Reminiscences of a Stockblogger

Oct 11

On AI, Math and Cryptography

Because I haven’t written in this blog in a little over two years, and because AI has been my primary vehicle for investing for the last two years, I have a lot of thoughts about AI that need to be prefaced before I get to anything else.

Even though some of them may sound a little dated by now, well, I have to start (again) somewhere. And while I was going to talk about a couple of stocks at the end of this post, Akamai and Circle, both of which have not done well, and explain why I think that was, but the post got out of control long so I will leave that for next time. So instead, this is just a second, state-of-where-my-mind-is-at post, and I’ll talk about more specific stocks shortly.

I wanted to bring up Akamai and Circle because they have been AI related stocks that really haven’t worked. Which makes them outliers, at least for me. AI has been the only thing that has consistently worked for me for about the last 2 years.

When I have tried to wade into an non AI name, it has had about a 50/50 chance of blowing up in my face.

Just thinking of just the last few months, I have, at various times, gotten long Boston Scientific, Abivax, BuildersFirst Choice, and Amer Sports. All of these stocks have been somewhere between disasters and duds.

My non-AI loser stocks have definitely outnumbered the winners for some time now.

Fortunately, the AI names I have owned have mostly worked.

Yet I am always uneasy about the AI names. Not for the most commonly held reason though. My uneasiness is NOT that I buy into the commonly held bear case of the big AI bears, the guys like Michael Burry and Ed Zitron. I actually suspect they won’t be proven right for quite a while yet (they will eventually of course, because stocks always have a bear market).

Even though there are a lot of different ways of saying the commonly held AI bear case, I believe you can boil it down to a few points and one big unknown that determines their outcome. Those points being:

Spending is so big that it can’t possibly realize the return that it expects
GPUs depreciate too fast to have their investment realized
Creative financing mechanisms are being used to keep the boom going and those will collapse like they always do

Those three points gather their potency if one future development occurs: AI does not deliver on demand.

If AI delivers enough demand, then none of the above worries matter. Money will be made that will paper over shortcomings and the boom with continue.

For now, I think that demand will prevail and that the rally has legs. That is my tentative conclusion (always tentative because it depends on what is happening next and, I admit, before Muse I beginning to waffle that the doomers may be right).

But anyway, this is the common bear-side worry. But not the source of my own unease.

Instead, my uneasy feeling has more to do with how quickly it is getting better. And my inclination that this is just going to accelerate.

For a bit of background on where I am coming from…

In the fall of last year, I sort of decided that I was going to figure out how AI worked.

I had been in and out of different AI stocks through some of 2024 and 2025. But I never really had conviction. I was always torn between the “it’s a bubble” bear camp and the promise of productivity and profits. And so I was always selling out too soon.

I don’t know why it takes me so long to realize obvious things, but it was then that I realized the only way I was going to gain that conviction (or not) was if I figured out how the dumb thing worked.

I got this book called Why Machines Learn, that gave me a good headstart. I followed that up by going through AI papers, a lot of papers, starting with this list from AI researcher Ilya Shutskeyver, who contributed many papers to the subject and was a co-founder of OpenAI, of the 30 most important papers he thinks you need to read to understand AI.

These papers and others are all available for free and are very useful for understand the math and science that goes into AI and how we got to this point. Once you get through them you can tackle pretty much any new idea that comes out and have a good understanding of whether its meaningful or not. I’ve kept at it, reading about what the open labs are doing and, where possible because unfortunately it is all behind closed doors now, what OpenAI, Anthropic, Google and Meta are doing as well.

This learning process was useful for conviction, just as I hoped it would be. What has stuck with me the most is that this stuff is surprisingly simple. It’s all based on math that I learned in undergrad engineering. Today it involves matrices that are enormous, I mean they are hundreds of billions of parameters now, but the actual operations you are doing are not that different than what I learned in first and second year university.

I’m not the only one who has been blown away by the simplicity. Sutskever said himself that one of the reasons he got into the field was because when he read a couple AI papers after his under-grad and couldn’t believe how simple the math was compared to what he was doing with his other math classes and projects. It was a hint to him that this was onto something.

So that’s the first piece of my uneasiness. AI is pretty simple.

Second bit of background. One of the papers that Sutskever lists above is what is known as the Scaling Laws paper.

You hear a lot about scaling laws and what they mean for AI, from bulls and bears alike. What they are usually talking about is this paper called Scaling Laws for Neural Language Models. One of the writers of this paper is, interestingly enough, Dario Amodei, who is the guy in charge of Anthropic now.

This is one of the easier papers to read because it isn’t really exploring any new method or technique. It is just investigating what happens when you make models bigger.

It was written in 2020. It is hardly ancient history.

It didn’t really set out with the expectation to find out what it found. In fact, it was sort of assumed by everyone in AI at the time that eventually as models got bigger things would break down. If you go through the previous 20 or 30 years of AI research, there is this sort of underlying assumption that you can’t just keep making things bigger.

Which sort of goes with the territory. Science is all about simplifying things down through clever solutions. Brute force is frowned upon.

With that expectation, the big discovery of the scaling laws paper was that no, that’s not right. The expected wall doesn’t happen and, in fact, model performance seems to follow a remarkably consistent pattern, called a power law, whereby output gets better regardless of model size.

A power law is just a really simple equation that says that when you make some quantity, say x, bigger, the resulting y scales by a much larger (or, depending on the exponent, smaller) amount. It looks like this:

When they found that models followed a power law is was an aha moment. It meant that A. you could basically extrapolate how well a model would perform as it got bigger. And B. It followed a law where outputs got big much faster then the inputs, which tends to be profitable over time.

The paper identified three key inputs by which model performance scaled: the number of parameters (ie. How big the matrix is), the size of the training dataset, and the amount of compute you use.

They found that these relationships held across more than seven orders of magnitude. Again, no sign of anything breaking down or even hinting that it would break down across a really wide range of sizes.

We are now six years past when that paper was written and so far the power law still hasn’t broken down.

Maybe we shouldn’t expect it to. Or at least, maybe we will be surprised with how big it can get before it does.

Power laws are everywhere in nature. Earthquakes, animal sizes, tree networks, blood vessels, river networks, there is just a lot of nature that follows a relationship of power laws.

Which is to say that when you start thinking about all this you start to wonder if we (being humanity that is) really stumbled onto something here. We’ve tapped into something very simple but that scales. Which certainly makes you go hmmm.

None of this is to say anything is for certain about any particular outcome for AI. Nothing is.

But when you put together the simplicity and the scaling I think we are more likely to be surprised by how quickly AI improves, and as it improves creates more use cases, and with that creates more demand, then we are likely to be surprised that its been a big capital spending black hole that has no use.

The problem with the internet, which everyone likes to compare as the example of a bubble that popped, is that the technology didn’t move fast enough to keep up with the infrastructure being built. If we could have had a Blackberry (the phone, not the pager) in 1998, an iPhone in 2001 and streaming Netflix by 2004, maybe the bubble wouldn’t have burst? We just would have just kept laying fiber and building cell towers.

All of this is by way of background to understand where my uneasiness lies. My uneasy feeling is about how quickly AI is getting better. Which brings me to why I was reminded of that this week.

It started with this sort of cryptic (yes, pun) tweet:

Who is toptickcrypto? It has been a long time and I don’t remember who exactly toptickcrypto is the monikor for anymore but I do remember he was a fund manager, maybe he still is, that he got into crypto in like 2018 or something, and he knows a lot about stocks and crypto.

Him not feeling so good made me want to investigate what he was feeling not good about. Which took me down the rabbit hole of AI models and math. And it all was a little unnerving.

It all starts with this release by OpenAI.

OpenAI published 722 math papers that together solved 372 math problems that had previously been unsolvable. They did it with an unreleased version of their model.

So that’s pretty crazy. And while I see an instance or two of people refuting the solutions, I see much more acceptance admitting what OpenAI did was real.

But beyond that, what Toptickcrypto is pointing out, and what makes him as a bitcoin/crypto guy not feel so good, is the rather ominous omission of those 722 problems: that 0 of them are cryptographic.

Cryptographic means solving a problem that deals with encryption techniques (you know, those things we depend on for literally EVERYTHING on the internet).

Which of course makes one wonder if A. they just happened to decide not to tackle cryptography or B. the cryptographic results are a little scary (ie. good at breaking encryption) and they don’t dare publish them or everyone would freak out.

This was not unnoticed in the crypto world, where there was some freaking out done.

Let me just preface this by saying that crypto, as the name implies, uses cryptography. Also, because of the way crypto is structured, crypto is probably the #1 most vulnerable place to be hacked if cryptography techniques are hacked.

To point to just one of the posts from the community, from Vitalik Buterin, the guy who founded Ethereum and therefore should be taken seriously:

This isn’t the whole tweet. The whole tweet is worth reading because Vitalik has put a lot of thought into this. I have two thoughts on his thought.

First, saying “I don’t recommend anyone scramble to move their funds” is what every bank manager says before the bank run. I’m not saying there is going to be bank run, I’m just saying this isn’t something you want to be saying.

Second, he is admitting that AI is making major steps in math, and that it is unlikely that those steps aren’t happening in the area of cryptography. And based on that, his solution for you, for now at least, is to start moving your crypto to wallets that haven’t had their public key exposed (it turns out that if you have never sent money from a crypto wallet you have never shared your public key and thus it can’t be hacked). Which again, is a bit like saying, take your money from the bank and put it under your floorboard and you’ll be okay.

I’m going to spend just a minute explaining how this all works and why this could eventually be a problem.

First, in crypto, pretty much all crypto, you have a public key and a private key. Both of these are just very long strings of numbers.

The public key is what you send out to the world to transact. The private key is what you would never tell anyone about, and ideally you would keep it locked in a bank vault somewhere. If someone knows your private key, they essentially own all the crypto in your wallet.

The path between the two looks like this:

private key ──(step 1: elliptic-curve math)──▶ public key ──(step 2: hash)──▶ address

The worry is that first “elliptic-curve math” step. The real problem with that step is the “math” part.

I haven’t really bothered to understand exactly what elliptic curve math is, but I can tell you a few things about it.

First, it’s a nifty technique whereby if you go in one direction (calculating the number that is your public key from the number that is your private key) it is relatively easy to do. But if you go the other way, calculating your private key from your public key, it becomes very, very hard. So hard that no one has ever figured out how to do it in a way that wouldn’t take many, many, many years.

However, it is not necessarily impossible. No one has ever proved that going backwards in elliptic-curve math is theoretically impossible. It just has been proven over decades of use that no one has been able to hack it, which means it’s practically impossible.

That is an important distinction, and suddenly quite a relevant one. Because yes, we have something that no human being has figured out a shortcut around. But AI is figuring out all kinds of whacky shortcuts.

Last detour, I promise.

When I was looking into all this I stumbled on this blog post, which talks about the mathocalypse. Anyway, the author of the blog’s wife is a complexity theorist, which means she deals with really hard math. His wife was reviewing one of the AI proofs from OpenAI. And she had this to say about it:

So there are a couple of ways you can take this.

First, you can take it in the mainstream “AI-isn’t-all-it’s-cracked-up-to-be-and-makes-things-up-all-the-time” sort of way. If you did, you would conclude AI is clearly spitting out gobbledygook and we have nothing to fear with this whole math proof stuff.

Or… the other way to take it is a little more seriously. What if AI is simply making connections that are so far from being obvious to us that they sound like gobbledygook (to an expert in the field, keep in mind) but are really fundamental to what you need to know to solve the problem?

Again, you sort of go hmmm. Can’t know for sure but it is alarming.

Of course, all this strikes me as something that could be bad for crypto at some point. And particularly bad for companies like Coinbase or Robinhood. The idea that everyone in crypto is not going to be able to transact without exposing themselves is not a good scene.

But not just yet. None of this matters just yet because fortunately OpenAI has tackled 700+ math problems but is leaving cryptography alone…. riiight. But most of crypto was up on Friday so it is true to say that no one cares about this yet. Citrini, the research substack, had this big piece on AI agents using stablecoins on Thursday, which helped crypto and actually makes a lot of sense and should send the crypto space much higher as long as (or until?) AI doesn’t cut it off at the knees.

The bigger thing for the plain vanilla common stocks I own is why this is a great example of where my uneasiness lies.

What I have been trying to lay out here is why things are progressing very quickly, why things are likely to continue to progress quickly, and from that, why the proper conclusion to draw is not the bear case that its all going to fall apart because of lack of demand, but instead that the real bear case is that at some point AI could break something in bad way.

There is no immediate action to take from that conclusion. In fact, everything I am saying is actually very bullish on AI as long as and until something bad happens. I think token usage will just keep rising, use cases that we haven’t even thought will keep materializing and Ed Zitron, Michael Burry and Paul Kedrosky will keep yelling at the sky.

But I think you also need to stay vigilant here, because we have quite possibly latched onto something that is lightening in a bottle. And I don’t think any of us can say with certainly where it is going to take us.

Sep 30

6 Comments

WHY I THINK META MUSE COULD CREATE THE NEXT LEG FOR THE AI STOCKS

I am back posting. I have been gone for 2 and a half years but have not be gone from investing. Or from writing, I just haven’t been putting it up on a blog. I’ll maybe talk about that at some point, but maybe not. I mean who really cares right? Live in the present.

As for the present, I have been using Meta Muse for 5 days now and I think this is could be the next game changer for AI.

Which is good. Because I have been a little nervous about what will come next with AI. And that if nothing did come next, that the market may have been in for a fall.

Let’s step back in time and review. In January, it became pretty clear that the whole AI-for-coding thing was going to take off. AI was naturally good at coding because it is, after all, a language model, and coding is pretty much the easiest language to learn, at least in terms of definite structure.

And that happened. It was a little delayed is all.

During the first days of the Iran war AI stocks sold off along with everything else. But even then it felt like they were just biding their time, waiting for a pause in the war so that they could go on a run as coding took off. Which is what happened in April.

I remember what I was thinking in March. That period was a tug-a-war for me, trying to answer this question: why is the market not going down more with the Iran war a clear disaster?

I had two competing thoughts at the time.

One was the recognition of the old maxim (well, at least my old maxim) that goes something like this: “the market doesn’t care about bad things until it can’t ignore them any longer”.

This is something I have noticed over time. It is basically saying that markets don’t actually like to go down. So they don’t go down unless they have to.

However, there is a flip side to that maxim. Just because nothing really bad has happened to the market yet, that doesn’t mean it won’t. The market may still go off the cliff if things get just a little bit worse.

To that very worrisome possibility, I weighed the much more positive possibility that AI coding agents were just that big of a deal.

It was possible that the market was strong in the face of war and high oil prices simply because it saw that coding agents were that big of a deal. War wasn’t going to stop them. Inflation wasn’t going to stop them. And that you couldn’t push stocks down with them coming on the horizon.

As it turned out, it was this second possibility that was correct. Coding agents were a big deal. The market was right not to allow itself to be pushed down.

When the Iran war eased, even just at the margins, the result was a steep bull market that lasted through the spring and into the summer before it hit a wall.

What happened at that point, and caused the pause that we are still in today, is a recognition that coding can only take AI so far.

You can replace programmers with AI. You can generate more code faster. And for a subset of the population (ie programmers and people that work with them) this can be very meaningful.

It is very meaningful to the programmers that lose their job.

It is meaningful to the companies that gain in productivity from being able to generate more code faster.

It is even meaningful to software companies, as we see now that the doomer-ism that software was about to be obsolete was wrong. That, in fact, some software was growing faster then ever, and that most software companies were more profitable then ever, because they no longer had to couple growth with more programmers.

But AI-coding agents weren’t meaningful to most people. AI had disrupted a segment. But what would happen when that segment, which also just happened to be the fastest adopters of AI (programmers love technology!), became saturated?

That question is what got me thinking about what comes next? It is something I have thought about all through the last month.

It is a big question, much bigger then it seems at a glance. To understand why “something coming next” is really, really important, consider the AI build out that is upon us…

WHY WHAT COMES NEXT, MATTERS MOST

So… the thing about the AI buildout that makes it matter really amounts to this: It is TRULY MASSIVE.

AI capacity is usually measured in GW of power consumption. Power is directly related to how many GPUs and CPUs you can run. Measuring power is the same as measuring compute capacity.

There are different ways of defining what AI capacity is. I am going to describe it as usable AI capacity, which would be what you call “frontier-grade”. Frontier grade means you could use that capacity to train or serve a big frontier model on it, just so we can put a consistent set of numbers on it.

AI capacity is expected to be about 15 GW by the end of 2026.

It is expected to rise to 45 to 55 GW in 2027.

Planned datacenter capacity is even higher. We are up to 210 GW (!!) of planned capacity that will come out in maybe the next 5 years or so.

So the thing is, these numbers are essentially insane. Consider that Alberta’s total electrical generation capacity is like 12 GW.

We are increasing AI datacenter capacity by two Albertas next year. And the pipeline is for like 12-15 more Alberta’s.

Apart from the rather unreal size of what we are talking about, what made me even more uncomfortable in the summer was that it seemed like wealready had enough capacity for what we were doing with AI right now.

I use Claude regularly. I code with it. I use it in Excel. I ask it a bazillion questions, mostly about stocks.

I’ve never had Claude say, “Woah, slow down there buddy, I can’t handle all this right now, I’m a little overloaded”.

I’m simplifying this a little too much of course. There are lots of strings Anthropic can pull to manage usage without it becoming apparent to a user like me. But still, if we were really, really short of compute, I would sort of expect Claude “brownouts” on occasion, just like when the electrical grid gets overwhelmed.

That isn’t happening. Even at the margins.

Where I’m going with this is that if this isn’t happening now, and we are adding two Alberta’s of compute next year, well you start to wonder if we are going to tip over into a glut of AI capacity at some point.

Which wouldn’t be good for stocks.

AI IS THE MARKET?

There are two things that the casual observer of the stock market is not aware of right now:

Just how many stocks have participated in the AI binge
Just how badly stocks that are not participating in the AI binge are doing

On point #1, do you realize that companies like Enbridge, TC Pipelines and AltaGas trade at price to earnings multiples today that are about 50% higher then they were 4 years ago?

It’s true. I spent a bunch of time on this a few weeks back. These stocks used to trade at 4-6x EV/EBITDA. Now its like 8-10x.

While it is always impossible to separate out what specific reason a stock is trading higher, and you can argue that with these pipeline stocks maybe it’s the Carney government and their pro-development stance, or maybe a decline in hatred of oil and gas, and yeah, okay I concede some of that might be it.

But if you look at the charts of these stocks, they all really started going up with the AI boom.

The reason, I think, was simply the recognition that more AI means more power (ie. That 10 Albertas in the next few years) and more power means growth in moving natural gas (and a lesser extent oil) and so these companies get higher valuations because they aren’t just stagnant dividend paying pipeline stocks, they are sort of growth stocks now. If a pipeline stock is actually allowed to be called that?

That’s just one example. But it holds broadly. There are a lot of stocks and sectors that have been buoyed by AI, well beyond a bunch of chip stocks.

On to Point 2. The market is pretty close to all-time highs. But you’d never know it by looking at the average non-AI related stock. In fact, 52-week lows on the NYSE are skyrocketing and 52-week highs plummeting.

There are a lot of stocks not doing very well at all. Don’t get this wrong, I’m not making the argument that the market is a disaster. I’m just saying that, if it weren’t for AI, it probably would be doing pretty blah.

The overarching point I am making here is that if it is true that AI is being overbuilt in the short term, the market could be in some trouble.

Which brings me to Meta Muse.

MUSE DOES EVERYTHING YOU COULD ALREADY DO WITH AI, BUT BETTER (MAYBE MUCH BETTER)

Look, I’m not going to pretend that I am an AI expert with my finger on the pulse. But I do keep pretty close tabs on what’s out there, I use it every day, and so I think I have a (slightly) better than average grasp on the state of the industry.

And if I’m right, then Meta Muse is something new and different.

This isn’t immediately obvious. Muse doesn’t necessarily do anything new. I haven’t found something with Muse where I’m like wow, I couldn’t do that before.

But what Muse does, is it does things much, MUCH more easily then you could have done them before.

The product is billed as an assistant. And that is truly what it is. It is like having someone to do and keep track of all the things that are a hassle for you to do. And to do the things you never did do because you didn’t have the time to do them.

You couldn’t really say that about Claude or ChatGPT. Yes, you can get Claude to do anything, but half the time you are stuck with a python file or installing a C++ compiler. And most people don’t want to go there.

Here are some of the things with Muse I have done in the past 48 hours:

Had it build a process whereby every Friday it scans all the local flyers for certain groceries that we buy on a regular basis. It then finds the best deals on each of them and sends an email with the list to my wife, mom and myself.
Recreate (I already had something like this in Claude) a Daily brief newsletter in HTML format that comes out every day and summarizes all the AI content on my AI X.com list that I have curated
Had it remind me of a bunch of tasks I keep forgetting about such as replacing a light, watching a particular video, doing a warranty call, replacing the car key battery
Summarize a livestream on stocks that occurs every Friday afternoon. I also had it schedule in summarizing future livestreams for me, which it will do every Friday and send me an HTML of the summary
Helped me haggle with SiriusXM to reduce our subscription price by about 65%. Also add a reminder of when this deal ends in a year so I don’t start paying full price.
Build a Tetris game
Create a short newsletter-style PDF of the Akamai/Anthropic deal based on my notes, model and discussion I had with Claude on the deal
Add weekly reminders to take out recycling/green bins/waste at a particular time
Check Calgary to Toronto flights every week for each airline to look for a deal and send an email to us with what they find each week and whether they think there is a deal worth acting on.

There are a dozen more I am mulling around in my head. All of them could have been with Claude. But Muse made them so easy to do that, unlike Claude, I actually did them.

It was literally like a few sentence description for the grocery flyer check and it was done in a few minutes. It built the Tetris game, which plays just like Tetris, in 3 minutes. The AI Daily newsletter was something I did with Claude but I had to run .bat files every day to actually generate and so I only got around to it once or twice a week, whereas with Muse it just builds and creates the newsletter every day and I have it there every morning with my coffee and I don’t have to do anything to get it.

The things that Muse has that make it more useful are, I think, twofold.

One, with Muse you get your own virtual computer with memory and compute. It doesn’t need to save things on your computer. It isn’t always asking to access your computer or install something on it. And it is set up to remember stuff for you.

That last point is really important I think. Muse can be your memory, just like an assistant would be. And it can be long-term memory. Muse is going to remind me about the SiriusXM renewal in 12 months.

Two, Muse is a chat, so you have it up on your computer and phone and its just like you are talking to your assistant.

Now of course, the worry here, which is already all over the news, is privacy.

Do you want to give Muse your data?

The thing is, none of the tasks I listed above actually had me give Muse any of my data. In fact, the only task where it would have been helpful was in haggling with SiriusXM, because if I was willing to give Muse my name, phone number, address, it would have just haggled with the online Sirius assistant, who I think it just a bot itself, for me. Because I wasn’t, instead I just copied the prompts from Sirius into Muse and asked Muse how I should respond. It worked.

But other then that, Muse doesn’t really have to access to anything. In fact, because you get this little virtual computer in the cloud, it doesn’t even need to access your computer at all (whereas Claude or GPT did for some tasks).

Of course as I have it do more things, it will learn more about me. It already knows some things about the groceries we buy or the flight we take most often. Just like any assistant would.

But these stories I’m reading about it wanting your bank info or your email access, well… you can certainly do that, and maybe at some point everyone will do that, but it is far from necessary to get value out it.

WHY ANY OF THIS MATTERS

What all of this does is make me think that maybe we aren’t about to run into an AI glut.

I am starting to warm up to the idea that Muse ushers in the next leg of AI.

I am certainly not the only one that sees what Muse can do and, more importantly, how easy it can do it. While AI was basically something that was super useful for programmers, researchers and people that liked to just ask lots and lots of questions, now its useful for, as Chris Camillo calls it, “the normies”.

Normies would define people who don’t really care about AI one way or another but if you can tell them where they buy coffee cheaply or alert them to a deal on flights and do it without them having to build and run a python script, and do it for free, they are happy to take advantage of it.

Muse isn’t perfect. It fails in some tasks and there are certainly some people out there that are gleefully pointing out those failures.

But I think that misses the point. Muse is just going to get better. If AI’s trajectory so far is any indication, it is going to get better quickly. And if I’m right about how this is going to open up a whole new, and larger, addressable market of people, there are going to be other Muses that come around. There will be an Anthropic Muse and an OpenAI Muse and a Google Muse in short order.

With coding agents, you had quick adoption of what is really a pretty small group of people – programmers.

With Muse, and whatever other coding agents are on the horizon, I am sure adoption will be slower. Most people won’t use it. But the pool of potential adopters is now essentially anyone that uses Facebook. So its really, really big.

Muse is free. Which gives you this massive pool of people that can afford it, even if only a small fraction of them are willing to be early adopters.

The other consideration, insofar as what this means for AI demand, is that about half the tasks I’m getting Muse to do are recurring.

With Claude or ChatGPT, it is all one-offs. Ask this, ask that. With Muse, much of what it is really useful doing is reminding, checking or doing something over and over again – every day or every week or every second Thursday.

That is real recurring usage. Which is additive.

And for now, the trend is your friend.

Finally, Meta announced just today that they have Muse for small businesses. Which makes a lot of sense. And given some of the things I am having it do for me, it will clearly be helpful to small businesses, especially if they can’t afford that assistant employee.

You add all this together and you start to see a picture of how this could play out. If Muse and its soon-to-come descendants gain traction, usage is going to increase, maybe a lot. Agents use A LOT more tokens then people.

And that, is where I was going with all this.

Maybe we aren’t about to hit an AI supply wall. Maybe things are going to be okay. And maybe the AI trade has yet another leg higher in the near future?

Dec 17

16 Comments

Stocksatbottom

One of the most fortunate things that happened to me as I was just beginning to invest in stocks was stumbling on an investment newsletter called Stocksatbottom.com.

This was in the early 2000s. That time wasn’t like today, what with Substack subscriptions from every Tom, Dick and Harry with an X account and a keyboard. At that time most investment subscriptions were sent to you by postal mail. An email subscription with a website was a relatively new phenomenon.

Stocksatbottom was run by a guy named Richard Stoyeck. Stoyeck talked about his “team”, but honestly I think it was just him. If you looked Stoyeck up you’d find he had been a fairly high-level guy for one of the big investment firms, I think it was Bear Stearns. But in the late-90s he got accused of insider trading and lost his job. My guess is that he was in his 50s at the time.

Anyway around 2000 Stoyeck was probably wondering what to do with himself and so he decided to start-up this little internet investment newsletter called stocksatbottom.com.

Honestly, it was one of the best educations I could have had.

Stoyeck wasn’t a great writer but he was a great writer, if you know what I mean. The stocksatbottom email alerts and round table discussions were full of grammatical errors, sentences cut off in mid stream and Stoyeck’s proficiency with this new fangled email-thingy was clearly lacking because there were often multiple fonts, multiple paragraph spacings and at time whole sections repeated within a single missive.

I also don’t think Stoyeck really cared much about selling subscriptions. The newsletter never advertised from what I can remember. At times he would go months, MONTHS, without sending out a thing.

But when Stoyeck did send something out it was a pleasure to read. Stoyeck was amazing storyteller. I have no idea if the stories he told were true but if they were he was connected to power brokers in Washington and Wall Street and he knew all kinds of inside baseball kind of info that was just so interesting to read. He’d weave these stories into a thesis about a stock. He’d start off talking about the guy he knew in the Lyndon Johnson administration that headed up the Tet offensive and was friends with Frank Sinatra and by the end of the piece he was telling why this is why you should buy Disney (btw I wonder this might be the time to buy Disney?).

It was such great stuff. When a new stocksatbottom email would show up I would actually get excited – and not “oh, a new idea to make money” excited but “I can’t wait to read this story” excited.

It really is a skill.

Anyway, there were a lot of lessons that I learned from Stocksatbottom and Stoyeck. But the one that sticks with me the most is what Stoyeck based his whole newsletter around.

Stoyeck was like, look, you don’t have to go to small caps, you don’t have to go to micro caps to make money in stocks. Take a look at the 200 biggest stocks in the S&P. Go through their 52 week or 104 week charts. What do you see? You see ups and downs. Sometimes big ups and big downs.

Now pick one of the downs and then pick the next up. How much is the difference? It can be 30%, 50%, sometimes 80-100%.

Stoyeck was talking about stocks like GE, Bank of America, Disney, McDonalds – the big names at the time. He would say these are great companies. They aren’t going away. Sometimes they have a stumble. Disney releases John Carter. McDonalds decides to make pizza. The market goes ape-shit and says the sky is falling. You buy the stock.

It was such a simple, simple idea. Don’t over think it. Sure, check the numbers. Make sure the valuation makes sense. Sure the market has found fault in the name and there is probably good reason to have some fear. That is why the stock is down. If there wasn’t a good reason, the stock wouldn’t be down. The markets not stupid.

But as Stoyeck repeated over and over: once everyone is talking about it, it’s already in the stock. Forget about it. Ask yourself, what’s not in the stock?

Once the market forgets about what it has priced in and moves on, then it will look to the next thing. The stock starts to recover. Pretty soon the market is talking about good news. That new McCafe is taking share. I bet you that those old princess movies are ripe for another round.

That’s when you sell. Nothing goes straight up. There’s always another stock being hated and you buy that one instead.

That was the essence behind Stocksatbottom.com. Every year that I subscribed Stoyeck would wash, rinse and repeat with GE, Pfizer, Bank of America, Proctor & Gamble, Office Depot, Macys, Goldman Sachs, Walmart and so on.

I was thinking about stocksatbottom this week because of what we went through in the last 3 months.

At the end of September I wrote this post about how much carnage there was across the market even though the indexes weren’t down that much. As is often the case, the indexes followed suit. Stocks were down big in October. It was a trying month. We were getting ready to move at the end of October, and it was probably the best timing I could have asked for because it kept me from doing anything stupid. I just sat tight with those earlier buys even as I was down a lot.

A lot of the stocks that I mentioned in that post back in September kept going down all through October. Stoyeck’s picks rarely went straight up. They usually went down first, which would make you write them off and forget them. Then he’d put out a missive 12 months later telling you he was selling that name for an 80% gain.

Some stocks I mentioned at the end of September did bottom at the beginning of October even as the market continued down. Stoyeck always said was that stocks don’t all bottom at the same time, which is why you couldn’t wait.

Many of the names I listed are now up. Some of them, like HD, TGT, DLTR, the medical device names, the airlines, are up a lot. There have been lots of 30%, 40% moves in medium/large caps even if you didn’t catch the bottom.

Some are not up much at all. As they say, there’s always money in the banana stand.

As Stoyeck showed and as this market swoon and rally has proven once again, there are many ways to skin a cat. You don’t have to dig into tiny microcaps, or pick SaaS growth or even find great businesses that you buy and hold forever. Stoyeck had a heck of a run by simply buying low and selling high on stocks like Bank of America and Home Depot over and over again, and telling a good story every time. Nice work if you can get it.

Oct 9

3 Comments

Hoping for a Turnaround at Mercury Systems

In the last few weeks I went through a whole bunch of sectors that are being blown out of the water. Many of these sectors are getting creamed because no one eats any more. But there are a few that are falling for other reasons. One of these is the defense sector, where I found no relationship between its demise and the introduction of weight loss drugs.

Because I don’t think that Ozempic will eliminate armed combat I went through the defense sector stocks and bought three of them. I bought some Lockheed Martin, RTX and Mercury Systems. Lockheed Martin and RTX are big and diversified so I don’t want to bother writing about them. Mercury Systems is more interesting.

Mercury is a sub-component supplier to the big defense contractors. They make chips (things like power amplifiers and limiters, switches, oscillators, filters, equalizers, digital and analog converters, chips, MMICs and memory and storage devices), boards and sub-assemblies (things like switched fabrics and boards for high-speed input/output, digital receivers, graphics and video, along with multi-chip modules, integrated radio frequency and microwave multi-function assemblies and radio frequency tuners and transceivers) and full systems (display and communication systems for aircraft, sensor and scanning systems for aircraft). These products are used as part of programs won by the direct defense contractors like Lockheed Martin, who use them and put into their end-products like aircraft, UAVs, radar systems and such. They sell to 25 different defense contractors.

Mercury was a high flying stock for years and then in 2022 it fell out of bed.

Mercury was a growth stock up until this year. The company grew revenue at an average rate of 22% per year from 2014 until 2023. Then it stopped.

HOW DID IT HAPPEN?

What happened? While there are lots of sordid details the essence of it is the same thing that happens to a lot of these growth stocks. The growth disappears, margins fall and then there is no basis for the rosy outlook that has buoyed the valuation.

This was followed by a couple activist investors, Starboard and Jana Partners (they still own 5% and 7% of the company), getting involved and trying to sell the company. That failed and the stock fell further.

The CEO and CFO were replaced, expectations have been reset with downward revisions to revenue and margins for this year and next, and the stock languishes in the $30s.

COST OVERRUNS ON DEVELOPMENT

That is the big picture. The more detailed picture of what is leading to reduced revenue and margins is this:

Mercury has 20 programs that are under-performing. These are all (or almost all) development programs – so programs where they care coming up with a new product that will eventually be manufactured, and because they are contracted to develop these projects from the larger defense contractors, they get paid for it.

These 20 programs are going over cost and are draining margins. Mercury noted on their last call that their portfolio is 90% firm fixed cost, so when a program goes over budget it falls 100% into margin. These 20 programs accounted for $56mm of margin decline in F2023 and $29mm in Q423 – which is to say the vast majority of the under-performance.

Having cost overruns on development programs also leads to lower revenue. “As total program cost increases on firm fixed price contracts the measure of progress on those programs decreases, resulting in a delay or reversal of revenue in the period the costs are recorded”.

That is the specific issue with 20 programs. More generally, all the development programs that Mercury is currently working through is impacting margins. Mercury’s usual mix between development and production is 20/80 but right now it is 40/60. Development programs, even when they are performing at expectation, have a much lower margin profile – in the low 30s versus in the low 40s for production.

A MARGIN MESS

As a result of all this Mercury’s gross margins are a disaster. They were down to 26.6% in FQ423 versus 41.3% in the previous year. About 11.5% of this is from the 20 challenged programs alone. The rest has to do with the mix of greater development programs and some unfavorable manufacturing variances.

That led to $21.9mm of aEBITDA in FQ423 vs $71.6mm in FQ422.

Some of this margin mess going to continue into their FQ124. They said expect negative EBITDA and lower YoY revenue. They don’t expect a real improvement in the business until the second half of their F24, which is after January.

Because the development programs have dragged on, it has impacted Mercury’s cash generation. Mercury was a consistent FCF generator or years but in F22 and F23 they were FCF negative. This is because their inventory and receivables grew, which in part was because of development programs that were buying inventory to go into production but then not going into production. COVID related supply chain issues didn’t help. This is expected to reverse here in 2024.

Mercury has said that working capital should be about 35% of sales. That would be about $350mm whereas right now its about $700mm.

WHAT’S BAD IS GOOD

The good news is that development programs eventually become production programs and having all these development programs bodes well for future growth.

Mercury’s model is to develop products that will be used across multiple DoD programs. So once they get through the development stage, multiple programs should benefit.

These are long-term headwinds. Mercury said on their FQ3 call (this was the former CEO) that the programs would complete in the next 2-3 quarters and reiterated something similar on the Q4 call (their new CEO said this). This excess of development programs that are coming in above budget should sort itself out by next summer.

Their bookings and backlog don’t seem too bad to me. Their backlog gives them 70% revenue visibility for 2024, which exceeds their historic coverage ratio.

Of course, they also had 80% coverage ratio going into Q4 and they still missed guidance.

REBUILDING TRUST

The story here is about getting margins back to normal and then seeing these development programs turn into production programs and generate revenue growth. Mercury expects that in 2024 they can get their EBITDA margin up to 16.8%-18.5%. The longer term goal is getting it to 20%+.

But these margins will be below YoY level in the first half of F24. So it will take time. And the market will be skeptical because they have disappointed in the past.

As recently as the Nov 2022 call (FQ123) Mercury said they thought they could get to 20% EBITDA margins in F23. And they said on the FQ2 call that EBITDA margins would be 30% in Q4. That was reduced to 18% guidance in FQ3 and then it came in at 8%. One analyst on the FQ4 call demonstrated the disbelief: “I still don’t understand how it got cut 45% in 90 days” which is about as close as analysts come to saying WTF are you doing?

There is a lot of trust that is going to have to be rebuilt after this level of miss this fast.

I think there is also a lot of skepticism simply because the previous CEO was misrepresenting what was happening in the business as Mercury tried to sell itself. He kept calling out “supply chain” as the issue.

We expect margins to naturally return to pre-pandemic levels as we overcome current execution challenges and as the supply chain conditions continue to normalize. Further margin expansion will follow is the late-stage development programs transition to production, and as we return to a more normal 80/20 business mix over time.

In retrospect, it seems like the former CEO was lumping in supply chain with the real issues, saying stuff like “we’re driving continuous improvements in new product development, supply chain, operations and program execution”, which made analysts think the problems were transient supply issues out of the companies control and not program issues that were.

The new CEO is straightening that out and now its clear there was more going on. Here is the broader explanation from the Q3 call:

In fiscal ’19 through fiscal ’21, we achieved a significant level of design wins, both organically and through acquisition, especially as related to the physical Optics Corporation acquisition. These design wins were predominantly within secure processing and mission avionics, 2 of our key strategic growth areas and translated into development contracts in our backlog. The onset of COVID in fiscal ’20 and the transition to remote work added latency to our development efforts.

Slightly thereafter, supply chain delays began to limit availability of critical components followed by the great resignation, which created labor constraints across a number of our program executing functions. We began to see some margin reduction in fiscal ’21 and fiscal ’22, partially offset by lower R&D expenses as more engineers charge labor directly to these development programs.

This resulted in increased levels of CRAD as discussed in many of our prior earnings calls and public filings. The higher engineering labor content, coupled with low unit volume on most development programs contributes to average gross margins in the low- to mid-30s on these programs.

I don’t think supply chain was what analysts were being told it was. It was really that they had ordered inventory that wasn’t getting used because their development projects were off-track. That is my suspicion anyway. One analyst called it out with the new CEO on the Q4 call, asking why all of a sudden there were no mentions of supply chain after hearing that the problems were nothing but supply chain before that.

There is also skepticism because the number of problem development programs increased pretty substantially from Q3 to Q4. In Q3 it was 12. In Q4 it was 20. So there is probably a bit of wondering what it is in Q124.

As a consequence of all these half-truths and the skepticism it has created, I feel like the new management team is really being conservative on guidance. When pushed they admitted they are erring on the side of caution here and the numbers bore that out.

WHAT IS IT WORTH?

During the years that Mercury was a growth company, they maintained pretty consistent EBITDA margins. The dip in margins in 2021 and 2022 could have just been COVID supply chain, more maybe it was the beginning of the bigger issues we see now. It’s hard to really say. But margins didn’t really collapse until this year.

At today’s price of $36 I estimate that the stock pricing in basically no growth (say 3% average for the next 10 years) and at least a four year grind to get back to historic EBITDA and FCF margins. Which is to say that there is very little of a potential turnaround priced in.

If Mercury turns it around, recovers to historic margins and shows that they are a 10%+ grower again, this is quickly an $70-80 stock.

There are a bunch of ways of getting to that valuation. A DCF model can give you $30-$35 fair value at 3% growth and $80+ at 13%. If you look at it from a multiple perspective, Mercury traded at an average of 21.5x EV/EBITDA over the period from F2014-F2022. If I use that multiple, assume that they recover to 19% EBITDA margins in F2025 and, after not growing at all in F2024 they get back to 13% growth in F2025, I get a 2025 stock price of $71.

Everything points to a current stock price that (not without reason) is discounting a lot of skepticism about the turnaround, about the numbers and about Mercury’s ability to pull out of this slump. And the upside is roughly a double. Which I think makes it a good risk/reward.