Motley Fool Money - Peloton Needs A Wizard

Episode Date: May 2, 2024

We’re talking big moves and Big Oil as earnings season continues. At (00:21) Jason Hall and Deidre Woollard break down Peloton’s CEO shift and Big Oil’s big profits. At (16:40) Tom Chivers, author of Everything Is Predictable, explains how the Bayesian theorem underlies much of modern life and investing. Companies discussed: PTON, SHEL, XOM, COP Host: Deidre Woollard Guests: Jason Hall, Tom Chivers Producer: Ricky Mulvey, Chace Przylepa Engineers: Dan Boyd Learn more about your ad choices. Visit megaphone.fm/adchoices

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Can the turnaround continue when the turnaround CEO leaves? Motley Fool Money starts now. Welcome to Motley Fool Money. I'm Deidra Willard here with Motley Full contributor, Jason Hall. Jason, how's your Thursday going? It's going great. It is earning season. So much happening. And we got a bit of a bombshell this morning. We did indeed. I wanted to bring you on to talk big oil. But before we do that, we've got to talk about that bombshell. So Peloton CEO, Barry McCarthy, he's out. He's leaving company after just a little over. or two years. You know, it's so interesting, he was supposed to be the guy who's going to turn it around. He's got the Netflix credibility. I don't know. I'm wondering what's next. The earnings

call was kind of full around about the turnaround talk. We're still turning around, but this turnaround is taking a lot longer than anyone thought it would. Yeah, there's no doubt about it. And it certainly has gotten more complicated because on the surface, you look at it and it feels like, hey, mission accomplished, at least a big part of the mission of turning the company around getting its financial house in order trying to right size operations. They touted being cash flow positive, free cash flow positive this quarter. But man, the more I've been thinking about this and reading a few follow-up stories of what's going on, maybe the story is more complicated than we think. So here's how I'm thinking about it, Deidre. So let's start with the McCarthy story, because we have two things,

right? We have the leadership changes, that turmoil that's happening, how that's going to affect the business. And then we have the, where are we with the turnaround story? So starting with McCarthy first, it definitely feels really sudden, and maybe it is. We don't know all the conversations that have been happening in the boardroom in recent weeks and months. We did hear, however, that one activist investor, back to last fall, was sending private letters to the board kind of agitating for McCarthy's ouster. And I'm sure that played some role in this. McCarthy's also 70.

He's not, you know, a young whippersnapper. He's been doing this for, you know, 30 years at this point as an executive with a lot of big companies dealing with finance. And I'm sure it's been a marathon, a full-contact marathon to mix metaphors here for him the past couple of years. But then here's the other part of it, too. Look at the entire executive team, Deidre. These are people he brought in, almost all of them. There's one content executive that's been around since before the company went public. but in terms of the people that are running the organization,

dealing with the finance operations, it's a completely different group. So the culture has to be very different, and their focus has to be very different than it was under the founder that McCarthy replaced. And then looking at the operations, looking at cash flows, again, you could say part of the job is done, the generated free cash flow this quarter.

They talked about, like, that's the expectation going forward. And then part of the question becomes, okay, well, who's the replacement going to be? You would think that you'd want McCarthy to hang around for a little bit longer as more than just a consultant to the business, which he's saying he's going to advise. You have a couple of relatively young but experienced co. How do you describe them? Is it co-interim or interim co-CEOs, DeGro?

It's a little confusing. We don't usually get interim co-CEOs. Right, right. And they're both on the board. One is the chairperson. and she's stepping out of that role. And one of their largest investors, who is one of the board members,

is going to be taking over as board chair. But it's possible one of those may be a good fit. Karen Boone, her background was in finance, and it seems like they're looking for a chief growth officer to be CEO. Maybe Chris Bruzzo, maybe that's more his role. He spent a lot of years as the chief experience officer at EA, his entire career is marketing and brand management. I'm sure they're going to be trying to find that person.

And that kind of leads us to the next part, right? The turnaround. Well, and it's interesting because if either of them were going to be the potential CEO or co-CEO, why not just start now? I'm not sure if Peloton is looking for somebody else, someone with a McCarthy style like credibility and, you know, sort of financial charisma, you know, a known name. I mean, that could be it too. But yeah, let's dive into the earnings just a little bit because, I mean, they're not bad.

They're not blowing the doors off or anything, but users only have been down around 1%. They've still got 6.4 million users. And there's stuff to like here. I just don't necessarily see that the turnaround part in terms of the growth of the business is happening. Yeah, I think if we were to step back and pull turnaround out of the conversation to think about this, as a patient rushed into the ER, this is stabilization, right? The business is stable at this point.

Yeah, year over year, total users fell a little bit. But if you look at from the prior quarter, which was the holiday quarter, calendar quarter, it increased a little bit. Some of that's surely seasonal, right? They gained some people that bought bikes or treads or whatever and became users. But then really the important user number is paid users. That number has been incredibly stable year over year. That number is not down from where it was year ago. It's actually up a tiny bit.

And it's up a little bit from the prior quarter, too. and that number matters more. Now, subscription revenues are not necessarily growing. And again, that's what you really want to see. So it feels like, again, it's a stable business, generated about $9 million in positive cash flow in the quarter. Still generating really big operating losses, Deidre, though. So I think that's an important thing.

I started peeling back the layers a little bit. And looking at that cash flow story, one thing that really stood out to me, they drew down about $75 million of inventory in the quarter. quarter. So on a gap basis, you know, you still recognize the margins the same way. And selling those bikes and stuff as low margins, right, mid-single-digit margins. But you generate a lot of cash for those things. Because if these have been sitting in inventory for months, you've already paid for all the components that made it, right? So cash out may have happened in a prior period in the cash in. So you can keep doing that. They're going to keep drawing down inventory for a while.

But at some point, inventory is going to stabilize. And the question is going to be what are the cash flows look like once that happens. And that makes me say, stable is one thing, turn around and returning to growth. That's a very, very different story. And that's where I think the skill set for the next CEO is going to be important, because we're definitely going to need a wizard, right, at this point. Yeah, I think we want someone who can get away from the hardware and more into the app and things like that, since it seems to be, to some extent, that's where things are headed. Yeah, there's no doubt about that. Let's move on to, to big oil. We've got some earnings. We've also got some news. Exxon is set to close on that $60 billion

deal to acquire Pioneer. I was announced last year, but now we have the FTC finally. They've reached the deal. This is going to happen. One person not coming along to the finish line there is Pioneer's former CEO, Scott Sheffield. But I'm curious, what is this deal going through mean for Exxon? I know it's all about the Permian Basin, right? It is. It's about the Permian. So this is that massive shale play in West Texas and moves a little bit north there too. But the bottom line is that this is some of the cheapest, easiest to access oil in the world at this point. And ExxonMobil already has a substantial position in the Permian. And a lot of Pioneers plays are adjacent to that. So that's extra valuable. So it's not just getting those assets, which this is what it is. It's

an asset buy. They're not buying Pioneers business. They're buying Pioneers oil and gas reserves. That's really what it comes down to. And they're going to be able to leverage that because they already have a lot of operations in that area. And a lot of their contractors that they use are operating in that same area and operating in those same pioneer fields. But the little things that they'll be able to do, like doing longer laterals in assets they have now, that now they have the Pioneer assets right next door. So that means less drilling activity, potentially better margins. So I think some of the things they'll do on the margins that can make this more profitable, could be really useful. Things like only having the CEO of ExxonMobil and

not Sheffield, another CEO, so it's really driving out operating costs in terms of like administrative people. That's another way that these things sort of pay off. So I think it's just business as usual and just getting better, cheaper, more oil that's aligned with where a big part of ExxonMobil's North American business is already focused. Big oil is still seeing big profits, It's maybe not as monstrously big as over the past couple of years. Shell reported today, they had 7.7 billion in profits. What's interesting, though, is they're going to put $3.5 billion into share buybacks during the next quarter. They've already had $13.2 billion buybacks over the last 12 months, 20% higher dividend per share than the first quarter of last year. So I'm wondering,

part of the investing thesis right now with Big Oil, with these profits, is it just that they're going to be really, really good to shareholders as long as the profits are also, fully good? Yeah, I think so. And hopefully it remains this way. These are our colleague, Tyler Crowe, he and I've covered the oil and asked gas industry together for over a decade at this point. And recently we were discussing it. He told me that he thinks that the U.S. oil industry, and I think this carries over to the European majors like Shell as well, it hasn't been this healthy in maybe 40 years, which is remarkable. And it's a product of what happened in the prior decade, really coming out of 2014, that boom when we saw oil prices skyrocket and say over $100 a

barrel for almost three years and then fell to the 30s. What happened is all these oil and gas companies ended up with all of these resources that they developed that the cost to get the oil and gas out of the ground was so much higher than the market was realizing. And it gutted the industry. And the companies have spent seven or eight years kind of sweating assets, really innovating, getting better at getting well out of the ground for cheaper. And that's gotten this point, and it kind of came to a head in the pandemic, where demand crashed for a quarter and then skyrocketed, and everybody had to really get back to work to meet demand.

But everybody has been more disciplined. So the thing that's tempered it now and has forced them to stay disciplined, Didera, is interest rates of skyrocketed. A lot of that early drive up through the early 2010s, money was cheap. Money's not cheap anymore. So they can't just go chase growth. They actually have to be responsible allocators of capital. And it seems like broadly, they continue to do that with smart growth,

not chasing growth, chasing cash flow growth, buying back shares, taking whatever's left after you meet your capital program and you're in the repurchases that you want to do, paying it in dividends and grow the dividend a little bit. So interest rates are helping us right now because they're keeping management from doing dumb things. That makes sense. The other thing that's happening, of course, is the energy transition. Shell is to some extent a part of this. And, you know, it's interesting because shareholders see this, but they want it to go faster, right?

So there's a shareholder proposition led by activist shareholders follow this, which asks for it to align its medium term carbon emissions reductions with Paris climate agreement. Shell talked about this a little in their earnings call. And their response is sort of like, trust us, we've got this. But what do you make of this and other ongoing calls for Big Oil to kind of pick up the pace of what they're doing? So first thing, I want to be abundantly clear here. I completely support anything we can do to support reduced emissions. I have a kid, right? You think about the future. And I think it's really, really important that we address this massive existential risk to humanity. But I will say that when it comes to these companies, the leverage position has shifted. Back in the late 20 teens, 2017 to 2020, there was a big push. And it seemed like there was a lot of momentum on the side of activists and those pushing for these companies to reduce their emissions. And, I mean, it was even so big as the Rockefeller Foundation, you know, the guy that founded, basically created the global oil industry,

the Rockefeller Foundation decided to divest itself from the oil and gas industry completely. And there was a big push broadly to kind of really coerce these companies to really focus on carbon reduction. They were struggling back then. They couldn't, they didn't have a leg to stand on to say, well, you know, we're just going to keep making a lot of money because they weren't making a lot of money. So they kind of had to kow a little bit and try to really work on these initiatives and really lead with a lot of climate reduction, at least in the conversations that they were having.

And the game has changed, Deidre. These companies are very, very profitable now. It seems like the ESG movement has taken a big blow in recent years. And the companies, the management, I think they realize that they have got some of the leverage back and they can say, look, we're just going to do our thing. We're going to be responsible. We're going to try to make economic decisions that are smart about carbon reduction, but our goal is to produce energy and generate cash flow for our shareholders. And then the shareholders can figure out what to do with the cash once we pay it to them. Yeah, and I think transitions always take longer than we'd like them to, and that's always a thing to keep in mind. Yeah, they take longer and then they happen faster. Right. It's so, yeah,

very, very slow and then very, very fast. Also, Conoco Phillips, they reported today as well, not quite as as as, Shell, production a little bit higher than expected, profits a little bit lower. you're looking at both natural gas and crude oil when you're looking at these companies. And just wondering about how, as an investor in these types of companies, you're thinking about the relationship between both natural gas and oil prices. This is a good opportunity to start by drawing the distinction between Conoco-Phillips and these other supermajors like Shell, ExxonMobil, et cetera. Conoco-Philips is a pure play on exploration and production.

They go find the oil and gas, they produce it, and then they sell it to somebody that's to do something with it or they sell it to, they market it directly through like refueling stations and that kind of thing. So that means that ConocoPhillips is very much a leverage bet on the price of oil and gas, right? If your shell, you have a petrochemicals business. You have refining operations that are less susceptible to the ups and downs of oil prices. You have that built-in diversification that a company like ConocoPhillips doesn't have. And with that said, you start looking at oil and looking at natural gas, and they share the reality that supply and demand drive their prices, right? Now, the difference is where that supply is and where the demand is. Oil is very much a

global commodity, whether it's oil, a refined product like jet fuel or gasoline or diesel, it may be refined in North America and then used in Europe or refined or produced in South America and refined in Africa and then used in the United States, right? These things move a lot because they're relatively speaking. They're relatively cheap to move. They're just liquids and you can move them pretty easy. Natural gas is by and this is starting to change, but historically has been exclusively consumed in the market that it was produced in

because it is so expensive to move it. You have to take it, super cool it to condense it enough to get enough energy density to get enough of it on a vessel. to then move it overseas, right? The vessels are very expensive. Then you have to take that gas, you have to gasify, that liquid and gasify it, and pump it into the pipeline of wherever it's going to be used.

So it's complex and very, very expensive. So that means that the shipping costs in the markets that don't have it, Japan, for example, most of Europe, their cost of natural gas is far higher than it is, say, in North America, where we have gobs and gobs of natural gas that's low cost. The other big difference, Deidre, of course, is oil, mostly transportation fuels for the most part. Some for petrochemical manufacturing. Natural gas, that's utility, right?

Your power plants, home heating, industrial heating, cooking, that sort of thing. So the use cases are a little bit different for them, too. Yeah, that totally makes sense. Thank you so much for your time today, Jason. Deidre, it was great being on with you. These days, I'm all about quality over quantity, especially in my first. closet. If it's not well-made and versatile, it's just not worth it. That's honestly what I love Quince. The fabrics feel elevated, the cuts are thoughtful, and the pricing actually makes sense.

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Tom Chivers, author of Everything is Predictable, how Bayesian statistics explain our world, shares how the world of an 18th century mathematician impacts our daily lives. Well, you call the Bayes theorem a theory of not quite everything, and I think that's a good place to start. So tell us a little bit about what this theorem is and why it is the theory of not quite everything. Sure. Well, I would say everything to do with intelligence is prediction, right? When we say we understand something, to a first approximation, what we mean is that we can predict it. We have a good model of it in our brains and we can predict it. And, you know, when we're always predicting the future. Almost everything we do is predicting the future. Like if you go to the shops, you are predicting, you know, so you're going to go and buy some breakfast cereal, you know, you are predicting that the shop will have breakfast cereal. When you apply for a job, you're, you're, you're, you're, you're, you're, you're, you're, you're, you're,

predicting that the job will, that there's a reasonable chance you'll get the job and that you will enjoy it and think, you know, we'll be glad that you did when you do so. What Bayes theorem is, is the maths of prediction, or the math, if you like. You know, all these predictions are uncertain that no one, you're never 100% certain that the shop will have breakfast cereal. You're never 100% certain that the job will, that you'll get the job or the job will be a good one. But you've, you can sort of put an estimate, you wouldn't probably do it in numbers, but you have an estimate of how likely it is that that will happen. And if you apply or if you walk to the shops, then you are sort of, you're betting, you're betting that this prediction is real. And all

bases is the maths, the maths of how you, that describes how you make predictions and how you update those predictions in the light of new evidence. It's this really simple one line equation and it is, it's the heart of it all. It's at the extent to which you are making good predictions, is the extent to which you're being Bayesian, and the extent to which you're making bad predictions is the extent to which you're not being Bayesian. And it's just, it's just, it's like the iron law of prediction. Do you sort of mean? Well, the reason I read the book and I was interested is thinking about this from an investor standpoint and a stock picking kind of standpoint. So the way I'm looking at it is there's two things, right? You've got, you're trying to predict

the macro cycle. And you've got a lot of priors from that from previous cycles. And I mean, surely you've seen everyone trying to predict the recession and failing miserably. And then you also are trying to do it with individual companies based on other companies that have been had a similar trajectory or things like that. So how does Bayesian theory kind of play into both of those? First, I should say, I'm not a stock picker. Funnily enough, if I'm allowed to name drop from my mom's uncle, my mom's great uncle, was a really brilliant stock picker.

He was this guy called John Maynard Keynes, who you may have heard of. And he was, yeah. And so he made incredible amounts of money on the stock market and then lost it all and then picked it again and did it and did it again. So I'm sure I'd be a huge disappointment to him. But so I don't want to give any, no one should take anything I'm about to say as investment advice, right? That's really crucial.

I think that like the key from a Bayesian framework is like you never have certainty in anything. You always have confidence levels in whatever. So in your estimate. So you might have, you might think it's 60% likely that Google will outperform the market or something like that. And then you see some, I don't know, so they've just bought a bunch of Nvidia chips or they've, I don't know, their latest smartphone looks really good. And so you adjust it up. And or you, you see that their latest AI, generative AI has just completely flopped and started doing really weird results. And so you adjust your estimate down.

I think that, so, you know, just sort of keeping in your mind that you never have to say, I think this is, this is good or this is bad. you can say, I'm 70% confident that this thing is good. And moving up and down as new evidence comes in and just constantly sort of updating yourself. The other thing I think that's relevant when you're thinking about investors and stock pickers themselves, because, I mean, we know, don't we, that the average stock picker does not beat the market. And if you see someone get lucky over a year and make money over a year, how sure should we

be that they are genuinely skilled and how lucky should we, how much should we think that they are just, they had a couple of big trades and got lucky. And with Bayesianism, you never have to make a judgment, a final judgment. You can just say early on, you say, my prior is that for any given stock picker, there's only a 30% chance that they're going to beat the market or whatever the correct number is, you know, 30% maybe lower. But if after a year, they're up there, they're ahead of the market, you say, okay, well, I will adjust that opinion up. If they're doing it after two years, you adjust your opinion up. And, you know, when you've got Warren Buffett, he's been doing it for 50 years,

you can be extremely confident without ever being certain that this guy has, is genuinely skilled and has, you know, has something, has an insight into the market that most of us, certainly I do not, you know. So I think that would be how I'd apply a Bayesian framework to it. And yet there are some of us that are better at this than the rest of us. You have this section in the book about the super forecasters. So what's the difference between the super forecaster? and the rest of us, because there's a lot of forecasters out there, but I don't think many of them

are super forecasters. No. No, well, this is a thing. I mean, I don't know how many of your listeners would have heard the term before, but it was this great, it was Phil Tetlock. He's still working scientists, but when he was young and in sort of early 30s in 1984, he sat in on various meetings in the White House, I think it was, about people worrying, you know, people predicting what was going to happen next with the Soviet Union.

Something happened that none of them were predicting, and that was the election, the appointment of Gorbachev and who was a liberal reformer, and it completely changed the whole thing. But everyone sort of said, this unexpected thing confirmed what I already thought. You know, that was the, that was the, that was the, that was Tetlock's big sort of takeaway from it. So he set up this thing called the Good Judgment Project, which assessed how,

it was literally getting people to make hundreds and hundreds and hundreds of predictions, time-limited, explicit, falsifiable predictions like, you know, will the yen be higher than the dollar on the 7th of December, 1986, whatever, you know. And then judging how people did. And he found that the median prediction was no better than random guessing. It was just, it was, people were completely rubbish, basically. But there was a subset of people. It's on a spectrum. Some people are really rubbish.

Actually, like, I think actually anti-correlated with truth. And some people were much better. and they arbitrarily picked the top 2% and said these ones will call the super forecasters. And they did loads better. They upperformed CIA analysts and stuff. The ways they do better is they do things like breaking stuff down and breaking questions down into component parts. Like it's a Fermi estimate if you've heard the term.

So if you ask someone how many piano tuners there are in Chicago, they won't just say, I don't know about 2000. They'll say, well, you know, how many people, what percentage of people own a piano? How many people are there in Chicago? You know, breaking it down, how long does it take to you? a piano, these sort of questions and trying to break the question down in smaller parts. They'll do things like they'll keep score. So if they make a prediction and it's wrong, they'll make that down rather than just

allowing that to disappear from their brain and sort of only remember the times they've got it right. And so they can learn from it. I think from the Bayesian point of view, the really important thing they do is they use prior information. They'd be explicitly use what a Bayesian were called priors. So a friend of mine who's a super forecaster, he said his example he always gives. and I cite it in the book, I think, is imagine you're at a wedding and someone asks you,

which is a pretty inappropriate thing to ask at a wedding actually now I think about it. But imagine someone asks you this at a wedding, how likely do you think this couple is to go the distance? And a non-superforecaster might sort of just use the information around them, sort of like, oh, they look so in love, they look so happy. You know, they're staring into each other's eyes, 95%, you know. But a super forecaster would try and find some appropriate reference class, some appropriate base rate, from which to adjust, you know, so they're like, well, the, about one in three British marriages end in divorce, so I'll start from there, you know, and then I see how closely they're looking

into each other's eyes and adjust up or down from there, given, deciding on, depending on whether I think they're looking in an appropriately loving way, you know, so that, and that's just, that's explicitly Bayesian. That is, they call it using an outside view that is finding a reference class or a sort of base rate and then adjusting with the inside view, the details in front of you. But I mean, that is exactly the same as having a prior probability and adjusting it with new data. And it is crucial to, it is crucial to a good forecasting. I mean, it's tricky sometimes, because when you're doing things like predicting whether Russia will invade Ukraine in 2022, what's your reference class? Is it the percentage of time, you know, the percentage of probability

that a given country will invade a different given country? Is it the percentage, you know, is it the number of times Russia invades Ukraine per century? Is it, do you update by the number of tanks that they put on the border? I don't know. It's complicated and it's not in, and that's a lot of the skill is finding a good reference class. But that's what you do. That's how you start out. Hey, fools. Just a quick programming note as we wrap today's episode. This is my final show in the host seat for Motley Fool Money. As I leave The Fool, I want to share just how much I've appreciated learning from all of the analysts during my time here. And all of the time you, our listeners, have spent tuning into my conversations on the podcast. Thanks for listening and full on. 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 ourselves stocks based solely on what you hear. I'm Deidre Willard. Thanks for listening. We'll see you tomorrow.