Is Artificial Intelligence Already a Bubble?

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INGRID: 60% of occupations will be affected by artificial intelligence. This is a bold prediction on AI, but one that we have to consider with this new, highly disruptive technology. This is going to be the focus of our TDAM Talks podcast today. My name is Ingrid Macintosh. Now, the distinct pleasure of welcoming Kevin Hebner, managing director and global investment strategist of TD Epoch to the podcast.

Kevin has recently written three engaging thought leadership papers on generative AI and equity markets, and he has also been traveling the globe presenting this topic to institutional prospects and consultants alike. Kevin, welcome.

KEVIN: Thanks, Ingrid!

INGRID: So I led the podcast off with a pretty alarming stat. Can we start there? Can you elaborate on how you think I will be reshaping the labor landscape?

KEVIN: Can I change your framing a little bit?

INGRID: Absolutely.

KEVIN: Well, 60% I don't think is bold. I think that's almost certainly an understatement. And I don't think it's alarming. I think it's exciting, but a majority people are more worried than excited about A.I., and I think it's wrong. I think there's going to be a lot of benefits to those fears. Thinking about 60%, for example, 60% of occupations today did not exist in 1950.

KEVIN: If you think about the percentage of occupations that were impacted by the PC and Internet, but certainly 60% plus by electricity, I think it's everybody. And when you have general purpose technologies like the Internet, electricity, the steam engine, the printing press, they end up impacting almost everything. That's, in fact, a definition of a general purpose technology.

INGRID: So we've been talking a lot at the investment table around what will be those impacts of AI. And one of the first benefits we talk about is this concept of, you know, productivity and this productivity surge. You talk a little bit about that and how we should be thinking about it from an investment perspective.

KEVIN: Yeah, I think we can frame it bottom up as well as top down. So bottom up, there's been quite a few studies of peer reviewed published studies looking at this. So for example, with coders, if they start using Copilot, on average productivity goes up 55% with writers, but an average, their productivity goes up 40%. And in fact, so far with generated by the two key uses are writing marketing copy as well as coding.

Taxi drivers, it increases their productivity by 8% because they have less downtime. They're better finding where their their next ride would be. Radiologist. A majority of radiologists are now using AI to help them detect whether an image is worrisome or it's not worrisome. The release last week by Open AI of the solo tool and so you can put in, say, 14 words of text and then you get a two minute very detailed video.

I think this decreases the cost of creating video game content or animation content by like 90%. It's enormously productive. So I think that's the way to think about it. Bottom up is aggregating from different occupations and tasks. You can also have a top down perspective on it.

INGRID: I think we could do a whole separate podcast, Kevin, on, you know, what does this mean for traditional education models, the university model, traditional learning. That's another entire conversation. I know in your in your presentations and road shows, you talk about some of the industries that are lower impact. Can you kind of touch on that? Who's less impacted by or what is less disrupted slash benefitted by?

KEVIN: I yes, but I'm not going to let you get away with throwing out education.

INGRID: But I do think it is a change.

KEVIN: It is super exciting both the healthcare and education sectors. With education, there are a number of AI companies involved with this. And if you think about education, the way we educate people really hasn't changed much since Socrates and Plato, since a thousand years, with the introduction of universities. Since we started increasing ... well ... really creating public school systems in the late 19th century to help an agricultural workforce thrive in a manufacturing environment.

So there is a lot of room for these new tools to give each student an exciting, more interactive, empathetic, personal tutor. But to do that, they have to come up against boards of education, teachers, unions, parents and ultimately the world of bits change quickly.

INGRID: I want to make sure, because this is a bit of an investment podcast, we want to make sure that we head on some of the pieces that our listeners might be listening for, which is some of the investment themes. And I've heard you talk about this concept of the “winner take most” dynamic. Can you talk a little bit about that?

KEVIN: Yes. So we've been working on this for a long time, Bill Priest and I - for the last eight years. And it's an inherent feature of digital tech, including AI that you have companies with enormous upfront investments in intangible capital and that allows you to reduce your marginal cost, your price, create enormous consumer surplus, which is why people love Google

Search and social media and these sorts of things. But what it means is that you do have to move fast and break things, try to get market share and then create moats. So that new companies can come in and take away your position. But it means that in all areas of the AI stack that can include the big platforms, cloud companies, semiconductors and all levels of semiconductors, design equipment, fabs, as well as all the applications that will come out of this.

All of them have this winner takes most feature, which means you're going to have an average, say, 3 to 4 companies getting the bulk of free cash flow margins return on invested capital. Everyone else is sort of struggling. And that's reflected in the type of market concentration we see today. But that's been building up over at least the last decade.

INGRID: And when we chatted ahead of this podcast, you'd mentioned, you know, you have been on the road in front of a number of institutional investors around the world, and you highlighted some of the themes that you're hearing from them when they talk to their asset managers. Can can I ask you to share some of that?

KEVIN: So the one thing that really surprised us in the last week, we had 15 meetings with institutional investors and consultants. Everybody talked about how they are using and how they should be using A.I. to increase their productivity, come up with new insights, improve their performance, and that runs the gamut from compliance through fundamental analysis, data analysis. It's not just a quantitative tools, marketing cells, functions and so forth.

So I think that's very interesting. And I'd say the consensus view is that within the next five years, this increase is a productivity of the investment management industry by 20%. That's a pretty chunky number. If there were differences of view in terms of whether that means they're going to be more people in the roles of fundamental analysis or quad marketing cells, I think there will be more because all of us have a long to do it.

Less was lots more things that we'd like to be doing, different asset classes, all sorts of things I think will let us add even more value to our clients. But that that was what really surprised me from the week is it was every meeting.

INGRID: You also talked a little bit about what you're hearing in terms of dispersion of returns that have similar pension funds are seeing. And I think, you know, this takes us back to the Magnificent Seven conversation, but can you talk about that a bit, please?

KEVIN: Yeah. So a lot of people were concerned that the underweight technology and that is because some of the big tech names, for example, NVidia or Tesla, it's very difficult to be market-cap-weighted (and) overweight them given issues about valuations and how much earnings growth is already in the price. So people are underweight tech, broadly underweight innovation that's led them to underperform in a very concentrated market.

And a number of people would bring this up and wonder, you know, how can they address this for the performance and I think there's both short term and long term posits to that.

INGRID: And they will have to see how that plays out. I’m going to get back in a moment to some of the impacts of regulation, you'd also mentioned this is not evenly distributed globally and today at least, the U.S. seems to be poised to sort of lead this push, this concept of U.S. exceptionalism.

KEVIN: Yeah, one of our clients last week, he joked that the future's already here ... is it's not evenly distributed. And I think I think there there is a lot of truth to that. In terms of U.S. exceptionalism, U.S. leadership that comes from a number of facets. One is the VC (Venture Capital) ecosystem in the United States. It's also present in Toronto and Montreal and other places, but it's really deep in Silicon Valley, New York and a couple other cities.

And this developed after World War Two, a lot of it funded by the Department of Defense and even recent companies, very much funded by the Department of Defense. So the VC ecosystem, I think is special. A second feature is U.S. has a very light regulatory touch. Everything does get regulated in the U.S., but very light certainly relative to Europe, for example, in what all this means as the U.S. attract the majority of private sector investment in AI, it also tracks almost all AI talent.

And if you look at the leaders in AI in the United States, they're born in India or China or France or the U.K. They're from all over the world. Some of them are born in the United States, but attracting all the money, attracting all the talent and these infrastructural advantages. So we think U.S. exceptionalism relative, say, to Europe or China and also some some smaller regions, I think continues.

INGRID: In Canada, to be fair, has a long history in in the foundation of the genesis of IATA. So we'll wrap us all up together in the North America lens. You started ... you open the the narrative on regulation. I think this is a really important one because, we're still in that first year plus since the real explosion of generative AI, ChatGPT, etc..

And I think, regulation is running quickly to catch up. But can you talk about how you first see the path forward with respect to the regulation of AI? And then on top of that, winners, losers, how that might affect are there some industries that will be more harnessed or hindered a little bit in terms of pace of change as a result of regulation?

KEVIN: Certainly regulation is a hot topic and there's a lot of focus on it. There's actually over 400 bills, draft bills, that have been tabled at the state level in the United States. That's up from about 30 a year ago. And a lot of this, I don't think is terribly meaningful. And there's a couple of reasons why we don't think the regulatory structures are going to be in place, I think not just in the next year or two, but I think it's going to take a decade.

One reason is we use the metaphor you have to skate to where the puck is going, where the parties and we don't really know where the puck is going. It's a novel, new technology. Even the last 18 months, things have changed a lot. And so you do have to wait until ... you know it's you're trying to regulate AI as an industry before it's an industry.

So I think it's sort of premature now. Now, a second reason is the Cold War 2.0 aspect of it. The U.S. doesn't want to put regulatory barriers in place that would slow U.S. development relative to China. There's a couple of reasons for that. A third reason is regulatory capture. Big tech are very active in terms of lobbying D.C. the biggest private sector companies, terms of spending money in DC are all tech companies, and they're trying to make sure that the regulatory infrastructure in place reflects their preferences and their views, how they see the industry developing.

And there isn't very much AI talent in government, so that's allowed, I think in the tech industry and one good example of that is social media. We've known for at least a decade of the pernicious impact that social media has on a lot of people, especially young people. But there's really been no effective legislation put in place. And this is really crazy.

But I think people who believe that we're going to be very aggressive and quick in putting regulations in place, they do they do have to think about this. And I think that's a big issue. Finally, if you look at the history of regulation in the U.S. and every new industry does get regulated. It typically takes between ten and 20 years after you have a commercially viable product until you get a regulatory infrastructure in place.

So we think there's going to continue to be a lot of talk, a lot of motion about it, but real action, I think this is not something for 2024 or 2025, but probably 2030 and later.

INGRID: And sort of a follow on question. I work for a large regulated bank. Are there places where you see that organizations will be slower to adopt, irrespective of the regulatory environment, but just because of the risks of unknown? And do you see sort of, you know, traditional players in certain sectors being hindered from from the perspective of a risk aversion from being able to keep up with newer, more agile companies like that, a different lens that we apply to to reviewing companies.

KEVIN: Yeah, I think regulation, existing regulation for the finance industry, health care industry is definitely an example. Education as well. So highly regulated industries, it is always more difficult for them to change. But when we start thinking about, you know, outside of tech, which industries are going to be more affected, we were like more coding and writing. But for example, because AI is impacting creative fields so quickly, it's going to be very important, say, for Disney, Pixar, Ghibli studios, video game developers, and these are actually very big industries.

And they face what we call the innovators dilemma. You have a new disruptive technology coming along and you have to pivot to this new technology and it's very difficult to do. Kodak is a classic example, and it's difficult not because you're bad, it's difficult because you're very good at what you do. So it's love. You love your products, your salespeople love what you do.

This is also a problem, say, for auto majors, because they have to pivot from internal combustion engines to autonomous vehicles and electric vehicles. And I think that's an extremely difficult pivot for them to have. So there's regulatory issues I think will make it slow. But I think the big thing is the “innovators dilemma”. You have this new disruptive technology and you can think about how how we do things - you have to reinvent yourself.

And that's hard to do because you're really good at quite a few things.

INGRID: Yeah, that concept of what is your value - what is your special niche? You know, I talked to before about Kodak, right? Yeah. How did they missed the boat, Right.

KEVIN: Yeah. Kodak invented digital photography. And so they knew it was coming. They understood the technology, but they died a death from a thousand cuts because they're so focused on the things that they're really good at. And the customers loved them that entrenched salesforce and they couldn’t move, you know, sort of one example of an interesting pivot just in the last couple of years is Facebook changed their name to met a couple of years ago because they were all in on the metaverse.

KEVIN: And the last year, one morning, Mark Zuckerberg wakes up, wakes up and says, no, we're not a metaverse company anymore. We're all in on A.I.. And so major pivot with companies like Facebook. And I think maybe you can do that when when you have one individual holding so much sway in a company broadly, I think that's really difficult.

INGRID: And it does. You know, when investment management teams look at company leadership and we look over the last 10, 15, 20 years, some of the leaders of the largest companies, the biggest tech companies at first looked like mavericks, looked like extreme. Is there something differently that you look at now when you look at leadership in companies against the backdrop of this parabolic rate changes or something different that makes leaders or companies great?

KEVIN: Well, I think that's definitely the case. And if you look at Microsoft, which has been one of the top five tech companies every year for the last 30 years, they have fantastic leadership. (Satya) Nadella (CEO of Microsoft) - he is visionary in terms of the role of AI on a computing. And so they're very good pivoting and changing the focus of the company.

Other companies like Google seem to be less good at that, but I think that that is very important to have an idea of where the puck is going and then changing the resources within the company. The focus of the company about that, that's clearly critical.

INGRID: Couple of last questions for you. The first one is the one that's on everybody's mind. Is this a bubble? Is it over? Is it run too fast? How do you respond when you get that question? Because I'm sure you get it.

KEVIN: So there's three reasons to to believe we're in a bubble of sort of environment. What is the concentration of returns? And there's been two previous times when stock market returns have been so concentrated and a small number of companies. One was the late 1990s, the other was the late 1920s. And neither of those cases ended up very, very well for investors.

A second reason is valuations are clearly stretched. They're rich, not as much as the 1990s. And the third reason is the euphoric tone of so much commentary about this is changing the world. It's happening right now and very much like the 1990s. But what's different now from that is that in the late 1980s companies were burning cash and ultimately what led to the tech bubble bursting was headlines in newspapers like Barron's saying all these companies are burning cash, we're running out of liquidity.

This can't happen for very much longer. And I think something similar might happen this time. But you have these companies now reducing a lot of cash. Operating margins are very impressive free cash flow generation and return on invested capital above 20%. These things are all fantastic, but we're in stage one. Stage one is: we're building out the infrastructure, investing in picks and shovels.

But at some point we need to move to stage two. So stage two is when we start to have killer apps, the benefit households, the benefit corporations. And there is going to be this this chasm, this abyss. I think it's at least 2 to 4 quarters from now. I don't think it's further than four years from now.

But at some point people are going to be saying, where's the beat? We've invested tens and tens of billions of dollars in building up the infrastructure. How does this really help companies and households? And we're not there yet. And I think when we get to that point and we start to see headlines in Barron's, the Wall Street Journal, Globe & Mail and so forth, emphasizing this point that we don't have these great killer apps yet, then I think there is a retrenchment and a rethinking in the market.

I don't think this is going to happen in 2024, but it could happen in 25 or 26.

INGRID: Last question for you. What is the risk that nobody's thinking about right now in terms of this theme? Because I think we're covering a lot of the ground and these are common discussions. I know you've had these conversations and maybe you get this question from the investors you're speaking to, but is there a risk is there something that that people are thinking about, either from an investment perspective or really from a social construct perspective as it comes to AI?

KEVIN: Well, in my conversations with clients and prospects, what is clear, I think, is people believe this happens much more quickly than it does. And I'm a fan of the history of innovation, and it always takes decades because you move from the worlds of bits and atoms to institutions and power structures. So it takes a lot longer than people think.

And so I think people are a bit over their skis now like they were in the 1990s. And I think this is going to cause an issue. But in terms of risk, the other risk that sometimes people bring up is we've had a number of examples of new technologies that have that hype, but ultimately there's not a whole lot underneath.

And you could think about cryptocurrencies as one example, but I don't think this is a cryptocurrency, I don't think this is a cloud that's going to pass. I think it's much more akin to the Internet, PC, electricity... And so far I think it is a general purpose technology that does impact lots of different sectors, occupations. And, you know, is is a big theme for investors this year, next year for the next decade.

INGRID: Kevin, thank you so much for your thoughts. I'm sure this will be the first of many conversations that you and I have on the subject, because as you say, I think, you know, as we go quarter by quarter, the landscape is going to change. New information will come available. Again. Thank you so much for your time and joining me today.

KEVIN: Oh, thank you Ingrid.

INGRID: And for our listeners, thank you for tuning in today. Please remember you can subscribe to us on Spotify, Apple, Google and Amazon to ensure you don't miss out on our content material. And also visit our Web sites to learn and see more of our thought leadership. On the subject of AI and the impacts on markets. G'day, everyone, and stay safe.