Bill Barker and Deidre Woollard discuss:
- Reasons for skepticism when it comes to AI hype.
- How long a leash unprofitable software companies may have.
- If being located in Silicon Valley is still an advantage for tech companies.
Asit Sharma and Ricky Mulvey break down the basics of expectations investing and give a framework for applying it to individual companies.
To catch full episodes of all The Motley Fool’s free podcasts, check out our podcast center. To get started investing, check out our quick-start guide to investing in stocks. A full transcript follows the video.
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This video was recorded on Sep. 07, 2023
Deidre Woollard: Welcome to Motley Fool Money. I’m Deidre Woollard here with Bill Barker. Today we’re going to look at some recent results through the window. Everyone’s favorite subject, AI. How you doing today, Bill?
Bill Barker: I’m well, thanks.
Deidre Woollard: Well Bill, we’ve talked a lot about AI on this show. It’s the story of the year. We’ve got massive profits, massive stock increases. I’ve been doing a Tech Thursday. But this week it’s different because I could talk a little bit about some unprofitable tech. We’ve had three companies report recently that I would say, they have a valid claim to an AI use case. You’ve got Asana, C3.ai and UiPath. None of those are exactly household names. They’re all in the business of selling AI services to other companies. But my concern is, and I’m really eager to get your take on this, I’ve talked with some analysts about the potential for overspend on both sides because you’ve got buyers out their companies. They feel they need to spend everything on AI to be with it. Then on the other side, you’ve got the companies that feel they have to offer something or anything or AI to be part of the conversation. Is this a recipe for an AI bubble, an AI disaster? What do you think?
Bill Barker: Well, I would go back to something you said initially is that there have been some massive profits and there have been relatively few companies that have realized massive profits from AI. There are plenty of stocks that have done massively well this year and certainly Nvidia is pocketing real money by providing the picks and shovels of all the AI work that’s going on. Where do you have the recipe for a poppable bubble is? I think when the actual profitability becomes completely untethered from the stock movements. The stocks can anticipate profits and price those into stocks and when the profits don’t actually appear, then sooner or later, the air is going to go out of a bubble. That I would say is the recipe. It’s early to declare that there are over-estimated profits for AI or those profits are expected to be too soon, but that’s where the bubble would pop.
Deidre Woollard: The sooner or later part I think is the part that gets. We don’t really know where we are in the cycle at this point.
Bill Barker: No, we’re certainly in the hype cycle.
Deidre Woollard: Yes.
Bill Barker: There’s some reason to be in the hype cycle. That in early days of dot-com the Internet bubble, there was plenty of hype. There was plenty that got realized over time in a lot of ways, but the hype came before the profits and the companies that ultimately delivered a profitable business model are many times the size that they were back then. But there were many others that never got to profitability.
Deidre Woollard: Well, and that’s really the important part here. I want to start with Asana. This is a work productivity platform, which AI and work productivity that makes a certain sense. They have this thing called the work graph. It’s their single point of truth they call it for work. They say they can add AI to this as a work wrangler. It puts things together, it makes suggestions. All of that makes sense. But Asana isn’t at its core, an AI company. If you’re an investor in Asana, you’ve got some AI aspirations here. But again, it’s a company that’s not profitable. How do you factor in the AI points?
Bill Barker: Well, I would start by being discounting the application of the single point of truth, which sounds religious to me.
Deidre Woollard: [laughs] Okay.
Bill Barker: It say AI get it as a sales pitch and it would be great if there were some single point of truth in any aspect of life. I understand why that is a goal for them to develop, but I don’t think they’ll ever get there on that and I think that investors can look at AI. The A I here, you’ve got Asana Intelligence as a small part of Asana as a whole. I think they’re more in the, me too, category of we’ve got some AI then this is an AI company. They’ve gotten some of the benefits of going along with the, we’re in this group too, but that’s more of a stock movement than the actual company’s business, which is growing at a healthy clip, but not as healthy as it used to be and this is the law of large numbers. When you’re growing the top line at the high teens which is where they are now, it’s just a different valuation multiple than where the company had been set years ago.
Deidre Woollard: Well, and like other tech companies that we’ve talked about, they’re having the same problem of the slowing macrocycle and it’s taking longer to close deals. We’ve seen that for about a year. But you mentioned something about the weird in this two thing that I want to talk about because you’ve got a CEO, Dustin Moskovitz. He’s got the cred. He’s been in Silicon Valley for forever. He’s one of the founders of Facebook and he was talking on the earning call about having this advantage when it comes to AI, because he knows all the players. It’s in their backyard. Is that really the advantage that he’s making it out to be?
Bill Barker: I don’t know. Not having spent the time in Silicon Valley? [laughs] I’m going to speculate. I’ve put a spotlight on the fact that this opinion should be discounted through that light. That’s where plenty of the talent that you would want to hire is. But do you need and is the talent staying there or going around the country to work? I don’t know that you have to do your shopping for the best coders in Silicon Valley today, the way you did five years ago. But having frequent interactions with intelligent people who are invested and know the field and know where they think it’s going has got to be a bit of a competitive advantage. But a competitive advantage against lots and lots of other companies that can pretty much say the same thing. I would think.
Deidre Woollard: I think that’s true and one of the things I’ve been thinking about from a real estate perspective and just from an overall perspective of, the previous belief was that you had to be in Silicon Valley and then it seemed to be less that way. The OG’s of tech are saying that that’s the case now again, for AI, and I have my skeptical hat on about that.
Bill Barker: If all the ones saying it are the ones in Silicon Valley, then [laughs] you’d be able to discount the degree to which that must be true, because I’m sure that there are others who are outside of Silicon Valley who are saying no. We’re doing great without being subject to Silicon Valley real estate prices and the troubles of living there, and we are out finding talent throughout the world. I think that it is. Look, if I wanted to learn more about AI, I would probably find myself in Silicon Valley talking to people out there. There’s something to it. But ultimately it shows up in the numbers, and if it doesn’t show up in the numbers, you can keep selling that story, but that’s all it is.
Deidre Woollard: Asana, also, they said this phrase a couple of times, that they want to move upmarket. Upmarket, I always get a little bit worried about upmarket because I know it’s always harder to go upmarket than it is to go downmarket. It feels they’re going to spend a lot of money. They’ve had some wins on this. They had a major cybersecurity platform they said is switching to Asana. They talked about some other big wins, but starting out small, how hard is it going to be for them to get bigger and get those bigger and bigger companies? I’m worried that they’re going to spend a lot of time, a lot of money trying to capture those big dogs and get them to switch.
Bill Barker: Well, ultimately you’re right. It’s about how much they’re spending in pursuit of this. Now, they are having some success. From the last quarter, the number of customers with annual spend above $5,000 was 20,000 plus customers that grew 15%. Annual spend over $100,000 increased 20%. That did increase a bit faster to 550 sum. That’s where they’re targeting. If they pull it off the bigger customers, especially a large base of big customers is great for the business. But for every customer who’s spending 100,000 they’ve got 40, they’re spending well somewhere above 5,000. I think it makes sense to pursue it. But if it doesn’t, as we come back to a few times, if it doesn’t show up on the bottom line sooner or later, then it’s not the right strategy. They need to get to profitability.
Deidre Woollard: The path to profitability thing is what I wanted to focus on today because AI is just like, I worry that it’s a little bit of a smokescreen for people and that it’s. Well, we’re going to be more profitable now and I want to talk a little bit about C3.ai because I feel like this is one of those stories. They should have that AI advantage. They got for good reason, a lot of the AI hype, they were doing AI enterprise before a lot of other companies, they’re tickers AI. Then they’ve got good contracts. They’ve got contracts with the Department of Defense. They’re working with the major Cloud providers. But this profitability question, and they talked about in their earnings, that they’re going to turn from focusing on profitability, on getting to that gap profitability, to investing in generative AI. Does that worry you? It seems like they’re just pushed off that date quite a bit.
Bill Barker: It’s not a get-out-of-jail-free card.
Deidre Woollard: Right.
Bill Barker: To the extent that management there or anywhere else think that it is. They’ll learn the lesson over time. C3.ai actually has more of a justifiable story that, “Hey, we’re going to focus on all of the potential of AI than many others.” That is their game. I think that what it does, when you say as they have, “Well, we’re pushing off the profitability that we thought we could hit on whatever, an adjusted basis this fourth quarter or something like that, we’re no longer going to pursue that,” is, they’re telling you, “We’re raising the ceiling, but we’re also lowering the floor on where things could go.” If you’re profitable, and have a predictable continuation of your profitability, you’ve got a floor under what can happen with your business and your stock. If you never get there, you may go crashing through the floor. But they’re in the AI game. The ceiling is very high. It makes sense to pursue the ceiling. The market on one day, took the stock down, whatever it was, 15% or so, but stocks almost tripled this year. They’re playing with house money. If they can’t get to profitability reasonably quickly on the business side of things, with the stock having virtually tripled, there’s a path to a secondary. If they need to raise cash, this is a good market to raise cash through an increased stock price.
Deidre Woollard: They don’t have a lot of debt, so they’ve got runway, they’ve got time. It’s just a question of, at some point, does everybody get a little impatient with it? Certainly, the drop indicates that there is definitely some impatience. There are a lot of people on line. You’d have the chatter that happens on line after results. There seems to be a lot of impatience with this one.
Bill Barker: Some impatience with this one. It’s way up from where it was at the beginning of the year. As I say, there was a little bit of house money to play with there in terms of, “Well, we can take a 20% hit to the stock, and everybody’s still going to be happy.” Who’s been along for the ride this year. Look over the long term, C3.ai is still down, 70% or whatever it is, from its high back in 2001. But in the recent past, they’re in the right spot having AI as their business, in their name, as their ticker. They’ve reaped some of the rewards from that. They haven’t turned it into a profitable business as of yet, but I think there’s a little bit of a leash for all of the companies that are in the AI space right now. It’s incumbent upon them to deliver what they have promised over the next quarter, I think, and not to back further away from the profitability picture.
Deidre Woollard: There’s only so far you can push that question off. It is going to come back up. It’s interesting, thinking about earnings and results this season. I’m hearing these two things from business-to-business companies. The one is, we’re more responsible now, we’re focused on profitability, and we’re cutting costs, and we’ve seen certainly the impact of tech layoffs, and things like that. But I’m also hearing that we’re going to spend a lot on AI. We’ve got C3.ai, Asana, and UiPath that all of their earnings were like, profitability will come, but don’t worry about it. All these companies have been public for three years or less. Does it make sense to give them that pass in order to let them really have that runway? At what point do you think that runway stops? Is there a point in the cycle when, all of a sudden those bills come due? It feels it happened a little bit with other companies, Meta comes to mind on that, at some point investors just get impatient?
Bill Barker: Yeah, if you’re talking about the three-year horizon that these companies have been around, going back two-and-a-half, three years, there was a complete pass on profitability at that point in time. That’s when the market peaked in 2001, especially for the NASDAQ companies. Today, a lot of companies have had to find religion on profitability, but AI is a special use case. It’s a bit of a fair fight between the promise of these huge piles of gold that somebody is going to land upon in AI, and maybe many companies, but they all got priced at points this year as if all of them would land upon a pile of gold. They’re not all going to. I’m not saying that these three won’t, in some respect, but it’s still, I think, a lot of hope in all these companies. They’re not being priced on traditional valuation metrics yet.
Deidre Woollard: Good point, and reasons to be cautious.
Bill Barker: You can play this game focusing on the profits. Now Nvidia got profits, but I mean, the multiple on those profits is rather eye-opening. Whether simply getting end profits that are growing fast, you can come up with a mathematical equation to justify that price. It’s still trading quite close to its all time high. You can focus simply on companies that are profitable, if you want, and still be exposed to some upside. Certainly, in the case of Nvidia, the plenty of upside this year, and the others that are also engaged in the use of AI at a high level. They’re not the small caps that we’re talking about here which have a ceiling, which is 3, 4, 5, 6 times the stock price. We know the ceiling is at least that high, because they’ve all been 3, 4, 5 times the price they are right now. If a sufficient amount of people get excited as we’re excited three years ago in unlimited growth, and maybe they get back to those prices someday.
Deidre Woollard: Awesome. Thank you for your time today, Bill.
Bill Barker: Thank you.
Deidre Woollard: You may have heard the phrase expectations investing, but what does it really mean? Asit Sharma and Ricky Mulvey kick off two part series on the topic.
Ricky Mulvey: Asit, we’re going to put some growth cases of companies, maybe a bit on trial for the listener. We’re going to do that through this framework called expectations investing. We’re first going to give the framework, and then we’re going to use that with some practical applications with four companies. This segment you’re going to hear the intro, and then over the weekend, we’ll get to dive in with some of those case. First Asit, can you just provide an introduction to expectations investing to maybe a listener who’s never heard of it before?
Asit Sharma: Absolutely, Ricky. First, I beg listeners lower your expectations. But this is a style of investing that is most associated with Michael Mauboussin, who’s a very famous investor and analyst theoretician. He wrote a book with Alfred Rappaport, another academic, called Expectations Investing. This is more of the fruition, I think, of his life’s work in investing his how to understand how companies should be valued by the individual investor. I’m actually going to read you an excerpt from the second chapter of the book, Expectations Investing, which is very interesting. Ask you for reflection. I think that’ll be a good jumping off point to understand how this works. Here we go. “Traditional discounted cash flow analysis requires you to forecast cash flows to estimate a stock’s value. Expectations investing reverses the process. It starts with the stock price, a rich and underutilized source of information, and determines the cash flow expectations that justify that price.”
Ricky Mulvey: I think it’s interesting because it assumes that the market is a bit efficient than a lot of stock investors would like to, perhaps, admit. I also think that there’s a part of my brain that is, “I’m lazy. I don’t want to add this extra step. I don’t even like doing a discounted cash flow model, so you’re adding more work for me. What the heck?”
Asit Sharma: Totally, I actually think this suits the lazy personality more than a DCF model. But let’s talk about what you alighted on. I think that’s so important. This book posits that the stock price is a rich source of information. There’s a lot that’s reflected in there. Different investors with different tools have all come together in a market place, and as a communal exercise, they’ve assigned a price in the market to a stock. Theoretically, if there’s a lot of good information out there, and there are a lot of knowledgeable people with good tools who are assessing stock price, and they have a balance of supply and demand, that price should represent some very decent cooperative assumptions. What this book is saying, is that, yes, if you build up a traditional idea of cash flows, try to ascertain what all those future cash flows are worth, and then discount them back to the present value. That’s a worthy exercise, and a large part of the investment community does it. But you can also do the opposite. Start with that stock price, work backwards, and ask, “What are the assumptions behind this? How long will it take for the cash flows to justify this stock price? What’s driving the stock price; the assumptions that everyone is building in? The book takes you back to some really fundamental basics. It identifies three main value drivers, which are easy for even the most novice of investors to understand. Sales growth. The rate of sales growth is a driver of value. Operating profit, so the margin percentage, how much money you make off of each sales dollar drives value. Incremental investment; the rate of investment, how much do you need to invest in fixed assets and working capital to drive that next dollar of sales or next dollar of profits? These simple concepts, if you understand them, can give you an edge in investing. Why? Because Mauboussin and Labcorp also say that at some point the crowd is going to revise its expectations of a business based on how these value drivers are changing, and you as the practitioner of expectations investing can get ahead of that game by studying what’s really moving the business and projecting that there’s going to be a revision in the market’s expectations, and therefore, a revision in the price.
Ricky Mulvey: Yeah, I think what’s interesting about that, even if you don’t go into any of the frameworks that Mauboussin describes, is it implores one to start from a neutral position. Don’t seek to say, what’s this company going to do in the future, but rather, what is baked into the assumptions by the market right now? Then you can make a judgment perhaps about what the rate of return is relative to the cost of capital moving forward.
Asit Sharma: I like that because if you take it the other way, so if you do the discounted cash flow model, you’re actually put uncomfortably in the other position. Which is you are building inputs and assumptions to try to project cash flows out into the future. You become a non-neutral observer in that exercise. Whether you like it or not, you’re making a ton of decisions about what the company will do to build your model up. That is a worthy exercise, as I said. But in this view of things, you can be a little more imprecise. If you’re focused more on what pushes the business, what drives those dollars, and how that impacts how other investors will see the stock price, let’s say, a year or two or three from today, to me, again, it’s better for people who want to understand why a business should be valued from its resources, how it applies its capital, then the DCF, which again you can spend a lot of time on and be very wrong, the more inputs you need to build. We’ll get into this. When we talk about some specific companies, we can contrast and compare these two ways of looking at life.
Ricky Mulvey: Well, one thing that Mauboussin has said on some podcasts is that investors have to earn the right to use yardsticks like a price to earnings multiple and enterprise value, to EBITDA multiple earnings before interest, taxes, depreciation, and amortization. How do you think investors earn that right if they want to practice this expectations investing approach?
Asit Sharma: Earning the right to use the multiples is pretty simple. If you simply dig in using the why question, you’re on your way to earning that right. I do agree with him. I have been guilty in the past, earlier in my investing career, of looking at companies in wildly different industries with wildly different balance sheets, different capital structures, and just assigning one ratio, let’s say a forward PE ratios to take one year’s Ford earnings, look at the price in relation to that and assess, is this reasonably valued? Is it expensive? Is it cheap? I’ve taken like old growth industries versus start-up type IPO companies and said to myself, well, this one has such a high PE ratio. It’s obviously overvalued. I think, for most investors, understanding the top of the valuation metric and the bottom is so important. I’ll go to my old saw. Ricky, you’ve heard me talk about this one before. Return on invested capital is posited by lots of investors as being a really simple and grounded and rational way to look at a company. Compare the price to its return on invested capital. The potential to produce incremental dollars on your investments. I think it’s one of the most complicated metrics out there, because understanding why a company has gotten to this point in its invested capital base, or how it’s developed that return, what that looks like in the future isn’t as simple as it looks. Just taking that metric and saying this company has a high ROIC or a low ROIC without context is really hard. Bottom line, when you start putting context around a ratio, you’re already earning that right. Never take them in isolation.
Ricky Mulvey: I always think, I think we’re seeing this in some cases as investors in 2023 perhaps become a bit more cranky and impatient than they were maybe before the pandemic, that a lot of these growthy companies may say, don’t judge us based on our price earnings multiple or our ability to become profitable because we’re still an early stage growth company in comparison. There’s now this huge disconnect between the investors that may be saying, you know what? No, the cost of capital is higher, my expectations have changed, and you need to get profitable immediately.
Asit Sharma: Yeah, I mean, for sure, that’s maybe the obverse case of what I was saying, but it’s totally true. Investors look at how you invest your capital. Depending on the interest rate environment, also, as we’ve all seen, that has an effect. You’re going to require something different out of a company. When the value of those future dollars decreases because of inflation, interest rates, then you get a little more impatient. That can certainly cut that way as well.
Ricky Mulvey: Are there any maybe less common metrics that expectations investors like to use in order to get an understanding of a company’s value?
Asit Sharma: I think for expectations investing style investors, it’s less about specific metrics and more about just building a very simple spreadsheet. Not too dissimilar, I mean, in theory from a reverse DCF, where you’ve stack these components. You look at revenue. You look at the costs that are associated with that revenue. You derive free cash flow. You see what reinvestment looks like, and then you go to the next year. You’re building year by year the value of the company. You’re also playing with what’s called the price implied forecast period. Let’s break that down. Now, in traditional DCF models, there’s something called a forecast period. That’s the time that the market expects a company to generate its returns on the incremental capital it invests. You’ll see there’s always a point in DCF models like five years or 10 years, where the rest is all into perpetuity, and then you lump those cash flows together, discount them back. This is interesting. Again, I think that expectations investing is so much geared toward a layperson’s idea of how the world works in investing. My idea, the concept is so much simpler. It’s saying, look, there’s a price out there for a stock. This company is going to throw off cash flows for many years. How many years is it going to take for the cash flows to justify the current stock price? When you build a spreadsheet out in expectations investing, more than looking at metrics, that’s really your starting point, is to figure out, OK, I’m at seven years here of this company’s projected cash flows when I discount those back for it to justify what I’m paying today. But I also know some other things about this business. I actually think they can get that return faster. They can justify that stock price faster. If I buy the company today, I’ve got an edge over competitors. This might be the way an expectations investing type personality looks at future cash flows versus maybe pulling a metric for easy use.
Ricky Mulvey: I think that’s a good place to stop for the introduction. On Saturday, we’re going to have a full show with case studies. We’re going to move from General Motors all the way to Nvidia to see how the expectations investing framework can help investors understand where those companies are currently at.
Deidre Woollard: As always, people on the program may have interest in the stocks they talk about, and the Motley Fool may have formal recommendations for or against, so don’t buy or sell stocks based solely on what you hear. I’m Deidre Woollard. Thank you for listening. We’ll see you tomorrow.