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Speaker 1: Welcome to Trillions.

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Speaker 2: I'm Joel Webber and I'm Eric Belchunis.

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Speaker 1: This is going to be a fun episode, Eric.

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Speaker 2: Yeah, I've been wanting to do this one for a

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Speaker 2: while a.

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Speaker 1: While, and we had to jump through them some hoops

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Speaker 1: to make it happen. But it seems like a great

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Speaker 1: moment to put the spotlight on the United States of America.

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Speaker 1: We have President Trump back in the Oval Office. He

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Speaker 1: has signed so many executive orders so far, but underlying

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Speaker 1: it all is sort of like, you know, this attempt

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Speaker 1: to kind of reassert American dominance. When you think about

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Speaker 1: the equity market in particular, USA is always number one.

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Speaker 3: Right er.

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Speaker 2: Yeah, And you know, on our team, we have this

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Speaker 2: persistent chat and we've actually it's come down to basically

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Speaker 2: like mocking Europe. It's it's like they're the you know,

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Speaker 2: we just had a full leaders at the Super Bowl.

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Speaker 2: It's like they're the Cleveland Browns or the like, they're

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Speaker 2: just a bad team that just can never get going.

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Speaker 2: And then the index that we specifically are just marvel

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Speaker 2: at is the Nasdaq one hundred. We call it the

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Speaker 2: eighth Wonder of the World. It just keeps on freight

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Speaker 2: training everything year in, year out. This is the cues,

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Speaker 2: and it kind of captures like that American innovation tech

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Speaker 2: thing even better than the S and P five hundred

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Speaker 2: and so when you compare the cues to like investing

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Speaker 2: in Europe, it's just so night and day. But at

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Speaker 2: some point it's so much cheaper pete price to earnings

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Speaker 2: ratio wise in Europe, everybody's like, oh, it's got to

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Speaker 2: go back, but it never goes back. It might go

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Speaker 2: back for two months, but then people come right back

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Speaker 2: to the queues. And so I just think that the

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Speaker 2: US stock market in particular, that the Nasdaq stocks and

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Speaker 2: the mag seven are almost like abnormally unusually great where

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Speaker 2: they deserve some kind of a scientific breakdown to analyze

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Speaker 2: what's really behind all this because it almost breaks your

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Speaker 2: brain a little bit.

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Speaker 1: Well you know who's not a scientist, me or you,

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Speaker 1: But it turns out we work with one. His name

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Speaker 1: is Steve how he's a quantitative research at Bloomberg. He

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Speaker 1: has a PhD. You started talking to him a while

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Speaker 1: ago about America and it's greatness, right, would you learn?

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Speaker 2: Yeah, because you know, we were looking at tech and

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Speaker 2: innovation in other countries and it's just not. They have

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Speaker 2: it there, but no one cares. Why is it rewarded

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Speaker 2: in the US and not in other countries? And we

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Speaker 2: had an interesting conversation about the culture here, the regulations,

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Speaker 2: the mindset, this sort of like you can do it

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Speaker 2: and who cares if you fall in your butt? Like

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Speaker 2: that doesn't exist everywhere else. Like a lot of people

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Speaker 2: are just like get in line and hammer, you know

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Speaker 2: what I mean? Like America is a special place, and

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Speaker 2: we don't do everything great. We're not number one and

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Speaker 2: a lot of things, Joel, but when it comes to

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Speaker 2: like the stock market, man.

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Speaker 1: We kick ass.

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Speaker 2: The US has four percent of the world's population, twenty

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Speaker 2: percent of the world's GDP, and our stock market is

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Speaker 2: fifty five percent of the whole world. Right, you got

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Speaker 2: to go. I think number two is something like Japan,

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Speaker 2: where Europe is a continent, is like eight percent or

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Speaker 2: something like that.

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Speaker 1: So joining us on this episode, Steve, how quantitative researcher

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Speaker 1: have Bloomberg in disease? This time on Trillions Captain America. Steve,

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Speaker 1: welcome on Trillions.

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Speaker 3: Thank you very much for having me finally, and Eric, yeah,

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Speaker 3: good to see you.

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Speaker 1: So, Steve, Bloomberg is a big place. I work in

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Speaker 1: the news group. Eric works in Bloomberg Intelligence. What is

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Speaker 1: Bloomberg indsease and what's your day job?

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Speaker 3: So Bloomberg Indisses is part of Bloomberg's data team now

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Speaker 3: and I am a quant researcher for Bloomberg Indices. And

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Speaker 3: we create investible indices or portfolios which can be then

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Speaker 3: licensed by as a managers and as owners to track right.

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Speaker 3: We create investment products and inducees that we license, and

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Speaker 3: then once in a while, if we come up with

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Speaker 3: something that we've seen that we think interesting that's worth

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Speaker 3: expanding a bit more than we then run long form

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Speaker 3: white paper.

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Speaker 1: Okay, so he's written some white papers. Did you read them?

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Speaker 2: Yeah, I mean I skimmed them.

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Speaker 1: Okay, all right, we're gonna we're gonna learn a little

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Speaker 1: bit of.

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Speaker 3: The pricing power. But you she said, was very good.

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Speaker 2: Yeah. And the innovation, I mean, they're they're they're very good.

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Speaker 2: And the Index group is obviously mentally in the same

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Speaker 2: ballpark as us because we write passive.

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Speaker 1: But he sells stuff.

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Speaker 2: True. Yeah, you're on the revenue side of the building.

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Speaker 1: Yeah, it's important. There's a disclaimer there, which is, you know,

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Speaker 1: we're not going to turn on the We're not going

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Speaker 1: to talk our own business.

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Speaker 2: No, No, I'm part of the terminal value add.

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Speaker 1: So Steve, just big picture what makes America fundamentally with

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Speaker 1: the research that you do, the data that you see,

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Speaker 1: why is American innovation greater than anywhere else in the world?

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Speaker 3: So h if you so went real this white pipe

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Speaker 3: of because you know, we created a index to try

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Speaker 3: to capture innovation as a factor systematically, right, you know

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Speaker 3: out there there's new people talk about we try to innovation.

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Speaker 3: How do you actually do it? Right? There's the r ETF,

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Speaker 3: which is an activetf F you know would selects them

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Speaker 3: and you know it does what it does right. And

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Speaker 3: then there's accuse, which is actually, when you think about it,

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Speaker 3: not intentionally innovation index, just as all the companies that

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Speaker 3: happens to be listed on the NAS Exchange, which of

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Speaker 3: course has agglomeration effect, right, everybody sort of congregates that.

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Speaker 3: But so it's not intentional. So how do you actually

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Speaker 3: do it? So we start to actually look into some

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Speaker 3: systematic accounting attributes. So R and D is a natural

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Speaker 3: place to look, right, Companies spend and report R and

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Speaker 3: D not or always consistently, and they may sort of

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Speaker 3: attribute things a little bit differently, but generally the direction

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Speaker 3: is correct. And people previously have looked at R and

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Speaker 3: D intensity, you know, so normalized by sales so they

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Speaker 3: can compare companies in the cross section. That's one way

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Speaker 3: to do it, but we found actually was even better.

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Speaker 3: It's actually to look at persistence of R and D spending, right,

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Speaker 3: companies that grow their R and D spending three consecutive

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Speaker 3: years year and year right, so, and by screening for

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Speaker 3: these such as gets expensive. Yes, that gets expensive, but

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Speaker 3: but more than that, it also means that there's a

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Speaker 3: highly grow of persistence that needs to be funded by

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Speaker 3: internally generated cash flow. On top of it, you probably

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Speaker 3: soft indicative of the skills that you have the confidence

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Speaker 3: in your own ability to convert right R and D activities,

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Speaker 3: which is inherently very expensive and on certain into valuable

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Speaker 3: intentionibal capital.

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Speaker 2: I want to go even deeper and just okay, you

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Speaker 2: grew up in I grew up in America. I basically

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Speaker 2: was raised outside. I went down played with people like

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Speaker 2: droll down the street. We got into football games in

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Speaker 2: the backyard. Then I entered little league games, had to

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Speaker 2: get along with teammates from all over the place. There

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Speaker 2: was also a feeling of optimism in the in my

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Speaker 2: upbringing at all times pretty much it was like anything's possible.

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Speaker 2: And then you go to high school and college and

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Speaker 2: you're frequently grouped with people to work on tasks and

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Speaker 2: as a team try new things, and.

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Speaker 1: I break breakdown problems.

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Speaker 2: Breakdown problems, and there's also credit from being the one

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Speaker 2: that does the good thing, like there's a number one

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Speaker 2: winner kind of celebrated here. Not to mention the bankruptcy

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Speaker 2: laws and the regulatory environment, it just seems like, I'm

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Speaker 2: not sure how you replicate that.

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Speaker 3: I think recently there was a very good interview that

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Speaker 3: Jeff Bezos gave at the Old Book. You know, he

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Speaker 3: mentioned that one of the biggest, you know, I think

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Speaker 3: reasons why America manages to have such a vibrant tach

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Speaker 3: sector innovative industry is because of the presence of a

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Speaker 3: risk capital market, which is different from a banking market,

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Speaker 3: right financial markets. You know, if you think about landing

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Speaker 3: right banks land Europe has a very good banking industry,

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Speaker 3: but the ability to have risk capital venture right to

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Speaker 3: invest in things that has one hundred chunds or even

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Speaker 3: less to work out. That's gonna when it pays off,

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Speaker 3: pays out one to to one hundred and one to

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Speaker 3: one thousand. That is the type of risk taking that

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Speaker 3: comes with I think both culturally and also past dependence. Right,

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Speaker 3: some of it has to do with cultural this idea

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Speaker 3: of being encouraged as a child to try things and

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Speaker 3: not be told that you likely will not succeed, but

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Speaker 3: to be told just try it. Right. Then, there was

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Speaker 3: also the fact that Americas happened to have succeeded in

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Speaker 3: the tech industry, a bunch of people succeeded by concentrated

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Speaker 3: entrepreneurship and equity appreciating, giving them the confidence of saying, yeah,

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Speaker 3: I can take a risk investing in in venture.

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Speaker 2: But if you take venture capitalists could invest in the

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Speaker 2: European I mean they're not like just legally allowed to

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Speaker 2: invest in the US like they they if they thought

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Speaker 2: Europe was the better odds to get that like Unicorn,

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Speaker 2: they would go there, right.

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Speaker 3: So, like, well, part of it I think to be

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Speaker 3: fair to Europe is that Europe. When I recently, I've

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Speaker 3: taken a few trips business trips to Europe to try

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Speaker 3: to understand how to actually get European clients to buy stocks,

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Speaker 3: and one we've noticed that there's just such a huge

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Speaker 3: amount of fragmentation of markets. It's impossible to get a

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Speaker 3: huge market right because everyone little country still has its

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Speaker 3: own language, you know, own regulation and just customs, and

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Speaker 3: it's impossible to scale what product. So if you're an airbnb, right,

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Speaker 3: you can have the entire US market. If you succeed here,

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Speaker 3: you become this behemoth and then go overseas. Everybody is lightweight,

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Speaker 3: you know. So I think it's a combination of issues.

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Speaker 3: I think cultural, you know, sort of is certainly at

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Speaker 3: the core of it, but you know, there are other

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Speaker 3: sort of institutional factors.

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Speaker 2: This idea of international also is something Bogel I wrote

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Speaker 2: a piece about a month ago, and I was like,

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Speaker 2: Bogel was kind of right about international. In ninety three

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Speaker 2: is the first time he had written, you don't really

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Speaker 2: need international. The US has all the capitalism you need.

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Speaker 2: If you had followed his advice, man you would have

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Speaker 2: really benefitted by not going international. So a lot of

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Speaker 2: people that I know, advisors and portfolio managers, they're always

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Speaker 2: in this dilemma of whether to rotate internationally, but it

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Speaker 2: just doesn't ever pan out. And I think that's part

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Speaker 2: of why this matters now is that we got big

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Speaker 2: valuations here, low valuations in Europe. But does Europe have

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Speaker 2: to go up just because it's due?

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Speaker 3: Well, so, the one thing is that if you recall

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Speaker 3: a post dot com bubble in the early few thousands,

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Speaker 3: you know that was a period when a bunch of

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Speaker 3: sort of things that we are not used to. How

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Speaker 3: did it happen?

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Speaker 1: Right?

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Speaker 3: International, I'll perform the US right, Europe, I'll perform the

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Speaker 3: US EM I'll perform the US value, I'll perform growth right,

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Speaker 3: and bones actually outperform stocks, you know, for many years

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Speaker 3: until things sort of then resumed. It's long run trajectory

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Speaker 3: and you have to triumph of the optimists. But you

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Speaker 3: know there are conditions in which you know.

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Speaker 2: I think the point you're making, though, which is is

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Speaker 2: a good control. Bogo really had very deep wisdom, and

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Speaker 2: he knew US would have bad years. He was going

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Speaker 2: to stay invested. I think if you add international and

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Speaker 2: bonds and this other stuff, when they go up and

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Speaker 2: the US goes down, it gives you comfort and like

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Speaker 2: it helps you hang in there. So if you have

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Speaker 2: a behave, if you're somebody who can't have the intestinal

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Speaker 2: fortitude to just stick to the US. But the returns

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Speaker 2: I'm talking about are actually inclusive of those bubbles bursting

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Speaker 2: two thousand and eight, two thousand and one, International way lags.

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Speaker 2: But you're right, there's these sections of time that could

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Speaker 2: be years where you're going to underperform. But I think

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Speaker 2: in Bogel's case, he was like, I'm committed to the

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Speaker 2: S and P five hundred. I don't need the feeling

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Speaker 2: that something else in my portfolio is up, which is

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Speaker 2: a level of wisdom that's hard to get to.

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Speaker 3: Yeah, I mean, part of it is exactly because the

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Speaker 3: US is such a huge economy with such a tremendous

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Speaker 3: complexity that there is everything that's happening right inside of

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Speaker 3: individual countries that's only exposed to oil or banking on

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Speaker 3: and so on and stuff forth. So this national boundary

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Speaker 3: division was always obitring the US place. I never really

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Speaker 3: like the comparison or two lines of a Europe US

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Speaker 3: up performing Europe because it's not the Apple Store comparison.

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Speaker 3: If you actually compared sort of sector weights adjusted or

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Speaker 3: equity duration adjusted, you will find that the difference, what

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Speaker 3: there is, a premium difference, is not as big as

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Speaker 3: one might have thought.

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Speaker 1: Or see if I want to bring it back to

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Speaker 1: your your paper and how you think about building in

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Speaker 1: disease that can help capture investment. Right, So you brought

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Speaker 1: up persistent R and D being the data point that

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Speaker 1: is like kind of the north start for the style investing.

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Speaker 1: How do you go about getting that data? By the way,

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Speaker 1: it's all you know, the companies are doing this, but

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Speaker 1: like what do you see when you look at those numbers?

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Speaker 3: So companies, first of all, that they increase, they are

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Speaker 3: required to you know, sort of disclose their R and

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Speaker 3: D and you know spending expenditures and you know for

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Speaker 3: a long time depending on or coman treatment. If R

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Speaker 3: and D spending is immediately allowed to be expensed, they

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Speaker 3: are actually incentivized, you know, to expand and so they

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Speaker 3: can reduce their textbook you know income. But more broadly,

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Speaker 3: we're just looking for, you know, in a systematic fashion,

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Speaker 3: metrics that can most succinctly, sort of in the sufficient

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Speaker 3: sufficient statistic way identify companies accurately that fit the profile

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Speaker 3: we're looking for. So in this case, we're looking for

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Speaker 3: innovative companies. You know, what is one metric that really

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Speaker 3: captures companies? You know ability to innovate, and we zoomed

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Speaker 3: in on the R and D spending metric in particular.

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Speaker 3: We found that more than the intensity is the persistence

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Speaker 3: of R and D spending that really allows you to

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Speaker 3: sort of screen down to the companies that are genuinely innovative.

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Speaker 1: Well, it's an example of the company that goes that

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Speaker 1: big on R and D but maybe isn't as persistent,

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Speaker 1: and what's that? What's that when that's more persistent?

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Speaker 3: So like, I don't want to go into an individual company,

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Speaker 3: but more more more likely I think it's one thing

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Speaker 3: is like if you look at, for example, the art

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Speaker 3: portfolio versus the you know, sort of the queues or

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Speaker 3: or in this case, the Bloomberg be invent Index, which

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Speaker 3: which is which we created and it's comfortable to the cues.

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Speaker 3: And here I do want you I want to give

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Speaker 3: credit to my colleague Garl Panzea who who you know,

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Speaker 3: helped create this this metric. Uh, is that if you

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Speaker 3: go back to compare the rc ETF you know membership,

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Speaker 3: the companies in there spend a tremendous amount of money

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Speaker 3: on R and D. Right, and if you normalize by

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Speaker 3: net sales, the average ratio is perhaps higher in fact

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Speaker 3: than the companies that are contained in this portfolio. But

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Speaker 3: if you compare the performance over time sort of you know,

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Speaker 3: this index is tremendously outperformed the art portfolio over time.

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Speaker 3: Now why is that. It's because the companies that held

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Speaker 3: in her profile in the portfolio tend not to be

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Speaker 3: consistently profitable. As a result, they don't consistently spend the

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Speaker 3: R and D. They sort of one of those things.

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Speaker 3: They spent one year when they have profits, the next

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Speaker 3: year they spend less, and so on and so forth.

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Speaker 3: So the average is high, but the persistence is not

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Speaker 3: very high. And as we all know, so those of

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Speaker 3: us who are trying to innovate, it's one of those

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Speaker 3: things where you can't really just you have to get it, sticks,

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Speaker 3: stick to it. You have really you know, persevere through,

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Speaker 3: you know, uh, and that's how you menually make the discovery.

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Speaker 3: And in fact, if you compare the US you know

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Speaker 3: index and why he has performed Europe. You know, going

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Speaker 3: back to our discussion earlier, the biggest difference between US

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Speaker 3: and Europe is not even the level, right, is actually

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Speaker 3: the percentage of companies that have persistence a high degree

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Speaker 3: of persistence. And we started the millennium actually with the

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Speaker 3: US and Europe being at similar levels of intensity and

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Speaker 3: percent of companies that have uh, sort of three consecutive

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Speaker 3: years R and the growth. And it was over the

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Speaker 3: last twenty years that difference really diverged, right Uh. And

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Speaker 3: you can so in the in the paper, you have

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Speaker 3: these sort of tables. You can check out these numbers.

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Speaker 2: That's why, in my opinion, it's not just R and

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Speaker 2: D spending. It's a U S thing. And and I

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Speaker 2: talk to people on Twitter life and they say, well,

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Speaker 2: you can get growth here. You just it's hard to

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Speaker 2: get growth other places. Sometimes there's these speculative bubbles like

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Speaker 2: in China, but pound for pound, you're just you're just

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Speaker 2: going to get more growth here in some of these

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Speaker 2: big companies that are in the US, they're just growing.

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Speaker 1: But Steve's going to bring it back to persistent R

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Speaker 1: and D being that thing.

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Speaker 2: And if you go, yeah, but that that that exists

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Speaker 2: in Europe too, but nobody cares.

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Speaker 3: Like that, not to nearly the same extent right over.

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Speaker 2: The last time in percent increase, that's right.

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Speaker 3: The share of companies that sort of really remained persistent

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Speaker 3: in R and D spending just dropped, right, and bisector

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Speaker 3: of the sector, you can compare them, Whereas in the

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Speaker 3: US tech sector is you know, sort of over fifty percent.

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Speaker 3: You know, in Europe you're talking about half that and

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Speaker 3: actually peaked in twenty thirteen, and actually it is even

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Speaker 3: smaller today. In other words, I think it's a kind

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Speaker 3: of a in the American the case is kind of

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Speaker 3: a virtual cycle, right when you have these innovative companies,

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Speaker 3: and I show in the paper that when they invest

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Speaker 3: persistently R and D, it produces intensible capital which then

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Speaker 3: becomes truly valuable, right, and it spins out cash flow.

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Speaker 3: Right if Apple invents the iPhone, invents the AirPods, if

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Speaker 3: becomes a six of a product, and that cash flow

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Speaker 3: funds R and D. Because R and D is inherently

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Speaker 3: a very opaque, asymmetric information activity, right, you don't really

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Speaker 3: want to erase external financing to fund it because nobody's

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Speaker 3: going to know how you're spending it. So you want

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Speaker 3: to use internally generated cash flow, which is why what's

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Speaker 3: nice about this metrip we created is that we don't

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Speaker 3: need explicitly even screen for cash flow because these companies

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Speaker 3: that persistently invest in R and D automatically are profitable

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Speaker 3: and general cash flow and they invest in themselves. So

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Speaker 3: which is why today you're seeing the AI race is

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Speaker 3: all the hyperscalers that are investing capacs by the hundreds

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Speaker 3: of billions themselves. They're not boring, right, So what.

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Speaker 1: You're describing is effectively a flywheel, right, this is the

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Speaker 1: R and D flywheel that that's what makes me.

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Speaker 2: Let's say we decided to go into Europe. Europe had

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Speaker 2: an ice run for six months like two years ago.

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Speaker 2: Is that flywheel going to start there and keep getting rewarded?

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Speaker 2: In other words, are the European companies going to spend

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Speaker 2: it as well, our work as hard as in the US,

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Speaker 2: where that R and D is rewarded with these intangible

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Speaker 2: brands and you know, huge, great products that we have.

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Speaker 3: Yeah, I think that's probably a challenge. I mean, France,

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Speaker 3: I think just announced one hundred billion euro projects try

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Speaker 3: to replicate or to to emulate America Stargate AI project.

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Speaker 3: I think their challenge there is I think again, all

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Speaker 3: these factors that are not pointing in the right direction, right,

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Speaker 3: so they don't have a you know, sort of unified

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Speaker 3: single market. The language is not English. It's hard for

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Speaker 3: them to draw talent. They have a lot of you know,

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Speaker 3: so well trained French engineers from their cause, but you know,

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Speaker 3: sort of it's hard for them to I think, get

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Speaker 3: your scale. You talk about the work culture, they don't

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Speaker 3: have as much. I think the text code probably makes it,

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Speaker 3: you know, such the people are not as incentivized to

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Speaker 3: work those long hours, sleep on the factory floors and

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Speaker 3: so on. I mean I'm generalizing here. I mean they

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Speaker 3: are really genuinely creative, ingenious, hardworking people in Europe. Like

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Speaker 3: there's the Mistraw model, AI model that's really I think

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Speaker 3: the state of the art. But I think, what if

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Speaker 3: you're sort of, so to speak, generalizing there is think

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Speaker 3: those factors that work against them?

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Speaker 1: Okay, So I used to take meetings a number of

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Speaker 1: years ago with Huawei. Do you know what Huawei? The

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Speaker 1: stat that they would point to persist in R and D. Yeah,

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Speaker 1: and it was a big number, and they would like

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Speaker 1: show it how much bigger they spent, how much more

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Speaker 1: money they spent on R and D than American companies.

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Speaker 1: So let's bring China into this. China spins a ton

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Speaker 1: on R and D. What is that? How does that

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Speaker 1: challenge your assumptions.

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Speaker 3: In so so you got cleary. You know, so this

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Speaker 3: episode is a cuptain in America, right, and the last

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Speaker 3: time years, America is far and away the biggest, most

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Speaker 3: successful you know, spender and creative innovation.

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Speaker 1: By the way, see you're Captain America. That's that was

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Speaker 1: That was the joke.

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Speaker 2: Yeah, that was the nickname you got.

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Speaker 3: That's funny, even though I'm not.

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Speaker 2: I know it adds to the humans run with it.

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Speaker 3: So so so if you look in the paper and

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Speaker 3: Fogus figure travel, like I was sort of doing a

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Speaker 3: tabulation of like what percentage of the companies you know,

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Speaker 3: in various national stock markets that are persistent R and

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Speaker 3: D spenders and what market share they take up in

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Speaker 3: the overall stock market. America is you know, obviously number one,

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Speaker 3: right in terms of both accounts, but number two is China.

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Speaker 1: Right.

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Speaker 3: They started a decade negligibly low, right close to zero

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Speaker 3: asserted they started the millennium right in the early two

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Speaker 3: thousand when China was much so further behind in terms

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Speaker 3: of economy development. But they caught up in a very

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Speaker 3: spectacular fashion. So if you look at the chart of

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Speaker 3: you know, sort of that pay figure twelve, that that

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Speaker 3: ascend and rise in R and D expenditures and the

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Speaker 3: degree of persistence, you know, almost catching up with America,

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Speaker 3: you know, a sector bisector.

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Speaker 1: Right when we started talking about this, there was this

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Speaker 1: great email exchange that led into an eventful weekend. I

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Speaker 1: just want to like look back on that for a second.

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Speaker 1: The email that you sent was a couple of weeks ago,

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Speaker 1: January twenty fourth, and you said there should be a

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Speaker 1: fun one with all the hysteria about Chinese AI kneecapping

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Speaker 1: American AI. And then on Monday that was Deep Seek Monday,

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Speaker 1: when everybody woke up and was like, what is Deep Seek?

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Speaker 1: So you sent that email and I was like, you know,

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Speaker 1: it's great to have that time set stamp because now

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Speaker 1: we're going to go back and revisit it. How does

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Speaker 1: your thesis hold up from before the Deep Seek Monday

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Speaker 1: to the after Deep Seek week.

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Speaker 3: So within the context of what we are talking about today,

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Speaker 3: you know, innovation systematic, you know, innovation factor. I think

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Speaker 3: it is actually ties in perfectly and we'll see later

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Speaker 3: why this entire phenomenon actually ties in very naturally to

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Speaker 3: what we were talking about, the American culture. American culture

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Speaker 3: of innovation. And two the ex time we have this

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Speaker 3: great geopolitical rivalry between US and China. What people don't,

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Speaker 3: I think often appreciate, fully appreciated in America, is that

436
00:21:59,440 --> 00:22:05,000
Speaker 3: China is really copies itself in America's mirror image. Right. China,

437
00:22:05,520 --> 00:22:09,200
Speaker 3: my generation represents a generation where we grew up looking

438
00:22:09,280 --> 00:22:11,919
Speaker 3: up to America when we when we first grew up,

439
00:22:12,040 --> 00:22:14,320
Speaker 3: we used to say thing of the world as the West, right,

440
00:22:14,320 --> 00:22:17,400
Speaker 3: the outside world, that the abroad, and then we realized

441
00:22:17,800 --> 00:22:19,600
Speaker 3: not all Western countries are born equal.

442
00:22:19,880 --> 00:22:20,120
Speaker 1: Right.

443
00:22:20,240 --> 00:22:23,239
Speaker 3: There is Europe, which is the Old World, and then

444
00:22:23,280 --> 00:22:27,600
Speaker 3: there's America, in particular the Silicon Valley. And gradually we

445
00:22:27,680 --> 00:22:30,280
Speaker 3: have had millions of Chinese students coming over the study

446
00:22:30,280 --> 00:22:34,080
Speaker 3: in America. They acquired that American sort of can do spirit,

447
00:22:34,240 --> 00:22:38,240
Speaker 3: the entrepreneurship, the ability to reinvent the wheel and not

448
00:22:38,359 --> 00:22:40,840
Speaker 3: be afraid, not be laughed in your face. And that's

449
00:22:40,840 --> 00:22:44,080
Speaker 3: why shan Zen is China's Silicon Valley. And that's why

450
00:22:44,119 --> 00:22:48,760
Speaker 3: you as increasingly seeing sort of Chinese Chinese innovation success,

451
00:22:48,960 --> 00:22:52,320
Speaker 3: which we will talk about how China sort of succeeded

452
00:22:52,720 --> 00:22:55,600
Speaker 3: sort of by copying America, not just you know, in

453
00:22:55,680 --> 00:22:58,399
Speaker 3: terms of literally the blueprints, but also more importantly in

454
00:22:58,440 --> 00:23:01,880
Speaker 3: a cultural sense. And that's the reason why I think

455
00:23:01,960 --> 00:23:04,600
Speaker 3: China has Actually it's one of those things where you

456
00:23:04,600 --> 00:23:06,879
Speaker 3: you know, you can't really catch up overnight, right, you

457
00:23:06,920 --> 00:23:09,359
Speaker 3: have to sort of practice and practice and practice, just

458
00:23:09,520 --> 00:23:11,959
Speaker 3: with any sport, right for you to hashman to get

459
00:23:12,040 --> 00:23:14,920
Speaker 3: get to get onto the game, actually shoot with a

460
00:23:15,000 --> 00:23:18,720
Speaker 3: high degree of percentage, you know. So I think that's

461
00:23:18,760 --> 00:23:20,600
Speaker 3: what we are looking at. So America still has a

462
00:23:20,640 --> 00:23:24,520
Speaker 3: tremendous lead, but China presents a formidable challenge.

463
00:23:24,800 --> 00:23:27,640
Speaker 1: So let's bring it back to AI specifically. US has

464
00:23:27,920 --> 00:23:30,760
Speaker 1: obviously been at the lead of this. The deep seek

465
00:23:30,840 --> 00:23:34,040
Speaker 1: moment sort of suddenly suggests maybe there's different ways to

466
00:23:34,080 --> 00:23:36,680
Speaker 1: go about making better AI models. How does that sit

467
00:23:36,720 --> 00:23:37,920
Speaker 1: with your research?

468
00:23:39,200 --> 00:23:41,240
Speaker 3: So to the extent, you know, so my research is

469
00:23:41,320 --> 00:23:45,720
Speaker 3: well identifying you know, innovative companies, you know, and we

470
00:23:45,800 --> 00:23:48,600
Speaker 3: indeed actually sort of find the same cuss out of

471
00:23:48,600 --> 00:23:51,119
Speaker 3: companies that are succeeding here in America through our index

472
00:23:51,160 --> 00:23:53,240
Speaker 3: and people can look at the membership. And we have

473
00:23:53,440 --> 00:23:57,199
Speaker 3: also using applied the same methodology to China identified you know,

474
00:23:57,240 --> 00:24:00,320
Speaker 3: sort of similarly creative and you know it come in

475
00:24:00,400 --> 00:24:04,160
Speaker 3: China that are doing well. That with respect to Deep Seak,

476
00:24:04,240 --> 00:24:07,560
Speaker 3: I think it's actually a very good illustration that, you know,

477
00:24:07,640 --> 00:24:09,680
Speaker 3: innovation is one of those things where you're sort of

478
00:24:09,720 --> 00:24:12,880
Speaker 3: paddling upstream, right, you are always sort of persevering and

479
00:24:12,920 --> 00:24:14,800
Speaker 3: so that so you can make progress and you otherwise

480
00:24:14,800 --> 00:24:17,560
Speaker 3: you'd be caught up. The Deep Sick moment is really

481
00:24:17,600 --> 00:24:19,600
Speaker 3: nothing special other than the fact that we are continuing,

482
00:24:19,800 --> 00:24:21,960
Speaker 3: continuing along the scaling law. Right, you know, sort of

483
00:24:21,960 --> 00:24:25,359
Speaker 3: costs of compute has always gotten cheaper. When you guys

484
00:24:25,359 --> 00:24:28,359
Speaker 3: were first entering, you know, the workforce and computers were

485
00:24:28,440 --> 00:24:31,000
Speaker 3: much slower, right today computers are tiny and on the

486
00:24:31,040 --> 00:24:33,080
Speaker 3: phone on the phone much faster. And then there's nothing

487
00:24:33,119 --> 00:24:34,520
Speaker 3: special about it other than the fact that you have

488
00:24:34,640 --> 00:24:38,320
Speaker 3: happened to have come from China and further innovation will

489
00:24:38,359 --> 00:24:41,000
Speaker 3: take place in America. What Deep Sick actually has actually

490
00:24:41,119 --> 00:24:43,960
Speaker 3: done was not even necessarily novel in the sense of

491
00:24:44,000 --> 00:24:47,040
Speaker 3: the invented brand new techniques. Those techniques have actually been

492
00:24:47,200 --> 00:24:50,840
Speaker 3: already published by Google right a year and a half ago.

493
00:24:50,880 --> 00:24:53,320
Speaker 3: Google didn't manage to actually make it sort of commercially

494
00:24:53,520 --> 00:24:56,560
Speaker 3: work as well as Deep Sick did, but generally the

495
00:24:56,640 --> 00:25:00,679
Speaker 3: idea is that one thing we've learned. Hardware advantage is

496
00:25:00,720 --> 00:25:04,800
Speaker 3: one thing, but you know, human capital is still ultimately

497
00:25:04,840 --> 00:25:05,720
Speaker 3: the most valuable.

498
00:25:07,000 --> 00:25:09,760
Speaker 2: At the beginning, I mentioned that the US stock market

499
00:25:09,840 --> 00:25:13,439
Speaker 2: is fifty five percent of the global market cap. Do

500
00:25:13,480 --> 00:25:17,000
Speaker 2: you see that, you know, growing to sixty seventy. I mean,

501
00:25:17,359 --> 00:25:20,200
Speaker 2: given how small we make up four percent of the population,

502
00:25:20,280 --> 00:25:23,600
Speaker 2: that is a big disparity. Obviously, where do you see

503
00:25:23,640 --> 00:25:27,680
Speaker 2: that number going. Do you see the money consistently rewarding

504
00:25:27,720 --> 00:25:30,159
Speaker 2: the US like the flywheel, or do you think that

505
00:25:30,200 --> 00:25:32,920
Speaker 2: the valuations will just get too wide and you just

506
00:25:33,160 --> 00:25:35,760
Speaker 2: have to go to Europe for like, just because it's cheap.

507
00:25:36,359 --> 00:25:38,400
Speaker 3: Well, I think I think of it in the two

508
00:25:38,400 --> 00:25:41,160
Speaker 3: waysn't fundamentally, I don't think it's an accident. Right, stock

509
00:25:41,200 --> 00:25:44,160
Speaker 3: market is not magic? Right America? I think, even though

510
00:25:44,320 --> 00:25:47,000
Speaker 3: has such a tiny population, is one of the world's

511
00:25:47,080 --> 00:25:50,000
Speaker 3: unique immigration country. Right is the immigration system is a

512
00:25:50,040 --> 00:25:53,000
Speaker 3: funnel the hye B system, which was much talked about

513
00:25:53,080 --> 00:25:55,879
Speaker 3: a few weeks ago. Essentially it's a funnel to funnel

514
00:25:55,880 --> 00:25:59,680
Speaker 3: for the world's I think, sort of finest talents. Right, So,

515
00:26:00,000 --> 00:26:02,560
Speaker 3: even though you know the population is very big, you

516
00:26:02,640 --> 00:26:07,400
Speaker 3: manage to attract the world's brightest, most best educated population

517
00:26:07,560 --> 00:26:10,760
Speaker 3: that's been excited by other countries' expense. Right, so, whilst

518
00:26:10,760 --> 00:26:14,000
Speaker 3: that doesn't change, I see in America having a structural advantage.

519
00:26:14,040 --> 00:26:16,199
Speaker 3: Now where that change, we don't know. Right there are

520
00:26:16,200 --> 00:26:20,640
Speaker 3: seemed to be some political winds shifting and with respect

521
00:26:20,640 --> 00:26:22,080
Speaker 3: to whether or not so we can have a stock

522
00:26:22,160 --> 00:26:25,119
Speaker 3: market bubble crash, and that's always possible. But this is

523
00:26:25,119 --> 00:26:28,800
Speaker 3: what happens afterwards when America continue to have that structural

524
00:26:28,800 --> 00:26:31,280
Speaker 3: advantage with respected the rest of the world. That remains

525
00:26:31,320 --> 00:26:33,520
Speaker 3: to be seen over the next few years.

526
00:26:33,760 --> 00:26:36,040
Speaker 1: Steve Hown, this has been fascinating. Thanks so much for

527
00:26:36,080 --> 00:26:37,000
Speaker 1: joining us on Trillions.

528
00:26:37,040 --> 00:26:38,120
Speaker 3: Thank you very much for having.

529
00:26:43,600 --> 00:26:46,560
Speaker 1: Thanks for listening to Trillions. Until next time. You can

530
00:26:46,600 --> 00:26:51,400
Speaker 1: find us on the Bloomberg Terminal, Bloomberg dot com, Apple Podcasts, Spotify,

531
00:26:52,080 --> 00:26:54,480
Speaker 1: or wherever else you'd like to listen. We'd love to

532
00:26:54,560 --> 00:26:57,880
Speaker 1: hear from you. We're on Twitter, I'm at Joel Weber Show,

533
00:26:58,280 --> 00:27:02,919
Speaker 1: He's at Eric Baltuno's. This episode of Trillions was produced

534
00:27:02,960 --> 00:27:07,080
Speaker 1: by Magnus Hendrickson. Bye