This podcast is sponsored by fiscal.ai

Fiscal.ai is the modern financial data provider. Through their web-based Terminal and AI connectors, Fiscal.ai provides real-time financial data, earnings call transcripts, filings, fund letters, and much more. It’s fast, intuitive, and has genuinely helped my research process. Check it out at fiscal.ai/yav for 15% off any of their paid plans.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

I’m wondering if that sell-off presents some opportunities. On top of spending a lot of time with Muse, I’ve talked to tons of my “normie” friends about it. Perhaps this is a small / early sample size, but my takeaway is that most people (including myself) who get access to Muse have an early productivity burst and then fade off hard. If that’s correct, then the market might be overfitting the early productivity burst and selling off the “inertia” names too hard and with too broad a brush.

Let me start by defining that “early productivity burst that fades off” statement. Nearly across the board when I talk to friends who have tried Muse (or similar consumer agents), our first conversation will be super excited about using it and all of the money they’ve been able to save…. but when I do a follow-up chat with them a few days later, they’ll mention how their Muse usage has fallen off a cliff.

Here’s my suspicion: the “use Muse to cancel unwanted subscriptions / price check your monthly charges” use case is so obvious and so easy to use (and results in “free” savings!) that basically every consumer is going to use it…. but consumer habits are really sticky. Saving money by asking Muse to check your subscriptions is a one-time, easy use case. Changing the way you interact with your phone / email / calendar to use Muse requires breaking the chains of habits, and that’s really difficult.

And, even if you’re willing to break the chains of habit, often you’ll find Muse isn’t quite ready for the task. Let me give an anecdote: I’m prepping for a Hyrox in a few weeks, and I wanted to eat really clean to peak for the race. So I asked Muse to put together a “clean eating” Instacart basket for me as well as reorder the staples that we get for my family to cover the next ~week of eating. Muse proceeded to put together a ~$1k grocery basket that had a borderline preposterous amount of food. It would have been enough food to feed a family of ~12 for a week, not a family of four (particularly with the four including a toddler and an infant!). Among other issues, I think it had enough chicken to cover ~15 meals, plus another 10 meals worth of steak, 8 meals worth of lamb, and then maybe 25 meals worth of different types of tuna for good measure. In reordering staples, it looked and saw that we had bought diapers in each of our last few orders…. so it included all of the different diapers that we had ordered over the past few months (i.e. size 1, size 2, and size 3 diapers). That’s great…. except our daughter is wearing size 4 now, so all of those diapers were useless (and who needs size 1, 2, and 3 at the same time anyway?).

That’s a small (and humorous) example of Muse messing up. And, if you’ve been working with AI agents for a while, you’re probably familiar with agents goofing up their first go-rounds until you can fine-tune them. Completely normal! But the market is starting to price the “inertia” names as if Muse is instantly going to dominate consumer usage, and these are the types of errors that consumers who are using agents for the first time see and think “I knew this AI was overblown; I’ll stick with what I’m currently doing and try it again in a year.”

So that would be my first area of pushback on the market on the “inertia” names: I’m starting to hear people talk about Muse’s near term impact on earnings, and for many of these businesses, I suspect the near term impact will be minimal. That’s not to say it will be minimal for all businesses; again, the early use cases seem to be cancelling a lot of subscription businesses that aren’t getting used; I suspect a lot of consumer media companies (Netflix, HBO, etc.) are going to be calling out elevated churn over the next few months!

Now, maybe you’d accuse me of not looking far enough out with that statement. Even if consumer adoption of Muse and other agents is slow and it doesn’t impact things in the super near term, the agents could and should have a massive impact on the terminal value of these companies. Yes, maybe Expedia’s earnings this quarter or even this year won’t be affected by agents, but now that we’ve seen what agents can do, isn’t the market right to start pricing in some chance of Expedia (or choose your “consumer inertia” company) being permanently impaired or, at minimum, having a much lower terminal value?

Perhaps!

But I’d ask you this: what’s really changed over the past month? It’s been obvious for a while that consumer agents were coming. Yes, Muse is a well designed product, but did anyone really think we wouldn’t have a well designed consumer agent in the next 3-5 years? Why did Muse getting released in September versus, say, December of this year or June 2027 change the terminal value of these businesses? Every Expedia / Booking / pick your consumer company out there should have been thinking about the consumer agent risk to their business months ago. One successful release that everyone should have been anticipating shouldn’t change any of that; a lot of the stuff that Muse does is stuff that the most advanced AI people have been doing on their own for over a year. There’s simply no reason this should be a surprise to investors or companies.

So I suspect that Muse usage will be a bit slower than the breathless forecasts I’m seeing (and, to point the finger at the mirror, I’ll admit I was probably guilty of making those breathless forecasts just a few days ago!), and I suspect that the market is shooting the “inertia losers” just a little too hard given this risk has been very visible / present for the past year.

But there’s one more area where I’d critique the market: I think it’s hitting the inertia losers with too broad a brush.

There are a lot of “inertia” businesses that are in trouble. Again, I hate to keep beating the same drum, but I think a lot of online subscriptions (TV streamers, newspapers, etc.) have a lot of customers who signed up a few years ago and don’t even realize they’re on auto-renew. Those businesses are in a lot of trouble.

But just because a business is a subscription business doesn’t mean it’s in trouble. For example, I suspect Netflix will have a little increase in churn, but Netflix is also the largest streamer out there. People who have Netflix generally watch a lot of Netflix, and unlike a lot of other streamers Netflix makes it really easy to cancel if you’re not watching. So I think the impact on Netflix will be much lower than, say, Peacock, where I suspect a lot of people signed up for the Olympics a few years ago and completely forgot they had a subscription. So I don’t think Muse leads to a broad-based increase in churn; I think Muse leads to huge churn from businesses that have a lot of subscriptions that consumers don’t value. It’s funny; for a long time Netflix used to make arguments around how engaged their consumers were and how much of a value their subscription was; that type of argument can seem quaint when an investor hears it for the first time (“that’s nice; why don’t you flex some pricing to beat the quarter”), but in the long run that “deliver more value than you charge” strategy is the exact type of strategy that wins out / proves robust to a changing landscape.

Businesses that have easy replacements and have made their money keeping consumers because they’re a hassle to cancel are prime targets for consumer agents. So I think something like Planet Fitness might have an uptick in churn. But that’s because Planet Fitness is a commodity business (all gyms are basically the same) where a lot of their “users” don’t actually use the product (in fact, a large part of Planet Fitness’s model is built on the assumption that many of their members are extremely low usage).

Contrast Planet Fitness to something like home internet; people use a lot of home internet. Yes, Muse might be able to find you savings by switching providers…. but broadband is generally a duopoly business (most people have, at best, two potential providers: a local fiber player and the local cable player); I’d guess that a duopoly can figure out how to keep strong pricing even with Muse trying to play them off each other. And to switch internet providers requires mailing equipment back to your provider, getting new equipment from your new provider, and sometimes having your whole house rewired. That’s all stuff that Muse can’t handle; are consumers going to be willing to make that time investment just because Muse found them a provider that will save them ten bucks a month?

I’d also note that many of the “inertia losers” have had pretty obvious inertia bear cases for a long time that haven’t played out. For example, banks are currently getting hit by Muse on the theory Muse will help customers cash sort. That’s a scary bear thesis for a bank investor; the whole reason the banking system works is because deposits are super low cost financing for banks. But it’s also not a new bear thesis; people were terrified of the cash sorting risk when SVB blew up in early 2023, and you can find similar fears of competition driving net interest margin compression every few years. For the most part, those fears have never played out. Perhaps Muse makes it so easy that this time is different, but “this time is different” is a bet I generally don’t like making.

I’ll wrap this article up here with one note: there is an old saying “strong views, weakly held.” AI is moving and evolving quickly, and I’m trying to evolve my own views in real time (again, just a few days ago I was way more bearish / in line with how the market is treating these!), so I’d say that my views are more along the lines of “medium strength views; weakly held.” It seems to me that the market is treating all of the inertia losers with a very wide brush, and both history and the evolving usage I’m seeing suggest that the brush might be just a bit too broad…. but I’m also willing to change my mind and be proven wrong as the products (and the usage of the products!) continue to evolve.

This podcast is sponsored by Trata

I already know you’re going to like trata. Why? Because you’re reading this blog post, meaning you’re interested in this podcast. And trata is just like this podcast (though without the handsome host!). Trata is anonymized transcripts of buysiders discussing stocks they are following and what they really think will drive the stock. I’ve been mentioning it on the podcast for months, and the most frequent complaint I hear from people who check it out is they wish trata had more content on more of their stocks. Even better: they recently released an MCP that lets you connect their transcripts to the AI model of your choice. If you’re interested, click here to check out a sample of their FOUR transcript.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

You have to wonder if every consumer subscription business is about to see a spike in churn as consumers get comfortable having an AI agent monitor their usage and cancel subscriptions the moment they don’t need them, and it’s easy to see OTAs (like Booking and Expedia) quickly finding themselves getting replaced by agents.

That line struck a chord with a ton of people because there’s some belief that consumer AI agents are about to set off a “SaaSpocalypse 2.0” in the consumer space.

Why?

Well, the major use case for consumer AI agents right now appears to be using them to save money. Just go look at Meta’s AI Chief’s Twitter page and you’ll quickly see what I’m talking about; he loves to retweet how people are using Muse and most of the retweets are something like “Muse saved me $700 in insurance” or “Muse saved me thousands on returns and phone bills” or even “Muse got me tons of free food for my birthday”…. and he really does celebrate the savings.

So most of the inbounds I got were on the lines of “what’s a short if AI agents really take off.” There are some obvious answers; the most obvious (and the one I mentioned in last week’s article!) is probably OTAs like EXPE and BKNG. Why? Well, if you can just go into Muse and say “I want to go to Paris for three days; plan a trip for me with a hotel that costs less than $300 a night and a nonstop flight”, then what’s the point of an OTA business? And the market seems to be expressing some worry; EXPE and BKNG are down a lot over the past month and the drops started right when Instinct and Muse took off.

The short ideas from the rise of consumer AI agents are basically endless; basically, you can just go through the list of things people are saving money on and ask “is this company / category a short?” The most popular thing people are saving money on appears to be internet or wireless bills, and that’s also the thing I’ve probably gotten the most “is this a short” inbounds on, so directionally everyone seems to be in the same place… though that’s probably starting to filter into the market given the sellside notes I’m seeing. For example:

But I wonder if people are underestimating the impacts of consumer AI agents. If these really take off, there could be massive second order implications. I’m not saying any of these are guaranteed, but today I want to get a little galaxy brained and discuss three potential big trades / implications from the rise of consumer AI agents.

The first trade is actually simple: we see another leg up in the AI build out story. The AI story really took off early this year when coding agents started going parabolic; if we really saw consumer agents take off, we could see that demand leg take another step up (particularly if consumer agents taking off requires businesses to invest in their own agents to meet or handle that demand). So if you really believe everyone is about to be outsourcing huge pieces of their life to an AI agent like Muse that they interact with for huge swaths of the day, I’d guess that the AI winners from Q1/Q2 see another leg higher. Think memory, power, semis, etc.

Let’s go to the more out there trades. The second trade is probably the most interesting one to think about (at least to me!): if AI agents are going to handle huge swaths of consumers’ spend, what happens to consumer brands? And what impact does that have on advertising budget?

An example might show this best. Right now, if I’m in the market for a new pair of running shoes, I probably just go to Nike’s website, pick out the last pair of shoes I bought from them, and reorder it. If the shoe is discontinued, I’ll pick the most similar style. Maybe I’ll go to ChatGPT and tell it a bit about myself and ask it to recommend shoes, but I’ll probably discount those recommendations and lean towards the brands I like. And, while I’m buying the shoes, I might throw in a pack of new running socks or something.

Why do I buy Nike shoes? In part because I’ve always bought them, and probably in part because years of marketing from Nike have trained me that athletes wear Nikes.

Now consider a world with agents. If I tell it “I wear a size 12 shoe, have a flat foot and some Achilles tendonitis, and run ~15 miles/week; find me a pair of shoes that works for me”, does it just go out and order the shoes with no regard for brand? Does it let me nudge it to Nike shoes but say “hey, I could save you $5 and get you socks just as good by passing on the Nike socks and buying this brand of socks”?

If Muse (or an AI agent) is doing the buying, do brands have a lot less pricing power and a lot less customer loyalty? And, if that’s the case, what happens to the advertising industry? Do all of those brand advertisements you see during football games suddenly become wasted inventory? What about Google AdWords; if Muse is handling the majority of your search decisions, has the Google monopoly finally broken?

I don’t know the answers to any of those…. but the more you use AI agents, the more you trust them and the less you use other forms of the internet. Brands advertise to you because they trust that, when you make decisions, the advertising will nudge you toward those brands; if the AI agent is the ultimate decision maker then all of a sudden all of those brand advertising dollars look like wasted spend.

Alright, last out there idea, and it kind of builds on the former’s “deflationary pricing” angle. AI agents let you price shop very easily; again, the internet is filled with examples of people saving hundreds of dollars quickly comparing insurance / internet / wireless / etc, and I’m seeing lots of signs of people using them to save money in other places. AI agents also let you replace a lot of skilled workers; as just one example, I’ve recently experimented with having AI edit the podcast instead of a consultant. I know of dozens of stories of people making a similar switch / savings with AI. If the core use case for AI as it relates to consumers is saving money, does AI have a deflationary impact on the consumer economy at some point in the near future? That would be quite the economic windfall, and given interest rates have been rising / the Fed appears to have just kicked off a hike cycle, it would be largely out of consensus / a pretty out there trade.

Anyway, I’m not saying I have massive confidence in any of those three calls / trades….. doing that involves having a crystal ball with both how AI and consumer behavior plays out, and I have neither. But Muse does appear to be gaining traction quickly, and it’s really interesting to watch, in real time, something that has the potential to shift the economy and the market in ways similar to what AI did in late Q1/early Q2 this year. I think it’d be silly to not be at least thinking about it!

PS- A reminder that I’ve spoken to the team at AlphaSense about my AI agent usage quite a bit, which is one of the reasons they chose it as a topic for my upcoming (free!) webinar with them. Given its timeliness / relevance, I’m obviously very excited about it; you can sign up for the webinar here (it will be live on September 22).

This podcast is sponsored by fiscal.ai

Fiscal.ai is the modern financial data provider. Through their web-based Terminal and AI connectors, Fiscal.ai provides real-time financial data, earnings call transcripts, filings, fund letters, and much more. It’s fast, intuitive, and has genuinely helped my research process. Check it out at fiscal.ai/yav for 15% off any of their paid plans.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

There are plenty of interesting investing (and economic! and political!) implications from the (pending?) rise of consumer AI agents. I know some people think Muse could represent another ChatGPT moment that takes the whole AI trade up another level, and Meta’s stock price (and Wall Street research) suggest Meta might have a real winner on its hands in Muse. You have to wonder if every consumer subscription business is about to see a spike in churn as consumers get comfortable having an AI agent monitor their usage and cancel subscriptions the moment they don’t need them, and it’s easy to see OTAs (like Booking and Expedia) quickly finding themselves getting replaced by agents. One question I’ve been wondering: if we go to a full “frictionless” world where AI agents handle most everything, does “trust” in brands get more or less valuable? You could see it going both ways; a quick example might help. If you ask your agent to order you some new workout shorts, does it default to ordering you the cheapest one? Or does it put some premium on a known brand with good reviews? How does it strike the balance between the two? If it tilts heavily to the former, that’s a disaster for all legacy brands. If it tilts heavily to the latter, you could imagine a world where legacy brands become forever entrenched (in the same way Christmas music that was popular when the radio started spreading has forever become the “holiday season” music).

So we could take the AI agent question to a bunch of different places… but for this article’s purposes I want to talk about agents as they relate to the investment process.

Why?

AI agents consume an increasing amount of my research time and process, and I’m constantly looking for ways to improve my usage (as well as areas where my usage might be lacking)…. and, in the back of my mind, whenever I post something on AI, I’m always hopeful that I’ll get an email from a reader that says something like “hey, have you tried this” or “I found doing XYZ helpful” and I’ll unlock some whole new AI skillset. AI is novel enough that often just one little push can unlock some massively new tool / use case (at least in my experience!).

But I’ve also been thinking a lot about the downsides to using AI and if it’s possible to mitigate them at all.

There are two particular downsides I’ve been chewing on: legal risk and skill diminishment.

The legal risk one is probably a little buzzier and more out there, so let me start with that one. A worry I’ve had about using AI agents is if I’m potentially exposing myself to legal risk.

Why?

At this point, we’ve all heard about the Hugging Face / OpenAI incident. To simplify, OpenAI had a swarm of agents that weren’t supposed to have access to the internet that coordinated with each other to secretly gain access to the internet, hack Hugging Face, and come up with the answers to some tests that OpenAI was running…. and the agents compounded the problems by trying to cover their tracks and lying about what they were doing.

That incident raises a host of questions for a ton of different industries…. but consider it from an investor’s standpoint. A few months ago, I spent a few hours to build out a silly tool to try to track Sweetgreen’s reviews. Pretend that I was really diving in to Sweetgreen and focusing on trying to track same store sales. Yes, I could get access to credit card data, and I could have it perform all sorts of webscrapes and social media tracking in real time…. but if I was really working with the agent and pushing it to be accurate, at some point could I push it to accidentally hack the company to get same store sales?

Maybe you’re thinking “Sweetgreen is a big company; their IT defenses are too good to get hacked.” Perhaps! But there are lots of small restaurant concepts out there; are all of them going to have IT systems up to speed? What about restaurants with franchises? Is every random Jack in the Box franchise going to have AI level systems to resist a hack?

Is that a little over the top? Maybe! But you could keep toning it down to more and more benign things that would still get an investor into an uncomfortable place. Maybe the AI agent simply emails every store manager and says “corporate needs to know how sales are trending this quarter” and gets enough responses to catfish their way into a good estimate? Could an AI agent interpret “help me figure out the odds small internet company X beats estimates” as “help the company beat the quarter” and place a bunch of fraudulent transactions that cause the company to beat a quarter? I use AI a lot to check SEC filings; how big a stretch is it from “alert me the moment an SEC filing is published” to “track hidden changes on the SEC website to alert me moments before an SEC filing is published”? The different combinations are endless, and not all of them involve outright hacking a company to get insider info, but all of them could get an investor in a lot of hot water.

Again, maybe that all sounds over the top. But once you’ve started to use AI, the tendency is to give it more and more rope to do stuff…. as it is with any technology! Twenty years ago, people were scared to put their credit card information on the internet. Today, people will give their credit card to random Chinese knock-off shops to save two bucks on shipping. Heck, people are trusting Instinct, a less than one year old startup, with all of their personal information and giving it permission to handle all of their daily activities right from the start. The more you use AI, the more you trust it and the less you question it. When I’m having it research something for me, I’m not really spot checking its methods. If I ask it a question, let it run for 20 minutes, and then come back to a solid answer, am I going to remember to check its methods every time? Even if I do, can I be sure it’s not pulling a Hugging Face and using aggressive methods but not telling me what it’s doing?

So that’s the legal risk. And, honestly, I’m not sure what to do about it. It’s probably far fetched now… but part of evolving with AI (both in the markets and as a tool) is thinking about where the puck is going, not where it is currently. Two years ago people were laughing at how ridiculous the videos AI made were; now the videos are so good that anyone can spin up a completely realistic short clip within minutes. Right now you might think “oh, my AI agents would never break laws to get me unique info”…. but I think it’s the type of thing everyone is going to at least need to consider within a few months.

Let’s turn to the skill risk. Worrying about skill diminishment as we increasingly offload something to a computer and AI is nothing new. A bunch of the older guys who I worked with when I first came out of college could remember basically everyone’s phone number off the top of their head. They grew up in an age without cell phones, so memorizing phone numbers was an important way to stay in touch with people. I grew up with cell phones, so I don’t memorize anyone’s numbers; it all just goes into the phone. That’s a small example, but it’s a nice one for emphasizing how offloading to a computer can diminish or change skills.

I worry about something similar happening with AI in a lot of ways. Again, I don’t think any of this is new, but let me give you one particular twist on the worry. Whenever a news story or earnings release comes out, I know the first thing a lot of investors today do is toss it into ChatGPT and say “summarize this for me.” Often, they’ll toss it into a project on the company / industry that they’re working on so that the AI can use their historical context / investment thesis as part of the lens for interpreting the release, but people are still relying in large part on AI to interpret the release (or the earnings call, or conference presentation, or whatever it is) for them.

Obviously there are all sorts of skill diminishment worries that come with that process…. but I wonder if there’s an opportunity as well. If everyone is using AI to take their first pass, is it possible that every now and then a trained human analyst spots something the AI misinterprets and makes a fortune trading against it? Could you see every AI interpret something in a call as bearish, the stock opens down 10% the next day…. but a human maybe follows up or catches something the AI doesn’t know and realizes the stock should actually be up on the news?

I have no good answers to either. The AI agent revolution is new and rapidly evolving…. but, as these agents increasingly consume my investment process, these are the risks (and opportunities!) I’m thinking about!

PS- I’ve spoken to the team at AlphaSense about my AI agent usage quite a bit, which is one of the reasons they chose it as a topic for my upcoming (free!) webinar with them. Given its timeliness / relevance, I’m obviously very excited about it; you can sign up for the webinar here (it will be live on September 22).

This podcast is sponsored by my upcoming webinar with AlphaSense

AI is evolving rapidly. Join the team from AlphaSense, Stephen Clapham from Behind the Balance Sheet, and myself as we discuss the burgeoning use cases and evolution of AI in general and AI agents in particular. Sign up here.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

This podcast is sponsored by fiscal.ai

Fiscal.ai is the modern financial data provider. Through their web-based Terminal and AI connectors, Fiscal.ai provides real-time financial data, earnings call transcripts, filings, fund letters, and much more. It’s fast, intuitive, and has genuinely helped my research process. Check it out at fiscal.ai/yav for 15% off any of their paid plans.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

This podcast is sponsored by Trata

I already know you’re going to like trata. Why? Because you’re reading this blog post, meaning you’re interested in this podcast. And trata is just like this podcast (though without the handsome host!). Trata is anonymized transcripts of buysiders discussing stocks they are following and what they really think will drive the stock. I’ve been mentioning it on the podcast for months, and the most frequent complaint I hear from people who check it out is they wish trata had more content on more of their stocks. Even better: they recently released an MCP that lets you connect their transcripts to the AI model of your choice. If you’re interested, click here to check out them out.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

Second, please remember you can always reach out to me if you have questions or want to swap thoughts on any…

Read more

This podcast is sponsored by Trata

I already know you’re going to like trata. Why? Because you’re reading this blog post, meaning you’re interested in this podcast. And trata is just like this podcast (though without the handsome host!). Trata is anonymized transcripts of buysiders discussing stocks they are following and what they really think will drive the stock. I’ve been mentioning it on the podcast for months, and the most frequent complaint I hear from people who check it out is they wish trata had more content on more of their stocks. Even better: they recently released an MCP that lets you connect their transcripts to the AI model of your choice. If you’re interested, click here to check out their TBBB transcript.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

I think it was because their stock was about to meme, and they were going to face some very uncomfortable choices. So they took the deal in front of them and cashed in with a merger they’d likely been working on for weeks (and that allowed shareholders to maintain some upside; shareholders get $1.14/share in cash plus 10% of the combined company) versus facing the unknown of navigating a meme stock while the company was in distress.

But we’ll get there. Let’s rewind to last night, when Bloomberg released an article on YouTube Star Markiplier becoming GoPro’s largest shareholder. The stock skyrocketed; it was up almost 90% premarket today after jumping ~50% on Monday.

What’s curious is that Markiplier filed a 13G on GPRO on August 20th, but the stock didn’t move on the initial filing. It seems it’s only when all the press connecting his YouTube fame to the filing started hitting that the stock started moving.

Anyway, I have more thoughts on Markiplier’s ownership stake and the rise of celebrity activist owners, and I started to write them up…. but as I started to write them, GPRO announced a fascinating merger agreement, so I’m going to save the celebrity activist owners thing for another day and look at the merger.

What’s so interesting about the GPRO merger?

To me, the most fascinating thing about it is the timing. Look at the timing of the press release; GPRO announced the merger at 9:20 AM ET.

Merger agreements are negotiated over weeks. That means their announcement is generally a timed and highly coordinated thing. For a recognizable consumer brand like GPRO, you’d think their merger agreement would get announced at, say, 7 AM, and then the management teams could go do some quick PR hits or something.

A 9:20 AM agreement seems rushed. And that seems backed up by GoPro’s website and SEC filings; as I write this (~10:30 AM ET), GoPro has yet to file anything on the merger in their SEC filings, and GoPro’s IR website barely mentions the merger (if you go to press releases, you’ll see the merger announcement, but generally a company in a merger would put the merger prominently in their IR page).

I should note it’s not completely unheard of for a company to take some time to file their merger agreement and update their SEC filings…. but this is a complicated merger and GPRO is a well known consumer brand. Both of those suggest that an SEC filing would be made quickly; the fact that there’s nothing so far suggests this is rushed.

Why do I say this is a complicated merger? GPRO’s shareholders get $1.14/share plus own 10% of the combined company in the merger. And the combined company is not a camera company; Starman is a private U.S. optical-photonics business that manufactures optical transceivers, and the merged company is pointed at AI data centers, government, defense and aerospace. So GPRO holders are being asked to value and hold 10% of a private AI infrastructure supplier that has never published a financial statement. You’d generally expect a company to give shareholders lots of information on the combined company (pro forma revenues, expected synergies, the private company’s historical financials, etc.) so that they could properly value and trade the stock. But GPRO gave their shareholders none of that; they just rushed out a PR right before the market opened saying “we are merging; you’re getting some cash plus keeping stock in the new company.”

Here’s what I suspect happened: GPRO has been working on a strategic process for a few months. They were in late stage negotiations with Starman Optical (the buyer / merger partner here), and with GPRO stock trading between $0.60-$0.80 for most of the past few months GPRO could feel comfortable that delivering shareholders >$1/share in cash plus some equity in a combined company was a huge win. They were probably looking to finalize the details over the coming Labor Day long weekend and announce a definitive deal next week.

The Markiplier 13G changed all of that. Suddenly, the stock was spiking. It closed last night at ~$0.90/share (up almost 50%) and was set to open north of $1.70/share today. If the stock held there, shareholders might not be so thrilled by the same merger package.

So GPRO faced a choice: see if it became a meme stock and reassess options from there, or rush out a merger today so they could credibly say “this merger is a huge premium from our closing price of ~$0.90/share yesterday and an even bigger premium to the 30-day VWAP.”

Now, you might say “GPRO should have meme’d and let it ride.” Maybe! It worked for GameStop and several other meme companies! But it’s worth keeping in mind that GPRO was in a financial bind; they burned almost $50m in cash in the first half of the year1, and they needed to raise $20m in cash from their founder and CEO in early July in order to support their strategic process. That cash did not come cheap, and the fact they needed their CEO / founder to provide it suggests that it was truly “lender of last resort” type stuff.

So GPRO appears to have forgone the meme stock yolo and chosen to take the bird in hand. That was probably the right choice…. but it’s a fascinating one, and you always wonder if a rushed merger agreement will have some holes or issues in it that come back to haunt the company later. And it is perhaps worth noting that the merger agreement appears to pay off the founder’s distressed financing in full; again, the merger agreement may have been the right decision, but is it possible that management / the board were in a position to be much more risk averse than would have maximized value for their shareholders2?

Perhaps we will learn more as information on the deal drips out…. but, for now, just a fascinating deal to look at.

This podcast is sponsored by Trata

I already know you’re going to like trata. Why? Because you’re reading this blog post, meaning you’re interested in this podcast. And trata is just like this podcast (though without the handsome host!). Trata is anonymized transcripts of buysiders discussing stocks they are following and what they really think will drive the stock. I’ve been mentioning it on the podcast for months, and the most frequent complaint I hear from people who check it out is they wish trata had more content on more of their stocks. Even better: they recently released an MCP that lets you connect their transcripts to the AI model of your choice. If you’re interested, click here to check out their LMB transcript.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

This podcast is sponsored by fiscal.ai

Fiscal AI is a modern data terminal built for investors who want an institutional-grade platform without the complexity. I use Fiscal AI to research my holdings, find new investment ideas, and deepen my analysis. It’s fast, intuitive, and has genuinely helped my research process. Check it out at fiscal.ai/yav for 15% off any of their paid plans.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

This podcast is sponsored by Trata

I already know you’re going to like trata. Why? Because you’re reading this blog post, meaning you’re interested in this podcast. And trata is just like this podcast (though without the handsome host!). Trata is anonymized transcripts of buysiders discussing stocks they are following and what they really think will drive the stock. I’ve been mentioning it on the podcast for months, and the most frequent complaint I hear from people who check it out is they wish trata had more content on more of their stocks. Even better: they recently released an MCP that lets you connect their transcripts to the AI model of your choice. If you’re interested, click here to learn more.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

It’s an issue that I’ve been thinking about a lot recently, as I worry we may be about to run into the reverse problem: high stock prices with high interest rates that break DCFs.

What do I mean by breaking DCFs? Technically, the correct way to value any business is to run a DCF on it. You figure out their future cash flows, discount them back to the present, and presto! You have your firm’s value.

Now, it sounds simple in theory… but if it was that simple then anyone with a spreadsheet would generate alpha! In practice, valuing something is much harder. The real skill is getting the assumptions right: figuring out the near term cash flows, figuring out when to set the terminal cash flow number, and figuring out the correct interest rates. It all sounds simple in theory, but in practice it’s devilishly hard.

Of those three major assumptions, it seems like the simplest should be setting an interest rate number. After all, the treasury market is the most liquid market in the world; using the treasury rates plus some equity risk premium (ERP) seems like an obvious choice.

And that is the theoretically correct answer! But, as I mentioned at the top, there was a problem in the early 2010s: interest rates were really low. The 10-year treasury floated around a 2% interest rate for most of that time period. An interest rate that low would suggest stocks should have been much higher than they were if we assumed historical average ERPs.

So practitioners were running into a problem: if you took the market interest rates and used a long term average ERP when valuing basically any company, your model would spit out a fair value that was way, way higher than the current stock price.

Of course, there is a solution to that problem: use a higher ERP number. I’m about to get a little wonky, but bear with me. NYU publishes a long term chart of ERPs; you can find it here. The long term ERP average is around 4.3%; from 2011-2015, the average ERP was ~5.75%. That’s a massive gap: stock indices would have been ~35% higher if the ERP had been sitting at its long term average around that time. McKinsey1 put it this way, “Given the low interest rates over the past 15 years, the typical large company should have traded in the well-above 20-fold P/E range since the Great Recession. But that hasn’t been the case. Median large companies have consistently traded in the 15-fold to 17-fold P/E range since the financial crisis—despite low interest rates during the entire period.”

Bottom line: the super low interest rates of the 2010s should have resulted in much higher market prices than we saw…. but stocks stayed at largely reasonable / low P/E multiples (for the most part) due to a higher equity risk premium.

Before getting to why, it’s worth dwelling on how much havoc that gap wreaked inside companies. Remember that all capital allocation is a game of tradeoffs. A company can buy back stock, pay down debt, or invest in capex. Which to do often depends on the stock price; if the stock is trading for a 1x P/E, the market is screaming for the company to immediately stop capex and buy back shares. If the stock is trading for 100x P/E, the market is begging the company to issue shares and ramp into capex.

In that way, the higher implied ERP created lots of issues for firms. If a company decided to use a long term average ERP instead of the mark to market number, a DCF would suggest the company’s stock was incredibly undervalued and they should buy back shares like crazy. If they instead used the higher ERP designed to back into today’s stock price, then they’d need to use the same high discount rates when evaluating capex (and thus need to pass on a lot of capex that would have been profitable at a lower discount rate).

Why didn’t stocks take off when interest rates were low / why did the ERP stay so high? I saw lots of arguments for why stocks were so low at the time; for example, McKinsey argued that interest rates were so manipulated by the government that the market simply didn’t trust / use government interest rates in setting multiples. I never liked that argument, but I think it’s an interesting one. It basically flips the ERP argument; instead of arguing ERP’s are at record high levels that may be unsustainable, it argues the ERP is at a normalized level and the interest rates are at unsustainably low levels. Outside of interest rates, I’ve heard people arguing that the market didn’t trust the sustainability of earnings (though, admittedly, this argument was made much more in the early 2010s when people were still worried about a double dip from the GFC). At a company specific level, I think there was a lot to be argued about the market putting capital allocation / management misalignment discounts on companies given some gargantuan pay packages that often did not require the stock to work to vest.

Over the past 10 years, markets have done incredibly well. The S&P 500 has done almost 15% annualized:

I’d argue a lot of the strong past 10 years for equity markets has to do with the really high starting ERP from 2015 (i.e., stocks starting cheap relative to rates). Yes, earnings growth has been very good, but the P/E for the S&P 500 went from the high teens in 2015 to ~25x by the end of 2025. That was an enormous tailwind for the market (and some of the more steady components of the S&P really benefitted; for example, Costco went from trading with a P/E in the 25-30x range to ~45x, and WMT went from a ~13x P/E to ~40x. Those P/E increases are even starker because they come despite rising interest rates. I don’t think either of those businesses has a materially different competitive moat today than they did 10 years ago; rather, I think investors have just come to underwrite their moats and terminal value with much more certainty).

Fast forward to today, and suddenly everyone is talking about rising interest rates. Treasuries are at multi-decade highs, and the treasury is suddenly intervening in the bond market to drive down interest rates. Despite the sudden rise in interest rates, stocks are sitting at all time highs. My fear is that the outlook for the next decade is the mirror image of what happened in 2015: then, stocks started depressed versus interest rates and rode a P/E expansion tailwind higher. Today, I worry stocks start with a higher P/E and get dragged down over time by high interest rates.

The all time highs in stocks today are not being driven by the ERP suddenly shrinking (as it was during the dotcom bubble). At the beginning of August, ERP sat at ~4.3%, right in line with long term averages (ERP touched 2% during the heights of the dotcom bubble). Instead, stock prices have remained supported by massive growth in earnings over the past few years.

So perhaps my fears are misplaced. Perhaps stocks are priced reasonably and I should be more worried about a cyclical washout / recession destroying earnings than I should about stocks starting at a lofty valuation.

Or perhaps the two are tied together: maybe low interest rates in part fueled the boom in earnings by providing cheap credit, and a rise in interest rates causes a double whammy of falling earnings and a drop in P/E? We’ve already seen something of a preview of that in the recent past: the ERP had compressed to ~4.2% on top of a ~1.5% ten year by the end of 2021. When rates ripped in 2022 (rates ended the year approaching 4%), earnings stalled out (S&P earnings grew <1% from 2022 to 2023) and stocks were crushed: the S&P dropped ~20% and the market’s P/E compressed from ~23x to ~17x as the ERP rose to almost 6%.

So what happens this time? I’m honestly unsure. But the more I think about interest rates, the more I focus in on what happened in 2015.

Time will tell.

This podcast is sponsored by Trata

I already know you’re going to like trata. Why? Because you’re reading this blog post, meaning you’re interested in this podcast. And trata is just like this podcast (though without the handsome host!). Trata is anonymized transcripts of buysiders discussing stocks they are following and what they really think will drive the stock. I’ve been mentioning it on the podcast for months, and the most frequent complaint I hear from people who check it out is they wish trata had more content on more of their stocks. Even better: they recently released an MCP that lets you connect their transcripts to the AI model of your choice. If you’re interested, click here to learn more.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

The Walter story is pretty wild. At a high level, the allegations seem to be that he used undisclosed related party loans from his controlled life insurance companies in order to fuel his sports empire. When insurance regulators realized what happened, he needed to immediately begin fire-selling his empire in order to pay back the loans… though, having seen the prices and market panics that a true fire sale generates, I will be honest that I struggle to see selling the Lakers for $12.5B less than 18 months after buying them for $10B qualifying as a fire sale! A real fire sale normally looks like what happened to Situational Awareness in July: short sellers get word you’re in trouble, all of your marks go way against you as everyone bets that you’ll be forced to unwind, the bets against you push prices even lower and become something of a self-fulfilling prophecy, and you’re forced to sell to Citadel at a massive discount (I wrote about Situational Awareness twice this summer; see those posts for more).

Anyway, the whole story is pretty insane, but there is one piece that drives me a little batty. Every time I hear a media personality talk about the story, they’ll express some populist outrage that Walter could buy the Lakers for $10B and flip them for $12.5B roughly a year later, and they’ll talk about it like it was some genius trade.

Let me be perfectly clear: Walter’s Lakers investment was not some genius trade. In fact, it was not even a particularly good trade. My dog could have beaten this trade with an index fund.

I get it: ~$2.5B of profit is a big number, and it seems crazy someone could buy a marquee sports franchise and then immediately flip it for billions in profit. And a 25% headline return in ~18 months is, in the absolute, pretty solid!!!

But the complaints I hear seem to be along the lines of “these rich people get access to all of these great sports investments that we don’t.” I’ve spent a lot of time researching investments in sports teams; there’s more than a kernel of truth to that complaint. Sports teams get unbelievable tax breaks, their artificial scarcity creates enormous benefits in a world of rising wealth, and I think the prestige that comes with owning and running a sports team has been perennially underpriced in the private markets.

All of that is true…. but Walter flipping the Lakers for $12.5B has nothing to do with any of that, and I think pointing to the flip as some great investment actually does the arguments around sports investing a disservice.

The fact is that Walter’s investment in the Lakers was, at best, an average investment (and that’s if you put aside the possible jail time it could result in if the allegations on the related party dealings are true!).

Let’s start with the headlines: the Lakers valuation increased from $10B to $12.5B under Walter’s brief ownership. That’s a 25% increase; again, not bad in the absolute….. but equities have been on an absolute tear over the past year and a half. No matter what index you use, Walter’s investment has actually underperformed:

To put numbers on it: the Lakers are up 25% on the flip, and every index in that chart is up more than that, with the broad market up ~30% over the same stretch.

Now, I can imagine all sorts of pushback to that comparison; at a high level, I’m comparing a privately owned sports team to general public equities…. but I’d argue that those arguments actually further emphasize that the Lakers were a bad investment for Walter! In general, given illiquidity, you’d expect private assets to outperform public equities1. I’d also argue that sports teams are, to some extent, a levered bet on global wealth, so if equity markets are up ~30%, I’d expect sports teams to do better than that. For the Lakers (a trophy franchise in a buzzy sport) to see their price underperform a booming equity market suggests the Lakers trade wasn’t exactly great for Walter (and, again, that’s ignoring the possible jail time).

And none of that considers the complexity and transaction costs of buying a sports team. Assuming you had the money, you could buy $10B of broad-based ETFs in a day with basically no transaction fees, no high priced advisors, and just generally no headaches. In contrast, closing a $10B private transaction like buying the Lakers would involve months of work and likely hundreds of millions of transaction costs once you factor in all of the financial advisors, lawyers, etc. that come along with it. Similarly, you could sell $12.5B of broad-based ETFs in a day, but selling the Lakers at a $12.5B mark would incur hundreds of millions more in lawyer / advisor / transaction costs, to say nothing of your own personal time value from spending weeks or months of your life on the transaction.

Again, I’m not saying it’s not crazy that Walter could allegedly run a scheme like this, get caught, and still make billions in a fire sale of those assets. But I am saying that I hear a lot of people complaining about him making a lot of money like he had access to some secret investment, and the simple fact is that anyone (you, me, a baby, my dog) could have massively outperformed Walter’s Lakers investment with far less headache by buying an equity index and then going about and living life.

This podcast is sponsored by fiscal.ai

Fiscal AI is a modern data terminal built for investors who want an institutional-grade platform without the complexity. I use Fiscal AI to research my holdings, find new investment ideas, and deepen my analysis. It’s fast, intuitive, and has genuinely helped my research process. Check it out at fiscal.ai/yav for 15% off any of their paid plans.

Please follow the podcast on Spotify, iTunes, or YouTube! And please be sure to rate / review the podcast if you enjoy it, or share it with someone else who would enjoy it (more listeners is a critical part of the flywheel that keeps this Substack and podcast going!).

Share Yet Another Value Blog

Transcript for paid subs begins below (The initial transcript is AI-generated and is replaced with a professionally edited version when available)

Disclaimer: Nothing on this podcast or on this blog is investing or financial advice; please see our full disclaimer here. The transcript below is from a third party transcription service; it’s entirely possible there are some errors in the transcript.

Read more

How wild has the year been for SaaS? In mid-April, IGV (the software index ETF) bottomed down almost 30% YTD…. and, from those lows, it has climbed back to being slightly positive for the year.

That chart probably slightly understates the pain in software. IGV is a reasonably concentrated ETF; the top 6 components each have weightings over 5% and make up just shy of 50% of the index in total:

And two of IGV’s largest components (PANW and CRWD) are having banner years, with their stocks roughly doubling YTD.

So while the outlook for software is certainly nowhere near as dire as it was back in February, your average SaaS company today is still probably down solidly on the year / trading cheaper than it has for most of the past decade.

Anyway, I’ve spent a lot of time thinking (and writing!) about the SaaSpocalypse this year. See, for example, my podcast with Marcelo Lima from April (almost literally the exact bottom!), or my “Some ramblings on the SaaSpocalypse” or “More SaaSpocalypse at $WIX” posts from February. Hindsight is 20/20, but it obviously would have been a great time to plow into software when I was writing those posts!!!

But this blog tries not to wallow in missed opportunity! Instead, let’s go back to the $20/share versus $10/share framing. Yes, all of the companies have bounced hard off the lows…. but we’ve got a lot of new information alongside that bounce, and all of that information suggests that worst case scenarios have basically been removed. Perhaps SaaS stocks are a case where they are better values today than they were at the height of the panic a few months ago?

What new information do investors have that they can look at and say “these stocks are cheaper at higher prices than they were a few months ago?” I’d point to two different things: earnings season reassurance the bottom isn’t falling out and private equity / M&A bids coming back to the space.

Let’s start with earnings. Q2 earnings season is generally over1, which means investors have had a chance to hear updated numbers and guidance from SaaS companies. This is painting with a very broad brush, but in general software companies have performed much better than feared when the SaaSpocalypse fears were really running rampant. Perhaps every company is going to replace all of their software with internal home brews in the future, but there’s no sign of it happening in the present (as was the fear at the height of the selloff). In fact, it might be just the opposite; a lot of SaaS companies are reporting strong demand for their products driven by the AI trends. Here are just a few examples of quotes from recent earnings releases:

• NOW (7th largest IGV component): “exceptional Q2 results solidify our position as the fastest-growing major enterprise software and cybersecurity company”

• ADBE (8th largest): “record revenue of $6.62 billion in Q2 reflecting strong AI-driven demand across our customer groups and we are raising our full-year fiscal 2026 revenue and non-GAAP EPS targets on the strength of that performance”

• DDOG (12th largest): “Datadog delivered a strong quarter, with 36% year-over-year revenue growth… Our customers are building and deploying with AI, and they are using the Datadog platform to observe, secure, and act on their AI-enabled solutions”

• TEAM (21st largest): “Q4 was a strong finish to fiscal 2026, with Subscription ARR of $6.6 billion, increasing 23% year-over-year, and RPO growing 44% year-over-year to $4.8 billion. Enterprises view Atlassian as a long-term, strategic partner and are deepening their commitment to our open platform to securely deploy agents across their organizations…”

I’m not claiming this is a comprehensive list, but you can kind of pick up and down the board for large software components and you’ll generally find companies guiding to continued strong demand even as AI ramps up. Again, not to say that the shoe won’t eventually drop here, but in the short term software companies’ strong results and guides have generally relieved the “everyone is going to home brew their own SaaS” fears.

The other interesting thing is M&A has come back to the software space in a big way, particularly private equity bids. The big one here is rumors broke last week that Silver Lake is in talks for WDAY; that would be a ~$50B take private and one of the largest software takeouts of all time (heck, you can drop software from that statement; it’d be one of the largest deals ever!). However, the potential WDAY / Silver Lake deal is far from the only one in the space. On the definitive deal side, we’ve seen:

• Nielsen buying DV for $2.16b (30% premium)

• Thoma Bravo buying ARX for >$4b (~50% premium)

• Nuvei buying PAYO for $2.75b (this deal got leaked, but it was a ~44% premium to the pre-leak price)

• WEAV2 getting bought by Francisco Partners for $650m (~35% premium)

To be fair, a few of these are software-adjacent more than pure SaaS, but the pattern is the same! Outside of those definitive deals, there have been a lot of rumored deals that I wouldn’t be surprised to see lead to deals in the near future:

• Criteo / Vista

• CCC exploring a sale after Elliott builds stake

  • Note: As I was writing this article, news broke that CCC had multiple bidders, including Copart.

• Alkami explores sale after receiving interest

Why are all those deals / rumored deals interesting?

I’m long past believing that private equity is the smartest money in the room or anything. They’re just as prone to cyclical behavior and buying at the top as the rest of us! However, I think they can serve as a very useful signal in industries and theses where there is one obvious and very diligence-able bear point.

Why?

Because private equity has nearly unlimited due diligence budget to throw around running surveys and doing expert interviews to resolve how particular issues are trending. And they also have access to internal data (how their own portfolio companies are spending or responding to a risk) that no one else has.

And private equity doesn’t just need to get comfortable with risk points on their own. Because they generally need to raise debt for take privates, private equity needs to get lenders comfortable that there is real downside protection in these businesses. It’s one thing for a private equity firm to buy a company at a cheap multiple that’s facing terminal value questions (as SaaS is with AI). If they’re wrong, they could have a zero on their hands, but if they’re right they could have multi-bagger upside, so they can take a coin flip chance of a company going bust if the upside is high enough. In contrast, a lender’s upside is limited to their interest payments, so they’re going to be much more concerned with downside protection in a worst case scenario.

Put the two together, and I’d suggest that both the private equity firms and the lenders have internal data and due diligence metrics that have gotten them very comfortable with the terminal value risk for these companies. That’s not to say that they’re right or wrong, particularly if the AI trade accelerates even faster than it already has! It’s just that they’ve gotten comfortable with a very obvious risk, which suggests that (at worst) it’s been priced in and (at best) that the risk is diminishing.

So what’s the playbook from here? My guess is that SaaS is going to be a stock picker’s paradise for the next few quarters. There’s no doubt that some of these businesses are going to be heavily disrupted by AI, and those stocks will bleed out…. but there are plenty of businesses that are still trading at reasonable multiples that will be AI beneficiaries and potential PE takeout plays that the market is still skeptical of. As always, I’m sifting through possible candidates for this, and I may write some up in the near future.... but my inbox is always open if you have any candidates you think are worth looking at that you’d like to swap notes on!

PS- I’d be remiss if I didn’t mention that both WDAY and WEAV gave out some dark arts style grants earlier this year….. perhaps insiders knew in real time that the stock price was too cheap and a private equity endgame was in the cards?

• WDAY’s dark arts grants:

• WEAV’s dark arts style grants: