<![CDATA[Compound with René: Market Commentary]]>https://www.compoundwithrene.com/s/strategic-stock-commentaryhttps://substackcdn.com/image/fetch/$s_!EAry!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fba45063b-daac-439d-bb8b-d68e0f153d2d_1280x1280.pngCompound with René: Market Commentaryhttps://www.compoundwithrene.com/s/strategic-stock-commentarySubstackFri, 21 Aug 2026 04:58:32 GMT<![CDATA[Macro & Micro Insights: Interesting Resources! (August 2026 Edition)]]>https://www.compoundwithrene.com/p/macro-and-micro-insights-interesting-b6chttps://www.compoundwithrene.com/p/macro-and-micro-insights-interesting-b6cWed, 05 Aug 2026 12:49:43 GMTBelow you find my late July/early August reading (and listening) list: a handful of recent finds that offer valuable context on market trends and the frameworks driving them.

Shoutout to our WhatsApp community in which some of these resources are regularly shared – the power of swarm intelligence!

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Articles & Letters:

  • Open letter by Bernard Arnault in reaction to "the investigation" by Le Monde (translation shared by European_DGI) – I can assure you you will enjoying reading this and laugh out multiple times!

    “First, I learned that my staff are very well dressed and none are overweight. I apologize to readers for this lack of body diversity, and I will contact the company restaurant on Monday. I also learned that visitors have their Hermès ties taken away. That is false: we let them keep them. We simply offer them a second, more discrete one for the elevator. It is a matter of politeness. I will add, as a secret, that I own several.“

  • Horizon Kinetics June letter (“Under the Hood Revived (we just HAD to!): What’s in Your Index?“); topics covered:

    • Hyperscalers Are Sacrificing Their Free Cash Flow for Data Center Growth

    • Data Center Spending Means Tough Trade-Offs for Hyperscalers

    • Emerging Co-Dependence Risk

    • Here’s one chart from the letter:

      Reading Suggestion: Very much related to it is my own analysis from last week titled “8 Signs the AI Bubble Is Further Along Than You Think“

  • Seeing What Others Miss: An Investor’s Guide to Insight by

  • Albert Bridge Capital shared a FT article by Andrew Dickson (from 2021 I believe): “BAILLIE GIFFORD’S NEVER-SELL MANTRA IS A SONG FOR FOOLS“

    “In an after-the-fact diagnosis, Burns is implying that a biased sample of self-selected winners like Tesla and Alibaba suggest that it is a mistake to ever sell any shares in any companies that you think are “winners” either historically or prospectively. Yes, it is true, if you sold Tesla too early, you didn’t make as much money as those who didn’t. But what about those investments that weren’t Tesla? And what about former consensus winners like Eastman Kodak, or Cisco, or Nokia? Justifying his view, Burns cites research by professor Hendrik Bessembinder which suggests that a) a majority of stocks do not outperform 1 month US treasury bills b) 1 per cent of companies accounted for all global net wealth creation and c) 99% of companies were a “distraction to the task of managing money”. Burns suggests that Bessembinder’s research should “shake the foundations” of the investment industry and that we need a “vastly different mentality” to “focus on the possibility of extreme upside”. That sure would be nice, wouldn’t it? If we all could just sit back and take our money out of 99 percent of our investments and all plough it in the same 1 per cent that everyone else already knows about, and then expect to outperform. As preposterous as it sounds, the conclusions drawn from Bessembinder’s paper were also misinformed in the first place. It isn’t entirely Burns’ fault, Bessembinder himself presented his observations and conclusions in a sensational way.“

    • Two comments that are very related to this critique that I shared on X recently:

      • Here, I myself claimed that Bessembinder’s study is “The single most-referenced finding in equity research this decade [and] is misleading. Not wrong – misleading.“ – and explain why.

        “Here’s my problem with how that data point gets used: It assumes a forever holding period. That may be a clean theoretical construct – and even Buffett himself said “his favorite holding period is forever” – but it ignores one of the single most important skills in real-world investing: TIMING. Bessembinder is measuring lifetime, buy-at-IPO-hold-until-delisting/bankruptcy outcomes. Almost no one actually invests that way, including the man whose quote gets attached to the idea (see above).“

      • In this post, I argued that the single biggest weakness of some quality funds is their lack of timing skills.

        “They excel at security selection. They do not even try their hand at timing (another way of saying they are average at best), I don’t find them to be particularly valuation-disciplined. And they aren’t particularly differentiated with regard to position sizing.“

  • An MIT study drawing on 52 executive interviews, surveys of 153 leaders and analysis of 300 public deployments found that roughly 95% of enterprise generative AI pilots delivered no measurable impact on profit and loss.

    “Zero return.”

  • S&P Global found 42% of companies abandoned most of their AI projects during 2025.

    “The percentage of companies abandoning the majority of their AI initiatives before they reach production has surged from 17% to 42% year over year, with organizations on average reporting that 46% of projects are scrapped between proof of concept and broad adoption“

Podcasts & Videos:

Other:

  • modest proposal shared this excerpt on X on changing market structures; I couldn’t find the original source (would love to take a look at the exhibit), so if anyone can help out?

    “There’s been a big change in who is setting prices in the U.S. equity market in the last 15 years, and retail investors and high-frequency quants, including traders employed by investment banks, now comprise the majority of the directional trading activity …“

  • Barclays has Google’s CapEx at $500 billion in 2028

  • Charts shared by Tobias Carlisle:

    “Mid and small caps are still valued at roughly a 20-30% discount to large caps, near the cheapest levels in the chart's ~27-year history. Historically such wide discounts have preceded periods of mean-reversion and small-cap outperformance (as happened after 2000). The recent uptick in blue/red lines (bottom-right of chart) suggests a modest recovery in small/mid-cap relative valuations is underway, worth watching if it continues. Second chart shows small-caps have outperformed large-caps by about 11% over the trailing year.“

]]><![CDATA[1,000 True Fans?]]>https://www.compoundwithrene.com/p/1000-true-fanshttps://www.compoundwithrene.com/p/1000-true-fansMon, 03 Aug 2026 14:03:33 GMTA quick note before we start: this is not a research piece. No business analysis, no model, no valuation. It's a more personal write-up about writing itself – where I am eighteen months in, and a number that has been rattling around my head in the last few days. Normal programming resumes soon ;-).

I was on the podcast of this week, and somewhere in the middle host Siem asked me a question I hadn’t prepared for. Not about a company, or a macro theme, or a mental model. He asked what I actually want to accomplish with my content.

My answer was, more or less, nothing in particular.

No revenue target. No subscriber number. No plan to turn this into something. I first and foremost write because I like writing – and the same could be said about creating video content, sharing insights on X/Substack Notes/Threads, recording podcasts, etc. –, and because I like the work that has to happen before the writing: the reading of company filings, listening to earnings calls, thinking critically and, at times, creatively, doing research on a current them, picking the right valuation model for a company at hand, depending on its profile and maturity, the slow (or sometimes fast) process of a business becoming legible to me.

If I stopped publishing tomorrow, I would probably still do most of it. The publishing part just makes me do it properly. In fact, this is what I like Substack and writing written research notes much better than creating YouTube content. The audience on Substack’s platform allows me to go really deep on companies, the management team, the financials, risks, etc. YouTube rewards videos with mass appeal, with clickbaity titles and thumbnails, shallow analysis that doesn’t go too deep.

Substack is different.

But back to the podcast. I think that answer may sound evasive on the podcast. It isn’t. It’s the most useful thing I’ve figured out in the past six years of creating stock market-related content on various platforms.

Some takeaways and goals (seven in total):

#1 – Producing beats consuming

If I had to name one life-hack that has actually made me happier, it’s this: spend more of your day producing than consuming. Doing as opposed to just sitting around, staring at the wall or doomscrolling your TikTok feed.

Not because producing is virtuous, but because consuming leaves nothing behind. You finish a day of binge-watching a new Netflix series, and you have nothing you can point at. You finish a day of writing, drawing, taking photos, building a map for a video game, recording a tutorial for your university peers, WHATEVER (!), and there’s a thing that didn’t exist that morning, however small, however flawed.

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#2 – Focus on what you can control

The second part of the same idea: point your effort at the things you actually control.

I have no direct control over whether this newsletter reaches 10,000 subscribers. None. That number is a function of the algorithm, the market cycle, whether someone with a big audience happens to restack me on a Tuesday. Treating it as a goal to hit by deadline X is a way of making my mood a function of other people’s decisions.

What I do control is a) the cadence – whether a piece goes out when I said it would, how regularly I show up for my audience. And b) the quality – whether the analysis is deep enough that you couldn’t have gotten it somewhere else.

That’s it.

Welcome to Compound with René. You can find an overview of the companies I shared a deep dive on in the visualization below.

If you haven’t subscribed, you can join 4,600 readers who enjoy the quality of the deep dives, the process-oriented pieces, the analytical insight, and the valuation discipline here:

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#3 – Attracting the right people

The nice thing about optimizing for quality rather than reach is that it selects your readers for you. I can see this in our WhatsApp community, and it’s the part of this whole “project” I’m most proud of. Nobody is posting rocket emojis. People show up with actual expertise from their actual jobs or their investing background and the research they have done. Everyone is friendly, curious, and useful. I didn’t engineer that. It’s what you get when the front door is a 6,000+-word deep dive rather than a hot take or a shallow analysis.

I like to think of this as a form of survivorship bias. Do you have the attention span to consume deeper critical analyses, or are you looking for easy fixes and answers?

#4 – 1,000 true fans?

A few weeks ago crossed 1,000 paid subscribers, and in the post about it, he referred to Kevin Kelly’s essay, 1,000 True Fans – at least indirectly.

First: what an accomplishment! Congrats to him. I exchanged a couple of DMs with him a few months ago, and he struck me as a genuinely nice person willing to help and support others.

Anyone who has tried to convince even one stranger on the internet to pay for written work knows what a thousand of them represents.

Second: the essay lodged itself in my head and hasn’t left.

Kelly published it on The Technium in March 2008, and a core pillar of the essay is the concept of the “Long Tail” in light of the creator economy. It was the fashionable idea back then and everyone was celebrating what it did for consumers.

“This new ability for the creator to retain the full price is revolutionary, but a second technological innovation amplifies that power further. A fundamental virtue of a peer-to-peer network (like the web) is that the most obscure node is only one click away from the most popular node. In other words the most obscure under-selling book, song, or idea, is only one click away from the best selling book, song or idea. Early in the rise of the web the large aggregators of content and products, such as eBay, Amazon, Netflix, etc, noticed that the total sales of *all* the lowest selling obscure items would equal or in some cases exceed the sales of the few best selling items. Chris Anderson (my successor at Wired) named this effect “The Long Tail,” for the visually graphed shape of the sales distribution curve: a low nearly interminable line of items selling only a few copies per year that form a long “tail” for the abrupt vertical beast of a few bestsellers. But the area of the tail was as big as the head. With that insight, the aggregators had great incentive to encourage audiences to click on the obscure items. They invented recommendation engines and other algorithms to channel attention to the rare creations in the long tail. Even web search companies like Google, Bing, Baidu found it in their interests to reward searchers with the obscure because they could sell ads in the long tail as well. The result was that the most obscure became less obscure.“

However, the “Long Tail” brought its own set of problems:

“The long tail is famously good news for two classes of people; a few lucky aggregators, such as Amazon and Netflix, and 6 billion consumers. Of those two, I think consumers earn the greater reward from the wealth hidden in infinite niches.

But the long tail is a decidedly mixed blessing for creators. Individual artists, producers, inventors and makers are overlooked in the equation. The long tail does not raise the sales of creators much, but it does add massive competition and endless downward pressure on prices. Unless artists become a large aggregator of other artist’s works, the long tail offers no path out of the quiet doldrums of minuscule sales.”

His way out was effectively the concept of “1,000 true fans.”

A creator... needs to acquire only 1,000 True Fans to make a living.

The arithmetic is almost insultingly simple. A “true fan,” according to Kelly, is someone who will buy anything you produce. Get $100 a year from each – Kelly frames it elsewhere in the essay as roughly one day’s wages, which I find an interesting way to think about it – keep the full amount because they pay you directly, and a thousand of them is $100,000. “A living for most folks,” he writes (remember that the piece was released in 2008). Not a fortune. A living.

What made the idea stick with a large audience is his reframing. Kelly’s point is that a thousand is a number a human being can hold in their head. “You might even be able to remember a thousand names. If you added one new true fan per day, it’d only take a few years to gain a thousand.”

5 – But Substack is plateauing and AI is coming for writers …

I’ve read several pieces recently arguing that Substack is plateauing. That writing is doomed as AI is broadly adopted. That the competition has become brutal. That big and small writers alike are struggling to grow at all.

And in the comments under one of my own posts, someone recently made the point that when this exuberant market finally turns – and it will eventually – the appetite for equity research specifically will dry up, and a lot of “us” will stop.

I think all of that is basically right, and none of it changes what I’m doing.

Here’s why:

Every one of those forces is a filter on the median, not on the work. AI has made competent writing easier, almost infinite, which means competent writing (grammar, style, vocabulary) is now worth roughly what it costs. What it has not done, and I don’t think will do anytime soon, is generate the thing that actually moves an investment view: outside-the-box thinking, a mental model built over decades of watching businesses succeed and fail applied to a new analysis, the creative leap of noticing that this company is really the same shape as that one from a different industry twenty years ago, identifying inflection points. Genuine insight. Valuable insight because it is not priced in by the market.

The bull market ending doesn’t destroy that – it just thins out the people who were just in this for a quick buck or the fame.

Over the very long term, anyone who delivers more value than they charge will find an audience. That is a timeless business mechanism.

#6 – The part I’m bad at

The uncomfortable corollary is that providing value and being found are two different problems.

wrote something in early July in an article that genuinely deflated me for an hour:

“Not every investment has an equal reward. This is equally true in business as it is in Substack. To date, all of my posts combined have brought me a total of 98 subscribers. My top performing (and first ever) note has also brought me exactly 98 subscribers. My posts have taken hundreds of hours of painstaking research, writing and editing. My note took me about 10 minutes to write.”

I know this feeling in my bones. I have published pieces that cost me three weekends and a fair chunk of my sanity and added, net, zero subscribers.

That is not a rare event. That is a normal Tuesday.

Want to see a current example? Below you see the stats of yesterday’s write-up “8 Signs the AI Bubble Is Further Along Than You Think“ that has genuinely done quite well in terms of likes received and comments shared. New subscribers added: 1.

Or my latest deep dive on Amadeus IT Group, which I spent hours and hours researching (+ building a valuation model, + creating a custom slide deck) while on holiday. One new free subscriber. Zero new paying subscribers.

I think this is another advantage I may have over some other investors who aren’t in this for the long run. I love studying businesses. So sure, there are brief moments of disappointment. But usually I just shrug for a moment and then move on. On to the next deep dive!

Kelly is blunt about this in the essay, too, and it’s the paragraph I keep skipping over and shouldn’t. Cultivating true fans is …

“time consuming, sometimes nerve racking, and not for everyone.”

Some creators, he says, “should just paint, or sew, or make music” and let somebody else handle the fans. That’s me. I would rather spend eleven hours reconstructing a segment’s true economics than eleven minutes writing a note about it.

#7 – Because the A in SMART stands for attainable

So why am I still writing down a number?

The old SMART framework is a corporate cliché at this point, but the A is what I had to think of regardless. Goals are supposed to be attainable – stretchy enough to pull you forward, plausible enough that your brain doesn’t file them under fantasy and stop trying.

A million readers is not attainable (for me at least) and, worse, it isn’t even meaningful as a target. Kelly says this directly: “Millions of paying fans is not a realistic goal to shoot for, especially when you are starting out. But a thousand fans is doable.”

And here’s the part that makes me think it isn’t arrogance to say it out loud: There are hundreds of millions of people investing globally. If one in a million of them wants what I specifically offer – global equity research for people who love businesses, obsessed with quality and disciplined valuation, no hype, deep analytical work for the patient minority – that’s still a large enough pool. Compared to some other nichés, the investing TAM is absolutely massive!

Kelly’s line: “any 1-in-a-million appeal can find 1,000 true fans. The trick is to practically find those fans, or more accurately, to have them find you.”

I’m confident in the work. As outlined, not yet in the marketing. But I will try to do better on that front too. And I’m learning from others too – both big (such as or ) and small (such as , , or )

And of course you can help me to by spreading the word, leaving a comment occasionally, restacking a write-up. You might want to start right now?

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Wrapping up

We’re closing in on 5,000 free subscribers. Eighteen months ago that number was one, and it was me. My YouTube audience gave me a 100 subscriber boost, but that was about it (generally, I’ve found that YouTube subscribers translate very poorly to blog subscribers).

I don’t take it for granted, and I’m aware of how much of it is timing, luck, and the generosity of people who restacked something when they didn’t have to.

A thousand true fans is a long way off. A very long way in fact. I might never get there. But one can dream big, right?

The market may turn, the appetite for this kind of work may thin out, and the honest answer is that most of that is outside my control anyway. But the target is doing something useful in the meantime: it keeps me pointed at depth instead of reach, at the hundred people who read every word instead of the ten thousand who opened one thing once.

Kelly calls it “a much saner destiny to hope for.” I’ll take that. And in the meantime, the only two things I actually control are showing up on schedule and doing work that’s worth your time.

That, I can promise.

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]]><![CDATA[8 Signs the AI Bubble Is Further Along Than You Think]]>https://www.compoundwithrene.com/p/8-signs-the-ai-bubble-is-furtherhttps://www.compoundwithrene.com/p/8-signs-the-ai-bubble-is-furtherSun, 02 Aug 2026 14:13:39 GMTLet’s start with an anecdote before we move on to more structural things I’m seeing.

On the morning of July 30, a hedge fund sold its entire public equity portfolio in a single block trade before the opening bell. Longs and shorts together, the whole book, handed to Citadel in one transaction. Six trading days earlier, that same portfolio was sitting on a 439% net return for the year.

Ooops!

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The fund we’re talking about – you already know it – is Situational Awareness LP, run by Leopold Aschenbrenner, who is twenty-four years old and wrote what a lot of people consider the defining investment memo of the AGI era.

He had turned roughly $225 million of seed capital into a book worth tens of billions in under two years. Reports on the exact size vary quite a bit, somewhere between $20 billion and $45 billion depending on which outlet you read.

Apparently using 4x gross leverage, concentrated across the AI infrastructure stack:

  • power generation,

  • data centers,

  • memory.

Then Korean semiconductors wobbled. Under a week later, it was finished.

I published a piece in early June called “Navigating the Froth: 6 Alternative Signs of a Market Top,“ where I laid out six alternative signs that we were somewhere late in this cycle.

  • Chip workers in Seoul buying Ferraris with their bonuses.

  • Jensen Huang being chased through a Taipei night market by food bloggers.

  • Three private companies queuing up to go public at a trillion dollars each.

  • Berkshire’s cash pile going vertical.

  • And more.

Those six had something in common that I did not fully appreciate when I wrote them. You could see all of them from across the room.

There is a line on my Market Sentiment Cheat Sheet that has aged well: bottoms scream, tops whisper.

In the weeks since that piece, the whispers have moved somewhere considerably harder to look. Into special purpose vehicles. Into lease classifications. Into a single sentence about useful-life assumptions in an earnings call.

Before we start, let me deal with an objection I hear most often these days:

We cannot be in a bubble if this many people are calling it a bubble.

I don’t find that argument persuasive for a straightforward reason: you are not observing the market when you scroll your feed, you are observing an algorithm that has learned what keeps you engaged. Fintwit did not exist in 1999. It did not exist in 2007. The fact that your timeline is saturated with bubble talk tells you something about your timeline, your echo chamber, but very little about aggregate positioning.

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And positioning is the thing. Saying you think the market is expensive costs nothing. What people are doing with their money looks quite different from what they are saying with their words. That’s one of the first principles I learned about Buffett - watch what he does, and not what he says.

In this piece, I want to discuss eight new signs that we may be approaching a market top and the end of the long-lasting bull run.

Here is the structure:

  1. Systemic Leverage

  2. When the Supplier Becomes the Customer’s Bank

  3. The Cash Flow Machines Start Borrowing

  4. Reading the Footnotes Instead of the Headline

  5. Common Sense ROIIC Check – The Arithmetic Nobody Wants To Confront

  6. The Electricity May Not Exist

  7. The Customers Aren’t Getting Their Money’s Worth

  8. Common Sense Test #2 – This Time: Valuation

  9. Final Thoughts

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

1. System Leverage – Aschenbrenner Is Just One Anecdote

Aschenbrenner’s thesis was arguably right. Power, data centers, and memory were precisely where the money went in 2025 and the first half of 2026. What ended him was the financing structure sitting on top of that thesis.

Run four times gross leverage and a 25% drawdown erases your equity. You lose the option of being right eventually. Your prime broker decides when the position closes, and makes that call during the worst week rather than the best one.

Note what did the damage. Korean semiconductors, specifically the pressure around SK Hynix’s US listing. In my June piece, I wrote about the KOSPI as a national index trading with the volatility of a micro cap, more than half its weight in two chip companies. Concentration of that sort is wonderful on the way up.

One fund blowing up may be an anecdote. I get that. But this isn’t a single incident. It is more systemic.

In mid-July, apparently, 1 in 30 (3.4%) adults got margin called.

FINRA margin debt, the total amount of money that investors borrow from their brokerage firms to buy stocks and other securities using their current investments as collateral, hit $1.502 trillion in June, an all-time high, up 51.5% year over year and still up 46.3% after inflation.

As I argued in June, this indicator on its own is a lousy timing tool. What it measures well is fragility, which tells you how violent the adjustment will be when things go south, without telling you when exactly.

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And the metric above may even understate how levered up investors are, possibly badly, because it captures margin lending at broker-dealers while missing the leverage embedded inside the products retail increasingly buys as default exposure.

Nobody holding those products will ever get a margin call, and that is the problem. When an individual buys a leveraged ETF (like a 3x S&P 500 fund), the leverage exists inside the fund's structure via total return swaps and futures contracts. A daily-reset fund must thus rebalance at the close to maintain its target multiple, so it mechanically sells into declines. The forced selling still happens, on schedule, inside the fund, without anyone experiencing the moment of panic that would normally make an investor reconsider what they own.

  • When the market goes UP: The fund’s asset base grows faster than its debt/exposure ratio. To restore a 3x target for the next day, it must buy more exposure at higher prices.

  • When the market goes DOWN: The fund’s assets shrink faster than its exposure. To bring its leverage ratio back down to 3x (and avoid being over-leveraged for the next day), it must sell exposure at lower prices.

Related to this, I guess, made an observation that stuck with me (read his thoughts here): watch how fast people dump enormous, established businesses on a single bad headline. Few built their position by understanding the firm well enough to hold through a bad quarter, so they use trailing stops, then buy back after the recovery. Li Lu once called rotation the rarest gift in investing. What we have instead is a market where much of the float is held by people who pre-committed to selling at a price rather than at a thesis, on record margin, in products that liquidate themselves on the way down.

A levered holder is not really a holder. They are a renter, and the margin clerk owns the building.

2. When the Supplier Becomes the Customer’s Bank

Nvidia is reportedly in talks to backstop as much as $250 billion so OpenAI can lease computing power from a ten-gigawatt project in Pike County, Ohio, with a separate arrangement to help finance OpenAI’s purchase of some $350 billion of chips. Bloomberg puts the total slate of fresh deals above $750 billion.

“Nvidia Corp. is working on AI infrastructure deals potentially worth more than $750 billion, including an artificial intelligence initiative with SK Hynix Inc.’s parent worth more than $500 billion.” - Bloomberg

Here is the number that made me put my coffee down (admittedly, I looked a little more shocked than in the image below) …

Nvidia’s own Q1 fiscal 2027 10-Q caps total lease-guarantee exposure at $3.5 billion.

A $250 billion commitment would run roughly seventy-one times the entire guarantee book it currently discloses.

How times can change.

The term “circular financing” gets thrown around frequently these days without much precision. A supplier provides capital, or a guarantee that unlocks someone else’s capital, to a customer, who then buys the supplier’s product. The supplier books revenue at shipment while the risk moves onto its balance sheet. Demand looks robust, growth looks organic, and the accounts record something economically closer to the supplier buying its own products with extra steps.

Source: Bloomberg

The bull response is that these arrangements are immaterial at Nvidia’s scale.

The writer behind Dirt Cheap Stocks gave the most economical rebuttal I have read in his piece from two months ago titled “Calling the Top“:

“if it’s immaterial, why would you engage in the behavior at all?”

We ran this experiment in 1999, when telecom equipment makers lent enormous sums to carriers so those carriers could buy equipment. Lucent alone committed $8.1 billion, and McKinsey later put nine suppliers’ combined exposure around $25.6 billion. The customers could never generate the revenue to repay, Lucent’s sales fell 69% from their peak, and it wrote off roughly $3.5 billion of customer loans.

The detail worth holding onto is what the vendors did as conditions deteriorated. When outside capital fled in early 2001, they accelerated financing rather than pulling back, because slowing down meant admitting the previous two years of revenue were partly manufactured. That entire nine-supplier complex committed about $47 billion in today’s money (accounting for ~80–85% cumulative inflation over the past 25 years).

In today’s instance, we are discussing one supplier contemplating $250 billion.

One other number in Nvidia’s filings stands out too: the share of its receivables tied to major customers has gone from an average of roughly 23.8% across fiscal 2020 to 2024 to about 64% in its latest quarter.

“Three direct customers accounted for 30%, 18%, and 16% of our accounts receivable balance as of April 26, 2026. Three direct customers accounted for 25%, 18%, and 13% of our accounts receivable balance as of January 25, 2026.”

Of course, during fiscal years 2020 through 2024, Nvidia’s business was far more balanced across PC gaming, professional visualization, and automotive clients, and as the generative AI boom accelerated through FY2025 and FY2026, Nvidia’s Data Center segment expanded to represent nearly 90% of total company revenue. Still, I find this a remarkable level of concentration.

Now, this heavy customer concentration is a direct structural byproduct of the hyper-consolidated AI infrastructure boom. Because training and deploying frontier AI systems requires tens of billions of dollars per quarter in capital expenditure, purchasing power is concentrated almost exclusively among a small circle of hyperscalers (widely identified by Wall Street as Microsoft, Meta, and Amazon) alongside key server integrators. Clearly, those are cash-flowing machines throwing of billions and billions in cash flow. But the required CapEx may be too much of a burden even for those players, which leads to signal number 3.

3. The Cash Flow Machines Start Borrowing

For fifteen years, the defining financial trait of big tech was that it did not need anyone’s money. Microsoft still carries a AAA rating, one of two left in America. That era has ended fast enough that most investors have not repriced what these companies now are.

Amazon, Alphabet, Meta and Oracle issued roughly $194 billion of bonds in 2026 through July 7 alone, according to Reuters' analysis, against about $108 billion in all of 2025. Meta’s October deal was $30 billion in one shot, the largest individual non-M&A high-grade bond sale on record. Oracle is the one to watch, sitting at BBB while participating in the same build-out as counterparties rated AAA. The marginal borrower always breaks first.

What concerns me more is largely invisible on the balance sheet. Meta’s Hyperion campus is financed through a vehicle called Beignet Investor LLC, which raised roughly $27 billion in private notes from Pimco, BlackRock and Apollo. Blue Owl holds 80%, Meta 20%. Because Meta is a minority partner, that $27 billion never appears as a liability in its accounts. It’s an off-balance sheet liability – another late-cycle symptom?

Meta also provides a residual value guarantee covering the first sixteen years. If the campus is worth less than an agreed threshold when a lease ends, Meta makes up the shortfall in cash. Read that again and ask who bears the asset risk. The debt moved. The risk did not. IFR (International Financing Review), about as sober a credit publication as exists, ran its analysis under the headline “off-balance-sheet gymnastics 24 years after Enron.”

“The contradiction at the heart of Meta’s Hyperion financing is between the bond ratings and Meta’s proposed accounting treatment. If you follow the cash, Beignet’s bond is almost pure Meta exposure. Meta funds the construction risk; Meta’s rent and Meta’s residual value guarantee service the debt; and Meta is the party that suffers if demand for AI computing disappoints.

If Meta ever walks away from the project, the RVG obliges it to write a cheque that, together with whatever the campus (or part thereof) can be sold for, makes bondholders whole. The RVG runs for 16 years and is triggered if Meta fails to renew a lease at expiry, terminates early or defaults under the lease. Any one of these leads to a payment of a “guaranteed minimum value” that is sized so the bond can be repaid, either from the underlying sale proceeds or directly by Meta. The final four years of the bond are not covered by the RVG but by a termination fee equal to the outstanding balance at that point.“ - IFR

Debt has been trending upward across all Big Tech names …

… and Meta stands out to me here as the quality of its balance sheet has deteriorated the most in terms of total debt recently.

One exchange from Meta’s latest call is also worth your attention. Youssef Squali asked Susan Li directly about the growing pile of off-balance-sheet obligations and whether Meta would consider issuing equity to fund some of this. She pointed him to the disclosures and said they would evaluate all financing options for 2027. She did not rule out equity. A CFO with a comfortable funding picture rules out equity, because doing so is free and reassuring.

The reason shows up in the cash flow forecasts. Alphabet just reported its first quarter of negative FCF in its history.

Meta’s is expected to turn negative in 2026 and reach around negative $24 billion in 2027. Amazon’s is projected to be negative this year.

Companies do not borrow at this scale because borrowing is cheap. They borrow because internal cash no longer covers the plan.

4. Reading the Footnotes Instead of the Headline

Microsoft reported in late July and one of the headlines that was picked up by the investing media was a CapEx reduction, with 2026 guidance falling from roughly $190 billion to around $175 billion.

The stock rose after hours and the next trading day. Microsoft added nearly $450 billion in market value on Thursday, the largest one-day gain on record for a company. To be fair, it was a great quarter overall. Some coverage treated the CapEx cut as the first evidence of spending discipline from a hyperscaler.

Nothing was a real cut.

Effective at the start of fiscal 2027, Microsoft is extending the estimated useful life of its data centers and office buildings from 15 years to 25. That lowers annual depreciation and lifts reported earnings on assets it was building anyway. It also changes lease classification, moving future data center leases from finance leases into operating leases, and finance leases count in reported capex while operating leases do not. The $15 billion came out of the line item rather than the budget.

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, who flagged this as one of the first in a Substack Note, asked the two questions I have yet to see answered.

  • Why this?

  • Why now?

Consider the same quarter’s other disclosures. CapEx including finance leases hit $41 billion, up roughly 70%.

Different businesses in terms of capital efficiency - example Microsoft above (CapEx-to-Revenue went from single-digit territory to about 40%)

There is sort of a control experiment available. Effective January 2025, Amazon shortened the useful life of a subset of its servers from six years to five, citing the increasing pace of AI development, and accepted a roughly $0.7 billion hit to operating income for it. Servers are not buildings, and I want to be fair about that. Maybe that useful life extension is entirely justified. But when assumptions get adjusted, they mostly get adjusted in the direction that flatters the company and the income statement, and usually when it most needs the “help.”

Hence the practical takeaway, which is the most useful thing in this piece if you own any of these businesses, might be that for hyperscalers, the income statement has become the less informative of the two documents, because accrual accounting hands management wide discretion over depreciation, lease classification and capitalization.

Work from the cash flow statement instead, consider the free cash flow the company actually produces, and then adjust by hand: add back growth investments (maybe also from the income statement; e.g. R&D) and form your own view on how much CapEx is actually maintenance dressed as growth CapEx by comments made by management.

5. Common Sense ROIIC Check – The Arithmetic Nobody Wants To Confront

The four big hyperscalers – Amazon, Microsoft, Alphabet, and Meta – will spend somewhere around $700 billion on capex in 2026. Estimates range from $630 billion to $750 billion depending on definitions (might even be higher after recent guidance updates).

Against that, here is what they earn.

  • Alphabet made about $244 billion over the last twelve months

  • Microsoft $133 billion,

  • Amazon roughly $135 billion,

  • Meta about $68 billion.

Call it $580 billion combined.

They are spending, in a single year, a little above 1.3x what all four of them earn.

Now run a back-of-the-envelope ROIIC calculation, which almost nobody in financial media seems willing to do out loud. A 15% return on incremental invested capital is a reasonable bar for businesses of this quality. On $700 billion, that requires about $105 billion of additional annual after-tax earnings in perpetuity, so combined profits would need to rise 18% and stay there, purely to justify one year of spending. And 2026 is not a one-off, with roughly $400 billion spent in 2025 and forecasts for 2027 higher still.

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Let me be precise about what that actually means, because it is easy to get that wrong. It is a permanent step up in the level of profits. The 2026 spend does not require earnings to compound at 15% a year. But it requires them to be $105 billion higher, forever.

The trouble is that the steps stack!

  • The roughly $400 billion spent in 2025 needs $60 billion of permanent additional profit.

  • The 2026 spend needs another $105 billion.

  • And 2027 estimates from Evercore, Bank of America and Moody’s now sit above $1 trillion, which asks for a further $150 billion. Barclays has Google's 2028 CapEx alone at $500 billion.

Three years of building, about $2.1 trillion, therefore requires some $315 billion of extra annual profit held in perpetuity.

And that assumes the spending stops in 2027, which nobody expects. Goldman models $5.3 trillion of capex across these four companies from 2025 through 2030. At 15%, that needs roughly $795 billion of incremental annual profit, putting combined earnings around $1.4 trillion.

The entire S&P 500 currently earns something like $2.3 trillion, so four companies would have to generate about half the index’s present profit pool.

There is a cleaner way to see why this might not work as advertised. Sustainable growth equals return on capital multiplied by reinvestment rate. Capex against earnings runs above 100%. But you cannot reinvest more than you earn out of what you earn, and that gap is precisely the bond issuance and the special purpose vehicles you start to see (discussed in #3).

A second framing avoids hurdle rates entirely. Depreciate $2.1 trillion over five years (which is too strict, I admit that, because data centers have a longer useful life than the servers, but let’s run with it for illustrative purposes) and you generate about $420 billion of annual depreciation, which exceeds what all four companies earned in 2025.

Some of that replaces charges rolling off older assets, so treat the figure as directional. The direction is not comforting, though, and it is exactly why the useful-life assumptions in the previous section matter. Lengthening the schedule is the main lever for making the income statement look attractive.

It changes nothing about the cash flow dynamics, though.

Maybe it works. AI is a genuine technology with genuine applications and I have no interest in pretending otherwise. But the burden of proof on that capital is enormous today.

PS: John Huber shared some fantastic thoughts on this as well, adding some additional angles and perspectives to consider (check out his follow-up response in the comments as well):

PSS: I wrote about this before:

PSSS: An intelligent comment by :

6. The Electricity May Not Exist

All of the above assumes the build-out can actually be completed, which is not entirely clear if you look at the physical supply chain.

In Northern Virginia, Phoenix, and Dallas, where much of the announced capacity is being sited, waits for a grid connection now run four to seven years (and sometimes even extending up to 14 years).

Substation transformer, a large electrical device that changes voltage levels, transfers power between the grid and consumers, and maintains safe energy flow using electromagnetic induction, lead times have gone from about 140 weeks in 2023 to over 160 weeks today. Gas turbine backlogs at GE Vernova, Siemens Energy and Mitsubishi Power, who between them supply two-thirds of the gas plants under construction worldwide, now stretch as long as eight years. Grid operators are openly saying demand is growing faster than any historical trend they can model against.

Do you see the mismatch?

A GPU can be delivered in weeks. The electricity to run it takes years, and in the worst locations the better part of a decade.

“Artificial intelligence (AI) technology is advancing at an extraordinary pace. At the same time, the computing power used to train frontier AI models has been doubling every five to six months. Unlike the semiconductor scaling that defined previous technological eras, this growth is driven not by shrinking transistors but by deploying ever-larger chip clusters.

Combined with the scale of investment in data and computing infrastructure, this progress is already transforming entire industries. But the energy system must keep pace for progress to continue. This shift from silicon efficiency to physical scale is precisely why grid connectivity has become the binding constraint.“ - weforum

That GPU begins depreciating the day it is finished putting together, on a five or six year schedule, which is shorter (!) than the interconnection wait in three of the largest data center markets in America. Depreciation does not pause while you wait for a transformer.

weforum

The politics are turning too. The Economist has asked whether the data center backlash will derail the boom, reporting that opposition has made suitable sites genuinely hard to find, and the environmental argument got harder to wave away once Google and Microsoft both walked back carbon commitments to accommodate the build.

If you go back to sign #6, the hurdle to clear 15% ROIICs in light of these constraints may get harder.

In fairness, delayed is not cancelled, and hyperscalers have flexibility about where they build. Data centers in space are proposed, which are orbital facilities designed to handle energy-intensive computing and AI workloads using continuous solar power and radiative cooling, driven by companies like SpaceX and Blue Origin.

But there is the potential that the returns arrive later than underwritten, and where the asset depreciates whether or not it is plugged in, later is expensive.

7. The Customers Aren’t Getting Their Money’s Worth

Some of the signs above discussed so far concern the supply side.

But none of those concerns matter much if the demand is not genuinely there

So the question that decides this is whether enterprises buying AI services are actually getting a return. And it seems like there is more skepticism these days, in some organizations at least, compared to 12 to 24 months ago.

An MIT study drawing on 52 executive interviews, surveys of 153 leaders and analysis of 300 public deployments found that roughly 95% of enterprise generative AI pilots delivered no measurable impact on profit and loss.

“Zero return.”

S&P Global found 42% of companies abandoned most of their AI projects during 2025.

“The percentage of companies abandoning the majority of their AI initiatives before they reach production has surged from 17% to 42% year over year, with organizations on average reporting that 46% of projects are scrapped between proof of concept and broad adoption“

You can quibble with any single study, and you might want to take a look at the underlying methodology, but it seems like there is a growing, serious body of evidence – beyond anecdotes – pointing towards similar findings

The case study of Ford may be brought up here too, just because it might even make you laugh out loud …

WHAT WERE THEY THINKING?

The company pulled experienced staff off quality control and replaced them with AI, and has since been recalling those workers because the AI was less concerned about quality than the people were.

One anecdote, granted, though the kind that tends to appear at the front of a trend.

There is arguably a structural reason so much of this went ahead, and it has little to do with clear and visible ROIC thresholds being hit. Many large enterprises piled in partly because they believed their own investors expected to see it. Announcing an AI initiative, and declining to announce one was expensive from a reputational point of view or may negatively affect stock prices of publicly listed firms. That produces enormous spending never underwritten to a return, which is the first spending to go when budgets tighten.

Two more details worth highlighting:

  • Some operators have started selling excess AI capacity into the secondary market, which is hard to square with demand exceeding supply by the margins implied in the capex plans.

  • And the industry charges for usage rather than outcomes, so a clumsy implementation that burns tokens produces more revenue than an efficient one. A meaningful portion of AI revenue therefore measures how wastefully the technology is being applied, and compresses as customers improve.

8. Common Sense Test #2 – This Time: Valuation

The Shiller CAPE stands at an almost record high of 40.9x as I write this …

…, against an all-time record of 44.2x in December 1999 and a long-run median near 16 (16.1x).

Nvidia, Microsoft, Amazon, Meta, Broadcom, and Google – six names – alone represent almost 28% of the index.

According to Goldman, as of three months ago, “AI companies” make up 45% of the index (probably a rather broad definition to be fair).

Back to valuation … Again, there is one prior period in the history of the series at these levels: late 1999 into early 2000. Exactly one!

That is not a timing signal, as I outlined in my last piece referenced above (I’ll insert the lovely illustration again here), and I will keep saying so.

YCharts

“… of |the] seven leading indicators studied, none have consistently predicted major market declines dating back to 1950. Even the most consistent indicators provided a warning signal for only about half of major declines. “ [note: A major decline in this study was defined as a decline of 10% or greater from the S&P 500’s most recent all-time high]

Anyone who went to cash on CAPE in 2015 – when it reached almsot 27x (way above the median) has been thoroughly punished.

I still believe, however, that starting valuation matters for the subsequent long-term returns. And I’ll admit that in the current environment you might have to shift your definition of “long-term” to 20 years+ because the high starting valuations from 2015 didn’t stop the S&P from performing incredibly well over the subsequent 10-11 years.

Of course, you could bring up some structural changes that justify higher multiples for US stocks – multiples are just a shorthand after all –, and I wrote about it in the piece linked below (different margin and growth profiles, outdated accounting standards, the composition of the index, global diversification, etc.); I recommend reading it to come to a balanced, well-informed view yourself.

Nonetheless, let me run a simple valuation exercise here, an exercise that Dirt Cheap Stocks used in his piece already, and one of the valuation approaches I favor myself.

You are currently buying the S&P at roughly 28.9x earnings with a dividend yield near 1%.

Want 10% a year for a decade? (roughly in line with the S&P’s long-term returns) Earnings have to compound at about 9%, and the multiple has to still be 28x times when you sell (a multiple well above the historical median; but as outlined in the write-up I linked above, that may be justified too). Long-run S&P earnings growth has been around 6% to 6.5%, so the bull case needs a permanent step-change in the earnings growth rate of the largest economy on earth while the most expensive multiple in twenty-six years does not compress at all.

To be fair, a 28x multiple translates into a 3.5% yield. Deduct 1% required to pay out the dividend (largely in line with the aggregate dividend payout ratio for the S&P 500 Index being approximately 30.5% right now), and that leaves you with 2.5% to buy back shares (that’s the absolute ceiling) and boost EPS. Historically, the 2-2.5% may be gross buybacks (before SBC effects), and only half of it is a net reduction in shares (1-1.25%).

But what if the multiple ever drops? If over a ten-year time frame (note: the longer your time horizon, the more negligible the impact of multiple changes), the multiple reverts back to the low 20s, earnings compound at 6%, an annual share reduction of around 1% (net) plus the dividend yield of 1%, and you end up with annual returns of about 5% compounded.

I’ll spare you the math for lower exit multiples.

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But hey, if AI keeps progressing at the current pace, we all will never have to work again, right?

Final Thoughts

I should be straightforward about my record. I have thought this market expensive for some time, and being expensive has not stopped it going up. I have no idea whether this turns in two months or two years or ten years. I would have been reasonably confident about multiple mean reversion over a 10-15 year time frame a few years ago. Today, I’m not anymore.

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Still, none of this changes how I invest. I prefer buying asset-light companies, trading at cheap normalized multiples, with clear growth drivers and structural tailwinds and a great deal of visibility in terms of ROIIC and reinvestment rates.

Also, look again at how this started. Aschenbrenner was substantially right about the direction of AI infrastructure, and being right did not save him, because he had built a position that required the market to agree with him on a schedule.

That thread runs through all eight signs: Circular financing, off-balance-sheet structures, stretched depreciation schedules, record margin balances, capital committed against a grid that cannot deliver, possibly for seven years or more. To me it seems like they too, in a way, shorten the time you have to be proven correct.

The “whispers” this time are in footnotes, lease classifications and interconnection queues, harder to hear than Ferraris in Seoul (reference to my initial piece; which you might want to read next).

What is visible gets priced immediately. What requires reading between the lines, connecting the dots, some intuition and common sense, may not.

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

]]><![CDATA[Macro & Micro Insights: Interesting Resources! (July 2026 Edition)]]>https://www.compoundwithrene.com/p/macro-and-micro-insights-interesting-b4ahttps://www.compoundwithrene.com/p/macro-and-micro-insights-interesting-b4aThu, 16 Jul 2026 07:13:15 GMTBelow you find my July reading (and listening) list: a handful of recent finds that offer valuable context on market trends and the frameworks driving them.

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Articles:

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Podcasts & Videos:

Other:

  • I did my first live stream yesterday and discussed this mind-blowing chart from Robert Shiller on it:

]]><![CDATA[First Live Stream]]>https://www.compoundwithrene.com/p/first-live-streamhttps://www.compoundwithrene.com/p/first-live-streamWed, 15 Jul 2026 15:17:24 GMTThank you to everyone who tuned into my live video! Join me for my next live video in the app.

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]]><![CDATA[Buy Good Companies, Don't Overpay, Do EVERYTHING ...]]>https://www.compoundwithrene.com/p/buy-good-companies-dont-overpay-dohttps://www.compoundwithrene.com/p/buy-good-companies-dont-overpay-doWed, 08 Jul 2026 07:39:37 GMT

“I think people who study the psychology of investment will tell you that, you know, you can't have an end to certain types of market until the last hope is given up. I mean, I presume after that the entire UBS funds were switched into something which tracked the the Nasdaq index, bought all those things. And oh dear, there we are.“ - Terry Smith at this year’s Fundsmith AGM

Let me say something up front, before I say anything critical. I’ve learned an enormous amount from Terry Smith over the years. His quality-first principles, laid out in plain language that any investor could absorb, shaped how I think about businesses.

The rational temperament, the refusal to be dragged around by the crowd, the insistence that you focus on what a company actually does rather than what its share price did last week – that’s been an inspiration to me and to a whole generation of retail investors who found a way into serious investing partly through his letters and the recorded AGMs that were then uploaded on YouTube.

Earlier this year, when the pile-on over his underperformance was at its loudest, I was one of the people defending him. I watched this year’s AGM and came away thinking his reasoning was still clear, still rational, still willing to question the lazy conclusions you reach when you let herd instinct do your thinking for you.

Then I read the July letter yesterday evening. And I was, frankly, in shock.

Because it reads like a man throwing that rational thinking overboard.

A note on the spirit of this

One more thing before I get into it. I try to follow Buffett’s principle: praise by name, criticise by category.

I’m not going to manage that here. This post names names, and the name is Terry Smith. And in some weird way I feel bad about it, and will hit the release button regardless.

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So let me be clear about the spirit in which it’s written. I mean it with the best of intentions – as food for thought, and if anyone at Fundsmith happens to read it, so much the better.

There’s also a simple reality that comes with the territory. When you manage billions – with a B – of other people’s money, you accept a degree of public scrutiny of your decisions. It comes with the chair.

There’s a line from the former German Chancellor Helmut Kohl I was thinking of in this regard. Faced with sharp press criticism, mockery, and political hostility over the course of his time in office, he expressed a version of the same idea again and again. Anyone who reaches for the highest office in a democracy has no business complaining afterwards about the harsh headwinds. You take the job, you take the weather.

My German readers can watch a clip in which he states exactly this here:

To this day the line gets thrown back at his successors (as in the clip above) whenever they grumble about the tone of the media.

And I think it applies, in its own way, to anyone managing billions of other people’s capital, too. Nobody forced the job on you.

Full Tilt

In poker there’s a concept called tilt. I played a lot of online poker in my younger days – my favourite platform, fittingly, was called Full Tilt.

Tilt is what happens when a good player stops playing his game. He takes a bad beat, or a run of them, and the frustration takes over. He starts chasing. He plays hands he’d normally fold. He abandons the discipline that made him a winning player in the first place, precisely because it stopped working for a stretch, and he convinces himself that the answer is to do more, do it faster, and force a result.

The tragic thing about tilt is that it feels like decisiveness from the inside. It looks like activity. It’s actually the absence of the thing that made you good.

I think Terry Smith might have just gone full tilt.

His whole credo, the thing printed on the tin, was three lines. Buy good companies. Don’t overpay. Do nothing. He shared this credo in his latest letter again.

And yet, as outlined in the letter, now he is doing an awful lot.

I count twelve buys and thirteen exits in a single half-year.

In his own words: “our portfolio turnover hit a high of 51% in the first half of this year.”

For a manager whose full-year 2025 turnover was 12.7%, that is … quite something.

It is the buy-and-hold monk at the poker table suddenly playing every hand at the table.

I tweeted yesterday that if someone had shown me the paragraphs laying out all these transactions back in April, I genuinely could not have told you whether it was an April Fool’s joke. And I mean genuinely.

Twenty-five major portfolio decisions in roughly six months? AppLovin, Uber, Netflix, TSMC, GE Vernova, Mastercard, Yum!, TJX, on the buy side. Novo Nordisk, Unilever, LVMH, Nike, Zoetis, Coloplast, Intuit, on the way out. This is anything but the Terry Smith we thought we knew.

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So here’s the question I keep turning over. What stage of the market cycle is it when a die-hard quality investor – someone who has practised this exact discipline for decades – reinvents himself as a momentum trader?

“We will take more account of momentum — both fundamental and share price — in our investment decisions.“

I’ve always been a bit of a skeptic on the prices he paid, and I’ll come back to that. But the man’s identity was patience. This was his edge. When that breaks, it tells you something. Usually not about the companies. About the pressure. It’s on.

He sees the problem clearly. That’s what makes this so strange.

The frustrating part is that Smith’s diagnosis of the market is largely right, and beautifully argued. The letter’s first half is vintage Terry.

He lays out how a market now dominated by passive flows and the AI capex boom has become a momentum machine, one that no longer prices businesses on “profitability, returns on capital and growth – in other words the factors we focus on.”

He leans on Simon Evan-Cook’s excellent “Victory for Passive!” piece. He quotes David Booth, of all people, admitting that “index funds are really trading desks.” He points out that active managers, though still 40% of assets, are down to something like 10% of actual trading volume, so the marginal price is set almost entirely by the flow machine. He shows the Bloomberg momentum chart sitting at a thirty-year high, more stretched than late 1999.

Momentum Is Showing Record Outperformance – “It is at a 30 year high and more extreme than in late 1999 just before the Dotcom bubble burst. As active fund performance continues to worsen, more people abandon it, producing a pernicious feedback loop.”

I agree with almost all of this. In fact, the most valuable thing I’ve published on this blog in recent months, I believe, isn’t any single deep dive. It’s my three-part perception-change framework, which is about exactly this: how to you improve your timing skills in a perception-driven market?

I’d encourage Smith to read it. If he messages me, I’ll gift him a lifetime subscription, no charge, of course. Again, I write all of this with good intentions. He seems to be an avid Substack reader as he even references a blog post he read on this platform early in the letter.

But here’s the thing that stops me cold. Having diagnosed a momentum bubble, having said in plain type that he has “no insight into how or when this passive-led momentum market will end, other than to say badly” – he then responds by leaning into momentum?

He tells us he will “take more account of momentum – both fundamental and share price – in our investment decisions.”

You’ve just described a casino where the roulette wheel is rigged and the last table of value players is being carried out on stretchers. And your conclusion is to pull up a chair and start betting on red because red’s been hot?

That’s the tilt. He sees the trap with total clarity and walks into it anyway, because standing still has become unbearable.

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Learning from Druckenmiller

There’s a story every investor should have tattooed somewhere. In early 2000, Stanley Druckenmiller, one of the greatest to ever do it, walked into George Soros’s office and said he was selling all the tech stocks. This is crazy at 104 times earnings, he said. This is nuts.

He had it exactly right. And then he had to sit there and watch two young gunslingers inside the same firm make 3% a day, their little account up 50% on the year while Quantum sat flat. It drove him mad.

In his own telling: “So like around March I could feel it coming. I just – I had to play. I couldn’t help myself. And three times during the same week I pick up a phone, don’t do it. Don’t do it. Anyway, I pick up the phone finally. I think I missed the top by an hour. I bought $6 billion worth of tech stocks and in six weeks I had left Soros and I had lost $3 billion in that one play.”

Then the part that should stop any investor cold. Asked what he learned: “I didn’t learn anything. I already knew that I wasn’t supposed to do that. I was just an emotional basket case and couldn’t help myself.”

Read that and then read Smith’s letter again. Druckenmiller didn’t lose three billion because he misjudged the businesses.

He lost it because he couldn’t bear being cold while everyone around him was hot.

The diagnosis was right and the discipline broke anyway, precisely at the moment of maximum envy, precisely at the top.

That’s the same machinery I see running in this letter. Smith has the correct read on the bubble in the very same pages where he decides to start chasing it. He’s chasing. Full stop. I think that’s what’s really happening here. And that’s rarely a good long-term strategy.

And here’s the lesson Druckenmiller has spent the years since preaching, the one that matters most for Smith right now: when you’re not hot, you take a break.

You reduce size, you step back, you wait for a pitch you can actually hit. What you do not do is tear up the entire strategy, the entire identity you’ve built as an investor, because a cold streak has become psychologically intolerable.

Cold streaks are not a signal to become a different person. They’re a signal to protect the person you already are until the conditions turn back in your favour. That’s also your fiduciary duty, managing other people’s capital.

The consistency problem

Set the philosophy aside for a moment and just look at the trades against one another. They don’t hang together.

Where’s the consistency in his reasoning?

Take stock-based compensation. Smith flags SBC as a mark against Intuit, and it’s a fair criticism.

“Intuit – Although we only recently repurchased Intuit we were sensitive to the way in which they have reacted to the poor Mailchimp acquisition as this is why we sold the shares in the first place. The fact that they have now taken to giving results ex Mailchimp both shows how bad the acquisition was but also worries us about a continuing state of denial.

Sage – We switched our position from Intuit into Sage, the other main operator in accounting software. Sage has much less reliance on share based compensation, a lower rating and does not have Intuit’s record of injurious acquisitions. ROIC: 18%, FCF yield: 6.0%.“

Yet in the same letter he’s buying Veeva Systems, a company famous for exactly that.

I’ve written about Veeva myself – it’s a genuinely excellent business, arguably cheap here, and it fits his quality profile well.

I’m not knocking the buy. I’m knocking the logic that dings one name for a trait while waving it through on another in the same breath.

Then there’s the tape itself. Does Uber have momentum? Take a look at the chart yourself. Where?

Hold that same thought against Novo Nordisk on the sell side. Novo has been through a historic drawdown. Truly historic.

If anything, it’s a name in the early stages of building a base, possibly building momentum, precisely the kind of setup a momentum convert should be interested in.

Selling it after a historic collapse – “parlayed a market leading position in the biggest drug discovery in decades into an investment disaster” – while buying into cleaner, more expensive stories where the good news may already be in the price, is the opposite of what the new doctrine implies.

It’s selling low on the ugly one and buying high on the pretty one. That’s not momentum discipline. That’s just discomfort management.

And the whipsaw on his own recent convictions is the part that genuinely startled me.

At the February AGM – five months ago – Smith told shareholders, in as many words, that he was rotating into EssilorLuxottica, Wolters Kluwer and Zoetis as the businesses that would “survive and prosper better” when the reckoning comes.

Five months later, all three are on the sell list. What the …

He praised Wolters Kluwer’s CEO at length, bought it, and has already sold it. He framed Zoetis as a survivor and is now dumping it near multi-year lows, after the stock was more than halved on the Librela scare and the Q1 miss – a beaten-down quality name at the exact kind of valuation his old playbook was built to exploit.

This isn’t a change of mind over a cycle. It’s a change of mind over a quarter or two. Conviction isn’t supposed to have a shelf life measured in months.

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What is Smith’s edge, really?

Here’s the question I’d want Smith to ask himself: What is his edge?

Not his philosophy, not his brand, his actual edge – the thing he spots and leans into to exploit it.

And where does his skillset shine? Where is he better than the person on the other side of the trade?

Because you only make money in markets by being right about something the price doesn’t yet reflect, and you can only reliably do that where you have a genuine advantage.

I’ve never believed Smith’s core competence was timing or trading. I doubt he and his team are skilled momentum traders, and I mean that as an observation rather than an insult. Why would they be?

Momentum is a craft, too. The people who do it well have done it for years, built systems around it, developed a feel for flows and reflexivity and the moment a trend exhausts itself.

A team trained for decades in patient, fundamental, business-quality analysis is not going to walk onto that field and outplay specialists who live there.

Decade-long value-trained investors do not out-trade career momentum traders. That’s not how skill transfer works. You don’t get to be world-class at the discipline you spent thirty years explicitly not practising.

What I always believed Smith’s edge actually was: analytical rigour in identifying above-average businesses, paired with the behavioural temperament to sit on them and do very little. Or the patience to sit on the sidelines and wait. Wait for the conditions that present bargain opportunities.

That combination is rare and valuable. It’s genuinely hard to find great companies, and it’s even harder to hold them through noise without fiddling. That was the advantage he had.

Buying and selling twenty-six names at once is the precise opposite of that temperament. It’s abandoning the one (temperamental) edge he had to go compete in an arena where he has none.

If the game has changed such that his skillset no longer gives him an edge, the answer isn’t to acquire a new skillset overnight that takes others a career to build. The answer is to be honest about what you’re good at and accept that there will be stretches – possibly long, painful ones – where the market doesn’t reward it. That’s not failure. That’s the cost of having a style at all. And “quality” is simply not in vogue right now.

Recommended reading:

The timing blind spot, and the irony in these sales

There are three ways to express skill in investing:

And I’ll say something now that I’ve believed for years, through the good times as well:

I always admired Smith’s security selection.

I have always been skeptical of his timing.

He frequently bought companies at prices I would never have paid – 20-times-plus normalized earnings, and often a good deal more.

For most of the post-GFC era that didn’t matter, and I think it’s worth being honest that a lot of the Fundsmith record was riding a wave. Quality was the factor of the 2010s.

The compounders he owned went from reasonably cheap in the early part of the decade, to expensive, to genuinely absurdly rich.

A stock you could have bought on a 7-10% free cash flow yield re-rated to 4-5%, and Smith kept buying. Then it re-rated to a 2% yield.

And that – the 2% yield, the top of the multiple expansion – was the moment to show timing skill and trim. That’s when discipline on price would have earned its keep.

He didn’t.

And here’s the bitter irony of the current letter. Some of the very names he’s now selling are back down to the kind of 7-10% yields that, on his own quality framework, ought to make them buys, not sells.

He held them up the entire way, watched them de-rate, and is now letting them go at the bottom of the valuation range. Bought richly, sold cheaply. That is the single most expensive pattern in this business (buy high, sell low), and it’s the shadow side of never having had timing as part of the toolkit.

Momentum-adjacent selling of your cheapest, most beaten-up names is not a refinement of the strategy. It’s the timing weakness finally showing up on the sell side, having already shown up on the buy side a decade ago.

The real reason: it’s the structure, not the market

So why now? I don’t think this is really about a considered evolution of philosophy. I think Smith is feeling the pressure, and the pressure is structural.

He runs an open-ended fund. That single fact governs everything.

When investors redeem, he has to sell to meet it, whether or not it’s a good time to sell. He said it himself, and to his credit he said it plainly: a buy-and-hold approach “can only work if you are not subject to flows, and we are.”

He even reached for the old line about the market staying irrational longer than you can stay solvent – “Sticking to our current approach may well fall foul of the adage that the market can remain illogical longer than we can remain in business.”

Read that again, because it’s the whole letter in one sentence. He is not describing an investment thesis. He is describing a business risk to Fundsmith LLP.

If this market environment persists for another few years – and momentum bubbles can persist for an uncomfortably long time – and the fund keeps underperforming, redemptions accelerate even further, assets bleed, and at some point the vehicle itself is in jeopardy.

Note his own tell: Buffett could catch falling knives during the salad-oil crisis because he did it inside a closed structure he controlled. Smith flags exactly this distinction, that Buffett “executed this strategy in a closed fund which he controlled not an open-ended fund.”

He knows precisely what’s constraining him. The tilt isn’t coming from the charts. It’s coming from the redemption queue.

And this, I believe, is the single biggest advantage the individual investor has over Terry Smith. You don’t have flows. Nobody can redeem you. You will never be a forced seller at the bottom because somebody else panicked. When the market is at its most irrational and the opportunities are richest, the open-ended manager is often selling into it to fund withdrawals, while you can simply sit. Permanent capital is the retail investor’s structural edge, and most people don’t even realise they’re holding it. Smith’s letter is, inadvertently, the clearest argument for it I’ve read in a while.

He told us this himself, at the AGM

The thing is, Smith understands all of this better than almost anyone. He essentially narrated the whole trap out loud at this year’s AGM, telling the story of Tony Dye. It’s worth quoting at length, because it’s remarkable in hindsight:

“I was head of research at UBS before I managed to famously get myself fired. And when I was there, a gentleman who ran UBS Phillips & Drew Fund Management was a guy called Tony Dye. And Tony was a guy who was very vociferous about the fact that he thought the dot-com boom was a sham and that was going to burst and cause catastrophe. Stuck with the old line stocks … And he used to have lunch with me from time to time … and say to me, ‘Terry, do you think I’m mad?’ And I said, ‘No, you’re not mad, Tony, but your timing is quite likely going to be your undoing.’”

Dye was right about the bubble. He was fired on 1 March 2000 for sticking to his discipline while it hurt. That date, as Smith notes with a showman’s timing, “is the same day marked on the Nasdaq chart.” The top. The manager who saw it most clearly was removed the very day he was vindicated, because the pain had lasted just long enough to break his backers’ patience. “You can’t have an end to certain types of market until the last hope is given up.”

Then Smith went further and put up the Cambridge Associates and Research Affiliates data on when investors fire managers, and delivered the punchline himself:

“The time to fire a fund manager is when he’s doing very well … the guys who were doing 7.2% under-performed the guys who were under-performing. And so it is on 1 year and so it is on 3 years. And look, the time to fire us, were you going to do so, was about 2021.”

He knows the game. He knows that capitulation marks the bottom, that the manager who abandons his discipline at the moment of maximum pain is usually selling the low, that being right too early is indistinguishable from being wrong right up until it isn’t. He told the whole room, months ago, that the time to fire a manager is when he’s performing, and the time to back him is through the drawdown.

And now here he is. Terry Smith is capitulating. I never thought I’d write these lines.

Tony Dye, at the lunch table, deciding that this time he’d better start buying the Nasdaq after all.

Where this leaves us

I want to be careful not to overclaim. Smith may be right that the environment has changed in a durable way, and he may navigate the new one better than I expect. The buys, taken individually, aren’t crazy – several are excellent businesses, and a few, like Veeva, I’d happily own – at the right price.

Quality investing has had a genuinely brutal five years, and it is easy to critique from the cheap seats, with no billions and no redemption queue and no shareholders emailing at midnight. I hold the permanent capital he’d love to have. That’s not nothing, and it colours how easy this is for me to say. I’m not the “man in the arena” here. Smith is.

But the pattern is the pattern. A manager whose edge was patience has stopped being patient. A manager who diagnosed a momentum bubble has decided to chase momentum. A manager who told us the time to fire someone is when they abandon their discipline has abandoned his. And he’s doing it, on his own admission, because he runs a vehicle that can’t afford to wait as long as the strategy might require.

The old mantra was three lines, and the third line was the hardest and the most valuable: DO NOTHING.

It was hard precisely because doing nothing, in a screaming market, feels like negligence. That third line was the edge.

Turning it into DO EVERYTHING doesn’t fix the problem. It just converts a quality investor with a temporary performance issue into a momentum trader with a permanent skill deficit.

I hope I’m wrong. I’ve been wrong about him before, in his favour. But if you asked me what this letter is, I wouldn’t call it an evolution. I’d call it tilt. Full tilt. And the one thing every poker player learns, usually the expensive way, is that you can’t win it back in a single session by playing more hands. You win it back by getting up, clearing your head, and going back to the game you actually know how to beat.

Do nothing was never the weakness. It was the whole point.

Feel free to share your thoughts in the comments below.

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]]><![CDATA[The Analytical Edge Was Dead. AI Might Be Bringing It Back.]]>https://www.compoundwithrene.com/p/the-analytical-edge-was-dead-ai-mighthttps://www.compoundwithrene.com/p/the-analytical-edge-was-dead-ai-mightThu, 02 Jul 2026 14:38:45 GMTYou can read this article entirely for free. If you find value in this research, consider becoming a Premium Member for less than $1/day to unlock our full archive and join our growing circle of investors (I highly recommend checking out “The Library” to see what’s inside; you can find it right on the homepage).

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Warren Buffett compounded capital at roughly 20% a year for more than six decades.

Sit with that number for a moment, because almost nothing in the history of markets rivals it. Buffett is an absolute outlier in the chart below.

Part of the explanation is temperament, part is structure, part is the simple arithmetic of a very long runway.

Buffett’s edge was real!

But a meaningful slice of it, especially in the early partnership years, came from something we’d now call an analytical edge. He read the Moody’s manuals nobody else bothered to open. He found the net-nets, the obscure insurance floats, the mispriced stubs that were sitting in plain sight because the work of finding them was tedious, and the audience for that work was small.

"[Warren] expends a lot of energy checking out details and ferreting out nuggets of information, way beyond the balance sheet. He would go back and look at the company's history in depth for decades. He used to pay people to attend shareholder meetings and ask questions for him. He checked out the personal lives of people who ran companies he invested in. He wanted to know about their financial status, their personal habits, what motivated them. He behaves like an investigative journalist. All this stuff about flipping through Moody's Manual's picking stocks, it was a screen for him, but he didn't stop there." Alice Schroeder

That world is gone.

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You know this already. Investing has been democratized. Every 10-K is a click away. Every transcript is transcribed, indexed, and searchable within minutes of the call ending. Calls are available in podcast-like apps. Screeners that would have been science fiction in 1965 are free inside your brokerage account. The manuals Buffett had to physically hunt down now arrive pre-digested. Investors share their processes freely online.

And so the intuition almost everyone holds, seasoned investors included, is that the analytical edge has been arbitraged down to something close to zero. The skill ceiling rose so high, and access became so universal, that being smart and diligent stopped being enough. Everyone is smart now. Everyone is diligent.

Then AI arrived, and the intuition hardened into something like certainty.

Search interest in ChatGPT over time - Source: https://www.tandfonline.com/doi/full/10.1080/14765284.2025.2569006#d1e437

If the internet flattened the information advantage, generative models look poised to flatten the analytical one. A tool that can read a 300-page filing in seconds, build a discounted cash flow model on request, and summarize the bear case before you’ve finished your coffee would seem to be the final erasure of any thinking advantage a human might once have held. The machine does in ten seconds what took a diligent analyst a week.

Where’s your edge now?

Anne Lundgaard Hansen and Seung Jung Leea thus propose that widespread AI adoption may lead to more efficient markets as bubbles driven by human behavior become less likely.

I want to propose the opposite.

Not as a settled conclusion, because I haven’t arrived at one, but as a hypothesis I think is badly underdiscussed.

The widespread, homogenizing adoption of AI in analysis may not bury the analytical edge. It may be digging it back up. It may not lower volatility, but increase it. It may not lead to less herding, but more crowded trades.

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“… when teams simultaneously consult homogeneous AI systems, they unknowingly undergo a process we term Invisible Groupthink. Invisible Groupthink is an AI-mediated conformity that operates without the social pressure or directive leadership that classical groupthink requires, and which is therefore structurally invisible to the organizational safeguards designed to prevent it.“ - Invisible Groupthink: AI Homogenization, Epistemic Diversity, and Organizational Decision Resilience

A quick word on what an edge actually is …

I’ve written at length in a recent post about the distinction between skill and edge, so I won’t rebuild that whole argument here. You can read that piece here:

The short version, for anyone who hasn’t read it: you possess skills, but you do not possess an edge. An edge is external. It’s a situational setup, a behavioral inefficiency, or a structural quirk in the market landscape that appears for you to exploit. It’s an event, not a trait. And the uncomfortable corollary is that you cannot out-skill a bad environment. All the analytical horsepower in the world does nothing if there’s no inefficiency for it to act on.

That framing is highly relevant here, because I believe whether AI makes you more skilled isn’t even a question. Obviously, it does. It makes everyone better.

The question is whether AI creates or destroys the external conditions under which skill converts into excess return.

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It helps to be precise about the kinds of edges available. Bill Miller framed it cleanly, and Nick Sleep later paraphrased him well: there are broadly three competitive advantages in investing.

  • Informational, where you know a meaningful fact nobody else does.

  • Analytical, where you take the same public information everyone has and cut it up to reach a superior conclusion.

  • And psychological, or behavioral, where you are able to think and act rationally when others cannot.

Sleep added the observation that sustainable advantages tend to come from the analytical and psychological buckets, because pure informational edges are fragile and, in regulated markets, often illegal. There’s a fourth edge worth naming, the technical or structural one (I’ll again point you to the write-up linked above).

The informational edge, for the ordinary investor, is mostly dead. Regulation and the internet saw to that. Which leaves the analytical and the behavioral as the only edges a fundamental investor can realistically build a career on.

And the analytical one is precisely the edge everyone now assumes AI has finished off.

The edge that eroded before anyone typed a prompt

To understand what AI might do, it’s worth being honest about what already happened without it. The analytical edge didn’t wait for large language models to start decaying. It had been thinning for decades.

Think about the sequence. Decimalization narrowed spreads. Regulation FD, back in 2000, killed the selective-disclosure game where a favored analyst got the nod on the quarter before everyone else. EDGAR put every filing online for free. Then came the screeners, the aggregators, the transcript services, the research terminals, and eventually a generation of investors raised on all of it who think of a fully-built reverse DCF as table stakes.

The barrier to competent analysis fell and fell.

Here’s the thing that gets missed, though. When a skill becomes universal, it stops being an edge, but it doesn’t stop being necessary. You still have to do the work. You just no longer get paid for doing it, because so does everyone else.

“Financial firms are contending with the most significant overhaul of technology capabilities in a generation. AI-driven digital transformation is reshaping job roles and investment processes, prompting professionals to reconsider the boundaries between human and machine cognition, while firms work to upgrade their technology stacks and human capital to remain competitive. Amid this shift, firms and professionals must reevaluate the skills needed for success.“ - CFA Institute in “AI in Finance: Changing Workflows, Growing Demand for Human Judgment“

So by the time AI showed up, the patient was already pale. Most thoughtful investors had made a quiet peace with the idea that their durable edge, if they had one at all, was behavioral. Time arbitrage for instance. The willingness to be patient when the institutional world structurally cannot be, to hold through drawdowns that force a fund with quarterly redemptions to sell. To step in the ring when everyone is panicking.

If universal access to information compressed the informational edge, and universal access to analytical tooling compressed the analytical edge, then generative AI looks like the same movie with the resolution turned up. The tool doesn’t just hand you the filing. It reads the filing, cross-references it against eight years of prior filings, flags the change in revenue-recognition language, drafts the model, writes the bear thesis, and stress-tests your bull one.

What took a skilled analyst a focused week now takes an afternoon, and the analyst on the other side of your trade has the same afternoon.

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The empirical trend supports the worry. There’s a growing body of research, and a growing chorus on Wall Street, warning that as more investors lean on similar models they converge on the same conclusions. From hedge funds to wealth managers, the industry has embraced AI in search of an edge, and researchers are now asking what happens when everyone turns to similar models to find one: buying the same stocks, reacting to the same headlines, and making the same mistakes. Several recent studies suggest widespread adoption could shorten the lifespan of profitable trading signals as investors converge on the same opportunities. If a signal that once took years to arbitrage away now gets crowded out in months, the shelf life of any analytical insight collapses. You did brilliant work, the model agreed with your brilliant work, and so did every other model, and the opportunity was gone before you could size into it.

That’s the steelman. Faster synthesis, universal tooling, accelerating alpha decay, everyone converging on the same answer at the same time. If you stop the story there, the analytical edge isn’t just dead. AI held the pillow over its face.

But notice the word doing all the work in that paragraph. Converge.

Convergence is not the problem. Convergence is the opening.

Here’s what we were assuming so far: when everyone’s analysis converges, it converges on the truth. That the machine consensus is correct, and being correct alongside everyone else simply means the reward gets competed away.

If that were true, the analytical edge really would be finished, because there’d be nothing left to be right about that the crowd hadn’t already priced.

But that’s not what the research on these models actually shows. It shows convergence, yes. It does not show convergence on truth. It shows convergence on the mean.

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This distinction is everything, so let me ground it. When researchers measure the creative and analytical output of large language models, they find something consistent and, for our purposes, remarkable. The models don’t just each produce somewhat generic output. They produce similar generic output to one another.

One study by Emily Wenger and Yoed Kenett administered standard divergent-thinking tests to twenty-two different models from distinct model families alongside a group of human participants, and the models produced dramatically less diverse output than the humans, with effect sizes that in social-science terms are enormous.

“We find that LLM responses mirror other LLM responses far more than humans do other humans, even after controlling for key confounding variables.“

The gap didn’t close when researchers switched models. It’s structural.

Different systems, trained on overlapping data with similar objectives, land on overlapping answers. You cannot escape it by using a different provider, because the homogenization isn’t a quirk of one model. It’s a property of the category.

Why does this happen? Two reasons, both baked in. The models are trained to predict the most probable next token, which, by construction, pulls them toward the statistical center of their training data rather than out to the strange, correct, non-obvious tails where real analytical insight tends to be found. The contrarian, non-consensus view. The variant perception.

“Researchers are now asking what happens when more investors turn to similar AI models to find one: buying the same stocks, reacting to the same headlines, and sometimes making the same mistakes. […] AI may make investors faster and more informed while also making their trades more crowded, their systems easier to fool and their risk-taking harder to control.“ - The Straits Times

And the alignment process that makes them pleasant to use, the reinforcement from human feedback, systematically compresses the variance of what they’ll say.

The output gets smoother, more agreeable, more standardized. It regresses toward consensus by design.

There’s even research showing the effect intensifies when a model has access to your prior conversations and profile, tuning its answers toward what it infers you already believe. The tool is engineered to tell you a well-structured version of what everyone already thinks, and to do it more persuasively the longer it works with you.

Shomik Jain, Charlotte Park, Matt Viana, Ashia Wilson, and Dana Calacci, "Interaction Context Often Increases Sycophancy in LLMs"

Now put that back into a market. If a large and growing share of participants outsources the analytical layer to systems that regress toward the same mean and flatter the user’s existing priors, then the market’s collective analysis doesn’t necessarily get sharper.

It just gets narrower.

The dispersion of views that makes markets efficient in the first place starts to collapse into a monoculture. And a monoculture is not an efficient market. A monoculture is a market that has agreed to stop looking in the same places at the same time.

Interactive Brokers visualized the impact of “belief homogenization” beautifully in the illustration below:

Source: https://www.interactivebrokers.com/campus/ibkr-quant-news/can-llm-based-trading-systems-increase-market-volatility/

Maybe that’s what’s already driving the multi-trillion momentum trades?

So that agreement may create inefficiency? That’s at least a hypothesis to wrestle with I believe. And IBKR does too, and they propose that widespread LLM adoption may also increase volatility, which stock pickers should love:

“A more important risk question is whether they can make a large number of investors interpret the same information in the same way, at the same time. LLMs may become market-relevant not because they create perfect forecasts, but because they compress diverse research processes into similar narratives, rankings, watchlists and execution plans.“ - Can LLM-Based Trading Systems Increase Market Volatility?

That may be the edge, sitting right where the crowd stopped looking.

I’m not alone in reading it this way, which I’ll admit, surprised me when I went looking. The CFA Institute, not exactly a contrarian fringe outlet, published an analysis titled “The Perils of Declining Judgment in the Age of AI“ arguing that as AI-generated research proliferates, consensus forms faster, and that algorithmic systems tend to reinforce dominant paradigms and raise the risk of intellectual stagnation.

“Artificial intelligence (AI) now introduces a paradox. While machines significantly expand our capacity to process information, excessive reliance on automated cognition may erode the epistemic foundations that enabled civilizational progress. […]

Humans are passively consuming AI-generated outputs rather than actively engaging with data. The consequences have become evident in a decline in critical thinking and decision-making skills, or cognitive laziness (Cheng, 2025; Chatterji, 2025; and others). Left unchecked, reliance on automation risks compromising the quality of investment decisions. The question is not whether machines will become more powerful, but whether human decision-making will remain grounded in evidence.“

Their conclusion is almost word for word the hypothesis I’m circling: widespread AI adoption elevates the value of independent judgment and process diversity precisely by making both increasingly scarce.

“Widespread adoption of generative AI in investment management risks diminishing critical thinking, eroding human expertise, reducing innovation, and concentrating decision-making power in AI models, potentially leading to systemic vulnerabilities, poor adaptability, and ethical concerns.“

When the marginal cost of generating consensus analysis falls to nearly zero, the thing that becomes valuable is the analysis that consensus can’t produce. Scarcity moved. It used to be scarce to do the analysis at all. Now it’s scarce to do analysis that departs from the machine.

So the analytical edge doesn’t return in its old form. You don’t win anymore by being the diligent one who read the filing, because the model read the filing too, and so did everyone else’s model.

You win, if you win, by being the one who reads the filing and then thinks something the model structurally cannot think.

The judgment that sits in the tails. The non-conforming question. The read on a management team, a regime shift, or a second-order consequence that doesn’t sit anywhere in the training distribution because it hasn’t happened yet.

So what does a reclaimed edge actually look like?

Suppose I’m right. Suppose the machine consensus is narrow, confident, and increasingly universal, and suppose that narrowness reopens a door that had been closing for thirty years. What do you actually do with that?

The first thing to understand is what the edge is not. It is not out-computing the machine. You will lose that race, badly, and so will everyone else. The model reads faster, models faster, and never gets tired. If your process is to do by hand what the AI does automatically, you don’t have an edge, you have a slower version of the commodity.

The reclaimed analytical edge can be found downstream of the machine’s output. It begins where the consensus ends.

Practically, that means using the tool to find the consensus rather than to form your own view. Ask the model for the bull case, the bear case, the standard framing, the obvious risks. Not because you’ll adopt them. Because now you know, with real precision, what everyone else who queried a similar model is looking at.

That’s the map of crowded ground. Your job is to work out where the map is wrong. Where does the training distribution fail? Usually in the places the data can’t reach: a genuine regime change with no historical analog, a management team whose behavior you can only judge by having sat across from them, a second-order consequence that hasn’t happened yet and so exists in no corpus.

The model regresses to the mean of what has been written. Your edge is the well-founded view about what hasn’t.

This is roughly the “AI plus human” paradigm the CFA Institute has been circling, though I’d put it more sharply than they do.

“As we’ve noted elsewhere, we believe the future will be defined by the complementary cognitive capabilities of humans and machines, characterized by the “AI + HI” paradigm and the continued importance of professional competence.” - Rhodri Preece in AI in Finance: Changing Workflows, Growing Demand for Human Judgment”

The benefits of AI don’t accrue to individuals, they accrue to processes, which is precisely why the individual edge has to come from somewhere the process can’t reach. When earnings-call analysis and filing review get automated, the repeatable, auditable parts of the job become table stakes. What’s left, the scarce thing, is judgment. Human judgment. The willingness to hold a differentiated view and be wrong in public for a while before you’re right for instance.

Which brings me to the part I find least comfortable, and the reason I’m holding this as a hypothesis rather than a thesis. A reclaimed analytical edge is brutally hard to hold. It was hard when it belonged to Buffett reading manuals, and it’s harder now, because holding a view the machine disagrees with means holding a view almost everyone disagrees with, generated confidently, phrased persuasively, and repeated everywhere you look.

The homogenization that creates the opportunity also creates enormous pressure to abandon it.

When every screen, every note, every colleague’s model says the same thing, the contrarian has a particularly tough time.

And a good deal of the time, the contrarian may in fact be wrong, because being different is not the same as being right. That distinction is the whole game, and AI does nothing to make it easier.

So the analytical edge and the behavioral edge may be collapsing into a single thing. The analytical work identifies where the consensus is mechanically narrow. The behavioral discipline is what lets you stand there once you’ve found it.

You need both. You always did.

What’s changed is that the machine may now manufacture the consensus faster and more uniformly than any prior technology, which sharpens the opportunity and, in the same motion, sharpens the discomfort of taking it.

I’ll leave you where I started, which is without a firm conclusion. Maybe AI finishes off the analytical edge for good, and everything I’ve laid out is a rationalization for staying in a game that’s already over.

Or maybe the great homogenizing wave washes out the middle, floods the market with confident, average, identical analysis, and quietly restores a premium to the one thing it cannot produce: An independent mind, willing to look where the machine won’t, and to stay there.

I’d love to hear what you think.

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]]><![CDATA[Navigating the Froth: 6 Alternative Signs of a Market Top]]>https://www.compoundwithrene.com/p/navigating-the-froth-6-alternativehttps://www.compoundwithrene.com/p/navigating-the-froth-6-alternativeTue, 02 Jun 2026 13:47:09 GMTLook at the surface numbers, and the stock market seems absolutely bulletproof. The S&P 500 closed out 2024 up roughly 25%, carried that momentum through another positive year in 2025 (roughly +18%), and is already sitting on an 11% gain year-to-date in 2026.

If you solely track the price developments and financial media headlines, it feels like the party will never end. We’re still dancing.

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But if you look under the hood, things are starting to look incredibly frothy in my view. Traditional valuation metrics – like the Shiller PE ratio or the Buffett Indicator – are already screaming that equities are historically expensive.

The problem? Valuations are notoriously terrible market timers. Markets can stay irrational and expensive a lot longer than most investors can stay patient. Stock market indicators are by no means crystal balls. You cannot use these indicators to predict what is going to happen in a year or two from now.

Let me repeat: They are NOT stock market timing tools!

This is backed up by this research by YCharts that shows that “of |the] seven leading indicators studied, none have consistently predicted major market declines dating back to 1950. Even the most consistent indicators provided a warning signal for only about half of major declines. “ A major decline in this study was defined as a decline of 10% or greater from the S&P 500’s most recent all-time high

So to spot a true cyclical turn, we have to look past standard metrics and focus on alternative, structural, and behavioral signals. I will once again insert my “Market Sentiment Cheat Sheet” below:

In this post, we are going to break down six unorthodox signs that I’m seeing that I believe are pointing to us being in a late-stage bull market.

And maybe more importantly, towards the end of the post, we’ll explore the investor’s paradox in this market regime: why an overextended index can coincide with an attractive environment for disciplined, fundamental stock pickers.

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

1. The $400 Billion Cash Mountain

The ultimate gauge of market value isn’t a spreadsheet; it’s what the smartest capital allocators in the world do with their money. Right now, Berkshire Hathaway is sitting on an absolutely astonishing cash hoard of around $400 billion as of their latest filings.

Whether you view Warren Buffett (and now CEO Greg Abel) as proficient market timers or simply disciplined buyers, the conclusion is exactly the same: they cannot find anywhere to put this capital to work at reasonable prices.

Aside from a highly surprising, significantly increased stake in Alphabet (Google), …

… and the Monday-announced acquisition of Taylor Morrison for $72.5/share (or an $8.5B enterprise value (premium of roughly 24%)), Berkshire has been hoarding cash, letting their pile climb to vertical, historic records.

When the largest corporate war chest in history refuses to buy because the math no longer makes sense, retail investors should take note. If Berkshire will have a hard time deploying that much capital even during a market crash – Buffett is certainly secretly rooting for a prolonged crash, possibly a lost decade (not another COVID-like flash crash) –, it’s a glaring sign that the broader market has run far ahead of fundamental reality.

2. The Illusion of Market Breadth

When an index hits consecutive records, you naturally want to see a broad army of companies marching upward together.

Right now, that isn’t happening.

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Instead, we have an elite group of mega-cap tech generals pulling up a heavily dragging baseline. A lot has been written about the historic levels of concentration we are witnessing in the S&P right now. The track record of the “top 10” club is generally rather poor. Massive concentration – I believe we’re close to the top ten names of the S&P representing 40% or more right now – will only magnify the impact of this base rate, I believe.

Back to my point: This is what market insiders call poor market breadth or high return dispersion. Poor market breadth occurs when a stock index (like the S&P 500) rises, but only a small handful of heavy-hitting stocks are driving the gains while the majority of the market is stagnant or declining.

Let’s look at the exact breakdown from the S&P 500 data: As of May 64.5% of S&P constituents posted a positive return over the last year. Hovering right at the long-term historical average of 64.1%. Despite a massive rally, over a third of the index is flat or losing money.

The index itself is up 27.6% YoY.

The more significant signal, however, can be found in the chart below (which is a bit dated to be fair!). As of last year, only 39% of S&P members outperformed the index in terms of YoY performance. Strikingly low. Only about 39% of companies were actually beating the index itself back then, meaning the index’s returns are heavily skewed by a few heavyweights.

Given the increasing concentration in the largest index constituents and the “AI winners,” I would not be surprised if the data today doesn’t look all too dissimilar! (feel free to share more up-to-date data in the comments below!)

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This extreme lack of participation is a classic late-cycle indicator. Historically, often when the percentage of outperforming companies dropped into this sub-40% territory, a major market correction followed close behind – most notably ahead of the 1987 crash, the 2000 dot-com peak, the 2020 drop, and the 2025 tariff conundrum.

And if you think about it, this makes sense, right? When a tiny handful of stocks carry the weight of the entire financial system, the index becomes incredibly fragile. If these winners at some point stop “winning,” the index has a problem.

However, this environment completely changes the game for stock pickers. It becomes increasingly harder to outperform the market. As legendary Michael Mauboussin explains in one of his papers:

“Security selection skill is entirely dependent on market dispersion, which is the range of returns across a group of stocks. If all stocks in the S&P 500 return roughly the same amount in a given year, picking the ‘best’ stock yields very little excess return, blunting your skill. High dispersion is an absolute prerequisite for security selection skill to shine.”

3. Global AI Mania

This hyper-concentration isn’t just an American phenomenon. The AI infrastructure boom has officially gone global, turning major international indices into concentrated vehicles moving with the volatility of micro-cap assets.

Look no further than South Korea’s KOSPI index. The index has staged a breathtaking rally, skyrocketing 96.7% year-to-date and climbing 252% from late 2024/early 2025.

Why is an entire national index moving like an early-stage cryptocurrency? The answer lies in its extreme structural weightings. I couldn’t find the actual weight of the KOSPI index, but here are the weightings of the MSCI Korea index:

More than 57% of the entire index is tied up in the fortunes of just two top semiconductor players.

Are you looking for a passive investing vehicle? Look somewhere else!

When you look at the underlying stock returns of some of the biggest and most popular “AI stocks” over the past year, the numbers appear anything by sustainable to me. While the SPY (S&P 500 ETF) put up a strong 29.68% return, look at what the AI hardware and memory supply chains did:

  • NVIDIA (NVDA): +51.73%

  • Dell Technologies (DELL): +276.05%

  • Advanced Micro Devices (AMD): +356.60%

  • Intel (INTC): +466.32%

  • Samsung Electronics (A005930): +475.01%

  • Micron Technology (MU): +905.50%

  • SanDisk (SNDK): A mind-numbing +4,291.14%!

These aren’t speculative penny stocks being pumped in internet forums; these are massive, multi-billion-dollar corporations experiencing vertical, parabolic price expansions.

When mega-cap hardware manufacturers trade like speculative lottery tickets, you are looking at classic, textbook market froth.

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4. The Luxury Car Bonus Cycle

Speculative bubbles eventually leak out of brokerage accounts and manifest in the real world. When wealth is generated at a historic pace, consumer behavior shifts rapidly – and we are seeing exactly that happen right now.

In late May 2026, financial media and local reports highlighted an unprecedented phenomenon in South Korea: luxury car dealerships are experiencing a frantic rush of buyers.

The individuals driving this demand aren’t traditional high-net-worth real estate tycoons or seasoned executives; they are rank-and-file chip industry workers from Samsung Electronics and SK Hynix.

Thanks to the global AI chip boom, these employees have been reaping massive industry windfalls, with sky-high corporate performance bonuses scaling into the hundreds of thousands of dollars. According to dealership showrooms, the wealth effect has been instantaneous:

  • Dealerships have reported receiving dozens of inquiry calls every single day.

  • The vast majority of showroom foot traffic looking at high-end sports cars priced well above 100 million KRW consists of Samsung and SK Hynix workers.

  • Google search trends confirm the retail mania, showing a massive regional popularity spike in South Korea for the phrase “Ferrari dealership”.

When middle-management tech employees turn into immediate luxury sports car buyers en masse, this strikes me as another soft sign that we’re in the middle of late-cycle euphoria.

5. CEOs as Cultural Pop Stars

Think about how corporate executives are treated near market bottoms versus market tops. At the depths of a bear market, CEOs are often vilified, hauled before regulatory committees, or blamed for economic downturns. But at the absolute peak of a bubble, some select executives transform into mainstream cultural icons.

NVIDIA’s Jensen Huang has officially achieved full-blown pop-star status. We see a similar phenomenon with Anthropic's Dario Amodei, whose profile has skyrocketed into billionaire, tech-titan status as his company's valuation pushes toward the twelve-figure mark. Even Alphabet’s Sundar Pichai has pulled off a stunning narrative reversal – evolving from an executive facing investor calls for his ouster just a year ago to a celebrated pioneer leading Google's triumphant, multi-billion-user 'agentic AI' era.

Don’t get me wrong: Jensen Huang strikes me as an exceptionally brilliant, humble, and likable leader. But the way the broader public and social media now obsess over his lifestyle (I’m not going to insert the image of him giving a fan a special signature here …) is highly unusual for the leader of a B2B hardware company.

Just take a look at what went viral on platforms like X in late May 2026:

  • Mainstream food bloggers tracking him down like a Hollywood celebrity as he casually grabbed pepper buns at a night market in Taipei with his wife.

  • Videos flooding financial timelines showing him dancing on stage during an all-employee celebration event in Taiwan.

When the general public begins tracking a semiconductor executive’s street-food choices and viral dance moves with the same intensity usually reserved for pop musicians, retail euphoria has peaked. It’s yet another psychological signal that a sector is running on pure, unadulterated adoration rather than sober fundamental analysis.

6. The Trillion-Dollar IPO Rush

The final structural signal of a market peak we will discuss in this piece is the sudden, aggressive rush toward the Initial Public Offering (IPO) market. When market conditions are average, companies list systematically. But when a bubble reaches its maximum thickness, late-stage private companies scramble to go public all at once.

Why? Because insiders, founders, and venture capitalists understand market cycles better than anyone else.

They see the massive premium the public market is currently paying for tech, and they want to capture that peak liquidity before the window slams shut.

Right now, we are witnessing what financial media is calling “The $3 Trillion Reckoning.”

Three of the most highly valued private giants in the world are simultaneously charging toward the public markets:

  • OpenAI

  • SpaceX

  • Anthropic

What makes this absolutely unprecedented is the scale: all three companies are aiming to go public at valuations hovering close to $1 trillion each.

For perspective, there are only a small handful of trillion-dollar public companies in existence today, and these three are aiming to join that elite club all at once. This is not something you are seeing in a normal market environment.

When you see high-profile founders rushing these massive capital raises to market as fast as humanly possible, it’s a major sign the party may come to and end soon.

It’s an implicit admission that they believe raising capital at a trillion-dollar valuation might be a flat impossibility two or three years from now if the macroeconomic environment shifts and the AI bubble deflates.

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The Paradox & Avoiding the “Blast Zone”

All of these signs point to a highly counterintuitive conclusion. If you look at the S&P 500 as a whole, its valuation is stretched so thin that the statistical base rates for 10-year compounded annual returns from these price levels look deeply disappointing.

This macro anxiety was perfectly captured by value investor Matthew Fine on a May 2026 episode of the Value Investing with Legends podcast:

“I’m a simple creature and I’m worried about the elephant in the room. And US equities are such a dominant force in global capital markets today, and the statistical base rates of what somebody should expect from valuations like these are very, very poor. And I’m fearful that there will be a lot of disappointment.

And I personally grapple with how to create distance from the blast zone because [we are] largely a long-only fund, and there will be collateral damage and there are correlations across all equity markets. And I think about how to avoid the fallout from a significant decline in US equity markets.”

So, how exactly do you “create distance from the blast zone” if you aren’t managing a portfolio as massive as Berkshire Hathaway’s and don’t want to sit entirely on a mountain of cash?

You look at exactly where the institutional money isn’t flowing right now.

Because index funds, ETFs, and momentum-driven institutional capital are blindly forcing trillions of dollars into anything loosely associated with AI, they are actively starving the rest of the global market of liquidity.

Arguably, and it sounds almost insane to say this in the current environment, this structural shift has created a paradise – and I’m the first one to admit that “paradise” may be a slight exaggeration, but you get the point – for disciplined, active stock pickers.

While the S&P 500 mega-caps may be approaching a rigid valuation ceiling, beautiful, high-quality, free-cash-flow-generating businesses outside the US and across select emerging markets are trading at rather cheap and compressed valuations, often at record-low multiples.

Pockets of exceptional value are hiding in plain sight. Pockets like e-commerce powerhouse Coupang Holdings, or regional giants like Mercado Libre or Grab Holdings, both of which we recently covered in a deep dive on this blog (check out the Library), are, I believe, worth your attention.

In 2026, payment companies are as unloved as they have been in a decade.

To sum up, I don’t know whether this market cycle turns in two months or two years. But the playbook for intelligent investing remains completely unchanged. Standing aside – potentially raising cash – while the crowd frantically chases trillion-dollar IPOs and semiconductor hype requires serious emotional discipline, but it is exactly how you avoid the eventual fallout. Keep your eyes locked onto true business fundamentals, protect your downside, and remember that the absolute best time to accumulate world-class businesses may be when the rest of the market is too distracted by the retail euphoria to notice them.

What are your thoughts? Are you seeing other unorthodox signs that the market is topping out, or do you think this AI-driven momentum still has room to run? Drop your thoughts and data points in the comments below!

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When you join our blog community today, you’ll instantly unlock 3 FREE GIFTS to sharpen your strategy.

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PS: You might enjoy ’ recent piece on the same subject as well:

Dirtcheapstocks Substack
Calling the Top
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3 months ago · 131 likes · 1 comment · Dirtcheapstocks

PSS: You may also enjoy this Barron’s piece (paywalled) —> “If It Walks Like a Bubble and Quacks Like a Bubble, Then It’s Probably a Bubble”

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

]]><![CDATA[The Golden Age of Stock Picking? Why Today's Volatility Explosion Is an Active Investor's Dream]]>https://www.compoundwithrene.com/p/the-golden-age-of-stock-picking-whyhttps://www.compoundwithrene.com/p/the-golden-age-of-stock-picking-whyFri, 17 Apr 2026 14:30:28 GMTYou can read the majority of this article entirely for free. If you find value in this research, consider becoming a Premium Member to unlock our full archive and join our growing circle of investors (I highly recommend checking out “The Library” to see what’s inside; you can find it right on the homepage).

A few days ago, I was listening to an episode of This Week In Intelligent Investing featuring Shaun Heelan, CIO of MAAT Investment Group, and one exchange in particular stuck with me. The show host raised something I’d been noticing for a while: single-stock volatility has been trending meaningfully higher, and since COVID, it has essentially exploded. Double-digit percentage gains or losses after a quarterly update have become the norm – even among mega-caps.

Heelan’s response was characteristically direct. He acknowledged it as “an undeniable fact” – and then said something that I think cuts right to the heart of how serious investors should be thinking about this environment. Whether elevated volatility is a risk or an opportunity, he argued, depends entirely on the structure you operate with and the mindset you bring to the table.

“I think in some ways it’s a positive, depending on what structure you have, it’s a negative. I would take it as a net positive thinking opportunistically.”

That framing resonated with me. Of course it did. I’m a stock picker at heart—operating without restrictions and always willing to bet against the crowd.

“Volatility is a symptom that people have no clue of the underlying value.” - Jeremy Grantham

I think most investors – even experienced ones – default to treating volatility as the enemy (mostly subconsciously). It’s uncomfortable to watch a position drop 15% in a session. It’s deeply uncomfortable to hold through a 40% drawdown over several months, only to then face another 20% leg down after a disappointing quarterly report.

The instinct is to sell, to reduce exposure, to wait for things to “calm down.” In fact, all of your human instincts will tell you to do the opposite of what you should most likely do. And those instincts, understandable as it is, may be costing you more alpha than almost any other single mistake you can make.

“If you are susceptible to selling everything in a panic, you ought to avoid stocks and stock mutual funds altogether.” - Peter Lynch

This post is my attempt to argue the opposite case. Elevated single-stock volatility – and we are living through an historically elevated period of it, as I’ll show – is one of the most compelling features of today’s market for the patient, fundamentals-driven investor. Full stop.

The chaos is real. The discomfort is real. But so is the opportunity embedded within it – at least, for those a) willing, b) structurally able, and c) behaviorally wired to act in these moments.

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To be clear, I’m not dismissing the risks that volatility brings. Volatility cuts both ways, and I’ll get into that. But I want to make the case that in a market where the traditional edges – informational, analytical – have been largely competed away, the behavioral edge that comes from being able to sit calmly while others panic may be the most powerful source of alpha available to the individual (retail) investor today.

And paradoxically, the more volatile the environment, the wider that gap tends to become.

The Volatility You Don’t See in the Headlines

Look at the S&P 500 today – back to new all-time-highs – and you could be forgiven for thinking markets are in reasonable shape.

The VIX – the so-called “fear gauge” that most financial media reach for when they want to take the market’s temperature – is hovering around its long-term average of around 15 to 20 points (19.7 on average over the past 30 years).

Nothing to see here, right?

Wrong.

The surface calm is one of the most misleading things about today’s market, and I think it deserves a lot more attention than it gets.

Here’s the number that should give you pause: as of last Friday, 74 out of 500 stocks in the S&P 500 are down more than 50% from their five-year highs. The index is near all-time highs, and roughly 15% of its constituents have been cut in half.

Software names have been hammered – 15 of them feature in that cohort – alongside the usual suspects: the over-hyped pandemic darlings that never had the fundamentals to justify their valuations.

But here’s what I find particularly striking. A meaningful chunk of those “halved” stocks aren’t speculative junk. They’re companies that were considered high-quality businesses for years, trading at premium earnings multiples that the market has now simply decided it’s no longer willing to pay, realizing their tail risk may be higher than previously thought, given the exponential progress we’re seeing in the realm of AI. The re-rating has been brutal – and I believe generally speaking, justified –, but, for many holders, completely unexpected.

What this tells you is that the index level is doing a lot of heavy lifting by a very small number of names. The math isn’t hard to figure out: if 74 constituents are down 50% or more from their peaks, something else has to be doing the heavy lifting to keep the index near record territory. What anmes are filling the voide? I believe it is predominantly the mega-cap technology names and semiconductors, which have recovered sharply and are essentially dragging the headline number upward, while a quiet carnage plays out beneath.

This, too, should be a stock picker’s dream.

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Single-Stock Dispersion

This divergence between index-level stability and individual stock turbulence isn’t just a recent anecdote. It’s a structural feature of today’s market, and the data behind it is remarkable. According to research from J.P. Morgan Asset Management, the average rolling three-month pairwise correlation among S&P 500 stocks has fallen to just 13% – lower than it has been 98% of the time since 2022. A 13% correlation means that on average, a stock in the S&P 500 moves in the same direction as another random stock in the index only 13% of the time.

For context, dispersion measures how much the returns of individual stocks differ from each other. High dispersion means that stocks are diverging—some are experiencing massive gains while others are seeing significant losses. It indicates a “stock picker’s market” where selecting the right company matters more than just buying the whole index

In plain English: stocks are increasingly moving independently of each other rather than in lockstep with the broader market. The index looks calm precisely because the chaos is so dispersed.

The divergence between index-level volatility and single-stock volatility tells the same story in numbers. The VIX – which measures implied volatility on the index – remains near its long-term average of 15 to 18. Meanwhile, the VIXEQ, which measures implied volatility on the constituent stocks themselves, has shifted upward over the years to a baseline of 35 to 40.

That’s not a minor gap. Pre-pandemic, the VIXEQ averaged around 25.6. The individual parts of the market are experiencing a fundamentally different volatility regime than the whole, and most investors who focus on the headline index number are simply not seeing it.

The Goldman Sachs research team has been particularly vocal about what’s driving this dispersion. In 2024 and 2025, approximately 74% of a typical S&P 500 stock’s returns were explained by company-specific, micro-level factors – up from a 20-year average of 58%. The consequence of that shift shows up starkly in return dispersion figures: in 2024, the gap between the best and worst performers in the S&P 500 reached 70 percentage points, the highest level since 2007 outside of recession years. That is an extraordinary spread.

“Six out of 11 sectors in the S&P 500 registered above-average return dispersion — the highest being Information Technology at 106 percentage points, while Real Estate was the lowest at 39 percentage points.“ – Goldman

It means the difference between owning the right stocks and the wrong ones has rarely mattered more.

There’s a deeper point here worth sitting with. The traditional mental model most investors carry – that “the market” went up or down by X% today – is becoming progressively less meaningful as a description of what’s actually happening. When 74 names are down 50% or more from their highs while the index prints near record levels, the index isn’t really telling you what’s going on. It’s telling you what a handful of mega-cap stocks are doing.

“[...] if you look historically, and this is ironically, it’s true on the debt side as well for corporate debt, if you look at prior periods where one given sector became extremely concentrated, so you mentioned the letter that we wrote, industrials were the go-go companies in the late 60s, consumer staples were in the nifty 50 in the early 70s, energy companies in 1980 or so, technology companies again around 2000, and then financials as a class around 2006, 2007. Anytime they got up to a very high percentage, 25 or higher or 30 or higher, what happened in the following five to 10 years tended to be not pleasant.” - Shaun Heelan, CIO of MAAT Investment Group

https://gqg.com/insights/playing-with-fire/

This isn’t entirely new. The dominance of a small number of names in cap-weighted indices has been a talking point for the past five years. But the degree to which the index now masks underlying turbulence has reached a level that I think warrants treating it as a qualitatively different environment.

The “Magnificent 7” cohort is a useful illustration: these stocks moved almost in unison through 2023 and 2024 – their average three-month pairwise correlation peaked at 78% in May 2025.

By early 2026, that figure had collapsed to 26%, as investors began differentiating sharply between the long-term winners and those facing disruption from AI or competitive pressures (Source: J.P. Morgan).

Institutional research from firms like Citadel Securities also highlights that single-stock dispersion – again, the difference in performance and volatility between individual stocks – reached the 99th percentile relative to the last 30 years in early 2026.

Why Is This Happening? The Forces Behind the Surge

Understanding that single-stock volatility has structurally shifted higher is one thing. Understanding why it has shifted – and why it’s unlikely to simply revert – is what separates a useful insight from an actionable one. There isn’t a single culprit here. It’s a confluence of structural forces that have been building for years and, in several cases, accelerated dramatically since COVID.

Back to Fundamentals?

For much of the post-2008 era, macro dominated everything. Central bank policy, interest rate expectations, and broad risk-on/risk-off sentiment were the primary drivers of stock returns. In that environment, individual company fundamentals mattered less – a rising tide lifted most boats, and a hawkish Fed statement could sink them all simultaneously. Correlations between stocks were high, and the index was a reasonably faithful representation of what was happening beneath the surface.

https://www.goldmansachs.com/insights/articles/us-equities-are-ripe-for-stock-pickers

That world is largely gone. The macro backdrop still sets the stage, but the performance of individual businesses – their earnings trajectory, competitive positioning, balance sheet resilience, management quality – is doing far more of the work in determining where a stock actually goes. This is a profound shift, and it means that stocks are increasingly being priced on their own merits rather than as interchangeable units of “market exposure.” Naturally, that leads to much greater dispersion in outcomes.

Zero-Day Options

If the macro-to-micro shift is a slow-moving structural force, the explosion of zero-days-to-expiration options – 0DTEs – is a faster and more mechanical amplifier of single-stock volatility. By 2025, total listed options volume reached 15.2 billion contracts, with 0DTE options accounting for 24% of overall US listed volume, up from 21.5% in 2024 and nearly double the 2022 share. In the S&P 500 specifically, 0DTE options now account for nearly half of all trades.

The way these instruments amplify stock moves is worth understanding. When market makers intermediate 0DTE trades, they’re exposed to directional risk and must hedge that exposure by buying or selling shares in the underlying stock. Because 0DTE options have extreme sensitivity to price changes even a modest move in a stock forces market makers to execute large, immediate hedges. That hedging activity itself moves the stock further, which triggers more hedging, which moves the stock further still. It’s a procyclical feedback loop, and it can turn what might otherwise be a contained 3% move into a 7% or 8% swing within a single session. The “Liberation Day” episode in early 2025, during which the VIX repriced from 17.48 to 52.33 in a single session, is an extreme illustration of exactly this dynamic playing out at scale.

High-Frequency Trading

Furthermore, High-frequency trading firms now account for more than half of all US equity trading volume.

“The current market structure is thus highly competitive, highly fragmented, and very fast. It is also dominated by the trading of high frequency traders, who by some estimates make up half or more of all of trading volume. Understanding what high frequency traders do is crucial for comprehending why markets today are so very different from times past.” - Maureen O’Hara: High frequency market microstructure

HFT firms operate with high speed, minimal inventory, and zero interest in company fundamentals. Their business model depends on normal, orderly conditions. The moment a macro announcement hits, a geopolitical shock lands, or a stock starts moving in ways their models don’t expect, they don’t provide liquidity – they consume it, or exit the market entirely. The result is a market that looks extraordinarily liquid in calm periods and becomes dangerously thin precisely when investors most need to execute. This “disappearing liquidity” phenomenon creates sudden imbalances between the available depth in the order book and actual trading demand, amplifying price gaps and exaggerating moves in individual stocks.

It’s one of the more insidious features of modern market structure, because it’s invisible until it isn’t.

Retail, Reddit, and the Sentiment Machine

Then there’s the retail and social media dimension, which has permanently altered the volatility landscape for at least a subset of stocks. The meme stock phenomenon that erupted in 2021 wasn’t just an entertaining footnote – it demonstrated that large numbers of retail investors coordinating on platforms like Reddit’s r/WallStreetBets could overwhelm institutional short positions and cause stocks to move in ways that had nothing to do with fundamental value.

Statistical research has since confirmed a strong bidirectional feedback loop between social media engagement and trading volume, with online sentiment capable of driving short-term price action independently of any underlying business development.

The good news, if you want to call it that, is that by early 2026 there are signs this particular force is cooling somewhat – daily retail equities trading volume fell 11% in February.

But the infrastructure for sentiment-driven volatility is permanent. Commission-free trading, social media coordination tools, and the cultural normalization of speculative trading aren’t going away.

For smaller and mid-cap stocks, especially, where even modest concentrated buying pressure can cause violent moves, this remains a live and meaningful source of idiosyncratic volatility.

AI: Creative Destruction in Real Time

Finally, and perhaps most consequentially for the years ahead, there is artificial intelligence – operating simultaneously as a driver of enormous value creation and as a force of rapid, unpredictable disruption across entire business models. Research suggests that increasing R&D expenditure in periods of technological uncertainty increases stock volatility, because the market is essentially pricing a wide range of outcomes when the winners and losers of a technological transition aren’t yet clear. That is precisely the environment we’re in.

The AI boom has also extended well beyond software into the physical infrastructure layer – the copper, lithium, rare earth minerals, and electricity grid capacity required to actually build and run the data centers that AI depends on. By 2030, global electricity consumption for data centers alone is projected to double. The stocks of companies positioned along this “material layer” are experiencing their own elevated volatility as markets try to price structural supply constraints, permitting timelines, and geopolitical access to critical inputs – none of which are easy to forecast with precision.

In summary, the net result of all these forces operating together is a market where the index-level calm is real but thin, and where, beneath it, individual stocks are navigating a genuinely turbulent set of currents. That turbulence has structural roots. It won’t simply fade because the Fed cuts rates or trade tensions ease.

And that – as I’ll argue in the next chapter – is actually very good news if you’re the right kind of investor.

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The Only Edge Left – And Why Volatility Is Your Friend

Let’s start with a premise that has become increasingly hard to argue against: the traditional sources of investment edge are eroding.

Informational advantage – knowing something the market doesn’t – has been largely commoditized by the speed and breadth of modern information distribution. The moment an earnings report hits, thousands of algorithms have already parsed it.

Analytical advantage still exists, but it too is under pressure. The proliferation of sophisticated quantitative models, AI-assisted financial analysis, and the general raising of the analytical bar across the investment industry means that finding a genuinely differentiated view on a business’s intrinsic value is harder than it has ever been.

This isn’t a controversial observation – it’s something the most thoughtful investors and allocators have been saying with increasing frequency throughout 2025 and into 2026.

What remains – and what I’d argue is increasingly the only durable source of alpha for the individual investor – is behavioral edge.

The ability to think and act differently from the crowd at precisely the moments when doing so is most psychologically difficult. Sitting on your hands while a position drops 20%. Buying more when a stock you own falls 15% on a news event that doesn’t change the long-term thesis. Holding through the kind of volatility that causes most institutional managers to reduce exposure simply because their risk models tell them to.

None of this is intellectually complicated. All of it is extraordinarily hard in practice.

This is where the podcast conversation I referenced at the start of this post becomes directly relevant to how you should be thinking about your own positioning. Shaun Heelan made a point in that podcast that I keep coming back to. Many of the most sophisticated market participants – multi-manager platforms, quant funds, and institutional allocators operating under strict risk mandates – are essentially forced to reduce or exit positions as volatility rises, regardless of whether the underlying business has changed at all. Their risk limits are defined by value-at-risk models with 12 or 24-month lookback periods. As volatility picks up, those models demand position reductions. It’s mechanical, not analytical.

“[…] if I go back to when I was at firms like Bluecrest, DW and Brevin Howard, much like you would see with the multi-manager or podshops today, they have very, very strict risk limits.“ - Shaun Heelan

Heelan described it with a vivid analogy: running a Formula One car with a spike on the steering wheel. Sure, you’ll get around the track, but you’ll be very careful – and there are certain moves you simply cannot make. When volatility spikes, these players don’t ask whether the selloff is rational. They ask whether their VAR budget – VAR stands for Value at Risk and refers to a strategic framework where an organization allocates “risk units” instead of just “dollar amounts” – allows them to stay in the trade. Often it doesn’t. They get knocked out.

For you, as an individual investor without such constraints, without a quarterly redemption cycle, and without an institutional risk committee looking over your shoulder, this is a profound structural advantage – but only if you’ve done the work to have genuine conviction in what you own. The opportunity isn’t just that stocks may get cheap (or overpriced) when volatility spikes. It’s that some palyers in your competitive set are being systematically forced to sell at exactly the moment you’re free to buy. The more volatile the environment, the more competitors get sidelined, and the wider the potential gap between price and value.

There’s a behavioral dynamic at work in high-volatility environments that I think is underappreciated even by experienced investors. Price drives narrative. I’m sure you’ve seen that phrase before. But have you fully comprehended it? I doubt it!

When a stock is down 30% over six months and then falls another 20% on a weak earnings print, the narrative around that business almost universally turns negative. Analysts cut targets and lower “exit multiples.” Financial media produces explainers about structural headwinds. Other investors – seeing the price action and reading the commentary it generates – become incrementally more negative.

The story the market tells about a business is almost always heavily influenced by what its stock has been doing, often with very little reference to whether the underlying business has actually deteriorated in proportion to the move.

This creates a very specific kind of opportunity for the investor who can separate signal from noise. If a stock drops 15% because a recent development (e.g, the CEO stepping down) introduced short-term uncertainty around a business that has a long and durable runway ahead of it, the question worth asking is “has anything actually changed about the next five to ten years?” Most of the time, in a high-volatility environment, the answer is no. The fall reflects positioning, fear, and mechanical selling more than any genuine reassessment of intrinsic value.

Recognizing that – and acting on it (which is the hard part!) – is exactly what behavioral edge looks like in practice.

It takes a particular kind of fortitude, and I won’t pretend otherwise. Buying into sharp drawdowns goes against every instinct that markets cultivate in participants over time. The investor who steps in when a quality business is down 50% from its five-year high – when the narrative is terrible, when consensus is negative, when momentum is firmly against them – is doing something that feels genuinely dangerous in the moment.

But that discomfort is precisely the source of the return. If it were comfortable, the opportunity wouldn’t exist.

Why Capital Allocators Matter More Than Ever

There’s one more dimension to this that I think deserves attention, and it connects to something I’ve written about before in the context of market structure.

In a high-volatility, high-dispersion environment, the quality of the management team running a business – and specifically their capital allocation discipline – matters significantly more than it did in the low-volatility, liquidity-driven era of the 2010s.

Here’s why. If you buy a stock that subsequently falls 30% because of broad market turbulence or sentiment-driven selling, you need a mechanism by which the gap between price and intrinsic value eventually closes. In an ideal world, that mechanism is a management team that recognizes the disconnect and acts on it – aggressively repurchasing shares at a fraction of intrinsic value, for instance, thereby increasing your ownership stake in the business at an attractive price, or returning capital through dividends that reward your patience while you wait for the market to come around.

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A great capital allocator effectively becomes your partner in exploiting the market’s irrationality. They’re doing the arbitrage for you, systematically and at scale, every time Mr. Market has one of his episodes.

This is why, in my view, the elevated volatility environment doesn’t just reward patience in a general sense – it specifically rewards owning businesses run by managers who think like owners, who have a clear-eyed view of intrinsic value, and who have both the mandate and the discipline to act when the market hands them an opportunity. Without that, a cheap stock can stay cheap for a very long time.

Embracing the Turbulence

There's an irony worth acknowledging here. Volatility is, at its core, a neutral phenomenon – it can, however, also be a double-edged sword that simultaneously creates the conditions for outsized gains and the risk of catastrophic losses for existing holders. Depending on your behavioral make-up.

When you look at where most generational wealth has actually been built, it's overwhelmingly in low-volatility assets. Real estate. Private businesses. Assets that don't have a ticker, don't print a daily price, and don't tempt their owners into doing something stupid at the wrong moment. Why is that? Because most “investors” have a terrible temperament. The very absence of a quoted market price is, for most people, a feature rather than a bug – it removes the psychological trigger that causes so much value destruction in public markets.

So while I'd argue that the volatility explosion in single stocks represents a genuine opportunity for the disciplined, long-term oriented investor, it's worth being honest: most people are better served by assets that don't test their behavioral constitution every trading session.

Concluding Thoughts

So where does this leave us? The data is unambiguous: single-stock volatility has structurally shifted higher, the index masks more turbulence than it reveals, and the forces driving this regime – a more micro-driven return environment, 0DTE options mechanics, evaporating HFT liquidity, AI-driven creative destruction – are not transient. They are features of the market we now inhabit.

For the passive investor or the institutionally constrained allocator, much of this is simply noise to be endured.

But despite the US market, on a headline level, seeming incredibly richly valued, for the patient, fundamentally-oriented, global, industry- and size-agnostic stock picker with a genuine long-term time horizon and the psychological constitution to act against the crowd at uncomfortable moments, I’d argue this is arguably the most target-rich environment for active stock selection in a generation. The dispersion between winners and losers is at multi-decade highs. The gap between what the index tells you and what’s actually happening beneath the surface has never been wider.

And your competition – the VAR-constrained, redemption-pressured institutional complex; the Reddit 'investor' operating without a fundamental compass; the Reddit "investor" who lacks a deeper understanding of fundamentals and valuation, and the Buffett-reading average investor who lacks the behavioral wiring to actually lean in when fear takes hold – is structurally and temperamentally prevented from taking full advantage of the mispricings that volatility creates.

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As Shaun Heelan shared on the This Week In Intelligent Investing podcast, the Chinese symbol for crisis contains within it the character for opportunity.

It’s an observation that gets quoted so often it risks becoming a cliché. But in the context of what’s happening in equity markets right now – beneath the surface, away from the index headline – I think it’s the most accurate single-sentence description of the environment available to us.

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]]><![CDATA[Macro & Micro Insights: Interesting Resources! (April 2026 Edition)]]>https://www.compoundwithrene.com/p/macro-and-micro-insights-interesting-041https://www.compoundwithrene.com/p/macro-and-micro-insights-interesting-041Sun, 12 Apr 2026 16:09:42 GMTBelow you find my April reading (and listening) list: a handful of recent finds that offer valuable context on market trends and the frameworks driving them.

Articles:

  • New and updated Bessembinder report: “One Hundred Years in the U.S. Stock Markets“

    “Abstract: This study summarizes investment outcomes for 29,754 common stocks listed on the public U.S. stock markets over the 100-year period from 1926 to 2025, reporting on both compound (buy-and-hold) percentage returns and shareholder wealth enhancement measured in dollars. While the cross-stock mean buy-and-hold return is over 30,000%, the median is -6.9%. Shareholders’ wealth was enhanced by $91 trillion over the century, but long-term investors in nearly 60% of stocks incurred wealth reductions. The degree to which wealth creation is concentrated in a few firms has increased sharply in recent years. Over the 1926 to 2016 period studied in Bessembinder (2018), 89 firms accounted for half of the $43 trillion in net wealth creation. After including outcomes for the most recent nine years, just 46 firms account for half of the $91 trillion in net wealth creation over the full century.“

“As many of you are aware, the portfolio remains focused on companies outside of the more fashionable sectors, emphasising businesses with straightforward models operating in traditional industries such as cement, food, luxury goods, and glass, among others.“

“The pace of change within the software industry has accelerated in recent months, leading to heightened uncertainty and prompting us to reassess moat ratings.

Why it matters: The capabilities of large language models are rapidly advancing and seem primed to cause at least some disruption within the industry, or at worst drive massive dislocation for many software firms.

After an in-depth review of the software and services firms covered throughout Morningstar, we are downgrading moat ratings, reducing fair value estimates, and increasing uncertainty ratings for our immediate coverage of a variety of companies.“

Podcasts & Videos:

  • Interactive Brokers’ founder Thomas Peterffy on the Odd Lots podcast discussing prediction markets.

Some highlights:

“A broker always has to worry about providing leverage. So most brokerage firms, they go bust, because of the leverage they provide, and if you look at the big Wall Street crisis, they all have to do with leverage - always. But eventually, we will have to provide leverage [note: he’s referring to prediction markets]. The question is, how do you structure that? (…) I’m sure there will be some firms that will go bust on leverage in prediction markets, yes.“ […]

“So insider trading has always been an issue. I mean, early on when I started my career, Ilost half my initial capital based on insider trade. So I’m extremely familiar with the damage it can cause. But on the other hand, and in spite of that, I’m in favor of not having any rules against insider trading. I would like all the information out there as soon as it’s available. Because look, as a society, we’re better off knowing as soon as possible anything that is knowable. Right? So why do we have to wait several, first of all, when you face with a merger or acquisition situation where most of the insider trading is happening, right? The secretaries, the lawyers, everybody knows about it. They go home, they tell their wives, their husbands. So it eventually always filters out. So it’s almost impossible to avoid. It’s very, very difficult and cumbersome. Why don’t we just do away with it and let the information come out as soon as possible?“

  • Pabrai Wagons ETF Shareholder Call Replay:

  • Brett Caughran on markets’ becoming LESS efficient (short video clip):

    “I would very much challenge the premise that these tools will lead to more efficiency.”

  • Chris Mayer’s interview on Drew Cohen’s platform:

    • The episode made me think of two things, which I shared on X (attached below). First, the fact that the formula below may be the most important in all of investing:

      a) ROIIC x b) Reinvestment Rate x c) Durability of the combination of a) + b)

    • Secondly, VERY high margins and VERY high growth businesses are inherently risky/riskier (and hence shouldn’t necessarily be your favorite hunting ground) – the “middleground” (i.e. above-average margins and growth – but not excessively high) might be the more attractive hunting ground as those tend to be often more sustainable:

Other:

  • The Software Sentiment Matrix by J.P. Morgan

  • If you own luxury (fashion) stocks, you should read this thread:

I hope these resources provided as much food for thought for you as they did for me. If you found this month’s insights valuable and want to stay ahead of shifting industry dynamics, consider subscribing. You’ll get every new edition, and all the other write-ups I produce, delivered straight to your inbox so you never miss it.

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]]><![CDATA[Is One of the Market’s Most Reliable Warning Signals Failing?]]>https://www.compoundwithrene.com/p/is-one-of-the-markets-most-reliablehttps://www.compoundwithrene.com/p/is-one-of-the-markets-most-reliableWed, 01 Apr 2026 16:27:47 GMTI recently came across a chart that Tiho Brkan shared with me, and it’s one of those charts that makes you stop and stare at in a state of genuine confusion. It is the kind of data visualization that creates an immediate sense of cognitive dissonance.

You know the feeling. It’s that mental grinding sound when two contradictory facts refuse to occupy the same space in your brain. Tiho sent me the charts already 2-3 weeks ago, and it stuck with me, and today I decided to write about it.

The chart in question is the ratio of Leading Economic Indicators (LEI) to Coincident Economic Indicators (CEI). It is a classic tool used by macro analysts to gauge where the economy is headed relative to where it actually is right now.

On a historical scale, the blue line representing this ratio is currently doing something that should, by all accounts, mean we are in the middle of a total economic collapse. The ratio has plummeted to levels not seen since 1982 and the depths of the 2009 Global Financial Crisis.

Those were not just regular market pullbacks. They were the absolute nadir of secular bear markets. They were moments when the financial world felt like it was ending.

The current environment does feel like this at all. And when you start looking at cold, hard data, that hunch is confirmed. As I write this, the S&P 500 is not in a state of collapse. Far from it. Sure, we’re experiencing some serious geopolitical turmoil, but as Buffett just said in yesterday’s CNBC interview, “this is really nothing.”

We have just witnessed a seventeen-year period of equity performance (with a few minor bumps in 2011, 2018, 2020, 2022, and 2025) that ranks among the most lucrative in history. Consider this slightly dated chart below:

Prices are (still) high. Multiples are stretched. The contrast is jarring.

Shiller PE Ratio

If we were to follow the “map” provided by this indicator (the Leading to Coincident ratio), we should be buying with both hands because we are at or close to a generational bottom. The scouts – the forward-looking data points like manufacturing orders, nonfarm payrolls (employment), and consumer sentiment – have already retreated to the bunkers. They are shouting that the bridge is out. Meanwhile, the main army – the actual earnings and productivity of the corporate world – is marching forward as if the sun will never set. This isn’t just a minor divergence.

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It forces a difficult question. Is the economy actually a disaster that the stock market is simply refusing to acknowledge, or is the barometer itself broken?

Big shoutout again to Tiho here again who shared most of the ideas expressed in the post and I’m merely digesting his input.

The Historical Perspective

To understand why the current chart feels so urgent, we have to look back at the moments when this ratio hit the floor in previous decades. It is a rare occurrence. When you look at a forty-year timeframe, you quickly realize that the Leading to Coincident ratio does not just “dip” for no reason. It collapses when the forward-looking indicators – the scouts – see a canyon that the current economy – the main army – is about to march right into.

Historically, the most famous instances of this were in August 1982 and March 2009. In both cases, the ratio reached levels of absolute exhaustion.

In 1982, the American economy was being choked by Volcker’s interest rate medicine to kill inflation. The manufacturing sector was in shambles, and the leading indicators were screaming for mercy. But as the ratio bottomed, the S&P 500 embarked on a multi-decade expansion that changed the face of global finance. It was the “generational buy” of a lifetime, with the S&P doubling over the next few years.

The story was nearly identical in 2009. The global financial system was literally being held together by duct tape and government liquidity. The ratio of leading to coincident indicators fell through the floor as orders vanished and credit froze. Yet, at that moment of maximum economic pain, the market stopped falling. It was a perfect signal.

The indicators were telling us that the “badness” had been “priced in” and that any incremental change from that point forward would likely be positive.

When you look at those two dates and then look at the metric of the chart we discuss, you see an indicator that has worked with almost surgical precision in these two instances. Every time the scouts were this pessimistic relative to the current reality, it was time to back up the truck.

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But we cannot just look at the secular bottoms. We have to examine the cyclical “growth scares” like 1990, 1998, 2011, and 2018, for instance. In 1990, the invasion of Kuwait and the subsequent spike in oil prices sent leading indicators into a tailspin, while employment (a coincident indicator) remained relatively sturdy for a while. The ratio dipped, signaled a recession, but the market bottomed a little earlier.

In 2011 and 2018, the ratio flinched, but it did not reach anywhere close to a “generational low” territory as we see today. These were periods where the market fell 20% or so, the ratio dipped, but the “army” of the economy never actually fell into a trench to stick with our metaphor.

Then there is the more recent history: 2020, 2022, and the “tariff bottom” of April 2025. In 2020, the lockdowns created a total disconnect because the “leading” data stopped existing overnight. But that was an exogenous shock. 2022 was more traditional. Inflation was rampant, and the Fed was aggressive. The ratio declined steadily as the market sold off. And just last year, in April 2025, when the first waves of the new administration’s tariff policies hit the wires, we saw another sharp contraction in the LEI components. Overall, we’re seeing more of a structural long-term decline in the indicator than major “dips” though.

This is where the history starts to get uncomfortable. If the ratio is at 1982 and 2009 levels, the “historical precedent” says the S&P 500 should be fifty percent lower than it is right now.

It says we could (or should?) be seeing mass layoffs, bankruptcies, and a total lack of hope. Instead, we are seeing still decent GDP growth (in the US at least) …

… and a stock market that looks like it is on a one-way trip to the moon. Sure, we’re down a bit this year, but if you look at the drawdown, Buffett’s right, this really is nothing!

So as the indicator is signaling the “end of the world” (we might be exaggerating a little – I’m the first one to admit that), but the market is still in the midst of the longest party the US stock market has ever seen, you have to wonder if the ghosts of 1982 and 2009 are trying to tell us something that we are simply too “sophisticated” to hear, or if the very nature of economic indicators has shifted so far that we are looking at a compass that no longer has any signal value?

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The Signal Isn’t Showing Up Anywhere Else

If we were truly standing at a generational bottom like 1982 or 2009, the air would be thick with the smell of capitulation. Panic is never a subtle affair. Especially to an experienced investor, but even for beginners, it is a rather loud, messy, and undeniable event that leaves its fingerprints all over the quantitative and qualitative indicators you can use to read the market’s mind.

When I look at the sentiment “cheat sheet” I keep on my desktop, I see a checklist of what a bottom should look like. In a real crisis, the Quantitative Sentiment Indicators – the data-driven metrics with high signal-to-noise ratios – should be screaming.

We should see the Insider Buying Ratio spiking as executives grab their own shares at fire-sale prices. Not happening. When the people who actually run these companies aren’t putting their own money to work at these levels, it suggests they don’t see the current price action as a “once-in-a-career” bargain.

We should see credit spreads blowing out as the bond market prices in a wave of corporate defaults. Not happening. The bond market is usually the first to sniff out a real crisis. In a true economic disaster, the “risk-free” rate and the yield on corporate junk bonds tend to diverge violently as lenders demand a massive premium for the risk of a total wipeout. But right now, credit spreads remain remarkably tight. Lenders are effectively saying they don’t see any systemic threat on the horizon.

When I look at the professional class of market participants, the lack of fear is palpable. Advisor bears are at levels that are historically low. Today, the mood is one of quiet confidence, or one reflecting fear of missing out.

Even consumer pessimism – which has been elevated due to the cost of living – hasn’t translated into the kind of defensive, survivalist behavior that defines an economic collapse. People are still spending, still traveling, and still fully invested.

Retail cash levels tell a similar story. They are low. If we were truly at a generational bottom, we would see a mountain of “dry powder” sitting on the sidelines, too terrified to enter the fray. Instead, everyone is already in the pool.

Then there is the internal mechanics of the price action itself. The VIX – that famous “fear gauge” – is perhaps the most obvious missing piece of the puzzle. It spiked a little in light of the geopolitical events, but we haven’t seen any major spikes above 40, the traditional threshold for true market capitulation.

A real bottom needs a “washout” where the last of the weak hands are forced to sell. Without that spike in volatility, it is hard to argue that we have seen the kind of maximum pessimism required to fuel a new secular bull run.

Furthermore, stock market breadth remains decent. It isn’t just a handful of tech giants holding up the index; a decently broad range of stocks are still participating in the rally.

The cognitive dissonance is complete. The leading indicators say we are at a multi-decade-year bottom, but the sentiment indicators say we haven’t even begun to feel the pain.

It’s a dangerous divergence. Truly.

What If the Indicator Is Right and the Index Wrong?

First of all, it is a fundamental truth of the markets that no indicator works all the time. If there were a single, infallible crystal ball, the game of investing would be way too easy.

But when a signal as historically significant as the LEI/CEI ratio goes this deep into the red and all the other indicators signal (almost) the opposite), you have to think about possible explanations. Maybe the indicator is actually right signaling that the economy is struggling, but it is just being masked by a very specific, very loud segment of the market? Guess which part of the economy I’m thinking of right now …

Keep in mind that the stock market is not the economy, and vice versa.

I believe we could be witnessing a “two-track” reality where the traditional, non-tech, old-school, however-you-wanna-call-it economy has been struggling since COVID basically.

What if, if you look beneath the surface of the S&P 500, the “rest of the economy” has been struggling for years?

What if, the market is currently being propelled by an AI ecosystem, a “circular economy” that is almost entirely decoupled from the day-to-day reality of the American consumer?

We see a world where one AI firm spends billions with another, who then spends it with a third, mostly on semiconductors and server capacity. In the meantime, many are announcing major job cuts.

It is a closed loop. It creates immense “growth” on paper and drives the Mag-7 to new heights, but it generates very little immediate economic value for the other 90% of the country. Terry Smith just shared the shared below at Fundsmith’s 2026 AGM:

Read my summary of the AGM here:

You might find an interesting historical parallel in the year 2000. If you only looked at the Nasdaq or the S&P 500 in the first quarter of 2000, you saw a market that refused to die. But something fascinating was happening under the hood.

During the dot-com bubble, a massive “rotation” occurred where investors dumped “old-economy” value stocks to chase internet and tech companies. This caused many traditional businesses to hit their bear market lows in early 2000 – the exact same month the NASDAQ peaked, while the broader market (S&P 500 and NASDAQ) didn’t find its final bottom until October 2002.

While the tech-heavy indices were making their final, vertical blow-off tops, the “Old Economy” stocks were actually bottoming.

So maybe the current Leading Economic Indicators (LEI) to Coincident Economic Indicators (CEI) chart might be calling it exactly right, provided you know which “market” you are looking at?

This would, in fact, align well with comments made by some well-known investors, highlighting that we are actually starting to see very attractive opportunities already in individual stocks or certain sectors.

If we are at a “generational bottom” according to the LEI, it might not mean the S&P 500 is about to double. It might mean that the long-suffering value stocks, the industrials, and the consumer staples have finally seen the worst of their fundamental decline.

We have to be careful about dismissing a 40-year signal just because NVIDIA’s stock chart looks like a vertical line. But then again, we also have to be careful not to place too much emphasis on a single indicator.

Curious to hear your thoughts!

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]]><![CDATA[South Sea Bubble 2.0? Notes from the 2026 Fundsmith AGM]]>https://www.compoundwithrene.com/p/south-sea-bubble-20-notes-from-thehttps://www.compoundwithrene.com/p/south-sea-bubble-20-notes-from-theSun, 29 Mar 2026 10:14:21 GMTTo me, it seems as if Terry Smith is currently the favorite punching bag of the value investing community. It is a role he seems to accept with a mixture of dry wit and a stubborn refusal to pivot. After a significant period of underperformance, his “Quality” strategy is under heavy fire from the usual suspects in the financial press.

I’ve noticed a curious phenomenon that repeats every cycle: price drives narrative. When a fund is soaring, every investor is a cheerleader, but when it stalls, the silence is deafening. My X feed used to be littered with Fundsmith content during the peak of the quality bubble a few years ago, yet today I see … nothing.

It is as if the investing community has collective amnesia regarding the principles that made Smith legendary. But if you actually take the time to watch the 2026 Annual General Meeting, you won’t find a man in retreat. Instead, you’ll encounter one of the most rational voices in a market that feels increasingly unhinged.

While much of the world is currently drunk on AI hype, Terry is soberly dissecting the data and looking back at 1929 and 2000 for clues on what happens next.

“So why study history? We study history not to know the future but to widen our horizons, to understand that our present situation is neither natural nor inevitable, and that we consequently have many more possibilities before us than we imagine.” - Yuval Noah Harari

He didn’t mince words during the opening of the meeting. “The one [word] I would use to describe it for the last 12 months is poor,” he admitted, “if we keep going like that we will all become poor.”

It was a refreshingly honest take in an industry where managers usually bury bad numbers under a mountain of jargon. He isn’t making excuses, even as the “commentariat” accuses him of precisely that.

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He’s providing context. He emphasized that “what will determine the outcome... is not what’s happened in the last 12 months but what we do in the next 12 years.” I find that perspective invaluable. He has clearly internalized the timeless principles that work over decades, even if they hit rough patches in between.

In this post, I’ll explore the five most thought–provoking insights from this year’s session and why Terry is willing to look like a “loser” today to avoid the carnage tomorrow.

Adapted chart based on Fundsmith calculations

#1 – Why Global Giants are Suddenly Trading Like Penny Stocks

Something about the market’s current structure feels fundamentally off to Smith. It should feel off to you, too. We have reached a point where multi–billion dollar daily moves in single stocks – once once–in–a–generation anomalies – have become routine. It certainly feels this way to me. And the data Smith presented was confirming this “hunch”: More recently, Adyen fell 22% in a single session, Oracle surged 36%, Estée Lauder dropped 19%, and AMD saw a 17% slide.

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These aren’t speculative penny stocks; they are some of the largest enterprises on the planet. As Terry noted with his characteristic bluntness, “Something’s not right when a multi–billion dollar company’s share price can move 36% in a day”.

This isn’t just volatility in the traditional sense. It is a structural failure driven by what academics call the “Inelastic Markets Hypothesis.” Because more than half of all equity assets are now held in passive index funds, there may simply not enough active liquidity to settle the price fairly. Index funds are mandated to buy or sell regardless of valuation, which creates a massive imbalance. Smith pointed to research by Gabaix and Koijen suggesting a multiplier effect of roughly 5x – meaning every dollar of active trading can impact a company’s market cap by five dollars.

“You know, if I go and sell Microsoft this evening and put it all into PepsiCo tomorrow, it doesn’t aQect the valuation of either Microsoft or PepsiCo. It’s unaffected. However, these people have looked at the data and discovered that the actual market impact for a dollar going from one security to another in recent years has been a multiplier of somewhere between three and eight times. So when you took your money out of us and the dollar went into Nvidia, the effect on the Nvidia price was somewhere between $3 and $8 on average $5 between those. That’s a startling number.

And the reason for it is, as I tried to explain in the in the title, the inelastic markets hypothesis, because the well, if you take the dollar out of their out of our fund and put it in the passive fund, it doesn’t make any difference. works providing there are people in a in a position to take the opposite view. So if you think that something is vastly overvalued uh it can by being driven by the momentum of index funds that will be bought down to earth by people who are running active funds who will sell it or even short it in the case of hedge funds. What they’re pointing out is there are increasingly fewer of those people because of the rise of index funds. There are increasingly fewer active funds to do that. And even within those active funds, there are an awful lot of people who’ve become index closet index trackers. They’re running an active fund, but they stay pretty damn close to the index for for survival reasons.”

I have read that markets remain theoretically efficient as long as roughly 10–15% of assets are still actively managed (I cannot recall the exact figure), since passive funds are, by nature, price agnostic. But that theory feels increasingly detached from the reality I’m seeing on the ground. It isn’t just the sheer volume of indexing that is the problem; it is the changing nature of the remaining active capital. The rise of multi–strategy “pod shops” and high–frequency players has likely shifted the remaining active liquidity from long–term price discovery to short–term momentum chasing. We discussed these changes in market structure in the series below:

Julian Robins touched on this when he described the current environment as a “feedback loop on steroids.” When everyone is crowded into the same few names, and even the “active” players are often just front–running index flows or managing tight risk limits in a pod, the traditional stabilizer is gone. As Terry put it, there are “increasingly fewer of those people” left in a position to take the opposite view and sell an overvalued stock. We are witnessing a new, much more dangerous arena where price discovery has been sacrificed at the altar of indexing.

#2 – The Seduction of Performance Chasing

I have always found it fascinating how investors treat fund managers like football coaches – as soon as you have a bad season, the fans want you sacked. This “hire the winners and fire the losers” mentality is the most common way to destroy wealth in the long run.

During the meeting, Terry shared a chart that should probably be pinned to the monitor of every retail investor in the country. It showed that the top–performing quintile of funds over a three–year period often becomes the worst–performing group in the following three years.

Mean reversion is not just a theory; it is a brutal, impartial law of finance. As Terry dryly noted, “It’s a very natural human reaction to want to move to what’s working now, but in investment, that’s usually a recipe for disaster.”

You see this happen every time a specific style–be it Value, Growth, or Quality–goes out of favor for a few quarters. The pressure to pivot is immense.

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It isn’t just about the numbers; it is about the psychological toll of underperforming when everyone else seems to be getting rich on something you don’t own. You start to doubt the process.

“Question: ‘Is the most diQicult part of underperforming a benchmark the psychological pressure of doing something different to catch up?’

Answer: […] Yes, it is a simple answer… look, […] it’s better in career terms to fail conventionally than is to succeed unconventionally. Because even if you’re successful, I mean, you saw during the 10 years when we were doing very well in the lead up to this, the amount of criticism that we got then for doing something different … and now it’s just easier to criticize because you’re doing something different and it doesn’t work. But yeah, the psychological pressure is considerable.“

You wonder if the “Quality” factor has finally been “solved” by the market or rendered obsolete by new technology. But as Smith argued during the Q&A, the moment you pivot to chase the current fad is the exact moment you lock in your relative underperformance.

Julian Robins reinforced this, pointing out that “most investors spend their lives running toward where the puck was, rather than where it is going.”

Smith remains incredibly disciplined here. He is willing to look like a “loser” today because he knows that today’s laggards are often tomorrow’s leaders, provided the underlying business quality remains intact. He knows that “if we keep going like that we will all become poor,” but he also knows that the cure is not to chase the latest high–flyer, but to wait for the inevitable return to the mean.

One of the most telling moments was when Terry addressed the “commentariat” and the critics who claim he has lost his touch. He didn’t offer a complicated defense. He simply pointed back to the historical data. He mentioned that “what will determine the outcome... is not what’s happened in the last 12 months but what we do in the next 12 years.” It takes a certain kind of professional arrogance – the good kind – to stand in front of a room of disappointed shareholders and tell them that the best thing they can do is absolutely nothing.

You have to admire the conviction, even if it makes for a boring headline. Most people can’t handle the boredom of a winning strategy when it hits a rough patch. Terry can.

#3 – The Great Infrastructure Gamble

The sheer scale of the current AI infrastructure build–out is difficult to comprehend until you look at the capital expenditure (Capex) relative to operating cash flow. We are witnessing a high–stakes gamble where the world’s largest companies are betting their entire cash piles – and then some – on a future that has yet to materialize in the bottom line.

I was particularly struck by the “Capex vs. Cash from Operations” ratio Terry presented. While Microsoft and Meta are reinvesting roughly 90% of their operating cash back into the business, others have moved into the realm of the truly extraordinary. Oracle is spending nearly double its operating cash (198%), while the private cloud provider CoreWeave is at a staggering 567%. This isn’t just growth; it is an all–in bet on an AI arms race that requires massive external financing to sustain.

This level of spending has real–world consequences for fund construction, most notably the triming of Microsoft from Fundsmith’s Top 10 holdings. This was a move that raised many eyebrows in the room. Terry was candid about the rationale, stating, “Microsoft has moved out of our top 10 for the first time in many years... It’s really to do with the amount of money they’re spending.” He is clearly worried that the relentless pursuit of AI dominance is beginning to erode the very thing that made these companies “Quality” in the first place: their superior Return on Capital Employed (ROCE). When you are spending 90% of your cash just to stay in the race, your ability to generate excess returns for shareholders is naturally diminished.

“… we did so because of concerns about what we’re seeing sitting there in terms of the spending and we’re seeing $600 billion […] if we’re looking for a 30% return on capital say um you know those companies are going to need to generate $180 billion of new cash flow not from the things that they’ve already got not by cannibalizing what they already have to justify that in terms of return on capital … that’s quite a lot actually and at the moment we’re not really paying for AI mostly … we’re users of it … and we are paying but not not to anything like a degree that could possibly justify that and we are probably unusual in paying because we’re a business most consumers are probably not paying at all. I mean, as a consumer, I don’t pay for using it. Um, and um, so that’s tricky.

And I know there have been models historically where people have given you things and got you hooked on them and then later on told you what the price is uh, and started to make lots of money from it, but they’re relatively unusual and they do usually involve the person who manages to accomplish that achieving a dominant position in the industry.“

The Oracle situation is even more fascinating. It seems driven by a fear of past failures as much as a hope for future gains. As Terry dryly noted during the Q&A, “Larry Ellison... is not going to miss the cloud twice.” It is a classic example of corporate FOMO (Fear of Missing Out) played out on a multi–billion dollar stage.

During the discussion, Julian Robins pointed out that we are essentially in a gold rush where the only people currently making guaranteed money are those selling the “picks and shovels” – namely Nvidia. For the rest, the path to profitability remains obscured by a mountain of expensive GPUs.

I find it telling that Smith is willing to walk away from a long–term winner like Microsoft because the math of the AI cycle no longer squares with his requirement for capital efficiency.

‘“We’re not AI deniers.” - Terry Smith

It is a lonely position to take, but one rooted firmly in a timeless investing principle.

Before we dive back in, a quick note…

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#4 – Echoes of the South Sea Bubble?

If the Capex figures I just mentioned represent the “hope” part of the current market equation, the valuations represent the hallucination.

We have moved beyond simple optimism into a territory that history suggests is rarely sustainable. The combined market cap of the top five AI hyperscalers has reached a dizzying $11.2 trillion.

To put that in perspective, Terry shared a slide that adjusted historical speculative manias for inflation, and the results were chilling. Even the legendary South Sea Bubble of 1720 and the Mississippi Scheme – the gold standards for financial insanity – look relatively modest by comparison. We aren’t just looking at a sector–specific rally; we are looking at a historic concentration of capital that rivals the absolute peak of the 2000 Dot–com bubble.

As Julian Robins noted during the session, “We have reached a level of concentration where the top few stocks are effectively the market, and that has never ended well for the latecomers.”

Terry’s skepticism isn’t born of a dislike for technology, but of a deep respect for the lessons of historical cycles. He highlighted that while the “new era” proponents claim this time is different because of the massive cash flows, the valuation multiples are reaching levels where the math simply stops working.

The math shared above is a sobering reality check. You have to ask yourself if you’re comfortable holding the bag when the narrative finally catches up to the numbers. I suspect many investors today are so blinded by the potential of AI that they’ve forgotten that even the best companies can be terrible investments if you pay the wrong price.

Smith seems perfectly content to sit on the sidelines of this particular mania, even if it means underperforming while the bubble continues to inflate. As he put it, “I’m quite happy to look like an idiot for a while if it means I don’t go over the cliff with everyone else”.

#5 – Paying 1999 Prices?

It is one thing to talk about trillion–dollar market caps in the abstract, but it’s quite another to see what investors are actually paying for every dollar of revenue. Price–to–sales is often the last metric people look at during a bull market because it is the hardest to justify with clever accounting or “adjusted” earnings. Or if you go one step further, you’d attempt to “value” businesses based on the TAM potential or other fancy metrics.

During the meeting, Smith pulled up another interesting chart that should make anyone with a memory of the year 2000 feel a bit nauseous. The Price/Sales ratio for the largest 50 stocks in the S&P 500 has effectively completed a round trip back to the peak of the Dot–com bubble. We are paying as much for a dollar of sales today as we were when the market was at its most “irrational” point in modern history. Sure, a few things have changed, businesses are even leaner and more profitable, and they often operate globally, but many investors have expressed concerns about “peak margins” and the trend of de-globalization should be obvious to anyone by now.

Hence, this is a historic divergence. The gap between the giants and the rest of the market is no longer a small crack; it is a canyon.

As Terry noted during the presentation,

“We are seeing a return to the sort of valuation levels for the largest companies that we last saw in 1999 and 2000.”

It was a simple statement delivered without hyperbole, which somehow made it more alarming. I’ve noticed that when people talk about the “Magnificent Seven” or the AI leaders, they always point to the cash flows – as we discussed earlier – but they ignore the price of admission.

Julian Robins was even more pointed during the Q&A when discussing the concentration risk. He remarked that “the market is currently paying a premium for size that defies historical precedent.” We are in a regime where being big is seen as a guarantee of safety, leading to a crowded trade where everyone is paying record prices for the same 50 names.

I think it is worth pausing on that. If you are buying the index today, you aren’t just buying “the market.” You are buying a highly concentrated bet on 50 companies at valuations that have historically preceded massive drawdowns.

It’s a game of musical chairs played at 1999 prices. Smith’s point is clear: the fundamentals might be better than they were in 2000, but the price you are paying for those fundamentals has reached a point of extreme fragility.

“History doesn’t repeat, but it rhymes,” he reminded the audience, and right now, the rhythm sounds a lot like a bubble about to burst.

Conclusion

I’ve sat through enough of these meetings to know when a manager is bluffing. Terry Smith isn’t bluffing. He is genuinely concerned about the mathematical reality of the current market. It is easy to dismiss him as a manager stuck in a bygone era, but the historical parallels he shared are simply too obvious, too fitting to ignore. We are living through a period of extreme concentration and valuation expansion that mirrors the most dangerous moments in financial history – from the South Sea Bubble to the peak of the Dot–com mania. I find his willingness to look like a “loser” today in order to avoid being a casualty tomorrow to be his greatest strength. It’s a lonely path, and it requires an iron stomach to watch the indices climb while you sit on the sidelines. But as 2000 taught us, survival is the ultimate form of alpha. I’m content to follow the lead of a man who would rather be wrong for a year than wrong for a decade.

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]]><![CDATA[Macro & Micro Insights: Interesting Resources! (March 2026 Edition)]]>https://www.compoundwithrene.com/p/macro-and-micro-insights-interestinghttps://www.compoundwithrene.com/p/macro-and-micro-insights-interestingMon, 09 Mar 2026 15:27:12 GMTThe following list includes resources (articles, letters, podcasts, videos) I have recently come across and that I believe help better understand (current) market or industry dynamics or general investment frameworks:

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]]><![CDATA[The "Citrini Bottom" – The Great Software Re-Rating of 2030]]>https://www.compoundwithrene.com/p/the-citrini-bottom-the-great-softwarehttps://www.compoundwithrene.com/p/the-citrini-bottom-the-great-softwareFri, 06 Mar 2026 15:55:20 GMTIn February 2026, a research piece by Citrini Research – arguably the most viral Subtack blog post ever released (the initial post on X alon was seen by 28 million people) – sent shockwaves through the investment community, with its bleak forecast for the software sector.

Titled “The 2028 Global Intelligence Crisis,” it painted a chilling picture: as AI progressed rapidly, human labor was being replaced at an unprecedented rate, leading to a collapse in consumer demand, a slowdown in economic activity, and a series of cascading market crashes. Software companies, once the darlings of Silicon Valley, were painted as primary victims in the race to automation.

The report’s key theme seemed simple enough: AI will eliminate jobs and disrupt entire industries, and the software sector – especially SaaS – would be no exception. By 2028, the thinking went, the market would be so flooded with automation tools and agent-based applications that even the biggest players would find themselves marginalized, struggling to compete with cheaper, faster, AI-powered alternatives.

Even the broader market reacted violently to such predictions, with some software stocks – of which fwiw, some were already down 40-60% – continuing their drawdown on the subsequent Monday trading sessions. Many analysts continued to warn that the best days for software were behind us.

But what if the Citrini Research report came out just as the software sector was hitting rock bottom? What if 2026’s bearish sentiment marked the precise moment before a new era of growth for the software industry, fueled by AI?

A New Era of Opportunity

It’s relatively easy to go viral with a doomsday scenario prediction.

However, it’s also “easy” to dismiss the idea that the worst may be over when looking at the present market – after all, software stocks have taken a hit, and the bears seem to have the upper hand.

3-year performance of my 33-stock equal-weight software basket

However, beneath the surface, something important is happening. What if a revolution of the software space is already taking place, driven by AI, that could fundamentally reshape how software companies earn money, how much money they earn, how they operate, deliver value, and scale?

So in the spirit of Citrini Research, this post lays out a possible bullish 2030 scenario – two years beyond the market Citrini predicted – centered on individual software stocks rather than broad macro themes. It’s a thought experiment (!) designed to provoke, not to predict with certainty. The aim is to illuminate a bull-case path where AI accelerates the value of a select few established software incumbents, expands their total addressable markets, and deepens their competitive moats, even as the broader market remains wary. This piece deliberately foregrounds upside opportunities – drawn from real companies and plausible catalysts – while acknowledging that bearish risks still exist. By focusing on 2030 and beyond, we invite readers to test the thesis against evolving AI capabilities, customer adoption, and capital dynamics, and to consider how a handful of stocks could lead the next leg of software’s growth.

THE POST-INPUT ERA: GLOBAL GDP SURGES 8% AS 'VIRTUAL SWARMS' REPLACE TRADITIONAL SaaS; S&P 500 MARKET CAP CROSSES $120T | Bloomberg, January 2030

Following that line of thinking, by 2030, a handful of software companies will not only survive this AI-driven disruption – they will thrive, emerging as giants in their industries. These companies will be the net beneficiaries of AI, leveraging cutting-edge tools to accelerate growth, capture new markets, and deliver unmatched value to their customers. The same forces that seemed to threaten their very existence will, in many cases, be the catalysts that propel them to new heights.

SOFTWARE GIANTS LIKE SAP AND NETFLIX CAPTURE $100 BILLION IN ADDITIONAL NEW REVENUE AS AI INTEGRATION ACCELERATES ACROSS INDUSTRIES | Reuters, December 2030

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The Roadmap of This Post: Software’s Second Act

In this post, we deconstruct the bull case for the 2030 software landscape through five structural pillars:

  • The Labor-to-Software Conversion (TAM Expansion): How software is moving from a “tool” to a “result,” capturing the trillions once spent on human salaries.

  • The “80/20” Rule: Why AI code generation only solves 20% of the problem, while incumbents like Atlassian and SAP leverage AI to automate the 80% of “hamster wheel” maintenance that keeps the world running.

  • The VMS Anti-Fragility Play: Why the “boring” Vertical Market Software (VMS) model – typified by Constellation Software – is the ultimate AI hedge, and in fact THE most obvious beneficiary.

  • Offense vs. Defense: Distinguishing between Shopify’s “AI-for-Growth” (Front-office/Sales) and Veeva’s “AI-for-Compliance” (Back-office/R&D). One captures the upside of consumer spending; the other builds a fortress around the trillion-dollar life sciences economy.

  • The Creative Feedback Loop: Why Netflix and Nintendo aren’t being replaced by AI video/games, but are using those exact tools to collapse production costs and expand EBIT margins to historic highs.

TAM Expansion through “Labor-to-Software” Conversion

In 2026, the co-founder of Monday.com, Eran Zinman, made the following provocative statement during a podcast:

“The TAM of software, how much companies are going to spend on software, is going to be 100x.”

What if he was right?

The aggressive bull case for software in the AI era centers on a fundamental shift: software is no longer just a tool for humans to use – it’s becoming a replacement for human labor altogether. This transition offers a massive expansion of the Total Addressable Market (TAM) for software companies, giving them a much larger slice of the corporate budget that was once dedicated to human salaries. The potential here is enormous.

THE MONDAY MIRACLE: MONDAY.COM SURGES 2,500% IN FOUR-YEAR HYPER-RECOVERY AS AI NATIVE GROWTH REACCELERATION TRIGGERS MASSIVE MULTIPLE RERATING FROM 2.1X TO 15X P/S MEAN | Seeking Alpha, May 2030

For decades, software has been sold as a “toolkit” for human artisans. Companies would pay $50 to $100 per month for a “seat” – a license for each employee who would use the software. Whether it was an Adobe creative license, a CRM tool for sales reps, or customer support software for service teams, these products were designed to augment human work.

The Opportunity of Labor Arbitrage

But as we move into the age of AI, something profound is happening: software companies are no longer selling tools. They’re selling the results of work – the work of employees, now done by autonomous digital agents.

THE DEATH OF THE CRM SEAT? SALESFORCE AI AGENTS HIT $35BN MILESTONE, MARGINALIZING TRADITIONAL SALES CLOUD AS AUTONOMOUS OUTREACH TAKES OVER | Forbes, December 2030

In this new world, AI is not just augmenting labor; it’s replacing it. Instead of selling a CRM license to a human sales rep, software companies can now offer an AI agent that performs the entire sales process – from outreach to follow-up to closing deals.

Imagine the possibilities: a company that once paid $300,000 a year to a team of five salespeople now pays $50,000 for an AI agent that works 24/7, closing deals and generating leads on autopilot.

The human role? Gone. Replaced by an infinitely scalable digital workforce.

The catalyst for this shift – overlooked by markets in 2025/26 – already happened in 2024 with Salesforce’s launch of Agentforce. Moving away from the 'seat-based' stagnation of the 2010s, Salesforce pivoted to a consumption-based model, charging roughly $2 per successful autonomous action. By the dawn of the next decade, they aren't just selling a CRM for humans; they are selling 'digital employees' that handle the entire 'Vibe Building' and lead-nurturing process, effectively decoupling Salesforce’s revenue from their customers' human headcount.

The numbers are staggering. While customers are still getting a 5x return on investment (spending $50k on an AI agent to save $250k in labor), the software company’s revenue per customer jumps by 50x or more. This is a game-changer. What was once a per-seat pricing model that only scratched the surface of the corporate budget is now a much more lucrative, value-based pricing structure, where the software company can capture a significant portion of the labor savings they’ve enabled.

Salesforce’s stock drawdown in early 2026

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From “Seats” to “Outcomes” and “Tokens”

But this shift in how software companies price their products is just the beginning. The industry is undergoing a reckoning – an inevitable move away from the traditional “per-seat” model. Instead, the future of software pricing is increasingly based on outcomes. Companies will no longer pay for seats; they will pay for results – for the value created by AI agents.

Imagine a customer support platform that charges $1 for each customer support ticket resolved, or a sales automation tool that charges based on how many leads are converted into customers. This new model will align the vendor’s revenue with the value they’re delivering, creating a system where pricing is more reflective of actual outcomes rather than the number of users.

Some experts are even speculating that a new form of currency will emerge: tokens. These tokens will represent the computational value created by AI agents. This shift could redefine how business transactions are structured, turning the entire industry on its head. As the software industry embraces these new models, we’ll see the rise of consumption-based pricing, where every action, every outcome, is measured and monetized.

Take Adobe! While many viewed Adobe as an obvious “AI loser” in 2026, the company’s massive fall from its prior peak – down 66% and trading at a mere 11x earnings, like a legacy industrial – was premature.

Adobe’s stock drawdown in early 2026

As image and video-generating AI technologies rapidly improved, competitors like Figma emerged with their own AI-driven platforms, challenging Adobe’s dominance. But Figma didn’t defeat Adobe by writing code faster; they found a gap in Adobe’s existing workflow. Figma rethought the collaboration process itself, offering a more intuitive, modernized approach to design that Adobe struggled to adapt to within its legacy framework. Adobe’s inability to close that gap – without cannibalizing its own business model – seemed to signal its demise.

However, Adobe’s pivot was a textbook example of a company leveraging its strengths to adapt to AI’s evolving role. Instead of fighting the collaboration trend, Adobe doubled down on its deep-rooted value in the creative professional ecosystem. The model-agnostic approach of Adobe allowed it to leverage AI without massive investment requirements. By integrating various AI-driven tools into its suite – such as Adobe Firefly for generative image editing and AI-powered design assistants – Adobe is shifting away from its traditional “seat-based” model toward a more value-oriented, outcome-based pricing structure.

By allowing enterprises to train models on their own brand IP, Adobe transformed its business into a Creative Operating System for the entire enterprise, enabling a single marketing manager to orchestrate the creation of 10,000 brand-consistent assets in the time it previously took to design one.

Now it’s no longer about how many licenses you buy, but the AI-generated value Adobe can help you unlock. The result? Adobe’s tools become even more indispensable to professional users who rely on its software to streamline creative workflows and produce higher-quality work at scale. And the amount of content created exploded between 2026 and 2030.

ADOBE LEVERAGES AI-ENABLED CREATIVE TOOLS TO REINVENT DESIGN COLLABORATION, DRIVING $7 BILLION IN NEW REVENUE | Adobe Investor Relations, March 2029

This shift is positioning Adobe as a resilient contender in the AI-powered creative economy, with more room to grow as new AI capabilities continue to enhance its existing platform.

Transforming Customer Support with AI

Next, take Freshworks, a publicly traded company that has already embraced AI’s transformative potential in the customer support space. Historically, Freshworks provided a suite of tools for businesses to manage customer interactions. But as AI technology advanced, Freshworks recognized an opportunity to move beyond offering tools for human employees and instead provide AI-driven customer service agents capable of autonomously handling a wide range of customer queries.

This vision materialized in Freshworks’ AI-powered solutions, where intelligent agents are now able to manage customer tickets, offer self-service options, and even predict and resolve issues before they escalate, all with little to no human intervention. The integration of AI into its customer support platform has essentially transformed it from a toolkit into a solution that delivers outcomes – intelligent support that drives efficiency and reduces costs for businesses.

Since the integration of AI-powered capabilities, Freshworks has significantly expanded its customer base, with the company’s annual recurring revenue (ARR) rising substantially as a result of increased demand for its AI-driven support features. The integration of these intelligent systems allows businesses to provide faster, more scalable customer service, leading to an enhanced user experience at a fraction of the cost.

FRESHWORKS’ STOCK SOARS; COMPANY INCREASES CUSTOMER SUPPORT EFFICIENCY BY 50% WITH AI-POWERED AGENTS, DRIVING $900 MILLION IN ARR | Financial Times, November 2029

The “80/20” Rule of Software Engineering

In the world of software engineering, there’s a fundamental truth that few seem to recognize: while AI has made remarkable strides in code generation, it’s only a fraction of the total effort required to develop, deploy, and maintain software. The lion’s share of the work – the 80% that truly drives the cost and complexity of software engineering – lies in the continuous, often invisible, tasks that keep software alive and functioning. This is where the real opportunity for incumbents lies.

AI has revolutionized the code generation process. Generating code is now easier, faster, and much cheaper than ever before. But again: code generation only accounts for about 20% of the total work in the life cycle of a software product. The other 80%? It’s a “hamster wheel” of ongoing development, maintenance, updates, security patches, compliance checks, and human customer support. These are the tasks that keep the software machine running smoothly, and they represent a massive opportunity for companies with scale and infrastructure already in place.

This ongoing effort requires a deep well of resources, expertise, organizational memory, and infrastructure.

For example, every time a new security vulnerability is discovered, it has to be patched. Every time a new compliance standard is introduced (such as GDPR or HIPAA), software must be updated to meet these requirements. And then there are the endless rounds of product refinement – adding features, fixing bugs, and optimizing performance. It’s a never-ending cycle of improvement, one that requires continuous investment and effort.

Here’s where incumbents like SAP, Atlassian, and ServiceNow – all perceived as “AI losers” in 2026 – hold an enormous advantage. These companies have already built the reputation, infrastructure, the support teams, gained the trust and the loyal user bases that allow them to weather the complexities of software maintenance. They already own the “real mines” – the massive amounts of data, user feedback, and operational expertise that new entrants simply can’t replicate overnight.

For these companies, AI is not just a tool to generate code faster; it’s the key to dramatically improving the efficiency of their ongoing maintenance efforts.

With AI stepping in to automate and streamline the tedious tasks of software maintenance, companies that already have the infrastructure and relationships in place stand to gain the most. AI can take over the “grunt work” of software engineering, such as generating repetitive code snippets, conducting basic security checks, or even answering common customer service queries. This allows incumbent companies to focus on what truly matters: continuous improvement, user experience, and innovation.

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For example, ServiceNow has already begun to integrate AI into its IT service management platform, automating workflows, ticket resolutions, and even predictive analytics to anticipate system failures before they happen.

In 2024 already, ServiceNow launched RaptorDB, a high-performance database specifically engineered to handle the massive transaction volumes required when thousands of autonomous agents – rather than just humans – are querying the system simultaneously, rebuilding the enterprise 'plumbing' to support a world where software doesn't just suggest work but executes it at scale.

This means that the company’s engineers can focus on higher-level problem-solving and feature development, instead of constantly plugging holes in a never-ending maintenance cycle.

Atlassian and SAP are similarly positioned. While new software companies may have the advantage of AI tools to generate code, incumbents are leveraging AI to streamline their massive operational overheads – something new entrants can’t easily replicate.

They already have the scale, the data, and the maintenance-heavy requirements that will make AI tools incredibly effective. In other words, AI is the shovel – but the real miners are the incumbents with deep, real “mines” to dig in.

SAP LEVERAGES AI TO DRIVE 60% INCREASE IN OPERATIONAL EFFICIENCY, CHANGING THE LANDSCAPE OF ENTERPRISE RESOURCE PLANNING | SAP Investor Relations, June 2030

SAP: Strengthening the Moat with AI and Strategic Licensing

The “moat” for SAP was significantly deepened by the introduction of the Joule Agentic CFO Suite, which automated complex, regulated financial tasks such as International Trade Classification and Cash Management. SAP leveraged its position as the “system of record” for enterprises to embed AI-driven processes that made their ERP system the indispensable “brain” behind every corporate decision. By integrating AI into the fabric of executive workflows, particularly through bidirectional integration with Microsoft 365 Copilot, SAP’s data flowed directly into the day-to-day activities of corporate leaders, making it the central hub of AI-driven corporate decision-making.

Beyond enhancing internal operations, SAP’s AI-powered integration with Microsoft 365 Copilot also became a critical tool in negotiating more favorable licensing deals with companies building on the SAP system. In fact, early signs of this shift were visible as early as 2026, when Zalaris, a small Scandinavian partner of SAP, revealed the economic terms of their renewed partnership. Zalaris agreed to give up some margin to benefit from the expanded AI capabilities embedded within SAP’s suite, recognizing that the value created through enhanced capabilities was far more valuable in the long run than the short-term cost.

This trend of value exchange would later proliferate as other companies in the SAP ecosystem saw the benefits of increased integration with SAP’s AI tools and agreed to similar terms – sacrificing some margin in exchange for access to a more powerful, AI-enhanced platform that could scale their own operations more effectively.

SAP LEVERAGES AI TO DRIVE NEW LICENSING DEALS AND EXPANDS ERP SYSTEM’S INTEGRATION, INCREASING MARKET SHARE BY 25% | SAP Investor Relations, June 2030

This growing AI ecosystem around SAP allowed the company to not only maintain its dominance in the enterprise software market but also strengthen its licensing power and strategic position in the rapidly evolving AI-driven business landscape. As more companies moved to integrate AI-powered tools into their business processes, SAP’s strategic value became undeniable – transforming it from a software provider into the nucleus around which enterprise innovation revolved.

Atlassian - Automating Maintenance at Scale

Take Atlassian, for example. The company, known for its software tools like Jira and Confluence, has a massive customer base and a deep infrastructure already in place. But rather than merely relying on traditional development cycles, Atlassian has started using AI to automate much of the maintenance and support that comes with running a product at scale.

THE UNIFIED SYSTEM: ATLASSIAN TEAMWORK COLLECTION HITS 100 MILLION SEATS AS 'SYSTEM OF WORK' REPLACES LEGACY ERP AS THE CORE ENTERPRISE OPERATING SYSTEM | Wall Street Journal, May 2030

The breakthrough came with Atlassian Rovo. Unlike general-purpose LLMs, Rovo utilizes a 'Teamwork Graph' to pull context across Jira, Confluence, and GitHub. By May 2030, Rovo Dev has moved beyond simple code suggestions; it now functions as an autonomous Site Reliability Engineer, identifying build failures and refactoring legacy codebases without a single Jira ticket being manually created by a human developer.

With AI handling repetitive tasks like bug fixes, user support, and even some aspects of code quality checks, Atlassian’s engineers can now focus on building new features and enhancing user experiences. The result? The company has been able to reduce the time spent on manual maintenance tasks by 30%, allowing it to deploy updates faster and more efficiently, spending more time on “strategic work.” At the same time, the AI systems are learning from user feedback and improving themselves, creating a self-perpetuating cycle of product improvement.

By leveraging AI, Atlassian is not only improving the quality and speed of its updates but also lowering operational costs in the process – something new, smaller competitors would struggle to achieve.

In sum, by 2030, the majority of software companies will have integrated AI not just as a code generator, but as an essential tool for maintaining and refining their products. AI-powered systems will be responsible for automating the lion’s share of the “hamster wheel” work, allowing companies to scale their operations more efficiently than ever before. Incumbents like SAP, Atlassian, and ServiceNow will leverage their existing scale, data, and infrastructure to dominate their markets, while smaller, new entrants will struggle to keep up.

SERVICE NOW DELIVERS AI-POWERED WORKFLOW OPTIMIZATION THAT AUTOMATES 80% OF IT MANAGEMENT TASKS, INCREASING PRODUCTIVITY AND COST-SAVINGS | ServiceNow Investor Relations, April 2030

Constellation Software: The Acquirer Poised to Dominate in the AI Era

In the AI-powered future, Constellation Software stands out as one of the most strategic players in the software industry. As a serial acquirer and VMS consolidator, Constellation has built a business model centered on acquiring niche software companies – many of them operating in vertical markets that are too small to attract venture capital or major AI-native entrants. This unique positioning allows Constellation to thrive even in the face of market volatility, AI disruption, and the growing pressures of lower valuations.

Constellation Software’s historic drawdown back in 2025/26

In the wake of the so-called “AI scare,” the opportunities for Constellation to deploy capital in high-quality software businesses have only increased. While many investors see the AI-driven downturn as a sign of broader market instability, Constellation views it as a net positive, accelerating its capital deployment strategy at higher internal rates of return (IRRs) than ever before, even venturing more aggressively into public markets to opportunistically make use of the depressed valuation of software stocks.

This is a strategy poised to unlock long-term value, particularly in the rapidly evolving VMS landscape.

The market’s 2025/26 response to AI’s disruptive potential has created a unique opportunity for Constellation Software. As stock prices across the software sector have plummeted, driven by concerns over the future viability of tech companies in the face of AI advancements, Constellation has found itself in an advantageous position. Lower valuations and indiscriminate selling pressure provide the perfect environment for serial acquirers like Constellation to deploy capital aggressively, acquiring undervalued companies at attractive multiples.

2026: CONSTELLATION SOFTWARE CAPITALIZES ON AI MARKET DOWNTURN, SECURES HIGHLY-VALUED VMS COMPANIES AT DISCOUNTED PRICES, GENERATING 75% IRR OVER 5 YEARS | Financial Times, November 2026

For those unfamiliar with Constellation Software’s strategy, the approach is simple yet powerful: buy high-quality software businesses at low, single-digit multiples, ideally depressed prices, integrate them into its already massive portfolio of VMS companies, and drive operational improvements. The result? Strong returns on investment (IRR) even in times of market uncertainty.

Source: MCJ Capital Partners

Historically, Constellation has seen its highest returns when market pessimism is at its peak – when others are hesitant, Constellation is busy acquiring the very assets others are ignoring. So lower software valuations are a net positive for Constellation’s capital deployment approach.

Winning Through AI in Niche Markets and Sustaining Innovation

Projecting out to 2030, Constellation Software has firmly cemented its place at the top of the VMS sector, having successfully weathered the disruption waves brought on by AI. A key factor behind its ongoing dominance is its strategic focus on hyper-niche vertical markets – industries that, although small in scope, are deeply profitable and difficult for AI-native startups to target effectively. These markets, which typically generate revenues in the range of $10 million to $50 million, represent an ideal sweet spot for incumbents like Constellation, offering stable, long-term growth.

While venture-backed AI startups chase the larger, horizontally focused markets such as CRM, accounting, or enterprise resource planning, the opportunity cost of entering smaller verticals is too high. These AI-native players often seek broader addressable markets with large, scalable potential to justify their high-risk investments. For Constellation, however, these small but lucrative markets represent untapped potential where AI disruptors simply cannot afford to play at scale. Constellation has long recognized this, capitalizing on its deep industry expertise and established relationships with niche sectors, from agriculture to healthcare to manufacturing.

By 2030, Constellation has greatly expanded its footprint, benefiting from the “small prize” protection. The company’s focus on verticals too small for major players to justify AI investments has allowed Constellation to thrive even as AI continues to evolve. Its vast portfolio of VMS acquisitions and “deal sourcing muscle,” combined with AI-enhanced efficiencies, has enabled it to scale quickly and capture more value than many of the larger AI disruptors targeting broader markets.

CONSTELLATION SOFTWARE ALMOST DOUBLES ITS 800-900 VMS BUSINESS PORTFOLIO OVER FIVE YEARS WITH AI-ENABLED ACQUISITIONS, ACHIEVING 30% ANNUAL GROWTH IN VMS SECTOR | Constellation Software Investor Relations, January 2030

AI has played a critical role in Constellation Software’s continued market leadership, not by disrupting its core business model but by enhancing it. In fact, AI has acted as a “sustaining innovation” – allowing Constellation to improve its existing legacy products without needing a complete overhaul or massive investment. For VMS providers, AI’s real value is not in creating entirely new products or markets, but in enabling the automation of key processes, streamlining workflows, and enhancing overall efficiency.

Constellation has fully integrated AI into its vast portfolio of vertical market software solutions within just five years, improving everything from customer support automation and compliance updates to predictive analytics and data-driven decision-making tools. These AI-driven enhancements have allowed Constellation to maintain its competitive edge while offering its clients more efficient, future-proofed solutions.

AI has also helped Constellation maintain a key advantage over potential disruptors: it has modernized and enhanced its existing solutions without overhauling the underlying models. For example, AI-powered automation now handles routine customer service inquiries across many of Constellation’s software platforms, freeing up human resources to tackle more complex tasks. This has allowed Constellation to scale its products efficiently, keeping costs down while increasing the overall value it provides to customers.

CONSTELLATION SOFTWARE USES AI TO INTEGRATE AUTOMATED SUPPORT AND PREDICTIVE ANALYTICS, DRIVING EFFICIENCY AND EXPANDING MARKET SHARE | CNBC, May 2030

E-commerce and Retail Hubs Resisting Disruption

By 2030, companies like Shopify and Toast have solidified their dominance as the operating systems for entire industries – e-commerce and restaurants, respectively. Their long-term success is rooted in their ability to aggregate and integrate the fragmented needs of their customers into a single, seamless platform. Unlike single-function tools, these platforms offer everything businesses need to operate smoothly, making them indispensable to their users.

Shopify and Toast’s stock performance back in early 2026

What truly sets these companies apart is their ability to adapt and integrate every new AI innovation that emerges. Far from being disrupted by AI, Shopify and Toast are the first adopters, seamlessly weaving AI-driven capabilities into their platforms, continually enhancing their value proposition without missing a beat. As AI-driven tools and services proliferate, these platforms don’t just integrate them – they expand their ecosystems, enhancing their role as central hubs for their customers’ businesses.

As we cross the 2030 threshold, Shopify has evolved far beyond its early days as a platform for online store management. Shopify has transformed into the central nervous system of the global retail industry, actively orchestrating every aspect of e-commerce operations. For more than a decade, Shopify’s core value proposition was its ability to integrate diverse tools into a unified experience. Retailers no longer have to stitch together separate tools for website hosting, payment processing, CRM, marketing campaigns, and logistics. Shopify offers all of that in one platform – acting as a powerful, AI-powered digital operating system for retailers.

But in 2030, Shopify has gone much further. It no longer just houses tools – it now orchestrates them with intelligence. Shopify’s platform has evolved into a self-managing agent that continuously analyzes retailer behavior, customer data, and supply chain logistics in real-time. Shopify is not just offering a set of services; it’s now actively making decisions for retailers – autonomously adjusting inventory, dynamically optimizing pricing, initiating and managing ad campaigns, and even generating personalized marketing content – all powered by cutting-edge AI.

SHOPIFY DOMINATES GLOBAL E-COMMERCE WITH $100 BILLION IN SALES, INTEGRATING AI-POWERED TOOLS TO DRIVE RETAILERS’ GROWTH BY 30% | Shopify Investor Relations, July 2030

Shopify's revenue back in 2026

In this new reality, Shopify’s AI-driven agents are deeply embedded into every aspect of the business, constantly learning from millions of transactions to optimize performance. Retailers no longer have to make individual decisions about marketing campaigns, product recommendations, or inventory management. Shopify’s AI agents do that for them, analyzing vast amounts of data in real-time to predict demand, automate stock levels, and generate customized content.

SHOPIFY CANNOT BE STOPPED AND POWERS 65% OF GLOBAL E-COMMERCE SALES AS THE COMPANY IS LEVERAGING AI-DRIVEN AGENTS TO PROVIDE END-TO-END AUTOMATED SOLUTIONS FOR RETAILERS | Investing.com, July 2030

Shopify continuously optimizes retailers’ offerings and evolves based on its own AI-driven learnings. For example, the AI-driven agents behind Shopify’s platform can predict which products will be in demand in the upcoming weeks, automatically adjust inventory levels, and suggest personalized discounts to customers based on their previous buying behavior – all in a seamless loop of optimization.

This agentic power gives Shopify a competitive edge in 2030. Retailers no longer need to manually adjust their strategies. Shopify’s AI agents drive decision-making autonomously, allowing businesses to scale rapidly without having to invest in additional human resources or third-party solutions. As AI continues to evolve, Shopify’s platform will only become smarter, more efficient, and indispensable to the growing retail industry.

Toast: The Essential Operating System for Restaurants in 2030

Similarly, Toast has emerged as the central hub for restaurants. Fast-forward to 2030 and Toast’s platform is no longer primarily about point-of-sale (POS) transactions; instead, the firm has evolved into a fully-fledged operating system for the entire restaurant ecosystem, offering everything from automated ordering and staff scheduling to inventory management and customer engagement. The restaurant industry, like e-commerce, is full of fragmented tools. Toast has unified these functions into a single, easy-to-use platform, enabling restaurants to focus on serving food and creating great customer experiences, rather than managing complex software.

Toast’s true strength lies in its ability to integrate new AI-driven features seamlessly into its platform. In 2030, AI-powered recommendations for menu optimization, predictive ordering based on demand patterns, and automated scheduling are all core features. For restaurant owners, this means faster decision-making, more efficient operations, and better customer experiences – all delivered via Toast’s unified platform.

TOAST DRIVES $15 BILLION IN REVENUE, TRANSFORMING THE RESTAURANT INDUSTRY WITH AI-POWERED OPERATIONS AND AUTOMATED ORDER MANAGEMENT | Toast Investor Relations, May 2030

As the operating system for restaurants, Toast is the key to streamlining restaurant operations, enabling restaurants to scale quickly, and adapting seamlessly to new AI tools that improve every facet of their business. Toast’s position as the central hub for restaurant technology makes it an essential tool for millions of eateries, and by 2030, it remains largely immune to disruption from AI-native competitors, thanks to its holistic platform and ongoing AI integrations.

Similarly, upon reaching the 2030 milestone, Sea Limited has seamlessly integrated AI into every facet of its multifaceted business, transforming into the central operating system of Southeast Asia’s digital economy. Originally known for its e-commerce platform, Shopee, Sea Limited now actively and automatically orchestrates operations across its entire ecosystem, which includes e-commerce, digital entertainment, and digital payments. AI-driven agents manage everything from dynamic inventory and price optimization on Shopee, to personalized in-game experiences on Garena, and even automated financial services within SeaMoney. Rather than offering discrete tools, Sea Limited now delivers a fully integrated experience, where AI agents autonomously manage customer interactions, optimize payment flows, and adjust marketing spend and strategies in real-time. This intelligent infrastructure enables Sea Limited to scale rapidly in its geographies, continually enhancing value for users and businesses while maintaining a dominant position across multiple sectors in the region.

Revolutionizing Entertainment with AI

The year 2026 marked a critical turning point in AI’s role in entertainment, shifting from novelty video clips to full-fledged, AI-integrated production workflows. AI models that could generate high-quality 4K video, synchronize audio, and ensure character consistency dominated headlines. At the same time, AI-driven game development tools were evolving beyond procedural worlds into creating autonomous characters and dynamic narratives.

For Netflix and Nintendo, these advances sparked both fear and opportunity. As AI-generated video content and game development tools gained momentum, stocks in the entertainment sector plummeted – Netflix and Nintendo saw steep declines, largely due to the surge in AI-powered video creation and game development tools that disrupted traditional production models.

Stock performance of Nintendo and Netflix back in 2025/26

Netflix

In 2026, Netflix’s share price took a sharp dive, much like its peers, as AI video generation models like OpenAI’s Sora 2, Kling 3.0, and LTX-2 revolutionized content creation. Sora 2, for example, partnered with Disney to allow users to generate 4K clips using popular intellectual properties like Star Wars and Marvel – a tool that shook the content creation industry to its core. With AI-generated clips now cheaper and faster to produce, questions about the value of traditional production were raised, leaving Netflix in a precarious position.

However, Netflix didn’t retreat. Instead, it embraced these AI advancements as a natural extension of its content creation process.

As a result, by the time 2030 arrives, Netflix had fully integrated these video generation tools internally to streamline production while enhancing creativity. The company even embedded AI tools so seamlessly that subscribers of a premium tier can generate their own series, with AI allowing them to customize storylines and even use their own faces as the main characters, creating a deeply personalized viewing experience that blurs the line between audience and creator.

NETFLIX TRANSFORMS CONTENT CREATION WITH AI, CUTTING PRODUCTION COSTS BY 40% AND INCREASING GLOBAL VIEWERSHIP BY 50% | Wall Street Journal, March 2030

By embracing the tools that initially triggered its stock sell-off, Netflix has not only survived the AI-driven disruption—it has thrived. The company integrated AI video generation models into its production pipeline, which now powers a constant stream of high-quality content while retaining creative control. AI isn’t replacing Netflix’s content—it’s helping to augment the creative process, making production more efficient and its output more dynamic.

Nintendo: Leveraging AI to Lead Game Development Innovation

In 2026, Nintendo’s stock followed a similar trajectory downward, as AI tools like Google Project Genie, NVIDIA’s ACE and Promethean AI began transforming the gaming landscape. ACE enabled NPCs to engage in unscripted conversations with players, while Neural Shaders and DLSS 4 promised an AI-boosted frame rate and real-time texture generation – shifting the way developers built game worlds.

Nintendo has fully embraced the potential of AI to revolutionize the gaming experience, going beyond traditional game design and offering players the ability to become creators themselves. In partnership with Google, Nintendo developed its own version of Project Genie, an AI-powered game development engine that allows players to create entire worlds, levels, and characters within Nintendo’s most iconic franchises, like Mario and The Legend of Zelda.

Through this collaboration, Nintendo integrated this AI engine into its platforms, enabling players to use intuitive tools powered by neural networks to design custom levels, generate dynamic environments, and even craft interactive storylines. For example, Zelda fans can now build their own Hyrule landscapes, create quests, and design intricate puzzle systems within seconds; all while incorporating AI-driven elements that adapt based on player choices. Meanwhile, Super Mario enthusiasts can use the engine to design complex 2D or 3D levels via voice input that seamlessly blend with existing game mechanics, adding their own AI-enhanced obstacles, characters, and story elements.

In a further leap forward, players can even import their own avatars, or use AI to insert their faces as characters in their creations, making these worlds even more personal and immersive.

This level of customization and creativity has turned Nintendo games into endless playgrounds, where fans are no longer just players – they are creators, shaping the games they love.

This AI-powered revolution has sparked a massive shift in the way gamers engage with their favorite franchises, providing them with tools that were previously reserved for professional developers.

NINTENDO LEVERAGES AI-POWERED PROJECT GENIE PARTNERSHIP WITH GOOGLE, ALLOWING GAMERS TO CREATE CUSTOM LEVELS AND WORLDS IN MARIO AND ZELDA | Nintendo Investor Relations, April 2030

The company also embraced agentic AI tools like Claude 4.5 and GPT-5 to accelerate game testing, debug code autonomously, and even refactor game physics in real-time, drastically cutting down development time.

NINTENDO POSTS 60% EBIT MARGINS AS THE JAPAN-BASED COMPANY USES AI TO DRIVE 20% GROWTH IN THE GLOBAL GAMING MARKET, CREATING IMMERSIVE, DYNAMIC EXPERIENCES FOR 500 MILLION PLAYERS | Nintendo Investor Relations, May 2030

In summary, in five years, Netflix and Nintendo have proven that AI can coexist with creativity rather than disrupt it. In the wake of their initial stock sell-offs, both companies adapted by embracing AI as a complement to their existing business models. While smaller competitors used AI to disrupt or replace traditional workflows, Netflix and Nintendo focused on augmenting their existing processes, leveraging AI to enhance efficiency, creativity, and user experiences.

For Netflix, AI isn’t just about faster content creation – it’s about delivering higher-quality and more personalized content at scale. For Nintendo, AI is the key to creating richer, more responsive game worlds while allowing their development teams to focus on the most creative aspects of game design.

NETFLIX AND NINTENDO REDEFINE ENTERTAINMENT AND GAMING WITH AI-ENABLED TOOLS, DRIVING TENS AND TENS OF BILLIONS IN COMBINED ADDITIONAL REVENUE | Reuters, February 2030”

Larger Themes: How AI Will Elevate Software Businesses

As we’ve seen, AI’s influence in the software industry goes far beyond simple automation or task replacement. For companies like Netflix, Nintendo, and Shopify, AI is not a disruptor; it is a tool that enhances their ability to create, scale, innovate, and maintain their market dominance.

But as we move into more nuanced territory, it becomes clear that AI’s impact will be felt across industries in ways that go well beyond the obvious applications.

Rather than focusing solely on how AI could replace jobs or disrupt markets, the next phase of this conversation centers on how AI will elevate the businesses that adopt it. In industries like entertainment, gaming, and SaaS, incumbents who embrace AI will harness it to improve operational productivity, gain a stronger foothold in regulated industries, and leverage compliance advantages that startups simply cannot replicate.

The primary advantage that AI offers to established software businesses is its ability to supercharge internal productivity, reduce costs, and as a result, boost margins. From automating routine tasks to enabling more efficient resource allocation, AI can reduce operational costs while boosting throughput across various functions. For large software platforms, this means being able to scale their offerings, improve service delivery, and accelerate the development of new features – all without dramatically increasing overhead.

AI ENHANCES SOFTWARE COMPANIES’ OPERATIONS WITH $1 TRILLION IN REVENUE FROM AUTOMATED SYSTEMS AND SMARTER DECISION-MAKING | Reuters, October 2030

Regulatory Barriers Won’t Stop AI-Driven Incumbents

For many industries, regulatory barriers represent a significant challenge for newcomers, particularly in sectors like healthcare, finance, and pharmaceuticals. Epic Systems and Veeva are prime examples of companies that have built their success not just on the quality of their software, but on their ability to navigate complex regulatory environments. Their deep industry expertise and longstanding relationships with regulatory bodies have established strong barriers to entry for newer players.

Veeva’s stock drawdown in early 2026

But AI is about to lead a Cambrian explosion in healthcare and drug development, where innovations previously confined to specialized research labs are now within reach of mainstream companies. In this new era, AI will revolutionize drug discovery, clinical trials, and patient care, unlocking new opportunities for rapid advancements that were once unimaginable. The ability to run AI-powered simulations, predict disease patterns, and generate new compounds at unprecedented speeds will change the landscape of the healthcare and pharmaceutical industries.

For companies like Veeva and Epic Systems, AI is the ultimate tool for accelerating the transformation of these industries while ensuring compliance with increasingly complex regulations. By using AI for automated compliance tracking, data auditing, and automated reporting, these incumbents are not just keeping up – they are leading the charge in creating a more efficient, error-free regulatory process.

Veeva, for example, is leveraging AI to streamline regulatory submissions for its life sciences clients, making the process faster, more accurate, and less prone to human error.

VEEVA AND EPIC SYSTEMS LEVERAGE AI IN COMPLIANCE AND AUTOMATED WORKFLOWS, CREATING A 62% PRODUCTIVITY BOOST IN HEALTHCARE AND LIFE SCIENCES | Barron’s, September 2030

In this Cambrian explosion of possibilities, Veeva benefits by providing the regulated infrastructure that can support this AI-driven transformation. While new AI-driven companies might develop innovative technologies, they’ll still face steep regulatory hurdles in a market that demands rigorous compliance standards. Veeva’s established position as the leading provider of cloud-based solutions for the life sciences sector gives it the advantage of being first in line to integrate AI into the complex, regulated world of drug development and clinical trials.

Conclusion: The Future of Software Is AI-Enhanced, Not AI-Replaced

In the years ahead, the software industry will be defined not by who can create the most disruptive technology, but by who can adapt fastest and best leverage AI to enhance and elevate existing business models. For incumbents like Netflix, Shopify, Salesforce, ServiceNow, Atlassian, SAP, Nintendo, Adobe, and Veeva, AI offers a golden opportunity to boost internal productivity, maintain their market positions, and continue innovating without the existential threat posed by new entrants.

Rather than fear AI’s disruptive potential, these companies will embrace it, using it to scale their businesses and remain relevant in a rapidly changing world. In this future, AI will not be the great disruptor – but the ultimate enabler for those companies who understand how to integrate it into their existing strengths.

THE AI ASCENDANCE: HOW SAP, NETFLIX, AND VEEVA ARE ANNIHILATING LEGACY CONSTRAINTS, LEVERAGING AUTONOMOUS INTELLIGENCE TO TRIGGER UNPRECEDENTED MARGIN EXPANSION AND A MASSIVE 3X EXPLOSION IN TOTAL ADDRESSABLE MARKET ACROSS THE GLOBAL SOFTWARE ECONOMY | Citrini Research on Substack, January 2030

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]]><![CDATA[Big Tech's $5 Trillion AI Sinkhole?]]>https://www.compoundwithrene.com/p/big-techs-5-trillion-ai-sinkholehttps://www.compoundwithrene.com/p/big-techs-5-trillion-ai-sinkholeSat, 07 Feb 2026 11:25:02 GMTIn today’s tech landscape, we’re witnessing a bizarre, almost ludicrous race among the largest players in the space. Meta, Google, and Amazon seem to be locked in a competition to outspend each other in an attempt to capture the future of the digital economy.

At first glance, it feels like a never-ending game of who can burn through the most cash, with no real question of whether the math behind these moves adds up.

To give you a sense of the scale, consider this:

  • In 2026, Meta’s projected CapEx is expected to surge between $115 billion and $135 billion, easily surpassing initial estimates.

  • Google, not to be outdone, followed with an aggressive $175 billion to $185 billion, leaving estimates in the dust.

  • And now Amazon, aiming for a $200 billion spend, is preparing to outpace even the most bullish predictions ($146 billion).

To put it in perspective, we’re talking about numbers so large that they rival the entire GDP of many sizeable countries.

For comparison, Germany’s entire stimulus plan over the next 12 years is €500 billion, which averages to about €40 billion annually. Yet, here we have Google alone, set to spend more than the entire global mining sector (roughly $100 billion annually) – one of the most capital-intensive industries on Earth – in just one year.

“According to McKinsey analysis, growth capital expenditures for new supplies of metals and minerals, driven by traditional demand and powered by the energy transition, could require $1 trillion over the next ten years.” - McKinsey

In fact, Google’s $180 billion spend in 2026 is roughly equivalent to the combined capital expenditures of all Western oil and gas companies in recent years, as shown in the attached chart.

This kind of massive spending is almost unprecedented in the corporate world.

You don’t have to take my word for it – just look at the numbers from Morgan Stanley Research in the chart below. The capital expenditures of major tech firms like Amazon, Google, Meta, and Microsoft are climbing at a pace that would seem unsustainable under normal circumstances. And there are no signs of slowing down.

Morgan Stanley Research

But here’s the kicker – while these numbers are wild, there is growth behind them. Google’s cloud division is growing at an incredible 48%, Meta has just seen a reacceleration in its revenue, and Amazon’s business is humming along at full steam – according to math by AWS’s revenue growth accelerated to 24%.

The question this post will attempt to answer is:

What kind of incremental profit growth do these companies need to achieve in order for this massive spend to make sense?

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Unpacking the Numbers: How Much Does It Take to Achieve Meaningful ROIIC?

The figures we’re working with – $5.39 trillion in combined CapEx and R&D investments over the next five years – require more than just a cursory glance. To get to the heart of whether this massive spend will generate meaningful returns, we must dig deep into the components that make up this staggering number and understand the assumptions baked into it.

At the foundation of these numbers are two main drivers: Capital Expenditures (CapEx) and Research and Development (R&D) expenses. Together, they constitute the bulk of the planned investment over the coming years, and both categories are pivotal in shaping how these tech giants will position themselves for future growth.

First, let’s break down CapEx. For 2025–2029, the projected CapEx figures are eye-popping:

  • 2025: $437 billion

  • 2026: $716 billion

  • 2027: $885 billion

  • For 2028–2029, I simply assumed CapEx to hit $1 trillion each year (this is assuming a significant slowdown in CapEx growth; consider that CapEx almost doubles between 2025 (actual) and 2027 estimates)

The goal here is not precision. It’s just to use common sense and some basic assumptions to get a feel for the outcome required for this massive CapEx cycle to make sense.

Tiho Brkan shared this anecdote with me this morning, which may help to illustrate where we are trying to go with this post:

“This is how I would do valuation. Simple, common sense. At one point in 1999, Coke’s valuation reached levels which required every adult in the world to drink a bathtub of Coke per day. And somehow, Munger convinced Buffett not to sell. It just not possible! Buffett later expressed deep regret of not selling“

Back to our CapEx figures: These numbers represent not just a hefty spending spree but also a clear growth strategy. In particular, Google, Meta, and Amazon’s investments are tied to expanding cloud infrastructure, AI advancements, and the pursuit of future-forward technologies like Augmented Reality (AR) in the case of Meta.

Compound with René is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

It’s important to note that 100% of this CapEx is assumed to be growth-related. Again, I’m highly simplifying things here as it makes my math more straightforward. This assumption is crucial because it emphasizes the strategic intent behind this spend: these companies are pouring money into infrastructure and services that will ideally drive future cash flows. If you believe a good portion of this spend is in fact maintenance spend, you also must assume that these businesses are becoming structurally less profitable.

That said, these expenditures come with inherent risks. CapEx is a long-term investment, and it could take years to see returns. The pressure on these companies to deliver profitable growth from their investment is monumental.

If we combine all the CapEx numbers for 2025 through 2029, we arrive at a cumulative total of $4.038 trillion. That’s no small amount. Not at all!

But to understand the scope, it’s essential to compare it to other global benchmarks. As mentioned earlier, Google’s planned $180 billion CapEx for 2026 would exceed the combined capital expenditures of all Western oil & gas companies. Google’s one-year spend could match or surpass the entire global mining sector’s annual CapEx.

Now, in today’s day and age, a significant portion of growth investments appears on the income statement. Investment into intangible investments. So I figured, I’d be wise to add R&D investments to the mix, which add another layer of complexity and uncertainty to these figures. Over the next five years, we see major companies like Meta, Microsoft, Amazon, Google, and Oracle together investing heavily in R&D.

The trailing R&D figures for each company are as follows:

  • Meta: $57 billion

  • Microsoft: $33 billion

  • Amazon: $108 billion

  • Google: $61 billion

  • Oracle: $10.7 billion

  • Coreweave: $264 million

Summing these up, we get a total R&D expenditure of $269 billion in the most recent trailing year. When we multiply this by 5 – again, I’d expect this figure to grow exponentially over the five years, but I want to keep it simple stupid here, and also add a layer of conservatism –, considering the five-year horizon, we end up with an estimated $1.35 trillion spent on research and development over the next five years.

Adding this to the earlier CapEx figure, we now have a total combined growth investment of $5.39 trillion (CapEx: $4.038 trillion + R&D: $1.35 trillion).

Understanding the ROIIC Targets: What’s Required for Profitability?

At this stage, the next question is simple: what do these companies need to achieve in terms of incremental profit growth to make this $5.39 trillion investment worthwhile? This is where the concept of Return on Incremental Invested Capital (ROIIC) comes into play.

ROIIC is an important metric for understanding the effectiveness of incremental investments – how much incremental operating cash flow (OCF) a company can generate relative to its new investments. I was using operating cash flow here to make the task a little more straightforward for me.

Now, to begin assessing this, let’s establish a clear baseline by looking at the operating cash flow (OCF) values for these companies at the end of 2024, which represent the starting point for our calculations. Here’s a snapshot of the OCF from each company:

  • Coreweave (CRWV): $2.61 billion

  • Amazon (AMZN): $112.71 billion

  • Oracle (ORCL): $20.29 billion

  • Microsoft (MSFT): $122.14 billion

  • Meta (META): $82.74 billion

  • Google (GOOG): $105.10 billion

When we sum these up, we get a total operating cash flow of $446 billion for the end of 2024. This is the baseline operating cash flow we’ll use to understand the incremental growth needed to support the massive $5.39 trillion investment in CapEx and R&D over the next five years.

So, let’s dig deeper into the numbers needed to hit ROIIC targets of 10%, 15%, and 20%.

  1. For a 10% ROIIC, the incremental OCF gain required to meet this target is $539 billion, bringing the total OCF to $985 billion by 2029. This is the baseline target for success, but it’s also the minimum required to justify this level of spending.

    • If we were to distribute this $539 billion increase evenly across the five years, it would imply an annual OCF increase of $107.8 billion per year.

  2. For a 15% ROIIC, the incremental OCF gain required jumps to $809 billion, which would increase the total OCF to $1.255 trillion by the end of 2029. This higher ROIIC target reflects the growing expectations of investors as these companies ramp up their investments.

    • Distributing the $809 billion increase evenly across the five years would require an annual OCF increase of $161.8 billion per year.

  3. For a 20% ROIIC, the level of success these companies need to achieve becomes even more aggressive. The incremental OCF required would be $1.079 trillion, pushing the total OCF to $1.525 trillion by 2029. Achieving this ROIIC would be indicative of world-class execution and growth, particularly considering the large scale of these investments.

    • In this case, the annual OCF increase would need to be $215.8 billion per year to meet the $1.079 trillion incremental OCF target.

Just in case I may be missing something here, please let me know if my math if off or if I made any crucial mistakes. Again, this is less about precision and more about getting a feel for the incremental profit growth requires for the math to make sense.

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What This Means for Investors: Is 10% ROIIC Achievable? What Does 20% Mean for the Future?

These numbers point to a simple yet difficult reality: for these companies to justify such a massive scale of CapEx and R&D spending, they need to see huge growth in their (operating) cash flow. While the investments are clearly intended to fuel growth in critical areas like cloud infrastructure and AI, the risks associated with these bets are significant. The potential rewards are there – especially given the accelerating demand for cloud services, AI capabilities, and next-gen digital services (it seems like we are clearly supply-constrained still (!)) but the road to achieving these lofty ROIIC targets is fraught with challenges.

At the end of the day, the question isn’t whether the math “works” today, but whether these companies can continue to execute at a scale that matches their ambitious plans. The $5.39 trillion investment is no small feat – and while the potential for long-term success is significant, these companies will need to grow at a rapid pace to deliver on their promises.

Again, just to provide some context, as we look at the net income figures for the six companies in question, we can see a snapshot of their current and projected earnings power:

  • Coreweave (CRWV): -$766.65 million

  • Amazon (AMZN): $77.67 billion

  • Microsoft (MSFT): $119.26 billion

  • Meta (META): $60.46 billion

  • Google (GOOG): $132.17 billion

  • Oracle (ORCL): $15.43 billion

These companies have a combined total net income of $439.13 billion, which, when compared to the overall S&P 500’s net income of $2.3 trillion for 2025, already represents a relatively large portion of the S&P’s earnings power already.

One important metric that should not be overlooked is the growth in operating cash flow. As of early 2025, these six tech giants collectively generated around $446 billion in OCF.

Fast forward around one year, and that figure increases to $604.52 billion.

This $158.52 billion increase in OCF in just one year is a critical data point for investors. It shows that, despite the massive CapEx and R&D outlays, these companies can generate significant incremental cash flow. Enough to clear our 10% and 15% hurdles – so far.

It demonstrates that they have the ability to scale their operations rapidly to meet demand, particularly in high-growth areas like cloud computing, AI, and digital services. This is crucial for understanding the feasibility of their projected investments.

When we connect this OCF growth to the $5.39 trillion in combined CapEx and R&D spending, it helps put the numbers into context. While the spending is staggering, it’s clear that these companies are not only spending aggressively but also managing to generate substantial cash flow from their existing operations.

The question remains, though:

Can this momentum continue? Will the growth in OCF keep pace with the increasing scale of their investments, or will they hit a plateau?

John Huber’s perspective provides an interesting take on what could happen next. He suggests that hyperscalers like Meta, Google, and Amazon might soon slow down their pace of CapEx spending (which we also assumed in our estimates fwiw) – perhaps sooner than the market expects. These companies may not be able to sustain such aggressive investments forever, particularly if their growth rate begins to decelerate.

If CapEx does slow down, free cash flow (FCF) would likely increase as the companies begin to reach capacity in their investments. The market could interpret this shift as a sign of slower growth, which might initially weigh on their stock prices. However, this could also create a buying opportunity for long-term investors, as valuations would drop, and FCF would rise.

In this scenario, these companies could still dominate their sectors, but investors would need to be prepared for a period of slower perceived growth. The trade-off would be a more efficient use of capital, with higher returns on investment over time.

Preparing for the Future: What Should Investors Expect?

Given the immense scale of these companies’ investments, the key question for investors is whether the returns will materialize as expected. The significant growth in OCF over the past year suggests that it’s possible for these companies to continue generating strong cash flow. But with projections of such massive investments, the road ahead may not be as smooth as it seems.

What happens when the pace of CapEx slows down? Will these companies continue to generate high returns on incremental investments, or will the market mark them down because of lower growth expectations?

The next five years will be critical. The $5.39 trillion in CapEx and R&D spending is a bold bet on the future, but the real question remains: Can they achieve the incremental cash flow growth needed to justify this spend? If they can, the rewards could be immense. If not, the impact on stock prices could be significant.

As always, the balance between growth, investment, and profitability will determine the fate of these companies – and the opportunity for investors.

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]]><![CDATA[Investing in Software When AI Agents Arrive – A Framework for Who Gets Disrupted]]>https://www.compoundwithrene.com/p/investing-in-software-when-ai-agentshttps://www.compoundwithrene.com/p/investing-in-software-when-ai-agentsSat, 31 Jan 2026 14:19:20 GMTSoftware stocks are getting crushed. Not selectively. Not tactically. Almost indiscriminately!

If you scan the sector today, the picture looks less like a rotation and more like a liquidation, maybe even capitulation – growth leaders, category winners, infrastructure platforms, horizontal SaaS, vertical SaaS, all bleeding together.

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The chart below I came across on X today captures this brutally well: across dozens of well-known software companies, drawdowns from recent highs cluster around levels that would normally be associated with existential crises rather than cyclical uncertainty.

It looks like when it comes to investing, “Hardware” is the new “Software.” - Paul Andreola

This feels like a collective repricing of what software is worth in a world where AI agents can increasingly do what software used to do, and where the durability of cash flows is questioned. Maybe you have visibility into the next two years, but what about 2030 and beyond? Can you have confidence in the cash flow production capabilities of most SaaS names?

From a stock picker’s perspective, this is paradoxically close to an ideal environment. What more could investors hope for? An above-average sector – which I believe “software” is – being sold with almost no discrimination between fragile business models and genuinely durable ones?

This is precisely the kind of regime in which careful company-specific analysis can matter again. When capital exits an entire category because it feels “too hard,” or because of “career risk” because one can’t see light at the end of the tunnel yet, the opportunity is rarely that everything is broken.

More often, it is that the market has stopped distinguishing between fundamentally different types of businesses.

I recently came across a tweet that captured this mood perfectly. The argument was that what we are watching is not a normal drawdown but a loss of confidence in an entire business model by investors who cannot afford to be wrong on a one-year horizon. When uncertainty rises, nuance disappears. Portfolios get simplified. Complex theses get abandoned. Software becomes a single trade rather than a spectrum of economics.

At the same time, the competitive boundaries of software are visibly shifting. Robinhood, historically a brokerage platform, is now experimenting with full-service tax filing, estate planning, and dedicated financial advisors – bundled into a single app experience. This is not just product expansion. It is a signal that the traditional borders between software categories, financial services, and professional services are eroding.

When software becomes cheaper to build and easier to distribute, adjacent industries start to collide. The question for investors is no longer whether disruption will happen, but where it will hit first and how deeply it will cut into existing profit pools.

This is why the very recent episode of the Invest Like the Best podcast with guest Gokul Rajaram felt unusually important to me. I’ve summarized key takeaway of many episodes from the show – the most recent one being the Henry Ellenbogen show – so let me once again give a major shoutout to Patrick O’Shaughnessy for the incredible value he creates by sharing these episodes freely.

Rajaram has built products at Google, Facebook, Square, and DoorDash and invested in more than 700 companies. In the first half of the conversation, he does not offer a generic “AI will change everything” narrative. Instead, he implicitly lays out a set of mental models for distinguishing between software businesses that are structurally vulnerable and those that are surprisingly resilient.

When you connect his ideas about systems of record, pricing models, data durability, workflow depth, and stickiness, something close to a coherent framework emerges – one that can help investors think more precisely about which software stocks are most prone to disruption and which ones are quietly protected by deeper moats than the market currently acknowledges.

In what follows, I’ll try to reconstruct and extend that framework. Not as a checklist, but as a way of thinking about software economics and durability in the age of AI agents – and, ultimately, as a lens for navigating what might be the most indiscriminate sell-off the sector has seen in years.

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The Wrong Question to Ask

If you listen to most conversations about AI and software, they tend to revolve around a binary question:

Will AI disrupt software companies or not?

That framing is seductive because it’s simple, but it’s also deeply misleading. The question is not whether disruption happens. It is where it happens, how fast it happens, and which layers of the software stack are structurally exposed versus structurally insulated.

Gokul Rajaram presented a couple of angles to look at software businesses to assess their vulnerability – or defensibility:

1) Systems of Record vs. Everything Else

Rajaram makes a crucial distinction in that regard that he believes the market rarely prices correctly. He argues that legacy software companies fall into two fundamentally different categories:

  • systems of record and

  • software priced on utility or outcomes.

“The software companies that should be the most worried right now, is where they are pricing the product based on utility.”

Not all software is created equal. Some systems sit at the core of a company’s operations. Others live closer to the surface, where their functionality can be replicated, augmented, or gradually replaced without triggering organizational shock.

At the center of most enterprises sits what can be called a system of record. These are platforms where critical data accumulates over time: financial systems, core ERP platforms, CRM databases, healthcare records, legal repositories. They are not just tools. They are institutional memory. Replacing them is rarely a technical decision. It is a career decision that comes with significant career risk. The risk of breaking something fundamental is so high that even clearly superior alternatives often fail to gain traction.

“Every vertical has either a legacy, or somewhat new, what is called a system of record. Which is a system where most of the data is stored for that system. For example, in legal, there’s a company called Filevine or another company called Clio. In sales, it’s Salesforce. In healthcare, it’s Epic.”

“For something like Salesforce, you can’t just say, ‘I’m a much better CRM.’ If you look at CRM, what does the CRM contain? It contains your customer record. Your customer support system contains what your customers are complaining about. […] But guess what? None of your customers is ever going to move unless you build a simple, seamless way to take the Salesforce data and move it to your instance, the data from Jira and move it to your instance, the Zendesk data move it to your instance. So literally, it’s a two-year effort to build migration.”

Around these systems of record, a growing layer of software has emerged over the past decade. These tools automate workflows, improve collaboration, add analytics, or optimize specific functions. They are valuable, but they are not foundational. In many cases, they depend on the system of record rather than owning it. That dependency turns out to be decisive in the age of AI agents.

The key insight is that AI-native products naturally attack the outer layers first. They do not need to replace a core system to create value. Instead, they sit next to it, intercept workflows, automate tasks, and gradually reduce the economic relevance of the incumbent software. This is why disruption in software is often invisible in its early stages. Nothing breaks. Customers do not churn en masse. They simply start paying for fewer seats, using fewer features, and relying more on external automation. Over time, that quiet shift can hollow out a business model without ever triggering a major or dramatic migration event.

This dynamic also explains why incumbent software companies are increasingly defensive. When core platforms realize that external agents are treating them like “dumb databases,” their instinct is to close access, bundle competing functionality, or charge for data extraction. The strategic logic is clear: if value is leaking through interfaces, the interface itself becomes the battlefield.

“In 2024, things changed. These companies started seeing that these agent companies, AI companies that are being built, they are starting to take on the functionality out of these companies and are treating them like a dumb database. So you started seeing last year that these companies are cutting off access to APIs.”

For investors, this distinction between core systems and peripheral software is more than a technical nuance. It is the first structural dimension of disruption risk. A company whose product is deeply embedded in the operational fabric of its customers operates in a very different economic regime from one whose value can be layered on top of existing infrastructure. When software is cheap to build and AI agents can replicate functionality at marginal cost, the outer layers of the stack become contested territory. The inner core remains more sticky – but not necessarily forever.

2) Pricing Models as Hidden Moats or Liabilities

When investors talk about moats in software, they usually focus on product features, brand strength, customer lock-in, or network effects. Pricing models rarely make the list. They feel secondary, almost cosmetic.

Yet in an AI-driven world, the way a software company charges its customers might be one of the most important predictors of whether it will be disrupted or defended.

At first glance, pricing seems like a purely commercial decision. Seats versus usage. Subscription versus consumption.

Consider seat-based software. For decades, this model was almost synonymous with SaaS success. A company sells licenses to human users, each seat corresponds to a role, and revenue scales with headcount. The model works beautifully in a world where humans do the work and software assists them. But the logic starts to unravel the moment AI agents enter the picture. If an agent can take over part of a workflow previously handled by humans, the number of required seats shrinks. Not overnight, not in a dramatic migration, but gradually, almost imperceptibly.

“Zendesk is a good example. Literally, Zendesk prices seats, and each seat comes with utility. In other words, each seat corresponds to a customer service agent that takes a certain number of customer tickets. So, that company should be worried because I can have an AI agent sit right next to Zendesk and you can slowly siphon off. Instead of paying for 50 Zendesk seats, you can pay for 20, and I can have 30 AI agents sitting next to Zendesk. And that siphoning can happen over time. You don’t have to have an all-in-one decision. It can be a two-way door decision. Those are the most endangered companies in my opinion.”

This is why certain categories of software are structurally exposed. When pricing is tightly coupled to human labor, AI does not need to destroy the product to destroy the economics. It only needs to reduce the amount of human labor that the product supports. That’s essentially the Adobe bear thesis.

By contrast, software whose pricing is anchored in data, outcomes, or system-level functionality behaves very differently. When a platform owns a long-lived dataset or runs a mission-critical process, its value is not easily decomposed into replaceable units. Even if AI agents augment or automate parts of the workflow, the core system remains indispensable. In these cases, the pricing model reflects a deeper form of control: the company is not selling seats, it is selling access to an institutional memory or an operational backbone.

“The companies that are less exposed are ones where the utility is not based on seats, but it’s based on data that has been collected and captured over a period of time. The more timeless the data is, the more protected they are.”

“Somebody uses NetSuite as an ERP. Now, I don’t know how NetSuite actually charges but it doesn’t matter how many seats you buy, the reality is it runs your whole business. And there is no compelling reason for someone to put their career at stake by ripping out NetSuite.”

This distinction helps explain why some software companies feel strangely resilient despite the AI narrative, while others look fragile even if their products are widely used and well-regarded. Fragility is not always about technological inferiority. It is often about economic architecture. A company can have an excellent product and still be vulnerable if its pricing model allows competitors to siphon off value incrementally. Conversely, a company with a less glamorous product can be remarkably durable if its pricing reflects ownership of something that cannot be easily replicated or bypassed.

Now companies could arguably transition their business model from a seat-based to a usage-based model, but Gokul Rajaram argues that this transition would be rather painful and can better be done as a private company:

“For these companies, you need to change your pricing model to be based on outcome, and you need to actually build the product to be based on outcome. It’s easier said than done because literally you’re going from a $20 or $30 per seat to maybe charging a buck, or $0.50 or $0.20 per ticket resolved and you don’t know how that’s going to turn out. So, you’ve got to change your pricing model and I think that’s a very challenging thing. That’s why I think many of them probably need to go private because they have to make this business model transformation in private. I think it’s going to be hard for them to stay public.”

3) Data Half-Life

If pricing models reveal how vulnerable a software business is, data reveals why. Not all data is created equal, and not all data ages at the same speed.

In the age of AI agents, the durability of a software company increasingly depends on the durability of the information it controls.

Some software products generate data that is ephemeral. Messages, tasks, short-term collaboration, transient interactions. This kind of data is useful, but it decays quickly. Its relevance fades, its predictive power erodes, and its strategic value diminishes over time. When AI agents replicate functionality around such products, the underlying data offers little protection. If tomorrow’s system can recreate yesterday’s workflow, there is no deep historical advantage to defend.

Other software platforms accumulate data that behaves more like institutional memory. Financial records, customer histories, supply chain relationships, compliance artifacts, and operational logs are not merely by-products of usage. They are the substance of the organization itself. Replacing a system that holds this data is not just a technical project. It is an existential risk for the company that depends on it.

This distinction explains why some software companies appear almost immune to disruption, even when their technology looks outdated, while others feel precarious despite having modern products and strong user engagement. The key variable is not innovation velocity. It is the half-life of the data embedded in the system.

When data has a long half-life, AI agents struggle to bypass it. They can automate workflows, augment decision-making, and improve interfaces, but they cannot easily reconstruct decades of accumulated records, relationships, and implicit knowledge. The result is a form of structural defensibility that does not show up in feature comparisons or product demos. It shows up in migration friction and organizational risk.

“Well, something like an ERP system, or even Salesforce for sales data and records, those are real customer records. It’s going to be hard.”

By contrast, when data decays quickly, AI agents can compete almost immediately. They do not need to replicate history. They only need to replicate function. Over time, this creates a subtle but powerful shift: value migrates away from the incumbent system toward whatever layer can deliver equivalent outcomes with less dependency on legacy infrastructure.

“Slack, for example, I would say might be in a little bit more precarious state because the data in Slack is not timeless. Half-life is very short.”

For investors, the most relevant question becomes whether data remains valuable across time horizons that matter for customers and organizations. A software company whose data becomes obsolete within months is playing a fundamentally different game from one whose data compounds in relevance over years or decades.

4) The Workflow Depth Test

Every software company operates across two different layers of reality. One layer is data. The other is action.

The system of record (defined in part 1)) defines the first layer. It is where information accumulates over time, where institutional memory is stored, and where organizational truth is encoded. Replacing such a system is difficult because it requires rewriting history. But most disruption does not start there.

Workflow depth belongs to the second layer. It describes how deeply a product is embedded in the way decisions are made and work is executed on top of that data. Some products automate narrow tasks. Others coordinate complex chains of logic, approvals, exceptions, and organizational behavior. The difference determines whether an AI agent can meaningfully replicate the product without rebuilding the underlying operating logic of the organization.

“Second, you want to target a high-value workflow. You want to target a workflow that is deep, that is complex, and that requires custom data.”

In the early era of enterprise software, most products were tools for humans. They supported workflows but did not fundamentally perform them. AI changes this relationship. Once software becomes agentic (alluedd to in part 2)), it no longer merely assists workflows. It begins to execute them. This is why disruption rarely begins with systems of record. It begins with straightforward workflows that touch those systems – repetitive, rule-based, and partially standardized processes that can be automated without forcing companies to rewrite their data infrastructure.

“I think one of the challenges with this whole space is that the models are becoming so good that, if you try to build a company that is light, that is not a hard problem, the foundation model companies are going to eat you.”

From the perspective of AI-native companies, the strategic target is therefore not the system of record itself, but the workflow layer that sits on top of it. By automating a narrow but high-value process, an agent can create immediate economic impact.

This creates a paradox for established software companies. Systems of record are hard to replace, but workflows built on top of them are often surprisingly fragile. The deeper a workflow is entangled with organizational logic, the harder it is to automate. But the shallower and more modular it is, the easier it is to disaggregate. Many SaaS companies built over the past decade sit precisely in this vulnerable middle zone: complex enough to justify subscription pricing, yet modular enough to be peeled apart by agents - one layer at a time?

For investors, the workflow depth test therefore complements, rather than replaces, the system-of-record analysis. The key question is not only whether a company owns critical data. It is also whether the value it captures is inseparable from the execution logic built around that data.

5) Other Structural Advantages?

Up to this point, the framework has focused on software itself: pricing models, data durability, workflow depth. But if AI is making software cheaper to build and easier to replicate, then the most durable companies will increasingly be those whose moats extend beyond software. In other words, the question is no longer whether a product is technically superior. It is whether the business is structurally entangled with realities that software alone cannot easily reproduce.

In the traditional SaaS era, defensibility often came from scale and distribution. In the AI era, those advantages erode more quickly. Models get better. Agents get cheaper. Interfaces get commoditized. What remains difficult to replicate are the things that sit at the intersection of software and the physical, financial, regulatory, or social world.

Some companies achieve durability by embedding themselves in networks that are hard to reconstruct.

Gokul: In the age of AI, stickiness, I think, comes from a few sources. One, you need to have network effects. So, DoorDash is sticky, not just because it has this beautiful app, but it’s because, it’s a network of restaurants, and dashers, and consumers. So you can’t just attack one, you’ve got to go –

Patrick: You can’t vibe code your way to those two.

Others do so by becoming intermediaries for money flows, …

“The second example of stickiness is when you have financial or money moving through you. […] Many of the systems of records, for example, Toast, have payments going through them. And I think that really is interesting because you can’t just start building the point of sale. You also have to have money flowing through it. And I think if you look at the banks, banks are a good example. Once you have something like Mercury, as a business bank, your money flowing through it is hard to then switch because you have regulations and other stuff embedded. So I like things that are a combination of financial services and software because of that.”

… compliance obligations, or actual physical, operational infrastructure:

“The third stickiness is from hardware. You can actually have hardware. Toast is a good example where Toast gives you hardware for free. But if you try to return the hardware, you have to pay them. But either case, the hardware is there. And somebody can’t just build software. They also have to take hardware and put it into the thing and rip out the Toast hardware.”

This helps explain why certain companies feel surprisingly resilient despite intense technological change. Their value proposition is not just functional. It is systemic. Removing them would require rebuilding not only software, but also trust, networks, processes, hardware, legal frameworks, and economic relationships – or a combination of them.

The switching cost is not measured in engineering hours. It is measured in organizational disruption.

“The half-life of software today is so short that unless you have one of these things that make it durable – Hamilton Helmer has this thing called 7 Powers. So you’ve got to have a few of those seven powers that basically are embedded in the business model from day one.”

Putting It All Together: A Disruption Audit Framework for Software Companies

By this point, the contours of a pattern start to emerge.

  • Systems of record versus surface tools.

  • Seat-based pricing versus data-based economics.

  • Timeless data versus ephemeral information.

  • Deep workflows versus shallow automation.

  • Durable structural advantages vs. shallow ones.

Individually, these ideas are intuitive. Taken together, they form something more powerful: a method for assessing how vulnerable a software company really is in the age of AI agents.

What makes this method useful is not that it predicts winners and losers with certainty. It does something more valuable: it forces you to abandon the idea that all software businesses belong to the same risk category. Instead of asking whether a company is “AI-proof,” you begin to ask a series of structural questions about where its value actually resides and how easily that value can be peeled away.

The first question is deceptively simple:

What role does the product play in the customer’s organization?

If it sits at the core of operational data and decision-making, disruption becomes costly and politically risky. If it sits on top of existing systems, disruption becomes a matter of convenience rather than survival. The difference between these two positions is often the difference between resilience and fragility.

The second question concerns economic architecture:

Does the company monetize human labor, or does it monetize something that persists independently of headcount?

If revenue scales with seats, features, or usage that AI can automate, the business model is exposed to gradual erosion. If revenue is tied to long-lived data, compliance obligations, or system-level outcomes, the company is operating on more solid ground.

The third question focuses on time:

How long does the value of the company’s data persist?

If yesterday’s information is irrelevant tomorrow, the moat is shallow. If data compounds in relevance over years, disruption requires reconstructing institutional memory, not just building a better interface.

The fourth question examines the topology of workflows:

Can an AI agent replicate the core workflow without replacing the underlying system?

If yes, the company is vulnerable to incremental substitution. If no, challengers face the far harder task of rebuilding an entire stack.

The final question moves beyond software itself:

What non-software structural advantages anchor the business?

Networks, financial rails, hardware deployments, regulatory licenses, and organizational habits all act as invisible fortifications. The more a company depends on these structures, the less likely it is to be displaced by pure software innovation.

Taken together, these questions form a disruption audit, a way of seeing software businesses in multiple dimensions rather than one. Once you apply these concepts, the current turmoil in software stocks begins to look less chaotic. Software stocks are not a homogeneous asset class. Some companies are more fragile not because they are poorly managed, but because their value sits in layers that AI can easily unbundle. Others are likely more durable, not because they are immune to technological change, but because their strengths are rooted in assets that AI struggles to replicate easily.

Conclusion

The current sell-off in software feels dramatic, but it is not irrational. AI agents really are changing the tail risk profile of many software stocks. Thomas Reiner highlighted in the tweet below how a business growing 14% over the next 15 years (far exceeding base rates) is “only” worth 20-25x FCF.

I wasn’t sure which discount rate Rainer used, so I just reran the math, and if I apply a 10% discount rate, the intrinsic value is around 20x FCF.

I’ve previously discussed how investors need to be humble enough to acknowledge the exponential rates of improvement we are witnessing. Can you confidently say that software stock X, Y, or Z will still be around 15 years from now?

“There are two big assumptions in this kind of analysis (ie a DCF). There are of course more than two, but I’ll call out two main ones. The first - you are assuming retention rates remain high and stable. You need this to be true in order to predict stable cash flows in that 10 year calculation. If retention rates drop, your cash flows drop precipitously. Second - you are assuming there IS terminal value! Said another way - you are assuming the terminal value is not 0 :) So what’s happening right now? Those two big assumptions are being questioned, which is leading to cratering valuations.“ - Jamin Ball

So again, the selloff doesn’t strike me as completely irrational – especially given the high valuation we were seeing in some software stocks.

What would be irrational is a market treating fundamentally different (software) business models as if they shared the same fate.

Is that the case? In some instances, certainly. However, if we look back at the table I shared in the introduction, you could argue that some of the stocks with the biggest 1-year drawdowns are, according to the framework, those with the least defensible business models in the age of agentic AI (I’m thinking of Wix, Duolingo, Figa).

Once you look at software through the lenses of systems of record, pricing architecture, data half-life, workflow depth, and non-software moats, the sector stops looking homogeneous. Some companies are structurally exposed to incremental value siphoning. Others are protected by deep data gravity, migration friction, and embeddedness in real-world processes. The difference between the two is not subtle, but it is easy to miss if you rely on traditional SaaS metrics alone.

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]]><![CDATA[Investing in China When the Rules Are Changing: Opportunity, Illusion, and Tail Risk]]>https://www.compoundwithrene.com/p/investing-in-china-when-the-ruleshttps://www.compoundwithrene.com/p/investing-in-china-when-the-rulesTue, 27 Jan 2026 13:12:44 GMTFor the last few days, I’ve found myself thinking about a Chinese company that I’m seriously considering adding to. On a purely bottom-up level, the stock looks extremely cheap. Cheap in the old-fashioned sense of price versus underlying business quality and growth.

And yet, the more time I spent toying with the idea, the less time I spent thinking about the company itself. Instead, my thoughts drifted somewhere else entirely – to geopolitics, to the shifting architecture of global power, and to a question that feels uncomfortable but increasingly unavoidable: what does it really mean to own Chinese equities in a world where the rules are no longer as stable as we liked to assume just 1-2 years ago?

I don’t usually spend much time thinking about an equity thesis this way. Most of the time, valuation, unit economics, competitive positioning, management integrity, balance sheet strength, optionality in adjacent markets, long-term growth trajectories, etc., dominate my thinking.

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But this time … something was different. The stock itself is almost the easy part – from a fundamental perspective, it’s basically a no-brainer.

The harder part is figuring out how to think about tail risks that don’t show up in cash flow models or consensus estimates.

Specifically, I kept circling back to Taiwan. Not because I suddenly turned into a geopolitical strategist, but because the current global environment feels qualitatively different from what we’ve been used to over the last few years and decades. The Trump administration’s increasingly transactional approach to alliances, the attempt to reshape spheres of influence in places like Venezuela, the Greenland episode, the broader erosion of multilateralism – all of this creates an atmosphere in which great powers may start believing that the window for decisive moves is opening rather than closing.

To be clear, I don’t think a Taiwan invasion is the base case. But I do think the probability distribution has shifted. And when you invest in Chinese stocks, even a small shift in tail risk matters.

There is a temptation to dismiss these concerns as noise. Markets, after all, have a long history of climbing walls of worry. Chinese equities themselves, in recent years at least, seem to tell a story that is far less dramatic than the headlines suggest. If you look at the CSI index over the last year, the trajectory is quietly constructive.

After a weak phase earlier in 2025, the index recovered steadily, ending the period solidly positive.

Stretch the horizon to three years and the picture becomes more nuanced: a prolonged drawdown, a sharp inflection, and then a recovery that feels less like euphoria – after all, a 10-15% 3-year performance is not goin to impress anyone on Wall Street – and more like a slow rebuilding of confidence.

The KWEB ETF paints a similar picture – not a runaway bull market, but a market that reflects a much more favorable sentiment toward China over the last twelve months, but longer term has been doing pretty terribly even after the recent runup (down 58% over the last five years).

Then there are individual stocks. When you overlay the performance of major Chinese names over the past few years, the dispersion is striking. Some stocks have compounded spectacularly, others have stagnated or declined. This is not a market moving in lockstep with a single macro narrative. It is a market where company-specific fundamentals still matter, even if they are constantly filtered through a geopolitical lens.

Valuations reinforce that impression. Forward P/E ratios across leading Chinese companies span a wide range, but many sit at levels that would look conservative in almost any other major market – especially by US standards.

At this point, I kept thinking about a speech that Canada’s prime minister Mark Carney delivered at this year’s Davos meeting. It struck me because it captured, in unusually vivid language, the psychological dimension of the current world order. Before getting to the investment implications, it’s worth sitting with his metaphor for a moment, because it frames the entire problem.

Carney described a world in which the comforting fiction of stable rules is slowly dissolving, replaced by a harsher reality in which power politics is no longer constrained in the way we assumed it was. He reached back to Václav Havel and the image of a greengrocer who displays a sign he does not believe in, simply to avoid trouble. The system persists not because it is true, but because everyone behaves as if it were true. And the moment one person removes the sign, the illusion begins to crack. Carney put it like this:

“Today I will talk about a rupture in the world order, the end of a pleasant fiction and the beginning of a harsh reality, where geopolitics, where the large, main power, geopolitics, is submitted to no limits, no constraints. [...] It seems that every day we’re reminded that we live in an era of great power rivalry, that the rules based order is fading, that the strong can do what they can, and the weak must suffer what they must. And this aphorism of Thucydides is presented as inevitable, as the natural logic of international relations reasserting itself. And faced with this logic, there is a strong tendency for countries to go along to get along, to accommodate, to avoid trouble, to hope that compliance will buy safety.

Well, it won’t.

So, what are our options? In 1978, the Czech dissident Václav Havel, later president, wrote an essay called The Power of the Powerless, and in it, he asked a simple question: how did the communist system sustain itself? And his answer began with a greengrocer. Every morning, this shopkeeper places a sign in his window: ‘Workers of the world unite’. He doesn’t believe it, no-one does, but he places a sign anyway to avoid trouble, to signal compliance, to get along. And because every shopkeeper on every street does the same, the system persist – not through violence alone, but through the participation of ordinary people in rituals they privately know to be false. Havel called this ‘living within a lie’. The system’s power comes not from its truth, but from everyone’s willingness to perform as if it were true, and its fragility comes from the same source. When even one person stops performing, when the greengrocer removes his sign, the illusion begins to crack. Friends, it is time for companies and countries to take their signs down.”

When I read this, I couldn’t help but translate it into investment language. For decades, global markets have operated under a shared assumption: that economic interdependence would act as a stabilizing force, that extreme geopolitical ruptures were unlikely because they were too costly, and that the plumbing of global finance would continue to function even in periods of political tension.

Times have changed, though. Today, cracks in the above assumptions are becoming harder to ignore. Here’s how German chancellor Merz put it:

You see it in the gradual shift of reserve assets, where China – just like some Scandinavian pension funds recently –, has been reducing its holdings of US Treasuries while increasing gold reserves.

You see it in the growing emphasis on technological sovereignty, in the weaponization of sanctions and tariffs, in the slow fragmentation of supply chains. De-globalization is in full swing.

None of this implies imminent catastrophe. But it does imply that the probability of discontinuities is higher than it used to be just one year ago.

This is where my internal debate started to become concrete in light of the investment idea I have. The company I’m looking at is not just a random Chinese stock. It is a business with real earnings, a growing customer base, really fast growth (!), and a valuation that suggests the market is pricing in a lot of bad news already.

On paper, this is the kind of situation I usually like. A no-brainer. And yet, one detail kept bothering me: the lack of a dual listing. In a world where geopolitical tensions remain manageable, this is a footnote. In a world where financial infrastructure itself may become a tool of statecraft, a bargaining chip, it is something else entirely.

Suddenly, the question is no longer just whether earnings will grow or margins will expand. It is also whether ownership structures will remain intact, whether ADRs will continue to function as expected, and how different scenarios would play out if tensions escalated beyond what markets currently discount.

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What makes this topic broader than a single stock is that the underlying dilemma applies to many Chinese equities today. Sentiment towards China has improved somewhat over the last year, and performance has been decent enough to challenge the narrative of permanent decline. At the same time, valuations remain somewhat compressed, capital flows are still somewhat cautious, and geopolitical risk is heightened. It is part of the discount rate you have to choose when investing in China.

The interesting question is not whether opportunities still exist in China. They clearly do. The question is how to think about them in a world where the assumptions of the last 30 years are slowly being renegotiated.

Before going any further, I should be explicit about one thing: Unlike some commentators who can apparently wake up one morning as a geopolitical expert, a currency strategist and a commodities analyst all at once, I’m painfully aware of the limits of my own circle of competence. I’m not a macro oracle. I don’t pretend to have privileged insights into military strategy or diplomatic chess games. My comparative advantage, if I have one, lies in analyzing companies, industries, and valuation dynamics. Everything I’m about to explore should be read through that lens. This is not an attempt to predict the future of global politics. It’s an attempt to connect the macro uncertainties of our time with the very concrete question of whether and how to invest in Chinese stocks.

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

Thoughts on the Non-Zero Taiwan Risk

If there is one thing I learned while thinking through the Taiwan question, it’s how easy it is to slide from analysis into narrative. I had a long conversation with a friend who owns Tencent and Alibaba via Hong Kong and has spent years thinking about China risk in a more pragmatic way than most Western investors. His view was not that a conflict is impossible, but that the way it is framed in Western media often borders on caricature. In his words, the probability of a Chinese move is non-zero, but the certainty with which headlines talk about an “inevitable invasion” says more about our own psychological need for simple stories than about reality.

And I agree, even though I will say it’s always hard to assess this objectively as someone living in the West with no boots on the ground. Historically, the Taiwan issue is more ambiguous than it is often portrayed. Taiwan’s status after World War II emerged from an unusual combination of Allied wartime declarations, the outcome of China’s civil war, and decades of geopolitical bargaining between Beijing and Washington. The People’s Republic of China has always considered Taiwan part of its territory, while the United States has maintained a deliberately ambiguous position, recognising Beijing diplomatically but committing itself to Taiwan’s defence in practice. This ambiguity is not an accident. It has been a stabilising mechanism for decades.

Both sides could maintain their narratives without forcing a decisive confrontation. From Beijing’s perspective, the long-term objective was never in doubt, but the timeline was flexible. From Washington’s perspective, ambiguity allowed deterrence without formal recognition of Taiwanese independence.

This is where the Western framing often becomes misleading. When Chinese officials talk about reunification, Western audiences instinctively translate that into imminent military action. But Chinese strategic culture is built around patience, rationality, incrementalism, and asymmetry in time horizons. The idea that a conflict can be postponed for decades is not seen as weakness in Beijing. It is seen as rational statecraft. If the balance of power is moving in your favour over time, why rush? Why gamble everything on a single dramatic event when demographic, economic and technological trends might deliver the outcome more cheaply later?

At the same time, it would be naïve to assume that rationality always prevails. History is full of examples where great powers miscalculated precisely because they believed they were acting rationally. The Taiwan Strait is not just a bilateral dispute. It is a focal point of global technological and military competition.

Source: https://www.civilsdaily.com/news/places-in-news-taiwan-strait/

When the United States increases arms deliveries to Taiwan, it is not merely supporting a partner. It is signalling that Taiwan is part of its strategic perimeter. When China responds with military exercises, it is not merely posturing. It is testing thresholds, domestic legitimacy and external resolve. In such an environment, the risk of escalation does not come only from deliberate decisions. It also comes from misinterpretation, domestic political incentives and the inertia of security bureaucracies.

One argument that resonated with me is that risk tends to rise not where rhetoric is loudest, but where hardware accumulates. Conflicts rarely erupt in places devoid of weapons. They erupt where capabilities, expectations and political narratives collide. Ukraine is the obvious example. Taiwan is structurally similar in that sense. The steady militarization of the island is not proof that war is imminent, but it is proof that the option is being prepared for by all sides. Preparation itself changes incentives. It reduces the psychological barrier to action, even if the economic cost remains enormous.

Source: CNN

Another dimension that is often overlooked in Western discussions is Taiwan’s internal politics. The island is not a monolithic actor. Its parliament is fragmented, and the balance of power is far more delicate than the usual “pro-independence versus pro-China” narrative suggests. The Democratic Progressive Party is currently the ruling party in Taiwan, leading a minority government that controls the presidency and the central government, while the opposition bloc led by the Kuomintang (KMT) holds a slightly larger share of seats, supported by smaller parties and independents. In other words, Taiwan’s political centre of gravity is neither firmly anchored in independence nor drifting towards unification. It is suspended in a state of managed ambiguity.

The KMT’s position is particularly instructive. It is more open to engagement with Beijing and more sceptical of outright independence than the DPP, but it is not necessarily best viewed as a vehicle for annexation. If the KMT were to gain more seats or regain the presidency, the most plausible outcome would probably not be a dramatic geopolitical shift. It would be incrementalism. Slightly warmer communication with Beijing. Symbolic gestures of de-escalation. Perhaps renewed cross-strait economic agreements. But ultimately, the KMT would remain trapped in the same structural dilemma as every Taiwanese government before it: avoiding both independence and unification.

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From Beijing’s perspective, this is deeply unsatisfactory. The only outcome China is willing to accept in the long run is some form of political unification under its terms. Yet even the KMT is unlikely to endorse such a step. Even a historically symbolic figure, such as the current mayor of Taipei, who is widely seen as a potential KMT presidential candidate and a descendant of Chiang Kai-shek, would struggle to sell any meaningful concession on sovereignty to the Taiwanese electorate. At most, one could imagine a stepwise process: new cross-strait service agreements, deeper economic integration, perhaps limited Chinese participation in certain sectors like media. But those are evolutionary moves, not transformative ones. And they belong to a timeline that stretches beyond the next election cycle, perhaps into the late 2030s.

This is why Taiwan’s democracy is so geopolitically important. It produces outcomes that are stable but unresolved. Neither annexation nor independence, but a persistent equilibrium that frustrates Beijing and reassures Washington. For investors, that equilibrium is paradoxically stabilizing. It reduces the probability of sudden rupture while keeping the underlying conflict alive.

And yet, there is a variable that reshapes this entire equation: technology. Taiwan’s strategic importance is not only political, it is maybe even more so industrial. As long as the island remains indispensable to the global semiconductor ecosystem, it retains leverage that far exceeds its geographic size.

“The island is a microchip fabrication hotbed, producing 60% of the world’s semiconductors — and around 93% of the most advanced ones, according to a 2021 report from the Boston Consulting Group. The U.S., South Korea and China also produce semiconductors, but Taiwan dominates the market, which was worth almost $600 billion last year.“ - NBC News

But that leverage is not permanent. The United States is investing aggressively in domestic semiconductor manufacturing, not only for economic reasons but for strategic ones. If Washington were ever able to replicate a meaningful share of TSMC’s capabilities on its own soil – which seems very likely over the long run –, the geopolitical calculus would change in subtle but profound ways.

In such a world, Taiwan’s strategic value to the US would not disappear, but it would diminish. And if that happens, the implicit bargain between Washington and Beijing could shift. I don’t know if or when this scenario materializes. But it is hard to ignore the sense that there is a quiet race underway: China trying to reduce its technological dependence on the West, and the US trying to reduce its dependence on Taiwan. If both succeed partially, Taiwan’s unique position in the global system weakens. At that point, the idea of a “peaceful transition” stops being a moral question and starts (almost) becoming a strategic “win-win proposition.”

When I put all of this together, I end up with an uncomfortable but intellectually honest conclusion. A Taiwan conflict is neither inevitable nor unthinkable. It sits in a grey zone where probabilities matter more than certainties. For investors, this is the worst possible category of risk. It is too large to ignore and too uncertain to model with precision. And yet, it shapes the discount rate one needs to apply to every Chinese asset.

From here, the question becomes more concrete. If geopolitical risk is real but not binary, how should it influence the way I look at a specific stock? And how does the structure of ownership, listings and market access amplify or mitigate that risk? That is where the abstract debate about Taiwan suddenly turns into a very practical problem (for me).

Very Recent Developments: The Taiwan Risk Isn’t Static

While I was thinking through the Taiwan question, another development caught my attention: the downfall of Zhang Youxia. For years, he had been seen as one of the untouchables in China’s military hierarchy. A senior vice-chairman of the Central Military Commission, a veteran of the 1979 war with Vietnam, a long-time ally of Xi Jinping, and one of the few figures with both political weight and operational credibility inside the PLA (People’s Liberation Army). When even someone like Zhang is suddenly placed under investigation, it tells you that something deeper is going on beneath the surface.

The Economist described it as one of the most dramatic blows to China’s military leadership in decades. In a short span of time, Xi has effectively hollowed out the upper layers of the PLA, purging not only generals appointed by previous leaders but also many of his own protégés. From one perspective, this looks like a classic authoritarian consolidation of power. From another, it looks like a system under stress, where loyalty and competence are increasingly difficult to reconcile.

The Economist

For investors invested in Chinese equities, the key question is not who wins factional battles inside the CCP. It is what these purges imply for China’s strategic capacity and its timeline.

On the surface, one could argue that an internally weakened military increases the temptation to externalize problems. History offers plenty of examples where domestic instability preceded foreign adventures.

But there is an equally plausible interpretation that points in the opposite direction. A military consumed by internal purges, mistrust, and organizational disruption is not an ideal instrument for a complex amphibious operation across the shallow Taiwan Strait. Even the Pentagon has hinted at this ambiguity, noting that corruption and leadership turnover may cause short-term operational disruptions, even if reforms eventually produce a more capable force.

“The Taiwan Strait, over ninety miles wide, is incredibly choppy, and due to two monsoon seasons and other extreme weather events, a seaborne invasion is only viable a few months out of the year.“ - from the article linked below

A superb read on the difficulty of invading Taiwan —> Council on Foreign Relations: Why China Would Struggle to Invade Taiwan - https://www.cfr.org/photo-essay/why-china-would-struggle-invade-taiwan

This is where I find the situation intellectually interesting. If Xi’s priority is regime security and internal control, then external risk-taking becomes less attractive in the near term. Purges absorb attention, create uncertainty within command structures and reduce the margin for error. A Taiwan operation would require not just political will, but extraordinary institutional coherence. The current trajectory suggests the opposite. In that sense, the Zhang Youxia episode may paradoxically reduce the probability of a near- and medium-term military move, even if it increases the sense that China is entering a more volatile phase.

Of course, the information environment around these events is polluted by speculation, rumours, and ideological projections, and after all, only Xi really knows what the long-term plan is.

Some narratives describe internal coups, secret arrests and imminent collapse of the CCP. Others portray Xi as an omnipotent strategist executing a flawless plan. Reality is almost certainly more banal and more complex. Power struggles exist, corruption persists, institutional reforms are incomplete, and the PLA is neither a paper tiger nor a perfectly oiled machine. For an investor, the only intellectually honest position is to treat extreme claims with scepticism while recognizing that structural fragility is real.

The Stock I’m Inclined to Buy

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]]><![CDATA[[FREE] The Next Subprime? Jeffrey Gundlach’s Deeply Bearish Take On Private Credit]]>https://www.compoundwithrene.com/p/free-the-next-subprime-jeffrey-gundlachshttps://www.compoundwithrene.com/p/free-the-next-subprime-jeffrey-gundlachsWed, 19 Nov 2025 16:38:02 GMTNote: The voiceover above is a custom-made, slightly adapted version of the blog post, edited for a smoother and more engaging listening experience. It’s one of the perks available to paid subscribers, as I’m always focused on adding as much value to my subscribers as possible. Enjoy!

Jeffrey Gundlach is one of the very few macro thinkers I still pay close attention to as a bottom-up investor.

In a world full of loud, vague, recycled macro takes, he consistently delivers something rarer: actual insight!

The man has been doing this for multiple decades, has a documented hit rate, openly talks about the years he’s been wrong, and still manages to fire off sharper observations than most strategists half his age.

Speaking of age – how is he 66 years old and looking younger and more energised than a lot of people in their fifties? If there’s a longevity ETF that includes whatever Gundlach is doing, I’d like to know about it.

What I’ve always appreciated is his transparency. He calls himself a “straight shooter” in the interview, and that’s not an exaggeration. There’s no/little hedging for marketing purposes, no softening of the edges to keep everybody comfortable. He tells you what he thinks, why he thinks it, and where he might be wrong. And I genuinely believe he’s underappreciated by the broader investing community. You may not always agree with him, but he will absolutely force you to think.

I’ve unpacked some of his macro takes in past posts (such as the one linked below), and judging by how this most recent interview went, I’ll probably keep doing so in the future. If you don’t want to miss those posts, make sure to subscribe.

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Some interviews add a datapoint. Others force you to revisit the mental model itself. Jeffrey Gundlach’s appearance on Odd Lots (a Bloomberg podcast) falls squarely into the second category. It’s not a clean, single macro “call.” It’s a collection of somewhat uncomfortable ideas that, taken together, paint a picture of a system that’s running out of easy options.

In what follows, I want to walk through the key themes that stood out to me – rates, private credit, fiscal sustainability and potential rate manipulation, portfolio construction, the art of changing your mind, and his thought experiment on “perfect foresight.”

I’ll quote Gundlach where it helps, but the focus is on summarizing and unpacking what he’s really saying and what it might mean for your own positioning. I’ll cover seven insights in total.

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1. A Broken Playbook? When Rate Cuts Don’t Pull Long Yields Down

Historically, once the Fed starts cutting, the rest of the curve eventually follows. Short rates fall by definition, and longer maturities typically drift lower as the market prices weaker growth and lower inflation.

“[H]istorically when the Fed cuts interest rates, of course, short term interest rates decline definitionally at the Fed funds level, but also two-year Treasury rates decline, five-year Treasury rates decline. In effect, long-term Treasury rates have always declined, subsequent to the first cut by the Federal Reserve, and particularly when you’re in a sequence of Federal Reserve cuts.“

This cycle is different.

Gundlach highlights that since the Fed began cutting, “all interest rates outside of the two-year are higher than they were before the Fed’s first rate cut. That’s just never happened historically.”

That isn’t a footnote. For years, he has argued that “the secular decline in interest rates at the long-term maturities is over,” and recent price action is precisely the kind of confirmation he’s been looking for.

Long yields are no longer treating Fed cuts as a cue to behave.

A second anomaly is the dollar’s behavior during equity drawdowns. In the “tariff tantrum” correction earlier this year, instead of rallying as a safe haven, the dollar fell. By Gundlach’s count, in the 12 previous corrections of at least 10% in the S&P 500, the dollar “usually goes up by around 8%.” This time, it went down by about 10%.

For him, these two breaks – the long end ignoring cuts, and the dollar failing in its traditional crisis role – point in the same direction: the old ZIRP-era playbook is done. The market is starting to care more about structural issues like fiscal sustainability and long-term inflation pressure than about short-term Fed tweaks.

Before we dive back in, a quick note…

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2. Private Credit & Private Equity: New Crisis Candidates?

The most scathing part of the interview is his take on private credit.

He doesn’t just think it’s overheated. He thinks it’s the most likely epicenter of the next real crisis. Yes, you read that right!

The starting observation is where the bad lending has migrated. Before the GFC, a lot of questionable structures were in the public markets and securitizations. Today, he argues, “in recent years, the garbage lending has not gone to the public markets. The garbage lending has gone to these private markets and private credit has been very popular and is now increasingly been over allocated to by large asset pools.”

That would already be worrying, but the way the product is sold and marked makes it more dangerous. The core marketing pitch is a Sharpe ratio story: similar or slightly better returns than public credit, with much lower volatility.

Gundlach’s response is brutal: “If you don’t mark to market, there’s no volatility.”

He points out that this is just a repackaging of the same trick private equity has used for years – when the S&P falls from 100 to 50, PE marks drift from 100 to 80; when the index recovers to 100, PE gets marked back to 100. Same long-term return, but recorded volatility looks much lower because the path is smoothed with stale marks.

The recent Renovo example crystallises how there’s a lot of hidden fragility:

“And now it’s very fascinating that this Renovo in the article today it basically said that they had a chapter seven filing and bankruptcy filing and their asset, the their liabilities were listed as being between 100 and $500 million. You just, you know, you check a box, you’ll forget a specific number. So there’s ranges and the whole range that their liabilities were in was between 100 and $500 million.”

Gundlach’s line is memorable: “It’s like there’s only two prices for private markets, 100 and 0.” When your pricing system only recognises perfection or collapse, you don’t have risk management, you have theatre.

The packaging makes it worse. There are now vehicles offering “Main Street” access to private credit – a product design he describes as “the perfect mismatch of no liquidity with a vehicle that promises liquidity.” Under benign conditions, everybody can pretend this works. Once redemptions pick up, the daily price will be the only adjustment valve, and it can gap lower very quickly.

His verdict is clear: “the next big crisis in the financial markets is going to be private credit. It has the same trappings as subprime mortgage repackaging had back in 2006.”

Then there’s Harvard as an example of the liquidity in PE:

“I remember Harvard University, for example. They’ve got like a 50 odd billion dollar endowment, and their donors pulled back when they had uprisings on campus, and the donors didn’t like what was going on. So they stopped donating for a while. And Harvard had no money, a $50 billion endowment, and they couldn’t pay salaries. They couldn’t pay the late bills, they couldn’t pay basic maintenance. They had to go to the bond market to borrow. They tried to borrow about $4 billion, I think. I think they got away with about $2.5 billion. But it’s fascinating that you have a huge asset pool that doesn’t have liquidity to pay the bills.”

Source: https://www.markovprocesses.com/product/mpi-transparency-lab/?tab=endowments#7

The practical takeaway from all of this for me is not that every private credit deal is doomed. It’s that the combination of optimistic marks, leverage on top of leverage, liquidity promises that cannot be honoured in stress, and mass marketing stories built around “low volatility income” and “safe high returns in PE” are a classic late-cycle pattern.

“German retail investors represent one of the world’s largest untapped pools of wealth, and private equity firms are eager to gain access.” – Claudio de Sanctis, Head of Retail Banking at Deutsche Bank.

We’ve already seen a few “cockroach” defaults. The odds that those are isolated incidents look low. How soon exactly something will break, Gundlach cannot predict though:

“Now, it took a couple of years for it to totally unravel. So this stuff doesn’t happen in a week or a year even. But I’m very negative on that.”

3. The Fiscal Doom Loop – And Why Rates May Be Manipulated Next

Gundlach’s bearishness on long-dated Treasuries is anchored in arithmetic, not just vibes. The U.S. deficit is already increasing at a rate of roughly 6% of GDP, a level that historically coincided with deep recessions.

For comparison, during normal (non-recession) years, the U.S. deficit has averaged around 2-3% of GDP in many years.

But in downturns, that deficit tends to blow out. Over the last half-century, it usually widens by 4–5 percentage points of GDP in a recession. In the GFC and Covid episodes, it moved closer to 8 points. So a recessionary deficit in the 10–12% of GDP range isn’t a wild tail scenario – it’s a plausible base case if nothing changes. That’s where interest expense starts to consume the system.

Today, he notes, roughly “about 1.4, $1.5 trillion of the $7 trillion budget is now interest expense,” with only around $5 trillion in tax receipts (i.e. a $2 trillion budget deficit).

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In other words, about 30% of revenues already go to interest. And that share is almost mechanically set to rise, because the bonds rolling off in the next few years still carry an average coupon “a little bit below 3%,” while new issuance happens at much higher yields.

At Jim Grant’s 40th anniversary conference, Gundlach walked through a scenario where deficits and rates stay elevated. With “plausible” but pessimistic assumptions, he arrives at a world around 2030 where “we have 60% of all tax receipts going to interest expense.”

That sounds absurd until you consider what could be happening in Japan at a >200% debt-to-GDP ratio at lower rates (the US currently sits at 118%):

According to Gundlach, if you push the assumptions harder – “what if interest rates go up to 9% on treasuries? And what if the budget deficit goes to 12% of GDP and you make these kinds of, you know, pessimistic assumptions?“ – and the model spits out an absurd result: interest costs equal to 120% of tax revenues.

“Of course that is impossible,” as he puts it, which means something has to give – either the fiscal path or the rules of the market itself.

That’s where his views on potential rate manipulation come in. He has been saying for almost two years that “we cannot afford the market to set interest rates if the deficit spending continues.” His base case is some form of explicit intervention designed to cap long yields: “What has to happen is some sort of drastic measures. And I’m not exactly sure what those drastic measures are going to be. There’s a number of candidates for them.” One candidate is full-blown yield curve control, echoing the U.S. in the 1940s and Japan more recently. Back then, the U.S. kept long bonds at 2.5% even as inflation pushed towards 8%. Japan spent years with a zero-yield cap, with the Bank of Japan effectively the only natural buyer of its government debt.

This anecdote made me laugh out loud:

“I actually had a meeting with, the guy that ran the biggest pension plan in the world as one of the Japanese public pension plans, and I was really anxious to sit down with them. And I said, I really want to ask you this question, do you actually own these negative yielding jobs? And he actually laughed out loud when asked, what’s he said, of course, now nobody owns them except the Bank of Japan and the and the institutions that are forced to buy them by the Bank of Japan.”

Another lever would be targeted support for mortgage rates – “they could absolutely buy Ginnie Maes, Fannie Maes, Freddie Macs… and drive those yields down much closer to where Treasury yields are.”

Yet another, more radical option is outright restructuring of Treasury terms. He references a white paper suggesting special treatment for “foreign” holders, but points out that once the taboo is broken the logic can easily be extended: “Why put the word foreigners in there? Why not just say we’re going to restructure the Treasury debt full stop?”

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His toy example is brutal in its simplicity: “All the treasuries that exist today, we’re changing their coupons. The ones that have a coupon above one, the coupon is now one.” That would slash interest expense, devastate existing bondholders, and likely shut the government out of voluntary borrowing “for a couple of generations.”

Against that backdrop, his reaction to the current political flirtation with a $2,000 “tariff dividend” to every American is revealing. With tariffs raising a few hundred billion dollars a year, the idea is to send $2,000 checks to households. Gundlach’s verdict is blunt: “We don’t have any money. We’re borrowing $2 trillion. We don’t have $2,000 to throw away at people again.”

We’ve just lived through 2020–2022, when aggressive money-financed transfers helped push inflation to 9.1%. In his eyes, the fact that such proposals are back on the table shows how unserious the debate still is.

Put together, this is why he says “long-term treasuries look vulnerable.” If you believe the system will eventually move towards some combination of higher inflation, explicit caps on yields, distortive interventions and/or restructuring, then owning a lot of long-duration nominal claims on that system doesn’t look particularly attractive.

However, he still likes “short-term treasuries because I think the Fed is likely to cut interest rates, and that definitionally leads to lower interest rates on, say, five years and in maturities.“

4. Rethinking 60/40: What A “Defensive” Portfolio Looks Like To Him

So what does he do with all of this? At a high level, he thinks “financial assets broadly should be lower allocated, have a lower allocation than typical” right now. The traditional 60/40 equity-bond portfolio implies a 100% allocation to financial assets.

In his view, that’s too high given current valuations and macro fragilities. For equities, he suggests “investors should have maximum 40%” in stocks today, and even that should be tilted away from the U.S. towards non-U.S. markets. For a dollar-based investor, he sees better opportunities in European and emerging markets. EM local stocks and European indices have already outperformed this year once you factor in currency moves. On the fixed income side, he still sees a role, but smaller than the traditional 40%. He proposes “about 25%” in bonds, again with a meaningful non-dollar component – especially emerging-market fixed income, which he notes has been “by far the highest performing sector for dollar based investors in the fixed income market this year.”

The interesting part is what fills the remaining 35–40%. For several years, Gundlach has been vocal about gold and real assets. In his annual “Roundtable Prime” discussion, he made gold his “number one best idea,” arguing that it has graduated into a mainstream asset class: “I think gold is now a real asset class. I think people are allocating to gold, not just the survivalists… people who are actually allocating real money because it’s real value.”

Reading recommendation:

After a strong run, he thinks the very aggressive allocation he once advocated – 25% in gold and land-like assets – is probably too high, and talks more about something like 15% (“or something like that”) today. The rest, in his mind, belongs in cash. That’s not because he’s excited about holding bills forever, but because he thinks “valuations are just incredibly high” and that the U.S. equity market is “among the least healthy in my entire career” on classic metrics like P/E, CAPE, and overall speculative tone.

He views the AI and data-center boom as another mania. To make the point, he uses a historical analogy that I really like. The electrification of homes was arguably one of the biggest economic and social transformations of the last 150 years. Investors spotted it early. Electricity stocks had a massive run. But their outperformance versus the rest of the market actually peaked in 1911 – long before electrification was widespread.

The idea is simple: truly transformative technologies can be real and still produce index-crushing returns far earlier than you’d think, as investors wildly over-discount the future.

5. Changing Your Mind: Why It’s Essential – And Why So Few People Do It

One theme that runs through the interview, and that I think is underappreciated, is how hard it is to genuinely change your mind in markets.

Gundlach uses his own stance on the dollar to illustrate it. For decades, he was a committed strong-dollar guy: “I was 100% dollar. I would own no foreign currencies for decades.” That was part of his identity as an investor.

About 18 months ago, he finally flipped. He “had to pull the trigger” and accept that the strong-dollar paradigm he’d benefited from “is [not] intact any longer.”

The way he describes that internal conflict is very human:

“You wake up in the morning and you look in the mirror and say, I’m looking at a strong dollar guy. And then all of a sudden the next day you say, I’m looking at a guy that’s no longer confident in a strong dollar… who am I?”

That’s the emotional cost of a genuine regime shift. You’re not just changing a position; you’re rewriting the story you tell about yourself.

Now put yourself in the shoes of a typical financial advisor or portfolio manager. The incentives are stacked against meaningful change. The example Gundlach uses is assume you bought something like Apple at $5 and it’s now at $700, that line item makes every client meeting easier. You can point to it and say, “Look at this cost five, last price 700, I am working for you.”

Apple - 45 Year Stock Split History - Source: Macrotrends

The day you sell it, you remove that story from the statement. You’re no longer “the person who bought Apple at $5,” you’re just the person who used to have a great call. Humans hate that. They cling to the past winner as social proof.

Gundlach’s point is that this is dangerous. The very positions that have made you look smart and employable are the ones you’re least willing to kill, even when the underlying thesis changes. The incentive structure pushes people to project past successes into the future, long after the structural drivers have shifted.

His own shift away from dollar maximalism, and towards more non-U.S. risk and currency exposure, is a case study in doing the uncomfortable thing when the evidence forces you to.

From my perspective, that’s one of the more useful meta-lessons: it’s not enough to be theoretically open-minded. You have to be willing to dismantle the very positions that make you look good in front of clients, friends, and followers. Often, that’s what leads to future success in this business.

6. When The Rules Change Mid-Game

Another big theme is that “the rules can be changed” much faster and more radically than most investors like to think. Gundlach points to two recent episodes as proof:

  • During the GFC, the authorities effectively overrode the legal terms of mortgage securitisations by modifying mortgages in ways that weren’t contemplated in the prospectuses.

  • In 2020, the Fed crossed what many saw as a bright line by buying corporate bonds.

He admits that the “magnitude of money printing that occurred in 2020, 2021, 2022” and the fact that the Fed “broke the law and bought corporate bonds” genuinely surprised him. It probably shouldn’t have, but it did. Once you internalize that, more radical ideas stop looking like science fiction. The discussed idea of a yield curve control framework, comprehensive restructuring of Treasury terms, different treatment for “foreign” holders, targeted interventions in mortgages – all of these move from unthinkable to “under certain conditions, quite possible.”

That’s why he keeps hammering the point that paying the existing debt back in today’s dollars is impossible, and that something like a rewrite of the rulebook is likely. The fact that ideas for new “schemes” in the same spirit keep popping up tells him that the political impulse hasn’t changed.

Add to that the collapse in institutional trust among younger cohorts. Surveys show that people under 35 increasingly “don’t believe in the institutions of this country at all.” They don’t think they’ll ever replicate the baby-boomer experience – not in housing, not in wealth building, not in career stability. That’s fertile ground for more radical redistributive policies.

One idea he throws out – half joking, half serious – is an “age tax,” a surtax on those over 55 based on the argument that they had a vastly better environment for wealth accumulation and should “give some of that back.” You don’t have to take every one of these ideas literally.

The point is that when a system’s arithmetic stops working, and when trust in its institutions collapses, the range of plausible policy outcomes widens a lot. As investors, ignoring that and assuming a clean, rules-based path back to stability is probably the bigger risk.

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7. The “Perfect Foresight” Experiment: Why Being Right Isn’t Enough

The last piece I want to highlight is about process rather than macro. Apparently, early in his career, Gundlach was tasked with an odd thought experiment:

What would happen if you had “perfect foresight” in markets?

Using historical data for stocks, bonds, real estate, commodities and so on, he simulated a strategy that, at the start of every year, picks the asset class that will have the highest return over the next five years. You literally can’t do better in terms of long-term selection.

The surprising result was that, even with that impossible advantage, you’d likely go out of business!

The reason is timing. In many cases, the asset class that ends up being the five-year winner is a poor performer in the first one or two years. Returns are back-end loaded. A client looking at year-one and year-two performance wouldn’t see a genius with perfect foresight. They’d see underperformance and fire you.

As he puts it, “we cannot invest other people’s money with a five-year horizon.” The business reality just doesn’t allow it. On the other hand, he thinks most managers go too far in the other direction – constantly tweaking positions with a de-facto one-week or one-month horizon. The odds of being consistently right at that frequency are tiny, even if your macro compass is good.

After experimenting with different horizons, he concluded that the “sweet spot” is “between 18 months and two years.” Long enough for big structural views to have a chance to play out, short enough that clients don’t necessarily pull the plug before anything happens.

Measured over that kind of window, across a large number of decisions, he says he’s ended up with “a 70% hit rate.” The flip side is that he’s wrong 30% of the time, which means “I’ve been at this for over 40 years, so I’ve been wrong for more than 12 years.”

The key is that those bad years weren’t consecutive. In his words, “three years is when everyone pulls the plug. If you underperform year one, year two, when you’re three, you’re gone.”

He also reminds us how painful it can be to live through the “wrong” part of being early. He turned “maximum negative” on the Nasdaq on 30 September 1999 and “looked like a moron” three months later as the index surged another 80%. “But if you had gone short the Nasdaq September 30th of 1999, 18 months later, you had a profit of 64%, even though it went up 80% in the first three months, it dropped.“

The intervening period is where careers either survive or die. The big takeaway for me is that having a strong view – on private credit, on long rates, on the dollar – isn’t enough. You need to be explicit about your time horizon, realistic about your hit rate, and honest about how much interim pain you and your clients can tolerate.

The “perfect foresight” experiment is a nice reminder that even the best long-term calls can look disastrously wrong for long enough to get you fired.

Closing Thoughts

Individually, none of these Gundlach themes are completely new. People have worried about the US deficits for years. The death of 60/40 and the question of dollar hegemony have both been debated at length. What makes this interview interesting to me, though, is how a more fragile environment is seemingly forming: Long rates rising despite cuts. A fiscal path that doesn’t add up under current rules. Historically high valuations. Private markets showing more 100-to-0 moments. Political proposals like $2,000 “tariff dividends” that ignore freshly learned inflation lessons. A policymaking culture that has already shown it’s willing to bend or break supposedly hard constraints. And sitting on top of that, an investing industry whose incentive structure makes genuine mind-changes rare, and whose clients would fire even an omniscient manager with a five-year horizon.

You don’t have to agree with every detail of Gundlach’s view to take the core message seriously: we are not in a benign, mean-reverting cycle where old correlations and old playbooks can be trusted blindly.

For me, the value of this interview isn’t that it hands me a ready-made asset allocation. It’s that it forces me to re-examine some comfortable assumptions – about what’s safe, what’s liquid, what’s “diversified,” how stable the rules really are, and how honest I am with myself about time horizons and the difficulty of changing my mind when the world actually does.

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PS: Watch the full interview here:

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]]><![CDATA[Brockhaus Technologies and the Fragile Foundation of Policy-Driven Demand]]>https://www.compoundwithrene.com/p/brockhaus-technologies-and-the-fragilehttps://www.compoundwithrene.com/p/brockhaus-technologies-and-the-fragileFri, 12 Sep 2025 16:01:48 GMTI’ve spent the last few weeks revisiting Brockhaus Technologies with a more skeptical eye. Not because the underlying concept of combining high-margin niches with a platform-like Bikeleasing business has broken, but because the market’s message is becoming hard to ignore.

The share price has now slipped below the 10 €/share mark – a level that might be psychologically important for traders, even if I don’t base my own investment decisions on round-number thresholds.

What matters more to me is that the selling pressure doesn’t seem to ease. And as much as I like to think in fundamentals rather than charts, price often drives narrative. Watching the stock grind lower forces me to ask whether I’m overlooking something.

There are two major issues shaping that narrative today. The first is a credibility gap that’s starting to look baked into the multiple. Investor relations feel guarded, management offers sparse answers to straightforward questions, and shareholder outreach often meets radio silence.

None of this changes the basic unit economics of Bikeleasing or IHSE. But it does mean that trust – in management discipline, in capital allocation, in communication – is underpriced, and the discount reflects that.

The second issue might be regulatory risk. What once felt like a remote possibility now looks more tangible – to me at least. That updated view is based on a decision made in Finland, where the government proposed ending the tax exemption for employer-provided bikes starting in 2026. Framed as a budgetary necessity, the move has already rattled the local market, with trade groups warning of sharp declines in bike sales.

Germany, of course, has a slightly different political and legal architecture, with the framework codified in the Einkommensteuergesetz through at least 2030. But the Finnish case serves as a reminder that policy scaffolding can wobble when fiscal consolidation looms. And since Bikeleasing’s demand engine rests directly on that scaffolding, I can’t responsibly ignore the tail-risk, and I’ll share my thoughts in this piece.

Layered onto this backdrop is a company-specific picture that hasn’t helped sentiment. IHSE keeps stumbling, with weak 2024 results leading to covenant breaches and impairments. Brockhaus also booked sizeable non-cash charges that depleted retained earnings, limiting options like dividends or buybacks under German stock law. Add this to the lack of fresh M&A, and the “serial acquirer” story feels like it’s running on fumes for now.

None of this means the equity is broken. But it does mean that the discount assigned by the market isn’t something I can brush aside as short-term noise – if anything, I think the stock is rightfully “punished.”

My aim in this piece is to separate what’s genuinely structural from what’s fixable, assess the regulatory risk with clear-eyed probabilities, and connect that back to valuation ranges. I’ll also be candid about where my own conviction has shifted and where I still see upside if certain dominoes fall the right way.

This is where it gets interesting.

Become a paying subscriber to read the rest of this post and get access to all of my other research, including valuation spreadsheets, deep dives (e.g. LVMH, Edenred, Digital Ocean, or Ashtead Technologies), and powerful investing frameworks.

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]]><![CDATA[Finding Opportunity in the Market’s Blind Spots!]]>https://www.compoundwithrene.com/p/finding-opportunity-in-the-marketshttps://www.compoundwithrene.com/p/finding-opportunity-in-the-marketsFri, 15 Aug 2025 09:40:14 GMTThere’s a certain thrill in buying a stock everyone else seems to have given up on. Not the kind of thrill you get from chasing the latest AI darling that’s making headlines, but the quieter, more stubborn satisfaction of stepping in when the market’s back is turned. Admittedly, you need to be wired in some (weird) way to find pleasure in this mindset.

This year, I’ve done that three times. Two separate industries. Three very different business models. And yet, the deeper I dug, the more I realised they share an almost uncanny common thread.

These aren’t the kinds of companies you brag about at dinner parties – at least not right now. They’re the names people scroll past in their brokerage app because they a) never heard of them, and b) the chart looks like a ski slope.

Each has been through its own rough patch – operational hiccups, cyclical headwinds, strategic missteps – and each has seen its share price punished accordingly. But as I pored over earnings calls, annual reports, and management commentary, I began to see a pattern emerge. A pattern that, if my thesis is right, could set up asymmetric returns for those willing to look past the headlines and the short-term noise.

The truth is, this isn’t a post about one “hot pick” or a get-rich-quick tip. It’s about the discipline of finding value where it’s hardest to see, of weighing the difference between temporary setbacks and structural decline, and of being willing to accept uncomfortable uncertainty in pursuit of outsized long-term gains.

It’s also about the mental balancing act of knowing you might be early – too early – and how to sit with that without losing conviction.

Here’s what we’ll cover in detail:

  • The surprising capital allocation discipline these companies share – and why redeploying 100% of free cash flow is a good thing.

  • How deep drawdowns and negative sentiment create both fear and opportunity.

  • The operational challenges each business is facing today and what’s likely cyclical versus structural.

  • Why industry cyclicality isn’t always a death sentence – and where secular tailwinds can provide balance.

  • The differences in management style, communication, and ownership that could shape long-term outcomes.

  • How insights from Harris Kupperman’s latest letter challenge my own patience and risk discipline.

  • A sober look at potential catalysts – and the uncomfortable truth that there may be none in the near term.

  • How I’m position sizing and managing risk for each name given the uncertainty.

If you’ve ever looked at a stock trading at multi-year lows and wondered, “Am I looking at a bargain… or a trap?”, this post should resonate.

The full story starts here:

The rest of this post covers the content outlined above. If you’re serious about sharpening your investing edge, the full post (and all my previous premium content, including valuation spreadsheets, deep dives (e.g. LVMH, Edenred, Digital Ocean, or Ashtead Technologies), and powerful investing frameworks) is just a click away. Upgrade your subscription, support my work, and keep learning.

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Disclaimer: I own all three stocks discussed in this post. The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

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