Michael Zeuner: 00:08
Hi everyone. This is Michael Zeuner, one of the managing partners at WE Family Offices. Thanks for listening to The Wealth Enterprise Briefing. I’m joined today by Sam Sudame, a familiar voice on this podcast. Sam is our global head of macro, and we’re going to have a conversation to address a question that we’ve been getting a fair amount from families that we work with, which at its essence is, are we in a bubble? Is this 1929 all over again? And you know, do we need to be worried about valuation levels, etc. And Sam, I know you’ve been, you’re a student of history, you’ve been looking at this topic. I suspect you’re going to want to put a wider lens on it, not just 1929 but you know, other examples of exuberance would be the 19 nineties.com bubble. We could go back to the 1870s right with the railroad bubble. Let’s, let’s talk a little bit about where we’re at. What you see that looks like it might have some similarities to those prior circumstances, and what if anything you think might be different and what should we take away as investors? So Sam, over to you.
Sam Sudame: 01:24
All right. Thank you. Michael, so the topic of AI and its investments and what has been doing for markets has been central this year. So first, let me talk about how much impact it’s had this year. So you know, we’ve had a two track economy and markets for most of the year. So the AI economy and markets have done very well, and the rest of it, the traditional economy, the rest of the stock market, did not do as well through August, but has started doing a little bit better after that, and AI has been the hero for the economy. So the OECD estimates that the US economy would have been in a recession in the first half of this year without the AI capital expenditures. And so, you know, questions are rising about the sheer quantity of investment. So what we see is that for the rest of this decade, the estimates of AI capex spending is about three to 5 trillion globally, and most of it is in the US and most of it is done by the hyper scalers. This has been really an important driver now of the US economy. So AI capex, as we’ve seen this year, has become a powerful cylinder of economic growth with much more to go. There’s been questions about is there over building in AI capacity? And one of the ways we can look at that is by seeing the vacancy rate in data centers. Right now, it is extremely low. And JP Morgan estimated that for the data centers that are being built, 75% are being leased in construction. And a lot of the companies called hyperscalers, which are building these data centers, they have a very large backdrop of orders. So we’re seeing right now just this nascent rise in AI investment. But when we start looking at transformational technologies in history, the railroads in the middle to late 1800s the automobiles in the 1920s telecoms that were happening in the 1980s and 90s, the capital requirement was enormous, and it was actually a larger part proportion of the GDP than we’re seeing now. And moreover, the pattern that we’re seeing, the amount of AI spent to GDP is still following the pattern of those revolutionary technologies in the past.
Michael Zeuner: 04:06
So, so that’s interesting, because what you’re suggesting is that, on a relative basis, what we’re seeing right now with AI is not out of the ordinary or out of the norm from these other transformative technologies, you know, over the last 200 you know, 150 years or so. But I guess the question is, when you look at what investors are willing to pay for shares of some of these hyperscalers, or the data centers, you know, capacity, or for the chip manufacturers, how do you feel about valuation, given also historical precedent.
Sam Sudame: 04:55
So the mag seven companies, the Nvidia’s and the Googles and Amazons, they’re called hyper scalers. They have driven the S&P500 returns since chatGPT was launched about three years ago. But those returns were backed up by strong earnings. So when you look back at the 1990s especially around the 1995,1996, those stocks were also going up, and they were driven by earnings. And then something happened. You know, it’s human nature, that sentiment became over, I guess, bubbly, very over enthusiastic, and it’s lifted the stock prices far beyond what the fundamentals had justified. So to take an example Cisco, comparing Cisco in 1990s to Nvidia today, these are prime examples. Cisco was an amazing company of the 1990s it’s still an amazing company today. It was the key part of the infrastructure of dotcom, the Internet technologies, just like Nvidia is today. So but what had happened, especially when you get to 1998 through 2000 the price skyrocketed. The price was really built on a bubble of over expectation on earnings, earnings that NVIDIA, despite being an amazing company, could never reach. I’m sorry that Cisco could never reach. When you look today, Nvidia’s stock price has gone in lockstep with its earnings. Its stock price has skyrocketed over these last three years because its earnings have skyrocketed over these past three years
Michael Zeuner: 06:51
And how would you what would you be looking for to see if there were signs of a bubble? Because I think on the one hand, as long term investors, we’re always trying to look through short term market volatility. But on the other hand, if a bubble is building, right, we want to be, you know, cautious and think about portfolio strategy and the context of that, which I think you know, can have different implications depending on where you are. If you don’t have any exposure, right? Do you buy in if you have exposure, do you reduce, right? There’s, there’s different lenses one could use. But how do you look at whether we’re just in a rising market, because earnings are melting up, and therefore stock prices are melting up, versus whether we’re truly in a bubble? How do you how do you assess when? When would you know?
Sam Sudame: 07:43
So a very when you look at technology stocks, their business momentum is very strong, and that’s one of the reasons why they have IPE ratios. So one of the ways to handle it is something called a PEG ratio, the price to earnings to growth. What so you compare that valuation versus its inherent growth rate. When we look at that PEG ratio, that is where the signs of a bubble in technology starts. So for example, back in the.com era, that PEG ratio was about more than four times. That’s extraordinarily expensive when the market was paying way too much for the growth process. Today, that PEG ratio is around 1.7 times. That’s quite reasonable for fast growing companies. So that’s one of the metrics that we use.
Michael Zeuner: 08:43
And so I guess the other perspective is that, if you think about the railroads or electricity or automobiles or the internet, all of things that were transformative technologies in their time and that transformed the economy, most of those led to a bubble and over exuberance, which burst right in the example of the autos and electricity in the 20s, right in the banking situation, right? Really, really terrible consequences, right? And you know, depression, etc, but at the same time, here we are, you know, 100 years later, and we’re still using cars and we’re still using electricity and we’re still using railroads, and clearly we’re still using the internet. So how do you think about navigating as an as a long term investor focused on fundamentals as opposed to trading? How do you think about navigating through, even if we’re not there yet, right by different metrics, including the PEG ratio, how do you think about navigating through through short term over exuberance and, you know, bubbles
forming?
Sam Sudame: 09:56
Sure. So what we want to look at is, what is driving the stock price? Are these based on assumptions that will never be met? If that’s the case, then it’s a bubble that or stocks are clearly then over valued. But when you start seeing a strong demand and the market is rationally applying a proper valuation metric, then we think that that the market is still same. Now it is human nature, and it has been, in all those examples that you mentioned, whether it’s railroads or autos or internet, excess investment took place excess investment because of over optimism, and that over optimism spilled into stock valuations. And finally, those assumptions that underpin the prices could not be met, and those stocks plunged, but the technologies remained and remain. Transformation, we think AI is also part of one of these super cycles, and we will look for measures where there is over capacity, in terms of capacity, in terms of investment, such as the vacancy rates. We’ll also look at threats such as what could cause the earnings in in hyperscalers to not be met. One of these type of risks is that they can’t meet their business backlog because there isn’t enough energy supply. In other words, there’s an energy bottleneck. So we will have to keep our eyes on those type of risk factors as well.
Michael Zeuner: 11:35
Which I guess Sam, if you think about the fact that, sure, there could eventually be a bubble that forms, you know, and there are many factors that go into valuation of companies, including interest rates, including overall macroeconomic growth rates, including geopolitical factors, right which are also at play right now in our system. It’s why we we counsel investors to not make a bet on any particular single outcome, and to have portfolios that balance exposure to the growth, to the AI, to the compelling opportunity, but also have offsetting exposures to sectors that might zig to the extent these growth sectors zag, and having a portfolio with multiple risk exposures is, remains critically important, even if we’re in the early innings of this AI transformation. Do you agree with that?
Sam Sudame: 12:39
Absolutely diversification is really one of the best risk management tools, by spreading your eggs across many baskets so that you have multiple drivers in a portfolio.
Michael Zeuner: 12:53
Well, and presumably to the extent that the bubble does form and then bursts, you’re protected in other parts of your portfolio, and can just ride it through. But in any case, I think I hear you being loud and clear that you are not certainly in a place where you think we’re in bubble territory, and that so far the increase in stock and equity valuations of coming out of the AI revolution are keeping in check with fundamental earnings and other other metrics that you look at. I think Sam will keep a very close eye on it in the coming months, but thank you for that perspective, and look forward to talking again soon.
Sam Sudame: 13:34
Thank you, Michael.
Disclosure: 13:39
The Wealth Enterprise Briefing is for informational and educational purposes only and does not consider the specific investment objectives, financial situation, or particular needs of any listener. The information in the briefing is not a recommendation of any security and should not be relied upon as investment legal or tax advice.
