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Speaker 1: Welcome trillions.

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Speaker 2: I'm Joel Whatever and I'm Eric Belchunas.

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Speaker 1: Eric, maybe you've noticed the media tech landscapes are obsessed

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Speaker 1: with artificial intelligence right now you think, yeah, yeah, you

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Speaker 1: used GPT yet.

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Speaker 2: Indirectly, but I totally know about it. It's impressive, although

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Speaker 2: you know, I'm still trying to make sense of it all.

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Speaker 2: I will say that it does seem like every two

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Speaker 2: years something just hits the zeitgeist with the Wall Street

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Speaker 2: Hype Machine, blockchain, and then it was like ESG. I

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Speaker 2: just feel like now AI is like front and center.

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Speaker 2: This is the new new thing. Anything with that associated

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Speaker 2: with it is going to have success. Is it a

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Speaker 2: bubble maybe? Is it just something to satiate the neat

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Speaker 2: marketers or is it truly the next one? Or is

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Speaker 2: it truly the next big thing? I will say I

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Speaker 2: was at the SEC's first ever Investment Division of Investment

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Speaker 2: Management conference in DC two fridays ago. Gensler spoke at

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Speaker 2: this conference and he said AI is going to be

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Speaker 2: bigger than the Internet, and they're looking into how to

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Speaker 2: regulate it and whatnot. So that was an eye opener

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Speaker 2: because I do find sometimes you don't know at first

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Speaker 2: whether something's just this huge hype marketing thing or it's

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Speaker 2: really worthy of all that attention. I'm not sold totally,

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Speaker 2: but certainly in the ETF world, there's a ton of

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Speaker 2: attention that's coming about AI, and we're going to continue

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Speaker 2: to see ETFs that have AI in the name doing

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Speaker 2: a variety of things, and so we should cover it

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Speaker 2: for sure.

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Speaker 1: Yeah, and we didn't ready to talk about this for

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Speaker 1: a second, but I think we've got two perfect guests

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Speaker 1: to kind of walk us through it. One's going to

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Speaker 1: be Rebecca Sen at Bloomberg Intelligence, who's been watching this

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Speaker 1: space closely. And then we've got Dave masa chief strategy

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Speaker 1: officer at round Hill Investments, which just launched an ETF

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Speaker 1: called Chat this time on Trillions AI Mania. Dave, Rebecca,

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Speaker 1: Welcome to Trillions.

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Speaker 3: Thank you for having me.

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Speaker 4: It's pleasure to be here.

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Speaker 1: Rebecca. I want to start with you. You, like I said,

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Speaker 1: you've been watching the space closely. There's there's two distinct

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Speaker 1: ways that we can talk about this, so let's just

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Speaker 1: be clear about that. How do you break it down?

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Speaker 3: So if we look at all of the ETFs that

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Speaker 3: haven't mention of AI Autonomous robotics, there's really two ways

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Speaker 3: that we classified it. The first are ETFs that track

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Speaker 3: AI companies, so that's the like of ourc roundhill ball

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Speaker 3: there ETF that just launched that day we'll talk about.

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Speaker 3: But then the second category are really ETFs that utilize AI.

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Speaker 3: So these are ETFs that had an artificial intelligence that's

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Speaker 3: telling them which stocks to buy and sell, when to

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Speaker 3: buy and sell, and the likes of those would be fourth.

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Speaker 3: In Korea, they have a core ETFs and they just

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Speaker 3: launched one last week. But then the other one is

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Speaker 3: also AI EQ, which is tracking the IBM supercomputer and

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Speaker 3: they analyze thousands of data points all day long, twenty

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Speaker 3: four to seven, and they say that they could do

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Speaker 3: the work of a thousand research analysts. So there's really

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Speaker 3: two categories that we found.

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Speaker 1: So yeah, one is a thematic bet on the sector, right,

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Speaker 1: and the other is AI is coming for all of

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Speaker 1: our jobs and it's just going to take over in

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Speaker 1: either mint money or drive it portfolios into the ground

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Speaker 1: or somewhere between.

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Speaker 2: Yeah, So let's just chew through the second version and

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Speaker 2: then we'll get to Dave and the thematic play. The

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Speaker 2: first version is AI powered ETF, So these are ETFs

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Speaker 2: that use AI to invest. Now, I consider this just

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Speaker 2: sort of like an evolution of smart beta smart beta

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Speaker 2: because it's using algorithms and numbers and stats. It's crunching

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Speaker 2: numbers to try to figure out a way to get alpha.

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Speaker 2: That's smart beta is sort of what that is already,

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Speaker 2: and there's been AI for a couple of years. It's

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Speaker 2: not totally brand new. AIEQ, as Rebecca mentioned, was the

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Speaker 2: one that was using IBM's Watson supercomputer. We recently looked

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Speaker 2: at this one. The returns aren't great. It's lagging the

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Speaker 2: S and P by quite a bit. I dug into why,

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Speaker 2: and my conclusion was it's just trades too much. It's

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Speaker 2: turnover is really high. It's going through stocks left and right.

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Speaker 2: And this brings up the bigger problem with AI powered ETFs,

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Speaker 2: which is you can't really solve the problem that regular

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Speaker 2: managers have, which is costs impeding onto your return. So

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Speaker 2: AI is gonna, in my opinion, have to figure out

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Speaker 2: a way to maybe limit costs, limit trading, and limit

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Speaker 2: the fee in order to have outperformance. So it's not

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Speaker 2: like AI is like somehow discovered some holy grail. It's

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Speaker 2: gonna face the same challenges that regular managers have and

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Speaker 2: that smart beta has, both of which have learned to

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Speaker 2: get cheap and limit turnover, and they've started to succeed

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Speaker 2: because they've done that. So I think AI is early

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Speaker 2: a lot of ETFs that have come out using AI

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Speaker 2: or pretty high fees, the turnovers high. I think over

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Speaker 2: time we might see one or two succeed if they

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Speaker 2: can get some performance. But I'm a little more bearish

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Speaker 2: on this side of the fence versus the thematic place.

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Speaker 1: But Rebecca, how could this version of AI powered ETFs?

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Speaker 1: How could this evolve more going forward?

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Speaker 3: So I think going to air points. If we look

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Speaker 3: at all of the AI powered etf from a performance standpoint,

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Speaker 3: just looking at year to date, they actually LaGG the

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Speaker 3: S and P five hundred. So looking at all of

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Speaker 3: the ETFs, the AI powered ETF on average return three

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Speaker 3: percent versus the ETFs that track AI companies return twenty percent.

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Speaker 3: And they're also more expensive. So we found that on

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Speaker 3: average they cost roughly seventy five basis points versus fifty

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Speaker 3: basis points. And so in terms of where the growth is,

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Speaker 3: I think with the AI powered ETF, you really need

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Speaker 3: to find the right fund managers, as Eric was saying,

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Speaker 3: a lot of this, A lot of them churt, so

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Speaker 3: they have a higher training cost. And so even though

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Speaker 3: a lot of these funds are powered by a supercomputer

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Speaker 3: an AI, there's still someone tweaking the model. There's still

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Speaker 3: someone tweaking the code. So I think that also impacts

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Speaker 3: the performance of the fund.

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Speaker 1: Okay, but the big phenomenon that we're seeing with the

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Speaker 1: chat gbts of the world, generative AI is sort of

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Speaker 1: what this is called, right, how much generative AI is

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Speaker 1: even in these AI powered ETFs or is there yet

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Speaker 1: another chapter to what this investment future could look like.

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Speaker 3: I think this is where round Hill differentiates themselves with

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Speaker 3: their new ETF, because they really are in that generative space.

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Speaker 3: I think a lot of the ETFs that were launched

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Speaker 3: previously are a little bit more traditional. They are quantitative based,

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Speaker 3: but there is still someone in the background that's tweaking

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Speaker 3: the model, that's changing it. And so I think in

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Speaker 3: terms of where the future goes there as we adopt

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Speaker 3: AI more and more. And I think chat GPT is

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Speaker 3: a perfect example. Since chat gbt launched in November, they

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Speaker 3: got more than one hundred million users in less than

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Speaker 3: two months. So to put that in context, how many

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Speaker 3: users do you think it took Uber to How long

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Speaker 3: do you think it took Uber to get one hundred

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Speaker 3: million users a year?

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Speaker 1: Two years, three years, six years, six years, okay.

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Speaker 3: Six years. So Instagram took two years and Spotify took

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Speaker 3: four years, and so chat GBT got one hundred millionars

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Speaker 3: million users in just two months. And so this really

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Speaker 3: shows that not only is there a hype into this,

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Speaker 3: but people are really invested and interested in this. And

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Speaker 3: I think as you get more and more data in

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Speaker 3: the AI space, that is only going to improve and grow.

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Speaker 3: So I think if we look at specifically at ETFs,

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Speaker 3: the ETFs that are powered by AI, they don't have

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Speaker 3: enough data points and a lot of the technology when

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Speaker 3: they first launched. I think AIEQ eric that launched a

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Speaker 3: while ago, and looking at all of the robotics ETF,

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Speaker 3: I think the first ETIF that launched was in two

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Speaker 3: thousand and six, and so if we look at where

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Speaker 3: technology is now versus in two thousand and six, it's

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Speaker 3: improved a lot and it's only going to grow exponentially.

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Speaker 2: That also the biggest challenge, and what nobody can get around,

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Speaker 2: is that nobody really knows the future. It's just hard

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Speaker 2: to predict the future and robot Ai Smart Beta Active Manager,

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Speaker 2: it's just very difficult. My guess is one of these

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Speaker 2: breaks out the pressro goes with like robots Win, that

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Speaker 2: gets some money, but then it underperforms and maybe sees

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Speaker 2: some outflows.

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Speaker 1: And you've said you're saying that you've seen this movie before.

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Speaker 1: I've seen this movie before.

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Speaker 2: So now what Rebecca talked about Chat GPT and the

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Speaker 2: frenzy to get on there, I think that speaks more

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Speaker 2: to thematic play. In other words, let me buy some

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Speaker 2: stocks that are going to benefit from this and let

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Speaker 2: me get a piece of that action. That is where

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Speaker 2: the thematic side comes from. Bringing Dave, now, I think

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Speaker 2: that's a perfect intro. Dave, you've launched a new ETF

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Speaker 2: called Chat. You're the chief strategy officer at Roundhill. What

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Speaker 2: was the thought behind this and how long have you

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Speaker 2: been working on it?

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Speaker 4: Well, I think Rebecca hit the nail on the head,

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Speaker 4: which was exciting for us when thinking about AI is

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Speaker 4: not really just AI itself. This has been around for

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Speaker 4: some time, but AI's been waiting for the killer app,

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Speaker 4: and the killer app with Chat gibt and that made

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Speaker 4: it so that because of its use and you biquitous nature.

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Speaker 4: All you need is computer access or a smartphone and

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Speaker 4: you can use Genera of AI in your daily life.

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Speaker 4: That it opened up our eyes that this is not

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Speaker 4: necessarily a theme or a fad that's happening ten fifteen

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Speaker 4: years in the future. This can happen today and now

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Speaker 4: we're seeing CEOs or as you noted, Gary Genzer, discussing AI,

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Speaker 4: general AI and material ways because that's really where the

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Speaker 4: productivity goes. I think it's exciting and everyone can be

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Speaker 4: focused on humanoids and robots and thinking that's AI, and

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Speaker 4: it is. But the ability to do so on sort

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Speaker 4: of a micro level is what Genera of AI is doing,

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Speaker 4: and that's really the idea behind creating an ETF just

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Speaker 4: for that space, and chat provides exposure around thirty companies

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Speaker 4: that are at the forefront of general AI, whether it's

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Speaker 4: the picks and shovels of a company like Navidia, or

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Speaker 4: companies that are exposed to it directly through ownership about

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Speaker 4: OpenAI like Microsoft, or a handful of other names that

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Speaker 4: folks may not is familiar with who are involved in

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Speaker 4: the space.

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Speaker 1: How quickly were you able to move on this.

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Speaker 4: We launched the EUTF color I should say a couple

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Speaker 4: of weeks ago, and so we moved really quickly, and

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Speaker 4: this is an idea that we were using it. We

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Speaker 4: started looking at chat EBT using it again either in

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Speaker 4: our workflows or just to have fun and realize, say,

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Speaker 4: there's something here. Can we do some research to identify

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Speaker 4: other companies that may be exposed to it because it

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Speaker 4: is a very nascent space, And it turns out once

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Speaker 4: you do that peel back the onion, even just one layer,

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Speaker 4: you can see that companies sort of have been poised

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Speaker 4: to do this, and now that the intention is there,

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Speaker 4: we're really beginning to see more and more folks latch

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Speaker 4: onto it.

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Speaker 1: So how do you figure out who's a real deal

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Speaker 1: player here that has unique exposure? Like an Nvidia comes

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Speaker 1: to mind, right, where you've got a chip maker who

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Speaker 1: is all about the creating the chip four AI applications,

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Speaker 1: and Microsoft, which is a backro of chat GPT right,

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Speaker 1: But then like there's a kind of a falloff. I mean,

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Speaker 1: I'll keep Google sort of in that camp or Alphabet

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Speaker 1: in that camp, maybe because we know that they'll have

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Speaker 1: something significant with bard. But after that, like, how do

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Speaker 1: you figure out who's legit versus just generating more hype

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Speaker 1: from the hype machine.

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Speaker 4: Yeah, this is really important because I think what we're

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Speaker 4: gonna find is, and we saw this with crypto and blockchain,

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Speaker 4: there's got to be a handful of companies that perhaps

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Speaker 4: changed their name we all know one from Long Island famously,

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Speaker 4: or companies that are using it but not really exposed

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Speaker 4: to it. Perhaps maybe some consumer names come to mind,

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Speaker 4: a Wendy's or Pepsi. So what we're doing is two things,

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Speaker 4: and I call it a kind of a talking to

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Speaker 4: talk and walking the walk approach. You need both here

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Speaker 4: to identify those companies that are truly exposed. So first

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Speaker 4: and foremost we're transcript Score and so here we're looking

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Speaker 4: at a proprietary keyword analysis of public documents, whether they

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Speaker 4: are company filings, transcripts, presentations, or press releases to see

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Speaker 4: if that company has references to various AI and related

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Speaker 4: technology terms. And I you know, you'd imagine a company

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Speaker 4: like Navidia jumps to the top of that list. But

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Speaker 4: then importantly we also have what we call a sector score,

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Speaker 4: and that's to do some real quantitative research to understand

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Speaker 4: is a company actually spending or has direct revenue exposed

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Speaker 4: to generate AI and the related technologies, and so companies

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Speaker 4: that are doing more or have higher portion of revenue

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Speaker 4: coming from that, or are spending on it for the

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Speaker 4: future from a true R and D standpoint, are going

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Speaker 4: to get higher score. So you pair those together, you know,

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Speaker 4: subject to some standard market cap and liquidity requirements, you

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Speaker 4: end up with a portfolio relatively concentrated in the grand

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Speaker 4: scheme of things that has exposure to microcaps, small caps,

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Speaker 4: mid caps, and of course some of the megacap names

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Speaker 4: that are powering this forward. So investors can expect to

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Speaker 4: see around twenty five to thirty securities going forward. It's

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Speaker 4: a global portfolio, a lot of US exposure, but a

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Speaker 4: lot of China exposure in the portfolio as well. And

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Speaker 4: this is going to adapt through time, which is one

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Speaker 4: of the reasons why we're excited that this is actually

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Speaker 4: an active approach.

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Speaker 1: How do you figure out the waitings?

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Speaker 4: The waitings are really i'd say a derivation of both

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Speaker 4: the transcript score and the sector score. So subject so

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Speaker 4: companies that have are talking about it a lot and

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Speaker 4: are actually spending on it, we're going to get higher rates.

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Speaker 4: Hence the video being the top holding, and then and

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Speaker 4: then so on. So if you look at our top

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Speaker 4: ten holdings, uh, you know, it's names like Navidia, Microsoft,

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Speaker 4: C three, AI, Alphabet and then AMD another semiconductor which

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Speaker 4: is making some inroads with g GPUs and the chips needed.

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Speaker 3: For general AI.

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Speaker 1: It's interesting.

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Speaker 2: I was at the inside ETFs conference and I looked

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Speaker 2: at and I was on an AI panel, and I

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Speaker 2: can't say I'm a total expert, but I looked at

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Speaker 2: some of the ETFs and I brought shot up and

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Speaker 2: my one critique of it. This is before Navidia's earnings.

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Speaker 2: I looked at and I said, you know, I don't

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Speaker 2: love theme ETFs that have big cap names at the

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Speaker 2: top because I already own those in my low cost

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Speaker 2: beta core. I don't need to be redundant. Then navidia

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Speaker 2: earnings came out and I felt like maybe they were

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Speaker 2: justified a little more because Navidia is the top holding,

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Speaker 2: and they felt a lot of that juice. But typically

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Speaker 2: I find DAVE in a fledgling area where the theme

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Speaker 2: might not be totally ripe enough for a full ETF

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Speaker 2: equal weighting or some kind of modified market cap waiting

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Speaker 2: to give you more exposure to the small and mid

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Speaker 2: cap space could be better because it helps differentiate the

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Speaker 2: ETF more from BETA.

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Speaker 1: Well, I think you.

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Speaker 4: Raised a good point, and we could have this conversation

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Speaker 4: about a variety of different ETF areas. It's whether it's

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Speaker 4: factor based, smart, beta, ESG is a whole other can

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Speaker 4: of worms and stomats.

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Speaker 1: Right.

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Speaker 4: The way I think about it for this space is

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Speaker 4: it would be I think disingenuous not to have the

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Speaker 4: exposure to a company like the Video or Microsoft or

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Speaker 4: Alphabet in a material way in this portfolio, and because

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Speaker 4: they're exposed to it, they get a higher weight. But

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Speaker 4: you know, again in the top ten holding there's names

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Speaker 4: like iFly Tech, since Time Group, even C three AI

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Speaker 4: to some extent, a company like More you know, or

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Speaker 4: a risk in networks that are not megacap growth. Right,

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Speaker 4: So even among the thirty names, we have a wide

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Speaker 4: representation of companies across the market cap spectrum. For US,

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Speaker 4: it's really are you truly exposed to genera of AI

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Speaker 4: or not? And that's what we're going to hang our

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Speaker 4: hat on. And of course this market has been rewarding

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Speaker 4: that that may not always be the case going forward.

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Speaker 4: But if we can provide that exposure again in a

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Speaker 4: concentrated way to companies truly exposed to jener of AI,

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Speaker 4: then we feel like we're doing.

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Speaker 1: Our job here.

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Speaker 3: So I guess going off that Dave, there are seventy

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Speaker 3: ETFs that has AI mentioned in their description and roughly

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Speaker 3: sixteen billion in assets. We expect that by twenty thirty

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Speaker 3: there's going to be one hundred and fifty ETFs with

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Speaker 3: the mention of AI. Given how crazy and everyone's interest

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Speaker 3: in this area with thirty five billion, what do you

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Speaker 3: tell investors that say, you know, how do I pick

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Speaker 3: which ETFs? A lot of them do have AI in

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Speaker 3: their wording. There's a lot of hype in there. And

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Speaker 3: to Eric's point, you know, just looking at NASDAQ one

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Speaker 3: hundred and sm P five hundred, they have all the

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Speaker 3: six names alphabet Navidia, Apple, Microsoft, and so what would

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Speaker 3: you say to investors?

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Speaker 4: Yeah, so it's funny. I did actually a research paper

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Speaker 4: that was published at this point, probably ten years ago

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Speaker 4: about givin ndtfs, and at the time it was crazy

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Speaker 4: because it had first marked that there was one hundred

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Speaker 4: divid endtfs. So now as you all know, there's hundreds more.

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Speaker 4: And if you look at the performance dispersion of them.

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Speaker 4: They were massive, right, and at the end of the day,

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Speaker 4: a big difference was whether they give it in growth,

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Speaker 4: which is more quality, or give it in yield there's

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Speaker 4: more value. And I think the same can be said

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Speaker 4: about the Mattos that when I in this market, it's

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Speaker 4: very easy to put a buzzy name in an ETF

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Speaker 4: name and see if that gets people's attention. I think,

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Speaker 4: particularly post COVID experience and some of the selloff, investors

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Speaker 4: have become more discriminating about the funds that they own

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Speaker 4: and they understand that, you know, like our friend Todd

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Speaker 4: Rosenblue says, you need to know what you own. And

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Speaker 4: some people just want to buy either the largest or

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Speaker 4: most well known, but it might not be the exposure

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Speaker 4: you want. So I always advocate take a look at

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Speaker 4: one what is the process to get the names in there?

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Speaker 4: If it's an index process, take a quick look and

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Speaker 4: understand the index methodology. You don't need to be an

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Speaker 4: expert in any of this space, but can you understand

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Speaker 4: what they're trying to do and then do the whole

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Speaker 4: things generally reflect that? Right, So if we're in our

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Speaker 4: case saying we're going to provide exposure to companies at

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Speaker 4: the forefront of GENERATIVII, and a name like Navidia isn't

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Speaker 4: a top holding, I'd probably question that. But also if

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Speaker 4: there's names like sense Time Group, which is a leader

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Speaker 4: in China in creating a computer focused AI marketplace, they

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Speaker 4: have their own large language model, then that makes sense too.

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Speaker 4: So this is going to be a space where I

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Speaker 4: think there's there's, as noted, a ton of continued investor interest.

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Speaker 4: There could be right or wrong reasons to buying any

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Speaker 4: of these ETFs, but for us, when it comes to

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Speaker 4: what is really focused on, generative AI chat stands alone.

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Speaker 1: Okay, Dave, I'm curious. You've got this thematic option, You've

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Speaker 1: laid out the case for it. At what point what

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Speaker 1: would it take to put that engine that we talked

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Speaker 1: about earlier in the show, the AI powered ETF engine,

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Speaker 1: into the thematic thing, and then you have an AIM

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Speaker 1: powered AI thematic ETF. What would that take? Yeah?

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Speaker 4: So, look, look, we are huge believers in the transformation

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Speaker 4: that jenerava I can bring both to everything from enterprise

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Speaker 4: software where our director of research publisher report focused on

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Speaker 4: an estimate of our tam of over one hundred and

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Speaker 4: twenty billion in a ten years time, and also the

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Speaker 4: consumer applications. But the idea that AI can be used

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Speaker 4: to identify the company systematically at this point in time,

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Speaker 4: I questioned that to me, I would agree at the

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Speaker 4: intro to the show that this may just be the

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Speaker 4: next evolution of quantitative investing. When I was a assistant

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Speaker 4: quant portfolio manager in the mid two thousands, we had

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Speaker 4: three components to our SoC selection model and we actually

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Speaker 4: outperformed before the global financial crisis consistently, and one of

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Speaker 4: those was priced book. It was so simple, but it worked,

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Speaker 4: and then guess what happens? It gets arbitraged away. I

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Speaker 4: think with AI, it's all dependent AI powered ets, AI

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Speaker 4: powered investment processes. It all matters at this stage of

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Speaker 4: development of is it being powered by humans appropriately?

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Speaker 1: Now?

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Speaker 4: Over time, as general of AI continues to improve, as

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Speaker 4: large language models and other use cases begin to become

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Speaker 4: more real time, we have to remember a lot of

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Speaker 4: the genera of AI potential right now is still having

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Speaker 4: that kind of backward looking learning. It's we are just

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Speaker 4: at the cusp of it being applied across a wide

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Speaker 4: range of industries. Then maybe I'd have more confidence in

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Speaker 4: those particular approaches, But for now, our quantitative process, both

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Speaker 4: sort of on the transcript side and then on the

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Speaker 4: sector analysis side, gives me more confidence that will be

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Speaker 4: identifying the names that will continue to be exposed to

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Speaker 4: general of AI in the future.

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Speaker 1: Is there any busy work that you can unleash AI

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Speaker 1: on to improve your daily life?

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Speaker 4: Dave, Uh, Well, one thing that you know, I think

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Speaker 4: people are experimenting with, and we've heard some stories about

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Speaker 4: about ai US is going right and also going wrong.

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Speaker 4: You know, there's there's things that I use to help

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Speaker 4: me craft uh to almost serve as an editor on

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Speaker 4: a daily basis, for for whether it's a blog that

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Speaker 4: I'm writing or just other research that I'm doing to

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Speaker 4: power that. So we're using it. In fact, if you

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Speaker 4: go on the research section of Roundhill Investments website, we

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Speaker 4: will we will note where where articles are being helped

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Speaker 4: to be written by chat GBT. Now, I think the

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Speaker 4: generative text is where all the easy attention is being paid.

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Speaker 4: But I think in the short term we're going to

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Speaker 4: see people experimenting with image generation, uh, sound generation and

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Speaker 4: things of that nature. So again we are just at

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Speaker 4: the cusp of that. But yeah, busy work is something

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Speaker 4: that we're looking to offload pretty frequently, uh to to

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Speaker 4: help to help guide us and be that assistant for us.

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Speaker 2: So this brings up a good point with ETF research.

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Speaker 2: I always tell my team put as much as your

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Speaker 2: voice in your writing as possible, get as much human

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Speaker 2: in those words, because some of this stuff is going

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Speaker 2: to be automated if you are dull, you know, And

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Speaker 2: I do think of this one. You know, the Hollywood

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Speaker 2: writers are on strike. There was this one billboard from

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Speaker 2: this woman who who was at the strike and it

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Speaker 2: said chat GPT never had childhood trauma. And I do

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Speaker 2: think that nothing will replace the human at the end

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Speaker 2: of the day for certain tasks. But again, dull, repetitive tasks,

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Speaker 2: I just see just automated. We do it at Bloomberg

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Speaker 2: on several types of data stories. There's a lot of

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Speaker 2: automated stories already, but I think research and you're editing

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Speaker 2: BusinessWeek over there, how much of your let's say, ten

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Speaker 2: years from now, how much of the copies can.

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Speaker 1: Be written by AI? No comment, no comment, I've stumped roll. Yeah, No,

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Speaker 1: I mean, look like we have there's a long ways

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Speaker 1: to go, Like is AI going to conduct interviews no,

436
00:23:00,720 --> 00:23:03,320
Speaker 1: So I think there's a long way for a lot

437
00:23:03,359 --> 00:23:05,919
Speaker 1: of it, and I think that you're right, there's a

438
00:23:05,920 --> 00:23:08,280
Speaker 1: lot of things that are rote that can be disrupted.

439
00:23:09,200 --> 00:23:11,480
Speaker 1: But I think, you know, the question for humans is

440
00:23:11,520 --> 00:23:13,399
Speaker 1: like where you add value, and I think there's a

441
00:23:13,480 --> 00:23:15,399
Speaker 1: lot of things that humans will still add value to.

442
00:23:15,640 --> 00:23:26,560
Speaker 3: So we interviewed Kathy with and asked her thoughts on AI,

443
00:23:26,600 --> 00:23:28,679
Speaker 3: and she said, it's going to add any It's going

444
00:23:28,760 --> 00:23:30,760
Speaker 3: to be the area that adds the most value in

445
00:23:30,800 --> 00:23:33,200
Speaker 3: the tech sector. And I think when we look at AA,

446
00:23:33,240 --> 00:23:35,320
Speaker 3: there's really two areas that we can look at. Most

447
00:23:35,320 --> 00:23:38,680
Speaker 3: people probably look at it from a software and hardware perspective,

448
00:23:39,040 --> 00:23:41,760
Speaker 3: but her view is that if we take Tesla for instance,

449
00:23:41,800 --> 00:23:45,119
Speaker 3: the autonomous driving of getting from point A to point

450
00:23:45,119 --> 00:23:47,760
Speaker 3: B safely is another way that AI is going to

451
00:23:47,800 --> 00:23:50,199
Speaker 3: have a huge influence in our daily lives. And so

452
00:23:50,240 --> 00:23:51,840
Speaker 3: I think it's interesting as we look at a lot

453
00:23:51,840 --> 00:23:54,119
Speaker 3: of these companies to see how they evolve. You know,

454
00:23:54,119 --> 00:23:56,040
Speaker 3: we don't know what they're going to do in five, ten,

455
00:23:56,160 --> 00:23:58,920
Speaker 3: twenty years time, and so there's a lot of growth potential.

456
00:23:59,680 --> 00:24:03,320
Speaker 1: Dave, So if Kathy would has been there's nobody more

457
00:24:03,400 --> 00:24:08,280
Speaker 1: bullishean tech in the future and AI than Kathy? Could

458
00:24:08,359 --> 00:24:13,479
Speaker 1: you imagine a world where Chat invests in ARC or

459
00:24:13,560 --> 00:24:15,120
Speaker 1: is that not enough of a pure play?

460
00:24:16,480 --> 00:24:20,119
Speaker 4: Wow, that is an interesting way to think about it.

461
00:24:20,160 --> 00:24:24,400
Speaker 4: I think my short answer would be never say never.

462
00:24:25,359 --> 00:24:27,439
Speaker 4: But I don't think as of now, that's really the

463
00:24:27,480 --> 00:24:31,040
Speaker 4: exposure that we're or intention that we're we're looking to

464
00:24:31,080 --> 00:24:35,800
Speaker 4: have right is And also people I think are looking

465
00:24:35,840 --> 00:24:39,680
Speaker 4: to Chat to serve that direct exposure to jenitor of

466
00:24:39,680 --> 00:24:42,560
Speaker 4: AI and kind of for now having another fund look

467
00:24:42,640 --> 00:24:44,360
Speaker 4: to do that, it may not make as much sense.

468
00:24:44,800 --> 00:24:48,959
Speaker 2: Yeah, that's a little like just bad form if you're

469
00:24:49,000 --> 00:24:49,600
Speaker 2: an ETF.

470
00:24:50,080 --> 00:24:50,560
Speaker 1: It's funny.

471
00:24:50,600 --> 00:24:55,240
Speaker 2: There was a cannabis mutual fund that used HMMJ for

472
00:24:55,280 --> 00:24:57,119
Speaker 2: the longest time. It had like ten percent. There was

473
00:24:57,119 --> 00:24:59,960
Speaker 2: an African mutual fund that used AFK. It's happened sometimes

474
00:25:00,160 --> 00:25:02,040
Speaker 2: here and there, But I generally ETFs don't want to

475
00:25:02,119 --> 00:25:04,680
Speaker 2: use other ETFs unless they're an ETF vtfs.

476
00:25:04,800 --> 00:25:09,360
Speaker 1: Yeah, okay, okay, Dave, first time on trillions. Welcome by

477
00:25:09,359 --> 00:25:12,080
Speaker 1: the way again. Uh, there's a question that we often

478
00:25:12,119 --> 00:25:15,040
Speaker 1: ask first timers, and I'm gonna ask it of you

479
00:25:15,080 --> 00:25:18,040
Speaker 1: now because you've got an epic ticker, Chat is a

480
00:25:18,280 --> 00:25:22,520
Speaker 1: great one. What is your favorite ETF ticker other than

481
00:25:22,760 --> 00:25:23,120
Speaker 1: your own?

482
00:25:24,960 --> 00:25:28,080
Speaker 4: So Chat is a pretty good one. The Roundhell Investment

483
00:25:28,119 --> 00:25:30,840
Speaker 4: team has a great number of.

484
00:25:31,040 --> 00:25:33,000
Speaker 1: Storied history of great tickers.

485
00:25:33,200 --> 00:25:38,680
Speaker 2: We'd don't forget about the twenty five million dollar bad

486
00:25:38,760 --> 00:25:42,480
Speaker 2: Boy Meta's that's probably the greatest ticker of all time,

487
00:25:42,520 --> 00:25:43,800
Speaker 2: simply because of the price tag.

488
00:25:44,200 --> 00:25:45,880
Speaker 1: But you can't use any of those.

489
00:25:46,640 --> 00:25:48,640
Speaker 4: Yeah, you'd have to keep me around all day because

490
00:25:48,640 --> 00:25:50,480
Speaker 4: I could go to my time at Direction and some

491
00:25:50,560 --> 00:25:56,080
Speaker 4: of the great tickers there. Gosh, drip, it's really hard

492
00:25:56,680 --> 00:25:57,760
Speaker 4: to pick a few.

493
00:25:57,840 --> 00:26:00,399
Speaker 3: Just pick one in Asia it's a random number exactly.

494
00:26:00,680 --> 00:26:03,359
Speaker 2: By the way, Asia is so boring, Like in the

495
00:26:03,440 --> 00:26:06,720
Speaker 2: China ETFs, the tickers are six numbers. It's like five

496
00:26:06,840 --> 00:26:09,800
Speaker 2: five O three three one, And it's like, come on,

497
00:26:10,920 --> 00:26:11,639
Speaker 2: the AI.

498
00:26:11,440 --> 00:26:12,679
Speaker 3: Tells you what your ticker will be.

499
00:26:13,680 --> 00:26:16,240
Speaker 4: I'm old school, and I'm so I'm gonna go with

500
00:26:16,320 --> 00:26:19,800
Speaker 4: something like move, you know from from our friends over

501
00:26:19,840 --> 00:26:23,679
Speaker 4: at van Ax. That was to me one of like

502
00:26:23,720 --> 00:26:26,960
Speaker 4: the original cool tickers. I think it still plays a role.

503
00:26:27,880 --> 00:26:30,120
Speaker 4: So I always have a soft spot.

504
00:26:29,920 --> 00:26:32,880
Speaker 2: For that one wholesome, wholesome pick that's probably the most

505
00:26:32,920 --> 00:26:34,360
Speaker 2: popular pick moo tan.

506
00:26:34,840 --> 00:26:37,359
Speaker 1: You know it's funny. That's the thing I like about it.

507
00:26:37,359 --> 00:26:40,959
Speaker 2: Like you know the other It's likable and so I

508
00:26:41,040 --> 00:26:44,600
Speaker 2: like verbs. Yeah, that's why chat's good. Chat's also verban

509
00:26:44,720 --> 00:26:46,600
Speaker 2: nown You got a two for there.

510
00:26:47,480 --> 00:26:49,760
Speaker 4: Well, I'm trying to make it an adjective and an adverb,

511
00:26:50,320 --> 00:26:52,280
Speaker 4: so give us, give us a few months.

512
00:26:52,400 --> 00:26:55,920
Speaker 1: Yeah, all right, Dave Rebecca, thanks so much for joining

513
00:26:56,000 --> 00:26:56,560
Speaker 1: us on rallians.

514
00:26:57,800 --> 00:27:09,199
Speaker 5: Thank you, thanks for having us, Thanks for listening to Trillions.

515
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Speaker 1: Until next time. You can find us on the Bloomberg terminal,

516
00:27:12,359 --> 00:27:17,040
Speaker 1: Bloomberg dot com, Apple Podcasts, Spotify, or wherever else you'd

517
00:27:17,040 --> 00:27:19,640
Speaker 1: like to listen. We'd love to hear from you. We're

518
00:27:19,680 --> 00:27:24,119
Speaker 1: on Twitter, I'm at Joel Webber Show. He's at Eric Baltuna's.

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00:27:25,240 --> 00:27:30,680
Speaker 1: This episode of Trillions was produced by Magnus Hendrickson. Bye