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Speaker 1: Because if you had a government that had its act together,

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Speaker 1: they would already be distributing an AI dividend the easiest

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Speaker 1: people to fire.

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Speaker 2: Other people you haven't hired yet.

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Speaker 1: And our data is getting sold in resold for hundreds

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Speaker 1: of billions of dollars a year and no one's seeing

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Speaker 1: it dive.

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Speaker 3: This is Gavin Newsom.

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Speaker 1: And this is Andrew Yang.

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Speaker 3: So much to talk about, obviously, all things AI. What's

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Speaker 3: going on with your new party, Forward Party, your new

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Speaker 3: cell phone company, what's going on in the world we're

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Speaker 3: living twenty four to seven with autopsies and the Democratic

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Speaker 3: Party and you know, all kinds of you know, press

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Speaker 3: conferences being canceled, including one today on AI from Donald

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Speaker 3: Trump and New EO there was about to provide. I

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Speaker 3: want to get to all of that, but first I

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Speaker 3: just want to check in on what is going on

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Speaker 3: with and you've got the lapel right now with the

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Speaker 3: Forward Party where to begin.

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Speaker 1: A Forward Party is a positive independent political movement that

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Speaker 1: things the two party system is going to lead us

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Speaker 1: nowhere good and unfortunately we're doing great in the sense

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Speaker 1: that people are increasingly despondent about the direction of the

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Speaker 1: two party system. So supporting positive candidates around the country

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Speaker 1: of any party with a special spot in our hearts

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Speaker 1: for independent candidates like Seth Bodnar who's running for US

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Speaker 1: Senate in Montana, Mike Dugan who's running for governor or Michigan.

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Speaker 2: And other candidates around the country.

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Speaker 1: It being an independent is the fastest growing clinical affiliation

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Speaker 1: in the US for good reason, and so Forward party

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Speaker 1: is growing all the time.

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Speaker 3: Well, let's talk about your primary when you ran, and

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Speaker 3: your primary focus that continues to this day. And I

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Speaker 3: appreciate your tenacity on it and have a little bit

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Speaker 3: of history myself with UBI, this notion of universal basic income,

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Speaker 3: and we can get to all things AI. But in

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Speaker 3: so many ways, you know, you were way ahead of

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Speaker 3: the curve in terms of calling these things out. This

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Speaker 3: notion of mencome, as it's been referred to and organized

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Speaker 3: in other countries. It's not necessarily a new idea, but

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Speaker 3: it's an idea really brought to the fore and scaled

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Speaker 3: and consciousness. But it's been a bipartisan idea. Over the

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Speaker 3: course of quite literally decades and decades, conservative economists, not

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Speaker 3: just progressive thinkers, have been promoting this fundamental idea of

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Speaker 3: monthly check's minimum income that is universally distributed to address

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Speaker 3: the anxieties, the burdens, and the stresses, particularly now in

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Speaker 3: an AI induced economy.

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Speaker 1: Yeah, and you were open to this too, man. I

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Speaker 1: remember you quoting Voltaire at an event about how work

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Speaker 1: stabs off like the three great sins, you know.

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Speaker 3: Itselves, Life's three great evils boredom, vice, and need.

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Speaker 1: Yeah, and look at this. And I was like, look

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Speaker 1: at look at Gavin quoting Voltaire.

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Speaker 2: I don't have to steal that, you know, since I

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Speaker 2: was quoting other people.

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Speaker 1: But yeah, this is something that is now front and

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Speaker 1: center because AI has arrived. And when I was running

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Speaker 1: for president back in twenty nineteen and twenty twenty, it

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Speaker 1: was still somewhat speculative or far out. But now it's here,

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Speaker 1: you know, spending seven hundred billion dollars on data centers

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Speaker 1: around the country, and the major tech firms are replacing coders,

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Speaker 1: and soon it's going to be customer service, and then

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Speaker 1: Waymo is going to replace the drivers, and like and

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Speaker 1: on and on and so the average California and the

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Speaker 1: average Americans like, Okay, what the heck is my kid

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Speaker 1: going to do when they graduate from college? Uh? And

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Speaker 1: in my view and by the way, not just my view,

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Speaker 1: but also no Daria Amiday is saying tax us put

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Speaker 1: in a token tax sam Almon's like National Wealth Fund

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Speaker 1: based on ai Elon musks as universal high income, which

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Speaker 1: I you know, quite enjoy.

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Speaker 2: You know that the the uh, the vision uh.

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Speaker 1: And so we need to get a move on because

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Speaker 1: more and more value is going to disappear into the cloud,

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Speaker 1: and more and more average families are going to be

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Speaker 1: looking up being like what the heck happened? So to me,

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Speaker 1: universal based income is the foundational step, but you have

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Speaker 1: to keep on moving in that direction. And Gavin, thank

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Speaker 1: you for pushing us down this road. One of the

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Speaker 1: biggest misconceptions about universal based income is that it somehow

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Speaker 1: is anti work. I have an Asian joke here, which

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Speaker 1: is I'm Asian, I love to work. But the fact is,

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Speaker 1: if you give people more money, they'll participate in the

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Speaker 1: market economy, they'll start businesses at higher levels. They'll give

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Speaker 1: to their congregation or religious community at higher levels. Like

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Speaker 1: it's going to supercharge the things that people actually want, which.

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Speaker 2: Will give us a lot more to do.

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Speaker 1: The other major source of jobs, in my opinion, after

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Speaker 1: the private sector retrenches, which it will would be the government.

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Speaker 1: And I'd much prefer communities figure out what they'd want

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Speaker 1: to pursue for work, then have the government come in

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Speaker 1: and say, hey, I've got.

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Speaker 2: A bunch of jobs for you.

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Speaker 1: Like, I don't think that's the vision that most Americans want.

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Speaker 3: When you were promoting UBI, you were promoting about one

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Speaker 3: thousand dollars? Was one thousand dollars if I recall specifically.

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Speaker 1: Yeah, oh, by the way, just for fun too. I

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Speaker 1: even wrote a book recently called Hey, Yang, where's my

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Speaker 1: thousand bucks? And the alternate title, which you'll enjoy, was

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Speaker 1: hey am I racist? Or are you Andrew Yang? Both

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Speaker 1: of which I have gotten. But yeah, it's a thousand

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Speaker 1: bucks a month.

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Speaker 3: Was a freedom dividend, so, and I love that you

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Speaker 3: framed it as you suggest a freedom dividend, and you

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Speaker 3: were going to pay for it. I mean, it's not inexpensive.

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Speaker 3: I think at the time they scored it, what two

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Speaker 3: point eight three trillion dollars or something like that?

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Speaker 1: Is that accurate order, mindu Toud It's a little bit

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Speaker 1: lower than that if you include current programs, which in

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Speaker 1: my plan would be kind of like a trade off.

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Speaker 2: It's like you get one or the other, and you

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Speaker 2: can elect.

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Speaker 1: But let's call it two trillion or two and a

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Speaker 1: half trillion, just for you know, the setup and the.

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Speaker 3: And the idea again was just to deal with the anxiety,

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Speaker 3: create at least a baseline of of money that allows

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Speaker 3: people not you know, makes the end of the month

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Speaker 3: a little bit less stressful, and then provides the opportunity

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Speaker 3: then to stretch the mind of imagination across the spectrum

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Speaker 3: of issues, including something you've been attached to for decades,

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Speaker 3: and that is a very aggressive entrepreneurial construct, this notion

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Speaker 3: of startups, which you invested a lot of time, energy

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Speaker 3: and resources in promoting. But what about this is you

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Speaker 3: suggest notion that UBI is charity versus this notion of

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Speaker 3: ownership that you Interestingly, even Sam Altman came out, as

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Speaker 3: you pointed to, recently suggesting maybe universal basic capital is

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Speaker 3: a better approach, this notion of an ownership society not

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Speaker 3: necessarily a charitable society that can make everybody feel a

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Speaker 3: sense of not just connection, but connection to a larger

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Speaker 3: cause called democracy, to one another in a broader sense.

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Speaker 1: I'm on board. And the fact is these models were

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Speaker 1: built on our data, and our data is getting sold

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Speaker 1: and resold for hundreds of billions of dollars a year,

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Speaker 1: no one seeing a dime, and a dividend is what

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Speaker 1: a stockholder.

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Speaker 2: A shareholder gets.

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Speaker 1: It's one of the things I framed as like a

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Speaker 1: stakeholder society where you get a dividend as a result

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Speaker 1: of being a member of the richest, most advanced country

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Speaker 1: in the history of the world. So there are different

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Speaker 1: names you could use, but that is exactly what I

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Speaker 1: was going for, which is like, look, we all have

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Speaker 1: an ownership stake in the future, and then we can

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Speaker 1: look at our kids and say, you have an ownership

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Speaker 1: stake too.

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Speaker 2: You're going to be all right, And when the AI wizards.

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Speaker 1: Develop more innovations, you're somehow getting like a tiny, tiny

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Speaker 1: slice of that.

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Speaker 3: It's interesting. I appreciate the frame in the context of that.

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Speaker 3: You know, to me, it's not even a debate for

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Speaker 3: us out here in California at least, it's both and

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Speaker 3: And let me give you a proof point. A few

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Speaker 3: years back, inspired by a lot of the work you

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Speaker 3: were doing and a lot of the commentary and the critique,

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Speaker 3: I just I love stress testing this idea of increasing

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Speaker 3: the number of tries be open argument interested in evidence,

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Speaker 3: and it led to us at a state level putting

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Speaker 3: out thirty five million dollars of general fund to seven

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Speaker 3: large scale UBI projects. And by the way, those projects

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Speaker 3: are fully functioning with the cohorts class the support on

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Speaker 3: their multi year pilots and the results of all of

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Speaker 3: these pilots. They targeted pregnant women, foster families, people sixty

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Speaker 3: five and over. In fact, we just trued it up

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Speaker 3: additional five millions, it's forty million. We're going to get

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Speaker 3: the results of all that an independent analysis of what

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Speaker 3: it produced and stress test literally in a matter of months,

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Speaker 3: and so California will at a scale geographic scale and

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Speaker 3: demographic scale, have the ability to to kick the tires further.

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Speaker 3: But we're also going to be piloting this notion of

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Speaker 3: university basic capital. At the same time, we're now rolling

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Speaker 3: out working with our what we created baby bonds years

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Speaker 3: ago before the Trump accounts five point five million California

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Speaker 3: accounts up to fifteen hundred dollars for all of our kindergarteners,

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Speaker 3: everyone was eligible. We put one point nine billion dollars

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Speaker 3: of general fund money to support this program and to

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Speaker 3: use these accounts for ownership for capital accumulation, focusing on

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Speaker 3: financial literacy, that notion of compounded you know, investments, and

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Speaker 3: so I'm very appreciative of what you're doing. I'm also

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Speaker 3: appreciative now we're broadening the conversation to capital, to equity,

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Speaker 3: to public equity, to dividends, and something broader than just income.

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Speaker 1: I'm pumped that you've been rolling these things out in California, Gavin.

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Speaker 1: I have a joke, which is it only counts if

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Speaker 1: money changes hands, you know what I mean? And in

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Speaker 1: your case, money is changing hands, because like I've run

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Speaker 1: companies and if you tell everyone how great a job

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Speaker 1: they're doing, that works for a little while, but then

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Speaker 1: eventually they're like, hey, you know, is there a bonus attached?

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Speaker 1: So the fact that these kids are getting baby bonds incredible.

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Speaker 1: Like I'm for it when California does it, and I'm

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Speaker 1: for it when the Trump crew does it, you know,

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Speaker 1: no matter what they name it. Like people ask me

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Speaker 1: on TV it's like, hey, what do you think of this?

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Speaker 1: And I was like, look, as far as I can tell,

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Speaker 1: it's money for babies. So you know, like I'm like,

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Speaker 1: I'm on board. You could call them something even worse,

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Speaker 1: I'd probably be okay with.

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Speaker 3: It, and I appreciate that, and I think it's important

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Speaker 3: for Democrats. By the way, we had a lot of

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Speaker 3: Democrats that were supporting you know, Corey Booker was out

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Speaker 3: there for years and years, Ted Cruz as well. You know,

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Speaker 3: it's a good idea. It's a good idea, and you

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Speaker 3: got to celebrate it to your point, even if you know,

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Speaker 3: some of us that are a little more partisan, you know,

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Speaker 3: are not happy about Trump accounts. I could call them

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Speaker 3: newsome accounts, they wouldn't be happy about it. We're gonna

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Speaker 3: we'll rise above that though.

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Speaker 1: Yeah, yeah, I just call it something neutral like America

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Speaker 1: accounts or you know, is pretty neutral.

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Speaker 3: I'm going to needier help. We call it cow Kids,

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Speaker 3: which is a lousy brand. But it's another thing.

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Speaker 2: All the kids could have been improved upon.

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Speaker 1: I got to say, by the way, I have enjoyed

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Speaker 1: your entire social media persona. And one of the things

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Speaker 1: I've said to folks is that Trump is a cocktail

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Speaker 1: of three communication styles, politics and then pro wrestling. He's

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Speaker 1: kind of a pro wrestling villain and a ww Hall

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Speaker 1: of Famer, and then three comedy insult comedy. And the

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Speaker 1: fact is pro wrestling in comedy, both have audiences of

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Speaker 1: millions every week that ignore cable news channels. And so

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Speaker 1: the fact that you are tapping into these other wavelengths

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Speaker 1: I think is very very savvy, and you know it

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Speaker 1: serves you well.

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Speaker 3: I appreciate that, and again not everyone does, so I'm

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Speaker 3: grateful for the recognition as we try to battle that

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Speaker 3: out for attention and just to get in that space. Yeah,

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Speaker 3: and I could agree with you more, especially again my

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Speaker 3: party Democratic Party just so often a little bit humor less.

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Speaker 1: No, no, ye.

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Speaker 3: Forgive the understatement.

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Speaker 1: It's it's fine. I mean, I'm just I'm this, you know,

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Speaker 1: giving someone a hard time. But it's something that I

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Speaker 1: personally have told people. It's like, look, I think Gavin's

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Speaker 1: smart to do it. I think it's working.

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Speaker 2: Uh.

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Speaker 1: And when I get asked about your prospects, which I

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Speaker 1: do get asked, I'm just like, Gavin is very very

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Speaker 1: strong in a room, like the dude seems like he

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Speaker 1: did just walk off.

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Speaker 2: A Hollywood set.

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Speaker 1: In a good way. I mean, this is a comment man,

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Speaker 1: like you're very handsome and like you know, tall and

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Speaker 1: son kissed and the rest of it. But I was like, look,

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Speaker 1: having been in these rooms. Being good in the room

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Speaker 1: matters very, very significantly. Like I think Gavin's going to

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Speaker 1: travel well and people are going to gravitate towards him.

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Speaker 1: So you know, just I mean as you can imagine,

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Speaker 1: because like I get asked about the field all the time.

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Speaker 1: I also tell people I think it's going to be

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Speaker 1: a governor because I think for folks are going to

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Speaker 1: want that kind of background and leadership.

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Speaker 3: I appreciate all that, and and and but and again

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Speaker 3: I'm not going to take the bait on any of it.

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Speaker 3: And I want to go back to this to AI.

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Speaker 3: I've been you know, I've been listening to you on

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Speaker 3: a bunch of podcasts, and I appreciate you know you

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Speaker 3: you've been out on this, you know, been calling us out,

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Speaker 3: not just been out on the road, UH talking about

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Speaker 3: AI and uh what's going on not just with jen AI,

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Speaker 3: but now with what's going with agentic AI as it

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Speaker 3: moves and mores into this next iteration and talking about compute,

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Speaker 3: not just talking about data centers, but talking about tokens,

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Speaker 3: talking about uh digital privacy and and and and this

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Speaker 3: notion of a digital dividend that can look many different

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Speaker 3: you know forms. Uh in shapes. But this notion of

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Speaker 3: AI anxiety, this notion that you know they're coming after

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Speaker 3: my jobs. We talk about white collar workers now having

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Speaker 3: so much more in common with blue collar workers. You

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Speaker 3: could be talking about a twenty five year that's not

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Speaker 3: getting any interviews, that just graduated a few years ago

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Speaker 3: and is having a hard time and sounding a lot

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Speaker 3: like you know, the old factory work in Ohio that

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Speaker 3: you know boot Cotta worker. White collar rhetoric is starting

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Speaker 3: to come together, and I think a new coalition in

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Speaker 3: many respects around anxiety. And so I'm curious where you

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Speaker 3: are on the spectrum, the doomer versus the utopia spectrum.

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Speaker 3: Is it coming sooner than we think, the displacement is

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Speaker 3: coming faster? Is it coming in different shades? Meaning the

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Speaker 3: people you don't hire is a form of that displacement

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Speaker 3: because those are the easiest people as you suggest to

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Speaker 3: fire the people you don't hire. Is it coming first

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Speaker 3: to entry level jobs, clerical jobs. Is it happening in

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Speaker 3: the physical AI space you talk about weimo, then we

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Speaker 3: could talk robots. I mean, give me a sense of

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Speaker 3: where you are on the spectrum of AI and the

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Speaker 3: spectrum of where we should be. What we should be

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Speaker 3: thinking about.

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Speaker 1: Well, first, let me share a story to help set

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Speaker 1: this convo up. And so I was a CNN commentator

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Speaker 1: for a while and at one point the team approached

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Speaker 1: me and said, hey, we're thinking of doing a TV

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Speaker 1: show called the Future of with Andrew Yang.

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Speaker 2: It'll be like the future of.

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Speaker 1: Healthcare, the future of transportation. I said, all right, that

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Speaker 1: sounds good. They come back ten days later said, hey, Andrew,

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Speaker 1: we ran a focus group bad News. Americans don't like

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Speaker 1: the future, you know, And that was, you know, kind

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Speaker 1: of tough to hear.

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Speaker 2: And this was a few years ago.

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Speaker 1: So when you talk about the hostility towards AI and

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Speaker 1: grads booing the commencement speakers, Americans don't feel great about

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Speaker 1: the future because they don't think it's going to include them.

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Speaker 1: They don't think they're going to be among the beneficiaries.

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Speaker 1: And they are stone cold correct, you know, like I

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Speaker 1: have AI, you don't, or maybe you can, you can

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Speaker 1: like use it, you can pay for it. But you're

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Speaker 1: talking about the formation of multiple trillion dollar companies. You're

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Speaker 1: gonna have our first trillionaires. And then they had the

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Speaker 1: average family looking up in central California or central Missouri,

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Speaker 1: is like, okay, Like I don't think I'm gonna win

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Speaker 1: as a result of this. And so if you give

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Speaker 1: me a chance to boo or to protest the data center, like,

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Speaker 1: I'll take that. And so the question is how do

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Speaker 1: you make that person feel like they're winning? And I'm

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Speaker 1: going to go back to what I said before, is

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Speaker 1: like it only counts the money changes hands. So if

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Speaker 1: the big winners from this, and I do think some

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Speaker 1: of the AI high chieftains are getting wise to this

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Speaker 1: because they know that people are turning on them so completely,

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Speaker 1: they've got to share the winnings as quickly and broadly

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Speaker 1: as possible. Now to your question of like where am

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Speaker 1: I in terms of how bad it's going to get

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Speaker 1: for workers and what sequence and the rest of it, No,

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Speaker 1: I look at the same data that you do, maybe

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Speaker 1: a better data than I do, because you're a governor

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Speaker 1: and you get like some.

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Speaker 2: Internal secret stuff.

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Speaker 1: But the big companies are replacing first coders, then customers serve,

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Speaker 1: then various white collar functions, then slowing down entry level highers.

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Speaker 1: College kids now have an underemployment rate of let's call

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Speaker 1: it forty eight percent or something along those lines. And

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Speaker 1: so it's getting sped up. I'm fifty one years old,

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Speaker 1: and I tell people, if I don't get dumber in

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Speaker 1: a given month, it was a good month. AI probably

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Speaker 1: got twice as smart in the same timeframe. So then

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Speaker 1: saying what we're going to do is train people and

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Speaker 1: have them upscale, I mean by the time I complete

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Speaker 1: the program, AI got eight times faster. So you know,

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Speaker 1: like what, that's not a real plan. So like it

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Speaker 1: probably sounds pretty pessimistic my analysis. But I sit down

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Speaker 1: with CEOs the same way you do, and the CEOs

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Speaker 1: tell me, look, I'm going to fire fifteen percent of

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Speaker 1: my staff this year, another twenty percent two years from now,

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Speaker 1: and another twenty percent two years later than after that.

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Speaker 2: Who knows?

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Speaker 1: Now are they going to go on CNBC and say

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Speaker 1: that probably not? But have I heard that from now

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Speaker 1: how a dozen different CEOs of both public and private companies, Yes,

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Speaker 1: I have, And so if you hear that over and

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Speaker 1: over again. And by the way, even for me running

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Speaker 1: Noble Mobile, our CTO came and said, hey, guess what,

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Speaker 1: We're going to take down the job posting for junior

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Speaker 1: engineers because I think I can now get it done

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Speaker 1: with AI. And so that's the you know, the easiest

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Speaker 1: people to fire, the people you haven't hired yet.

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Speaker 3: So you know, we're having the same conversations. And by

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Speaker 3: the way, the people, these folks running these frontier labs

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Speaker 3: are saying it out loud. I mean, as you noted,

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Speaker 3: Dario's saying it out loud and ananthropic open letter basically

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Speaker 3: an essay that was written by Sam Altman and open

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Speaker 3: AI around the anxiety. And so they see the you know,

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Speaker 3: they may not see quote unquote the pitchfork's coming, but

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Speaker 3: they certainly see into the future and the trend lines

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Speaker 3: here that are becoming headlines.

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Speaker 1: I mean Sam had a tax on his house. I

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Speaker 1: mean like that that was a modern day pitchfork. So

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Speaker 1: you know, I think they're feeling it. And by the way,

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Speaker 1: I think the lapse here, it's like the level of

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Speaker 1: federal dysfunction is actually like a major impediment because if

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Speaker 1: you had a government that had its act together, they

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Speaker 1: would already be distributing an AI dividend in my opinion,

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Speaker 1: and I think a lot of the AI companies would

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Speaker 1: be forking over cash to keep the pitchforks at bay

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Speaker 1: just out of enlightened self interest.

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Speaker 2: You know, like there's a sense.

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Speaker 1: That people are greedy beyond you know, like like any

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Speaker 1: other measure. And I just I think that enlightened self

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Speaker 1: interest from a lot of these tech CEOs is like, look,

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Speaker 1: they know they're going to have plenty of money. And

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Speaker 1: what I joke about is like life is better outside

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Speaker 1: the bunker than in the bunker, Like no matter how

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Speaker 1: much money you have, like that bunker is not that nice,

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Speaker 1: you know, like you know, miss the sunlight. So I

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Speaker 1: think that there is a grand bargain to be had,

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Speaker 1: but our government is asleep at the switch or just

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Speaker 1: cheerleading for the AI firms. And even if the II

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Speaker 1: firms are raising their hands and saying hey, like you know,

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Speaker 1: please do consider taxing me, at the same time, they

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Speaker 1: have one hundred and fifty million dollars in lobbying cash

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Speaker 1: that they'll use to bomb anyone who does something they

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Speaker 1: don't like. And so there are a lot of legislators

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Speaker 1: who are like, Okay, are you sincere about this or

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Speaker 1: are you just trying to make yourself look good? And

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Speaker 1: then if I suggest it, all of a sudden, I'm

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Speaker 1: going to have eight figure spent against me.

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Speaker 3: And I mean that's played out obviously, and I think,

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Speaker 3: what is it out in your neck of the woods?

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Speaker 3: Is the twelfth Congressional dis.

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Speaker 1: Alex Boris, Yeah, like who I find to be totally

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Speaker 1: reasonable past a sensible AI safety bill as a New

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Speaker 1: York state legislator, and.

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Speaker 3: Which, by the way, full disclosure, Andrew, was modeled after

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Speaker 3: the bill that we passed here in California to be

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Speaker 3: fifty three.

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Speaker 1: That's right.

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Speaker 3: So I couldn't agree with you more the sensibility of

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Speaker 3: the bill. It was a large language model at LM

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Speaker 3: Frontier Safety and Transparency bill and just the second in

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Speaker 3: the country and one, by the way, broadly embraced by tech.

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Speaker 3: But he hasn't necessarily been Yeah.

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Speaker 1: And by the way, the irony of the attacks on

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Speaker 1: Alex Boris Gavin are you have these ads of him

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Speaker 1: looking very mean and scary and then say he worked

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Speaker 1: for Palenteer, you can't trust him.

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Speaker 2: And meanwhile, the folks who.

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Speaker 1: Are funding that ad are the AI companies themselves that

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Speaker 1: are just trying to you know. And Alex Dorus is like,

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Speaker 1: I did work for them, and then I became a

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Speaker 1: day legislator and I'm trying to do good things. And

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Speaker 1: so this goes back to the conversation around incentives is

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Speaker 1: that legislators know it's like, look, if I take on

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Speaker 1: the AI industry in a way that they don't like,

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Speaker 1: it's going to reduce my job security. And so if I,

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Speaker 1: you know, kind of handwave or like walk around the

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Speaker 1: block a little bit like you know, I mean, and

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Speaker 1: I do think Republicans are more guilty of this right now,

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Speaker 1: because again they have just been reduced a cheerleader for

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Speaker 1: the industry even as their voters are turning on AI.

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Speaker 1: You know, there's like a fascinating disconnect. AI has a

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Speaker 1: twenty six percent approval rating right now, and I think

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Speaker 1: that's lower than ICE.

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Speaker 2: So it gives you a sense how bad it is.

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Speaker 1: And I think it's trending negatively, not.

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Speaker 2: Positively, even though it's already quite low.

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Speaker 3: What do you make of I mean, you know, obviously

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Speaker 3: with David Sachs, he's no longer quote unquote formally there

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Speaker 3: as the crypto and AIS are. I mean, Trump came

438
00:22:27,880 --> 00:22:30,480
Speaker 3: in very enthusiastically, sort of ripped off the band aid,

439
00:22:31,160 --> 00:22:35,440
Speaker 3: rolled back some of the executive order, at least directionally

440
00:22:35,520 --> 00:22:38,399
Speaker 3: rolled back some of the policies of the prior administration.

441
00:22:38,480 --> 00:22:42,960
Speaker 3: The Biden administration. There was efforts to undermine our safety

442
00:22:44,000 --> 00:22:48,159
Speaker 3: and frontier model legislation, led by not just members of

443
00:22:48,160 --> 00:22:52,399
Speaker 3: the Trump administration in sacks, but people like Ted Cruz

444
00:22:52,440 --> 00:22:55,399
Speaker 3: that didn't want to see any regulation. There was an

445
00:22:55,400 --> 00:22:59,960
Speaker 3: empty to preempt states from taking that role and responsibility.

446
00:23:00,440 --> 00:23:03,479
Speaker 3: And we're highlighting Alex and how he's the burden and

447
00:23:03,520 --> 00:23:06,159
Speaker 3: beneficiary because there are tech people that are buying him

448
00:23:06,200 --> 00:23:09,119
Speaker 3: as well. He's an interesting case study. There's nuance and

449
00:23:09,280 --> 00:23:12,320
Speaker 3: who's going after him, and then interestingly, who's supporting him.

450
00:23:12,600 --> 00:23:14,520
Speaker 3: A lot of folks out here in Silicon Valley are

451
00:23:14,520 --> 00:23:16,679
Speaker 3: actually supporting Alex. I think he's getting more of his

452
00:23:16,720 --> 00:23:20,760
Speaker 3: money from California than he's even is from Manhattan, in

453
00:23:20,840 --> 00:23:24,879
Speaker 3: an interesting twist in the saga of his election. But

454
00:23:24,960 --> 00:23:27,920
Speaker 3: what do you make of you know where Trump is now?

455
00:23:28,000 --> 00:23:32,160
Speaker 3: Particularly today where he allegedly was going to come out

456
00:23:32,880 --> 00:23:36,560
Speaker 3: with a new executive order and was going to have

457
00:23:36,560 --> 00:23:39,960
Speaker 3: a press conference. He pulled back the executive order press

458
00:23:40,000 --> 00:23:42,879
Speaker 3: conference the last minute. People are speculating as to why,

459
00:23:43,119 --> 00:23:45,479
Speaker 3: but it was a sense that now they've woken up

460
00:23:45,520 --> 00:23:51,080
Speaker 3: in a mythos world, anthropic put out mythos. They quickly

461
00:23:51,119 --> 00:23:57,120
Speaker 3: pulled it back cybersecurity red flags everywhere, and that may

462
00:23:57,160 --> 00:23:59,960
Speaker 3: have woken these guys up a little bit. No, Trump

463
00:24:00,200 --> 00:24:02,280
Speaker 3: and Bessett and others to we got to pay a

464
00:24:02,320 --> 00:24:04,520
Speaker 3: little more attention to her the safety sides of things.

465
00:24:05,119 --> 00:24:08,480
Speaker 1: Yeah, there was apparently a freak out that meet those

466
00:24:08,520 --> 00:24:11,560
Speaker 1: could be used to hack through a lot of systems

467
00:24:11,560 --> 00:24:16,280
Speaker 1: and infrastructure, and so that they did have a like

468
00:24:16,359 --> 00:24:19,119
Speaker 1: a wake up call. I don't know what's going on

469
00:24:19,240 --> 00:24:23,600
Speaker 1: with this delay, but what's interesting is that I think

470
00:24:24,280 --> 00:24:27,720
Speaker 1: Trump and some of his team have realized that a

471
00:24:27,760 --> 00:24:31,120
Speaker 1: lot of their people, their voters, are very very dubious

472
00:24:31,119 --> 00:24:34,000
Speaker 1: of AI, and that if left unchecked, AI could rip

473
00:24:34,040 --> 00:24:36,720
Speaker 1: through federal systems in.

474
00:24:37,040 --> 00:24:39,359
Speaker 2: A way that would be disastrous.

475
00:24:39,960 --> 00:24:44,200
Speaker 1: And so they're trying to be responsive, and I think

476
00:24:44,240 --> 00:24:48,639
Speaker 1: they will end up announcing some measures around having to

477
00:24:48,640 --> 00:24:51,480
Speaker 1: look at model Frontier models before they're released.

478
00:24:51,720 --> 00:24:53,280
Speaker 2: One of the jokes I have.

479
00:24:53,280 --> 00:24:55,760
Speaker 1: Been telling is that there are more regulations to open

480
00:24:55,800 --> 00:25:00,520
Speaker 1: a hot dog stand than launch a new that's going

481
00:25:00,600 --> 00:25:02,359
Speaker 1: to impact millions of people. It's like I could just

482
00:25:02,760 --> 00:25:05,399
Speaker 1: use use people as guinea pigs, whereas I couldn't use

483
00:25:05,400 --> 00:25:07,520
Speaker 1: them as guinea pigs on my hot dogs without someone

484
00:25:08,040 --> 00:25:11,359
Speaker 1: making sure that you know their kosher so to speak.

485
00:25:11,880 --> 00:25:14,680
Speaker 3: Yeah, I mean it's you know, nothing funny about that joke.

486
00:25:14,760 --> 00:25:16,560
Speaker 3: I mean it's it's fact, isn't it. I mean it's

487
00:25:16,560 --> 00:25:17,480
Speaker 3: a remarkable fact.

488
00:25:18,119 --> 00:25:18,359
Speaker 2: Yeah.

489
00:25:18,440 --> 00:25:22,639
Speaker 1: Man, these are these are strange times. But the public

490
00:25:22,680 --> 00:25:24,480
Speaker 1: is heading a certain way. I think there's a big

491
00:25:24,520 --> 00:25:28,439
Speaker 1: political void and I do think people in both parties

492
00:25:28,440 --> 00:25:31,080
Speaker 1: are going to move to fill it, which I applaud

493
00:25:31,160 --> 00:25:34,280
Speaker 1: because to me, it's it's crazy that we don't have

494
00:25:34,920 --> 00:25:39,359
Speaker 1: more sensible guidelines. We missed the boat on social media.

495
00:25:39,400 --> 00:25:42,359
Speaker 1: Our kids paid the price. We're missing the boat on AI,

496
00:25:42,920 --> 00:25:46,639
Speaker 1: and young people and workers, in my opinion, are about

497
00:25:46,680 --> 00:25:49,600
Speaker 1: to pay the price, and so there's going to be

498
00:25:49,680 --> 00:25:51,439
Speaker 1: a political backlash as a result.

499
00:25:52,480 --> 00:25:55,119
Speaker 3: So as we focus on safety and we focus on

500
00:25:55,440 --> 00:25:59,680
Speaker 3: the intended and unintended consequences of AI. In that prism,

501
00:26:00,080 --> 00:26:03,520
Speaker 3: we talk about jobs and job loss and the anxiety

502
00:26:03,800 --> 00:26:05,960
Speaker 3: and how it's beginning to manifest. Certainly a lot of

503
00:26:05,960 --> 00:26:09,600
Speaker 3: headlines that suggests it's already taking place in shape. It's

504
00:26:09,640 --> 00:26:13,640
Speaker 3: geographically dispersed, so it's a more challenging thing. It's not

505
00:26:13,840 --> 00:26:17,480
Speaker 3: reflected necessarily on the factory floor. It has been automation

506
00:26:17,640 --> 00:26:22,080
Speaker 3: generally certainly, but not reflected necessarily with clerical workers that

507
00:26:22,560 --> 00:26:25,600
Speaker 3: it's harder to see what's happening in terms of those

508
00:26:25,680 --> 00:26:30,000
Speaker 3: job losses. But warn Act, the Worn Act itself, it

509
00:26:30,040 --> 00:26:33,639
Speaker 3: seems to me it's worn out its usefulness. It was

510
00:26:33,800 --> 00:26:37,240
Speaker 3: designed like our labor laws nineteen thirties, These things were

511
00:26:37,240 --> 00:26:40,600
Speaker 3: designed for a world that no longer exists. Unemployment insurance

512
00:26:41,240 --> 00:26:45,520
Speaker 3: seems to me to have worn out its benefit. What

513
00:26:45,600 --> 00:26:50,280
Speaker 3: about employment insurance? What about the opportunity to read the

514
00:26:50,359 --> 00:26:54,239
Speaker 3: scale portable benefits? What about modernizing the Worn Act and

515
00:26:54,280 --> 00:26:57,879
Speaker 3: having early warning systems? What about I mean, what are

516
00:26:57,920 --> 00:27:01,160
Speaker 3: the conversations we need to be having now at scale

517
00:27:01,720 --> 00:27:05,239
Speaker 3: to reform our systems, to prepare in real time for

518
00:27:05,280 --> 00:27:07,600
Speaker 3: what's happening in real time on our watch.

519
00:27:08,760 --> 00:27:11,199
Speaker 1: Yeah, tying health insurance to jobs is super dumb. I

520
00:27:11,200 --> 00:27:14,280
Speaker 1: think most people realize that. And by the way, it's

521
00:27:14,320 --> 00:27:16,680
Speaker 1: an impediment to entrepreneurship. There are a lot of people

522
00:27:16,680 --> 00:27:19,600
Speaker 1: that would start businesses if they didn't need health insurance

523
00:27:20,200 --> 00:27:23,120
Speaker 1: for their families, and health insurance now is very expensive,

524
00:27:23,119 --> 00:27:25,040
Speaker 1: and so you know, like if I start a company,

525
00:27:25,080 --> 00:27:29,160
Speaker 1: I'm like kind of running a massive risk so portable

526
00:27:29,160 --> 00:27:32,000
Speaker 1: benefits would be to me like a massive step in

527
00:27:32,000 --> 00:27:35,480
Speaker 1: the right direction. But like I'm still for universal based

528
00:27:35,560 --> 00:27:40,760
Speaker 1: income or the equivalent, because I think it's going to

529
00:27:40,760 --> 00:27:45,320
Speaker 1: become increasingly necessary. And one of the topics I think

530
00:27:45,359 --> 00:27:48,080
Speaker 1: you and I both are passionate about is what's happening

531
00:27:48,080 --> 00:27:50,800
Speaker 1: to men and young men where labor first participation just

532
00:27:50,800 --> 00:27:54,840
Speaker 1: continues to decline. They have lower rates of both high

533
00:27:54,880 --> 00:27:59,879
Speaker 1: school and college graduation, lower rates of family formation and dating.

534
00:28:01,000 --> 00:28:03,240
Speaker 1: You know, they're they're going home, they don't think that

535
00:28:03,359 --> 00:28:07,160
Speaker 1: anyone has a use for them, and on the far end,

536
00:28:07,400 --> 00:28:10,160
Speaker 1: they do get radicalized by the Internet, they start blaming

537
00:28:10,200 --> 00:28:15,680
Speaker 1: someone for their problems. And so to me, like, there's

538
00:28:15,800 --> 00:28:19,000
Speaker 1: there are supports to try and help people who are

539
00:28:19,000 --> 00:28:20,879
Speaker 1: in jobs, but I just think we need to be

540
00:28:21,040 --> 00:28:25,320
Speaker 1: doing much more to stimulate activity and paths so that

541
00:28:25,480 --> 00:28:28,160
Speaker 1: folks feel like I bring something to the table, even

542
00:28:28,200 --> 00:28:30,120
Speaker 1: if what I bring to the tables just my magically

543
00:28:30,160 --> 00:28:32,119
Speaker 1: I did it in so I can come out and like,

544
00:28:32,200 --> 00:28:35,680
Speaker 1: you know, buy a beer. I mean, people aren't drinking either,

545
00:28:35,720 --> 00:28:38,120
Speaker 1: so I mean I should use a different example, you know,

546
00:28:39,040 --> 00:28:43,360
Speaker 1: like buy a burger, you know, with my friends, you know,

547
00:28:43,480 --> 00:28:47,520
Speaker 1: like some of the fundamental issues are are to me

548
00:28:48,440 --> 00:28:52,040
Speaker 1: only addressable with very, very big, dramatic actions. And one

549
00:28:52,040 --> 00:28:53,520
Speaker 1: of the things I like about you, Gavin is like

550
00:28:53,560 --> 00:28:56,040
Speaker 1: you hear this stuff and you're like, yeah, Voltaire, like

551
00:28:56,120 --> 00:28:58,320
Speaker 1: let's do it. I mean, not like you actually say that,

552
00:28:58,360 --> 00:29:01,600
Speaker 1: but but you know, but you you've got part of it.

553
00:29:01,640 --> 00:29:04,320
Speaker 1: Might be because you're the governor of California, which I

554
00:29:04,400 --> 00:29:07,000
Speaker 1: dare say is the most abundant state in the country

555
00:29:07,400 --> 00:29:09,040
Speaker 1: in terms of a growth mindset.

556
00:29:09,800 --> 00:29:12,920
Speaker 3: Yeah, no, well I appreciate that. And I again, you

557
00:29:13,080 --> 00:29:15,320
Speaker 3: just got to I mean the entrepreneur. Remember, I started

558
00:29:15,360 --> 00:29:17,400
Speaker 3: right out of college open my first business, grew it

559
00:29:17,480 --> 00:29:21,240
Speaker 3: to about twenty one small restaurants and hotels and wineries.

560
00:29:21,280 --> 00:29:23,200
Speaker 3: Not to impress you to say that, but to press

561
00:29:23,200 --> 00:29:26,320
Speaker 3: pond you that entrepreneurial mindset, a willingness to try to

562
00:29:26,360 --> 00:29:29,800
Speaker 3: take risks, not be reckless, recognition that you have agency,

563
00:29:30,080 --> 00:29:33,080
Speaker 3: you can shape the future. We're not victims. It's decisions,

564
00:29:33,080 --> 00:29:36,000
Speaker 3: not conditions that are determine our faith in future. Recognizing

565
00:29:36,400 --> 00:29:39,840
Speaker 3: that businesses can't thrive in a world that's failing. So

566
00:29:39,880 --> 00:29:43,960
Speaker 3: I want a customer base that is successful and thriving

567
00:29:44,080 --> 00:29:46,880
Speaker 3: and recognizing andrew in the spirit I think that defines

568
00:29:46,920 --> 00:29:49,320
Speaker 3: you and I that you can't be pro job and

569
00:29:49,360 --> 00:29:52,640
Speaker 3: anti business as well, that we should be supporting business

570
00:29:52,640 --> 00:29:54,120
Speaker 3: formation and entrepreneurialism.

571
00:29:54,160 --> 00:29:54,360
Speaker 2: Dude.

572
00:29:54,360 --> 00:29:55,880
Speaker 1: One of the most consistent things that happened to me

573
00:29:55,880 --> 00:29:58,880
Speaker 1: when I was running for president in Iowa and New Hampshire,

574
00:29:58,880 --> 00:30:00,880
Speaker 1: South Carolina, as I'd run to a business owner and

575
00:30:00,920 --> 00:30:04,120
Speaker 1: they'd say, hey, you're running as what I'd say, a Democrat,

576
00:30:04,520 --> 00:30:08,360
Speaker 1: And they really thought Democrats did not like them as

577
00:30:08,360 --> 00:30:10,720
Speaker 1: small business owners. And it made me so sad because

578
00:30:10,720 --> 00:30:12,320
Speaker 1: it was like, I came up as a small business

579
00:30:12,320 --> 00:30:14,560
Speaker 1: owner and like, you guys are the backbone and life's

580
00:30:14,560 --> 00:30:19,680
Speaker 1: blood of this town, of our workforce. So that was

581
00:30:19,680 --> 00:30:21,920
Speaker 1: something that really stuck with me. I mean, I'm sure

582
00:30:22,000 --> 00:30:25,440
Speaker 1: you know you get versions of it anyway. I mean,

583
00:30:25,480 --> 00:30:27,600
Speaker 1: but like, just like you, I came up as a

584
00:30:27,600 --> 00:30:31,160
Speaker 1: small business owner, and it's still what makes me tick.

585
00:30:31,840 --> 00:30:37,440
Speaker 3: Why are we, andrew then, as supporters and champions of

586
00:30:37,480 --> 00:30:40,960
Speaker 3: small business? Why do we have a tax system, particularly

587
00:30:41,480 --> 00:30:50,560
Speaker 3: when productivity and wealth are now being rewarded robots, technology, automation.

588
00:30:50,960 --> 00:30:54,560
Speaker 3: Why are we taxing then human labor? Why we have

589
00:30:54,640 --> 00:31:00,000
Speaker 3: payroll taxes and yet have tax code that actually rewards

590
00:30:59,840 --> 00:31:03,280
Speaker 3: automation in terms of ride offs exactly.

591
00:31:03,600 --> 00:31:07,600
Speaker 1: And I'm so glad you raised this, John Arnold vinode

592
00:31:07,600 --> 00:31:11,240
Speaker 1: Cosla Me sounds like maybe you two. Heck, even Jeff

593
00:31:11,280 --> 00:31:14,200
Speaker 1: Bezos the other day, if I hurt him right where,

594
00:31:14,760 --> 00:31:17,440
Speaker 1: you tend to tax things you want less of. So

595
00:31:17,560 --> 00:31:25,720
Speaker 1: taxing jobs via payroll taxes uh and other costs discourages hiring. Meanwhile,

596
00:31:26,160 --> 00:31:29,680
Speaker 1: if I do automate hundreds of thousands of jobs away

597
00:31:30,480 --> 00:31:33,280
Speaker 1: via AI, get to keep virtually all that money because

598
00:31:33,280 --> 00:31:35,760
Speaker 1: it's going to be going through some megacorp and you know,

599
00:31:35,840 --> 00:31:37,400
Speaker 1: and I'm not paying a lot of taxes on that.

600
00:31:37,560 --> 00:31:39,320
Speaker 2: Maybe I know make it so I have no profits.

601
00:31:39,840 --> 00:31:46,680
Speaker 1: So we're emphasizing the vortex that is AI and capital

602
00:31:47,080 --> 00:31:50,200
Speaker 1: and data as it sucks up more and more energy

603
00:31:50,320 --> 00:31:53,600
Speaker 1: and jobs. And then the small business owners just trying

604
00:31:53,600 --> 00:31:56,160
Speaker 1: to employ people in their community has to pay up

605
00:31:56,360 --> 00:32:00,720
Speaker 1: out the nose for uh, you know, their social Security

606
00:32:00,760 --> 00:32:04,880
Speaker 1: taxes and payroll taxes and healthcare in many cases. I mean,

607
00:32:04,880 --> 00:32:06,600
Speaker 1: we should be going line by line to what that

608
00:32:06,640 --> 00:32:08,840
Speaker 1: small business owner is paying and be like, let's see

609
00:32:08,840 --> 00:32:09,440
Speaker 1: if we can get rid of.

610
00:32:09,480 --> 00:32:13,960
Speaker 3: That unpacket where are you? And we talked briefly about

611
00:32:14,080 --> 00:32:18,120
Speaker 3: physical AI. You know we're seeing it. You brought up weemo.

612
00:32:19,600 --> 00:32:21,680
Speaker 3: Anyone who's been to San Francisco, you could be seven

613
00:32:21,760 --> 00:32:24,560
Speaker 3: deep at a stop sign with seven driverless cars in

614
00:32:24,600 --> 00:32:25,640
Speaker 3: front of you.

615
00:32:25,480 --> 00:32:27,240
Speaker 2: You must have been in them already, right, Gavin.

616
00:32:27,280 --> 00:32:30,920
Speaker 3: I mean I remember, Andrew, I'm old enough gray hairs

617
00:32:30,960 --> 00:32:33,800
Speaker 3: to prove it. When I was sitting there with Larry

618
00:32:33,800 --> 00:32:37,280
Speaker 3: and Serge in the parking lot at Google and basically

619
00:32:37,280 --> 00:32:40,560
Speaker 3: a golf cart that was driving itself, and just never

620
00:32:40,720 --> 00:32:44,760
Speaker 3: forget that experience. The first iteration of that decade or

621
00:32:44,800 --> 00:32:47,400
Speaker 3: so go I mean, now it's going to be Jobe

622
00:32:47,440 --> 00:32:50,560
Speaker 3: Aviation and Archer and others competing for the version of

623
00:32:50,560 --> 00:32:51,760
Speaker 3: the flying taxis.

624
00:32:52,520 --> 00:32:55,320
Speaker 1: Yeah. The first time I wrote in a waymos commercially

625
00:32:55,440 --> 00:32:57,400
Speaker 1: was in California last summer one night. I took a

626
00:32:57,440 --> 00:33:01,560
Speaker 1: little social media video and really enjoyed it. And then

627
00:33:01,640 --> 00:33:04,080
Speaker 1: after I got out and thanked the driver that wasn't there,

628
00:33:04,720 --> 00:33:06,760
Speaker 1: like I just thought like, yeah, we're fucked, which I

629
00:33:06,760 --> 00:33:10,600
Speaker 1: set out than it kind of, But it was a

630
00:33:10,640 --> 00:33:13,840
Speaker 1: great experience in the sense of I felt perfectly safe

631
00:33:13,840 --> 00:33:17,440
Speaker 1: after the first thirty seconds you don't have a driver.

632
00:33:17,920 --> 00:33:19,520
Speaker 1: In your case, I don't speak to myself, like I'm

633
00:33:19,600 --> 00:33:21,560
Speaker 1: kind of a public figure too, so sometimes not having

634
00:33:21,600 --> 00:33:23,600
Speaker 1: a driver is kind of preferable to having a driver.

635
00:33:24,400 --> 00:33:24,640
Speaker 1: You know.

636
00:33:24,840 --> 00:33:26,400
Speaker 2: You can like play the music you want if you

637
00:33:26,440 --> 00:33:27,440
Speaker 2: feel like it. You can have a.

638
00:33:27,440 --> 00:33:31,720
Speaker 1: Private, embarrassed conversation you want, you know. So I got

639
00:33:31,760 --> 00:33:33,640
Speaker 1: out and it was like, Yo, this thing is coming

640
00:33:34,080 --> 00:33:37,160
Speaker 1: fast and furious. And I'm someone who, by the way,

641
00:33:38,200 --> 00:33:43,360
Speaker 1: is more dubious of like robots performing certain types of tasks.

642
00:33:44,200 --> 00:33:46,240
Speaker 1: But I have very smart friends who are like, look,

643
00:33:46,400 --> 00:33:48,880
Speaker 1: the factories in China don't even have light switches anymore

644
00:33:48,920 --> 00:33:51,200
Speaker 1: because like it's all just robots doing stuff in the dark,

645
00:33:51,440 --> 00:33:53,640
Speaker 1: and they can run twenty four to seven, seven days

646
00:33:53,640 --> 00:33:57,200
Speaker 1: a week. So the robots are real and coming, and

647
00:33:57,440 --> 00:34:00,400
Speaker 1: America unfortunately has seated a lot of the robotics industry

648
00:34:00,400 --> 00:34:02,880
Speaker 1: and manufacturing, and so we don't see it in the

649
00:34:02,920 --> 00:34:04,800
Speaker 1: same way we don't feel it. Uh.

650
00:34:05,080 --> 00:34:07,080
Speaker 2: And I will say I think that there'll be.

651
00:34:07,120 --> 00:34:11,359
Speaker 1: Human plumbers, human age brack prepare, you know, for the foreseeable,

652
00:34:12,280 --> 00:34:15,600
Speaker 1: but apparently the robots are coming, you know. So it's

653
00:34:15,640 --> 00:34:17,879
Speaker 1: like AI and the cognitive work and the white collar work,

654
00:34:17,880 --> 00:34:20,880
Speaker 1: of which they're about seventy million jobs, and then the

655
00:34:21,280 --> 00:34:25,400
Speaker 1: robots are up next. Good times, Andrew Yang, like, you know,

656
00:34:25,440 --> 00:34:27,920
Speaker 1: I mean it's I you know, I mean I I

657
00:34:28,239 --> 00:34:31,200
Speaker 1: People sometimes joke Gavin about like, you know, am I

658
00:34:31,400 --> 00:34:33,520
Speaker 1: like a doomer? Am I optimistic? I mean I'm like

659
00:34:33,520 --> 00:34:36,560
Speaker 1: You'm an entrepreneur, so I'm optimistic by nature. But I

660
00:34:37,400 --> 00:34:41,839
Speaker 1: fear I wrote an article I said, I called this

661
00:34:41,880 --> 00:34:45,520
Speaker 1: process the fuckinging. It's like the fucking of white collar workers.

662
00:34:46,600 --> 00:34:48,080
Speaker 2: And uh. And then the robots will be the.

663
00:34:48,120 --> 00:34:51,120
Speaker 3: Next wave and the and a proof point of that

664
00:34:51,280 --> 00:34:54,960
Speaker 3: not to be modelinor or you know, too negative and

665
00:34:55,480 --> 00:34:58,960
Speaker 3: the subject, but the proof point of that certainly manifesting

666
00:34:59,000 --> 00:35:02,080
Speaker 3: now at scale here California and Fremont, the old factory

667
00:35:02,440 --> 00:35:05,239
Speaker 3: for Tesla where he was doing about half of the

668
00:35:05,239 --> 00:35:08,920
Speaker 3: global production. I mean, remember, Tesla happened here first. Future

669
00:35:08,960 --> 00:35:11,319
Speaker 3: happens here first. And I appreciate the future is a

670
00:35:11,360 --> 00:35:14,040
Speaker 3: four letter word in the context of the anxiety out there.

671
00:35:14,040 --> 00:35:16,759
Speaker 3: And I appreciate you reminding me of that, because the

672
00:35:17,160 --> 00:35:22,480
Speaker 3: future always has its you know, love it. But elon

673
00:35:22,680 --> 00:35:26,080
Speaker 3: converting that factory is the future first ev factory is

674
00:35:26,120 --> 00:35:29,240
Speaker 3: now pulled the Y and X model off that line,

675
00:35:29,360 --> 00:35:33,120
Speaker 3: is turning it over to humanoid robotics. Uh. And the

676
00:35:33,200 --> 00:35:36,320
Speaker 3: idea is to generate half a million to million units

677
00:35:36,360 --> 00:35:40,440
Speaker 3: of humanoid robots off that same assembly line. That's happening

678
00:35:40,880 --> 00:35:42,560
Speaker 3: starting to convert in real time.

679
00:35:42,840 --> 00:35:46,399
Speaker 1: Yeah. Yeah, they're pioneers in using robots in various ways.

680
00:35:46,400 --> 00:35:49,439
Speaker 1: Elon's a very big believer and you're seeing it, man.

681
00:35:49,840 --> 00:35:51,120
Speaker 1: You know, you can go over to Fremont.

682
00:35:51,120 --> 00:35:51,760
Speaker 2: It's true.

683
00:35:51,840 --> 00:35:55,360
Speaker 1: I mean, California is the vanguard of a lot of

684
00:35:55,400 --> 00:35:57,640
Speaker 1: what the rest of the country, you know, it gets

685
00:35:57,640 --> 00:35:59,640
Speaker 1: to experience a little bit later. Like you're weimo ride

686
00:35:59,640 --> 00:36:02,279
Speaker 1: a decade to go where it took you know, eight

687
00:36:02,360 --> 00:36:06,839
Speaker 1: or nine years for it to reach the highways. But

688
00:36:07,239 --> 00:36:08,600
Speaker 1: it is true, man, I love for a lot of

689
00:36:08,600 --> 00:36:13,400
Speaker 1: the country, the future is a scary thing. And the

690
00:36:13,440 --> 00:36:16,160
Speaker 1: next president, whoever that is, has a very very big

691
00:36:16,239 --> 00:36:19,680
Speaker 1: task trying to get people actually positive about the future

692
00:36:20,000 --> 00:36:24,239
Speaker 1: and not just the you know, values and feeling good

693
00:36:24,280 --> 00:36:28,280
Speaker 1: about things, but about reconstituting the future for their kids,

694
00:36:29,520 --> 00:36:32,120
Speaker 1: because you know, like the kids are coming home and

695
00:36:32,600 --> 00:36:34,399
Speaker 1: sometimes there's limit in the basement.

696
00:36:35,120 --> 00:36:38,160
Speaker 3: And as you know, well and I appreciate your reference

697
00:36:38,160 --> 00:36:39,960
Speaker 3: to everything that's going on with men and boys, and

698
00:36:40,080 --> 00:36:42,200
Speaker 3: you were kind enough not even mentioned the four x

699
00:36:42,280 --> 00:36:46,799
Speaker 3: higher suicide rates, the deaths of despair for young men

700
00:36:46,880 --> 00:36:49,319
Speaker 3: and boys, and help my party in the past, at

701
00:36:49,360 --> 00:36:52,040
Speaker 3: least is not focused enough attention. I think that's beginning

702
00:36:52,040 --> 00:36:55,040
Speaker 3: to change. A lot of leaders now recognizing the crisis

703
00:36:55,360 --> 00:36:56,200
Speaker 3: of our men and boys.

704
00:36:56,239 --> 00:36:59,080
Speaker 1: But Levin, I was told in the Democratic primary in

705
00:36:59,080 --> 00:37:01,279
Speaker 1: twenty twenty not to talk talk about it, and I

706
00:37:01,360 --> 00:37:02,680
Speaker 1: was I was like, well, what are you talking about?

707
00:37:02,719 --> 00:37:04,480
Speaker 2: Are these not human beings and Americans?

708
00:37:04,480 --> 00:37:06,719
Speaker 1: And they were like, there's a Democratic primary, Andrew, like,

709
00:37:06,760 --> 00:37:09,680
Speaker 1: you don't want to be talking about these problems. I

710
00:37:09,840 --> 00:37:12,080
Speaker 1: was told there's like a dog whishole, and it's like

711
00:37:12,080 --> 00:37:14,759
Speaker 1: a dog whistle I'm talking to talk about, Like I'm

712
00:37:14,760 --> 00:37:16,759
Speaker 1: not trying to signal any think it was like talking

713
00:37:16,800 --> 00:37:21,960
Speaker 1: about real life issues. I do want to talk a little.

714
00:37:21,680 --> 00:37:23,400
Speaker 2: Bit about what I'm building with Noble Mobiles.

715
00:37:23,440 --> 00:37:25,920
Speaker 1: I think you'd like it. I know you would do.

716
00:37:26,880 --> 00:37:29,000
Speaker 3: I want to talk about that because it's a good segue,

717
00:37:29,040 --> 00:37:30,640
Speaker 3: and let me introduce it a little bit, because I

718
00:37:31,000 --> 00:37:35,279
Speaker 3: really appreciate what what I'm told inspired you don't want

719
00:37:35,320 --> 00:37:37,799
Speaker 3: to stress tests whether or not I got this right

720
00:37:37,880 --> 00:37:40,680
Speaker 3: that you like, Look, any parent, you've got a couple

721
00:37:40,680 --> 00:37:43,160
Speaker 3: of kids, I've got four kids. We've all experienced. So

722
00:37:43,320 --> 00:37:45,480
Speaker 3: we just cannot compete with the damn phone. I don't

723
00:37:45,480 --> 00:37:48,080
Speaker 3: care how good you are as a parent. You know,

724
00:37:48,080 --> 00:37:50,640
Speaker 3: with all due respect, you know, the phone's just going

725
00:37:50,719 --> 00:37:53,000
Speaker 3: to kick your ass. It just will. It does. And

726
00:37:53,080 --> 00:37:57,000
Speaker 3: social media is just it's sucking these kids in the vortex.

727
00:37:57,320 --> 00:37:59,440
Speaker 3: They You and I are old enough. We used to

728
00:37:59,440 --> 00:38:02,840
Speaker 3: go online to search for knowledge. Now everything online is

729
00:38:02,880 --> 00:38:05,840
Speaker 3: searching for our kids. And these algorithms and they're just

730
00:38:05,920 --> 00:38:09,520
Speaker 3: wiring these guys. And that's you know, that manosphere, that

731
00:38:09,600 --> 00:38:13,520
Speaker 3: boyo sphere, it's really a boiosphere. Is you know created this?

732
00:38:13,960 --> 00:38:16,560
Speaker 3: You know, a real toxic environment, particularly for young men.

733
00:38:16,600 --> 00:38:19,000
Speaker 3: I think goes back to that question, but it connects

734
00:38:19,000 --> 00:38:21,040
Speaker 3: to two things you said earlier that I want to

735
00:38:21,080 --> 00:38:24,399
Speaker 3: just highlight and point out. You mentioned that we made

736
00:38:24,440 --> 00:38:26,960
Speaker 3: the mistake on social media and let's not make it

737
00:38:27,000 --> 00:38:29,640
Speaker 3: on AI. You mentioned the issue of men and boys

738
00:38:29,719 --> 00:38:32,200
Speaker 3: and work that Scott Galloway has been doing in that

739
00:38:32,280 --> 00:38:35,560
Speaker 3: space as well, and so you as a response to

740
00:38:35,600 --> 00:38:40,000
Speaker 3: this and the overwhelming evidence of what we've done to

741
00:38:40,040 --> 00:38:43,319
Speaker 3: our kids with these devices. Have come up with a

742
00:38:43,400 --> 00:38:48,640
Speaker 3: reading novel and interesting idea. It's called Noble Mobile. Tell

743
00:38:48,680 --> 00:38:49,239
Speaker 3: us about it.

744
00:38:49,600 --> 00:38:53,520
Speaker 1: Yeah, and Scott Galloway's an investor, one of the original

745
00:38:53,560 --> 00:38:57,319
Speaker 1: forces behind it, So check it out. I'm well known

746
00:38:57,440 --> 00:39:01,799
Speaker 1: for trying to make people broke, which I'm grateful for,

747
00:39:02,960 --> 00:39:06,359
Speaker 1: but also to your point, I've been concerned about our

748
00:39:06,480 --> 00:39:09,760
Speaker 1: kids and us spending too much time on what Hasan

749
00:39:09,840 --> 00:39:14,239
Speaker 1: Minhaj calls our rectangle of sadness, which if you think

750
00:39:14,239 --> 00:39:15,920
Speaker 1: about the last time he got really upset, it was

751
00:39:15,920 --> 00:39:18,759
Speaker 1: probably off of this guy or your kids being said

752
00:39:18,880 --> 00:39:22,640
Speaker 1: on this guy. And so I thought, how can we help?

753
00:39:23,560 --> 00:39:26,000
Speaker 1: And I was inspired by what Mark Cuban did with

754
00:39:26,120 --> 00:39:29,040
Speaker 1: cost plus drugs, where he bought generic drugs in Bullock.

755
00:39:29,080 --> 00:39:30,200
Speaker 1: And by the way, this is the sort of thing

756
00:39:30,239 --> 00:39:33,040
Speaker 1: that I know you're going to love because it's trying

757
00:39:33,080 --> 00:39:35,439
Speaker 1: to solve a large scale of consumer problem but doing

758
00:39:35,480 --> 00:39:38,640
Speaker 1: it in the marketplace. So then I went and looked

759
00:39:38,640 --> 00:39:41,000
Speaker 1: at our costs and figured what else can you maybe

760
00:39:41,040 --> 00:39:44,479
Speaker 1: cost plus an American life? So the average Californians cost

761
00:39:44,480 --> 00:39:52,000
Speaker 1: structure goes housing, healthcare, education, food, fuel, transportation, media.

762
00:39:51,640 --> 00:39:52,560
Speaker 2: And then wireless.

763
00:39:53,040 --> 00:39:55,520
Speaker 1: The average American is spending eighty three dollars a month

764
00:39:55,600 --> 00:39:59,240
Speaker 1: on their wireless plan. The average European is spending thirty

765
00:39:59,280 --> 00:40:02,320
Speaker 1: five dollars a month on their wireless plan. That Delta

766
00:40:02,840 --> 00:40:06,560
Speaker 1: Gavin comes to one hundred billion dollars a year in

767
00:40:06,960 --> 00:40:10,440
Speaker 1: extra spending on wireless. And of that one hundred billion,

768
00:40:10,480 --> 00:40:13,360
Speaker 1: eleven billion is going to Verizon shareholders as a dividend

769
00:40:13,360 --> 00:40:15,719
Speaker 1: every year. Seven billion is going to AT and T

770
00:40:15,800 --> 00:40:17,319
Speaker 1: shareholders as a dividend every year.

771
00:40:17,360 --> 00:40:19,080
Speaker 2: So you can see all this extra spend.

772
00:40:19,600 --> 00:40:22,160
Speaker 1: And so the first trick I had to pull was

773
00:40:22,480 --> 00:40:24,439
Speaker 1: can I get a better deal from any of these

774
00:40:24,440 --> 00:40:28,600
Speaker 1: carriers for Americans? So I went to the carriers, and

775
00:40:28,800 --> 00:40:32,480
Speaker 1: T Mobile said, we're into it in part because it

776
00:40:32,520 --> 00:40:35,080
Speaker 1: was me, Scott Galloway and some of our friends being like,

777
00:40:35,080 --> 00:40:37,200
Speaker 1: we're going to try and do this for the American people.

778
00:40:37,640 --> 00:40:41,640
Speaker 1: So number one, we cut your wireless bill typically in half,

779
00:40:41,680 --> 00:40:42,880
Speaker 1: down to forty two dollars.

780
00:40:42,920 --> 00:40:44,120
Speaker 2: But the kicker, and what you.

781
00:40:44,160 --> 00:40:47,319
Speaker 1: Described, is that if you use less data, we give

782
00:40:47,360 --> 00:40:49,680
Speaker 1: you the money back at the end of the month.

783
00:40:49,840 --> 00:40:52,560
Speaker 1: So it's an incentive to stop doom scrolling. When you're

784
00:40:52,600 --> 00:40:54,799
Speaker 1: done scrolling. You know you're costing yourself money, and we're

785
00:40:54,840 --> 00:40:56,960
Speaker 1: kind of simple. We don't like to cost ourselves money.

786
00:40:57,280 --> 00:41:00,960
Speaker 1: So the average Noble Mobile user uses seventeen to twenty

787
00:41:01,000 --> 00:41:05,680
Speaker 1: percent less screen time in month two because we realize

788
00:41:05,680 --> 00:41:08,680
Speaker 1: we're being chumps when we're getting sucked down a rabbit hole,

789
00:41:08,719 --> 00:41:11,760
Speaker 1: because that extra thirty minutes actually might cost us some money.

790
00:41:12,719 --> 00:41:15,759
Speaker 1: So that is Noble Mobile, and we're trying to solve

791
00:41:15,800 --> 00:41:20,880
Speaker 1: those two problems, both the money people are spending and

792
00:41:20,920 --> 00:41:23,960
Speaker 1: also how much excess time we're spending on our screens.

793
00:41:24,239 --> 00:41:26,800
Speaker 3: I love it, well done, and look any again, anyone

794
00:41:26,840 --> 00:41:29,000
Speaker 3: with kids. Yeah, but I love you said. It's not

795
00:41:29,040 --> 00:41:31,960
Speaker 3: just our kids. I mean we model the behavior, right,

796
00:41:32,000 --> 00:41:33,440
Speaker 3: It's not what we say, it's what we do. And

797
00:41:33,440 --> 00:41:35,839
Speaker 3: they're watching us on our phone complaining about them being

798
00:41:35,880 --> 00:41:39,719
Speaker 3: on theirs and wondering, you know, not even recognize this.

799
00:41:40,040 --> 00:41:42,840
Speaker 1: It's like you're in my house at my dinner table, Gavin,

800
00:41:42,880 --> 00:41:45,480
Speaker 1: because it's hard for me to lecture my boys when

801
00:41:45,560 --> 00:41:47,600
Speaker 1: then I'm going to like grab this thing, you know,

802
00:41:47,760 --> 00:41:51,400
Speaker 1: ten seconds later. And so imagine a phone plan that

803
00:41:51,560 --> 00:41:54,759
Speaker 1: could actually boost your kids allowance if they spend a

804
00:41:54,800 --> 00:41:56,440
Speaker 1: little bit less time on their phones, and then you

805
00:41:56,480 --> 00:42:00,160
Speaker 1: can have like a conversation about it every month see

806
00:42:00,200 --> 00:42:02,200
Speaker 1: like where the trends are and like what they're doing

807
00:42:02,200 --> 00:42:05,919
Speaker 1: more of and for yourself too, like you can turn

808
00:42:05,960 --> 00:42:09,200
Speaker 1: it into a friendly competition, which, by the way, this

809
00:42:09,239 --> 00:42:11,359
Speaker 1: competition I lose every month because I'm a heavy phone

810
00:42:11,440 --> 00:42:12,799
Speaker 1: user and I'm very open about it.

811
00:42:14,440 --> 00:42:14,920
Speaker 3: I love it.

812
00:42:14,960 --> 00:42:15,120
Speaker 1: Well.

813
00:42:15,120 --> 00:42:17,799
Speaker 3: It's part of that same trend that you know, even

814
00:42:18,000 --> 00:42:20,400
Speaker 3: it may not be at the scale the direction you

815
00:42:20,400 --> 00:42:22,680
Speaker 3: want to take it, but with flip phones now where

816
00:42:22,680 --> 00:42:24,920
Speaker 3: people are just trying to go back to you.

817
00:42:24,880 --> 00:42:28,200
Speaker 1: Know, what's old is new, good old man. I'm also

818
00:42:28,280 --> 00:42:31,920
Speaker 1: throwing parties around the country called offline parties where you

819
00:42:32,040 --> 00:42:34,520
Speaker 1: check your phone at the door and then it feels

820
00:42:34,520 --> 00:42:38,479
Speaker 1: like a nineties college party. We even had someone give

821
00:42:39,120 --> 00:42:41,440
Speaker 1: a guy their phone number on a napkin, just like

822
00:42:41,640 --> 00:42:43,800
Speaker 1: used to happen back in the day, because like no

823
00:42:43,840 --> 00:42:46,400
Speaker 1: one has their phone and they start talking and then

824
00:42:46,400 --> 00:42:48,080
Speaker 1: they're like, yeah, give me a call, but we don't

825
00:42:48,120 --> 00:42:51,680
Speaker 1: have your phone. But it's a forcing function because what

826
00:42:51,680 --> 00:42:54,560
Speaker 1: do we all do at a party the moment where

827
00:42:54,719 --> 00:42:57,040
Speaker 1: awkward or alone or board, we bust our phone out

828
00:42:57,440 --> 00:43:00,040
Speaker 1: and then that kind of like shuts the door. So

829
00:43:00,320 --> 00:43:04,719
Speaker 1: at these offline parties, people have to make eye contact

830
00:43:04,800 --> 00:43:07,000
Speaker 1: to have to look at each other. They kind of

831
00:43:07,040 --> 00:43:08,520
Speaker 1: have to you know, I wouldn't say have to drink.

832
00:43:08,520 --> 00:43:11,080
Speaker 1: I mean, obviously don't force anyone to drink, though we

833
00:43:11,400 --> 00:43:16,000
Speaker 1: give out some drink tickets. It is funny how people

834
00:43:16,080 --> 00:43:17,960
Speaker 1: I mean, it's not funny. It's terrible how people don't

835
00:43:17,960 --> 00:43:20,359
Speaker 1: party as much as they used to, Like you see

836
00:43:20,360 --> 00:43:25,239
Speaker 1: it in the nightlife districts in I'm sure La and

837
00:43:25,239 --> 00:43:27,279
Speaker 1: San Francisco. Have actually been to the downtown districts in

838
00:43:27,360 --> 00:43:32,000
Speaker 1: LA and San Francisco hosting these events. And let's just say,

839
00:43:32,040 --> 00:43:33,839
Speaker 1: like I felt like I was doing a service because

840
00:43:33,840 --> 00:43:36,919
Speaker 1: people would come and say like wow, like I haven't

841
00:43:36,960 --> 00:43:38,080
Speaker 1: had a night like this in a while.

842
00:43:38,800 --> 00:43:40,880
Speaker 3: Andrew, you're talking to trust me the right guy here.

843
00:43:40,880 --> 00:43:43,680
Speaker 3: Remember I'm in the wine business, in the restaurant and

844
00:43:43,719 --> 00:43:46,359
Speaker 3: bar business, so I'm living this, so you are.

845
00:43:46,480 --> 00:43:48,520
Speaker 1: That's actually where you and I first met. It was like,

846
00:43:48,640 --> 00:43:50,160
Speaker 1: you know, I think you were already governor, but it

847
00:43:50,160 --> 00:43:51,880
Speaker 1: was in San Francisco and it was like in a

848
00:43:52,640 --> 00:43:55,319
Speaker 1: like in a bar restaurant, and you talked about how

849
00:43:55,320 --> 00:43:58,120
Speaker 1: that's how you came up. And I'm sure it pains

850
00:43:58,120 --> 00:44:00,399
Speaker 1: you the same way it pains me, because we spent

851
00:44:00,440 --> 00:44:04,400
Speaker 1: all these evenings out in our young adult years well.

852
00:44:04,239 --> 00:44:07,200
Speaker 3: And it's socializing, it's connecting, it's a sense of community.

853
00:44:07,200 --> 00:44:09,080
Speaker 3: It's not just the drink. And I get that, you know,

854
00:44:09,120 --> 00:44:11,239
Speaker 3: I mean, I love what Scott, your partner has been

855
00:44:11,280 --> 00:44:13,439
Speaker 3: saying on this. He actually is going further. He wants

856
00:44:13,440 --> 00:44:17,200
Speaker 3: to lower the drinking age. But because he's that sincere

857
00:44:17,280 --> 00:44:20,759
Speaker 3: about this, the desperate need people to get off, you know,

858
00:44:21,320 --> 00:44:24,959
Speaker 3: you know, get back and connect again. I mean people,

859
00:44:25,120 --> 00:44:28,279
Speaker 3: young boys aren't even asking girls out on dates now.

860
00:44:28,320 --> 00:44:29,239
Speaker 3: They're scared to death.

861
00:44:29,600 --> 00:44:32,080
Speaker 1: My thirteen year old Gavin said to me and my

862
00:44:32,160 --> 00:44:35,319
Speaker 1: wife quite recently, I think I'm going to have an

863
00:44:35,360 --> 00:44:38,160
Speaker 1: AI girlfriend. And then we were both shocked and appalled,

864
00:44:39,440 --> 00:44:41,759
Speaker 1: and we asked him why, and he said, I think

865
00:44:41,760 --> 00:44:43,320
Speaker 1: it's going to be a lot easier than getting a

866
00:44:43,400 --> 00:44:46,399
Speaker 1: human girlfriend. And he's not wrong on that side, because

867
00:44:46,440 --> 00:44:49,480
Speaker 1: getting a human girlfriend is not easy. And so I

868
00:44:49,840 --> 00:44:52,560
Speaker 1: then said, hey, Christopher, this is what.

869
00:44:52,560 --> 00:44:54,080
Speaker 2: Making out with your AI girlfriend is going to be.

870
00:44:54,200 --> 00:44:56,440
Speaker 1: Like you and I pretend I was like not pleasant

871
00:44:56,480 --> 00:44:57,839
Speaker 1: at all, and I was like, this is what making

872
00:44:57,880 --> 00:44:59,000
Speaker 1: up your human girlfriends like it?

873
00:44:59,000 --> 00:45:00,880
Speaker 2: And I took his mom started kissing. I was like,

874
00:45:00,880 --> 00:45:03,279
Speaker 2: which is better? Which is better? You got to go

875
00:45:03,400 --> 00:45:05,480
Speaker 2: for the human even if it's harder.

876
00:45:06,000 --> 00:45:09,160
Speaker 1: Well, but this is what our kids are experiencing, where

877
00:45:09,880 --> 00:45:12,120
Speaker 1: they're living in a friction free digital world, and we

878
00:45:12,200 --> 00:45:15,960
Speaker 1: all know that real relationships actually come with a level

879
00:45:15,960 --> 00:45:18,080
Speaker 1: of friction. But that's the stuff of life, It's the

880
00:45:18,120 --> 00:45:19,000
Speaker 1: stuff of humanity.

881
00:45:19,400 --> 00:45:20,960
Speaker 3: What do you make of it? You know, there was

882
00:45:20,960 --> 00:45:24,600
Speaker 3: a headline this weekend that this last weekend that China

883
00:45:24,760 --> 00:45:28,799
Speaker 3: quite literally the headline was around China making sure people

884
00:45:28,880 --> 00:45:33,560
Speaker 3: don't have online girlfriends and how they're restricting access social media.

885
00:45:33,920 --> 00:45:37,200
Speaker 3: Their version of TikTok is radically different than ours, and

886
00:45:37,239 --> 00:45:40,040
Speaker 3: what content you can actually access in the hours you

887
00:45:40,040 --> 00:45:42,640
Speaker 3: can actually access it. You saw in China, And I'm

888
00:45:42,680 --> 00:45:46,680
Speaker 3: curious your thoughts on this. Where they actually find a

889
00:45:46,880 --> 00:45:51,759
Speaker 3: court find a company that fired a worker under the

890
00:45:51,800 --> 00:45:54,759
Speaker 3: basis on the auspices that that worker was replaced quote

891
00:45:54,800 --> 00:45:59,759
Speaker 3: unquote by a I and that worker was actually awarded

892
00:46:00,239 --> 00:46:03,920
Speaker 3: tens of thousands of dollars. What do you make of

893
00:46:04,120 --> 00:46:07,360
Speaker 3: that and what do you make of their approach to some.

894
00:46:07,280 --> 00:46:10,640
Speaker 1: Of this, You know, I think it's fascinating. I read

895
00:46:10,680 --> 00:46:14,279
Speaker 1: that same book you did, probably about how China's run

896
00:46:14,280 --> 00:46:17,239
Speaker 1: by engineers and we're run by lawyers, and so their

897
00:46:17,400 --> 00:46:20,879
Speaker 1: approach to problems very very different than ours, and their

898
00:46:20,880 --> 00:46:24,800
Speaker 1: advantages and disadvantages to both. You would you would say,

899
00:46:25,640 --> 00:46:29,000
Speaker 1: I can't imagine in America like trying to identify who

900
00:46:29,440 --> 00:46:32,239
Speaker 1: got fired due to AI and who didn't, because you

901
00:46:32,280 --> 00:46:36,080
Speaker 1: know it's going to happen in such a broad way.

902
00:46:36,280 --> 00:46:38,400
Speaker 1: I mean, our challenge, in my view, is trying to

903
00:46:38,400 --> 00:46:43,799
Speaker 1: get people excited about the innovation and progress that's going

904
00:46:43,840 --> 00:46:47,000
Speaker 1: on and like to feel included. And right now, you know,

905
00:46:47,200 --> 00:46:50,040
Speaker 1: like I think that's a really tough bar in terms

906
00:46:50,120 --> 00:46:55,960
Speaker 1: of China's moderating its own social media and banning AI girlfriends.

907
00:46:56,640 --> 00:46:58,600
Speaker 1: I mean, the fact is, we all know what's going

908
00:46:58,680 --> 00:47:03,080
Speaker 1: to happen in America. Our kids are going to date less,

909
00:47:03,120 --> 00:47:05,640
Speaker 1: fall in love less, get married less, have fewer kids.

910
00:47:06,040 --> 00:47:08,839
Speaker 1: Schools are going to close around the country, colleges are

911
00:47:08,840 --> 00:47:11,520
Speaker 1: going to close. You know, you're going to have increasing

912
00:47:11,560 --> 00:47:15,960
Speaker 1: demographic challenges because you have an aging population. I mean,

913
00:47:15,960 --> 00:47:19,040
Speaker 1: this is all written clear as day, and so China

914
00:47:19,120 --> 00:47:21,160
Speaker 1: saw something similar going on. Is like, you know what,

915
00:47:21,600 --> 00:47:25,240
Speaker 1: Ai girlfriends at the margin are going to actually reduce

916
00:47:25,280 --> 00:47:28,560
Speaker 1: the umph inside of our kids to go out and

917
00:47:28,600 --> 00:47:31,920
Speaker 1: meet each other. So let's get rid of them. You know,

918
00:47:32,120 --> 00:47:35,400
Speaker 1: you can see the rationale very very clearly, and I

919
00:47:35,560 --> 00:47:37,719
Speaker 1: do think we can borrow some pages from their book

920
00:47:37,719 --> 00:47:41,040
Speaker 1: in terms of actually figuring out what's happening with our

921
00:47:41,080 --> 00:47:43,879
Speaker 1: young people in particular and trying to get in front

922
00:47:43,880 --> 00:47:45,880
Speaker 1: of it, trying to help in real ways.

923
00:47:46,680 --> 00:47:50,800
Speaker 3: Well, let's I want to close on a beat of optimism.

924
00:47:50,880 --> 00:47:56,440
Speaker 3: There's a lot of punditry, obviously more broadly in compara contrast,

925
00:47:56,440 --> 00:47:58,920
Speaker 3: so in terms of the approach that China's taken in

926
00:47:58,960 --> 00:48:02,000
Speaker 3: the competition that marks I think a lot of the

927
00:48:02,000 --> 00:48:04,480
Speaker 3: conversation we're having as well with China as it relates

928
00:48:04,480 --> 00:48:09,160
Speaker 3: to superintelligence and cybersecurity and national security more broadly defined,

929
00:48:09,200 --> 00:48:12,680
Speaker 3: and how we have an advantage that issues around chips

930
00:48:12,680 --> 00:48:15,600
Speaker 3: and Americans stack all those debates that we didn't even

931
00:48:15,600 --> 00:48:20,080
Speaker 3: get into. But there's also a school of thought that

932
00:48:20,680 --> 00:48:24,080
Speaker 3: you and I may be a little too pessimistic about

933
00:48:24,200 --> 00:48:26,040
Speaker 3: where AI is taking us. That you know, I was

934
00:48:26,120 --> 00:48:30,719
Speaker 3: reading andrewsan associated blog the other day was pretty damn

935
00:48:30,800 --> 00:48:33,880
Speaker 3: convincing that you know, sure there will be some you

936
00:48:33,920 --> 00:48:36,200
Speaker 3: know there'll be a moment in time, there'll certainly be

937
00:48:36,280 --> 00:48:39,719
Speaker 3: categories of jobs and transitions, but we always mind those

938
00:48:39,760 --> 00:48:42,360
Speaker 3: transitions and that the doomers, or at least those that

939
00:48:42,400 --> 00:48:46,120
Speaker 3: are expressing so much alarm that are feeding the anxiety,

940
00:48:47,080 --> 00:48:52,279
Speaker 3: that they're just boneheaded once again, they're wrong, and that

941
00:48:52,280 --> 00:48:55,120
Speaker 3: we're going to come out with augmentation jobs that we

942
00:48:55,120 --> 00:48:58,080
Speaker 3: couldn't even conceive of. We're already experiencing those jobs today

943
00:48:58,560 --> 00:49:00,800
Speaker 3: in some respects, maybe not at scale on the consciousness

944
00:49:00,880 --> 00:49:03,719
Speaker 3: most people imagine. And a lot of the judgery, what

945
00:49:03,800 --> 00:49:06,600
Speaker 3: gets repeated gets replaced. That's a good thing. We move

946
00:49:06,640 --> 00:49:08,200
Speaker 3: away from that and we can find some of the

947
00:49:08,200 --> 00:49:11,520
Speaker 3: things that you're looking for with your UBI where I mean,

948
00:49:11,719 --> 00:49:15,560
Speaker 3: what do you say to that train of thought? And

949
00:49:16,040 --> 00:49:19,520
Speaker 3: I imagine you may identify a little bit with it.

950
00:49:20,080 --> 00:49:22,399
Speaker 3: What's a more positive picture you can paint here?

951
00:49:23,719 --> 00:49:29,000
Speaker 1: The positive opportunity is flipping from a scarcity economy to

952
00:49:29,040 --> 00:49:33,120
Speaker 1: an abundance economy where right now GDP might be eighty

953
00:49:33,239 --> 00:49:38,000
Speaker 1: to four thousand dollars a head in America, which is significant.

954
00:49:38,000 --> 00:49:40,080
Speaker 1: I mean, it's more even than it was when I

955
00:49:40,120 --> 00:49:43,200
Speaker 1: was running for president in twenty twenty, and AI is

956
00:49:43,239 --> 00:49:46,160
Speaker 1: going to push that past one hundred thousand dollars per head.

957
00:49:46,600 --> 00:49:48,880
Speaker 1: So we're getting to a point where you could meaningfully

958
00:49:49,840 --> 00:49:55,320
Speaker 1: situate people where people are actually living better and more fulfilled, happier, healthier.

959
00:49:55,600 --> 00:49:57,719
Speaker 1: It's just how do you take this value that's being

960
00:49:57,719 --> 00:50:01,000
Speaker 1: created and translated to the average Marria family or household.

961
00:50:01,000 --> 00:50:03,360
Speaker 1: And I think that's what the Andresen blog misses. It's like,

962
00:50:03,440 --> 00:50:05,600
Speaker 1: do I think they're going to be fantastic innovations, Yeah,

963
00:50:05,640 --> 00:50:09,200
Speaker 1: one hundred percent. Like do I think that that kid

964
00:50:09,239 --> 00:50:12,360
Speaker 1: down the street who right now is kind of listless

965
00:50:12,360 --> 00:50:15,680
Speaker 1: and directionless, is going to be, you know, dragged into

966
00:50:15,719 --> 00:50:18,400
Speaker 1: all these fantastic new opportunities. I do not you know

967
00:50:18,480 --> 00:50:21,360
Speaker 1: who else doesn't think so, the kid himself or their parents.

968
00:50:21,480 --> 00:50:24,840
Speaker 1: So like that that's the burden. I think the Andresen

969
00:50:24,920 --> 00:50:28,560
Speaker 1: School has to try and figure out how to bridge

970
00:50:28,560 --> 00:50:32,240
Speaker 1: that gap. But if we're ending on an optimistic note,

971
00:50:32,440 --> 00:50:35,799
Speaker 1: the top line, growth will be there, the opportunities will

972
00:50:35,840 --> 00:50:38,319
Speaker 1: be there for us to really address poverty and a

973
00:50:38,320 --> 00:50:40,080
Speaker 1: lot of other large scale problems in a way that

974
00:50:40,120 --> 00:50:43,560
Speaker 1: have not been possible before, a mindset of scarcely made

975
00:50:43,640 --> 00:50:47,160
Speaker 1: sense in this country. When are I mean not mine,

976
00:50:47,200 --> 00:50:49,000
Speaker 1: because you know I mean actually, Kevin, I don't know

977
00:50:49,040 --> 00:50:52,680
Speaker 1: if you know this. My parents met as immigrant students

978
00:50:52,680 --> 00:50:55,640
Speaker 1: at UC Berkeley in the sixties. My brother was named

979
00:50:55,680 --> 00:50:57,880
Speaker 1: after the Lawrence Hall of Science and was born in

980
00:50:57,920 --> 00:51:00,600
Speaker 1: San Francisco. So there's a lot of California my DNA.

981
00:51:01,520 --> 00:51:05,000
Speaker 1: But when Americans first showed up to these shores, if

982
00:51:05,040 --> 00:51:08,719
Speaker 1: they didn't grow or hunt their food, they died. So

983
00:51:08,760 --> 00:51:10,719
Speaker 1: there was like this mindset of scarcity that kind of

984
00:51:10,760 --> 00:51:12,520
Speaker 1: made sense. It's like, if you don't work, you die.

985
00:51:13,760 --> 00:51:17,799
Speaker 1: Now fast forward, AI is about to switch us to

986
00:51:18,120 --> 00:51:20,960
Speaker 1: a zone where we're going to have trillion dollar firms

987
00:51:21,000 --> 00:51:24,560
Speaker 1: and trillionaires and there's going to be more than enough

988
00:51:24,600 --> 00:51:28,359
Speaker 1: to go around, especially when the robots come, where if

989
00:51:28,400 --> 00:51:30,960
Speaker 1: we can get our acts together, we can actually solve

990
00:51:31,280 --> 00:51:34,440
Speaker 1: societal problems in a way that was not possible at

991
00:51:34,440 --> 00:51:36,760
Speaker 1: any other point in human history. So that's the hope.

992
00:51:36,840 --> 00:51:40,239
Speaker 1: That's a very California message, I dare say, because you know,

993
00:51:40,600 --> 00:51:44,359
Speaker 1: you guys have been walking this walk before. I meant

994
00:51:44,440 --> 00:51:45,920
Speaker 1: much of the rest of the country.

995
00:51:46,440 --> 00:51:50,080
Speaker 3: And I appreciate. I mean, the promise is not you

996
00:51:50,080 --> 00:51:52,040
Speaker 3: know is there. It's not just peril, it's it relates

997
00:51:52,080 --> 00:51:54,520
Speaker 3: to what could happen in the healthcare space in particular,

998
00:51:54,600 --> 00:51:58,800
Speaker 3: and we're already you know, seeing some real progress promoted,

999
00:51:58,960 --> 00:52:02,840
Speaker 3: you know earlier screen needs and detections and things as complicated.

1000
00:52:03,080 --> 00:52:05,600
Speaker 3: Is he created cancer and the like and you know,

1001
00:52:05,680 --> 00:52:09,560
Speaker 3: the sort of super MRI and the opportunities and drug development.

1002
00:52:10,080 --> 00:52:12,600
Speaker 3: Of course, the flip side of drug development biology is

1003
00:52:12,880 --> 00:52:15,120
Speaker 3: the downside of that in terms of the security risks.

1004
00:52:15,760 --> 00:52:20,000
Speaker 3: But that abundance mindset is I think also part of

1005
00:52:20,040 --> 00:52:22,920
Speaker 3: this broader conversation. But your point is, I think spot

1006
00:52:22,960 --> 00:52:24,880
Speaker 3: on and I think it's a good place to land

1007
00:52:24,920 --> 00:52:26,680
Speaker 3: because I think it is the bridge between the two.

1008
00:52:27,200 --> 00:52:30,719
Speaker 3: It's about growth and inclusion. It cannot be and we

1009
00:52:30,760 --> 00:52:33,080
Speaker 3: will have, as you suggest, you said it three times,

1010
00:52:33,239 --> 00:52:35,719
Speaker 3: we're gonna have the first trillionaires this year. And maybe

1011
00:52:35,760 --> 00:52:37,920
Speaker 3: I think it's plural, it's not just singular. I mean,

1012
00:52:37,920 --> 00:52:41,760
Speaker 3: certainly Elon will be on that list after SpaceX slash

1013
00:52:41,960 --> 00:52:46,200
Speaker 3: XAI slash Starlink goes public, and right behind them is

1014
00:52:46,239 --> 00:52:48,360
Speaker 3: you know, what's going to happen will be interesting to

1015
00:52:48,360 --> 00:52:50,520
Speaker 3: see what happens with sam Shop and open Ai and

1016
00:52:50,560 --> 00:52:54,040
Speaker 3: their IPO just a matter of weeks months here, all

1017
00:52:54,040 --> 00:52:55,880
Speaker 3: of this is going to take shape and an anthropic

1018
00:52:56,880 --> 00:53:01,319
Speaker 3: and that abundance is going to create moorings, and that

1019
00:53:01,400 --> 00:53:04,560
Speaker 3: concentration of wealth is going to create more I think,

1020
00:53:04,719 --> 00:53:09,080
Speaker 3: in security for even those that are the wealth creators themselves.

1021
00:53:09,120 --> 00:53:13,120
Speaker 3: And so this notion of sharing that abundance and having

1022
00:53:13,160 --> 00:53:18,800
Speaker 3: a society where everybody sees themselves as participating and fully

1023
00:53:20,160 --> 00:53:23,759
Speaker 3: you know, fully as you suggest, consider they see themselves

1024
00:53:23,800 --> 00:53:25,799
Speaker 3: in the future is foundational.

1025
00:53:26,080 --> 00:53:28,920
Speaker 1: Yeah, I'm pro innovation and pro success. I don't begrudge

1026
00:53:28,960 --> 00:53:33,839
Speaker 1: anyone their wealth, like as long as their neighbors aren't

1027
00:53:33,840 --> 00:53:36,640
Speaker 1: falling into the abyss you know, at scale, and I

1028
00:53:36,719 --> 00:53:40,040
Speaker 1: mean like that that's and that the thing I say

1029
00:53:40,080 --> 00:53:44,080
Speaker 1: to some of my very wealthy friends and they non agree,

1030
00:53:44,600 --> 00:53:47,760
Speaker 1: is that everyone is less happy in a vastly unequal society,

1031
00:53:48,400 --> 00:53:50,640
Speaker 1: you know, like no one wants to have bulletproof cars

1032
00:53:50,640 --> 00:53:52,840
Speaker 1: and private security for their kids and all this stuff,

1033
00:53:52,880 --> 00:53:56,480
Speaker 1: like it's miserable. And so the enlightened self interesting to

1034
00:53:56,520 --> 00:54:00,560
Speaker 1: do is like, look, I can be successful and let's

1035
00:54:00,680 --> 00:54:05,640
Speaker 1: solve some problems that these so that that's my school

1036
00:54:05,680 --> 00:54:08,400
Speaker 1: of thought. I you know, since you probably know some

1037
00:54:08,440 --> 00:54:12,560
Speaker 1: of these people, you probably naturally fall someplace similar. And

1038
00:54:13,160 --> 00:54:15,080
Speaker 1: that to me is something that even many of them

1039
00:54:15,080 --> 00:54:17,719
Speaker 1: will get behind. Like the caricature of some of these

1040
00:54:17,760 --> 00:54:20,160
Speaker 1: people is like they need every last dollar. It's like, look,

1041
00:54:20,880 --> 00:54:24,160
Speaker 1: you know, like sure they probably like their money, and

1042
00:54:24,200 --> 00:54:26,759
Speaker 1: they don't necessarily, you know, like want to port it

1043
00:54:26,800 --> 00:54:28,840
Speaker 1: over to the government in various ways because you know,

1044
00:54:28,880 --> 00:54:33,799
Speaker 1: they might not have like the highest confidence. But I

1045
00:54:33,840 --> 00:54:36,160
Speaker 1: think many of them can be drawn into a meaningful

1046
00:54:36,200 --> 00:54:39,239
Speaker 1: conversation about keeping society whole.

1047
00:54:39,760 --> 00:54:42,040
Speaker 3: I agree, And that's where I think we're missing that

1048
00:54:42,160 --> 00:54:45,600
Speaker 3: national leadership right now, and that opportunity presents itself, particularly

1049
00:54:45,640 --> 00:54:49,600
Speaker 3: in light of these IPOs, the situational moment demands of that.

1050
00:54:49,680 --> 00:54:53,160
Speaker 3: And look, if we quoted Voltaire, let's quote Aristotle, and

1051
00:54:53,880 --> 00:54:56,239
Speaker 3: he said it as well or better than we ever could.

1052
00:54:56,280 --> 00:54:58,360
Speaker 3: You can't live a good life. You can't live a

1053
00:54:58,360 --> 00:55:01,480
Speaker 3: good life in an unjust society. And that kind of

1054
00:55:01,680 --> 00:55:04,680
Speaker 3: wealth disparity, that kind of imbalance between the rich and

1055
00:55:04,719 --> 00:55:08,080
Speaker 3: the poor is the oldest, as Plutarch said, and most

1056
00:55:08,120 --> 00:55:10,920
Speaker 3: fatal ailment of all republics. So this society is just

1057
00:55:10,960 --> 00:55:13,960
Speaker 3: simply going to fray, and so I think this light

1058
00:55:14,000 --> 00:55:17,239
Speaker 3: and self interest, there's a real opportunity and shot to

1059
00:55:17,280 --> 00:55:20,160
Speaker 3: bridge that. So I appreciate that and thank you for

1060
00:55:20,200 --> 00:55:25,319
Speaker 3: giving me a shot to you know, of adrenaline, reconnecting

1061
00:55:25,360 --> 00:55:30,520
Speaker 3: with you talking in more entrepreneurial terms about the future

1062
00:55:31,480 --> 00:55:34,160
Speaker 3: and how we can accelerate it but steer it with

1063
00:55:34,239 --> 00:55:37,480
Speaker 3: the kind of guardrails and values that all of us deserve.

1064
00:55:38,800 --> 00:55:41,239
Speaker 1: Thanks Calvin, thanks for having me on