phenomenon that we've observed over time

00:31:23.020 | is the leading tech company in any city

00:31:26.620 | will aspire to be as large as the previous

00:31:28.900 | leading tech company in that city,

00:31:31.240 | but often not larger, right?

00:31:32.900 | 'Cause they have a model of success,

00:31:35.140 | and as long as they beat that level of success,

00:31:36.860 | they've kind of checked the box,

00:31:38.200 | like they've made it, and then they,

00:31:40.740 | but then in contrast, you're in Silicon Valley,

00:31:42.340 | and you look around, and it's just like Facebook,

00:31:44.140 | and Cisco, and Oracle, and Hewlett-Packard, and--

00:31:47.040 | - Gladiators.

00:31:47.880 | - Yeah, and you're just looking at these giants,

00:31:51.380 | and many of them are still,

00:31:53.100 | Mark Zuckerberg's still going to work every day

00:31:54.740 | and trying to do, and so these people are like,

00:31:58.280 | the role models are like alive, right?

00:32:00.260 | And they're like right there, right?

00:32:01.880 | And it's so clear how much better they are

00:32:03.960 | and how much bigger their accomplishments are,

00:32:05.460 | and so what we find is young founders

00:32:07.540 | in that environment have much greater aspirations, right?

00:32:11.220 | 'Cause they just, again, maybe at that point,

00:32:13.280 | maybe it's the social status,

00:32:14.340 | maybe there's an extrinsic component to that,

00:32:16.960 | but or maybe it helps calibrate that internal system

00:32:19.500 | to basically say actually, you know,

00:32:21.120 | no, the opportunity here is not to build a local,

00:32:23.220 | you know, what you may call local maximum form of success,

00:32:25.100 | but let's build to a global maximum form of success,

00:32:27.660 | which is something as big as we possibly can.

00:32:30.500 | Ultimately, the great ones are probably driven

00:32:32.380 | more internally than externally when it comes down to it,

00:32:34.700 | and that is where you get this phenomenon

00:32:36.260 | where you get people who are, you know,

00:32:37.460 | extremely successful and extremely wealthy

00:32:39.140 | who very easily can punch out and move to Fiji

00:32:41.700 | and just call it, and they're still working 16-hour days,

00:32:45.100 | right, and so obviously something explains that

00:32:47.420 | that has nothing to do with external rewards,

00:32:49.060 | and I think it's an internal thing.

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00:33:48.880 | - I've heard you talk a lot about the inner landscape,

00:33:53.700 | the inner psychology of these folks,

00:33:55.580 | and I appreciate that we're going

00:33:57.000 | even deeper into that today,

00:33:58.600 | and we will talk about the landscape around,

00:34:00.880 | whether or not Silicon Valley or New York,

00:34:02.520 | whether or not there are specific cities

00:34:03.860 | that are ideal for certain types of pursuits.

00:34:05.860 | I think there was an article written by Paul Graham

00:34:07.500 | some years ago about the conversations

00:34:10.460 | that you overhear in a city will tell you everything

00:34:12.480 | you need to know about whether or not you belong there

00:34:15.100 | in terms of your professional pursuits.

00:34:18.180 | Some of that's changed over time,

00:34:19.460 | and now we should probably add Austin to the mix

00:34:22.260 | 'cause it was written some time ago.

00:34:24.760 | In any event, I want to return to that,

00:34:27.120 | but I want to focus on an aspect

00:34:29.100 | of this intrinsic versus extrinsic motivators

00:34:33.140 | in terms of something that's a bit more cryptic,

00:34:35.680 | which is one's personal relationships.

00:34:38.740 | You know, if I think about the catalog of innovators

00:34:41.720 | in Silicon Valley, some of them, like Steve Jobs,

00:34:44.220 | had complicated personal lives,

00:34:45.860 | romantic personal lives early on,

00:34:47.540 | and then it sounds like he worked it out.

00:34:48.920 | I don't know, I wasn't their couples therapist,

00:34:51.820 | but when he died, he was in a marriage

00:34:55.460 | that for all the world seemed like a happy marriage.

00:34:58.060 | You also have examples of innovators

00:35:01.340 | who have had many partners, many children

00:35:03.400 | with other partners.

00:35:04.240 | Elon comes to mind.

00:35:05.840 | You know, I don't think I'm disclosing anything

00:35:07.840 | that isn't already obvious.

00:35:09.180 | Those could have been happy relationships

00:35:11.980 | and just had many of them.

00:35:13.480 | But the reason I'm asking this is you can imagine

00:35:15.960 | that for the innovator, the person with these traits

00:35:19.700 | who's trying to build up this thing, whatever it is,

00:35:23.700 | that having someone, or several people in some cases,

00:35:29.420 | who just truly believe in you,

00:35:32.340 | when the rest of the world may not believe in you yet

00:35:34.420 | or at all, could be immensely powerful.

00:35:36.760 | And we have examples from cults that embody this.

00:35:41.220 | We have examples from politics.

00:35:42.540 | We have examples from tech innovation and science.

00:35:46.260 | And I've always been fascinated by this

00:35:47.700 | because I feel like it's the more cryptic

00:35:49.760 | and yet very potent form of allowing someone

00:35:53.300 | to build themselves up.

00:35:54.960 | It's a combination of inner psychology

00:35:57.580 | and extrinsic motivation because obviously

00:35:59.500 | if that person were to die or leave them

00:36:01.380 | or cheat on them or pair up with some other innovator,

00:36:05.020 | which we've seen several times, recently and in the past,

00:36:08.680 | it can be devastating to that person.

00:36:10.220 | But what are your thoughts on the role of personal

00:36:13.600 | and in particular romantic relationship

00:36:15.340 | as it relates to people having an idea

00:36:18.720 | and their feeling that they can really bring that idea

00:36:21.460 | to fruition in the world?

00:36:22.980 | - So it's a real mixed bag.

00:36:24.500 | You have lots of examples in all directions.

00:36:26.580 | And I think it's something like following.

00:36:29.220 | So first is we talked about the personality traits

00:36:31.360 | of these people.

00:36:32.200 | They tend to be highly disagreeable.

00:36:33.980 | - Doesn't foster a good romantic relationships.

00:36:35.660 | - Highly disagreeable people can be difficult

00:36:37.420 | to be in relationships.

00:36:38.260 | - I may have heard of that once or twice before.

00:36:39.580 | A friend may have given me that example.

00:36:41.500 | - Yeah, right?

00:36:42.340 | And maybe you just need to find the right person

00:36:43.700 | who like compliments that and is willing to,

00:36:45.580 | there's a lot of relationships where like,

00:36:46.860 | it's always this question about relationships, right?

00:36:48.420 | Which is, do you want to have the same personality,

00:36:50.540 | growth profile, the same behavioral traits

00:36:52.100 | basically as your partner?

00:36:53.020 | Or do you actually want to have,

00:36:54.660 | is it an opposites thing?

00:36:56.140 | And, you know, I'm sure you've seen this.

00:36:57.980 | There are relationships where you'll have somebody

00:36:59.260 | who's highly disagreeable,

00:37:00.100 | who's paired with somebody who's highly agreeable.

00:37:01.500 | And it actually works out great.

00:37:03.140 | 'Cause one person just gets to be on their soapbox

00:37:04.700 | all the time and the other person's just like, okay.

00:37:06.980 | Right, and it's fine, right?

00:37:08.860 | It's fine, it's good.

00:37:10.340 | You know, you put two disagreeable people together,

00:37:12.260 | you know, maybe sparks fly

00:37:13.660 | and they have great conversations all the time

00:37:15.160 | and maybe they come to hate each other, right?

00:37:16.580 | And so, so anyway, so these people,

00:37:19.100 | if you're gonna be with one of these people,

00:37:20.520 | you're fishing out of the disagreeable end of the pond.

00:37:22.640 | And again, when I say disagreeable,

00:37:23.780 | I don't mean, you know, these are normal distributions.

00:37:25.900 | I don't mean like 60% disagreeable or 80% disagreeable.

00:37:28.780 | The people we're talking about are 99.99% disagreeable,

00:37:31.740 | right, so these are ornery people.

00:37:34.020 | So part of it's that.

00:37:35.940 | And then, of course,

00:37:36.780 | they have the other personality traits, right?

00:37:38.020 | They're, you know, super conscientious,

00:37:39.460 | they're super driven.

00:37:40.340 | As a consequence, they tend to work really hard.

00:37:41.920 | They tend to not have a lot of time for, you know,

00:37:43.560 | family vacations or other things.

00:37:45.340 | You know, and they don't enjoy them

00:37:46.780 | if they're forced to go on them.

00:37:47.740 | And so again, that kind of thing can fray in a relationship.

00:37:50.420 | So there's a fair amount in there that's loaded.

00:37:53.800 | Like somebody's gonna partner with one of these people

00:37:55.740 | and needs to be signed up for the ride.

00:37:57.340 | And that's a hard thing, you know,

00:37:59.140 | that's a hard thing to do.

00:38:00.740 | Or you need a true partnership with two of these,

00:38:02.460 | which is also hard to do.

00:38:04.260 | So I think that's part of it.

00:38:06.180 | And then look, I think a big part of it is, you know,

00:38:07.780 | people achieve a certain level of success

00:38:10.140 | and, you know, either in their own minds or, you know,

00:38:12.980 | publicly, and then they start to be able

00:38:14.940 | to get away with things, right?

00:38:16.120 | And they start to be able to, it's like, well, okay,

00:38:18.100 | you know, now we're rich and successful and famous

00:38:20.100 | and now I deserve, you know,

00:38:21.540 | and this is where you get into,

00:38:22.740 | I view this now in the realm of personal choice, right?

00:38:25.340 | You get in this thing where people start to think

00:38:26.980 | that they deserve things.

00:38:27.900 | And so they start to behave in, you know, very bad ways.

00:38:30.900 | And then they blow up their personal worlds as a consequence

00:38:33.660 | and maybe they regret it later and maybe they don't, right?

00:38:36.300 | It's always a question.

00:38:38.760 | So yeah, so I think there's that.

00:38:40.460 | And then I don't know, like, yeah, some people just need,

00:38:44.140 | maybe the other part of it is,

00:38:44.980 | some people just need more emotional support than others.

00:38:46.940 | And I don't know that that's a big,

00:38:48.320 | I don't know that that tilts either way.

00:38:50.000 | Like I know some of these people

00:38:51.340 | who have like great loving relationships

00:38:53.020 | and seem to draw very much

00:38:54.260 | on having this kind of firm foundation to rely upon.

00:38:57.140 | And then I know other people

00:38:58.020 | who are just like their personal lives

00:38:59.140 | are just a continuous train wreck

00:39:00.340 | and it doesn't seem to matter.

00:39:02.900 | Like professionally, they just keep doing what they're doing.

00:39:04.940 | And maybe we could talk here about like, you know,

00:39:07.700 | whatever is the personality trait for risk taking, right?

00:39:10.820 | Some people are so incredibly risk prone

00:39:13.260 | that they need to take risk

00:39:14.140 | in all aspects of their lives at all times.

00:39:15.980 | And if part of their life gets stable,

00:39:18.060 | they find a way to blow it up.

00:39:20.220 | And that's some of these people

00:39:22.260 | you could describe in those terms also.

00:39:24.120 | - Yeah, let's talk about that

00:39:25.780 | because I think risk taking and sensation seeking

00:39:29.540 | is something that fascinates me for my own reasons

00:39:32.720 | and in my observations of others.

00:39:34.580 | Does it dovetail with these five traits

00:39:39.220 | in a way that can really serve innovation

00:39:42.060 | in ways that can benefit everybody?

00:39:43.460 | The reason I say to benefit everybody

00:39:44.820 | is because there is a view

00:39:46.900 | of how we're painting this picture of the innovator

00:39:50.220 | as this like really cruel person.

00:39:53.180 | But oftentimes what we're talking about

00:39:54.500 | are innovations that make the world far better

00:39:56.600 | for billions of people.

00:39:58.080 | - Yeah, that's right.

00:39:59.560 | And by the way, everything we're talking about also

00:40:00.940 | is not just in tech or science or in business.

00:40:03.160 | It's also, everything we're also talking about

00:40:04.240 | is true for the arts, right?

00:40:05.640 | So the history of like artistic expression

00:40:07.840 | is you have people with all these same kinds of traits.

00:40:09.880 | - Well, I was thinking about Picasso

00:40:10.960 | and his regular turnover of lovers and partners.

00:40:13.820 | And he was very open about the fact

00:40:15.200 | that it was one of the sources

00:40:16.520 | of his productivity/creativity.

00:40:20.240 | He wasn't shy about that.

00:40:21.640 | I suppose if he were alive today,

00:40:24.020 | it might be a little bit different.

00:40:24.860 | He might be judged a little differently.

00:40:26.260 | - Right, or that was his story for behaving in a pattern

00:40:29.420 | that was very awful for the people around him

00:40:31.600 | and he didn't care.

00:40:32.440 | - Right, maybe they left him.

00:40:33.620 | - Yeah, like who knows, right?

00:40:34.840 | So it puts and takes to all this.

00:40:37.620 | But no, okay, so I have a theory.

00:40:39.620 | So here's a theory.

00:40:40.660 | This is one of these.

00:40:41.500 | I keep a list of things that will get me kicked

00:40:42.500 | out of a dinner party at topics at any given point in time.

00:40:45.900 | - Do you read it before you go in?

00:40:47.020 | - Yeah, I just, yeah.

00:40:48.020 | (laughing)

00:40:50.580 | I'll recall so that I can get out of these things.

00:40:53.180 | But so here's the thing that can get me kicked

00:40:55.540 | out of a dinner party, especially these days.

00:40:58.480 | So think of the kind of person where it's like very clear

00:41:02.140 | that they're like super high, to your point,

00:41:04.220 | somebody is super high output in whatever domain they're in.

00:41:06.180 | They've done things that have like fundamentally

00:41:07.620 | like changed the world.

00:41:08.580 | They've brought new, whether it's businesses or technologies

00:41:10.780 | or works of art, entire schools of creative expression,

00:41:15.260 | in some cases, to the world.

00:41:16.560 | And then at a certain point,

00:41:18.160 | they blow themselves to smithereens, right?

00:41:20.080 | And they do that either through like a massive

00:41:22.460 | like financial scandal.

00:41:23.460 | They do that through a massive personal breakdown.

00:41:25.920 | They do that through some sort of public expression

00:41:28.460 | that causes them a huge amount of problems.

00:41:30.140 | They say the wrong thing, maybe not once,

00:41:31.880 | but several hundred times,

00:41:34.100 | and blow themselves to smithereens.

00:41:35.820 | And there's this kind of arc,

00:41:38.020 | there's this moral arc that people kind of want to apply,

00:41:40.100 | which is like the Icarus flying too close to the sun.

00:41:43.620 | And he had it coming and he needed to keep his ego

00:41:45.980 | under control.

00:41:46.820 | And you get kind of this judgment

00:41:49.620 | that applies.

00:41:50.760 | So I have a different theory on this.

00:41:52.760 | So the term I use to describe these people,

00:41:54.840 | and a lot of, and by the way,

00:41:55.960 | a lot of other people who don't actually

00:41:56.960 | blow themselves up but get close to it,

00:41:59.340 | which is a whole other set of people,

00:42:01.640 | I call them martyrs to civilizational progress, right?

00:42:05.160 | So, and we're backwards, civilizational progress.

00:42:07.600 | So look, the only way civilization gets moved forward

00:42:10.520 | is when people like this do something new, right?

00:42:12.900 | 'Cause civilization as a whole does not do new things, right?

00:42:16.280 | Groups of people do not do new things, right?

00:42:18.320 | These things don't happen automatically.

00:42:20.320 | Like by default, nothing changes.

00:42:22.960 | The only way civilizational change

00:42:24.720 | on any of these axes ever happens

00:42:26.680 | is because one of these people stands up and says,

00:42:29.200 | no, I'm going to do something different

00:42:30.440 | than what everybody else has ever done before.

00:42:32.000 | So this is progress.

00:42:34.560 | Like this is actually how it happens.

00:42:36.280 | Sometimes they get lionized or awarded.

00:42:38.180 | Sometimes they get crucified.

00:42:39.880 | Sometimes the crucification is literal.

00:42:42.120 | Sometimes it's just symbolic,

00:42:43.560 | but like they are those kinds of people.

00:42:46.980 | And then, and then, and then murders.

00:42:48.440 | Like when they go down in flames, like they have,

00:42:51.360 | and again, this is where it really screws

00:42:52.720 | the people's moral judgments,

00:42:53.620 | 'cause everybody wants to have this sort of

00:42:55.880 | super clear story of like, okay,

00:42:57.140 | he did a bad thing and he was punished.

00:42:59.540 | And I'm like, no, no, no, no, no, no.

00:43:00.940 | He was the kind of person who was going to do great things

00:43:03.780 | and also was going to take on a level of risk

00:43:06.300 | and take on a level of sort of extreme behavior

00:43:08.620 | such that he was going to expose himself

00:43:10.520 | to flying too close to the sun,

00:43:12.220 | wings melt and crash to ground.

00:43:13.320 | But it's a package deal, right?

00:43:16.260 | The reason you have the Picassos and the Beethovens

00:43:18.680 | and all these people is because they're willing

00:43:20.360 | to take these extreme level of risks.

00:43:21.980 | They are that creative and original,

00:43:24.100 | not just in their art or their business,

00:43:25.820 | but in everything else that they do,

00:43:27.340 | that they will set themselves up to be able to fail.

00:43:29.500 | Psychologic, you know, a psychologist would probably,

00:43:31.380 | a psychiatrist would probably say, you know, maybe,

00:43:33.020 | you know, to what extent do they actually

00:43:34.120 | like have a death wish?

00:43:34.960 | Do they actually, you know, at some point,

00:43:36.260 | do they want to punish themselves?

00:43:37.300 | Do they want to fail?

00:43:38.120 | That I don't know.

00:43:39.360 | But you see this, they deliberately move themselves

00:43:42.060 | too close to the sun.

00:43:42.900 | And you can see it when it's happening,

00:43:44.580 | is like if they get too far from the sun,

00:43:46.580 | they deliberately move back towards it, right?

00:43:48.700 | You know, they come right back and they want the risk.

00:43:52.660 | And so anyway, like I, yeah, so martyrs

00:43:55.180 | to civilizational progress, like this is how progress happens.

00:43:58.460 | When these people crash and burn,

00:44:00.100 | the natural inclination is to judge them morally.

00:44:02.580 | I tend to think we should basically say, look,

00:44:04.820 | and I don't even know if this means like giving them

00:44:06.460 | a moral pass or whatever.

00:44:07.780 | But it's like, look, like this is how civilization progresses.

00:44:11.440 | And we need to at least understand

00:44:12.900 | that there's a self-sacrificial aspect to this

00:44:15.340 | that may be tragic and often is tragic,

00:44:17.780 | but it is quite literally self-sacrificial.

00:44:20.500 | - Are there any examples of great innovators

00:44:23.180 | who were able to compartmentalize their risk-taking

00:44:27.920 | to such a degree that they had what seemed to be

00:44:31.960 | a morally impeccable life in every domain

00:44:34.900 | except in their business pursuits?

00:44:36.860 | - Yeah, that's right.

00:44:37.700 | So some people are very highly controlled like that.

00:44:40.660 | Some people are able to like very narrowly,

00:44:42.980 | and I don't really want to set myself an example

00:44:44.780 | on a lot of this, but I will tell you like as an example,

00:44:46.700 | like I will never use debt in business, number one.

00:44:50.420 | Number two, like I have the most placid personal life

00:44:52.220 | you can imagine.

00:44:53.060 | Number three, I'm the last person in the world

00:44:54.860 | who's ever gonna do an extreme sport.

00:44:57.020 | I mean, I'm not even gonna go on the sauna on the ice bath.

00:44:59.100 | Like I'm not doing any of this.

00:45:01.140 | Like I don't end up doing anything.

00:45:02.020 | I'm not power skiing.

00:45:03.140 | - No obligation to do the ice bath.

00:45:04.860 | - I'm not on the Titan.

00:45:05.700 | I'm not going down to see the Titanic.

00:45:07.380 | - Thank goodness you weren't there.

00:45:08.260 | - I'm not doing any of this.

00:45:09.100 | I'm not doing any of this stuff.

00:45:09.980 | I have no interest.

00:45:10.820 | I don't play golf.

00:45:11.660 | I don't ski.

00:45:12.500 | I have no interest in any of this stuff, right?

00:45:13.780 | And so like there are, and I know people like this, right,

00:45:16.140 | who are very high achievers.

00:45:17.060 | It's just like, yeah, they're completely segmented.

00:45:19.460 | They're extreme risk takers in business.

00:45:21.020 | They're completely buttoned down on the personal side.

00:45:22.820 | They're completely buttoned down financially.

00:45:24.940 | They're scrupulous with following every rule and law

00:45:27.980 | you can possibly imagine.

00:45:29.300 | But they're still fantastic innovators.

00:45:30.860 | And then I know many others who are just like,

00:45:32.660 | their life is on fire all the time in every possible way.

00:45:36.780 | And whenever it looks like the fire is turning into embers,

00:45:38.820 | they figure out a way to like relight the fire, right?

00:45:41.420 | And they just really want to live on the edge.

00:45:45.300 | And so I think that's maybe--

00:45:46.860 | I think that's an independent variable.

00:45:48.660 | And again, I would apply the same thing.

00:45:49.820 | I think the same thing applies to the arts.

00:45:52.660 | Classical music is an example.

00:45:54.020 | I think Bach was, as an example, one of the kind of best

00:45:56.860 | musicians of all time, had just a completely sedate

00:45:59.700 | personal life, never had any aberrant behavior

00:46:02.260 | at all in his personal life.

00:46:03.580 | Family man, tons of kids, apparently pillar

00:46:05.940 | of the community, right?

00:46:07.380 | And so if Bach could be Bach and yet not burn his way

00:46:10.500 | through 300 mistresses or whatever, maybe you can, too.

00:46:15.700 | So in thinking about these two different categories

00:46:18.100 | of innovators, those that take on tremendous risk in all

00:46:20.500 | domains of their life and those that take on tremendous risk

00:46:23.140 | in a very compartmentalized way, I

00:46:25.340 | don't know what the percentages are.

00:46:27.180 | But I have to wonder if in this modern age of the public being

00:46:31.420 | far less forgivable, what I'm referring to

00:46:33.660 | is cancel culture.

00:46:36.220 | Do you think that we are limiting

00:46:38.700 | the number of innovations in total,

00:46:41.500 | like by just simply frightening or eliminating

00:46:45.020 | an enormous category of innovators

00:46:47.140 | because they don't have the confidence or the means

00:46:49.780 | or the strategies in place to regulate?

00:46:53.780 | So they're just either bowing out

00:46:55.500 | or they're getting crossed off.

00:46:56.820 | They're getting canceled one by one.

00:46:58.380 | So do you think the public is less tolerant than they

00:47:00.700 | used to be or more tolerant?

00:47:03.860 | Well, the systems that--

00:47:07.020 | I'm not going to be careful here.

00:47:08.420 | I think the large institution systems are not

00:47:14.100 | tolerant of what the public tells them

00:47:16.780 | they shouldn't be tolerant of.

00:47:20.260 | And so if there's enough noise, there's

00:47:22.500 | enough noise in the mob, I think institutions bow out.

00:47:25.820 | And here I'm referring not just to--

00:47:27.860 | they essentially say, OK, let the cancellation proceed.

00:47:31.540 | And then maybe they're the gavel that comes down,

00:47:34.340 | but they're not the lever that got the thing going.

00:47:36.980 | And so I'm not just thinking about universities.

00:47:38.980 | I'm also thinking about advertisers.

00:47:40.620 | I'm thinking about the big movie houses that cancel a film that

00:47:43.980 | a given actor might be in because they had something

00:47:46.180 | in their personal life that's still getting worked out.

00:47:48.560 | I'm thinking about people who are in a legal process that's

00:47:51.700 | not yet resolved, but the public has decided

00:47:53.900 | they're a bad person, et cetera.

00:47:55.700 | My question is are we really talking about the public?

00:47:59.060 | I agree with your question, and I'm going to come back to it.

00:48:01.380 | I'm going to examine one part of your question, which

00:48:04.180 | is is this really the public we're talking about?

00:48:06.220 | And I would just say exhibit A is

00:48:07.740 | who is the current front runner for the Republican nomination

00:48:11.460 | today?

00:48:13.380 | The public, at least on one side of the political aisle,

00:48:16.700 | seems very on board.

00:48:19.340 | Number two, look, there's a certain musician who flew too

00:48:23.900 | close to the sun, blew himself to smithereens.

00:48:25.860 | He's still hitting all-time highs on music streams

00:48:29.780 | every month.

00:48:30.820 | The public seems fine.

00:48:32.820 | I think the public might--

00:48:34.020 | I would argue the public is actually

00:48:35.660 | more open to these things than it actually maybe ever has been.

00:48:38.660 | And we could talk about why that's the case.

00:48:40.460 | I think it's a differentiation, and this

00:48:42.140 | is what your question was aiming at,

00:48:43.340 | but it's a differentiation between the public

00:48:45.220 | and the elites.

00:48:46.660 | So my view is everything that you just described

00:48:48.940 | is an elite phenomenon, and actually the public

00:48:50.980 | is very much not on board with it.

00:48:52.860 | Interesting.

00:48:53.540 | And so what's actually happening is the division--

00:48:55.740 | what's happened is the public and the elites

00:48:57.540 | have gapped out.

00:48:58.300 | The public is more forgiving of what previously

00:49:01.860 | might have been considered kind of ever an extreme behavior.

00:49:05.700 | Of Scott Fitzgerald, there are no second acts

00:49:07.540 | in American lives.

00:49:08.140 | It turns out completely wrong.

00:49:09.500 | It turns out there are second acts, third acts, fourth acts.

00:49:11.340 | Apparently, you can have a limited number of acts.

00:49:12.780 | The public is actually up for it.

00:49:14.220 | Yeah, I mean, I think of somebody like Mike Tyson.

00:49:16.940 | I feel like his life exemplifies everything

00:49:21.180 | that's amazing and great and also terrible about America.

00:49:24.900 | If we took Mike Tyson to dinner tonight at any restaurant

00:49:27.580 | anywhere in the United States, what would happen?

00:49:29.460 | He would be loved.

00:49:30.140 | Oh, he would be like--

00:49:31.140 | Adored.

00:49:31.660 | He would be-- the outpouring of enthusiasm and passion

00:49:34.820 | and love would be incredible.

00:49:37.540 | It would be unbelievable.

00:49:38.940 | This is a great example.

00:49:40.620 | And again, I'm not even going to draw--

00:49:42.200 | I'm not even going to say I agree with that

00:49:43.100 | or disagree with that.

00:49:44.020 | I'm just like, we all intuitively

00:49:45.820 | know that the public is just like 100%, like absolutely.

00:49:48.820 | He's a legend.

00:49:49.500 | He's a living legend.

00:49:50.420 | People love Mike.

00:49:51.220 | He's like a cultural touchstone.

00:49:52.420 | Absolutely.

00:49:52.860 | And then you see it when he shows up in movies, right?

00:49:54.460 | He shows-- I don't remember the--

00:49:55.940 | I mean, the big breakthrough I figured this out

00:49:57.340 | with respect to him because I don't really follow sports.

00:49:59.100 | But when he showed up in that, it was that first Hangover

00:50:01.500 | movie, and he shows up.

00:50:02.740 | And I was in a theater.

00:50:04.440 | And the audience just goes, banana's crazy.

00:50:07.460 | They're so excited to see him.

00:50:08.820 | Yeah, he evokes delight.

00:50:10.060 | I always say that Mike Tyson is the only person

00:50:11.980 | I'm aware of that can wear a shirt with his own name on it.

00:50:15.380 | And it somehow doesn't seem wrong.

00:50:18.180 | In fact, it just kind of makes you like him more.

00:50:21.780 | His ego feels very contoured in a way

00:50:24.660 | that he knows who he is and who he was.

00:50:27.300 | And yet, there's a humbleness woven in,

00:50:29.820 | maybe as a consequence of all that he's been through.

00:50:32.500 | I don't know.

00:50:33.900 | But yeah, people love Mike.

00:50:36.060 | Public closely.

00:50:37.380 | Exactly.

00:50:37.900 | Now, if he shows up to lecture at Harvard,

00:50:40.380 | I think you're probably going to get a different reaction.

00:50:42.020 | I don't know.

00:50:43.060 | I don't know.

00:50:43.860 | I mean, David Simon, the guy who wrote The Wire,

00:50:46.580 | gave a talk at Harvard.

00:50:48.860 | And it sounded to me, based on his report

00:50:51.300 | of that, which is very interesting, in fact,

00:50:54.540 | that people adore people who are connected to everybody

00:51:01.980 | in that way.

00:51:02.780 | I feel like everybody loves Mike from above his status,

00:51:06.300 | the sides, below his status.

00:51:08.900 | He occupies this halo of love and adoration.

00:51:12.980 | OK, all right.

00:51:14.540 | Yeah, and then, look, the other side of this is the elites.

00:51:17.060 | And you kind of alluded to this, or the institutions.

00:51:18.820 | So basically, it's like the people

00:51:20.060 | who were like, at least, nominally in charge,

00:51:21.620 | or feel like that they should be in charge.

00:51:22.860 | Yeah, I want to make sure we define elite.

00:51:24.620 | So you're not necessarily talking

00:51:25.500 | about people who are wealthy.

00:51:26.420 | You're talking about people who have

00:51:27.980 | authority within institutions.

00:51:29.380 | So the ultimate definition of an elite

00:51:30.980 | is who can get who fired, right?

00:51:34.540 | Like, that's the ultimate test.

00:51:35.860 | Who can get who fired, boycotted, blacklisted,

00:51:37.900 | ostracized, like when push prosecuted, jailed,

00:51:41.700 | like when push comes to shove, right?

00:51:43.660 | I think that's always the question,

00:51:45.060 | who can destroy whose career.

00:51:46.300 | And of course, you'll notice that that is heavily asymmetric

00:51:49.060 | when these fights play out.

00:51:50.340 | It's very clear which side can get the other side fired

00:51:52.500 | and which side can't.

00:51:54.260 | And so, yeah, so look, I think we live in a period of time

00:51:56.340 | where the elites have gotten to be extreme

00:51:58.140 | in a number of dimensions.

00:51:59.340 | And I think it's characterized by, for sure,

00:52:01.460 | extreme groupthink, extreme sanctimony, extreme moral,

00:52:05.620 | I would say, dudgeon, this weird sort of modern puritanism,

00:52:10.980 | and then an extreme sort of morality

00:52:13.100 | of punishment and terror against their perceived enemies.

00:52:17.140 | But I wanted to go through that, because I actually

00:52:19.660 | think that's a very different phenomenon.

00:52:21.180 | And I think what's happening to the elites is very different

00:52:22.580 | than what's happening in the population at large.

00:52:24.940 | And then, of course, I think there's a feedback loop

00:52:27.020 | in there, which is I think the population at large

00:52:29.020 | is not on board with that program, right?

00:52:31.300 | I think the elites are aware that the population is not

00:52:33.540 | on board with that program.

00:52:34.340 | I think they judge the population negatively

00:52:36.340 | as a consequence.

00:52:37.140 | That causes the elites to harden their own positions.

00:52:39.340 | That causes them to be even more alienating

00:52:41.260 | to the population.

00:52:42.260 | And so they're in sort of an oppositional negative feedback

00:52:45.100 | loop.

00:52:46.500 | And, yeah, it's going to be--

00:52:48.300 | but again, it's the sort of question, OK,

00:52:49.500 | who can get who fired?

00:52:50.500 | And so elites are really good at getting

00:52:53.940 | like normal people fired--

00:52:55.300 | ostracized, banned, hit pieces in the press, like whatever.

00:52:59.460 | For normal people to get elites fired,

00:53:01.060 | they have to really band together and really mount

00:53:03.740 | a serious challenge, which mostly doesn't happen,

00:53:06.100 | but might be starting to happen in some cases.

00:53:08.300 | Do you think this power of the elites

00:53:12.340 | over stemmed from social media sort of going

00:53:16.460 | against its original purpose?

00:53:18.020 | I mean, when you think social media,

00:53:19.020 | everything you're giving each and every person

00:53:21.620 | their own little reality TV show, their own voice,

00:53:24.380 | and yet we've seen a dramatic uptick

00:53:27.940 | in the number of cancellations and firings related

00:53:30.580 | to immoral behavior based on things that were either done

00:53:35.020 | or amplified on social media.

00:53:37.180 | It's almost as if the public is holding

00:53:38.980 | the wrong end of the knife.

00:53:41.180 | Yeah, so the way I describe it--

00:53:43.860 | so I use these two terms, and they're

00:53:46.260 | somewhat interchangeable, but elites and institutions.

00:53:48.340 | And they're somewhat interchangeable,

00:53:49.540 | because who runs the institutions?

00:53:50.660 | The elites, right?

00:53:51.460 | And so it's sort of a self-reinforcing thing.

00:53:55.260 | Anyway, institutions of all kinds, institutions,

00:53:57.220 | everything from the government, bureaucracies, companies,

00:53:59.580 | nonprofits, foundations, NGOs, tech companies,

00:54:02.720 | on and on and on, people who are in charge of big complexes

00:54:07.340 | and that carry a lot of basically power and influence

00:54:09.960 | and capability and money as a consequence

00:54:11.900 | of their positional authority.

00:54:13.780 | So the head of a giant foundation

00:54:15.860 | may never have done anything in their life

00:54:17.740 | that would cause somebody to have a high opinion of them

00:54:19.140 | as a person.

00:54:19.740 | But they're in charge of this gigantic multi-billion dollar

00:54:22.140 | complex and have all this power of the results.

00:54:24.180 | So that's just to define terms, elites and institutions.

00:54:28.340 | So it's actually interesting.

00:54:29.540 | Gallup has been doing polls on the question of trust

00:54:34.300 | in institutions, which is sort of therefore

00:54:36.180 | a proxy for trust in elites, basically

00:54:38.520 | since the early 1970s.

00:54:40.300 | And what you find-- and they do this

00:54:41.980 | across all the categories of big institutions,

00:54:44.340 | basically everyone I just talked about, a bunch of others,

00:54:46.580 | big business, small business, banks, newspapers,

00:54:49.080 | broadcast television, the military, police.

00:54:52.300 | So they've got like 30 categories or something.

00:54:54.340 | And basically what you see is almost all the categories

00:54:56.620 | basically started in the early '70s at like 60% or 70% trust.

00:55:00.220 | And now they've-- basically almost across the board,

00:55:03.100 | they've just had a complete basically linear slide down

00:55:05.900 | for 50 years, basically my whole life.

00:55:09.180 | And they're now bottoming out.

00:55:11.860 | Congress and journalists bottom out at like 10%.

00:55:16.140 | Like the two groups everybody hates

00:55:17.580 | are like Congress and journalists.

00:55:19.460 | And then it's like a lot of other big institutions

00:55:21.780 | are like in their 20s, 30s, 40s.

00:55:23.780 | Actually, big business actually scores fairly high.

00:55:25.980 | Tech actually scores quite high.

00:55:27.640 | The military scores quite high.

00:55:29.060 | But basically everything else has really caved in.

00:55:31.220 | And so this is sort of my fundamental challenge

00:55:33.740 | to everybody who basically says--

00:55:35.220 | and you didn't do this, but you'll

00:55:36.900 | hear the simple form of this, which is social media caused

00:55:40.060 | the current trouble.

00:55:41.540 | And it's called this an example.

00:55:43.020 | Collapse in faith in institutions and elites,

00:55:45.700 | let's call that part of the current trouble.

00:55:47.940 | Everybody's like, oh, social media caused that.

00:55:49.180 | I was like, well, no, social media is new in the last--

00:55:53.140 | social media is effectively new practically speaking since 2010,

00:55:55.940 | 2012 is when it really took off.

00:55:58.180 | And so if the trend started in the early 1970s

00:56:00.740 | and has been continuous, then we're

00:56:02.160 | dealing with something broader.

00:56:04.060 | And Martin Gurry wrote I think the best book on this called

00:56:07.340 | The Revolt of the Public, where he

00:56:08.760 | goes through this in detail.

00:56:09.920 | And he does say that social media had a lot to do with

00:56:13.300 | what's happened in the last decade.

00:56:14.820 | But he says if you go back, you look further,

00:56:16.700 | it was basically two things coinciding.

00:56:19.220 | One was just a general change in the media environment.

00:56:21.480 | And in particular, the 1970s is when you started--

00:56:24.460 | especially in the 1980s is when you started to get specifically

00:56:27.180 | talk radio, which was a new outlet.

00:56:29.820 | And then you also got cable television.

00:56:32.400 | And then you also, by the way, it's

00:56:33.940 | actually interesting in the '50s, '60s,

00:56:35.180 | you had paperback books, which was another one of these,

00:56:36.940 | which was an outlet.

00:56:37.780 | So you had a fracturing in the media landscape

00:56:40.340 | that started in the '50s through the '80s.

00:56:42.320 | And then, of course, the internet blew it wide open.

00:56:45.160 | Having said that, if the elites in the institutions

00:56:47.240 | were fantastic, you would know it more than ever.

00:56:49.560 | Previous information is more accessible.

00:56:51.240 | And so the other thing that he says and I agree with

00:56:53.400 | is the public is not being tricked

00:56:55.280 | into thinking the elites in institutions are bad.

00:56:57.640 | They're learning that they're bad.

00:56:59.560 | And therefore, the mystery of the Gallup poll

00:57:02.000 | is why those numbers aren't all just zero,

00:57:04.880 | which is arguably in a lot of cases where they should be.

00:57:08.080 | I think one reason that--

00:57:09.200 | Oh, and by the way, he thinks this is bad.

00:57:10.900 | So he and I have a different view.

00:57:12.240 | Here's our he, and I disagree.

00:57:13.440 | He thinks this is bad.

00:57:14.320 | So he basically says you can't replace elites with nothing.

00:57:18.120 | You can't replace institutions with nothing

00:57:20.500 | because what you're just left with

00:57:21.840 | is just going to be wreckage.

00:57:22.720 | You're going to be left with a completely, basically,

00:57:25.120 | atomized, out-of-control society that has no ability to marshal

00:57:28.240 | any sort of activity in any direction.

00:57:29.800 | It's just going to be a dog-eat-dog awful world.

00:57:32.980 | I have a very different view on that, which we can talk about.

00:57:35.680 | Yeah, I'd love to hear your views on that.

00:57:37.400 | Yeah.

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00:58:44.500 | The quick question I was going to ask before we go there is,

00:58:48.060 | I think that one reason that I and many other people

00:58:51.260 | sort of reflexively assume that social media

00:58:53.640 | caused the demise of our faith in institutions is,

00:58:58.640 | well, first of all, I wasn't aware of this lack

00:59:02.680 | of correlation between the decline in faith in institutions

00:59:06.180 | and the rise of social media.

00:59:08.220 | But secondarily, that we've seen some movements

00:59:11.560 | that have essentially rooted themselves in tweets,

00:59:16.560 | in comments, in posts that get amplified.

00:59:20.780 | And those tweets and comments and posts

00:59:22.860 | come from everyday people.

00:59:24.420 | In fact, I can't name one person who initiated

00:59:28.340 | a given cancellation or movement

00:59:30.620 | because it was the sort of dogpiling or mob

00:59:33.260 | adding on to some person that was essentially anonymous.

00:59:35.360 | So I think that for many of us,

00:59:36.400 | we have the bottom, to use neuroscience language,

00:59:38.440 | is sort of a bottom up and a perspective.

00:59:40.680 | Oh, someone sees something in their daily life

00:59:44.180 | or experiences something in their daily life

00:59:45.940 | and they tweet about it or they comment about it

00:59:48.700 | or they post about it.

00:59:50.160 | And then enough people dogpile on the accused

00:59:54.560 | that it picks up force and then the elites feel compelled,

00:59:59.560 | obligated to cancel somebody.

01:00:02.340 | That tends to be the narrative.

01:00:04.680 | And so I think the logical conclusion is,

01:00:06.460 | oh, social media allows for this to happen,

01:00:08.780 | whereas normally someone would just be standing

01:00:10.260 | on the corner shouting or calling lawyers

01:00:12.440 | that don't have faith in them.

01:00:13.460 | And you've got the like the Aaron Brockovich model

01:00:15.900 | of that turns into a movie,

01:00:18.960 | but that's a rare case of this lone woman

01:00:20.660 | who's got this idea in mind about how big institution

01:00:24.020 | is doing wrong or somebody is doing wrong in the world

01:00:27.380 | and then can leverage big institution, excuse me.

01:00:30.220 | But the way that you describe it is that the elites

01:00:32.820 | are leading this shift.

01:00:36.420 | So what is the role of the public in it?

01:00:39.680 | I mean, just to give it a concrete example,

01:00:42.120 | if for instance, no one tweeted or commented a me too,

01:00:47.980 | or no one tweeted or commented about some ill behavior

01:00:53.420 | of some, I don't know, university faculty member

01:00:55.960 | or a business person,

01:00:57.760 | would the elite have come down on them anyway?

01:01:01.440 | - Oh yeah, so it was happening.

01:01:02.580 | So based on what I've seen over the years,

01:01:05.900 | there is so much astroturfing right now.

01:01:09.660 | There are entire categories of people

01:01:12.060 | who are paid to do this.

01:01:13.980 | Some of them we call journalists,

01:01:15.380 | some of them we call activists,

01:01:16.420 | some of them we call NGO, non-profit,

01:01:18.820 | some of them we call university professors,

01:01:20.540 | some of them we call grad students,

01:01:21.660 | like whatever, they're paid to do this.

01:01:24.220 | I don't know if you've ever looked

01:01:25.040 | into the misinformation industrial complex.

01:01:27.600 | There's this whole universe of basically these funded groups

01:01:29.960 | that basically do quote, unquote, misinformation

01:01:32.960 | and they're constantly mounting these kinds of attacks.

01:01:36.080 | They're constantly trying to gin up

01:01:37.480 | this kind of basically panic

01:01:38.680 | to cause somebody to get fired.

01:01:40.080 | - So it's not a grassroots.

01:01:41.580 | - No, it's the opposite of grassroots.

01:01:43.000 | No, and almost always when you trace these things back,

01:01:45.200 | it was a journalist, it was an activist,

01:01:47.300 | it was a public figure of some kind.

01:01:50.840 | These are entrepreneurs in sort of a weird way.

01:01:55.520 | They're basically, they're paid their job mission calling.

01:01:59.840 | It's all wrapped up together.

01:02:00.880 | They're true believers,

01:02:01.720 | but they're also getting paid to do it.

01:02:03.040 | And there's a giant funding,

01:02:04.240 | I mean, there's a very large funding complex for this

01:02:06.240 | coming from certain high profile people

01:02:09.320 | who put huge amounts of money into this.

01:02:10.520 | - Is this well known?

01:02:11.520 | - Yes, well, so I mean, it is in my world.

01:02:13.360 | So this is what the social media companies

01:02:15.320 | have been on the receiving end of for the last decade.

01:02:17.920 | It's basically a political media activism complex

01:02:22.520 | with very deep pockets behind it

01:02:23.880 | and you've got people who basically,

01:02:25.200 | literally people who sit all day

01:02:26.820 | and watch the TV network on the other side

01:02:28.800 | or watch the Twitter feeds on the other side

01:02:29.980 | and they wait, they basically wait.

01:02:31.680 | It's like every politician,

01:02:33.120 | this has been the case for a long time now,

01:02:34.120 | every politician who goes out and gives stump speeches,

01:02:36.080 | you'll see there's always somebody in the crowd

01:02:37.240 | with a camcorder or now with a phone recording them

01:02:39.560 | and that's somebody from the other campaign

01:02:41.840 | who's paid somebody to just be there

01:02:43.400 | and record every single thing the politician says

01:02:45.600 | so that when Mitt Romney says whatever the 47% thing,

01:02:48.600 | they've got it on tape and then they clip it

01:02:50.440 | and they try to make it viral.

01:02:51.720 | So this stuff is, and again, look,

01:02:54.560 | these people believe what they're doing.

01:02:55.760 | I'm not saying it's even dishonest.

01:02:57.080 | These people believe what they're doing.

01:02:58.040 | They think they're fighting a holy war.

01:02:59.160 | They think they're protecting democracy.

01:03:00.440 | They think they're protecting civilization.

01:03:01.880 | They think they're protecting whatever it is

01:03:03.600 | they're protecting and then they know how to use the tools

01:03:07.640 | and so they know how to try to gin up the outrage

01:03:10.180 | and then by the way, sometimes it works in social cascades.

01:03:12.960 | Sometimes it works, sometimes it doesn't.

01:03:14.860 | Sometimes they cascade, sometimes they don't

01:03:16.720 | but if you follow these people on Twitter,

01:03:18.360 | this is what they do every day.

01:03:20.260 | They're constantly trying to light this fire.

01:03:22.440 | - Wow, I assume that it was really bottom up

01:03:25.360 | but it sounds like it's sort of a mid-level

01:03:27.560 | and then it captures the elites

01:03:29.600 | and then the thing takes on a life of its own.

01:03:31.600 | - By the way, it also intersects

01:03:32.560 | with the trust and safety groups

01:03:33.600 | at the social media firms who are responsible

01:03:35.880 | for figuring out who gets promoted

01:03:37.160 | and who gets banned right across this

01:03:38.960 | and you'll notice one large social media company

01:03:40.960 | has recently changed hands

01:03:42.080 | and has implemented a different kind

01:03:43.600 | of set of trust and safety

01:03:46.560 | and all of a sudden a different kind of boycott movement

01:03:48.380 | has all of a sudden started to work

01:03:49.640 | that wasn't working before that

01:03:50.880 | and another kind of boycott movement

01:03:52.380 | is not working as well anymore

01:03:53.840 | and so for sure there's intermediation happening.

01:03:57.000 | Like look, the stuff that's happening in the world today

01:03:58.960 | is being intermediated through social media

01:04:00.440 | 'cause social media is the defining media of our time

01:04:03.320 | but there are people who know how to do this

01:04:05.240 | and do this for a living.

01:04:06.980 | So no, I very much view this as a,

01:04:09.440 | I view very much the cancellation wave

01:04:11.640 | like this whole thing.

01:04:12.640 | It's an elite phenomenon

01:04:15.620 | and when it appears to be a grassroots thing,

01:04:18.000 | it's either grassroots among the elites

01:04:20.360 | which is possible 'cause there's a fairly large number

01:04:22.960 | of people who are signed up for that particular crusade

01:04:26.320 | but there's also a lot of astroturfing

01:04:27.680 | that's taking place inside that.

01:04:29.200 | The question is okay, at what point does the population

01:04:31.160 | at large get pulled into this

01:04:32.960 | and maybe there are movements at certain points in time

01:04:35.720 | where they do get pulled in

01:04:36.600 | and then maybe later they get disillusioned

01:04:38.120 | and so then there's some question there

01:04:39.440 | and then there's another question of like well,

01:04:40.960 | if the population at large is gonna decide

01:04:42.720 | what these movements are,

01:04:43.540 | are they gonna be the same movements as the elites want

01:04:46.280 | and are the elites, how are the elites gonna react

01:04:48.080 | when the population actually like fully expresses itself?

01:04:50.840 | Right, and so there's, and like I said,

01:04:52.200 | there's a feedback loop between these

01:04:53.680 | where the more extreme the elites get,

01:04:55.000 | they tend to push the population to more extreme views

01:04:57.000 | on the other side and vice versa

01:04:58.320 | so it ping pong's back and forth

01:04:59.960 | and so yeah, this is our world.

01:05:02.040 | - Yeah, this explains a lot.

01:05:04.200 | I wanna make sure that I--

01:05:06.040 | - By the way, Tyeeby, so Mike Shellenberger, Matt Tyeeby,

01:05:08.720 | a bunch of these guys have done a lot of work.

01:05:11.600 | If you just look into what's called

01:05:12.960 | the misinformation industrial complex,

01:05:14.960 | you'll find a network of money and power

01:05:16.580 | that is really quite amazing.

01:05:18.740 | - I've seen more and more Shellenberger showing up, right?

01:05:22.920 | And he's just like, on this stuff, he and Tyeeby,

01:05:25.520 | they're literally just like tracking money.

01:05:27.280 | It's just like, it's very clear how the money flows,

01:05:29.600 | including like a remarkable amount of money

01:05:31.240 | out of the government, which is of course,

01:05:33.440 | like in theory, very concerning.

01:05:35.000 | - Very interesting.

01:05:36.800 | - The government should not be funding programs

01:05:38.640 | that take away people's constitutional rights

01:05:40.340 | and yet somehow that is what's been happening.

01:05:44.160 | - Very interesting.

01:05:45.000 | - Yes.

01:05:46.120 | - I wanna make sure that I hear your ideas

01:05:48.660 | about why the decline in confidence in institutions

01:05:52.840 | and is not necessarily problematic.

01:05:56.180 | Is this going to be a total destruction

01:05:59.120 | and burning down of the forest that will lead to new life?

01:06:01.600 | Is that your view?

01:06:02.880 | - Yeah, well, so this is the thing.

01:06:04.220 | And look, there's a question if you're,

01:06:05.540 | there's a couple of questions in here,

01:06:06.560 | which is like, how bad is it really?

01:06:08.680 | Like, how bad are they, right?

01:06:11.240 | And I think they're pretty bad.

01:06:13.520 | A lot of them are pretty actually bad.

01:06:15.320 | And so that's one big question.

01:06:17.440 | And then, yeah, the other question is like, okay,

01:06:19.560 | if an institution has gone bad

01:06:21.200 | or a group of elites have gone bad,

01:06:22.480 | like can, it's this wonderful word reform, right?

01:06:24.840 | Can they be reformed?

01:06:26.120 | And everybody always wants to reform everything

01:06:27.600 | and yet somehow like nothing ever,

01:06:29.280 | quite ever gets reformed, right?

01:06:30.940 | And so people are trying to reform, you know,

01:06:32.480 | housing policy in the Bay Area for decades

01:06:34.000 | and you know, we're not building,

01:06:35.480 | we're building fewer houses than ever before.

01:06:36.920 | So somehow reform movements seem to lead to more,

01:06:39.080 | just more bad stuff.

01:06:40.960 | But anyway, yeah, so if you have an existing institution,

01:06:42.940 | can it be reformed?

01:06:43.780 | Can it be fixed from the inside?

01:06:45.280 | Like what's happened in universities?

01:06:46.320 | Like there's a lot of, there are professors at Stanford

01:06:48.000 | as an example who very much think

01:06:49.240 | that they can fix Stanford.

01:06:51.400 | Like, I don't know what you think.

01:06:52.760 | It doesn't seem like it's going

01:06:53.860 | in productive directions right now.

01:06:56.360 | - Well, I mean, there are many things about Stanford

01:06:57.880 | that function extremely well.

01:06:59.540 | I mean, it's a big institution.

01:07:00.920 | It's certainly got its issues like any other place.

01:07:03.680 | They're also my employer.

01:07:04.720 | Mark's giving me some interesting looks.

01:07:06.280 | He wants me to get a little more vocal here.

01:07:08.440 | - No, no, no, you don't need to.

01:07:09.400 | - Oh no, I mean, I think that, yeah, I mean,

01:07:12.000 | one of the things about being a researcher

01:07:13.480 | at a big institution like Stanford is,

01:07:15.200 | well, first of all, it meets the criteria

01:07:17.000 | that you described before.

01:07:18.120 | You know, you look to the left, you look to the right

01:07:19.800 | or anywhere above or below you and you have excellence,

01:07:23.680 | right, I mean, I've got a Nobel Prize winner below me

01:07:25.760 | whose daddy also won a Nobel Prize

01:07:27.440 | and his scientific offspring is likely to win.

01:07:30.280 | I mean, it inspires you to do bigger things

01:07:33.120 | than one ordinarily would, no matter what.

01:07:36.300 | So there's that and that's great and that persists.

01:07:39.540 | There's all the bureaucratic red tape

01:07:42.040 | about trying to get things done

01:07:43.360 | and how to implement decisions is very hard

01:07:46.400 | and there are a lot of reasons for that.

01:07:47.600 | And then of course there are the things that, you know,

01:07:49.680 | many people are aware of are there are public accusations

01:07:53.280 | about people in positions of great leadership

01:07:55.500 | and that's getting played out

01:07:56.680 | and the whole thing becomes kind of overwhelming

01:07:59.540 | and a little bit opaque when you're just trying

01:08:01.120 | to like run your lab or live your life.

01:08:03.440 | And so I think one of the reasons for this lack of reform

01:08:06.040 | that you're referring to is because there's no position

01:08:10.080 | of reformer, right?

01:08:11.880 | So deans are dealing with a lot of issues,

01:08:14.880 | provosts are dealing with a lot of issues,

01:08:16.320 | presidents are dealing with a lot of issues

01:08:17.920 | and then some in some cases.

01:08:20.180 | And so, you know, we don't have a dedicated role of reformer,

01:08:24.400 | someone to go in and say, listen,

01:08:26.320 | there's just a lot of fat on this and we need to trim it

01:08:28.960 | or we need to create this or do that.

01:08:31.040 | There just isn't a system to do that.

01:08:32.920 | And that's, I think, in part because universities

01:08:35.680 | are built on old systems and, you know,

01:08:38.080 | it's like the New York subway, it's amazing,

01:08:41.240 | it still works as well as it does

01:08:42.800 | and yet it's got a ton of problems also.

01:08:45.320 | - Well, so the point we can debate the university

01:08:48.520 | specifically, but the point is like, look,

01:08:50.080 | if you do think institutions are going bad

01:08:51.760 | and then you have to make it,

01:08:52.600 | number one, you have to figure out

01:08:53.520 | if you think institutions are going bad.

01:08:54.840 | The population largely does think that.

01:08:57.000 | And then at the very least, the people who run institutions

01:08:59.040 | ought to really think hard about what that means.

01:09:00.760 | - But people still strive to go to these places.

01:09:03.840 | And I still hear from people who, like for instance,

01:09:06.320 | did not go to college or talking about

01:09:08.080 | how a university degree is useless.

01:09:10.020 | They'll tell you how proud they are

01:09:11.240 | that their son or daughter is going to Stanford

01:09:13.380 | or is going to UCLA or is going to Urbana-Champaign.

01:09:16.300 | I mean, it's almost like, to me,

01:09:18.100 | that's always the most shocking contradiction is like,

01:09:21.660 | yeah, like these institutions don't matter,

01:09:23.300 | but then when people want to hold up a card

01:09:24.980 | that says why their kid is great,

01:09:27.320 | it's not about how many pushups they can do

01:09:29.280 | or that they started their own business most of the time,

01:09:31.720 | it's they're going to this university.

01:09:33.740 | And I think, well, what's going on here?

01:09:35.600 | - So do you think the median voter in the United States

01:09:37.260 | can have their kid go to Stanford?

01:09:38.500 | - No. - No.

01:09:39.340 | - No, and no--

01:09:40.180 | - Do you think the median voter in the United States

01:09:41.920 | could have their kid admitted to Stanford

01:09:43.080 | even with perfect SAT?

01:09:44.260 | - No. - No.

01:09:46.620 | - In this day and age, the competition is so fierce

01:09:50.180 | that it requires more.

01:09:51.180 | - Yeah, so first of all, again, we're dealing here,

01:09:54.100 | yes, we're dealing with a small number

01:09:55.820 | of very elite institutions.

01:09:57.220 | People may admire them or not.

01:09:59.380 | Most people have no connectivity to them whatsoever.

01:10:01.900 | In the statistics, in the polling,

01:10:04.560 | universities are not doing well.

01:10:06.220 | You know, the population at large,

01:10:07.620 | yeah, they may have fantasies

01:10:08.580 | about their kid going to Stanford,

01:10:09.500 | but like the reality of it is they have a very,

01:10:11.620 | say, collapsing view of these institutions.

01:10:14.280 | So anyways, this actually goes straight

01:10:16.240 | to the question of alternatives then, right,

01:10:17.600 | which is like, okay, if you believe

01:10:19.240 | that there's collapsing faith in the institutions,

01:10:20.880 | if you believe that it is merited, at least in some ways,

01:10:23.540 | if you believe that reform is effectively impossible,

01:10:25.400 | then you are faced, and we could debate each of those,

01:10:28.920 | but the population at large seems to believe a lot of that.

01:10:32.440 | Then there's a question of like, okay,

01:10:34.240 | can it be replaced, and if so,

01:10:36.300 | are you better off replacing these things

01:10:37.920 | basically while the old things still exist,

01:10:39.480 | or do you actually need to basically clear the field

01:10:41.820 | to be able to have a new thing exist?

01:10:44.040 | The universities are a great case study of this

01:10:45.560 | because of how student loans work, right,

01:10:48.040 | and the way student loans work is to be able to be a,

01:10:51.200 | to be an actual competitive university and compete,

01:10:53.840 | you need to have access to federal student lending,

01:10:55.520 | 'cause if you don't, everybody has to pay out of pocket,

01:10:57.480 | and it's completely out of reach for anybody

01:10:58.820 | other than a certain class of either extremely rich

01:11:01.400 | or foreign students.

01:11:03.180 | So you need access to federal student loan facility.

01:11:04.760 | To get access to federal student loan facility,

01:11:06.440 | you need to be an accredited university.

01:11:09.320 | Guess who runs the Accreditation Council?

01:11:11.920 | - I don't know.

01:11:12.760 | - The existing universities, right?

01:11:14.320 | So it's a self-laundering machine,

01:11:17.380 | like they decide who the new universities are.

01:11:19.000 | Guess how many new universities get accredited, right,

01:11:21.040 | each year to be able to, okay.

01:11:23.160 | - Zero. - Zero, right?

01:11:24.680 | And so as long as that system is in place,

01:11:27.800 | and as long as they have the government wired

01:11:29.680 | the way that they do, and as long as they control

01:11:32.200 | who gets access to federal student loan funding,

01:11:34.180 | like of course there's not gonna be any competition, right?

01:11:36.640 | Of course there can't be a new institution

01:11:38.080 | that's gonna be able to get to scale.

01:11:39.600 | Like it's not possible.

01:11:40.500 | And so if you actually wanted to create a new system

01:11:43.680 | that was better in the, I would argue dozens

01:11:46.000 | or hundreds of ways, it could obviously be better

01:11:47.720 | if you were starting today.

01:11:49.400 | It probably can't be done as long as existing institutions

01:11:51.800 | are actually intact.

01:11:53.100 | And this is my counter against to Martin,

01:11:55.000 | which is like, yeah, look, if we're gonna tear down the old,

01:11:58.480 | there may be a period of disruption

01:11:59.680 | before we get to the new, but we're never gonna get

01:12:01.420 | to the new if we don't tear down the old.

01:12:03.020 | - When you say counter to Martin,

01:12:04.080 | you're talking about the author of "Revolt of the Public."

01:12:05.680 | - Yeah, Martin Gurry, yeah.

01:12:06.520 | What Martin Gurry says is like, look, he said basically,

01:12:09.040 | what Martin says is as follows.

01:12:11.600 | The elites deserve contempt, but the only thing worse

01:12:15.200 | than these elites that deserve contempt

01:12:17.360 | would be no elites at all, right?

01:12:19.080 | And he basically says on the other side of the destruction

01:12:23.720 | of the elites and the institutions is nihilism.

01:12:26.240 | You're basically left with nothing.

01:12:27.320 | And then by the way, there is a nihilistic streak.

01:12:28.960 | I mean, there's a nihilistic streak in the culture

01:12:30.720 | and the politics today.

01:12:31.540 | There are people who basically would just say,

01:12:32.940 | yeah, just tear the whole system down, right?

01:12:34.980 | And without any particular plan for what follows.

01:12:37.720 | And so I think he makes a good point

01:12:40.460 | in that you wanna be careful that you actually have a plan

01:12:42.280 | on the other side that you think is actually achievable.

01:12:44.700 | But again, the counter argument to that is

01:12:47.140 | if you're not willing to actually tear down the old,

01:12:49.160 | you're not gonna get to the new.

01:12:50.640 | Now, what's interesting, of course,

01:12:52.360 | is this is what happens every day in business, right?

01:12:54.680 | So like the entire way, like how do you know

01:12:57.300 | that the capitalist system works?

01:12:59.100 | The way that you know is that the old companies,

01:13:01.460 | when they're no longer like the best at what they do,

01:13:03.560 | they get torn down and then they ultimately die

01:13:06.060 | and they get replaced by better companies.

01:13:07.560 | - Yeah, I haven't seen a Sears in a while.

01:13:09.200 | - Exactly, right?

01:13:10.640 | And we know, what's so interesting is we know in capitalism

01:13:13.800 | and a market economy, we know that that's the sign of health.

01:13:16.800 | Right, that's the sign of how the system

01:13:18.640 | is working properly, right?

01:13:20.080 | And in fact, we get actually judged by antitrust authorities

01:13:22.580 | in the government on that basis, right?

01:13:24.520 | It's like the best defense against antitrust charges is,

01:13:27.020 | no, people are like coming to kill us

01:13:28.160 | and they're doing like a really good job of it.

01:13:29.360 | Like that's how we know we're doing our job.

01:13:31.160 | And in fact, in business, we are specifically,

01:13:33.240 | it is specifically illegal for companies

01:13:35.380 | in the same industry to get together and plot and conspire

01:13:37.440 | and plan and have things like these accreditation bureaus.

01:13:39.760 | Like we would get, if I created the equivalent

01:13:41.920 | in my companies of the kind of accreditation bureau

01:13:44.200 | that the universities have,

01:13:45.040 | I'd get straight to federal prison.

01:13:46.640 | And a trust violation Sherman Act, straight to prison.

01:13:48.440 | People have been sent to prison for that.

01:13:50.440 | So in the business world, we know that you want

01:13:53.080 | everything subject to market competition.

01:13:54.660 | We know that you want creative destruction.

01:13:56.520 | We know that you want replacement of the old

01:13:58.240 | with superior new.

01:13:59.920 | It's just once we get outside of business,

01:14:01.240 | we're like, oh, we don't want any of that.

01:14:02.440 | We want basically stagnation and log rolling, right?

01:14:05.960 | And basically institutional incestuous entanglements

01:14:10.000 | and conflicts of interest as far as the eye can see.

01:14:12.800 | And then we're surprised by the results.

01:14:14.820 | - So let's play it out as a bit of a thought experiment.

01:14:17.640 | So let's say that one small banding together

01:14:22.640 | of people who want to start a new university

01:14:24.880 | where free exchange of open ideas,

01:14:27.480 | where unless somebody has egregious behavior,

01:14:31.420 | violent behavior, truly sexually inappropriate behavior

01:14:35.060 | against somebody, they're committing a crime, right?

01:14:37.560 | They're allowed to be there.

01:14:38.560 | They're allowed to be a student or a faculty member

01:14:41.700 | or administrator.

01:14:42.940 | And let's just say this accreditation bureau

01:14:45.820 | allowed student loans for this one particular university.

01:14:48.520 | Or let's say that there was an independent source of funding

01:14:50.460 | for that university such that students

01:14:52.180 | could just apply there.

01:14:53.140 | They didn't need to be part of this elite accredited group,

01:14:57.200 | which is it sounds very mafia like frankly.

01:15:01.120 | Not necessarily violent, but certainly coercive

01:15:03.140 | in the way that it walls people out.

01:15:06.160 | Let's say that then there were 20 or 30 of those

01:15:11.480 | or 40 of those.

01:15:12.780 | Do you think that over time that model

01:15:15.260 | would overtake the existing model?

01:15:17.820 | - Is it interesting that those don't exist?

01:15:19.960 | Remember the Sherlock Holmes, the dog that didn't bark?

01:15:24.800 | - It is interesting those don't exist.

01:15:26.800 | - So there's two possibilities.

01:15:28.020 | One is like nobody wants that, which I don't believe.

01:15:31.220 | And then the other is like the system is wired

01:15:32.740 | in a way that will just simply not allow it.

01:15:34.580 | Right, and you did a hypothetical

01:15:35.840 | in which the system would allow it.

01:15:37.660 | My response to that is no,

01:15:38.500 | of course the system won't allow that.

01:15:39.900 | - Or the people that band together have enough money

01:15:42.140 | or get enough resources to say,

01:15:43.920 | look, we can afford to give loans

01:15:46.660 | to 10,000 students per year.

01:15:48.880 | 10,000 isn't a trivial number

01:15:50.260 | when thinking about the size of a university.

01:15:51.860 | And most of them hopefully will graduate in four years

01:15:56.000 | and there'll be a turnover.

01:15:57.380 | And do you think that the great future innovators

01:16:01.880 | would tend to orient toward that model

01:16:05.340 | more than they currently do toward the traditional model?

01:16:08.220 | I mean, what I'm trying to get back to here is

01:16:09.660 | how do you think that the current model thwarts innovation

01:16:12.900 | as well as maybe some ways that it still supports innovation

01:16:17.460 | certainly cancellation and the risk of cancellation

01:16:20.760 | from the way that we framed it earlier

01:16:22.420 | is going to discourage risk takers

01:16:25.840 | of the category of risk takers that take risk

01:16:27.840 | in every domain that really like to fly close to the sun

01:16:31.840 | and sometimes into the sun.

01:16:33.280 | - Or are doing research that is just not politically.

01:16:35.980 | - Right, yeah, looking into issues that--

01:16:38.280 | - Palatable.

01:16:39.120 | - Right, that we can't even talk about on this podcast

01:16:42.440 | probably without causing a distraction

01:16:44.560 | of what we're actually trying to talk about.

01:16:46.080 | - But it gives up the whole game right there.

01:16:46.920 | - Right, exactly.

01:16:47.760 | So I keep a file and it's a written file

01:16:53.020 | because I'm afraid to put it into electronic form

01:16:55.480 | of all the things that I'm afraid to talk about publicly

01:16:58.220 | because I come from a lineage of advisors

01:17:00.340 | where all three died young and I figure if nothing else,

01:17:03.160 | I'll die and then I'll make it into the world

01:17:05.000 | and when I'll say five, 10 years, 20 years

01:17:08.340 | and if not, I know a certainty I'm going to die at some point

01:17:11.360 | and then we'll see where all those issues stand.

01:17:13.720 | In any event--

01:17:14.880 | - Is that list getting longer over time or shorter?

01:17:16.520 | - Oh, it's definitely getting longer.

01:17:17.920 | - Isn't that interesting?

01:17:18.760 | - Yeah, it's getting much longer.

01:17:20.360 | I mean, there are just so many issues

01:17:23.000 | that I would love to explore on this podcast with experts

01:17:26.960 | and that I can't explore just even if I had a panel of them

01:17:31.960 | because of the way that things get soundbited

01:17:35.240 | and segmented out and taken out of context,

01:17:37.680 | it's like the whole conversation is lost

01:17:39.940 | and so fortunately, there are an immense number

01:17:41.960 | of equally interesting conversations

01:17:44.840 | that I'm excited to have but it is a little disturbing.

01:17:49.040 | - Do you remember Lysankoism?

01:17:50.480 | - No.

01:17:52.320 | Lysankoism, so famous in the history of the Soviet Union,

01:17:54.600 | this is a famous thing.

01:17:55.440 | So there was a geneticist named Lysanko.

01:17:58.040 | - Yeah, that's why it sounds familiar

01:17:59.040 | but I'm not calling to mind what the context is.

01:18:00.880 | - Well, he was the guy who did communist genetics.

01:18:04.100 | The field of genetics, the Soviets did not approve

01:18:07.120 | of the field of genetics because of course,

01:18:08.920 | they believed in the creation of the new man

01:18:10.480 | and total equality and genetics did not support that

01:18:13.440 | and so if you were doing traditional genetics,

01:18:15.720 | you were at the very least fired if not killed

01:18:19.320 | and so this guy Lysanko stood up and said,

01:18:20.940 | "Oh, I've got Marxist genetics.

01:18:22.520 | "I've got like a whole new field of genetics

01:18:23.960 | "that basically is politically compliant."

01:18:25.960 | And then they actually implemented that

01:18:27.200 | in the agriculture system of the Soviet Union

01:18:29.220 | and it's the origin of one of the big reasons

01:18:31.120 | that the Soviet Union actually fell

01:18:32.400 | which was they ultimately couldn't feed themselves.

01:18:34.680 | - So create a new notion of biology

01:18:36.800 | as it relates to genetics.

01:18:37.920 | - Politically create biology, right?

01:18:39.160 | And so they not only created it, they taught it,

01:18:41.880 | they mandated it, they required it

01:18:43.280 | and then they implemented it in agriculture.

01:18:45.560 | - Interesting.

01:18:46.400 | - So yeah, so I never understood,

01:18:48.280 | there was a bunch of things in history

01:18:49.440 | I never understood until the last decade

01:18:50.840 | and that's one of them.

01:18:51.880 | - Well, I censor myself at the level

01:18:53.380 | of deleting certain things

01:18:54.880 | but I don't contort what I do talk about.

01:18:57.600 | So I tend to like to play on lush open fields.

01:19:01.980 | Just makes my life a lot easier.

01:19:03.460 | - This goes to the rot.

01:19:04.300 | This goes to the rot and I'll come back to your question

01:19:06.020 | but like this goes to the rot in the existing system

01:19:07.800 | which is we've, by the way, I'm no different.

01:19:09.360 | I'm just like you.

01:19:10.200 | Like I'm not trying not to light myself on fire either

01:19:12.260 | but like the rot in the existing system

01:19:14.320 | and by system I mean the institutions and the elites.

01:19:16.320 | The rot is that the set of things

01:19:18.200 | that are no longer allowed.

01:19:19.360 | I mean that list is like obviously expanding over time

01:19:22.280 | and like that's a real like historically speaking

01:19:26.120 | that doesn't end in good places.

01:19:27.860 | - Is this group of a particular generation

01:19:30.080 | that we can look forward to the time

01:19:31.480 | when they eventually die off?

01:19:33.000 | - It's sort of the boomers plus the millennials.

01:19:34.980 | So good news, bad news.

01:19:37.840 | I mean Gen X is weird, right?

01:19:39.120 | I'm Gen X.

01:19:39.940 | Gen X is weird 'cause we kind of slipped in the middle.

01:19:42.160 | We were kind of the, I don't know how to describe it.

01:19:45.860 | We were the kind of nonpolitical generation

01:19:47.440 | kind of sandwiched between the boomers and the millennials.

01:19:49.720 | Gen Z is a very, I think, open question right now

01:19:52.400 | which way they go.

01:19:53.320 | I could imagine them being actually like much more intense

01:19:56.400 | than the millennials on all these issues.

01:19:58.280 | I could also imagine them reacting to the millennials

01:20:00.040 | and being far more open-minded.

01:20:01.960 | - We don't know which way it's gonna go yet.

01:20:03.040 | - Where it's gonna go.

01:20:03.880 | It might be different groups of them.

01:20:05.200 | - I mean I'm Gen X also.

01:20:06.300 | I'm 47, you're--

01:20:07.440 | - 50, 50, yeah.

01:20:08.280 | - Right, so more or less same.

01:20:09.320 | So grew up with some John Hughes films

01:20:10.940 | and so where the jocks and the hippies and the punks

01:20:13.960 | and the, we're all divided and they were all segmented

01:20:16.720 | but then it all sort of mishmashed together

01:20:19.840 | a few years later.

01:20:21.800 | And I think that had a lot to do with, like you said,

01:20:24.400 | the sort of apolitical aspect of our generation.

01:20:27.880 | - We just knew, Gen X just knew the boomers were nuts.

01:20:30.280 | Like all the, I mean this is the canonical,

01:20:34.080 | one of the great sitcoms of the era

01:20:36.600 | was Family Ties with the character Michael P. Keaton.

01:20:39.120 | And he was just like this guy who's just like yeah,

01:20:41.060 | my boomer hippie parents are crazy.

01:20:42.560 | Like I'm just gonna like go into business

01:20:43.720 | and like actually do something productive.

01:20:44.980 | Like there was something iconic about that character

01:20:47.240 | in our culture and people like me were like yeah,

01:20:49.060 | obviously go into business.

01:20:50.460 | You know, go into like political activism.

01:20:51.920 | And then it's just like man, that came whipping back around

01:20:54.640 | with the next generation.

01:20:56.180 | So just to touch real quick on the university thing.

01:20:57.780 | So look, there are people trying to do,

01:20:59.200 | and I'm actually gonna do a thing this afternoon

01:21:00.680 | with the University of Austin which is one of these.

01:21:03.560 | And so there are people trying to do new universities.

01:21:06.340 | You know, like I say, it's certainly possible.

01:21:08.240 | I hope they succeed.

01:21:09.080 | I'm pulling for them.

01:21:09.900 | I think it'd be great.

01:21:10.740 | I think it'd be great if there were a lot more of them.

01:21:12.220 | - Who founded this university?

01:21:13.620 | - This is a whole group of people.

01:21:14.960 | I don't want to freelance on that

01:21:17.000 | 'cause I don't know originally who the idea was.

01:21:18.600 | - University of Austin, not UT Austin.

01:21:20.660 | - Yeah, so this is not UT Austin.

01:21:22.200 | It's called University of Austin or they call it,

01:21:24.040 | I think it's UAT, what is it, UATX.

01:21:27.320 | And so it's a lot of very short people associated with it.

01:21:31.400 | And they're gonna try to very much exactly

01:21:35.280 | like what you described.

01:21:36.120 | They're gonna try to do a new one.

01:21:37.280 | I would just tell you like the wall of opposition

01:21:39.560 | that they're up against is profound, right?

01:21:41.860 | And part of it is economic which is can they ever get access

01:21:44.400 | to federal student lending?

01:21:45.360 | And I hope that they can, but it seems nearly inconceivable

01:21:49.040 | the way the system's rigged today.

01:21:51.400 | And then the other is just like they're gonna,

01:21:53.480 | they already have come under.

01:21:54.440 | I mean, anybody who publicly associates with them

01:21:58.600 | who is in traditional academia immediately gets lit on fire.

01:22:01.120 | And there's like cancellation campaigns.

01:22:02.720 | So they're up against a wall of social ostracism.

01:22:05.600 | They're up against a wall of press attacks.

01:22:07.940 | They're up against a wall of people just like doing

01:22:10.800 | the thing, pouncing on any anytime anybody says anything,

01:22:13.300 | they're gonna try to like burn the place down.

01:22:14.720 | - This reminds me of like Jerry Springer episodes

01:22:18.360 | and Geraldo Rivera episodes where it's like if a team

01:22:23.200 | listened to like Danzig or Marilyn Manson type music

01:22:28.200 | or Metallica that they were considered a devil worshiper.

01:22:32.360 | Now we just laugh, right?

01:22:33.800 | We're like, that's crazy, right?

01:22:35.240 | People listen to music with all sorts of lyrics

01:22:37.240 | and ideas and looks and that's crazy.

01:22:40.800 | But there were people legitimately sent to prison,

01:22:44.800 | I think with the West Memphis Three, right?

01:22:46.340 | These kids out in West Memphis that looked different,

01:22:49.540 | acted different, were accused of murders

01:22:51.240 | that eventually was made clear they clearly didn't commit,

01:22:55.460 | but they were in prison

01:22:56.300 | because of the music they listened to.

01:22:58.020 | I mean, this sounds very similar to that.

01:22:59.980 | And I remember seeing Bumpersiggers

01:23:01.500 | free the West Memphis Three.

01:23:02.540 | And I thought this was some crazy thing.

01:23:04.620 | And you look into it and this isn't,

01:23:06.500 | it's a little bit niche, but I mean, these were real lives

01:23:09.900 | and there was an active witch hunt for people

01:23:13.500 | that looked different and acted different.

01:23:15.600 | And yet now we're sort of in this inverted world

01:23:18.500 | where on the one hand, we're all told

01:23:21.560 | that we can express ourselves however we want.

01:23:23.620 | But on the other hand,

01:23:24.460 | you can't get a bunch of people together to take classes

01:23:26.820 | where they learn biology and sociology and econ in Texas.

01:23:31.820 | Wild.

01:23:33.720 | - Yes.

01:23:34.540 | Well, so the simple explanation is this is Puritanism, right?

01:23:37.600 | So this is the original American Puritanism

01:23:40.440 | that just like works itself out through the system

01:23:42.560 | in different ways at different times.

01:23:44.000 | You know, there's this phenomenon,

01:23:44.840 | there's a religious phenomenon in America

01:23:46.240 | called the Great Awakenings.

01:23:47.840 | There'll be these periods in American history

01:23:50.020 | where there's basically religiosity fades

01:23:51.600 | and then there'll be this snapback effect

01:23:52.920 | where you'll have this basically, this frenzy

01:23:55.440 | basically of religion.

01:23:57.040 | In the old days, it would have been tent revivals

01:23:59.080 | and people speaking in tongues and all this stuff.

01:24:01.800 | And then in the modern world,

01:24:02.880 | it's of the form that we're living through right now.

01:24:05.880 | And so, yeah, it's just basically these waves

01:24:07.320 | of sort of American religious.

01:24:09.840 | And you know, remember like religion in our time,

01:24:12.320 | religious impulses in our time don't get expressed,

01:24:14.200 | you know, 'cause we live in more advanced times, right?

01:24:16.400 | We live in scientifically informed times.

01:24:17.760 | And so religious impulses in our time

01:24:19.320 | don't show up as overtly religious, right?

01:24:21.920 | They show up in a secularized form, right?

01:24:24.280 | Which of course conveniently is therefore

01:24:26.040 | not subject to the First Amendment

01:24:27.160 | separation of church and state, right?

01:24:28.600 | As long as the church is secular, there's no problem, right?

01:24:31.520 | And so, but we're acting out these kind

01:24:33.360 | of religious scripts over and over again

01:24:34.680 | and we're in the middle of another religious frenzy.

01:24:37.440 | - There's a phrase that I hear a lot

01:24:40.120 | and I don't necessarily believe it,

01:24:42.680 | but I want your thoughts on it,

01:24:43.680 | which is the pendulum always swings back.

01:24:46.520 | - Yeah, not quite.

01:24:47.760 | - So that's how I feel too, because you know, I'll take--

01:24:50.720 | - Boy, that would be great.

01:24:51.560 | - Take any number of things that we've talked about

01:24:54.100 | and you know, actually it's so crazy, you know,

01:24:57.440 | the way things have gone with institutions

01:24:59.840 | or it's so crazy the way things have gone with social media

01:25:01.880 | or it's so crazy, fill in the blank and people will say,

01:25:05.860 | well, you know, pendulum always swings back.

01:25:08.360 | Like it's the stock market or something, you know,

01:25:11.320 | after every crash, there'll be an eventual boom

01:25:14.040 | and vice versa.

01:25:15.520 | - By the way, that's not true either, right?

01:25:16.940 | - Right.

01:25:17.780 | - Most stock markets, we have of course survivorship,

01:25:19.280 | but it's all survivorship, everything is survivorship.

01:25:20.800 | It's all, everything you just said

01:25:21.720 | is obviously survivorship bias, right?

01:25:23.080 | So if you look globally, most stock markets over time

01:25:26.200 | crash and burn and ever recover.

01:25:27.800 | The American stock market has always recovered.

01:25:30.520 | - Right, I was referring to the American stock market.

01:25:32.040 | - Yeah, but globally.

01:25:33.380 | But the reason everybody refers to the American stock market

01:25:35.200 | is 'cause it's the one that doesn't do that.

01:25:37.160 | The other 200 or whatever that crash and burn

01:25:39.840 | and ever recover.

01:25:40.840 | Like, let's go check in on the, you know,

01:25:42.360 | an Argentina stock market right now.

01:25:43.600 | Like, I don't think it's coming back anytime soon.

01:25:45.360 | - Yeah, my father's Argentine and immigrated to the US

01:25:48.400 | in the 1960s, so he would definitely agree with that.

01:25:51.200 | - Yeah, like it doesn't come, you know,

01:25:53.080 | like when their stocks crash, they don't come back.

01:25:55.560 | So, and then, you know, like Senkoism,

01:25:57.080 | like the Soviet Union never recovered from like Senkoism,

01:25:58.960 | it never came back, it led to the end of the country.

01:26:01.560 | You know, literally the things that took down

01:26:02.980 | the Soviet Union were oil and wheat and the wheat thing,

01:26:05.480 | you can trace the crisis back to like Senkoism.

01:26:08.320 | And so, yeah, no, look, pendulum always swings back,

01:26:11.720 | it's true only in the cases where the pendulum swings back.

01:26:14.720 | Everybody just conveniently forgets

01:26:16.560 | all the other circumstances where that doesn't happen.

01:26:18.440 | One of the things people, you see this in business also,

01:26:21.320 | people have a really hard time confronting really bad news.

01:26:25.440 | I don't know if you've noticed that.

01:26:27.920 | I think every doctor who's listening right now

01:26:29.360 | is like, yeah, no shit.

01:26:30.200 | But like, there are situations, you see it in business,

01:26:33.400 | there are situations that, you see Star Trek,

01:26:36.000 | remember Star Trek, the Kobayashi Maru simulator, right?

01:26:38.800 | So the big lesson to become a Star Trek captain

01:26:40.360 | is you had to go through the simulation

01:26:41.480 | called the Kobayashi Maru, and the point was,

01:26:43.260 | there's no way to, it's a no-win scenario, right?

01:26:45.500 | And then it turned out like Captain Kirk

01:26:47.660 | was the only person to ever win the scenario,

01:26:49.200 | and the way that he did it was he went in ahead of time

01:26:51.200 | and hacked the simulator, right?

01:26:53.080 | It was the only way to actually get through.

01:26:54.560 | And then there was a debate whether to fire him

01:26:55.960 | or make him a captain, so they made him a captain.

01:26:58.920 | And the problem is, in real life,

01:27:01.480 | you do get the Kobayashi Maru on a regular basis.

01:27:03.720 | There are actual no-win situations

01:27:05.240 | that you can't work your way out of.

01:27:07.040 | And as a leader, you can't ever cop to that, right?

01:27:09.120 | 'Cause you have to carry things forward,

01:27:10.480 | and you have to look for every possible choice you can.

01:27:12.480 | But every once in a while, you do run into a situation

01:27:14.440 | where it's really not recoverable.

01:27:16.400 | And at least I've found people just cannot cope with that.

01:27:18.960 | And so what happens is they basically then,

01:27:21.000 | they basically just exclude it from their memory

01:27:23.080 | that it ever happened.

01:27:25.400 | - I'm glad you brought up simulators

01:27:26.720 | 'cause I want to make sure that we talk about

01:27:29.160 | the new and emerging landscape of AI,

01:27:32.640 | artificial intelligence.

01:27:34.000 | And I could try and smooth our conversation

01:27:39.380 | of a moment ago with this one

01:27:42.560 | by creating some clever segue, but I'm not going to.

01:27:45.220 | Except I'm going to ask, is there a possibility

01:27:48.940 | that AI is going to remedy some of what we're talking about?

01:27:53.120 | Let's make sure that we earmark that for discussion

01:27:55.120 | a little bit later.

01:27:56.120 | But first off, because some of the listeners

01:27:59.040 | of this podcast might not be as familiar with AI

01:28:01.840 | as perhaps they should be.

01:28:03.580 | We've all heard about artificial intelligence.

01:28:05.320 | People hear about machine learning, et cetera.

01:28:07.440 | But it'd be great if you could define for us what AI is.

01:28:11.180 | People almost immediately hear AI and think,

01:28:15.680 | okay, robot's taking over.

01:28:17.380 | I'm going to wake up and I'm going to be strapped to the bed

01:28:19.860 | and my organs are going to be pulled out of me.

01:28:22.520 | Robots are going to be in my bank account.

01:28:24.400 | They're going to kill all my children and dystopia for most.

01:28:29.400 | Clearly, that's not the way it's going to go.

01:28:34.560 | If you believe that machines can augment human intelligence

01:28:39.400 | and human intelligence is a good thing.

01:28:41.180 | So tell us what AI is and where you think it can take us,

01:28:46.180 | both good and bad.

01:28:49.240 | - Yeah, so there was a big debate

01:28:51.720 | when the computer was first invented,

01:28:53.080 | which was in the 1930s, 1940s.

01:28:55.540 | People like Alan Turing and John von Neumann and these people.

01:28:58.960 | And the big debate at the time was,

01:29:01.520 | because they knew they wanted to build computers,

01:29:02.960 | they had the basic idea.

01:29:04.960 | And there had been calculating machines before that

01:29:07.140 | and there had been these looms

01:29:08.760 | that you basically programmed with punch cards.

01:29:10.200 | And so there was a prehistory to computers

01:29:12.540 | that had to do with building

01:29:13.380 | sort of increasingly complex calculating machines.

01:29:15.680 | So they were kind of on a track,

01:29:16.760 | but they knew they were going to be able to build,

01:29:18.120 | they call it a general purpose computer

01:29:19.600 | that basically you could program

01:29:21.120 | on the way that you program computers today.

01:29:23.160 | But they had a big debate early on,

01:29:24.680 | which is should the fundamental architecture of the computer

01:29:27.240 | be based on either A, like calculating machines,

01:29:30.480 | like cache registers and looms and other things like that,

01:29:34.200 | or should it be based on a model of the human brain?

01:29:36.480 | And they actually had this idea of computers

01:29:38.920 | modeled on the human brain back then.

01:29:40.760 | And this was this concept of so-called neural networks.

01:29:43.620 | And it's actually fairly astonishing

01:29:45.120 | from a research standpoint.

01:29:46.600 | The original paper on neural networks

01:29:48.320 | actually was published in 1943, right?

01:29:50.600 | So they didn't have our level of neuroscience,

01:29:52.640 | but they actually knew about the neuron.

01:29:54.000 | And they actually had a theory

01:29:54.920 | of neurons interconnecting in synapses

01:29:56.680 | and information processing in the brain even back then.

01:30:00.080 | And a lot of people at the time basically said,

01:30:02.600 | you know what, we should basically have the computer

01:30:04.360 | from the start be modeled after the human brain,

01:30:05.980 | 'cause if the computer could do everything

01:30:08.160 | that the human brain can do,

01:30:09.240 | that would be the best possible general purpose computer.

01:30:11.220 | And then you could have it do jobs

01:30:12.800 | and you could have it create art

01:30:13.960 | and you could have it do all kinds of things

01:30:15.000 | like humans can do.

01:30:16.000 | It turns out that didn't happen in our world.

01:30:20.260 | What happened instead was the industry

01:30:21.920 | went in the other direction.

01:30:22.920 | It went basically in the model of the calculating machine

01:30:25.040 | or the cash register.

01:30:25.880 | And I think practically speaking,

01:30:27.400 | that kind of had to be the case

01:30:28.760 | 'cause that was actually the technology

01:30:30.140 | that was practical at the time.

01:30:32.960 | But that's the path.

01:30:33.800 | And so what we all have experiences with,

01:30:36.760 | up to and including the iPhone in our pocket,

01:30:38.340 | is computers built on that,

01:30:39.840 | basically calculating machine model,

01:30:41.400 | not the human brain model.

01:30:42.880 | And so what that means is computers,

01:30:44.340 | as we have come to understand them,

01:30:46.260 | they're basically like mathematical savants at best, right?

01:30:50.160 | So they're like, they're really good at like,

01:30:52.400 | doing lots of mathematical calculations.

01:30:54.280 | They're really good at executing

01:30:55.460 | these extremely detailed computer programs.

01:30:57.720 | They're hyper literal.

01:30:59.540 | One of the things you learn early when you're a programmer

01:31:01.720 | is as the human programmer,

01:31:03.320 | you have to get every single instruction

01:31:04.600 | you give the computer correct,

01:31:05.620 | 'cause it will do exactly what you tell it to do.

01:31:08.100 | And bugs in computer programs are always a mistake

01:31:10.980 | on the part of the programmer.

01:31:12.060 | - Interesting.

01:31:12.900 | - You never blame the computer.

01:31:13.720 | You always blame the programmer

01:31:15.020 | 'cause that's the nature of the thing

01:31:16.780 | that you're dealing with.

01:31:17.620 | - One down score off and the whole thing--

01:31:19.700 | - Yeah, yeah, and it's the programmer's fault.

01:31:22.300 | And if you talk to any programmer,

01:31:23.140 | they'll agree with this.

01:31:23.980 | They'll be like, yeah, if there's a problem, it's my fault.

01:31:25.480 | I did it.

01:31:26.320 | I can't blame the computer.

01:31:27.280 | The computer has no judgment.

01:31:28.500 | It has no ability to interpret, synthesize,

01:31:32.100 | develop an independent understanding of anything.

01:31:33.960 | It's literally just doing what I tell it to do step by step.

01:31:37.120 | So for 80 years, we've had this just,

01:31:39.320 | this very kind of hyper literal kind of model computers.

01:31:42.120 | These are called, technically,

01:31:43.400 | these are what are called von Neumann machines

01:31:44.880 | based after the mathematician, John von Neumann.

01:31:47.400 | They run in that way.

01:31:48.240 | And they've been very successful and very important

01:31:50.340 | and our world has been shaped by them.

01:31:52.140 | But there was always this other idea out there,

01:31:53.940 | which is, okay, how about a completely different approach,

01:31:56.220 | which is based much more on how the human brain operates,

01:31:59.220 | or at least our kind of best understanding

01:32:01.320 | of how the human brain operates, right?

01:32:02.820 | 'Cause those aren't the same thing.

01:32:05.100 | It basically says, okay, what if you could have a computer,

01:32:07.500 | instead of being hyper literal,

01:32:08.480 | what if you could have it actually be conceptual, right?

01:32:11.500 | And creative and able to synthesize information, right?

01:32:15.260 | And able to draw judgments and able to,

01:32:18.280 | behave in ways that are not deterministic,

01:32:21.120 | but are rather creative, right?

01:32:24.680 | And so, and the applications for this, of course,

01:32:27.560 | are endless.

01:32:28.400 | And so, for example, the self-driving car,

01:32:30.480 | the only way that you can make a car,

01:32:32.320 | you cannot program a computer with rules

01:32:34.400 | to make it a self-driving car.

01:32:35.400 | You have to do what Tesla and Waymo

01:32:36.880 | and these other companies have done now.

01:32:37.920 | You have to use AI, right?

01:32:39.200 | You have to use this other architecture.

01:32:40.960 | And you have to basically teach them

01:32:42.440 | how to recognize objects and images at high speeds,

01:32:45.040 | the same way, basically the same way the human brain does.

01:32:46.880 | And so those are so-called neural networks running inside.

01:32:49.540 | - So essentially let the machine operate based on priors.

01:32:53.060 | You know, we almost clipped a boulder

01:32:56.820 | going up this particular drive.

01:32:58.580 | And so therefore this shape that previously

01:33:00.760 | the machine didn't recognize as a boulder,

01:33:02.400 | it now introduces to its catalog of boulders.

01:33:05.320 | Is that sort of-

01:33:06.160 | - Yeah, a good example,

01:33:07.160 | or let's even make it even starker for self-driving car.

01:33:10.200 | There's something in the road.

01:33:11.280 | Is it a small child or a plastic shopping bag

01:33:13.640 | being blown by the wind?

01:33:15.880 | Very important difference.

01:33:17.840 | If it's a shopping bag,

01:33:18.800 | you definitely want to go straight through it

01:33:20.380 | because if you deviate off course,

01:33:21.760 | you might, you know, you're gonna make a fast,

01:33:24.000 | you know, it's the same challenge we have

01:33:25.400 | when we're driving.

01:33:26.240 | Like you don't want to swerve to avoid a shopping bag

01:33:27.820 | 'cause you might hit something

01:33:28.660 | that you didn't see on the side.

01:33:29.760 | If it's a small child, for sure you want to swerve, right?

01:33:31.960 | And so, but it's very, but like in that moment,

01:33:34.700 | and you know, small children come in different

01:33:36.200 | like shapes and descriptions

01:33:37.100 | and are wearing different kinds of clothes.

01:33:37.940 | - They might tumble onto the road

01:33:39.080 | the same way a bag would tumble.

01:33:40.640 | - Yeah, they might look like they're tumbling.

01:33:41.760 | And by the way, they might not be,

01:33:43.040 | they might be wearing a Halloween mask, right?

01:33:45.760 | They might not have a recognizable human face, right?

01:33:48.400 | Or it might be a kid with, you know, one leg, right?

01:33:51.180 | You definitely want to not hit those, right?

01:33:53.040 | Like, so you can't,

01:33:55.120 | this is what basically we figured out is,

01:33:56.720 | you can't apply the rules-based approach

01:33:59.080 | of a von Neumann machine to basically real life

01:34:01.560 | and expect the computer to be in any way understanding

01:34:03.900 | or resilient to change it

01:34:05.120 | to basically things happening in real life.

01:34:06.840 | And this is why there's always been such a stark divide

01:34:08.620 | between what the machine can do and what the human can do.

01:34:11.720 | And so basically what's happened is in the last decade,

01:34:14.320 | that second type of computer,

01:34:15.720 | the neural network based computer,

01:34:16.840 | has started to actually work.

01:34:18.760 | It started to work actually first interestingly in vision,

01:34:21.360 | recognizing objects and images,

01:34:22.560 | which is why the self-driving car is starting to work.

01:34:24.440 | - Face recognition.

01:34:25.280 | - Face recognition.

01:34:26.120 | - I mean, when I was started off in visual neuroscience,

01:34:28.000 | which is really my original home in neuroscience,

01:34:31.320 | the idea that a computer or a camera

01:34:34.120 | could do face recognition better than a human

01:34:35.880 | was like a very low probability event

01:34:40.460 | based on the technology we had at the time,

01:34:42.420 | based on the understanding of the face recognition cells

01:34:44.660 | and the fusiform gyrus.

01:34:45.760 | Now you would be smartest to put all your money

01:34:50.760 | on the machine.

01:34:51.760 | You know, you want to find faces in airports,

01:34:53.480 | even with masks on and, you know,

01:34:55.140 | at profile versus straight on,

01:34:57.480 | machines can do it far better than most all people.

01:35:00.580 | I mean, they're the super recognizers,

01:35:02.420 | but even they can't match the best machines.

01:35:05.600 | Now, 10 years ago, what I just said was the exact reverse.

01:35:08.920 | - Right, that's right.

01:35:09.760 | - All right, so faces, handwriting, right,

01:35:13.680 | and then voice, right, being able to understand voice.

01:35:16.300 | Like if you use, just as a user, if you use Google Docs,

01:35:18.640 | it has a built-in voice transcription.

01:35:20.040 | They have sort of the best industry-leading

01:35:21.280 | kind of voice transcription.

01:35:22.580 | If you use voice transcription in Google Docs,

01:35:24.160 | it's breathtakingly good.

01:35:25.400 | You just speak into it and it just like types

01:35:27.340 | what you're saying.

01:35:28.280 | - Well, that's good 'cause in my phone,

01:35:29.420 | every once in a while, I'll say,

01:35:30.320 | "I need to go pick up a few things,"

01:35:31.760 | and they'll say, "I need to pick up a few thongs."

01:35:34.100 | And so Apple needs to get on board

01:35:37.500 | with whatever the voice recognition is that Google's using.

01:35:39.840 | - Maybe it knows you better than you think.

01:35:41.500 | (laughing)

01:35:43.560 | - That was not the topic I was avoiding discussing.

01:35:46.040 | - No, so that's on the list, right?

01:35:47.160 | That's on your list.

01:35:48.520 | So look, there's a reason actually why Google's so good

01:35:52.480 | and Apple is not right now at that kind of thing,

01:35:54.160 | and it actually goes to actually the,

01:35:55.720 | it's actually an ideological thing of all things.

01:35:58.120 | Apple does not permit pooling of data for any purpose,

01:36:03.400 | including training AI, whereas Google does.

01:36:06.480 | And Apple's just like stake their brand on privacy

01:36:08.800 | and among that is sort of a pledge

01:36:10.260 | that they don't like pool your data.

01:36:11.740 | And so all of Apple's AI is like AI

01:36:13.800 | that has to happen like locally on your phone,

01:36:16.060 | whereas Google's AI can happen in the cloud, right?

01:36:18.200 | It can happen across pool data.

01:36:19.100 | Now, by the way, some people think that that's bad

01:36:20.760 | 'cause they think pooling data is bad.

01:36:22.360 | But that's an example of the shift

01:36:24.080 | that's happening in the industry right now,

01:36:25.160 | which is you have this separation between the people

01:36:26.960 | who are embracing the new way of training AIs

01:36:29.600 | and the people who basically, for whatever reason, are not.

01:36:32.580 | - Excuse me, you say that some people think it's bad

01:36:34.920 | because of privacy issues or they think it's bad

01:36:36.640 | because of the reduced functionality of that AI.

01:36:40.160 | - Oh, no, so you're definitely gonna get,

01:36:41.760 | so there's three reasons AIs have started to work.

01:36:45.400 | One of them is just simply larger data sets,

01:36:47.760 | larger amounts of data.

01:36:48.960 | So specifically the reason why objects and images are now,

01:36:53.040 | the reason machines are now better than humans

01:36:54.520 | at recognizing objects and images or recognizing faces

01:36:57.000 | is because modern facial recognition AIs

01:36:59.520 | are trained across all photos on the internet of people,

01:37:02.720 | billions and billions and billions of photos, right?

01:37:04.620 | A limited number of photos of people on the internet.

01:37:06.880 | Attempts to train facial recognition systems

01:37:08.960 | 10 or 20 years ago, they'd be trained on, you know,

01:37:11.100 | thousands or tens of thousands of photos.

01:37:12.940 | - So the input data is simply much more vast.

01:37:15.520 | - Much larger.

01:37:16.360 | And this is the reason, to get to the conclusion on this,

01:37:18.080 | this is the reason why ChatGPT works so well,

01:37:19.920 | is ChatGPT, one of the reasons ChatGPT works so well

01:37:22.620 | is it's trained on the entire internet of text.

01:37:25.140 | And the entire internet of text was not something

01:37:27.040 | that was available for you to train an AI on

01:37:28.960 | until it came to actually exist itself,

01:37:30.800 | which is new in the last, you know, basically decade.

01:37:33.040 | So in the case of face recognition,

01:37:35.140 | I could see how having a much larger input data set

01:37:38.060 | would be beneficial if the goal is to recognize

01:37:40.080 | Marc Andreessen's face because you are looking

01:37:42.520 | for signal to noise against everything else, right?

01:37:45.360 | But in the case of ChatGPT,

01:37:47.320 | when you're pooling all text on the internet

01:37:49.700 | and you ask ChatGPT to say,

01:37:52.640 | construct a paragraph about Marc Andreessen's prediction

01:37:56.360 | of the future of human beings over the next 10 years

01:37:59.640 | and the likely to be most successful industries,

01:38:04.560 | give ChatGPT that.

01:38:06.060 | If it's pooling across all text,

01:38:08.540 | how does it know what is authentically Marc Andreessen's text?

01:38:12.840 | Because in the case of face recognition,

01:38:14.340 | you have a, you've got a standard to work from,

01:38:18.120 | a verified image versus everything else.

01:38:21.340 | In the case of text,

01:38:23.960 | you have to make sure that what you're starting with

01:38:26.080 | is verified text from your mouth.

01:38:28.520 | So, which makes sense if it's coming from video,

01:38:30.960 | but then if that video is deep faked,

01:38:33.820 | all of a sudden what's true,

01:38:36.260 | your valid Marc Andreessen is of question

01:38:41.120 | and then everything ChatGPT is producing

01:38:43.720 | that is then of question.

01:38:45.240 | - Right.

01:38:46.080 | So I would say there's a before and after thing here.

01:38:48.000 | There's like a before,

01:38:48.920 | there's like a before ChatGPT and after GPT question, right?

01:38:51.840 | 'Cause the existence of GPT itself changes the answer.

01:38:55.040 | So before ChatGPT, so the reason,

01:38:57.100 | the version you're using today is trained on data

01:38:58.960 | up till September, 2021.

01:39:00.700 | They're caught up with the training set.

01:39:02.120 | Up till September, 2021,

01:39:03.860 | almost all texts on the internet was written by a human being

01:39:06.720 | and then most of that was written

01:39:08.320 | by people under their own names.

01:39:09.520 | Some of it wasn't, but a lot of it was.

01:39:11.300 | And why do you know this for me

01:39:12.440 | is 'cause it was published in a magazine under my name

01:39:14.280 | or it's a podcast transcript and it's under my name.

01:39:16.920 | And generally speaking, if you just did a search on like,

01:39:19.240 | what are things Marc Andreessen has written and said,

01:39:21.000 | 90 plus percent of that would be correct.

01:39:23.800 | And there, look, somebody might have written a fake,

01:39:25.960 | you know, parody article or something like that,

01:39:27.840 | but like not that many people were spending that much time

01:39:30.260 | writing like fake articles about like things that I said.

01:39:32.360 | - So many people can pretend to be human.

01:39:34.060 | - Exactly, right.

01:39:34.900 | And so generally speak, you can kind of get your arms

01:39:36.900 | around the idea that there's a corpus of material

01:39:38.660 | associated with me or by the way, same thing with you.

01:39:40.280 | There's a corpus of YouTube transcripts

01:39:41.940 | and other your academic papers and talks that you've given.

01:39:44.320 | You can kind of get your hands around that.

01:39:45.500 | And that's how these systems are trained.

01:39:47.060 | They take all that data collectively, they put it in there.

01:39:49.380 | And that's why this works as well as it does.

01:39:50.980 | And that's why if you ask ChatGPT to speak or write like me

01:39:54.200 | or like you or like somebody else,

01:39:56.660 | it will actually generally do a really good job

01:39:58.620 | 'cause it has all of our prior text in its training data.

01:40:02.000 | That said, from here on out, this gets harder.

01:40:05.300 | And of course, the reason this gets harder

01:40:06.660 | is because now we have AI that can create text.

01:40:09.500 | We have AI that can create text at industrial scale.

01:40:12.500 | - Is it watermarked as AI generating text?

01:40:14.500 | - No, no, no, no.

01:40:15.340 | - How hard would it be to do that?

01:40:16.260 | - I think it's impossible.

01:40:17.740 | I think it's impossible.

01:40:18.620 | There are people who are trying to do that.

01:40:20.300 | This is a hot topic in the classroom.

01:40:21.680 | I was talking to a friend who's got like a 14 year old kid

01:40:23.460 | in a class and there's like these recurring scandals.

01:40:25.720 | It's like every kid in the class is using ChatGPT

01:40:27.980 | to like write their essays

01:40:28.980 | or to help them write their essays.

01:40:30.780 | And then the teacher is using one of,

01:40:34.320 | there's a tool that you can use

01:40:35.780 | that it purports to be able to tell you

01:40:38.340 | whether something was written by ChatGPT,

01:40:39.940 | but it's like only write like 60% of the time.

01:40:42.540 | And so there was this case where the student wrote an essay

01:40:45.180 | where their parents sat and watched them write the essay

01:40:48.020 | and then they submitted it

01:40:48.900 | and this tool got the conclusion incorrect.

01:40:50.720 | And then the student feels outraged

01:40:52.180 | 'cause he got unfairly cheated,

01:40:53.240 | but the teacher is like, well, you're all using the tool.

01:40:55.240 | Then it turns out there's another tool

01:40:56.540 | that basically you feed in text

01:40:57.960 | and it actually is sort of,

01:40:59.700 | they call it a summarizer,

01:41:02.280 | but what it really is is it's a cheating mechanism

01:41:03.980 | to basically just shuffle the words around enough

01:41:06.840 | so that it sheds whatever characteristics

01:41:08.740 | were associated with AI.

01:41:09.960 | So there's like an arms race going on

01:41:11.860 | in educational settings right now

01:41:13.140 | around this exact question.

01:41:14.560 | I don't think it's possible to do,

01:41:16.460 | there are people working on the watermark.

01:41:17.620 | I don't think it's possible to do the watermarking.

01:41:18.980 | And I think it's just kind of obvious

01:41:20.100 | why it's not possible to do that,

01:41:21.220 | which is you can just read the output for yourself.

01:41:24.400 | It's really good.

01:41:25.720 | How are you actually gonna tell the difference

01:41:27.800 | between that and something that a real person wrote?

01:41:30.220 | And then by the way,

01:41:31.060 | you can also ask JetGPT to write in different styles, right?

01:41:33.560 | So you can tell it like,

01:41:34.940 | write in the style of a 15 year old, right?

01:41:37.120 | You can tell it to write

01:41:37.960 | in the style of a non-native English speaker, right?

01:41:40.380 | Or if you're a non-native English speaker,

01:41:41.620 | you can tell it to write in the style of an English speaker,

01:41:44.140 | native English speaker, right?

01:41:45.060 | And so the tool itself will help you evade.

01:41:47.860 | So I don't think that,

01:41:50.140 | I think there's a lot of people who are gonna want

01:41:51.660 | to distinguish, quote, real versus fake.

01:41:54.120 | I think those days are over.

01:41:56.100 | - Genie's out of the bottle.

01:41:56.940 | - Genie's completely out of the bottle.

01:41:58.220 | And by the way, I actually think this is good.

01:42:00.220 | This doesn't map to my worldview

01:42:02.380 | of how we use this technology anyway,

01:42:03.660 | which we can come back to.

01:42:04.980 | So there's that.

01:42:07.340 | So there's that.

01:42:08.340 | And then there's the problem, therefore,

01:42:09.680 | of like the so-called deep fake problem.

01:42:11.140 | So then there's the problem of like deliberate,

01:42:12.860 | basically, manipulation.

01:42:14.300 | And that's like one of your many enemies,

01:42:17.980 | if you're increasingly long list of enemies,

01:42:20.820 | like mine, who basically is like, wow,

01:42:23.820 | I know how I'm gonna get him, right?

01:42:25.260 | I'm gonna use it to create something

01:42:28.500 | that looks like a Hebrewman transcript,

01:42:29.860 | and I'm gonna have him say all these bad things.

01:42:31.220 | - Or a video.

01:42:32.060 | - Or a video, or a video.

01:42:32.900 | - I mean, Joe Rogan and I were deep faked in a video.

01:42:36.200 | I don't want to flag people to it.

01:42:37.820 | I won't, so I won't talk about what it was about,

01:42:39.880 | but where it, for all the world,

01:42:43.620 | looked like a conversation that we were having,

01:42:45.580 | and we never had that specific conversation.

01:42:47.900 | - Yeah, that's right.

01:42:48.740 | So that's gonna happen for sure.

01:42:49.580 | And so what there's gonna need to be

01:42:51.200 | is there's gonna need to be basically registries

01:42:53.020 | where basically you, like in your case,

01:42:55.540 | you will submit your legitimate content

01:42:59.100 | into a registry under your unique cryptographic key, right?

01:43:02.480 | And then basically there will be a way

01:43:03.620 | to check against that registry

01:43:05.000 | to see whether that was the real thing.

01:43:06.160 | And I think this needs to be done for sure

01:43:07.940 | for public figures, it needs to be done for politicians,

01:43:09.860 | it needs to be done for music.

01:43:11.660 | - What about taking what's already out there

01:43:13.380 | and being able to authenticate it or not?

01:43:15.180 | - In the same way that many times per week,

01:43:18.180 | I get asked, is this your account about some,

01:43:20.820 | a direct message that somebody got on Instagram?

01:43:22.900 | And I always tell them, look, I only have the one account,

01:43:26.560 | this one verified account,

01:43:28.200 | although now with the advent of pay-to-play verification,

01:43:32.000 | makes it a little less potent as a security blanket

01:43:35.060 | for knowing if it's not this account, then it's not me.

01:43:38.900 | But in any case, these accounts pop up all the time,

01:43:41.600 | pretending to be me.

01:43:42.560 | And I'm relatively low on the scale, not low,

01:43:47.560 | but relatively low on the scale to say like a Beyonce

01:43:51.400 | or something like that,

01:43:52.280 | who has hundreds of millions of followers.

01:43:54.200 | So is there a system in mind where people could go in

01:43:58.840 | and verify text, click yes or no, this is me, this is not me.

01:44:02.440 | And even there, there's the opportunity for people to fudge

01:44:05.740 | to eliminate things about themselves

01:44:07.280 | that they don't want out there by saying,

01:44:08.620 | no, that's not me, I didn't actually say that or create that.

01:44:11.680 | - Yeah, no, that's right.

01:44:12.520 | So technologically, it's actually pretty straightforward.

01:44:14.760 | So the way to implement this technologically

01:44:16.360 | is with public key, it's called public key cryptography,

01:44:18.280 | which is the basis for how cryptography information

01:44:20.720 | is secured in the world today.

01:44:22.200 | And so basically what you would do,

01:44:23.320 | the implementation form of this would be,

01:44:24.640 | you would pick whatever is your most trusted channel.

01:44:27.000 | Let's say it's your YouTube channel as an example,

01:44:28.760 | where just everybody just knows

01:44:30.120 | that it's you and your YouTube channel,

01:44:31.280 | 'cause you've been doing it for 10 years or whatever,

01:44:32.800 | and it's just obvious.

01:44:34.000 | And you would just publish,

01:44:34.880 | like in the about me page on YouTube,

01:44:36.800 | you would just publish your public cryptographic key

01:44:39.280 | that's unique to you, right?

01:44:40.920 | And then anytime anybody wants to check

01:44:42.680 | to see whether any piece of content is actually you,

01:44:44.700 | they go to a registry in the cloud somewhere,

01:44:47.680 | and they basically submit, they basically say,

01:44:49.440 | okay, is this him?

01:44:50.880 | And then they can basically,

01:44:52.040 | to see whether somebody with your public key,

01:44:54.560 | you had actually certified that this was something

01:44:56.800 | that you made.

01:44:58.340 | Now, who runs that registry is an interesting question.

01:45:01.080 | If that registry is run by the government,

01:45:02.520 | we will call that the Ministry of Truth.

01:45:04.620 | I think that's probably a bad idea.

01:45:06.760 | If that registry is run by a company,

01:45:09.000 | we would call that basically the equivalent

01:45:10.920 | of like a credit bureau or something like that.

01:45:12.920 | Maybe that's how it happens.

01:45:13.920 | The problem with that is that company now becomes

01:45:15.800 | hacking target number one of every bad person on earth,

01:45:18.840 | 'cause you can, if anybody breaks into that company,

01:45:21.280 | they can fake all kinds of things.

01:45:22.920 | - Yeah, they own the truth.

01:45:24.120 | - Right, they own the truth, right.

01:45:25.080 | And by the way, insider threat,

01:45:26.080 | also their employees can monkey with it, right?

01:45:27.840 | So you have to really trust that company.

01:45:29.640 | The third way to do it is with a blockchain, right?

01:45:31.500 | And so this, with the crypto blockchain technology,

01:45:33.640 | you could have a distributed system,

01:45:35.080 | basically a distributed database in the cloud

01:45:37.480 | that is run through a blockchain,

01:45:38.880 | and then it implements this cryptography

01:45:41.200 | and the certification process.

01:45:42.600 | - What about quantum internet?

01:45:44.320 | Is that another way to encrypt these things?

01:45:45.860 | I know most of our listeners are probably not familiar

01:45:47.800 | with quantum internet, but put simply,

01:45:49.720 | it's a way to secure communications on the internet.

01:45:53.220 | Let's just leave it at that.

01:45:54.440 | It's sophisticated, and we'll probably do a whole episode

01:45:56.720 | about this at some point,

01:45:57.540 | but maybe you have a succinct way

01:45:58.800 | of describing quantum internet,

01:46:00.060 | but that would be better, and if so, please offer it up.

01:46:04.520 | But is quantum internet going to be one way

01:46:06.800 | to secure these kinds of data and resources?

01:46:10.320 | - Maybe in the future, years in the future.

01:46:12.440 | We don't yet have working quantum computers in practice,

01:46:14.680 | so it's not currently something you could do,

01:46:16.560 | but maybe in a decade or two.

01:46:18.720 | - Tell me, I'm going to take a stab

01:46:19.980 | at defining quantum internet in one sentence.

01:46:21.600 | It's a way in which if anyone were to try and peer in

01:46:23.880 | on a conversation on the internet,

01:46:25.160 | it essentially would be futile

01:46:27.560 | because of the way that quantum internet changes

01:46:32.440 | the way that the communication is happening so fast

01:46:34.960 | and so many times in any one conversation,

01:46:37.120 | essentially changing the translation or the language so fast

01:46:40.080 | that there's just no way to keep up with it.

01:46:41.460 | Is that more or less accurate?

01:46:42.760 | - Yeah, conceivably, yeah, not yet, but yeah, someday.

01:46:45.840 | - So going back to AI,

01:46:48.140 | most people who hear about AI are afraid of AI.

01:46:50.560 | Well, I think most people who aren't informed.

01:46:54.600 | - This goes back to our elites versus masses thing.

01:46:57.120 | - Oh, interesting.

01:46:58.080 | Well, I heard you say that,

01:46:59.480 | and this is from a really wonderful tweet thread

01:47:04.660 | that we will link in the show note captions

01:47:06.600 | that you put out not long ago

01:47:08.980 | and that I've read now several times,

01:47:11.560 | and that everyone really should take the time to read it.

01:47:13.720 | It probably takes about 20 minutes to read it carefully

01:47:16.600 | and to think about each piece and I highly recommend it.

01:47:19.740 | But you said, and I'm quoting here,

01:47:23.540 | let's address the fifth,

01:47:26.880 | the one thing I actually agree with,

01:47:28.440 | which is AI will make it easier for bad people

01:47:31.160 | to do bad things.

01:47:32.600 | - Yeah, well, so first of all,

01:47:37.080 | there is a general freakout happening around AI.

01:47:38.980 | I think it's primarily, it's one of these, again,

01:47:40.460 | it's an elite-driven freakout.

01:47:41.520 | I don't think the man in the street knows cares

01:47:43.300 | or feels one way or the other.

01:47:44.300 | I think it's just not a relevant concept

01:47:45.900 | and it probably just sounds like science fiction.

01:47:47.840 | So I think there's an elite-driven freakout

01:47:50.580 | that's happening right now.

01:47:52.000 | I think that elite-driven freakout has many aspects to it

01:47:54.900 | that I think are incorrect, which is not surprising.

01:47:57.980 | I would think that given that I think the elites

01:47:59.180 | are incorrect about a lot of things,

01:48:00.640 | but I think they're very wrong about a number of things.

01:48:02.140 | They're saying about AI, but that said,

01:48:04.340 | look, this is a very powerful new technology, right?

01:48:06.940 | This is like a new general purpose,

01:48:08.380 | like thinking technology, right?

01:48:10.340 | So like, what if machines could think, right?

01:48:12.420 | And what if you could use machines to think

01:48:14.580 | and what if you could have them think for you?

01:48:16.200 | There's obviously a lot of good that could come from that,

01:48:19.140 | but also people, you know,

01:48:20.360 | look, criminals could use them to plan better crimes.

01:48:24.000 | You know, terrorists could use them to plan

01:48:25.160 | better terror attacks and so forth.

01:48:26.420 | And so these are going to be tools

01:48:28.340 | that bad people can use to do bad things, for sure.

01:48:31.460 | - I can think of some ways that AI could be leveraged

01:48:33.780 | to do fantastic things, like in the realm of medicine,

01:48:38.780 | an AI pathologist perhaps can scan 10,000 slides

01:48:45.420 | of histology and find the one micro-tumor seller aberration

01:48:50.620 | that would turn into a full-blown tumor.

01:48:53.020 | Whereas the even mildly fatigued

01:48:56.340 | or well-rested human pathologists, as great as they come,

01:49:00.500 | might miss that.

01:49:01.920 | And perhaps the best solution is for both of them to do it.

01:49:05.280 | And then for the human to verify what the AI has found

01:49:07.980 | and vice versa.

01:49:08.820 | - Right, that's right.

01:49:09.660 | - Right, and that's just one example.

01:49:10.900 | I mean, I can come up with thousands of examples

01:49:13.280 | where this would be wonderful.

01:49:15.120 | - I'll give you another one by the way, medicine.

01:49:17.780 | So you're talking about an analytic result,

01:49:19.300 | which is good and important.

01:49:20.180 | The other is like the machines are going to be

01:49:21.540 | much better bedside manner.

01:49:22.900 | They're going to be much better at dealing with the patient.

01:49:25.900 | And we already know there's already been a study.

01:49:27.540 | There's already been a study on that.

01:49:28.540 | So there was already a study done on this

01:49:31.140 | where there was a study team that scraped thousands

01:49:34.180 | of medical questions off of an internet forum.

01:49:35.860 | And then they had real doctors answer the questions.

01:49:38.200 | And then they had basically GPT-4 answer the questions.

01:49:40.820 | And then they had another panel of doctors

01:49:42.580 | score the responses, right?

01:49:44.460 | So there were no patients experimented on here.

01:49:46.340 | This was a test contained within the medical world.

01:49:49.020 | But then the panel of the judges,

01:49:51.960 | the panel of doctors who were the judges

01:49:53.620 | scored the answers on both factual accuracy

01:49:55.500 | and on bedside manner, on empathy.

01:49:58.500 | And the GPT-4 was equal or better

01:50:02.580 | on most of the factual questions analytically already.

01:50:05.880 | And it's not even a specifically trained medical AI.

01:50:08.940 | But it was overwhelmingly better on empathy.

01:50:12.100 | - Amazing. - Right?

01:50:13.300 | And so, and you know, I don't think, yeah, I don't,

01:50:15.940 | do you treat patients directly in your work?

01:50:18.180 | - No, I don't. - You don't, yeah.

01:50:19.020 | - I don't.

01:50:19.860 | We run clinical trials. - Right.

01:50:22.180 | - But I don't do any direct clinical.

01:50:24.340 | - So I have no direct experience with this,

01:50:26.700 | but from the surgeons, like if you talk to surgeons

01:50:29.500 | or you talk to people who train surgeons,

01:50:31.140 | what they'll tell you is like surgeons

01:50:32.500 | need to have an emotional remove from their patients

01:50:34.500 | in order to do a good job with the surgery.

01:50:35.980 | The side effect of that, and by the way,

01:50:37.580 | look, it's a hell of a job to have to go in

01:50:39.020 | and tell somebody that they're gonna die, right?

01:50:40.700 | Or that they have, so they're never gonna recover.

01:50:42.340 | They're never gonna walk again or whatever it is.

01:50:43.780 | And so there's sort of something inherent in that job

01:50:46.660 | where they need to keep an emotional reserve

01:50:48.180 | from the patient, right, to be able to do the job.

01:50:50.620 | And it's suspected of them as professionals.

01:50:53.220 | The machine has no such limitation.

01:50:55.180 | Like the machine can be as sympathetic as you want it to be

01:50:57.980 | for as long as you want it to be.

01:50:59.260 | It can be infinitely sympathetic.

01:51:00.420 | It's happy to talk to you at four in the morning.

01:51:01.980 | It's happy to sympathize with you.

01:51:03.300 | And by the way, it's not just sympathizing you

01:51:05.980 | in the way that, oh, it's just lying.

01:51:07.740 | You know, it's just making up words to lie to you

01:51:09.140 | to make you feel good.

01:51:10.340 | It can also sympathize with you

01:51:11.580 | in terms of helping you through all the things

01:51:13.220 | that you can actually do to improve your situation, right?

01:51:15.580 | And so, you know, boy, like if you'd be, you know,

01:51:18.740 | can you keep a patient actually on track

01:51:20.340 | with a physical therapy program?

01:51:21.540 | Can you keep a patient on track with a nutritional program?

01:51:23.780 | Can you keep a patient off of drugs or alcohol, right?

01:51:26.340 | And if they have a machine medical companion

01:51:28.660 | that's with them all the time

01:51:29.820 | that they're talking to all the time,

01:51:30.780 | that's infinitely patient, infinitely wise, right?

01:51:33.980 | Infinitely loving, right?

01:51:35.700 | And it's just gonna be there all the time.

01:51:37.300 | And it's gonna be encouraging.

01:51:38.300 | And it's gonna be saying, you know,

01:51:39.140 | you did such a great job yesterday.

01:51:40.180 | I know you can do this again today.

01:51:42.020 | Cognitive behavioral therapy is an obvious fit here.

01:51:44.500 | These things are gonna be great at CBT.

01:51:46.180 | And that's already starting.

01:51:47.220 | But you can already use ChatGPT as a CBT therapist

01:51:50.580 | if you want.

01:51:51.400 | It's actually quite good at it.

01:51:52.420 | And so there's a universe here that's,

01:51:55.100 | it goes to what you said.

01:51:55.940 | There's a universe here that's opening up,

01:51:57.300 | which is what I believe is it's partnership

01:51:59.520 | between man and machine, right?

01:52:01.180 | It's a symbiotic relationship,

01:52:02.700 | not an adversarial relationship.

01:52:04.220 | And so the doctor is going to pair with the AI

01:52:07.200 | to do all the things that you described.

01:52:08.740 | But the patient is also going to pair with the AI.

01:52:10.900 | And I think this partnership that's gonna emerge

01:52:15.060 | is gonna lead, among other things,

01:52:16.180 | to actually much better health outcomes.

01:52:18.180 | - I mean, I've relied for so much of my life

01:52:20.880 | on excellent mentors from a very young age and still now

01:52:25.500 | in order to make best decisions possible

01:52:29.380 | with the information I had.

01:52:30.820 | And rarely were they available at four in the morning,

01:52:34.480 | sometimes, but not on a frequent basis.

01:52:36.820 | And they fatigue like anybody else.

01:52:38.640 | And they have their own stuff like anybody else,

01:52:42.440 | baggage events in their life, et cetera.

01:52:44.540 | What you're describing is a sort of AI coach

01:52:48.440 | or therapist of sorts

01:52:50.200 | that hopefully would learn to identify our best self

01:52:53.900 | and encourage us to be our best self.

01:52:56.340 | And when I say best self,

01:52:57.780 | I don't mean that in any kind of pop psychology way.

01:53:00.180 | I mean, I could imagine AI very easily

01:53:02.140 | knowing how well I slept the night before

01:53:04.500 | and what types of good or bad decisions

01:53:06.260 | I tend to make at two o'clock in the afternoon

01:53:08.580 | when I've only had five hours of sleep,

01:53:10.340 | or maybe just less REM sleep the night before.

01:53:13.340 | It might encourage me to take a little more time

01:53:15.140 | to think about something.

01:53:16.600 | Might give me a little tap on the wrist

01:53:18.580 | through a device that no one else would detect

01:53:20.280 | to refrain from something.

01:53:23.480 | - Never going to judge you.

01:53:24.900 | It's never going to be resentful.

01:53:26.100 | It's never going to be upset that you didn't listen to it.

01:53:28.920 | It's never going to go on vacation.

01:53:30.880 | It's going to be there for you.

01:53:31.720 | I think this is the way people are going to live.

01:53:34.000 | It's going to start with kids

01:53:34.840 | and then over time it's going to be adults.

01:53:36.000 | And the way people are going to live

01:53:37.120 | is they're going to have a exactly friend, therapist,

01:53:39.680 | companion, mentor, coach, teacher, assistant,

01:53:43.260 | and that, or by the way, maybe multiple of those.

01:53:46.560 | Maybe we're actually talking about six

01:53:47.760 | like different personas interacting,

01:53:48.940 | which is a whole other possibility.

01:53:50.300 | But they're going to have-

01:53:51.140 | - A committee.

01:53:51.980 | - A committee, yeah, yeah, yeah.

01:53:52.800 | - A committee.

01:53:53.640 | - Yeah, exactly.

01:53:54.480 | Actually different personas.

01:53:55.300 | And maybe by the way,

01:53:56.140 | when there are difficult decisions to be made in your life,

01:53:56.980 | maybe what you want to hear is the argument

01:53:58.640 | among the different personas.

01:54:00.720 | And so you're just going to grow up.

01:54:03.720 | You're just going to have this in your life

01:54:05.120 | and you're going to always be able to talk to it

01:54:07.020 | and always be able to learn from it

01:54:08.320 | and always be able to help it make, you know,

01:54:09.840 | and like, it's going to be a symbiotic relationship.

01:54:14.000 | I think it's going to be a much better way to live.

01:54:15.120 | I think people are going to get a lot out of it.

01:54:16.760 | What modalities will it include?

01:54:18.680 | So I can imagine my phone has this engine in it,

01:54:22.960 | this AI companion,

01:54:24.360 | and I'm listening in headphones as I walk into work

01:54:27.600 | and it's giving me some, not just encouragement,

01:54:30.240 | some warning, some thoughts that,

01:54:33.100 | things that I might ask Marc Andreessen today

01:54:34.940 | that I might not have thought of and so on.

01:54:37.680 | I could also imagine it having a more human form.

01:54:40.720 | I could imagine it being a tactile,

01:54:43.000 | having some haptics or tapping to remind me

01:54:45.140 | so that it's not going to enter our conversation

01:54:47.220 | in a way that interferes or distracts you,

01:54:50.120 | but I would be aware, oh, right.

01:54:52.100 | You know, things of that sort.

01:54:53.440 | I mean, how many different modalities

01:54:55.680 | are we going to allow these AI coaches to approach us with?

01:54:59.820 | And is anyone actually thinking

01:55:01.320 | about the hardware piece right now?

01:55:03.160 | 'Cause I'm hearing a lot about the software piece.

01:55:05.220 | What does the hardware piece look like?

01:55:07.100 | - Yeah, so this is where Silicon Valley is going to kick in.

01:55:09.440 | So the entrepreneurial community

01:55:10.600 | is going to try all of those, right?

01:55:12.600 | By the way, the big companies and startups

01:55:14.280 | are going to try all those.

01:55:15.120 | So obviously, there's big companies that are working.

01:55:18.040 | The big companies have talked about a variety of these,

01:55:19.760 | including heads-up displays, AR/VR kinds of things.

01:55:23.920 | There's lots of people doing voice.

01:55:25.560 | The voice thing is, voice is a real possibility.

01:55:27.560 | It may just be an earpiece.

01:55:30.040 | There's a new startup that just unveiled a new thing

01:55:33.320 | where they actually project.

01:55:35.240 | So you'll have a pendant you wear on a necklace,

01:55:37.520 | and it actually literally will project images on your hand

01:55:40.900 | or on the table or on the wall in front of you.

01:55:42.520 | So maybe that's how it shows up.

01:55:44.920 | Yeah, there are people working on so-called haptic or touch-based

01:55:48.040 | kinds of things.

01:55:48.820 | There are people working on actually picking up

01:55:50.920 | nerve signals out of your arm to be able to--

01:55:56.440 | there's some science for being able to do basically

01:55:59.440 | like subvocalization.

01:56:01.360 | So maybe you could pick up that way, build conduction.

01:56:06.160 | So yeah, these are all going to be tried.

01:56:08.640 | So that's one question is the physical form of it.

01:56:10.720 | And then the other question is the software version of it,

01:56:13.100 | which is like, OK, what's the level of abstraction

01:56:15.140 | that you want to deal with these things in?

01:56:18.320 | Right now, it's like a question-answer paradigm

01:56:20.200 | in so-called chatbot.

01:56:21.360 | Ask a question, get an answer, ask a question, get an answer.

01:56:23.680 | Well, you want that to go for sure

01:56:25.080 | to more of a fluid conversation.

01:56:26.400 | You want it to build up more knowledge of who you are,

01:56:28.320 | and you don't want to have to explain yourself

01:56:29.600 | a second time and so forth.

01:56:30.960 | And then you want to be able to tell it things like, well,

01:56:32.360 | remind me this, that, or be sure and tell me when, x.

01:56:36.040 | But then maybe over time, more and more,

01:56:37.660 | you want it actually deciding when

01:56:39.360 | it's going to talk to you.

01:56:40.880 | And when it thinks it has something to say,

01:56:42.580 | it says it, and otherwise, it stays silent.

01:56:44.640 | And normally, at least in my head,

01:56:46.720 | unless I make a concerted effort to do otherwise,

01:56:50.160 | I don't think in complete sentences.

01:56:53.040 | So presumably, these machines could

01:56:58.040 | learn my style of fragmented internal dialogue.

01:57:02.080 | And maybe I have an earpiece, and I'm walking in,

01:57:04.720 | and I start hearing something.

01:57:07.280 | But it's some advice, et cetera, encouragement, discouragement.

01:57:12.100 | But at some point, those sounds that I hear in an earphone

01:57:17.100 | are very different than seeing something

01:57:19.080 | or hearing something in the room.

01:57:20.160 | We know this based on the neuroscience

01:57:22.500 | of musical perception and language perception.

01:57:24.720 | Hearing something in your head is very different.

01:57:27.720 | And I could imagine at some point

01:57:28.840 | that the AI will cross a precipice

01:57:30.640 | where if it has inline wiring to actually control

01:57:34.860 | neural activity in specific brain areas,

01:57:36.560 | and I don't mean very precisely,

01:57:37.980 | even just stimulating a little more

01:57:39.320 | prefrontal cortical activity, for instance,

01:57:40.960 | through the earpiece, a little ultrasound wave now

01:57:43.060 | can stimulate prefrontal cortex in a non-invasive way.

01:57:46.040 | That's being used clinically and experimentally.

01:57:48.440 | That the AI could decide that I need

01:57:52.160 | to be a little bit more context aware, right?

01:57:56.280 | This is something that is very beneficial

01:57:58.320 | for those listening that are trying to figure out

01:58:00.040 | how to navigate through life.

01:58:01.200 | It's like, know the context you're in

01:58:02.980 | and know the catalog of behaviors and words

01:58:05.020 | that are appropriate for that situation and not others.

01:58:07.440 | And this would go along with agreeableness perhaps,

01:58:11.700 | but strategic agreeableness, right?

01:58:13.760 | Context is important.

01:58:15.720 | There's nothing diabolical about that context is important,

01:58:17.800 | but I could imagine the AI recognizing,

01:58:19.900 | ah, we're entering a particular environment.

01:58:22.720 | I'm now actually going to ramp up activity

01:58:24.400 | in prefrontal cortex a little bit in a certain way

01:58:27.000 | that allows you to be more situationally aware

01:58:30.000 | of yourself and others, which is great

01:58:32.720 | unless I can't necessarily short circuit that influence

01:58:37.160 | because at some point the AI is actually then controlling

01:58:41.660 | my brain activity and my decision-making and my speech.

01:58:44.340 | I think that's what people fear is that once we cross

01:58:47.040 | that precipice, that we are giving up control

01:58:49.760 | to the artificial versions of our human intelligence.

01:58:52.760 | - And look, I think we have to decide.

01:58:54.400 | We collectively and we as individuals, I think,

01:58:56.160 | have to decide exactly how to do that.

01:58:57.680 | And this is the big thing that I believe about AI.

01:58:59.560 | There's just a much more, I would say, practical view

01:59:01.280 | of the world than a lot of the panic that you hear.

01:59:03.300 | It's just like, these are machines.

01:59:04.920 | They're able to do things that increasingly

01:59:06.640 | are like the things that people can do in some circumstances.

01:59:08.640 | But these are machines, we build the machines,

01:59:10.160 | we decide how to use the machines.

01:59:12.120 | When we want the machines turned on, they're turned on.

01:59:13.500 | We want them turned off, they're turned off.

01:59:14.840 | And so, yeah, so I think that's absolutely the kind of thing

01:59:17.320 | that the individual person should always be in charge of.

01:59:19.680 | - I mean, everyone was, and I have to imagine

01:59:22.180 | some people are still afraid of CRISPR, of gene editing,

01:59:25.120 | but gene editing stands to revolutionize our treatment

01:59:27.560 | of all sorts of diseases.

01:59:29.480 | You know, inserting and deleting particular genes

01:59:32.020 | in adulthood, right, not having to recombine in the womb

01:59:35.200 | a new organism is an immensely powerful tool.

01:59:38.480 | And yet the Chinese scientist who did CRISPR on humans,

01:59:42.480 | this has been done, actually did his postdoc at Stanford

01:59:45.880 | with Steve Quake, then went to China,

01:59:48.360 | did CRISPR on babies, mutated something,

01:59:50.680 | I believe it was the HIV, one of the HIV receptors.

01:59:53.820 | I'm told it was with the intention

01:59:55.760 | of augmenting human memory.

01:59:57.680 | It had very little to do, in fact,

01:59:59.160 | with limiting susceptibility to HIV per se

02:00:02.800 | to do with the way that that receptor

02:00:03.960 | is involved in human memory.

02:00:05.360 | The world demonized that person.

02:00:10.000 | We actually don't know what happened to them,

02:00:11.400 | whether or not they have a laboratory now

02:00:12.680 | or they're sitting in jail, it's unclear.

02:00:14.580 | But in China and elsewhere,

02:00:16.520 | people are doing CRISPR on humans.

02:00:18.600 | We know this, it's not legal in the US and other countries,

02:00:23.240 | but it's happening.

02:00:25.020 | Do you think it's a mistake for us

02:00:28.640 | to fear these technologies so much

02:00:30.520 | that we back away from them and end up 10, 20 years

02:00:33.080 | behind other countries that could use it

02:00:35.120 | for both benevolent or malevolent reasons?

02:00:38.640 | - Yeah, so there's always, and the details matter,

02:00:41.360 | so it's technology by technology,

02:00:42.960 | but I would say there's two things.

02:00:44.540 | You always have to think in these questions,

02:00:45.960 | I think, in terms of counterfactuals and opportunity cost.

02:00:48.640 | And so, CRISPR's an interesting one.

02:00:51.440 | CRISPR, you manipulate the human genome.

02:00:53.280 | Nature manipulates the human genome.

02:00:55.040 | Like, in all kinds of ways.

02:00:58.520 | - Yeah, when you pick a spouse

02:00:59.440 | and you have a child with that spouse,

02:01:00.680 | you're doing genetic recombination.

02:01:02.400 | - You are, really, yes.

02:01:03.680 | You are quite possibly, if you're Genghis Khan,

02:01:05.880 | you're determining the future of humanity, right,

02:01:08.240 | by those, like, yeah, nature, I mean, look, mutations.

02:01:13.240 | So, this is the old question of, like, basically,

02:01:17.080 | this is all state of nature, state of grace.

02:01:19.000 | Like, basically, is nature good,

02:01:20.720 | and then therefore artificial things are bad,

02:01:23.300 | which is kind of shocking.

02:01:24.140 | A lot of people have ethical views like that.

02:01:26.880 | I'm always at the view that nature's a bitch

02:01:28.920 | and wants us dead.

02:01:30.180 | Like, nature's out to get us, man.

02:01:32.320 | Like, nature wants to kill us, right?

02:01:34.240 | Like, nature wants to, like,

02:01:35.120 | evolve all kinds of, like, horrible viruses.

02:01:36.760 | Nature wants, you know, plagues.

02:01:37.880 | Nature wants to, like, do, you know, whether, you know,

02:01:39.960 | like, nature wants to do all kinds of stuff.

02:01:42.000 | I mean, look, the original,

02:01:42.840 | nature religion was the original religion, right?

02:01:44.680 | Like, that was the original thing people worshiped.

02:01:46.560 | And the reason was because nature was the thing

02:01:48.060 | that was out to get you, right,

02:01:49.800 | before you had scientific and technological methods

02:01:51.960 | to be able to deal with it.

02:01:53.640 | So, the idea of not doing these things, to me,

02:01:57.200 | is just saying, oh, we're just gonna turn over

02:01:58.760 | the future of everything to nature,

02:02:00.100 | and I don't think that that,

02:02:01.400 | there's no reason to believe that that leads

02:02:02.840 | in a particularly good direction or a bad, you know,

02:02:05.720 | that's not a value neutral decision.

02:02:07.480 | And then the related thing that comes from that

02:02:09.760 | is this, always this question around

02:02:11.080 | what's called the precautionary principle,

02:02:13.120 | which shows up in all these conversations

02:02:14.560 | on things like CRISPR, which basically is this,

02:02:17.440 | it's this principle that basically says

02:02:18.960 | the inventors of a new technology

02:02:20.240 | should be required to prove

02:02:21.460 | that it will not have negative effects

02:02:22.600 | before they roll it out.

02:02:23.800 | This, of course, is a very new idea.

02:02:26.920 | This is actually a new idea in the 1970s.

02:02:29.200 | It's actually invented by the German Greens,

02:02:30.560 | the 1970s.

02:02:31.680 | Before that, people didn't think in those terms.

02:02:33.720 | People just invented things and rolled them out,

02:02:36.640 | and we got all of modern civilization

02:02:38.560 | by people inventing things and rolling them out.

02:02:41.760 | The German Greens came up with the precautionary principle

02:02:43.600 | for one specific purpose.

02:02:44.760 | I'll bet you can guess what it is.

02:02:46.400 | It was to prevent--

02:02:49.480 | - Famine? - Nuclear power.

02:02:51.400 | It was to shut down attempts to do civilian nuclear power,

02:02:54.560 | and if you fast forward 50 years later,

02:02:56.500 | you're like, wow, that was a big mistake, right?

02:02:59.140 | So what they said at the time was

02:03:00.860 | you have to prove that nuclear reactors

02:03:02.080 | are not gonna melt down and cause all kinds of problems,

02:03:04.240 | and of course, as an engineer,

02:03:05.280 | can you prove that that will never happen?

02:03:07.020 | You can't.

02:03:07.860 | You can't rule out things that might happen in the future.

02:03:10.540 | And so that philosophy was used to stop nuclear power,

02:03:14.420 | by the way, not just in Europe, but also in the US

02:03:17.140 | and around much of the rest of the world.

02:03:18.580 | If you're somebody who's concerned

02:03:19.660 | about carbon emissions, of course,

02:03:20.940 | this is the worst thing that happened in the last 50 years

02:03:22.980 | in terms of energy.

02:03:24.160 | We actually have the silver bullet answer to unlimited energy

02:03:26.980 | with zero carbon emissions, nuclear power.

02:03:28.980 | We choose not to do it.

02:03:30.600 | Not only do we choose not to do it,

02:03:32.100 | we're actually shutting down the plants that we have now

02:03:34.860 | in California, and we just shut down the big plant.

02:03:37.480 | Germany just shut down their plants.

02:03:39.100 | Germany's in the middle of an energy war with Russia

02:03:41.760 | that we are informed as existential for the future of Europe.

02:03:44.220 | - But unless the risk of nuclear power plant meltdown

02:03:47.820 | has increased, and I have to imagine it's gone the other way,

02:03:51.420 | what is the rationale behind shutting down these plants

02:03:54.140 | and not expanding?

02:03:54.980 | - Because nuclear is bad, right?

02:03:56.180 | Nuclear is icky, nuclear is...

02:03:57.900 | Nuclear has been tagged.

02:03:58.980 | - It just sounds bad, nuclear.

02:04:00.840 | - Yeah, yeah, yeah.

02:04:01.740 | - Go nuclear.

02:04:02.580 | - Nuclear, well, so what happened?

02:04:03.420 | - We didn't shut down postal offices,

02:04:05.020 | and you hear it go postal.

02:04:06.060 | - So what happened was, so nuclear technology arrived

02:04:08.840 | on planet Earth as a weapon, right?

02:04:10.460 | So it arrived in the form of the...

02:04:12.060 | The first thing they did was in the middle of World War II.

02:04:13.900 | The first thing they did was the atomic bomb.

02:04:15.240 | They dropped it on Japan, and then there were all

02:04:16.980 | the debates that followed around nuclear weapons

02:04:18.620 | and disarmament, and there's a whole conversation

02:04:20.580 | to be had, by the way, about that,

02:04:22.180 | 'cause there's different views you could have on that.

02:04:24.460 | And then it was in the '50s and '60s

02:04:25.860 | where they started to roll out civilian nuclear power,

02:04:27.500 | and then there were accidents.

02:04:29.300 | There was like Three Mile Island melted down,

02:04:31.100 | and then Chernobyl melted down in the Soviet Union,

02:04:34.020 | and then even recently, Fukushima melted down.

02:04:36.260 | And so there have been meltdowns.

02:04:37.980 | And so I think it was a combination of it's a weapon,

02:04:40.980 | it is sort of icky.

02:04:42.620 | There's a lot of scientists sometimes say the ick factor,

02:04:45.420 | right, it's radioactive, it glows green.

02:04:49.260 | By the way, it becomes like a mythical fictional thing,

02:04:53.340 | and so you have all these movies of horrible supervillains

02:04:55.860 | powered by nuclear energy and all this stuff.

02:04:58.060 | - Well, the intro to "The Simpsons," right,

02:04:59.700 | is the nuclear power plant and the three-eyed fish

02:05:02.400 | and all the negative implications

02:05:05.620 | of this nuclear power plant run by,

02:05:07.940 | at least on "The Simpsons," idiots.

02:05:10.540 | And that is the dystopia where people are unaware

02:05:15.540 | of just how bad it is.

02:05:17.540 | - And who owns the nuclear power plant, right?

02:05:19.100 | This evil capitalist, right?

02:05:21.940 | So it's connected to capitalism, right?

02:05:24.900 | - So we're blaming Matt Groening

02:05:26.380 | for the demise of a particular nuclear power plant.

02:05:28.380 | - He certainly didn't help, right?

02:05:31.500 | But it's literally this amazing thing

02:05:33.020 | where if you're just thinking rationally, scientifically,

02:05:35.660 | you're like, okay, we want to get rid of carbon,

02:05:37.380 | this is the obvious way to do it.

02:05:38.500 | So, okay, fun fact, Richard Nixon did two things

02:05:43.180 | that really mattered on this.

02:05:44.000 | So one is he defined in 1971

02:05:45.540 | something called project independence,

02:05:47.280 | which was to create 1,000 new state-of-the-art

02:05:49.220 | nuclear plants, civilian nuclear plants in the US by 1980

02:05:52.140 | and to get the US completely off of oil

02:05:54.380 | and cut the entire US energy grid

02:05:56.020 | over to nuclear power electricity,

02:05:57.340 | cut over to electric cars, the whole thing,

02:05:58.940 | like detach from carbon.

02:06:00.340 | You'll notice that didn't happen.

02:06:03.460 | Why did that not happen?

02:06:04.300 | 'Cause he also created the EPA

02:06:05.780 | and the Nuclear Regulatory Commission,

02:06:07.520 | which then prevented that from happening, right?

02:06:09.120 | And the Nuclear Regulatory Commission

02:06:10.480 | did not authorize a new nuclear plant

02:06:11.980 | in the US for 40 years.

02:06:13.180 | - Why would he hamstring himself like that?

02:06:16.020 | - You know, he got distracted by him [laughs]

02:06:19.220 | by Watergate and Vietnam.

02:06:21.880 | - I think Ellsberg just died recently, right?

02:06:24.020 | The guy who released the Pentagon Papers.

02:06:25.420 | Yeah, so it's complicated.

02:06:27.540 | - But it's, yeah, yeah, yeah, exactly.

02:06:28.720 | It's this thing, yeah, he didn't,

02:06:30.100 | he left office shortly thereafter.

02:06:31.260 | He didn't have time to fully figure this out.

02:06:33.340 | I don't know whether he would have figured it out or not.

02:06:35.500 | Look, Ford could have figured it out.

02:06:36.620 | Carter could have figured it out.

02:06:37.580 | Reagan could have figured it out.

02:06:38.540 | Any of these guys could have figured it out.

02:06:39.780 | It's like the most obvious,

02:06:40.740 | knowing what we know today,

02:06:41.780 | it's the most obvious thing in the world.

02:06:43.600 | The Russia thing is the amazing thing.

02:06:44.860 | It's like Europe is literally funding

02:06:46.300 | Russia's invasion of Ukraine by paying them for oil, right?

02:06:49.420 | And they can't shut off the oil

02:06:50.740 | 'cause they won't cut over to nuclear, right?

02:06:52.540 | And then of course what happens, okay,

02:06:54.020 | so then here's the other kicker of what happens, right?

02:06:55.740 | Which is they won't do nuclear,

02:06:57.100 | but they want to do renewables, right?

02:06:59.260 | Sustainable energy.

02:07:00.220 | And so what they do is they do solar and wind.

02:07:02.900 | Solar and wind are not reliable

02:07:04.660 | 'cause it sometimes gets dark out

02:07:06.460 | and sometimes the wind doesn't blow.

02:07:08.540 | And so then what happens is they fire up the coal plants.

02:07:10.900 | Right, and so the actual consequence

02:07:13.020 | of the precautionary principle

02:07:14.380 | for the purpose it was invented

02:07:15.980 | is a massive spike in use of coal.

02:07:17.980 | - That's taking us back over 100 years.

02:07:19.820 | - Yes, correct.

02:07:20.820 | That is the consequence of the precautionary principle.

02:07:23.180 | Like that's the consequence of that mentality.

02:07:25.620 | And so it's a failure of a principle on its own merits

02:07:28.020 | for the thing it was designed for.

02:07:29.700 | And then there is a whole movement of people

02:07:31.880 | who want to apply it to every new thing.

02:07:33.260 | And this is the hot topic on AI right now in Washington,

02:07:36.540 | which is like, oh my God, these people have to prove

02:07:38.260 | that this can never get used for bad things.

02:07:39.860 | - Sorry, I'm hung up on this nuclear thing

02:07:41.660 | and I wonder, can it just be renamed?

02:07:46.260 | I mean, seriously.

02:07:47.820 | I mean, there is something about the naming of things.

02:07:50.400 | We know this in biology, right?

02:07:52.220 | I mean, you know, Lamarckian evolution

02:07:55.220 | and things like that, these are bad words in biology,

02:07:57.780 | but we had a guest on this podcast, Oded Reshavi,

02:07:59.800 | who's over in Israel, who's shown inherited traits.

02:08:03.200 | But if you talk about it as Lamarckian,

02:08:05.200 | then it has all sorts of negative implications,

02:08:07.260 | but his discoveries have important implications

02:08:10.480 | for everything from inherited trauma

02:08:12.580 | to treatment of disease.

02:08:14.200 | I mean, there's all sorts of positives that await us

02:08:16.460 | if we are able to reframe our thinking around something

02:08:19.260 | that yes, indeed could be used for evil,

02:08:21.260 | but that has enormous potential

02:08:24.100 | and that is an agreement with nature, right?

02:08:27.200 | This fundamental truth that, at least to my knowledge,

02:08:29.640 | no one is revising in any significant way anytime soon.

02:08:33.040 | So what if it were called something else?

02:08:35.340 | Instead of nuclear, it's called sustainable, right?

02:08:39.660 | I mean, it's amazing how marketing can shift our perspective

02:08:42.680 | of robots, for instance, or anyway,

02:08:46.020 | I'm sure you can come up with better examples than I can,

02:08:47.980 | but is there a good, solid PR firm

02:08:52.940 | working from the nuclear side?

02:08:55.120 | - Thunbergian, it's good, a Thunbergian.

02:08:58.620 | - Oh, Thunbergian. - Thunbergian,

02:09:00.440 | like if she was in favor of it,

02:09:02.600 | which by the way, she's not, she's dead set against it.

02:09:04.760 | - She said that. - 100%, yeah.

02:09:06.680 | - Based on- - Based on-

02:09:08.600 | - Murgian principles. - The prevailing ethic

02:09:11.340 | in environmentalism for 50 years is that nuclear is evil,

02:09:13.560 | like they won't consider it.

02:09:14.860 | There are, by the way,

02:09:15.700 | certain environmentalists who disagree with this,

02:09:17.220 | and so Stewart Brand is the one that's been the most public,

02:09:19.220 | and he has impeccable credentials in the space,

02:09:20.900 | and he wrote this- - Hoehler's Catalogs?

02:09:22.260 | - Hoehler's Catalog guy, yeah,

02:09:23.360 | and he's written a whole bunch

02:09:24.400 | of really interesting books since,

02:09:25.580 | and he wrote a recent book that goes through in detail.

02:09:27.660 | He's like, yes, obviously the correct environmental thing

02:09:29.940 | to do is nuclear power,

02:09:31.580 | and we should be implementing project independence.

02:09:34.020 | We should be building 1,000, specifically we should,

02:09:36.380 | he didn't say this, but this is what I would say.

02:09:37.620 | We should hire Charles Koch.

02:09:38.980 | We should hire Koch Industries, right?

02:09:42.620 | And they should build us 1,000 nuclear power plants, right?

02:09:44.980 | And then we should give them

02:09:45.820 | the Presidential Medal of Freedom

02:09:47.220 | for saving the environment.

02:09:48.700 | - And that would put us independent of our reliance on oil.

02:09:51.020 | - Yeah, then we're done with oil.

02:09:52.540 | Just think about what happens.

02:09:53.380 | We're done with oil, zero emissions.

02:09:54.980 | We're done with the Middle East, we're done.

02:09:57.300 | We're done.

02:09:58.120 | We're not drilling.

02:09:58.960 | We're not drilling on American land anymore.

02:10:00.340 | We're not drilling on foreign lands.

02:10:01.780 | Like we have no military entanglements

02:10:03.340 | in places where we're drilling.

02:10:04.340 | We're not despoiling Alaska.

02:10:06.300 | We're not, nothing, no offshore rigs, no nothing.

02:10:08.340 | We're done.

02:10:09.500 | And you basically just,

02:10:10.340 | you build state-of-the-art plants, engineered properly.

02:10:12.060 | You have them just completely contained.

02:10:13.380 | When there's nuclear waste,

02:10:14.220 | you just entomb the waste, right, in concrete.

02:10:16.540 | And so it just sits there forever.

02:10:19.580 | It's this very small footprint kind of thing.

02:10:22.260 | And you're just done.

02:10:23.660 | And so this is like the most,

02:10:25.220 | to me it's like scientifically, technologically,

02:10:26.780 | this is just like the most obvious thing in the world.

02:10:29.100 | It's a massive tell on the part of the people

02:10:31.060 | who claim to be pro-environment

02:10:32.060 | that they're not in favor of this.

02:10:33.660 | - And if I were to say,

02:10:35.380 | tweet that I'm pro-nuclear power

02:10:37.760 | because it's the more sustainable form of power.

02:10:39.720 | If I hypothetically did that today,

02:10:42.720 | what would happen to me in the-

02:10:44.280 | - You'd be a crypto-fascist, you know.

02:10:45.680 | (laughing)

02:10:48.100 | Dirty, evil capitalist, you know,

02:10:49.920 | a monster, how dare you, right?

02:10:51.340 | - I'm unlikely to run that experiment.

02:10:52.920 | I was just curious.

02:10:53.760 | That was what we call a Godankan experiment,

02:10:55.560 | experiment in our own hands.

02:10:56.400 | - Andrew, you're a terrible human being.

02:10:58.280 | - Wow.

02:10:59.120 | - We were looking for evidence

02:10:59.940 | that you're a terrible human being,

02:11:00.780 | and now we know it, right?

02:11:02.160 | This is a great example of that.

02:11:04.320 | I gave Andrew a book on the way in here with this,

02:11:06.280 | my favorite new book.

02:11:07.120 | The title of it is "When Reason Goes on Holiday."

02:11:09.680 | And this is a great example of it,

02:11:11.040 | it's the people who simultaneously

02:11:14.000 | say they're environmentalists

02:11:14.840 | and say they're anti-nuclear power,

02:11:15.900 | like the positions just simply don't reconcile.

02:11:18.280 | But that doesn't bother them at all.

02:11:20.580 | So be clear, I predict none of this will happen.

02:11:23.080 | - Amazing.

02:11:24.860 | I need to learn more about nuclear power.

02:11:27.080 | - Long coal.

02:11:28.320 | - Long coal.

02:11:29.160 | - Long coal, invest in coal.

02:11:30.960 | - Because you think we're just gonna revert.

02:11:32.240 | - It's the energy source of the future.

02:11:34.040 | Well, 'cause it can't be solar and wind

02:11:36.720 | 'cause they're not reliable, so you need something.

02:11:39.320 | If it's not nuclear,

02:11:40.160 | it's gonna be either like oil, natural gas, or coal.

02:11:42.480 | - And you're unwilling to say bet on nuclear

02:11:44.840 | because you don't think that the sociopolitical,

02:11:49.800 | elitist trends that are driving against nuclear

02:11:51.800 | are likely to dissipate anytime soon?

02:11:53.520 | - Not a chance.

02:11:54.360 | I can't imagine.

02:11:55.720 | It would be great if they did,

02:11:56.680 | but the powers that be are very locked in

02:12:01.680 | on this as a position.

02:12:02.680 | And look, they've been saying this for 50 years,

02:12:04.120 | and so they'd have to reverse themselves

02:12:05.360 | off of a bad position they've had for 50 years,

02:12:07.160 | and people really don't like to do that.

02:12:10.120 | - One thing that's good about this and other podcasts

02:12:12.120 | is that young people listen,

02:12:13.420 | and they eventually will take over.

02:12:15.400 | - And by the way, I will say also,

02:12:16.680 | there are nuclear entrepreneurs.

02:12:18.560 | So there are actually, on the point of young kids,

02:12:20.820 | there are a bunch of young entrepreneurs

02:12:22.120 | who are basically not taking no for an answer,

02:12:24.080 | and they're trying to develop,

02:12:25.280 | and particularly there's people trying to develop

02:12:26.600 | new very small form factor nuclear power plants

02:12:30.040 | with a variety of possible use cases.

02:12:32.720 | So look, maybe they show up with a better mousetrap

02:12:36.360 | and people take a second look, but we'll see.

02:12:38.880 | - We'll just rename it.

02:12:40.160 | So my understanding is that you think

02:12:44.360 | we should go all in on AI with the constraints

02:12:49.000 | that we discover we need in order to rein in safety

02:12:52.520 | and things of that sort, not unlike social media,

02:12:54.880 | not unlike the internet.

02:12:56.440 | - Not unlike what we should have done with nuclear power.

02:12:58.640 | - And in terms of the near infinite number of ways

02:13:04.680 | that AI can be envisioned to harm us,

02:13:07.280 | how do you think we should cope with that psychologically?

02:13:10.400 | You know, because I can imagine a lot of people

02:13:12.380 | listening to this conversation are thinking,

02:13:13.640 | okay, that all sounds great,

02:13:15.840 | but there are just too many what ifs that are terrible,

02:13:18.800 | right, you know, what if the machines take over?

02:13:20.760 | What if, you know, the silly example I gave earlier,

02:13:23.400 | but, you know, what if one day I get logged into my,

02:13:26.040 | you know, hard-earned bank account and it's all gone?

02:13:28.600 | You know, the AI version of myself like ran off

02:13:31.560 | with someone else and with all my money, right, right?

02:13:36.200 | My AI coach abandoned me for somebody else

02:13:39.420 | after it learned all the stuff that I taught it.

02:13:42.320 | It took off with somebody else, stranded, you know,

02:13:46.060 | and it has my bank account numbers,

02:13:47.960 | like this kind of thing, right?

02:13:49.360 | - You could really make this scenario horrible

02:13:51.160 | if you kept going.

02:13:52.000 | - Yeah, well, we can throw in a benevolent example as well

02:13:55.760 | to counter it, but it's just kind of fun to think about

02:13:59.680 | where the human mind goes, right?

02:14:01.920 | - So first I say, we got to separate the real problems

02:14:03.960 | from the fake problems, and so there's a lot,

02:14:05.560 | a lot of science fiction scenarios I think are just not real

02:14:07.840 | and the ones that you just cited as an example,

02:14:09.560 | like it's not, that's not what's gonna happen

02:14:11.000 | and I can explain why that's not what's gonna happen.

02:14:12.360 | So you should, there's a set of fake ones.

02:14:14.480 | The fake ones are the ones that just aren't,

02:14:16.800 | I think, technologically grounded, that aren't rational.

02:14:19.120 | It's the AI's gonna like wake up and decide to kill us all.

02:14:21.360 | It's gonna like, yeah, it's gonna develop the kind of agency

02:14:23.740 | where it's gonna steal our money, you know,

02:14:25.320 | money and our spouse and everything else, our kids.

02:14:28.040 | Like that's just, that's not how it works.

02:14:30.340 | And then there's also all these concerns, you know,

02:14:32.200 | destruction of society concerns, and this is, you know,

02:14:34.540 | misinformation, hate speech, deep fakes,

02:14:36.060 | like all that stuff, which I don't think is a real,

02:14:39.040 | is actually a real problem.

02:14:40.160 | And then there's, people have a bunch of economic concerns

02:14:42.600 | around, you know, what's gonna take all the jobs,

02:14:45.200 | and all those kinds of things, we could talk about that.

02:14:47.000 | I don't think those are, those are,

02:14:48.440 | I don't think that's actually the thing that happens.

02:14:50.520 | But then there are two actual real concerns

02:14:52.580 | that I actually do very much agree with,

02:14:53.980 | and one of them is what you said,

02:14:55.540 | which is bad people doing bad things.

02:14:57.540 | And there's a whole set of things to be done inside there.

02:15:01.020 | The big one is we should use AI to build defenses

02:15:03.600 | against all the bad things, right?

02:15:05.540 | And so, for example, there's a concern,

02:15:07.400 | AI is gonna make it easier for bad people

02:15:09.020 | to build pathogens, right, design pathogens in labs,

02:15:11.200 | which, you know, bad people, bad scientists can do today,

02:15:13.200 | but this is gonna make it easier to do.

02:15:15.400 | Well, obviously we should have the equivalent

02:15:17.160 | of an Operation Warp Speed operating, you know,

02:15:19.480 | in perpetuity anyway, right?

02:15:20.980 | But then we should use AI to build much better biodefenses,

02:15:24.080 | right, and we should be using AI today to design,

02:15:26.040 | like for example, full spectrum vaccines

02:15:27.680 | against every possible form of pathogen, right?

02:15:29.960 | And so defensive mechanism, hacking,

02:15:32.860 | you can use AI to build better defense tools, right?

02:15:35.040 | And so you should have a whole new kind of security suite

02:15:37.380 | wrapped around you, wrapped around your data,

02:15:38.700 | wrapped around your money,

02:15:39.540 | where you're having AI repel attacks.

02:15:43.620 | Disinformation, hate speech, deep fakes, all that stuff,

02:15:46.000 | you should have an AI filter when you use the internet,

02:15:48.220 | where, you know, you shouldn't have to figure out

02:15:50.700 | whether it's really me or whether it's a made up thing,

02:15:52.980 | you should have an AI assistant that's doing that for you.

02:15:55.100 | - Oh yeah, I mean, these little banners and cloaks

02:15:57.540 | that you see on social media,

02:15:58.540 | like this has been deemed misinformation.

02:16:01.240 | You know, if you're me, you always click, right?

02:16:03.400 | 'Cause you're like, what's behind the scrim.

02:16:05.200 | And then, or this is a,

02:16:07.560 | I don't always look at this image as gruesome type thing.

02:16:11.320 | Sometimes I just pass on that.

02:16:13.180 | But if it's something that seems debatable,

02:16:16.480 | of course you look.

02:16:17.440 | - Well, and you should have an AI assistant with you

02:16:19.520 | when you're on the internet,

02:16:20.360 | and you should be able to tell that AI assistant

02:16:21.520 | what you want, right?

02:16:22.720 | So yes, I want the full experience to show me everything.

02:16:26.260 | I want it from a particular point of view,

02:16:27.840 | and I don't want to hear from these other people

02:16:29.240 | who I don't like.

02:16:30.640 | By the way, it's gonna be my eight-year-old is using this.

02:16:32.400 | I don't want anything that's gonna cause a problem,

02:16:34.320 | and I want everything filtered.

02:16:35.280 | And AI-based filters like that that you program and control

02:16:38.560 | are gonna work much better,

02:16:40.040 | and be much more honest and straightforward and clear

02:16:42.300 | and so forth than what we have today.

02:16:43.640 | So anyway, so basically what I want people to do

02:16:45.840 | is think every time you think of like a risk

02:16:47.680 | of how it can be used, just think of like,

02:16:48.920 | okay, we can use it to build a countermeasure.

02:16:50.960 | And the great thing about the countermeasures

02:16:52.440 | is they can not only offset AI risks,

02:16:54.200 | they can offset other risks, right?

02:16:55.720 | 'Cause we already live in a world

02:16:57.080 | where pathogens are a problem, right?

02:16:59.400 | We ought to have better vaccines anyway, right?

02:17:01.920 | We already live in a world

02:17:02.760 | where there's cyber hacking and cyber terrorism.

02:17:04.320 | They already live in a world

02:17:05.200 | where there is bad content on the internet.

02:17:06.560 | And we have the ability now to build much better

02:17:08.720 | AI-powered tools to deal with all those things.

02:17:11.080 | - I also love the idea of the AI physicians.

02:17:14.760 | You know, getting decent healthcare in this country

02:17:18.640 | is so difficult.

02:17:19.600 | Even for people who have means or insurance,

02:17:22.160 | I mean, the number of phone calls and waits

02:17:24.440 | that you have to go through to get a referral

02:17:26.800 | to see a specialist, I mean, it's absurd.

02:17:28.920 | Like, I mean, the process is absurd.

02:17:30.920 | I mean, it makes one partially or frankly ill

02:17:34.200 | just to go through the process of having to do all that.

02:17:36.560 | I don't know how anyone does it.

02:17:38.480 | And granted, I don't have the highest degree of patience,

02:17:41.600 | but I'm pretty patient and it drives me insane

02:17:45.360 | to even just get a remedial care.

02:17:47.400 | But so I can think of a lot of benevolent uses of AI

02:17:52.040 | and I'm grateful that you're bringing this up in here

02:17:54.800 | and that you've tweeted about it in that thread.

02:17:56.800 | Again, we'll refer people to that

02:17:58.440 | and that you're thinking about this.

02:17:59.680 | I have to imagine that in your role as investor nowadays

02:18:03.080 | that you're also thinking about AI quite often

02:18:06.120 | in terms of all these roles.

02:18:08.080 | And so does that mean that there are a lot of young people

02:18:11.640 | who are really bullish on AI and are going for it?

02:18:15.200 | - Yeah.

02:18:16.040 | - Okay, this is here to stay.

02:18:16.860 | - Yeah. - Okay.

02:18:17.700 | - Yeah, oh yeah, big time.

02:18:19.160 | - Unlike CRISPR, which is sort of in this liminal place

02:18:21.600 | where biotech companies aren't sure

02:18:22.880 | if they should invest or not in CRISPR

02:18:26.560 | because it's unclear whether or not

02:18:28.120 | the governing bodies are going to allow gene editing.

02:18:30.160 | - Right.

02:18:31.000 | - Just like it was unclear 15 years ago

02:18:32.600 | if they were going to allow gene therapy,

02:18:33.920 | but now we know they do allow gene therapy

02:18:35.960 | and immunotherapy.

02:18:36.800 | - Right, okay.

02:18:37.640 | So there is a fight.

02:18:38.480 | Now having said that, there is a fight.

02:18:39.320 | There's a fight happening in Washington right now

02:18:41.320 | over exactly what should be legal or not legal.

02:18:43.560 | And there's quite a bit of risk, I think,

02:18:45.080 | attached to that fight right now

02:18:46.200 | 'cause there are some people in there

02:18:47.260 | that are telling a very effective story

02:18:49.120 | to try to get people to either outlaw AI

02:18:50.640 | or specifically limit it to a small number of big companies,

02:18:53.760 | which I think is potentially disastrous.

02:18:57.140 | - By the way, the EU also is like super negative.

02:18:59.920 | The EU has turned super negative

02:19:01.040 | on basically all new technology.

02:19:02.120 | So they're moving to try to outlaw AI,

02:19:03.800 | which if they succeed-

02:19:05.180 | - Outlaw AI?

02:19:06.020 | - Yeah, they just like flat out don't want it.

02:19:07.720 | - But that's like saying you're going to outlaw the internet.

02:19:09.400 | I don't see how you can stop this train.

02:19:10.940 | - And frankly, they're not a big fan of the internet either.

02:19:12.620 | So like I think they regret,

02:19:14.600 | the EU has a very, especially the EU bureaucrats,

02:19:18.120 | the people who run the EU in Brussels

02:19:19.800 | have a very negative view on a lot of modernity.

02:19:24.240 | - But what I'm hearing here,

02:19:26.200 | what calls to mind things that I've heard people

02:19:27.740 | like David Goggins say,

02:19:28.760 | which is there's so many lazy undisciplined people out there

02:19:32.700 | that nowadays it's easier and easier to become exceptional.

02:19:35.300 | I've heard him say something to that extent.

02:19:36.620 | It almost sounds like there's so many countries

02:19:38.460 | that are just backing off of particular technologies

02:19:41.660 | because it just sounds bad from the PR perspective

02:19:44.840 | that it's creating great low-hanging fruit opportunities

02:19:50.000 | for people to barge forward and countries to barge forward

02:19:52.480 | if they're willing to embrace this stuff.

02:19:54.140 | - It is, but number one,

02:19:55.200 | you have to have a country that wants to do that

02:19:57.720 | and those exist and there are countries like that.

02:20:00.240 | But, and then the other is look,

02:20:01.080 | they need to be able to withstand the attack

02:20:03.300 | from stronger countries that don't want them to do it.

02:20:06.920 | So like EU, like the EU has nominal control

02:20:09.740 | over like whatever it is, 27 or whatever member countries.

02:20:12.820 | So like, even if you're like whatever,

02:20:14.360 | the Germans get all fired up about whatever,

02:20:15.900 | like Brussels can still, in a lot of cases,

02:20:17.340 | just like flat out basically control them

02:20:18.920 | and tell them not to do it.

02:20:19.760 | And then the US, I mean, look,

02:20:21.200 | we have a lot of control over a lot of the world.

02:20:24.360 | But it sounds like we sit somewhere sort of in between.

02:20:26.920 | Like right now, people are developing AI technologies

02:20:31.320 | in US companies, right?

02:20:32.640 | So it is happening.

02:20:33.640 | - Yeah, today it's happening.

02:20:34.840 | But like I said, there's a set of people

02:20:36.100 | who are very focused in Washington right now

02:20:38.120 | about trying to either ban it outright

02:20:40.360 | or trying to, as I said,

02:20:41.840 | limit it to a small number of big companies.

02:20:44.560 | And then look, China's got a whole,

02:20:45.920 | the other part of this is China's got a whole different

02:20:48.080 | kind of take on this, right, than we do.

02:20:49.480 | And so they're of course going to allow it for sure,

02:20:51.600 | but they're going to allow it in the ways

02:20:52.660 | that their system wants it to happen, right,

02:20:55.700 | which is much more for population control

02:20:58.140 | and to implement authoritarianism.

02:20:59.980 | And then of course, they are going to spread their technology

02:21:03.220 | and their vision of how society should run

02:21:05.060 | across the world, right?

02:21:06.480 | So we're back in a Cold War dynamic

02:21:08.380 | like we were with the Soviet Union

02:21:09.380 | where there are two different systems

02:21:10.500 | that have fundamentally different views

02:21:12.320 | on concepts like freedom and individual choices,

02:21:15.500 | freedom of speech and so on.

02:21:17.540 | And we know where the Chinese stand.

02:21:19.220 | We're still figuring out where we stand.

02:21:21.300 | There are a lot of, so I'm having a lot of schizophrenic,

02:21:23.260 | I'm having specifically a lot of schizophrenic conversations

02:21:25.340 | with people in DC right now where if I talk to them

02:21:27.820 | and China doesn't come up, they just like hate tech,

02:21:30.380 | they hate American tech companies, they hate AI,

02:21:32.660 | they hate social media, they hate this, they hate that,

02:21:34.460 | they hate crypto, they hate everything,

02:21:35.580 | and they just want to like punish and like ban

02:21:37.820 | and like they're just like very, very negative.

02:21:39.860 | But then if we have a conversation a half hour later

02:21:41.860 | when we talk about China,

02:21:43.020 | then the conversation totally different.

02:21:44.620 | Now we need a partnership between the US government

02:21:46.420 | and American tech companies to defeat China.

02:21:48.820 | It's like the exact opposite discussion.

02:21:51.380 | - Is that fear or competitiveness?

02:21:53.180 | - On China specifically?

02:21:54.680 | - In terms of the US response in Washington,

02:21:57.660 | when you bring up these technologies,

02:21:59.980 | like I'll lump CRISPR in there,

02:22:02.300 | things like CRISPR, nuclear power, AI,

02:22:04.340 | it all sounds very cold, very dystopian to a lot of people.

02:22:07.960 | And yet there are all these benevolent uses

02:22:10.940 | as we've been talking about.

02:22:12.540 | And then you say you raise the issue of China

02:22:14.740 | and then it sounds like this big dark cloud emerging

02:22:17.500 | and then all of a sudden we need to galvanize

02:22:20.700 | and develop these technologies to counter their effort.

02:22:23.820 | So is it fear of them or is it competitiveness or both?

02:22:28.140 | - Well, so without them in the picture,

02:22:30.580 | you just have this basically,

02:22:32.780 | there's an old Bedouin saying as me against my brother,

02:22:35.860 | me and my brother against my cousin,

02:22:37.660 | me and my brother and my cousin against the world, right?

02:22:40.220 | So it's actually it's evolution in action

02:22:43.340 | if you want to think about it.

02:22:44.180 | If there's no external threat,

02:22:45.940 | then the conflict turns inward.

02:22:47.500 | And then at that point,

02:22:48.740 | there's a big fight between specifically tech

02:22:51.360 | and then I was just say generally politics.

02:22:53.180 | And in my interpretation of that fight

02:22:54.980 | is it's a fight for status.

02:22:56.040 | It's fundamentally a fight for status and for power,

02:22:58.380 | which is like, if you're in politics,

02:22:59.740 | you like the status quo of how power

02:23:02.020 | and status work in our society.

02:23:03.740 | You don't want these new technologies

02:23:05.180 | to show up and change things 'cause change is bad, right?

02:23:08.420 | Change threatens your position.

02:23:09.620 | It threatens the respect that people have for you

02:23:11.620 | and your control over things.

02:23:13.540 | And so I think it's primarily a status fight,

02:23:15.380 | which we could talk about.

02:23:17.500 | But the China thing is just like a straight up geopolitical

02:23:20.180 | us versus them.

02:23:21.500 | Like I said, it's like a Cold War scenario.

02:23:23.080 | And look, 20 years ago, the prevailing view in Washington

02:23:26.140 | was we need to be friends with China, right?

02:23:27.780 | And we're gonna be trading partners with China.

02:23:29.300 | And yes, they're a totalitarian dictatorship,

02:23:31.220 | but like if we trade with them over time,

02:23:32.660 | they'll become more democratic.

02:23:34.200 | In the last five to 10 years,

02:23:35.820 | it's become more and more clear that that's just not true.

02:23:39.340 | And now there's a lot of people in both political parties

02:23:42.120 | in DC who very much regret that

02:23:43.900 | and want to change too much more

02:23:45.380 | of a sort of a Cold War footing.

02:23:47.160 | - Are you willing to comment on TikTok

02:23:49.420 | and technologies that emerged from China

02:23:51.540 | that are in widespread use within the US,

02:23:54.420 | like how much you trust them or don't trust them?

02:23:56.300 | I can go on record myself by saying that early on

02:23:59.980 | when TikTok was released,

02:24:01.600 | we were told as Stanford faculty that we should not

02:24:04.500 | and could not have TikTok accounts, nor WeChat accounts.

02:24:07.920 | - So let's start with,

02:24:10.660 | there are a lot of really bright Chinese tech entrepreneurs

02:24:13.020 | and engineers who are trying to do good things.

02:24:14.940 | I'm totally positive about that.

02:24:16.820 | So I think that many of the people mean very well,

02:24:20.100 | but the Chinese have a specific system

02:24:22.140 | and the system is very clear and unambiguous.

02:24:25.500 | And the system is everything in China is owned by the party.

02:24:28.140 | It's not even owned by the state,

02:24:29.600 | it's owned by the party,

02:24:30.440 | it's owned by the Chinese Communist Party.

02:24:31.260 | So the Chinese Communist Party owns everything

02:24:32.660 | and they control everything.

02:24:33.900 | By the way, it's actually illegal to this day,

02:24:35.520 | it's illegal for an investor to buy equity

02:24:37.700 | in a Chinese company.

02:24:39.060 | There's all these like basically legal machinations

02:24:41.600 | that people do to try to do something

02:24:42.920 | that's like economically equivalent to that,

02:24:44.300 | but it's actually still illegal to do that.

02:24:45.920 | The Chinese have no intention,

02:24:47.280 | the Chinese Communist Party has no intention

02:24:49.120 | of letting foreigners own any of China,

02:24:51.120 | like zero intention of that.

02:24:52.620 | And they regularly move to make sure

02:24:54.200 | that that doesn't happen.

02:24:55.380 | So they own everything, they control everything.

02:24:58.580 | - Sorry to interrupt you,

02:24:59.420 | but people in China can invest in American companies

02:25:02.060 | essentially all the time.

02:25:02.900 | - Well, they can subject to US government constraints.

02:25:05.360 | There is a US government system

02:25:07.460 | that attempts to mediate that called CFIUS.

02:25:10.300 | And there are more and more limitations

02:25:12.560 | being put on that.

02:25:13.400 | But if you can get through that approval process,

02:25:15.660 | then legally, you can do that.

02:25:17.100 | Whereas the same is not true with respect to China.

02:25:20.400 | And so they just have a system.

02:25:23.500 | And so if you're the CEO of a Chinese company,

02:25:25.920 | it's not optional.

02:25:27.300 | If you're the CEO of ByteDance or CEO of Tencent,

02:25:30.000 | your relationship with the Chinese Communist Party

02:25:32.380 | is not optional, it's required.

02:25:33.940 | And what's required is you are a unit of the party

02:25:36.740 | and you and your company do what the party says.

02:25:38.780 | And when the party says we get full access

02:25:40.580 | to all user data in America, you say yes.

02:25:43.180 | When the party says you change the algorithm

02:25:45.020 | to optimize to a certain social result, you say yes.

02:25:47.680 | So it's whatever Xi Jinping and his party cadres decide,

02:25:53.680 | and that's what gets implemented.

02:25:55.660 | If you're the CEO of a Chinese tech company,

02:25:57.220 | there is a political officer assigned to you

02:25:59.940 | who has an office down the hall.

02:26:01.760 | And at any given time, he can come down the hall,

02:26:03.880 | he can grab you out of your staff meeting or board meeting,

02:26:05.920 | and he can take you down the hall

02:26:06.760 | and he can make you sit for hours and study Marxism

02:26:09.380 | Xi Jinping thought and quiz you on it and test you on it,

02:26:12.220 | and you'd better pass the test.

02:26:13.900 | Right, so it's like a straight political control thing.

02:26:17.260 | And then by the way, if you get crosswise with them, like,

02:26:20.140 | you know.

02:26:20.980 | - So when we see tech founders getting called up to Congress

02:26:26.540 | for, you know, what looks like interrogation,

02:26:29.980 | but it's probably pretty light interrogation

02:26:32.040 | compared to what happens in other countries.

02:26:33.580 | - Yeah, it's state power.

02:26:35.700 | They just have this view of top-down state power

02:26:37.940 | and they view it's their system

02:26:39.140 | and they view that it's necessary

02:26:40.300 | for lots of historical and moral reasons that they've defined

02:26:42.440 | and that's how they run.

02:26:43.280 | And then they've got a view that says

02:26:44.280 | how they want to propagate that vision outside the country.

02:26:46.700 | And they have these programs like Belt and Road, right,

02:26:49.220 | that basically are intended to propagate

02:26:50.740 | kind of their vision worldwide.

02:26:52.940 | And so they are who they are.

02:26:55.580 | Like, I will say that they don't lie about it, right?

02:26:57.520 | They're very straightforward.

02:26:59.060 | They give speeches, they write books,

02:27:00.260 | you can buy Xi Jinping's speeches,

02:27:01.500 | he goes through the whole thing.

02:27:02.340 | They have their tech 2025 plan,

02:27:04.500 | you know, it's like 10 years ago,

02:27:05.740 | their whole AI agenda, it's all in there.

02:27:07.840 | - And is there a goal that, you know, in 200 years,

02:27:10.880 | 300 years that China is the superpower

02:27:12.940 | controlling everything?

02:27:14.780 | - Yeah, or 20 years, 30 years, or two years, three years,

02:27:17.160 | yeah.

02:27:18.000 | - Well, they got a shorter horizon.

02:27:18.820 | - I mean, look, they're, yeah, they're, they're, I mean,

02:27:20.220 | if you, if you're, you know,

02:27:21.580 | and I don't know, everybody's a little bit like this,

02:27:22.800 | I guess, but yeah, if you're, they want to win.

02:27:25.940 | - Well, the CRISPR in humans example that I gave earlier

02:27:28.920 | was interesting to me because first of all,

02:27:30.760 | I'm a neuroscientist and they could have edited any genes,

02:27:35.340 | but they chose to edit the genes involved

02:27:37.200 | in the attempt to create super memory babies,

02:27:41.020 | which presumably would grow into super memory adults,

02:27:43.660 | and whether or not they succeeded in that isn't clear.

02:27:48.140 | Those babies are alive and presumably by now walking,

02:27:52.380 | talking as far as I know,

02:27:53.780 | whether or not they have super memories isn't clear,

02:27:55.560 | but China is clearly unafraid to augment biology

02:28:00.560 | in that way.

02:28:03.300 | And I believe that that's inevitable,

02:28:08.100 | that it's going to happen elsewhere,

02:28:09.960 | probably first for the treatment of disease.

02:28:11.840 | But at some point I'm assuming people are going to augment

02:28:14.580 | biology to make smarter kids.

02:28:16.280 | I mean, people not always,

02:28:18.460 | but often will select mates based on the traits

02:28:21.200 | they would like their children to inherit.

02:28:23.400 | So this happens far more frequently than could be deemed

02:28:26.760 | bad because either that or people are bad because people do

02:28:29.680 | this all the time,

02:28:30.500 | selecting mates that have physical and psychological

02:28:33.360 | and cognitive traits that you would like your offspring

02:28:35.840 | to have.

02:28:36.720 | CRISPR is a more targeted approach of course.

02:28:38.920 | The reason I'm kind of giving this example

02:28:40.960 | and examples like it is that I feel like so much of the way

02:28:44.380 | that governments and the public react to technologies

02:28:49.200 | is to just take that first glimpse and it just feels scary.

02:28:53.280 | You think about the old Apple ad of the 1984 ad.

02:28:58.020 | I mean, there was one very scary version

02:29:00.000 | of the personal computer and computers

02:29:01.760 | and robots taking over and everyone like automatons.

02:29:04.480 | And then there was the Apple version

02:29:06.420 | where it's all about creativity, love and peace.

02:29:08.500 | And it had the pseudo psychedelic California thing

02:29:10.580 | going for it.

02:29:11.480 | Again, great marketing seems to convert people's thinking

02:29:17.340 | about technology such that what was once viewed

02:29:20.840 | as very scary and dangerous and dystopian

02:29:24.520 | is like an oasis of opportunity.

02:29:26.920 | So why are people so afraid of new technologies?

02:29:30.120 | - So this is the thing I've tried to understand

02:29:32.020 | for a long time because the history is so clear

02:29:34.920 | and the history basically is every new technology

02:29:37.520 | is greeted by what's called a moral panic.

02:29:39.440 | And so it's basically this like historical freak out

02:29:42.960 | of some kind that causes people to basically predict

02:29:44.820 | the end of the world.

02:29:45.660 | And you go back in time and actually this historical

02:29:48.400 | sort of effect, it happens even in things now

02:29:50.260 | where you just go back and it's ludicrous, right?

02:29:51.800 | And so you mentioned earlier the satanic panic of the 80s

02:29:55.000 | and the concern around like heavy metal music.

02:29:57.480 | Right, before that there was like a freak out

02:29:59.060 | around comic books in the 50s, there was a freak out

02:30:01.640 | around jazz music in the 20s and 30s, it's devil music.

02:30:05.920 | There was a freak out, the arrival of bicycles

02:30:07.780 | caused a moral panic in the like 1860s, 1870s.

02:30:10.000 | - Bicycles?

02:30:10.840 | - Bicycles, yeah, so there was this thing at the time.

02:30:12.640 | So bicycles were the first very easy to use

02:30:16.160 | personal transportation thing that basically let kids

02:30:18.280 | travel between towns quickly without any overhead.

02:30:22.480 | You have to take care of a horse, just jump on a bike and go.

02:30:25.880 | And so there was a historical panic specifically

02:30:28.200 | around at the time young women who for the first time

02:30:30.600 | were able to venture outside the confines of the town

02:30:33.600 | to maybe go have a boyfriend in another town.

02:30:36.160 | And so the magazines at the time ran all these stories

02:30:38.580 | on this phenomenon, medical phenomenon called bicycle face.

02:30:41.920 | And the idea of bicycle face was the exertion caused

02:30:44.400 | by pedaling a bicycle would cause your face,

02:30:46.480 | your face would grimace and then if you run the bicycle

02:30:48.660 | for too long your face would lock into place.

02:30:51.360 | - Right, and then you would be unattractive

02:30:55.080 | and therefore of course unable to then get married.

02:30:57.580 | Cars, there was a moral panic around red flag laws.

02:31:02.760 | There were all these laws that created the automobile.

02:31:04.580 | Automobile freaked people out, so there were all these laws.

02:31:07.280 | In the early days the automobile in a lot of places

02:31:09.760 | you had to, you would take a ride in an automobile.

02:31:13.160 | And automobiles, they broke down all the time.

02:31:14.520 | So it would be, only rich people had automobiles.

02:31:16.520 | It would be you and your mechanic in the car, right,

02:31:19.360 | for when it broke down.

02:31:20.560 | And then you had to hire another guy to walk 200 yards

02:31:24.160 | in front of the car with a red flag.

02:31:26.280 | And he had to wave the red flag,

02:31:28.000 | and so you could only drive as fast as he could walk

02:31:29.840 | 'cause the red flag was to warn people

02:31:31.320 | that the car was coming.

02:31:32.960 | And then in, I think it was Pennsylvania,

02:31:35.160 | they had the most draconian version which was,

02:31:37.860 | they were very worried about the car scaring the horses.

02:31:40.680 | And so there was a law that said if you saw a horse coming,

02:31:44.080 | you needed to stop the car, you had to disassemble the car.

02:31:46.760 | And you had to hide the pieces of the car

02:31:48.360 | behind the nearest hay bale, wait for the horse to go by.

02:31:51.740 | And then you could put your car back together.

02:31:54.480 | So anyways, this example is electric lighting.

02:31:56.840 | There was a panic around whether this was gonna completely

02:31:59.040 | ruin, this was gonna completely ruin the romance of the dark

02:32:01.680 | and it was gonna cause a whole new kind of terrible

02:32:04.360 | civilization where everything is always brightly lit.

02:32:06.600 | So there's just all these examples.

02:32:08.440 | And so it's like, okay, what on earth is happening

02:32:10.240 | that this is always what happens?

02:32:12.280 | And so I finally found this book

02:32:13.600 | that I think has a good model for it.

02:32:15.900 | The book is called Men, Machines in Modern Times,

02:32:17.760 | and it's written by this MIT professor 60 years ago.

02:32:20.280 | So it predates the internet,

02:32:22.080 | but it uses a lot of historical samples.

02:32:24.000 | And what he says basically is he says

02:32:26.360 | there's actually a three-stage response.

02:32:28.160 | There's a three-stage societal response to new technologies.

02:32:30.520 | It's very predictable.

02:32:31.480 | He said stage one is basically just denial, just ignore.

02:32:35.320 | Like we just don't pay any attention to this.

02:32:36.840 | Nobody takes it seriously.

02:32:37.720 | We just, there's just a blackout on the whole topic.

02:32:40.760 | He says, that's stage one.

02:32:42.320 | Stage two is rational counterargument.

02:32:45.140 | So stage two is where you line up

02:32:46.640 | with all the different reasons why this can't possibly work.

02:32:48.640 | It can't possibly ever get cheaper, this, that,

02:32:52.040 | not fast enough or whatever the thing is.

02:32:54.360 | And then he says stage three,

02:32:55.480 | he says is when the name calling begins.

02:32:58.240 | So he says stage three is like, right, right.

02:33:00.440 | So when they fail to ignore it

02:33:03.160 | and they fail to argue society out of it,

02:33:05.820 | they move to the name calling, right?

02:33:07.120 | And what's the name calling?

02:33:08.120 | The name calling is this is evil.

02:33:09.400 | This is moral panic.

02:33:10.240 | This is evil, this is terrible, this is awful.

02:33:11.840 | This is going to destroy everything.

02:33:13.080 | Like, don't you understand like all this,

02:33:15.280 | this is just like, this is horrifying.

02:33:17.120 | And you, you know, the person working on it

02:33:18.680 | are being reckless and evil and you know,

02:33:20.060 | all this stuff and you must be stopped.

02:33:22.120 | And he said the reason for that

02:33:23.560 | is because basically fundamentally what these things are

02:33:26.040 | is they're a war over status.

02:33:28.160 | It's a war over status and therefore a war over power

02:33:30.720 | and then of course ultimately money,

02:33:32.620 | but status, human status is the thing.

02:33:35.040 | And so, and 'cause what he says is

02:33:36.960 | what is the societal impact of a new technology?

02:33:39.440 | The societal impact of a new technology

02:33:41.360 | is it reorders status in the society.

02:33:43.520 | So the people who are specialists in that technology

02:33:45.840 | become high status and the people who are specialists

02:33:48.280 | in the previous way of doing things become low status

02:33:50.600 | and generally people don't adapt, right?

02:33:53.000 | Generally, if you're the kind of person who is high status

02:33:55.300 | because you're an evolved adaptation

02:33:57.720 | to an existing technology,

02:33:59.240 | you're probably not the kind of person

02:34:00.600 | that's going to enthusiastically try to replant yourself

02:34:03.040 | onto a new technology.

02:34:04.440 | And so this is like every politician

02:34:06.320 | who's just like in a complete state of panic

02:34:07.760 | about social media, like why are they so freaked out

02:34:09.360 | about social media is 'cause they all know

02:34:11.080 | that the whole nature of modern politics has changed.

02:34:13.120 | The entire battery of techniques that you use

02:34:14.880 | to get elected before social media are now obsolete.

02:34:17.680 | Obviously the best new politicians of the future

02:34:19.780 | are going to be 100% creations of social media.

02:34:21.920 | - And podcasts. - And podcasts.

02:34:23.440 | - And we're seeing this now as we head towards

02:34:25.340 | the next presidential election,

02:34:26.560 | that podcasts clearly are going to be featured very heavily

02:34:30.360 | in that next election because long form content

02:34:32.740 | is a whole different landscape.

02:34:34.840 | - So this is exactly, so this is so far,

02:34:37.080 | so Rogan, Rogan's had like what?

02:34:38.340 | Like he's had like Bernie, he's had like Tulsi,

02:34:40.240 | he's had like a whole series of these.

02:34:41.080 | - RFK most recently and that's created a lot of controversy.

02:34:44.440 | - But also my understanding is like,

02:34:46.360 | I'm sure he's invited everybody.

02:34:47.420 | I'm sure he'd love to have Biden on.

02:34:48.980 | I'm sure he'd love to have Trump on.

02:34:49.980 | I'm sure he'd love to. - You'd have to ask him.

02:34:51.160 | I mean, I think that, you know,

02:34:53.280 | every podcaster has their own ethos

02:34:55.540 | around who they invite on and why and how.

02:34:58.720 | So I certainly can't speak for him,

02:35:00.440 | but I have to imagine that any opportunity

02:35:04.200 | to have true long form discourse that would allow people

02:35:08.080 | to really understand people's positions on things,

02:35:11.000 | I have to imagine that he would be in favor

02:35:12.920 | of that sort of thing. - Or somebody else.

02:35:14.120 | Yeah, or somebody else would, right?

02:35:15.200 | You know, some other podcaster undoubtedly would, right?

02:35:17.880 | And so there's, my point, exactly, I totally agree with you,

02:35:21.000 | but my point is if you're a politician,

02:35:22.560 | if you're, let's say a legacy politician, right,

02:35:25.440 | you have the option of embracing the new technology.

02:35:27.520 | You can do it anytime you want, right?

02:35:29.820 | But you don't, they're not, they won't.

02:35:32.520 | Like they won't do it and why won't they do it?

02:35:34.720 | Well, okay, first of all, they want to ignore it, right?

02:35:36.600 | They want to pretend that things aren't changing.

02:35:38.280 | You know, second is they want like,

02:35:39.600 | they have rational counterarguments for like

02:35:41.080 | why the existing campaign system works the way that it does

02:35:43.120 | and this and that and the existing media networks

02:35:44.840 | and like here's how you like do things

02:35:46.200 | and here's how you give speeches

02:35:47.180 | and here's the clothes you wear and the tie

02:35:48.560 | and the thing in the pocket square

02:35:49.620 | and like you've got your whole system.

02:35:51.200 | It's how you succeeded was coming up through that system.

02:35:53.000 | So you've got all your arguments

02:35:53.880 | as to why that won't work anymore.

02:35:55.400 | And then we've now proceeded to the name calling phase,

02:35:58.600 | which is now it's evil, right?

02:35:59.900 | Now it's evil for somebody to show up in, you know,

02:36:02.400 | on a stream, God forbid, for three hours

02:36:04.960 | and actually say what they think, right?

02:36:06.640 | It's gonna destroy society, right?

02:36:07.880 | So it's exactly right.

02:36:09.080 | It's like, it's a classic example of this pattern.

02:36:11.840 | And anyway, so Morrison says in the book,

02:36:14.360 | basically this is the forever pattern.

02:36:16.160 | Like this will never change.

02:36:17.960 | This is one of those things where you can learn about it

02:36:20.480 | and still not, the entire world could learn about this

02:36:22.400 | and still nothing changes because at the end of the day,

02:36:24.920 | it's not the tech that's the question.

02:36:27.200 | It's the reordering of status.

02:36:28.960 | - I have a lot of thoughts about the podcast component.

02:36:33.200 | I'll just say this because I wanna get back to

02:36:36.160 | the topic of innovation of technology,

02:36:39.520 | but on a long form podcast, there's no safe zone.

02:36:44.280 | The person can get up and walk out,

02:36:47.100 | but if the person interviewing them,

02:36:49.180 | and certainly Joe is the best of the very best,

02:36:52.580 | if not the most skilled podcaster in the entire universe

02:36:56.920 | at continuing to press people on specific topics

02:37:00.180 | when they're trying to bob and weave and wriggle out,

02:37:03.360 | he'll just keep either drilling or alter the question

02:37:07.360 | somewhat in a way that forces them to finally come up

02:37:10.500 | with an answer of some sort.

02:37:12.040 | And I think that probably puts certain people's cortisol

02:37:16.160 | levels through the roof,

02:37:18.440 | such that they just would never go on there.

02:37:20.320 | - Well, I think there's another deeper question also,

02:37:21.780 | or another question along with that,

02:37:23.000 | which is how many people actually have something to say?

02:37:25.480 | - Real substance.

02:37:28.840 | - Like how many people can actually talk in a way

02:37:30.840 | that's actually interesting to anybody else

02:37:32.300 | for any length of time?

02:37:34.140 | Like how much substance is there really?

02:37:35.480 | And like a lot of historical politics was to be able

02:37:38.160 | to manufacture a facade where you honestly,

02:37:40.160 | as far as like, you can't tell like how deep

02:37:43.040 | the thoughts are, like even if they have deep thoughts,

02:37:44.640 | like it's kept away from you,

02:37:45.960 | they would certainly never cop to it.

02:37:47.800 | - Well, it's going to be an interesting next,

02:37:49.560 | what is it about, you know, 20 months or so?

02:37:52.260 | - Yeah, yeah, yeah.

02:37:53.100 | - Leading into the next election.

02:37:54.000 | - The panic and the name calling have already started,

02:37:55.400 | so yeah, it's going to be.

02:37:56.240 | - Yeah, I was going to say this list of three things,

02:37:57.980 | denial, you know, the counterargument and name calling,

02:38:02.200 | it seems like with AI, it's already just jumped

02:38:04.880 | to numbers two and three.

02:38:06.520 | - Yes, correct.

02:38:07.360 | - And we're already at two and three

02:38:08.240 | and it's kind of leaning three.

02:38:09.680 | - Yes.

02:38:10.520 | - Yeah.

02:38:11.340 | - Yeah, that's correct.

02:38:12.180 | AI is unusual just because it had,

02:38:14.720 | so new technologies that take off,

02:38:16.120 | they almost always have a prehistory,

02:38:17.460 | they almost always have a 30 or 40 year history

02:38:19.200 | where people tried and failed to get them to work

02:38:20.800 | before they took off.

02:38:21.640 | AI has an 80 year prehistory, so it has a very long one.

02:38:24.640 | And then it just, it all of a sudden started to work

02:38:27.520 | dramatically well, like seemingly overnight.

02:38:30.980 | And so it went from basically,

02:38:32.800 | as far as most people were concerned,

02:38:34.080 | it went from it doesn't work at all

02:38:35.340 | to it works incredibly well in one step.

02:38:37.120 | And that almost never happens.

02:38:38.660 | And so I actually think that's exactly what's happening.

02:38:40.560 | I think it's actually speed running this progression

02:38:42.240 | just because if you use mid journey or you use GPT

02:38:45.400 | or any of these things for five minutes,

02:38:46.800 | you're just like, wow, like obviously this thing

02:38:48.900 | is going to be like, obviously in my life,

02:38:50.720 | this is going to be the best thing ever.

02:38:51.800 | Like this is amazing, there's all these ways

02:38:53.200 | that I can use it and then therefore immediately

02:38:55.760 | you're like, oh my God, this is going to transform

02:38:57.360 | everything, therefore step three.

02:39:00.760 | - Right, straight to the name calling.

02:39:02.660 | In the face of all this, there are innovators out there.

02:39:07.780 | Maybe they are aware they are innovators,

02:39:09.780 | maybe they are already starting companies,

02:39:12.620 | or maybe they are just some young or older person

02:39:16.620 | who has these five traits in abundance or doesn't,

02:39:21.080 | but knows somebody who does and is partnering with them

02:39:23.740 | in some sort of idea.

02:39:24.920 | And you have an amazing track record

02:39:28.980 | at identifying these people, I think in part,

02:39:31.580 | because you have those same traits yourself.

02:39:34.220 | I've heard you say the following,

02:39:37.580 | the world is a very malleable place.

02:39:39.840 | If you know what you want and you go for it

02:39:42.040 | with maximum energy and drive and passion,

02:39:44.480 | the world will often reconfigure itself around you

02:39:47.060 | much more quickly and easily than you would think.

02:39:49.980 | That's a remarkable quote because it says

02:39:53.280 | at least two things to me.

02:39:55.320 | One is that you have a very clear understanding

02:39:58.560 | of the inner workings of these great innovators.

02:40:02.280 | We talked a little bit about that earlier,

02:40:04.700 | these five traits, et cetera,

02:40:06.500 | but that also you have an intense understanding

02:40:09.900 | of the world landscape.

02:40:11.380 | And the way that we've been talking about it

02:40:12.840 | for the last hour or so is that it is a really intense

02:40:16.000 | and kind of oppressive landscape.

02:40:17.780 | You've got countries and organizations and the elites

02:40:20.420 | and journalists that are trying to, not necessarily trying,

02:40:25.320 | but are suppressing the innovation process.

02:40:28.540 | I mean, that's sort of the picture that I'm getting.

02:40:30.220 | So it's like we're trying to innovate inside of a vice

02:40:33.580 | that's getting progressively tighter.

02:40:35.460 | And yet this quote argues that it is the person,

02:40:40.140 | the boy or girl, man or woman who says,

02:40:43.900 | well, you know what, that all might be true,

02:40:46.860 | but my view of the world is the way the world's gonna bend.

02:40:50.420 | Or I'm gonna create a dent in that vice

02:40:52.860 | that allows me to exist the way that I want.

02:40:55.120 | Or you know what?

02:40:55.960 | I'm actually gonna uncurl the vice the other direction.

02:40:58.740 | And so I'm at once picking up a sort of pessimistic

02:41:03.380 | glass half empty view of the world,

02:41:06.520 | as well as a glass half full view.

02:41:08.940 | And so tell me about that.

02:41:10.980 | And if you would, could you tell us about that

02:41:13.820 | from the perspective of someone listening who is thinking,

02:41:16.420 | you know, I've got an idea

02:41:18.860 | and I know it's a really good one 'cause I just know.

02:41:22.780 | I might not have the confidence of extrinsic reward yet,

02:41:25.840 | but I just know there's a seed of something.

02:41:28.880 | What does it take to foster that?

02:41:30.840 | And how do we foster real innovation

02:41:34.080 | in the landscape that we're talking about?

02:41:36.200 | - Yeah, so part is I think you just,

02:41:38.160 | one of the ways to square it is I think you as the innovator

02:41:40.260 | need to be signed up to fight the fight, right?

02:41:42.640 | So, and again, this is where like the fictional portrayals

02:41:44.920 | of startups, I think take people off course,

02:41:46.500 | or even scientists or whatever,

02:41:47.560 | 'cause when there's great success stories,

02:41:49.600 | they get kind of prettified after the fact

02:41:52.620 | and they may get made to be like cute and fun.

02:41:54.800 | And it's like, yeah, no, like if you talk to anybody

02:41:56.660 | who actually did any of these things, like, no,

02:41:58.520 | it was just like these things are always just like

02:42:00.420 | brutal exercises and just like sheer willpower

02:42:02.980 | and fighting, you know, fighting forces

02:42:04.520 | that are trying to get you.

02:42:05.360 | So part of it is you just,

02:42:07.600 | you have to be signed up for the fight.

02:42:08.640 | And this kind of goes to the conscientiousness thing

02:42:10.700 | we're talking about.

02:42:12.080 | We also, my partner Ben uses the term courage a lot, right?

02:42:15.160 | Which is some combination of like just stubbornness,

02:42:17.520 | but coupled with like a willingness to take pain

02:42:20.560 | and not stop.

02:42:22.560 | And, you know, have people think very bad things of you

02:42:24.440 | for a long time until it turns out, you know,

02:42:26.280 | you hopefully prove yourself, prove yourself correct.

02:42:28.640 | And so you have to be willing to do that.

02:42:30.000 | Like it's a context sport, like these aren't easy roads,

02:42:33.880 | right, it's a context sport.

02:42:35.000 | So you have to be signed up for the fight.

02:42:37.400 | The advantage that you have as an innovator

02:42:40.760 | is at the end of the day, the truth actually matters.

02:42:44.840 | And all the arguments in the world,

02:42:47.160 | classic Victor Hugo quote is there's nothing more powerful

02:42:49.480 | in the world than an idea whose time has come, right?

02:42:51.560 | Like if it's real, right, and this is just pure substance,

02:42:56.400 | if the thing is real, if the idea is real,

02:42:58.960 | like if it's a legitimately good scientific discovery,

02:43:02.200 | you know, about how the nature works,

02:43:03.500 | if it's a new invention, if it's a new work of art,

02:43:06.720 | and if it's real, you know, then you do,

02:43:09.800 | at the end of the day, you have that on your side.

02:43:11.960 | And all of the people who are fighting you

02:43:14.000 | and arguing with you and telling you no,

02:43:15.520 | they don't have that on their side, right?

02:43:17.160 | It's not like they're showing up with some other thing

02:43:19.980 | and they're like, my thing is better than your thing.

02:43:21.640 | Like, that's not the main problem, right?

02:43:23.420 | The main problem is like, I have a thing, I'm convinced,

02:43:26.300 | everybody else is telling me it's stupid, wrong,

02:43:28.200 | it should be illegal, whatever the thing is.

02:43:30.020 | But at the end of the day, I still have the thing, right?

02:43:32.800 | And so at the end of the day, like, yeah,

02:43:35.120 | the truth really matters, the substance really matters

02:43:37.120 | if it's real, I'll give you an example.

02:43:39.240 | It's really hard historically to find an example

02:43:41.800 | of a new technology that came into the world

02:43:44.200 | that was then pulled back.

02:43:46.120 | And you know, nuclear is maybe an example of that,

02:43:49.360 | but even still, there are still, you know, nuclear,

02:43:51.000 | there are still nuclear plants like running today.

02:43:53.200 | You know, that still exists.

02:43:54.960 | You know, I would say the same thing as scientific,

02:43:57.400 | like, at least I may do this, I don't know,

02:43:59.000 | I don't know of any scientific discovery that was made

02:44:01.000 | and then people like, I know there are areas of science

02:44:04.340 | that are not politically correct to talk about today,

02:44:06.960 | but every scientist knows the truth, right?

02:44:09.600 | Like, the truth is still the truth.

02:44:11.520 | I mean, even the geneticists in the Soviet Union

02:44:13.200 | they were forced to buy into less egoism,

02:44:14.640 | like knew the whole time that it was wrong.

02:44:15.880 | Like that I'm completely convinced of.

02:44:18.120 | - Yeah, they couldn't delude themselves,

02:44:19.520 | especially because the basic training

02:44:21.560 | that one gets in any field establishes some core truths

02:44:24.040 | upon which even the crazy ideas have to rest.

02:44:26.520 | And if they don't, as you pointed out,

02:44:29.120 | things fall to pieces.

02:44:29.960 | I would say that even the technologies that did not pan out

02:44:33.880 | and in some cases were disastrous,

02:44:35.960 | but that were great ideas at the beginning

02:44:40.960 | are starting to pan out.

02:44:42.200 | So the example I'll give is that most people are aware

02:44:44.480 | of the Elizabeth Holmes Theranos debacle,

02:44:48.560 | to put it lightly.

02:44:49.600 | You know, analyzing what's in a single drop of blood

02:44:53.060 | as a way to analyze hormones and diseases

02:44:55.200 | and antibodies, et cetera.

02:44:56.800 | I mean, that's a great idea.

02:44:58.840 | I mean, it's a terrific idea

02:45:00.600 | as opposed to having a phlebotomist come to your house

02:45:02.560 | or you have to go in and you get tapped with, you know,

02:45:04.560 | and then pulling vials and the whole thing.

02:45:07.160 | There's now a company born out of Stanford

02:45:10.280 | that is doing exactly what she sought to do,

02:45:13.920 | except that at least the courts ruled

02:45:15.760 | that she fudged the thing

02:45:17.560 | and that's why she's in jail right now.

02:45:19.980 | But the idea of getting a wide array of markers

02:45:24.980 | from a single drop of blood

02:45:26.160 | is an absolutely spectacular idea.

02:45:28.160 | The biggest challenge that company is going to confront

02:45:30.320 | is the idea that it's just the next Theranos.

02:45:32.160 | But if they've got the thing and they're not fudging it

02:45:35.200 | as apparently Theranos was,

02:45:38.280 | I think everything will work out a la Victor Hugo.

02:45:42.920 | - Yeah, exactly. - Yeah.

02:45:44.240 | - 'Cause who wants to go back?

02:45:45.080 | Like, if they get to the work, if it's real,

02:45:48.980 | it's going to be, like, this is the thing.

02:45:50.920 | The opponents, the opponents,

02:45:53.120 | they're not bringing their own ideas.

02:45:54.460 | [laughs]

02:45:55.480 | Like, they're not bringing their,

02:45:56.600 | oh, my idea's better than yours.

02:45:57.640 | Like, that's not what's happening.

02:45:58.900 | They're bringing the silence or counterargument, right,

02:46:03.400 | or name calling, right?

02:46:04.840 | - Well, this is why I think people who need to be loved

02:46:08.420 | probably stand a reduced chance of success.

02:46:12.160 | And maybe that's also why having people close to you

02:46:15.000 | that do love you and allowing that to be sufficient

02:46:17.920 | can be very beneficial.

02:46:18.880 | This gets back to the idea of partnership and family

02:46:21.400 | around innovators, because if you feel filled up

02:46:25.140 | by those people local to you, you know, in your home,

02:46:28.680 | then you don't need people on the internet

02:46:30.720 | saying nice things about you or your ideas,

02:46:32.740 | because you're good and you can forge forward.

02:46:35.520 | Another question about innovation

02:46:38.260 | is the teams that you assemble around you.

02:46:40.260 | And you've talked before about

02:46:42.240 | the sort of small squadron model,

02:46:44.780 | sort of David and Goliath examples as well,

02:46:47.700 | where, you know, a small group of individuals

02:46:49.900 | can create a technology that, frankly,

02:46:53.100 | outdoes what a giant like Facebook might be doing

02:46:56.800 | or what any other large company might be doing.

02:47:00.940 | There are a lot of theories as to why that would happen,

02:47:03.200 | but I know you have some unique theories.

02:47:05.480 | Why do you think small groups

02:47:08.980 | can defeat large organizations?

02:47:11.280 | - So the conventional explanation is, I think, correct.

02:47:13.840 | And it's just that large organizations

02:47:16.300 | have a lot of advantages,

02:47:17.180 | but they just have a very hard time

02:47:18.920 | actually executing anything because of all the overhead.

02:47:23.440 | So large organizations have

02:47:25.360 | combinatorial communication overhead, right?

02:47:27.160 | The number of people who have to be consulted,

02:47:28.860 | who have to agree on things gets to be staggering.

02:47:31.140 | The amount of time it takes to schedule the meeting

02:47:33.260 | gets to be staggering.

02:47:34.700 | You know, you get these really big companies

02:47:36.140 | and they have some issue they're dealing with.

02:47:37.500 | And it takes like a month to schedule the pre-meeting

02:47:39.460 | to like plan for the meeting,

02:47:41.060 | which is going to happen two months later,

02:47:42.340 | which is then going to result in a post-meeting,

02:47:44.060 | which will then result in a board presentation,

02:47:46.120 | which will then result in a planning offsite, right?

02:47:48.860 | - I thought academia was bad,

02:47:50.000 | but what you're describing is giving me hives.

02:47:51.780 | - Kafka was a documentary.

02:47:53.660 | Yeah, like this is, yeah.

02:47:55.200 | So it's just like these are,

02:47:56.960 | I mean, look, you'd have these organizations.

02:47:58.180 | At a hundred thousand people or more,

02:47:59.360 | like you're more of a nation state than a company.

02:48:03.220 | And you've got all these competing internal,

02:48:05.420 | you know, it's the Bedouin thing I was saying

02:48:06.620 | before you've got all these internal,

02:48:07.580 | like at most big companies,

02:48:08.980 | your internal enemies are like way more dangerous to you

02:48:10.820 | than anybody on the outside.

02:48:12.780 | - Can you elaborate on that?

02:48:13.620 | - Oh yeah, yeah, yeah.

02:48:14.460 | You're a big, at a big company,

02:48:16.060 | the big competition is for the next promotion, right?

02:48:18.500 | And the enemy for the next promotion

02:48:20.200 | is the next executive over in your company.

02:48:22.740 | Like that's your enemy.

02:48:24.380 | The competitor on the outside is like an abstraction.

02:48:27.100 | Like maybe they'll matter someday, whatever.

02:48:28.780 | I got to beat that guy inside my own company, right?

02:48:31.460 | And so the internal warfare is at least as intense

02:48:34.060 | as the external warfare.

02:48:35.500 | And so, yeah, so it's just, I mean, this is just all the,

02:48:38.020 | you know, it's the iron law of all these big bureaucracies

02:48:40.300 | and how they function.

02:48:41.140 | So if a big bureaucracy ever does anything productive,

02:48:43.480 | I think it's like a miracle.

02:48:44.400 | Like it's like a miracle to the point

02:48:45.640 | where there should be like a celebration.

02:48:48.160 | There should be parties,

02:48:49.000 | there should be like ticker tape parades

02:48:49.980 | for like big, large organizations that actually do things.

02:48:52.100 | Like that's great because it's like, it's so rare.

02:48:55.520 | It doesn't happen very often.

02:48:56.420 | So anyway, so that's the conventional explanation.

02:48:59.220 | Whereas look, small companies, small teams,

02:49:01.460 | you know, there's a lot that they can't do

02:49:02.740 | 'cause they can't, you know,

02:49:03.580 | they're not operating at scale.

02:49:04.420 | They don't have global coverage and all these kinds of,

02:49:06.620 | you know, they don't have the resources and so forth,

02:49:08.220 | but at least they can move quickly, right?

02:49:10.700 | They can organize fast.

02:49:12.140 | They can have, you know, if there's an issue today,

02:49:13.720 | they can have a meeting today.

02:49:14.700 | They can solve the issue today, right?

02:49:16.380 | And everybody they need to solve the issue

02:49:17.660 | is in the room today.

02:49:19.020 | And so they can just move a lot faster.

02:49:21.220 | I think that's part of it,

02:49:22.060 | but I think there's another deeper thing underneath that

02:49:24.160 | that people really don't like to talk about

02:49:25.780 | that takes us back full circle to where we started,

02:49:27.580 | which is just the sheer number of people in the world

02:49:29.980 | who are capable of doing new things

02:49:31.100 | is just a very small set of people.

02:49:33.320 | And so you're not gonna have a hundred of them in a company

02:49:36.560 | or a thousand or 10,000.

02:49:37.860 | You're gonna have three, eight or 10, maybe.

02:49:42.780 | - And some of them are flying too close to the sun.

02:49:44.660 | - Some of them are blowing themselves up, right?

02:49:46.560 | Some of them are.

02:49:47.540 | So IBM, I actually first learned this,

02:49:49.420 | my first actual job job was at IBM when it was,

02:49:51.420 | and when IBM was still on top of the world

02:49:53.420 | right before it caved in in the early '90s.

02:49:55.140 | And so when I was there, it was 440,000 employees,

02:49:58.660 | which, and again, if you inflation adjust,

02:50:00.540 | like today for that same size of business,

02:50:02.480 | inflation adjusted, market size adjusted,

02:50:04.140 | it would be, it's equivalent today

02:50:05.340 | of like a two or three million person organization.

02:50:06.940 | It was like a, it was a nation state.

02:50:09.500 | There were 6,000 people in my division.

02:50:11.140 | You know, we were next door to another building

02:50:12.700 | that had another 6,000 people in another division.

02:50:14.460 | So you just, you could work there for years

02:50:16.280 | and never meet anybody who didn't work for IBM.

02:50:18.380 | The first half of every meeting was just IBM

02:50:20.420 | or just introducing themselves to each other.

02:50:22.160 | Like, it was just mind boggling, the level of complexity.

02:50:25.240 | But they were so powerful that they had,

02:50:28.660 | four years before I got there in 1985,

02:50:31.360 | they were 80% of the market capitalization

02:50:33.240 | of the entire tech industry.

02:50:34.840 | Right, so they were at a level of dominance

02:50:36.640 | that even, you know, Google or Apple today

02:50:38.300 | is not even close to, right, at the time.

02:50:40.640 | So that's how powerful they were.

02:50:41.860 | And so they had a system, and it worked really well

02:50:44.300 | for like 50 years, they had a system which was,

02:50:46.740 | most of the employees in the company were expected

02:50:48.560 | to basically rigid, follow rules.

02:50:50.800 | So they dressed the same, they acted the same,

02:50:52.240 | they did everything out of the playbook.

02:50:53.520 | You know, they were trained very specifically.

02:50:56.040 | But they had this category of people they called wild ducks.

02:50:58.960 | And this was an idea that the founder, Thomas Watson,

02:51:01.240 | came up with, wild ducks.

02:51:02.880 | And the wild ducks were, they often had the formal title

02:51:05.840 | of an IBM fellow, and they were the people

02:51:08.220 | who could make new things.

02:51:09.920 | And there were eight of them.

02:51:12.160 | And they got to break all the rules.

02:51:14.280 | And they got to invent new products.

02:51:16.040 | They got to go off and work on something new.

02:51:17.560 | They didn't have to report back.

02:51:19.040 | They got to pull people off of other projects

02:51:20.780 | to work with them.

02:51:22.600 | They got budget when they needed it.

02:51:24.260 | They reported directly to the CEO.

02:51:26.040 | They got whenever they needed.

02:51:26.960 | He supported them in doing it.

02:51:28.220 | And they were glassbreakers.

02:51:29.420 | And they showed-- the one in Austin at the time

02:51:32.020 | was this guy Andy Heller, and he would show up in jeans

02:51:34.200 | and cowboy boots.

02:51:35.840 | And you know, went amongst an ocean of men in blue suits,

02:51:38.840 | white shirts, red ties, and put his cowboy boots up

02:51:41.880 | on the table.

02:51:42.880 | And it was fine for Andy Heller to do that,

02:51:44.760 | and it was not fine for you to do that, right?

02:51:46.680 | And so they very specifically identified.

02:51:48.440 | We have almost like an aristocratic class

02:51:52.140 | within our company that gets to play by different rules.

02:51:55.060 | Now, the expectation is they deliver, right?

02:51:57.460 | Their job is to invent the next breakthrough product.

02:51:59.620 | But we, IBM management, know that the 6,000 person division

02:52:03.380 | is not going to invent the next product.

02:52:04.980 | We know it's going to be a crazy Andy Heller and his cowboy

02:52:07.460 | boots.

02:52:08.380 | And so I was always very impressed.

02:52:09.860 | And again, ultimately, IBM had its issues.

02:52:12.460 | But that model worked for 50 years, right?

02:52:14.980 | Worked incredibly well.

02:52:15.900 | And I think that's basically the model that works.

02:52:19.760 | But it's a paradox, right?

02:52:20.920 | Which is like, how do you have a large, bureaucratic, regimented

02:52:24.000 | organization, whether it's academia or government

02:52:26.460 | or business or anything, that has all these rule

02:52:28.880 | followers in it and all these people who

02:52:30.280 | are jealous of their status and don't want things to change,

02:52:33.080 | but then still have that spark of creativity?

02:52:37.620 | I would say mostly it's impossible.

02:52:39.800 | Mostly it just doesn't happen.

02:52:41.480 | Those people get driven out, right?

02:52:42.920 | And in tech, what happens is those people

02:52:44.640 | get driven out because we will fund them.

02:52:46.780 | These are the people we fund.

02:52:47.940 | I was going to say, I gather that you

02:52:49.920 | are in the business of finding and funding the wild dogs.

02:52:52.620 | The wild dogs, that's exactly right.

02:52:54.160 | And actually, this is actually going to close the loop.

02:52:56.400 | This is actually, I think, the simplest explanation

02:52:58.120 | for why IBM ultimately caved in and then HP sort of in the '80s

02:53:01.260 | also came--

02:53:02.140 | IBM and HP kind of were these incredible monolithic,

02:53:05.580 | incredible companies for 40 or 50 years.

02:53:07.760 | And then they kind of both caved in in the '80s and '90s.

02:53:10.140 | And I actually think it was the emergence of venture capital.

02:53:12.760 | It was the emergence of a parallel funding system

02:53:14.840 | where the wild dogs, or in HP's case,

02:53:16.900 | their superstar technical people could actually leave and start

02:53:19.440 | their own companies.

02:53:20.680 | And again, it goes back to the university discussion

02:53:22.480 | we're having.

02:53:23.040 | It's like, this is what doesn't exist at the university level.

02:53:25.600 | This certainly does not exist at the government level.

02:53:27.940 | And until recently in media, it didn't

02:53:29.820 | exist until there's this thing that we call podcasts.

02:53:32.480 | Exactly, right, exactly, right.

02:53:34.240 | Which clearly have picked up some momentum.

02:53:36.880 | And I would hope that these other wild duck

02:53:39.440 | models will move quickly.

02:53:42.320 | But the one thing you know, right, and you know this.

02:53:44.360 | The one thing you know is the people on the other side

02:53:45.880 | are going to be mad as hell.

02:53:47.320 | Yeah, they're going to-- well, I think they're past denial.

02:53:51.560 | The counterarguments continue.

02:53:53.780 | The name calling is prolific.

02:53:55.200 | The name calling is fully underway, yes.

02:53:58.600 | Well, Mark, we've covered a lot of topics.

02:54:01.920 | But as with every time I talk to you, I learn oh so very much.

02:54:07.360 | So I'm so grateful for you taking the time out

02:54:09.320 | of your schedule to talk about all of these topics in depth

02:54:13.040 | with us.

02:54:14.280 | I'd be remiss if I didn't say that it is clear to me

02:54:16.480 | now that you are hyper-realistic about the landscape.

02:54:21.800 | But you are also intensely optimistic about the existence

02:54:25.160 | of wild ducks and those around them that support them

02:54:28.360 | that are necessary for the implementation of their ideas

02:54:30.680 | at some point.

02:54:32.120 | And that also you have a real rebel inside you.

02:54:35.360 | So that is oh so welcome on this podcast.

02:54:38.640 | And it's oh so needed in these times and every time.

02:54:42.440 | So on behalf of myself and the rest of us

02:54:45.640 | here at the podcast and especially the listeners,

02:54:47.720 | thank you so much.

02:54:49.080 | Thanks for having me.

02:54:50.400 | Thank you for joining me for today's discussion

02:54:52.400 | with Mark Andreessen.

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Marc Andreessen: How Risk Taking, Innovation & Artificial Intelligence Transform Human Experience

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