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Speaker 1: Welcome to Fear and Greed Q and A, where we

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Speaker 1: ask an answer questions about business, investing, economics, politics and more.

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Speaker 1: I'm sure, alma us tech giants are spending literally hundreds

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Speaker 1: of billions of dollars in AI this year, but what

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Speaker 1: if the rewards in both profit and productivity take a

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Speaker 1: lot longer to arrive than they hope. Doctor Kevin Hebner

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Speaker 1: is global investment strategist at Epoch Investment Partners. He joins

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Speaker 1: me once again from New York. Kevin, Welcome back to

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Speaker 1: Fear and Greed.

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Speaker 2: Hey Sean, great to see you.

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Speaker 1: Let's start with the big numbers here. We're talking, I

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Speaker 1: think with six fifty billion, seven hundred billion US in

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Speaker 1: AI spending this year from the big tech companies. Where's

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Speaker 1: the money going? And then what's the payoff?

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Speaker 2: So it's it's an enormous amount of money. It's like

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Speaker 2: a third of Australia's GDP being spent this year. So

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Speaker 2: it's buckets of money. And there been spending like this

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Speaker 2: certainly in one hundred and fifty years we'd have to

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Speaker 2: go back to the railway. So it's a lot of

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Speaker 2: money in by AI. Capex, A lot of this is

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Speaker 2: data centers, which means GPUs data racks, all the connectors, begining,

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Speaker 2: cooling systems, all this sort of thing, the bones that

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Speaker 2: run AI systems. And we've been seeing the hyper scalers

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Speaker 2: in particular, but AI broadly increasing CAPEC spending forty percent

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Speaker 2: plus per year. And what's what's the purpose of this

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Speaker 2: ultimately is to get better training, better inference, better AI

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Speaker 2: systems with ideas. At some point there will be products

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Speaker 2: that consumers and businesses like and there'll be profits associated

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Speaker 2: with that. We just think it's going to take a

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Speaker 2: lot longer than most commentators and consensus expects to get

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Speaker 2: products that are good enough for consumers, industrial use and

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Speaker 2: for and productivity benefits of that.

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Speaker 1: Okay, I want to get to the product's point in

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Speaker 1: a moment, but before we get there, that sort of spend.

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Speaker 1: So the largest property company in Australia got a Goodman Group,

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Speaker 1: Greg Goodman as a CEO. He came out and he

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Speaker 1: just basically said there's materially less supply than demand for

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Speaker 1: data centers. He was talking about specifically, what I wonder

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Speaker 1: when there is that much money going into it, does

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Speaker 1: and let's talk about the US have the capacity to

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Speaker 1: provide all the engineers, all the nuts and bolts, all

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Speaker 1: the land for the spend.

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Speaker 2: Yeah, and that's a really good point. So, for example,

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Speaker 2: if you think twenty five twenty six years ago, when

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Speaker 2: we're building on the Internet. At that point they're laying

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Speaker 2: a lot of fiber OCTOIC cable in ninety seven percent

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Speaker 2: of that was dark, which means it wasn't let it

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Speaker 2: wasn't there wasn't data running through it. So you're building,

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Speaker 2: then you're building supply ahead of demand. This time there's

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Speaker 2: a lot of demand and supply is legging. So that's

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Speaker 2: one big difference between now and twenty five years ago.

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Speaker 2: And then your question is, well, do we actually have

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Speaker 2: the capability which could be physical capability, so can we

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Speaker 2: have the electricity, do we have the turbines the generation capability?

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Speaker 2: And then do we have the engineers to build all this?

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Speaker 2: And so there are a lot of bottlenecks. There really

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Speaker 2: are a lot of constraints. And then also we get

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Speaker 2: the chips actually manufactured by TSMC and Taiwan using machines

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Speaker 2: from ASML and the Netherlands, and there's only one company

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Speaker 2: that make the UV machines, only one company really that

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Speaker 2: makes the leading edge summis. So there's a lot of

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Speaker 2: bottlenecks in this process.

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Speaker 1: Okay, so what's that mean firstly for the build out,

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Speaker 1: for the spend and where it can be done, but

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Speaker 1: then the idea of when the returns actually start filtering back.

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Speaker 2: Yeah, so the build out, you know, we're running at

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Speaker 2: forty percent per year in terms of spend. In terms

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Speaker 2: of capacity, maybe it's lower than that because prices are

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Speaker 2: going up and there are are these bottlenecks, but people

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Speaker 2: are racing ahead, are you know. We have some concerns

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Speaker 2: about about that, but our big concern is just it's

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Speaker 2: always the case when you have these new tech waves,

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Speaker 2: going back to the steam engine and railways and electricity

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Speaker 2: and telephone, autos, computers, Internet, it always takes a long

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Speaker 2: time for these technologies to mature to get products that

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Speaker 2: are usable, so not just ninety percent reliable, but reliable

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Speaker 2: so that businesses can actually use these and consumers can

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Speaker 2: use this, and this takes This takes a long time.

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Speaker 2: So for example, given where we are, we're about three

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Speaker 2: years after chat GPT was released, so sort of the

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Speaker 2: current AI wave, so it's comparable to early nineteen ninety

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Speaker 2: nine with a tech wave compared to when Netscape went public.

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Speaker 2: At that point, for example, Mark Zuckerberg was still in

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Speaker 2: middle school. Google was not yet formed. So people are thinking,

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Speaker 2: we're going to have the products now, the companies now,

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Speaker 2: the profits now. It's still really early days. So this

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Speaker 2: stuff will be really important. It will change the world.

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Speaker 2: We will have humanoid robots in our homes, but not

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Speaker 2: until twenty forty five. It's going to take a long time.

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Speaker 2: And we get excited, right, and there's a lot to

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Speaker 2: get excited about. But this same we were really excited

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Speaker 2: in the late nineties, we're really excited with railways, electricity,

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Speaker 2: and that's what happens with each techwave.

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Speaker 1: So you talk about the March of the Nines and

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Speaker 1: you kind of alluded to it there. Ninety percent is

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Speaker 1: you can get to ninety percent, right, but getting to

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Speaker 1: ninety nine point nine nine nine percent, which is actually

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Speaker 1: where it becomes a great return and where people are

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Speaker 1: using it, where it's actually practical. I would think of

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Speaker 1: it as the last mile type thing. That's hard, isn't it.

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Speaker 2: Yeah, And so the idea is you can get up

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Speaker 2: to ninety percent reliability, which means you've got a cool demo.

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Speaker 2: So something you can show at a conference and everybody goes, wow,

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Speaker 2: this is so much fun. And you can do that

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Speaker 2: with the humanoid robot for example. Or you know, we've

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Speaker 2: had autonomous vehicles for forty years now, and so you

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Speaker 2: get to ninety percent this wow factor, that's really cool.

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Speaker 2: But you don't want Anton's vehicle that's ninety percent reliable

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Speaker 2: because it's going to be running over a lot of people.

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Speaker 2: There's going to be a lot of accidents. So you

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Speaker 2: need what's called the five nine. So ninety nine point

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Speaker 2: nine percent. That's sort of the rough metric that's used

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Speaker 2: in a lot of instances where products are ruled out.

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Speaker 2: In some cases the bar is much higher, Like if

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Speaker 2: you're running a nuclear power plant, you're not happy with

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Speaker 2: five nines. You probably want ten nines, but at minimum.

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Speaker 2: And and so if you're right, if I'm sending an

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Speaker 2: email to you and it's ninety percent reliable, that's sort

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Speaker 2: of okay. If I'm if I'm sending you a cat

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Speaker 2: video and it's ninety percent reliable, that's okay. If I'm

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Speaker 2: writing code that's going to be used in a nuclear

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Speaker 2: power facility, ninety set reliable is not going to cut it.

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Speaker 2: And so we get excited about ninety percent. There's the demo,

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Speaker 2: but then to actually something that can be used out

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Speaker 2: of firm like the firm that I work at, or

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Speaker 2: where you work at, or can be used in your home,

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Speaker 2: because the first time a robot kills Granny, the regulators

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Speaker 2: are going to come in and nothing's going to happen

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Speaker 2: for ten years. So you need the five nines and

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Speaker 2: the idea that you know, the length of time it

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Speaker 2: takes you to get to ninety is the same length

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Speaker 2: of time it gets you to ninety nine, ninety nine

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Speaker 2: point ninety nine and so on. So when you have

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Speaker 2: this really impressive demo, you're twenty percent of the way there.

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Speaker 1: Okay, so tell me what. Let's bring it back to investing. Here,

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Speaker 1: what sectors are at risk of over promising?

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Speaker 2: Well, so I think the idea here is that we

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Speaker 2: we've had lots of companies and you're trading on the

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Speaker 2: idea of we're going to have these big productivity gains,

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Speaker 2: big profit gains immediately. And so our view is investors,

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Speaker 2: at least until recently, we're over concentrated in US tech,

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Speaker 2: and so stick with US tech, but quality companies that

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Speaker 2: are producing free cash from now have real products, real clients,

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Speaker 2: So not so much hype companies which are burning free cash.

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Speaker 2: Who knows if they're have a product, who knows if

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Speaker 2: they have customers. So go with quality. But beyond you know,

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Speaker 2: tech and quality tech, look at other sectors that are

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Speaker 2: doing well, and look at markets beyond the United States,

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Speaker 2: and particularly we like global champions in the UK, Europe, Japan, China, Australia,

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Speaker 2: Canada and so on. And then outside of equities, you know,

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Speaker 2: this is all very good for infrastructure, so we need

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Speaker 2: a lot of infrastructure to run all this and we

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Speaker 2: were talking about a little bit of that before. It's

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Speaker 2: good for lots of different types of commodities as well well.

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Speaker 2: So just you know, until recently, investors have been overly

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Speaker 2: concentrated in US tech, so we're not necessarily bearished broadly

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Speaker 2: on the US equity market, but we think that we

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Speaker 2: need to diversify beyond these over concentrated bats. And part

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Speaker 2: of this alter reflect a bears for you we have

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Speaker 2: on the US dollar.

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Speaker 1: Okay, push forward ten years from here, what would say

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Speaker 1: like in the real economy for people, I mean at

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Speaker 1: the moment in the middle of reporting season in Australia.

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Speaker 1: Everyone is talking AI right now. I wonder actually whether

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Speaker 1: that's much more than a chatbot for many people, for

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Speaker 1: many of these organizations. But in ten years, what is

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Speaker 1: a successful company who is using AI? Sensibly? What do

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Speaker 1: they look like?

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Speaker 2: So you know, so if today is analogous to eighteen

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Speaker 2: ninety nine, so you're saying ten years from now, this

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Speaker 2: would be analogous to two thousand.

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Speaker 3: And nine, So GFC not kivin Well yeah no, but

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Speaker 3: in terms of tech, so Google's a real company and

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Speaker 3: yellow Pages just went bankrupt because of Google.

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Speaker 2: We have Facebook is a real company, Airbnb Uber. We're

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Speaker 2: seeing these real companies start to evolve, promising companies. Users

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Speaker 2: love them and we can see a path to free

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Speaker 2: cash flow. So I do think it takes that time.

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Speaker 2: And it's always the case when you have a new

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Speaker 2: tech wave, and we've studied ten tech waves over the

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Speaker 2: last two hundred years, that you don't necessarily know who

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Speaker 2: the winners are going to be, so you have to

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Speaker 2: be very cautious about that there are going to be losers.

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Speaker 2: And so now with the saaspocalypse, we're seeing a lot

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Speaker 2: of software companies get crushed. And that is that if

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Speaker 2: you have a good business and say the markets been

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Speaker 2: thinking you're going to have cash flow growth of twenty

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Speaker 2: percent per year. So you're training on a multiple, say

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Speaker 2: have twenty five or thirty times, maybe your growth going

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Speaker 2: forward is going to be half what you're thinking. That

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Speaker 2: it means your multiple has to come down from twenty

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Speaker 2: five to thirty downtownwards the market mean and number like

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Speaker 2: twenty Like that's a big drop, right, and so we

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Speaker 2: should be seeing that. And one of the tables we

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Speaker 2: like in our report is we show the top twenty

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Speaker 2: five tech companies sort of every year over the last

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Speaker 2: thirty years. But if you look at the top twenty

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Speaker 2: five tech companies in the world in two thousand, only

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Speaker 2: four of those are still in the top twenty five.

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Speaker 2: So if you look at the top twenty five companies now,

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Speaker 2: it's reasonable to think by the time we get to

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Speaker 2: twenty forty. You're talking, you were your question was saying

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Speaker 2: twenty thirty six, but say twenty forty, it's reasonable think

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Speaker 2: that a majority of today's top companies are out of

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Speaker 2: this sort of elite top list. And that's hard to imagine,

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Speaker 2: right because we look at me and say, oh, Google

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Speaker 2: so great, Microsoft so great, and so forth. But this

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Speaker 2: is what happens when you have a tech wave. It's

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Speaker 2: highly disruptive to everybody, including the incumbents, including the incumbents

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Speaker 2: that everybody loves that have wonderful products.

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Speaker 1: We are totally out of time, right, But Kevin, I've

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Speaker 1: got to ask you this SASPAC calls as in Software

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Speaker 1: as a Service as pocalypse is real. So companies like

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Speaker 1: tech companies, insurers. I think we've seen wealth managers recently

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Speaker 1: being sold off on the back of this. That is real.

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Speaker 2: Yes, I think this is what always happens. And one

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Speaker 2: lesson from previous tech waves is it's easy to respot

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Speaker 2: the losers than it is the winners. So you can

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Speaker 2: see the companies that are going to be vulnerable to

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Speaker 2: this new technology. And you're going to have AI native companies,

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Speaker 2: AI first companies supplanting incumbent companies that built on AI

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Speaker 2: but try to have their existing workflows or existing ways

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Speaker 2: of doing, existing products and customers. And this is called

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Speaker 2: the innovator's dilemma. And so you get this in every

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Speaker 2: tech wave. So it's quite normal. It's painful, but it's

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Speaker 2: quite normal.

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Speaker 1: Kevin, Thank you for talking to fear and greed.

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Speaker 2: Thank you, Sean.

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Speaker 1: That was doctor Kevin Hebner, global investment strategist at Epoch

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Speaker 1: Investment Partners. Remember this is general information only. Should always

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Speaker 1: see advice tailor it to you before making investment decisions.

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Speaker 1: I'm Sean Almer and this is Freer and Greed Q

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Speaker 1: and dah