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.

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.

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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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.

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.

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.

#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.

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

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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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• Aswath Damodaran’s response to Citrini’s “2028 GLOBAL INTELLIGENCE CRISIS“ report (X thread)

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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.

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.

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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.

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.

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.

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.

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

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.

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.

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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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.

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.

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.

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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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.

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.

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?

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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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.

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.

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.

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.

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.

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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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.

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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.”

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.

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.”

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.

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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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.

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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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Since the April tariff sell-off, stocks have staged an almost suspiciously clean recovery. Back in mid-April, the S&P 500 was down roughly 15% for the year. Now it’s up 10% YTD – a 30% swing! – and it’s done so without much resistance. Up, up, up we go.

And that, honestly, is what worries me. Markets that rise without stumbling make investors feel invincible, and invincible investors tend to overlook risk until it’s too late.

I was reminded of a post from Thomas Chua in late October 2024 titled Why I’m Taking Profits in the Stock Market Now. His timing wasn’t perfect – no one’s ever is – but if he used the April weakness to reinvest in quality companies that had been unfairly beaten down, it was still a good trade. Remember, while the S&P was “only” down 15% at the time, many of the so-called “tariff losers” were crushed far harder.

I had raised cash at roughly the same levels Thomas did, trimming or exiting positions in late February and early March. I’ll admit, I was a little frustrated that the deep pullback I was hoping for after Trump announced his ridiculous tariff scheme never fully materialized.

I wrote about this in May in You Missed the Bottom? Read This! Too Late to Buy? Too Early to Panic?, which touched on some of those same thoughts – I seemingly touched a nerve in this post, as to this day, it’s one of my most successful posts ever.

But even after the sharp rebound in late April, I felt that the trades I made – selling lower-IRR positions and building into higher-expected-return opportunities – left my portfolio in a better place.

Just yesterday, I summed up my current view in a tweet:

“I think the value of cash is close to the peak we saw at the beginning of the year – if not exceeding it! This may sound a little counterintuitive to some, but when things feel like ‘smooth sailing,’ when stocks are soaring to extreme highs, the value of cash is actually the highest, as phases of euphoria and outright complacency often precede market downturns.”

And that’s the crux of this post: we need to ask if now – while it feels almost riskless to keep riding this rally – is the moment to raise cash again.

Yesterday’s action almost perfectly illustrates the mood: stocks rallied after a better-than-expected inflation report, indexes hit record highs, and traders celebrated the possibility that the Fed will cut rates at its September meeting. It’s optimism bordering on complacency. Again.

Here’s the thing. I think I have a decent sense – my gut feeling – of where we are in the market cycle most of the time; what I’d call my “inside view.” But I’ve also come to believe in layering on the “outside view,” which is often the more reliable way to frame and assess things. The outside view is all about data: quantitative and qualitative signals that can either validate or challenge your gut.

So, in this post, I want to put my hunch to the test. I’ll share the market dashboard I often reference – a visualization of key indicators I’ve posted before – and walk through what it’s telling us now.

Before we dive in, here’s what we’ll cover:

• The specific market signals that suggest we’re back in high-risk territory, from valuation spreads to market breadth to speculative excesses.

• How these signals have behaved in past cycles, and what history says about their reliability.

• The counterarguments – signs that maybe it’s not time to get overly defensive just yet.

• Why market tops are a process, not a moment in time, and how I’m thinking about my own cash position right now.

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.

Reading the Market’s Dashboard: The Signals Flashing Today

So what makes me think now might not be the worst time to start raising cash? Let me begin with a chart I plan to reference repeatedly over the coming years – because if you truly internalize it, you’ll see the market in a different light. It’s simple, but it captures one of the hardest truths in investing: the riskiest times are rarely the ones that feel risky. They’re the ones that feel effortless.

When perceived risk is low, stocks are often climbing without resistance. Companies are reporting upbeat numbers, the financial media is celebrating winners, and the narrative is that nothing can go wrong. Yet under the surface, that’s when actual risk is often peaking. The line between confidence and complacency blurs, and investors – whether consciously or not – begin to stretch their assumptions about what can go wrong – and they tend to bid up prices of equities to levels that rarely produce good outcomes.

Acting countercyclically in these moments feels almost unnatural. It’s hard. It goes against your human nature (to stay with the crowd). It requires you to sell when everyone else is chasing higher highs, to slow down when the crowd is accelerating. It’s difficult, uncomfortable, and often lonely. But it’s also one of the most important habits to develop if you want to consistently beat the market over decades.

That brings me to the first signal – one that’s flashing red right now.

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Signal #1 – Valuations & the Vanishing Equity Risk Premium

If there’s one thing that deserves top billing in this list of signals, it’s valuations. And right now, they’re not screaming “cheap.” They’re doing the opposite. We’re back in territory where stocks are priced for near perfection, and the margin of safety – the cushion that allows you to be wrong and still come out fine – is paper-thin.

The first chart tells the story clearly. It compares the yield on equities – represented here by the CAPE yield (the inverse of the Shiller P/E) – with the yield on 20-year TIPS, a risk-free, inflation-protected instrument. The gap between the two can be considered the equity risk premium (ERP). In other words, it’s the extra return you earn for taking on the added uncertainty of owning stocks instead of guaranteed bonds.

Over the past two decades, that spread has swung widely. After the Global Financial Crisis, ERP was historically generous – stocks were cheap, yields were high, and investors were too scared to take advantage. In 2011, the CAPE yield was more than double the 20-year TIPS yield. Today? That gap is essentially gone. At around 0.1 percentage points, the equity risk premium is at its narrowest in the entire 20-year sample!

Apparently, during the dot-com bubble, the two lines actually inverted – risk-free bonds yielded more than stocks. We’re not quite there yet, but the direction is clear.

What does that mean in practice? If you own the market today, you’re taking on all the volatility, drawdown risk, and earnings uncertainty of equities… for barely any more return than you could lock in from a government bond (depending on the underlying earnings growth, of course). And if the ERP history teaches anything, it’s that low or negative premiums have rarely been the start of a great buying opportunity.

The second chart adds another layer. It shows the share of MSCI World stocks trading at more than 10 times EV/sales – a level that, outside of speculative bubbles, is extreme. In the late 1990s, that share spiked to just over 20%, right before the dot-com bust. After years of relative calm, it’s climbed again in the past few years, spiking during the pandemic liquidity boom, dipping briefly, and now rising again toward those late-90s heights. The sectors driving this are predictable: technology and related growth industries, the very same areas where narratives can outpace fundamentals for far longer than skeptics expect.

Put these two valuation perspectives together – the evaporating ERP and the high prevalence of richly priced growth stocks – and you get a market that’s priced for optimism. That doesn’t guarantee an imminent collapse. Valuations are blunt timing tools; expensive markets can get even more expensive. But it does mean that your forward expected returns, over the next five to ten years, are lower than average. And when the reward side of the equation is low, the relative value of cash – your dry powder – rises.

Signal #2 – Market Breadth: When Fewer Soldiers Are Carrying the Flag

Healthy bull markets aren’t just about the headline index numbers. They’re about participation. The strongest, most sustainable rallies are broad-based – many sectors, many stocks, all pulling in the same direction.

Conversely, narrow rallies, where gains are driven by just a handful of names, tend to be fragile. They can persist for a while, sometimes longer than skeptics think, but history suggests they rarely end well.

Right now, the market’s breadth looks… thin. If you compare the S&P 500 (market-cap weighted) to its equal-weight counterpart, you see a striking divergence. In April of this year, the two were running neck and neck – up 17% versus 20% since mid to late 2022. That suggested most stocks were keeping pace with the mega-caps.

But fast forward just a few months and the gap has exploded: the equal-weight index has underperformed by nearly 29 percentage points since April this year! The S&P’s recent highs have been pulled almost entirely by its largest constituents.

The picture gets starker when you look at other breadth measures. The “new highs minus new lows” line (to be found in the bottom half in the chart below) – the daily net number of stocks making 52-week highs versus those making 52-week lows – is a little underwhelming given that the S&P itself is at an all-time high. If this were a truly roaring, across-the-board bull market, you’d expect that line to be surging, like in 2017 or 2021. Instead, it’s muted.

Then there’s the percentage of S&P 500 stocks trading above their 200-day moving average – a long-term trend measure. We’re sitting at around 60%. That’s not bad, but it’s far from an “all-in” number. In past healthy uptrends, that figure has often pushed toward 80–90%. The fact that 40% of the index is still below its long-term trend while the headline index sets records suggests a market with some serious internal weakness.

Finally, we can’t ignore concentration. The weight of the top 10 stocks in the S&P 500 is hovering near record highs – levels we haven’t seen in a century. Goldman Sachs’ historical analysis shows that extreme concentration tends to cluster around market inflection points: 1929, the late 1960s, the dot-com peak, and now. High concentration by itself isn’t a sell signal – dominant companies can remain dominant (for some time) – but it does mean the market’s fortunes are disproportionately tied to the performance of a few giants. When leadership narrows to this degree, any stumble from those leaders can cause outsized damage to the index.

Breadth isn’t just an academic metric. It’s a measure of the market’s structural health. And right now, the patient is standing, but a lot of muscles aren’t firing.

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Signal #3 – Speculative Assets at Highs

One of the more subtle – but powerful – signals of where we are in the market cycle comes from looking at the most speculative corners of the investment universe. These are the assets that thrive when liquidity is abundant, investor confidence is high, and risk appetite is overflowing. They’re the first to run when the punchbowl is full… and often the first to get crushed when it’s taken away.

Right now, Bitcoin is hovering at all-time highs. On the surface, that might seem disconnected from equities – after all, crypto has its own ecosystem, its own narratives. But in practice, Bitcoin tends to behave like a high-beta, sentiment-driven asset. It thrives in periods of easy money, loose financial conditions, and investor euphoria. Over the past decade, sharp run-ups in Bitcoin prices have often coincided with late-cycle phases in equities, when risk tolerance is peaking.

It’s not just Bitcoin. During other euphoric market phases, we’ve seen this speculative fever spread to whatever the “shiny new thing” happens to be – from dot-com stocks in 1999 to housing in 2006 to NFTs and meme stocks in 2021. These assets share a common pattern: their valuations detach from underlying fundamentals, trading becomes narrative-driven, and the crowd piles in because the price is going up, not because the intrinsic value justifies it.

Why does this matter for a discussion about raising cash? Because speculative surges in fringe assets are often a byproduct of the same conditions that push broader markets to unsustainable heights: excessive liquidity, compressed risk premiums, and investor overconfidence. In the same way a fever tells you the body is fighting an infection, a speculative mania tells you the market is in an advanced stage of optimism.

Academic research supports this link. Studies on “cross-asset spillovers” show that when speculative assets like crypto, collectibles, or ultra-high-growth startups hit extremes, correlations with riskier parts of the equity market tend to rise. Investors treating gains in one area as “house money” often recycle that capital into other risky bets, further inflating valuations elsewhere.

The point isn’t that Bitcoin making new highs will cause stocks to fall. It’s that such highs are symptomatic of a market environment where capital is cheap, risk-taking is fashionable, and fear is scarce. It’s another signal.

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Signal #4 – Value Underperformance: A Market Preferring Story Over Substance

Another signal that caught my attention is the staggering underperformance of value stocks relative to expensive “glamour” names. Tobias Carlisle recently shared the data: since 1952, the cheapest decile of stocks on a price-to-cash-flow basis has outperformed the most expensive decile by roughly 18x. That’s not a typo. Over the very long term, paying less for a dollar of cash flow has been a winning strategy, and by a wide margin.

But since June 2014, the story has flipped – and flipped hard. Value has underperformed expensive stocks by about 77%. You can see it clearly in the chart: the green line representing value’s relative performance trends upward over decades, interrupted only by temporary reversals. Then, around 2014, it breaks decisively lower, plunging to underperformance levels we haven’t seen in modern market history.

Why does this matter for our discussion about raising cash? Because prolonged value underperformance is often symptomatic of a market that’s placing a very high premium on growth narratives, momentum, and future potential, rather than on current fundamentals. Investors are paying up – sometimes massively – for companies whose cash flows may lie far in the future. That can work beautifully in environments with low interest rates, abundant liquidity, and optimism about technological change. But it also leaves little room for error.

We’ve seen this before. The late 1990s were another extreme stretch where expensive growth crushed value. The mood back then was that “old economy” companies were obsolete; all the real returns would come from the “new economy.” For a while, that belief was self-reinforcing – expensive got more expensive, and valuation discipline looked quaint. But when the dot-com bubble burst, the pendulum swung hard back toward value.

Signal #5 – Hedge Fund Cash Levels: When Pros Are All-In

One of the more telling positioning indicators comes from hedge fund cash levels. The chart here tracks the unencumbered cash ratio – cash as a percentage of gross assets – for funds across different size cohorts. As of the latest data, cash levels for the top 10, top 11–50, and top 51+ funds are all near five-year lows. For the largest funds, cash is hovering around 2–3%. That’s about as close to “fully invested” as you get in professional money management.

Low cash levels can mean a few different things. In one light, it signals confidence. These managers see opportunity and want to maximize exposure. But in a contrarian framework, low cash is a yellow flag. It means there’s very little dry powder left to buy dips. If markets turn lower, instead of stepping in as liquidity providers, these same funds may become forced sellers to meet redemptions or rebalance exposures.

There’s also the behavioral angle. Hedge funds are in the business of generating returns and justifying fees. When markets are trending higher and clients are seeing double-digit gains from passive ETFs, the pressure to stay fully invested mounts. Nobody wants to be the manager who underperformed – albeit temporarily – because they held too much cash while the S&P ripped higher. That career risk can push managers to chase the tape, even when valuations are stretched and signals are flashing caution.

History shows that extreme positioning – whether in hedge funds, retail (more on this below), or mutual funds – often coincides with market inflection points.

To be clear, hedge fund positioning is not a perfect timing tool. Managers can remain fully invested through prolonged rallies. But as a part of the broader mosaic of signals, low cash levels fit the picture of a market where risk-taking is widespread, and defensive positioning is scarce. When combined with rich valuations, narrow breadth, speculative fervor, and value underperformance, it strengthens the case that we’re late in the cycle.

And we’re not done yet…

Signal #6 – Retail Equity Exposure: When Everyone’s Already In

One of the cleaner contrarian signals comes from looking at how much of their wealth households have allocated to equities. The FRED data on “Households and Nonprofit Organizations; Directly and Indirectly Held Corporate Equities as a Percentage of Financial Assets” is one indicator to consider. Today, that number is at its highest level on record – higher than during the dot-com bubble, higher than before the 2008 financial crisis.

Why is that important? Because equity ownership levels and future returns have historically had a negative correlation. When a large share of households’ financial assets are already in stocks, it means there’s less incremental buying power left. Everyone who’s inclined to be bullish is already positioned that way. The marginal buyer – the one who pushes prices higher – becomes harder to find.

The AAII Retail Investor Asset Allocation Survey paints a similar picture. Retail stock exposure is sitting near multi-decade highs, in the upper 60% range. That’s not the kind of number you see at market bottoms, when panic pushes equity allocations into the 40s. It’s the kind of number you see when optimism is entrenched.

And then there’s the anecdotal evidence – the kind of thing you don’t see in the data tables, but you feel in the market’s mood. How “back” are we? We’re so back that ARK Innovation ETF ($ARKK) just took in $800 million in a single day – its largest one-day inflow ever.

When speculative thematic funds start drawing this kind of money AGAIN, it’s a sign that risk appetite isn’t just healthy – it’s ravenous.

Signal #7 – Corporate Buybacks and the IPO Window

Corporate buybacks are back in force. Historically, these programs tend to peak when management confidence is high, balance sheets are flush, and valuations are elevated – precisely the moment when forward expected returns are least compelling. At market bottoms, when buybacks would actually deliver the best bang for the buck, they almost disappear as companies pull back to conserve cash. In that sense, buybacks often function as a contra-indicator: they tell us more about sentiment than about disciplined capital allocation.

Signal #8 – CNN Greed and Fear Index

The CNN Greed & Fear Index – which blends seven market sentiment measures like option activity, market breadth, and safe-haven demand – is currently signaling “greed.” I wouldn’t overstate its predictive power; it’s more of a temperature check than a timing tool. That said, it’s one more indicator leaning toward optimism, adding to the picture of an investor base that’s far from cautious.

Signal #9 – The AI CapEx Boom: Late-cycle tell or durable supercycle?

The ninth light on my dashboard is the AI capital-spending wave. It’s massive. The Morgan Stanley breakdown you see in the first chart estimates roughly $2.9 trillion of global data-center capex through 2028 (excluding power).

About $1.4 trillion is expected to be funded out of hyperscaler cash flows, but the rest leans on balance sheets and markets – roughly $200 billion of corporate debt, $150 billion via ABS/CMBS, an $800 billion private-credit opportunity, plus another $350 billion from PE/VC/sovereign sources. Translation: we’re not just talking about healthy reinvestment from profits; we’re talking about a financing machine pulling in every form of capital it can find.

The second chart brings the scale home from a different angle. Capex at the big platforms – Alphabet, Amazon, Meta, Microsoft, Oracle – has surged toward ~1% of U.S. GDP on a trailing-twelve-month basis. That’s extraordinary. Tech’s build-out has become macro-relevant in its own right. When an industry’s capex approaches macro scale, it can become a cyclical driver of both earnings and markets.

Here’s why this sets off my “top-ish” radar. Capex cycles tend to overshoot. Railroads in the late 1800s, telephone and cable build-outs in the 1990s, shale in the mid-2010s – the pattern is familiar. A real opportunity appears, early returns look phenomenal, capital floods in, suppliers scale, financing gets easy, marginal returns slide, and the leaders eventually dial back spend. Equity markets usually anticipate the turn well before management teams officially change guidance. I’m not predicting a bust in AI – far from it – but I respect how often peak investment intensity lines up with peak optimism and diminishing incremental returns.

Second, the financing mix matters. When internal cash flows fund the majority of a build, discipline is usually tighter. When outside capital fills the gap at scale – private credit, securitizations, supplier financing – the system can become more tolerant of lower hurdle rates. It’s subtle. Each participant is rational in isolation. Aggregated, it lowers the market’s overall cost of capital for the theme and encourages one more year of “must build.” That’s how you get overcapacity in compute, networking, and real estate even if the aggregate TAM ultimately proves huge.

How do I reconcile all that with the obvious counterpoint – that AI may be a genuine, decades-long productivity driver? I hold both thoughts. On a 10- to 20-year view, I’m optimistic about AI’s impact on profits and unit economics across many industries. On a 12- to 36-month view, the outside-view lesson is that capex super-cycles usually travel with rich valuations, rising concentration, and plentiful financing. We have all three. That’s why, as an investor, I treat the AI capex boom as a late-cycle feature even if the technology megatrend endures.

The Case Against Panic: What’s NOT Pointing to a Top

If there’s one lesson markets teach over and over, it’s that you want broad confirmation before making big portfolio moves. A single indicator flashing red can be more noise than signal, and even a cluster of warnings doesn’t guarantee we’ve hit “the” top. Market peaks tend to be messy processes rather than single-day events, and they often involve widespread froth across many fronts – not just a few hot readings.

Market tops also follow different patterns: Sometimes, major bull markets end with an explosive blow-off top (think of the dot-com bubble) – and sometimes they don’t. In 1929 and 1999, the frenzy of euphoria was off the charts, with stock markets turning into full-blown casinos.

Other times, however, trends simply lose steam and fade gradually, the bull market “rolls over” as in 1969, when Buffett famously recognized the peak and shut down his partnership. To me the current market setup feels more like the latter.

It’s important to always also consider what’s not lining up with the “time to raise cash” thesis.

Take the IPO market. If we were truly in a euphoric, late-cycle blow-off, we’d expect to see the kind of frenzy we saw in 2021 – hundreds of companies rushing to list, valuations pushed into the stratosphere, and deal sizes breaking records. That’s not the case today. Yes, activity has picked up from the 2022–2023 trough, but it’s still nowhere near the mania levels that tend to precede major tops. We’re in a recovery still below 2017-2019 levels, not a stampede.

That difference matters. An IPO boom is often a sign that insiders – those with the best knowledge of their businesses – are eager to cash out while the market is in a buying mood. When that appetite isn’t there, it suggests that at least part of the investor base is still somewhat cautious.

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Concluding Thoughts

If timing market bottoms is hard – and it is – then timing tops is even harder.

There’s no bell that rings at the peak, no flashing red light that says “this is it, time to get out, thanks for playing.”

At best, you can stack up enough warning signs to tilt the odds in your favor. But even then, expecting to nail the exact turning point is unrealistic. My preference has always been to err on the side of being a little early rather than too late. Being too early might cost you a bit of upside (and a lot of nerves); but being too late can cost you a lot more.

The challenge is that there are no completely reliable, single-shot signals of a market top. That’s because as discussed tops are rarely a one-day event – they’re a process. Conditions tighten, sentiment shifts, and liquidity starts to dry up, all in slow motion. Breadth often rolls over before the indexes do. Insider selling ramps up. Retail and institutional cash levels shrink, the VIX sinks to complacent lows, and credit spreads tighten to unsustainably narrow levels. There’s usually a euphoric narrative or hot fad dominating headlines – AI fits that bill today. Short-biased funds start shutting their doors, social media feeds overflow with hubris, people brag about returns, and M&A and buybacks push toward records.

Right now, we have nearly all of those elements in play. That’s enough to make me cautious.

After trimming DigitalOcean, I’m currently sitting on 15% cash, a level I feel comfortable with. To me, the market feels “top-ish” but not fully “bubbly” yet.

That’s a crucial distinction. It’s also why I’m not rushing to raise more cash at this point. Many of the stocks I own still look attractively priced (to me at least) relative to their long-term prospects, and the expected returns from these levels are, in my view, still compelling. Selling them just because the broader market feels stretched doesn’t make sense to me.

I do have a set of principles for raising cash more strategically – specifically, which positions to trim or exit first when the goal is to de-risk without hollowing out your best ideas. That’s a conversation I’ll save for a future post, because it deserves its own deep discussion.

For now, my takeaway is simple: the signs of froth are accumulating, but the case for an immediate, aggressive cash-raising move isn’t airtight. This is a time for balance – keeping some dry powder, staying selective, and letting the weight of the evidence, not just the loudest signal, guide your decisions.

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As a long-term-oriented investor and someone who has followed Brockhaus Technologies with great interest over the past few years, I would like to respectfully share a few thoughts, inspirations, and questions with you. My goal is not to criticize, but to offer constructive suggestions that, from my perspective, could help strengthen transparency, sharpen the company’s positioning in the capital markets, and ultimately increase long-term shareholder value.

Let me begin right away with some ideas that I hope you will find helpful:

1) Reduce complexity & focus on clear shareholder value metrics

You may have already come across the recent comments made by Paladin Asset Management in their latest investor letter (https://paladin-am.com/paladin-one/paladin-investorenbrief-q2-2025/). While I don’t agree with everything they wrote, I do strongly agree on one point: Brockhaus Technologies, primarily due to its ownership structure, appears incredibly complex. I believe it would be beneficial to report more metrics on an “adjusted for minority interest” basis. For existing investors, it's tedious to estimate these figures independently – and for new, potentially interested investors, the story may seem too opaque, especially given Brockhaus’s current size. With a market cap of ~€150 million and a free float of only ~€50 million (34%), the company’s narrative is, quite frankly, too complex for its scale.

Closely related to this is the urgent plea to focus on metrics that truly matter for long-term shareholder value creation – in particular, FCF (ideally FCF per share, and here of course “FCF per share available to BKHT owners,” not just Group-level FCF) as well as EPS.

The strong emphasis on adjusted earnings (with adjusted EBITDA seemingly the preferred metric), from which – at least more recently – little translates into actual cash flow, is extremely frustrating for long-term investors.

I’m sure you’re familiar with this legendary quote from Charlie Munger: “I think you would understand any presentation using the word EBITDA if every time you saw that word, you just substituted the phrase with ‘bullshit earnings.’”

2) Communicate better, more often, and more clearly

Recently, I couldn’t help but think of Brockhaus and your role as CEO when reading a tweet by a well-respected – though anonymous – investor who goes by the name Sidecar Investor. The core idea behind sidecar investing is simple yet powerful: instead of leading the investment charge, you ride alongside world-class capital allocators, trusting their judgement, discipline, and long-term vision. In that spirit, many of us are not just buying businesses – we’re backing the people who run them.

The key passage was this:

“How often do they communicate? When they do, is it technical jargon and double speak or are they a straight shooter? Do they set realistic expectations and tell investors bad things they don't have to? There are even clues in how they present themselves, what they wear at conferences, laugh at, and in their lifestyle.”

A major reason I became interested in Brockhaus was your appearance on the Lynx podcast (I’ve actually listened to it three times). My takeaway: he gets it! Long-term thinking, investing in high-quality businesses, being extremely patient and disciplined – very much in line with Terry Smith's philosophy: "Buy good companies, don’t overpay, do nothing."

Unfortunately, communication has deteriorated significantly since then. Especially in more challenging times, it's essential to maintain open communication with shareholders – not to cancel earnings calls altogether, as happened in the first half of the year. Yes, there may be tough questions on these calls – but that’s part of the game. Transparent and honest dialogue is foundational.

If the aspiration is to acquire “champions,” then the communication style should also be champion-like. If Roper Technology is the company’s north star, then let Roper’s communication style be a north star too. That includes acknowledging past mistakes – always! – and clearly outlining current challenges and weaknesses.

Here’s the full tweet for reference:

3) Investor Day 2025/Early 2026?

The last “Technology Day” was held over four years ago. Building on the previous point of improving communication, I would strongly encourage you to consider organizing an “Investor Day” – ideally later this year or in early 2026 – to clearly communicate the long-term vision, reduce perceived complexity, and introduce key leaders across the portfolio.

Take Bastian Krause at BLS Bikeleasing Service GmbH – a highly competent, personable founder. What is his long-term vision? How does he see the partnership with Brockhaus evolving? Let’s also hear from the executives at Probonio. Give IHSE its own strategic segment presentation.

This would also be the perfect venue to explain your ongoing strategic investments in more detail:

“As part of Bikeleasing’s ongoing transformation from a ‘single-product’ to a ‘multi-benefit’ platform, Germany’s second-largest bicycle leasing provider expects significantly higher personnel and other operating expenses in fiscal year 2025. The increased expenses are primarily attributable to strategic growth initiatives, in particular the rollout of the digital multi-benefit platform Probonio.de and the development of the used bicycle platform (Bike2Future.de), which was launched in 2024.”

How large are these investments exactly? How much is being allocated to personnel, product development, or marketing? What portion of these rising costs can be classified as growth investments versus recurring operating costs? How long do you expect these growth investments to remain elevated?

Again, this ties back to the second point: investors are forced to speculate – they shouldn’t. FY 2025 guidance suggests significantly lower margins (on an adj. EBITDA basis; I expect minimal profitability on an actual cash flow basis) – but what long-term FCFa2S (FCF available to BKHT shareholders) margin level do you believe is achievable over the long term?

Finally, regarding the growth investments in Probonio and Bike2Future: the question of opportunity cost arises. Assuming these are true growth investments and structural profitability is indeed higher than what we’ll see in 2025 (to quote from a previous blog post I wrote: “And when you think about my estimated steady-state FCF margin of 17% for the Brockhaus group, this additional profit has a disproportionately large impact. Using a simplified base-case of €30 million in FCF to Brockhaus, this €9.6 million addition boosts FCF by almost 30%.”), then Brockhaus is trading at a 4–6x multiple of FCF, which corresponds to a 16–25% yield.

That’s the benchmark your opportunity cost must be measured against – assuming you share my margin assumptions of course. That’s an immediate return with a very high degree of visibility. As John Huber recently wrote on his blog:

“Compounders are largely more expensive these days, but when a stock trades at a 20% FCF yield and is buying back shares, it has the same per share compounding potential as a 20% ROIC company that reinvests all earnings.”

4) Additional questions:

• Why was the new fee model for bicycle dealers not publicly communicated?

• What is your stance on Paladin Asset Management’s proposal to sell IHSE and focus the group fully on the Employee Benefits business?

• What exactly were the €1.6 million in costs related to the inbound M&A review?

I remain a committed and engaged shareholder – and I sincerely hope this letter is received in the spirit in which it is intended: as thoughtful feedback from someone who believes in the long-term potential of Brockhaus Technologies.

Kind regards,
René Sellmann

Note: 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!

“AI is growing faster and will be larger than any platform shift before, including the Internet, mobile, and cloud.” That’s what Nvidia CEO Jensen Huang said. Now, you never ask a barber if you need a haircut – but in this case, I think he might be right.

I've spent the last few months reading, listening, observing, and rethinking how AI might reshape the investment landscape. Not just in the obvious ways, but in the less visible ones too. And the more time I spend on it, the more convinced I become of a single idea:

Everyone is underweight AI.

That may sound like hyperbole. After all, hasn’t AI dominated every headline for over a year now? Don’t we already live in an era where Nvidia has joined the trillion-dollar club, where every software company has a “Copilot,” where self-driving cars are starting to scale across the U.S. – fast –, and where GPTs are pushing the limits of what machines can do?

Yes, on the surface, AI is everywhere. But when you look closer – at actual portfolio positioning – many investors are arguably still dramatically underexposed to the magnitude and velocity of this transformation.

It’s not just that the AI wave is massive. It’s that it’s accelerating. What’s unfolding isn’t a gradual trend, but a compounding force moving at a pace that’s hard to model, let alone price. Advances that once took a decade now show up in just one quarter. CapEx curves have gone vertical. Use cases are evolving in real time.

And yet, most portfolios still reflect a mental model from 2020 or earlier.

This post is my attempt to wrestle with what that means. Not just at a narrative level, but through the lens of capital allocation, disruption, and long-term investing strategy.

I’m not interested in hype cycles or clickbait predictions. I want to explore this with the mindset of a rational investor: Where are the risks? Where is the opportunity? How do you balance both?

And above all: How do you not get left behind?

Here’s what I’ll cover:

• Why AI may be bigger than the internet, cloud, or mobile – and what the data says about adoption speed, infrastructure demand, and platform-level shifts

• Why the speed of change matters more than most investors realize – and how it challenges traditional valuation, forecasting, and moat analysis

• The macroeconomic and societal implications – from productivity gains to labor displacement to the potential arrival of post-scarcity economics

• Two opposing investment mindsets – avoiding AI because of disruption risk vs embracing it for potential index outperformance, and how both can be valid

• Why the physical world still matters – and how AI could amplify, rather than replace, legacy industries that are hard to digitize

• How to express intelligent AI exposure in your portfolio – from first-order beneficiaries like chips and infrastructure to second-order plays in software and logistics

• Why valuation discipline is more important than ever – and how to avoid the “electricity trap” or a 1999-style blow-up even when the underlying tech is real

• How to think clearly when the future arrives too fast – and how mental models like the Lindy Effect, second-order thinking, and durability can help you navigate the noise

There’s a lot to unpack. So let’s dive in.

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1. Something Bigger Than the Internet?

Every generation gets its big platform shift. In the 90s, it was the internet. In the 2000s, mobile. Then came the cloud. Each wave seemed unthinkably big in its time — and each one ended up even bigger than expected.

But when people say AI might be bigger than all of those combined, I don’t think they’re being hyperbolic.

Coatue, one of the more forward-thinking tech investment firms in public and private markets, asked a bold question in a recent investor deck: Will AI reach 75%+ of total US market cap?

They believe this isn’t just another wave – it’s a new technology supercycle, one that could completely reorder sector weights and reshape how capital is allocated across the market.

The signs are already here.

In May 2025, BOND Capital published a report titled “Trends – Artificial Intelligence (AI)” that tried to quantify what’s happening. It didn’t just rely on opinion – it brought charts, lots ot charts. AI adoption is exploding. For instance, the number of GitHub repositories tagged with AI is going vertical. CapEx is spiking. DigitalOcean reported that its AI business is up 160%. Inference costs – the cost of running large AI models – have dropped 99.7% in two years. Energy per token has fallen 105,000x over the past decade. Training costs for frontier models are now flirting with the $1 billion mark, and yet companies are still pouring in.

Microsoft’s CEO Satya Nadella underscored just how massive this is during the company’s recent earnings call:

“We processed over 100 trillion tokens this quarter, up 5x year-over-year... including a record 50 trillion tokens last month alone.”

He wasn’t just flexing on usage metrics. He also signaled that demand is outstripping supply. Microsoft expects AI capacity constraints beyond June. That’s despite adding data center capacity at full speed. For an enterprise as large and well-capitalized as Microsoft to run into supply-side limitations is telling.

That brings us to CapEx. You’ve probably seen the headlines:

Amazon, Microsoft, Google, Meta, Oracle, Tesla – collectively planning $365 billion in infrastructure spend, a large chunk of which is AI-focused.

CoreWeave, a once-obscure cloud provider, is growing at triple-digit rates and raising billions to meet GPU demand. The scale here is unlike anything I’ve seen. These aren’t startups burning VC cash – these are possibly the emerging titans of the modern economy, and they’re all leaning into the same arms race.

Tech has outperformed all other sectors over the more recent past. And I’m not talking about shorter time period of 3-4 years – I’m referring to decades. The Nasdaq 100 has crushed other benchmarks over the last few decades. One dollar invested in the Nasdaq in 1985 is worth about $228 today. The same dollar in the Dow Jones? About $90.

Over the past 18 years, the Nasdaq’s 15.25% CAGR has been enough to beat the returns of almost any legendary investors. That’s not a knock on those investors – it’s a reminder that platform shifts reward index exposure to the right themes more than almost anything else.

As Tiho Brkan put it:

“It’s very difficult to beat the market, even for the greatest investors. Charlie Munger talked about this often.”

And that’s the mental model I keep coming back to. The “effort vs accuracy” tradeoff. Sure, you can spend countless hours building models, meeting management, and optimizing your portfolio. Or you can just buy a tech-heavy index and let the secular tide lift you. The internet era proved that. The cloud and mobile era reaffirmed it.

So what if AI is the next one?

What if all it takes to beat the market for the next 15 years is to be overweight AI?

Of course, it's not that simple. Valuations matter. Hype cycles exist. Some companies will go bust. But the larger point remains: we may be living through the early innings of a shift even bigger than those that came before – so big it’s hard to fathom for most.

And if that’s true, being underweight AI isn’t just a missed opportunity — it’s a liability.

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2. Speed, Scope, and the Shock of Acceleration

Most investors understand the concept of exponential growth in theory. In practice, it breaks brains.

We’ve all seen the chart: the slow, flat curve that suddenly bends up and goes vertical.

That’s what exponential adoption looks like. But there’s a difference between seeing that curve and living inside it. And make no mistake – right now, we are inside it.

The AI transformation isn’t following the decade-long diffusion path of previous tech waves. It’s compressing that arc into years – in some cases, quarters. The scope of what's changing is enormous. But it’s the speed that makes it especially destabilizing.

Look at Microsoft again. Just one year ago, token volume on their platform was a fraction of what it is today. Now it’s over 100 trillion tokens per quarter. That’s a fivefold increase, and still accelerating. Nadella didn’t just mention rising usage; he warned of capacity constraints. Despite bringing data centers online at full clip, demand is still outpacing infrastructure. That’s not normal tech growth - that’s system strain.

Zoom out, and the picture gets even more extreme. According to BOND, AI inference costs have fallen 99.7% in two years (see chart in the top left).

Energy per token has collapsed by a factor of over 100,000 in a single decade. These aren’t marginal gains. They’re orders of magnitude. And they’re happening faster than the market’s ability to digest them.

That’s why this moment feels different. Investors are used to trendlines. They like time to run DCFs, test assumptions, build valuation frameworks. But when tech moves this fast, those models become outdated almost as soon as they’re built.

Most software businesses today still operate under mental models shaped by the mobile and cloud era – where market leadership endured, moats thickened, and cycles played out over years. AI doesn’t play by those rules.

The implication? Forecasting gets harder. Competitive dynamics become less stable. Valuation multiples become harder to justify – or harder to dismiss – no one really knows.

Because in an environment of extreme speed, what matters most isn’t where things are now, but how fast they’re changing.

This isn’t just theory. We’re watching it happen in real time with agentic AI – a term that’s starting to show up more often in earnings calls, pitch decks, and investor memos. Agentic models don’t just assist users, they take actions autonomously, execute workflows, and start to replace human decision-making altogether. It’s not about answering your query – it’s about handling the entire task from end to end. Search, reason, act.

That’s a profound shift. Over the last few decades, software was designed around one core assumption: humans are at the center of the workflow. Agentic AI breaks that. It introduces a new architecture where the agent becomes the actor. Morgan Stanley recently highlighted how this change could redefine the $630 billion software market. Companies that adapt – architecturally, commercially, and culturally – might thrive. The rest? They’ll get left behind.

And here’s where it gets tricky.

Speed breaks things. It breaks assumptions, it breaks incumbents, and it breaks portfolios built around slow-moving variables. In most investing environments, change is an opportunity. But when change becomes unmodelable, it becomes a threat to every traditional analytical tool we rely on. Moats look shallower. TAMs become squishier. Time horizons shrink. Duration risk shows up in places you didn’t expect.

That’s why I keep saying: everyone is underweight AI. Not because they don’t believe in it, but because most portfolios aren’t built for this kind of acceleration. The tools we use weren’t designed for a world where fundamental assumptions get rewritten every quarter.

So what do you do? That’s what I’ll explore next.

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Note: 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!

When investors talk about bargains, they rarely think of megacaps. The prevailing wisdom is that inefficiencies show up on the fringes – in small caps, illiquid names, or obscure international plays.

But sometimes, bargains hide in plain sight. John Huber, the founder and portfolio manager at Saber Capital, recently pointed out that JPMorgan – yes, the bank – has outperformed five of the “Magnificent 7” over the last five years. Specifically, it’s beaten Amazon, Apple, Google, Microsoft, and Meta. Only Broadcom (which has taken over Tesla) and Nvidia did better.

Even more interesting was how Huber broke down the reason:

That last sentence really stuck with me. Because it challenges one of the dominant investing narratives of the last decade: that only fast growth and massive scale matter.

That you could ignore valuation entirely as long as you had quality and a long enough runway. That owning anything outside the Mag 7 meant you were settling for second-tier performance.

But if JPMorgan – a bank, no less – can quietly outperform five of the most powerful companies on the planet, what else is possible? Are we seeing the start of a broader shift, where other businesses finally start to outpace the titans?

It wouldn’t have seemed plausible ten years ago. From the aftermath of the financial crisis through the peak of the post-COVID tech boom, the Mag 7 simply crushed everything. An equal-weight basket of those seven stocks (including Tesla and not Broadcom) delivered +2,230% over the past decade, compared to just +259% for the S&P 500.

For a long time, beating the market without holding these names felt close to impossible. Their dominance was so total, so persistent, that many investors gave up even trying to look elsewhere.

But I think we might finally be at a turning point.

The tides may not have fully turned yet – but the current is definitely shifting. Valuations are rich. Reinvestment dynamics have changed. Capital allocation decisions are murkier.

And meanwhile, there’s a long tail of quality companies trading at reasonable multiples, growing at a modest but steady clip, with far more room for multiple expansion.

So no – this isn’t a post claiming the Mag 7 are “doomed” or finished. They’re still great businesses. But it is a post about the opportunity cost of sticking with yesterday’s winners.

It’s about the possibility that a growing number of “normal” businesses, with solid fundamentals and less fanfare, might quietly start outperforming.

JPMorgan may just be the first crack in the façade.

Here’s what I’ll explore:

• How JPMorgan outperformed 5 of the 7 Mag 7 stocks over the past five years – and what that says about valuation, modest growth, and capital returns

• Why the Mag 7’s dominance may be fading and how the next cycle could reward different traits

• What current valuation metrics (PE, EV/EBIT, PEG) reveal about risk and opportunity in today’s market

• Why big tech’s capital allocation has fundamentally changed, and how massive CapEx and uncertain returns could affect long-term outcomes

• What makes a reinvestment environment attractive vs. wicked, and why this matters more than ever when assessing future ROI (which in turn affects assigned multiples)

• The significance of Meta’s M&A spree and hiring push – and what it reveals about the competitive pressure inside big tech

• How many S&P 500 companies could realistically outperform the Mag 7 over the next five years – and what that means for active stock pickers

• What kind of companies might lead the next cycle, and why thinking beyond the top 20–30 names in financial media coverage is critical

• And finally, how I’m thinking about portfolio positioning in a world where tech no longer reigns supreme by default

Before we dive back in, a quick note…

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Part 1 – The Valuation Perspective

Valuation Was Once a Joke – Now It Matters Again

For years, valuation was the punchline. Anyone bringing up PE ratios or discounted cash flow models in the age of ZIRP and “infinite” TAMs was often waved aside as being too “old school.”

The idea was that if you were focused on cash flow or balance sheet strength, you just didn’t “get it.”

Multiples could be stretched to the sky if the company was growing fast enough – and if it wasn’t, well, maybe you shouldn’t own it in the first place.

But that era is fading. Interest rates are higher (for longer). Liquidity is scarcer. And the tolerance for wildly optimistic forward projections is dropping. Suddenly, valuation is not just relevant again – it's central.

The Magnificent 7 are still dominant businesses, but on most traditional valuation metrics, they no longer look remotely cheap. In fact, many (not all) of them now trade well above the S&P 500’s average multiple – a far cry from the early 2010s when they were the rare combination of growth and value.

A Simple PE Ratio Comparison Tells a Story

Let’s start with the basics. On a trailing PE basis, 5 out of the 7 Mag 7 names are currently trading above the S&P 500’s average. If you strip out Tesla – which sits in a category of its own with a multiple north of 180 – the valuation gap is still significant.

Companies like Nvidia and Microsoft, trading at multiples of 51x and 38x respectively, while still cash flow machines, are trading at very high near-term earnings multiples. Amazon and Apple? Their trailing valuations look optically rather rich too.

In contrast, JPMorgan’s PE ratio in July 2022 was just 8.3x. That’s not just cheap. That’s downright neglected.

Since then, JPMorgan’s multiple has expanded to 14.5x, which is still more than 50% below the average Mag 7 valuation. That multiple re-rating alone has added 20.44% in compounded returns – without even factoring in dividends or underlying earnings growth.

This is what happens when a modestly growing company gets re-rated from "cheap bank stock" to "decent quality equity."

The EV/EBIT Lens: Even More Lopsided

Valuation stories can look a bit different depending on which metric you use – and EV/EBIT adds another layer of nuance. On a next-twelve-month (NTM) EV/EBIT basis, 6 of the 7 Mag 7 companies trade at a higher multiple than the S&P 500. And again, the chart below is excluding Tesla.

This metric is particularly relevant because it strips out the effects of capital structure and shows how much investors are paying for operating earnings. In environments where capital costs have risen – like today – it becomes even more meaningful. High EV/EBIT multiples now demand much more from a company than they did three or four years ago. Every basis point of yield, every dollar of retained earnings, carries more weight. The hurdle to justify these valuations has risen sharply. And the Mag 7? Well, they’re not cheap by any means.

And it raises the question: What are investors really paying for?

If the answer is “AI growth” or “platform scale,” … well, these narratives eventually need to be paired with actual return on investment. And that's where things start to get tricky for most of the “AI plays.”

If you’re betting on Meta and Alphabet, you might be interested in a more bullish perspective:

PEG Ratios: The One Metric That Offers a Counterpoint

To be fair, not every metric screams “overvalued.” The PEG ratio – price-to-earnings relative to growth – softens the picture a bit. On a PEG basis, 4 out of the 7 Mag 7 stocks actually trade at more reasonable levels, especially when next year’s growth is factored in.

But the PEG ratio comes with its own trap: it assumes the growth is real, repeatable, and durable. And in some cases, that assumption is doing an awful lot of heavy lifting. For instance, if Nvidia’s future earnings stumble even slightly, or margins compress, the entire justification for today’s multiple begins to crack.

When expectations are this high, as they are for Nvidia and Microsoft, there's no room for missteps – and that’s often when performance starts to underwhelm.

JPMorgan as a Case Study in Multiple Expansion

Let’s return to JPMorgan for a moment. In July 2022, as mentioned, it was trading at 8.3x PE. Now, it trades at 14.5x. That’s a near doubling of its valuation multiple in just two years, and the stock has rallied hard as a result. Importantly, this happened without extraordinary revenue growth or a massive buyback announcement. Instead, it was driven by three modest but compounding factors: low starting valuation, modest but consistent growth, and disciplined capital return.

This is what makes the JPM case so compelling. It didn’t require some speculative leap of faith. It just required recognizing a decent business that had been left behind. And there are more of those out there than most investors realize.

In fact, when I ran a screen on the S&P 500 for companies trading at a LTM PE below 15x, with topline growth above 5% CAGR over the past three years, I got 40 names. That’s not a tiny niche.

Here’s the list. If you’re only allowed to invest in the US, start hunting!

That’s a real opportunity set. And interestingly, JPMorgan is still on that list, even after the run-up.

Valuation Is Just the Start – But It’s a Powerful Filter

Of course, not all of those 40 names will outperform. Valuation screens are blunt instruments. They don’t tell you if a company is operating in a declining industry, has structural headwinds, lacks reinvestment opportunities, or is run by a mediocre management team. That’s where qualitative research comes in.

But as a starting point, these screens remind us that there’s life outside the Mag 7.

And more importantly, outperformance doesn’t require perfection. It just requires not overpaying and giving yourself a margin of safety – something the market hasn’t prioritized in years.

The trap is thinking that because the Mag 7 have been exceptional performers, they must always remain so (recency alert bing bing bing!).

But history shows us that starting valuation is one of the most powerful predictors of future returns – especially when capital costs rise, the business model gets heavier, and new growth vectors become less certain (more on this below).

The thesis of this post is that the age of ignoring valuation is over. If anything, it was the exception all along.

Part 2 – The Capital Allocation Perspective

The Asset-Light Narrative Is Dead

One of the great myths of the past decade was that big tech was inherently capital-light. That you could scale up users, revenue, and margins without breaking a sweat – or the bank. Google didn’t need factories. Meta didn’t need inventory. Amazon was maybe the odd one out. But most of the Big Tech names were considered capital-light compounders. Machines that turned every dollar into three or four more, with minimal friction.

But that story’s over.

Today, capital allocation inside the Mag 7 looks very different. The top tech names are pouring tens of billions into physical infrastructure – mostly into data centers, chips, and AI-driven compute capacity. In fact, the tech megacaps plan to spend more than $300 billion in 2025 as AI race intensifies

The capex-to-cash-flow ratios are staggering. These companies are no longer asset-light software companies. They’re infrastructure businesses. And they’re investing like it. Here are some numbers:

• Microsoft is plowing 46% of its operating cash flow into CapEx

  

• Meta is close behind at 45%

• Alphabet comes in at 43%

  

• Amazon tops them all with a staggering 82% reinvestment rate

To put that into context: these ratios are higher than General Motors (42.6%) – a company we used to think of as the definition of capital intensity.

The idea that these firms are just floating above the physical world is no longer true. And that shift has real implications for how we think about return on invested capital, reinvestment risk, and valuation support.

Where’s the ROI?

The logical next question is: What are they spending on, and what kind of returns will they get?

The bulk of the money is going toward AI infrastructure. That includes everything from custom chips to massive data centers and high-performance compute clusters. The hope is that these investments will pay off through higher productivity, new products, and competitive advantage in the AI arms race.

But here's the uncomfortable part: The ROI is completely opaque. No one knows what the return on these investments will be. We don’t have good historical precedents. We don’t have long-tailed user behavior to analyze. And we definitely don’t have visibility into how much of the AI productivity gains will accrue to the platform layer vs. the application or consumer layer.

In other words, these companies are deploying capital at a staggering rate – and we’re flying blind when it comes to long-term payback.

That’s not to say the investments are dumb. Some may pay off handsomely. But the environment they’re operating in isn’t exactly conducive to high returns.

The Characteristics of a Wicked Reinvestment Environment

Let’s take a step back and ask: What makes for a great reinvestment environment? I’d argue the best ones share a few traits:

• Low or no competition

• High predictability

• A history of similar investments paying off

• Simple, modular rollout strategies (think restaurants or software)

• Clear, unit-level economics

• Optionality without excessive upfront capital

Now let’s contrast that with the current AI infrastructure buildout:

• The environment is highly competitive – every big player is spending aggressively

• There’s massive uncertainty about monetization models

• Competitors are well-funded and moving fast

• The ROI on investment is murky at best

• Many of the use cases are still experimental, and long-term demand is speculative

This isn’t a “roll out another store and earn 30% ROI” scenario. This is a wager on an uncertain frontier, one where the prize is unclear and the cost of staying in the game is escalating.

That’s a tough environment to earn alpha in. Especially when you’re spending at industrial scale.

The Return of M&A – and a Hint of Desperation?

There’s another twist here: acquisitions are back on the table, and they’re coming in hot. Meta has been particularly aggressive, trying to scoop up AI startups like Scale AI and targeting firms like Perplexity and Safe Superintelligence (SSI), the closely guarded startup co-founded by former OpenAI chief scientist Ilya Sutskever.

They’re also making wild recruitment offers to key AI talent – reportedly $100M+ “exploding offers” – job offers with very short deadlines for acceptance, often within 24 to 72 hours, after which the offer is rescinded – for OpenAI executives.

Now, with the Trump administration expected to take a more business-friendly stance and a FTC leadership, M&A may face fewer regulatory hurdles. But even so, the strategic rationale behind some of these moves feels less like a chess game and more like a scramble not to be left behind.

That’s a big psychological shift. These companies once set the pace. Now they’re reacting.

Of course, they have the cash to do it. And in theory, the platform advantage should still count for something. But when companies with $100B+ war chests start behaving like desperate startups trying to hoard scarce talent, it tells you something about the perceived threat level.

Why This All Matters for Investors

This shift in capital allocation is more than just a line on a cash flow statement. It fundamentally changes the investment thesis for these companies.

In the old days, you could underwrite big tech as capital-efficient cash machines. Minimal reinvestment required. Margins stable. Growth predictable. That’s what justified their premium valuations.

Now, they’re making massive bets into uncertain areas with unclear time horizons and opaque payoffs. And the market hasn’t fully adjusted its expectations.

That’s where the opportunity lies – not necessarily by shorting the Mag 7 (I’m not), but by recognizing how high the bar is for these names to keep compounding at historical rates. If those bets don’t work out perfectly – if ROI comes in below expectations, if competitive dynamics shift, if regulation catches up – the downside isn’t trivial.

Meanwhile, there are companies out there generating solid returns on capital, with far more predictable futures, trading at half the multiple.

The capital allocation game has changed. And investors who continue to treat the Mag 7 as infallible compounding machines are ignoring that shift at their peril.

Part 3 – Scenario Analysis

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Over the past couple of months, I’ve come to respect Gundlach as one of the very few people regularly appearing on CNBC, Bloomberg and the likes, who consistently operates outside the herd. He’s not just reacting to headlines or chasing lagging indicators. He’s thinking long term. And he’s reasoning by foresight – connecting dots, thinking in second- and third-order consequences, and surfacing signals that rarely make it onto mainstream investing TV networks.

There’s a clarity to his thinking that I find both refreshing and rare. Of course, not all of his predictions will be accurate, but more often than not, he’s been directionally right, which is why it’s worth listening when he speaks.

At the recent Bloomberg Global Credit Forum in Los Angeles, Gundlach once again did what he does best: step back from the chaos and offer a brutally honest view of where he thinks the macro winds are shifting.

His message this time? The old playbook is broken. The U.S. fiscal path is no longer sustainable. The so-called “safe” assets – long-dated Treasuries, the U.S. dollar – are starting to behave in strange and disturbing ways. Capital is beginning to move differently. And gold, once the province of doomsday preppers, is suddenly getting shelf space at Costco and headlines in mainstream finance.

“There’s an awareness now that the long-term Treasury bond is not a legitimate flight-to-quality asset,” Gundlach said bluntly. “A reckoning is coming.”

And that reckoning, if it plays out as he suggests, could reshape asset allocation in ways we haven’t seen since the dot-com bust or the global financial crisis. This interview was packed with so much hard-earned wisdom that I found myself taking notes just to make sure I internalize the key lessons. So what follows is not just a recap – it’s my own attempt to learn from it.

You can watch the interview yourself (linked below), or if you prefer a written summary of his key insights (I’ve identified seven in total) with added charts, read on!

Insight #1: The Debt Spiral Nobody Wants to Talk About

The first – and arguably most pressing – insight Gundlach hammered home is that the U.S. government’s interest burden is spiraling out of control. Of course, he’s not the first one to bring this up, but this really isn’t a theoretical future problem anymore.

It’s happening now, invisibly compounding in the background while quite a few market participants still treat Treasury bonds as risk-free assets.

“What people fail to appreciate,” Gundlach said, “is how much the average Treasury payment has gone up.” And he’s right. The average coupon on the Treasury market was below 2% not long ago. Now, it’s pushing 4%.

That sounds manageable until you consider the sheer size of the debt rolling over. Bonds issued in the ultra-low rate era of 2008/09 or even 2019 are now maturing, only to be refinanced at rates 300 to 400 basis points higher.

The result? A compounding interest bill on trillions of dollars.

Gundlach called it “untenable,” and it’s hard to argue with that characterization. If current deficits and interest rates persist, the U.S. will be spending staggering amounts just to service its existing debt. That crowds out future spending, policy flexibility, and eventually even the credibility of Treasury bonds as the global safe haven.

He added a key line that stuck with me: “We have to somehow figure out how we’re going to deal with $37 trillion.” Not in theory – but soon. The national debt is accelerating toward that number at a pace that should make any long-term investor uneasy.

“It will not take ten years. It won’t take five years. The panel before me said—and I think it’s true—that 2027 and 2028 are likely to be a window of tremendous opportunity. I think by then, the Treasury problem will be even more in focus than it is today, and it will weigh heavily on market behavior.

We need to restructure a lot of things in our system. We need to restructure institutions. We need to restructure political parties. We need to restructure our finances. All of these things have been in place—these are the waves of history.

Neil Howe calls it the Fourth Turning. He wrote the book that predicted the credit crisis around 2006. Pretty good. I know him fairly well, and we talk about the same concepts. That is, societies start with a pact: they have the means of production, and then they have the property system that allocates rewards.

Right after World War II, or the Civil War, it starts out with everybody buying into how the system works. But there’s a fundamental problem with the duality between property relations and the means of production. The means of production change in revolutionary ways—steam engine, radio, television, telephone, internet, AI. These are explosions of innovation.

But property relations don’t change very quickly. They evolve slowly, if at all. And over time, wealth inequality emerges—something we are now seeing in extreme form—where the people who benefit from the existing property relations resist any change because they are winning.

We now have a tremendous concentration of wealth and power. Property relations have become calcified, while the means of production are causing massive disruption, intensifying wealth inequality. At some point, the whole system says: this doesn’t work. We have to realign property relations to avoid slipping into a feudal system with lords and serfs. That’s essentially where we are, and we need to reconfigure everything.

The rising interest expense and unaffordability of government debt are just another offshoot of this. It’s all connected. We need institutions that people actually believe in.”

This is where Gundlach’s thinking becomes especially valuable. He doesn’t stop at describing the current data – he connects it to second- and third-order consequences. And one of those consequences, in his view, is that global capital is starting to reconsider the dollar.

“When the S&P fell 20%,” he noted, “the dollar usually would’ve gone up. This time, it went down. That’s strange.” Strange, and possibly telling. It suggests that we may be entering a phase where the market begins to price in not just short-term volatility, but long-term fiscal unsustainability.

If Gundlach is right, then the old assumption – that the U.S. can run perpetual deficits with no consequence – may soon be tested. And investors who still see Treasuries as a no-brainer ballast in a diversified portfolio might be in for a shock.

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Insight #2: The Death of the Long Bond as a Safe Haven

One of the most striking parts of Gundlach’s thesis is his argument that the long bond – once the cornerstone of any flight-to-safety strategy – simply doesn’t work anymore. And not in a vague or hypothetical way. It’s already breaking down, in real time.

“There’s an awareness now,” he said, “that the long-term Treasury bond is not a legitimate flight-to-quality asset.” That line deserves to be underlined. Because if Treasuries no longer serve that role, the implications are enormous – not just for portfolio construction, but for how markets behave during moments of stress.

Traditionally, when equities sold off and the Fed started cutting rates, yields would fall across the curve, especially at the long end. That dynamic created a powerful hedge: as stocks went down, bonds rallied, cushioning portfolios. But that’s not what we’re seeing now. Gundlach pointed out that after the Fed’s first rate cut in late 2023, the 10-year yield rose. And it’s continued rising since. “The yield curve is steepening,” he said – and not for the right reasons.

In Gundlach’s view, the market is starting to price in long-term structural risks. Rising deficits. Uncontrolled interest expense. A shrinking buyer base for Treasuries. There’s a growing realization that the U.S. might have to issue trillions in new debt every year just to stay afloat.

And if investors are no longer confident that those bonds will hold their value, especially in real terms, then demand starts to shift elsewhere.

That’s already happening. “Money is not coming into the United States the way it used to,” Gundlach said. “The dollar is falling. The long bond is no longer responding the way it’s supposed to.”

If you zoom out, what we’re seeing is a slow erosion of trust. The kind that doesn’t show up all at once – but chips away at fundamentals until the break is obvious to everyone. And by then, of course, it’s too late.

For long-duration bonds, the warning signs are flashing. Gundlach sees a potential setup where yields rise to 6% before the Fed is forced to step in with quantitative easing – and when that day comes, the rally could be explosive. But in the meantime? He’s staying away from the long end.

“The leading candidate,” he said, “is that they will announce quantitative easing on buying long-term Treasuries.”

“It’s a paradigm shift.” And that paradigm shift, in his view, could eventually force the Fed into an uncomfortable corner: resume quantitative easing not because the economy demands it, but because the bond market does.

So, if Treasuries are no longer a source of safety, then what is? That’s where gold enters the conversation.

Insight #3: Gold Isn’t for ‘Lunatic Survivalists’ Anymore

Gold has always occupied a strange space in the investing world – admired in theory, dismissed in practice. For years, it was treated like a hedge for preppers and pessimists. But according to Gundlach, that era is over. Gold is no longer a fringe asset. It’s become the new flight to safety.

“We have a tremendous paradigm shift going on,” he said. “The long bond is not a flight to quality asset. Gold is suddenly the flight to quality asset.”

Gundlach sees gold as reclaiming its role in the global financial system – not because of some conspiracy theory or apocalyptic scenario, but because of how broken the alternatives have become. When Treasuries no longer hedge risk, and when the dollar’s strength is suspect, the market starts to look for a new anchor. And that anchor, in his view, is gold.

It’s not just talk. Central banks have shifted from net sellers of gold to net buyers. That’s a major turn, after more than a decade of divestment. “They sold it at $300,” Gundlach said. “And now they’re buying it back at $3,000.” That may not make them savvy long-term investors, but it does reveal something about the direction of global capital.

Retail demand is also spiking. Costco can’t keep gold bars in stock. Once gold broke above $2,000, it didn’t drift higher – it ripped. It’s now treated as a liquid, legitimate, and increasingly desirable asset class.

Gundlach has owned gold for decades – since it was $300 an ounce. But this is the first time he’s talked about it like this: not just as an inflation hedge, or a portfolio diversifier, but as the replacement for Treasuries in moments of fear. That’s a radical shift in positioning.

He even made a quick comparison to Bitcoin. If you’re in it for volatility and potential upside, he argued, you might be better off simply leveraging gold. Over the last year, gold and Bitcoin are both up over 40% – but one of them has been around for millennia, and the other is still figuring out how to scale transactions and fight off regulators.

“I think gold has proven to be a source of growth. If you are a Bitcoin person, I would recommend instead of being a Bitcoin person, you take the same volatility and get gold and leverage it twice. Gold has outperformed Bitcoin year to date even though Bitcoin has done well. They are both up 40-plus percent. That's the place to be.“

To Gundlach, gold is now the real asset class. Not a speculative trade. Not a relic. But a core pillar for any investor questioning the durability of the dollar or the credibility of U.S. debt.

In a world where the old hedges are faltering, gold is doing exactly what it’s supposed to do. And more investors are finally taking notice.

Before we dive back in, a quick note…

I started this blog because most investing content either overwhelms or oversimplifies. I wanted to build what I wish I had ten years ago: a resource that teaches how to think, not just what to buy. If that resonates, becoming a paid subscriber isn’t just support of my work, it’s also a ticket to deeper insights, mental models, and decision frameworks that can change how you invest forever.

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Insight #4: Private Credit Is the New CDO

Gundlach didn’t mince words when asked where he sees cracks forming next. Without hesitation, he pointed to private credit – and the comparison he made couldn’t have been sharper. “Private credit today is analogous to the CDO market in the mid-2000s,” he said, referencing one of the most toxic epicenters of the global financial crisis.

That should make every allocator stop and think.

What worries him isn’t just the massive issuance – though there’s certainly been plenty of that. It’s the tremendous acceptance of the product. The universal buy-in. The marketing spin. The misplaced confidence. Gundlach described a recent private credit panel he listened in on: same language, same structure, same blind spots as CDO conference panels from 2006–07. That kind of déjà vu usually doesn’t end well.

His concerns are layered. First, the performance argument: private credit had great returns – but that was five to seven years ago, when it was still cheap and niche. Much of the performance data that gets cited today comes from that period. In the current environment, he pointed out, public credit has actually outperformed private credit for several quarters.

Second, the illiquidity. For instance, Harvard University, with its massive endowment – at $53.2 billion, Harvard's endowment is the largest in the world, with more than 70 percent of its portfolio given over to interests in hedge funds and private equity (PE representing around 40% of the fund) –, has had to issue debt twice recently to manage cash needs.

Now they’re reportedly looking to sell chunks of their private equity portfolio – at a discount. “If Harvard is publicly acknowledging liquidity problems,” he said, “I’ve got news for you – if there’s one cockroach in the kitchen, there’s never just one.”

That’s the problem with overinvestment in illiquid assets. When the selling starts, it doesn’t matter what the price is – some sell because they have to. Not because they want to. And as Gundlach reminded us, the strongest driver of investment behavior sometimes isn’t fear or greed – it’s need.

He’s seen this before. In 2008, he was raising money for distressed mortgage securities that were yielding north of 20% IRRs even under brutal default assumptions – but institutions had no cash. “I don’t have $10 million,” a Stanford endowment manager told him. “I have no money. We’re all locked up.”

That’s what happens in a forced-selling cycle. Prices fall not because value disappears, but because liquidity does. Gundlach sees the setup forming again. Too many allocators crowded into a space that promises yield without showing its risk – until it does. “The excess reward,” he said, “isn’t even close to what it used to be.”

If he’s right, private credit could be the next shoe to drop. And when it does, it won’t be polite or orderly. It’ll be fast, messy, and unforgiving. Just like last time.

Insight #5: Momentum, Mania, and the AI Echo Chamber

One of the more philosophical – and in many ways most valuable – parts of the interview came when Gundlach reflected on the nature of bubbles.

He wasn’t just talking about AI stocks. He was talking about how narratives metastasize, how expectations race ahead of reality, and how long it takes for promised revolutions to actually deliver.

“I feel the environment feels a lot like 1999,” he said. “AI – just map over dot-com.”

That’s not to say the underlying technology isn’t real. It is. Just like electricity was real in 1900, or the internet in 1995. But markets don’t price in reality – they price in imagination. The story takes over, and the multiples stretch until they no longer make sense. Then something breaks.

Gundlach even went historical: electricity was hailed as the future in the early 20th century – and it was. But the outperformance of electricity stocks peaked in 1911, long before the productivity gains fully arrived.

“That happened with dot-coms,” he added. “Some of those were great investments, but the enthusiasm becomes very excessive.”

That’s what he sees now. Not just in AI stocks themselves, but in the ecosystem forming around them – the suppliers, the credit flows, the capital formation based on ideas that haven’t yet materialized. It’s momentum on momentum. Until it isn’t.

“It’s a momentum trade,” he said. “And when momentum breaks, the first loss is the best loss – and it turns into a seller’s market.” That’s the dynamic he’s watching for.

He’s not saying AI won’t change the world – just that it won’t do it on the market’s timeline. And that disconnect between narrative and reality, between price and substance, is often where the worst losses hide.

I found this part of the interview especially insightful. Gundlach’s gift isn’t just a random hot macro take – his thinking reflects pattern recognition. He understands how bubbles form because he’s seen enough of them. He knows how long hype can sustain itself. But more importantly, he knows that when the turn comes, it’s always faster than anyone expects.

This isn’t a call to short AI. It’s a call to think critically – to separate transformative potential from unsustainable price action. And to remember that even great ideas can make terrible investments when everyone is already in the trade.

Insight #6: Global Rebalancing – The Shift Away from Dollar Assets

Gundlach’s most quietly radical point wasn’t about debt or gold or even private credit. It was about capital flows – and the subtle, slow-moving shift that could change everything: the reversal of money moving into the U.S.

Since the early 2000s, foreigners have poured trillions into U.S. assets:

“The previous group talked about capital flows, and one of the reasons the U.S. is underperforming at this point is that so much money has flowed into the United States since around 2005—or maybe 2015. There was a net investment position where foreigners were investing more into the U.S. than the U.S. was investing abroad, to the tune of $3 trillion. That was about 15 to 17 years ago.

Today, that net investment position has grown to over $25 trillion. And now, the dollar is falling. It’s not inconceivable that some of that $25 trillion—which came in over less than two decades—could start to go out.

This is a moment where, finally, you have a setup where non-U.S. investment makes a lot of sense. Even if you're a dollar-based investor, you should be thinking about increasing your allocations to non-dollar investments.“

And the signs of stress are already showing. As Gundlach pointed out, during the recent S&P 500 selloff, the dollar didn’t rise as expected. It fell. That’s a significant break from four decades of pattern. The “dollar smile” trade – where the greenback rises on either strength or fear – may be fading. If global capital starts reallocating toward other regions or currencies, the U.S. could find itself in a much less privileged position.

Gundlach isn’t all talk here. DoubleLine is actively shifting. “We’re starting to introduce foreign currencies into our funds,” he said – even in those traditionally aimed at dollar-based investors. That’s a bold move for a U.S. fixed-income shop and a clear signal that the dollar’s role as the anchor currency is no longer sacred.

The portfolio implication is clear: U.S.-centric positioning isn’t risk-free. In fact, the risk may be greatest where it once felt safest. Gundlach believes investors should begin increasing their allocations to non-dollar assets. Not in panic, but as part of a deliberate long-term adjustment.

“The stats are very uninteresting in the credit market—just as the valuation of the S&P 500 is incredibly uninteresting. When we had the big selloff in April, we were kind of asleep at the switch. The market was overvalued, and we should have been thinking more cautiously.

Well, it’s even more overvalued today, because while the S&P 500 is only down one or two percent, earnings estimates have been cut significantly. If you look at the forward P/E ratio, it’s actually higher now than it was at the all-time high back in February or early March.“

This doesn’t mean the dollar collapses tomorrow. But it does mean the tailwinds of the past two decades – massive capital inflows, persistent reserve currency demand, foreign central bank support – might not be there in the next two.

If you’re a dollar-based investor and you’ve never owned anything outside that framework, now might be the time to start thinking differently.

Insight #7: India – Buy It and Don’t Check the Statement

While most of the interview centered around macro instability, Gundlach didn’t leave things in doom-and-gloom territory. He ended with one of the clearest long-term investment ideas I’ve heard in a while – and it had nothing to do with Treasuries, credit spreads, or the Fed. It was about India.

“In 30 years, this will be a success,” he said. “You should invest in India… just hold it for your grandchildren’s college fund.”

That might sound quaint in a world obsessed with short-term catalysts and quarter-over-quarter guidance. But it’s classic Gundlach: zoom out, think thematically, and bet on structural forces that don’t need perfect timing.

Why India? He laid out the parallels: a massive population, a young and growing labor force, supply chain tailwinds, and a reform agenda (however messy) that rhymes with China’s arc starting in the 1980s. India today, in his view, looks a lot like China did 35 years ago – chaotic, corrupt, legally tangled… but full of untapped potential.

Importantly, he’s not saying it’ll be smooth or easy. Quite the opposite. The advice he gave – don’t open the statement – is a nod to the volatility and headline risk that come with emerging markets. But it’s also a call for mental discipline. If you believe in the long-term fundamentals, you have to let them play out. And that means tuning out the noise.

I think there’s a deeper lesson in this, too. In a world where many of the dominant narratives are crumbling – dollar supremacy, Treasury safety, private credit as a cure-all – there’s real value in finding clean, asymmetric bets that don’t rely on the same assumptions.

You won’t get a press release telling you when India becomes investable. It’s already happening – slowly, unevenly, and without much fanfare.

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What’s The Bottom Line?

What struck me most in this interview wasn’t Gundlach’s bearish tone or his trademark bluntness. It was the coherence of the message. Every point he made – from the ballooning U.S. interest expense to the mispricing in private credit and the rise of gold as a new safe haven – connects to the same core idea: we’ve entered a period where the things that once made investors feel safe are becoming the very things that may carry the most risk.

Long bonds, the U.S. dollar, even broad-based equity exposure in the U.S. – all of them are built on assumptions that feel increasingly fragile. That doesn’t mean we’re heading into some catastrophic collapse tomorrow morning. But it does mean we can’t afford to keep relying on old mental models just because they’ve worked in the past.

In this episode, we talk about what it's been like starting a paid blog, including what I love about the process and the time demands. We also touch on the importance of celebrating financial milestones with your finance buddies (and why your romantic partner usually couldn't care less), and dig into a recent Reuters report that criticzed Wise’s founder voting control and how the founder of Boston Beer calls this exact corporate governance structure “the purest form of corporate democracy.”

From there, we reflect on year-to-date investing performance, the idea of cash drag, and why performance-sharing online can feel like a lose-lose game. We close with a few stock notes: any names that are high on our watchlist?, thoughts on Apple’s underwhelming yearly event, Lululemon’s stock, and the unexpected Google–OpenAI partnership.

Hope you enjoy the episode as much as we did recording it.

(should be available on Apple Podcasts too soon)

If you're like most investors, chances are your portfolio has some degree of tech exposure. It might even be heavily tilted toward it.

For years – maybe decades – this tilt made a lot of sense. Tech was the growth engine, the compounder, the place where network effects, sticky products, and high-margin software businesses lived. It was where you could relatively safely stretch for 20% annualized returns by just betting on the Nasdaq Composite Index and still feel like a long-term, fundamentals-driven investor.

But 2025 feels… different. More chaotic. More fragile. And maybe even more dangerous.

The rise of generative AI isn’t just the next big thing – it’s an earthquake, making many investors feel excited (due to potential innovative breakthroughs) and uncomfortable (due to increased uncertainty) at the same time. ChatGPT has exploded from zero to 800 million users in two and a half years, clocking over a billion searches per day. That’s faster than Google, faster than TikTok, faster than anything we've seen before.

Fifty percent of S&P 500 companies now mention AI on earnings calls – a number that was close to zero just a few years ago. And yet, beneath all that excitement, there’s a gnawing sense that the tech landscape is shifting under our feet. Maybe violently.

This shift isn’t happening in a vacuum. The average lifespan of public companies in the US has been declining for decades – from above 60 years in the 1950s to 15 years today.

And with AI accelerating the pace of disruption, I expect that number to keep falling – maybe even sharply? What used to take decades to play out may now happen in just a few years. Structural advantages evaporate faster. Obsolescence sets in sooner. And the margin for strategic error keeps shrinking.

My perception is that many public market investors today underestimate how AI may undermine business models that not too long ago were considered “the best business models the world has even seen.”

The irony is that we’re living through a time when tech feels both more ubiquitous but may be less defensible than ever.

Moats are shrinking. Differentiation is collapsing.

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In 2022, building a new product might have required a dedicated team and a 12-month development cycle. By the late 2020s, that same product – at least in prototype or concept form – could be generated with just a few well-crafted AI prompts. The gap between idea and execution is collapsing.

The very speed at which innovation is happening – from AI generated movie productions (see video below), to AI-powered drug discovery platforms in healthcare, to humanoid robots navigating private households, to autonomous coding agents that debug and ship software with minimal human input – might be the thing that makes long-term advantage in tech investing increasingly elusive.

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A post shared by @wsj

This blog post is about risk – not volatility, but the kind of risk that actually matters: permanent capital loss. And specifically, the new kind of risk that investors with “tech exposure” face in a world where software might be turning into a commodity, and the barriers that protected incumbents for the last 10–15 years are suddenly being breached at light speed.

I’m not sounding the alarm just for the sake of it. I’m sounding it because it might be time to step back and ask:

• What are you really exposed to?

• What assumptions about tech are you still clinging to that might no longer hold?

• And how should we think about portfolio construction in a world where even the most entrenched players – Google, Microsoft, Amazon – are throwing tens of billions at a new technology they themselves don’t fully understand?

This post should serve as some food for thought!

Here’s what I’ll explore in this piece:

• How the explosive rise of AI is compressing competitive advantages across tech

• Why traditional tech moats like network effects, switching costs, and proprietary data are being eroded

• Why the current moment might be more structurally dangerous than the dotcom bubble

• How even Big Tech’s massive AI investments come with highly uncertain returns

• The debate over whether AI is just a feature or a full-blown paradigm shift

• What Mark Leonard’s take reveals about which businesses are more insulated from AI threats

• Why the physical economy still matters – and might actually be a source of hidden defensibility

• How to rethink tech exposure and portfolio construction for a world that’s changing faster than ever

Let’s unpack all of this.

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AI Mania and the Fragility Beneath the Hype

AI is having its Cambrian explosion moment. Everyone sees it. Everyone feels it. We’re witnessing fundamental change in real time! It feels nuts – often overwhelming.

The metrics are staggering. ChatGPT’s user base has grown 8x since launch, now hitting 800 million monthly users. India – not the US – is currently the largest national user base, accounting for over 14%. Daily (!) searches via ChatGPT recently passed one billion, doing so more than five times faster than Google ever did.

In parallel, AI is dominating corporate communication. As shown above, over half of the companies in the S&P 500 now mention AI on their earnings calls – a figure that was practically nonexistent just a couple of years ago. From tech to healthcare to energy, everyone’s trying to convince investors (and perhaps themselves) that they’ve got an AI strategy.

But here’s the thing: just because a business embraces AI, this business isn’t in a defensible position. In fact, the insanely high rate of change we are witnessing – the speed with which things are changing – might be what’s making this moment so uniquely risky. Not for consumers – they’re getting better tools every day – but for businesses with “AI exposure” (whether they know it or not) and, of course, investors too, particularly those anchored in the traditional tech playbook.

Because what AI is doing, almost invisibly, is compressing the time window in which competitive advantages matter. Features are cloned overnight. Products that once had 12-24-month leads now get replicated with a weekend hackathon. The friction to build, ship, and distribute new tools has collapsed. In some niches – CRMs, note-taking, developer tools – it’s already a free-for-all. Differentiation is vanishing at a rate that’s hard to mentally model.

And that has real consequences for investors. Because both the magnitude and the durability of return on invested capital are what ultimately drive long-term shareholder value. That’s what moats were supposed to protect. They bought companies time – time to reinvest at high rates, time to scale without being undercut, time to defend pricing power and margins while the rest of the market played catch-up.

But if that time window is shrinking across the board, then the kind of businesses that used to be considered rare and structurally advantaged may become even rarer – or, in some industries, may not exist at all in the same form.

That’s the fragility I’m talking about. AI doesn’t just accelerate innovation – it accelerates obsolescence. And while it might be too early to say who the long-term winners and losers are, it’s not too early to acknowledge that the rules of the game are changing, fast.

It’s a strange paradox: The tech sector has never been more dynamic. Yet as investors, we may never have had less clarity about what actually endures. And without clarity, visibility, or predictability, you cannot invest (because you cannot value assets with a reasonably high degree of certainty).

Before we dive back in, a quick note…

If this post already gave you something to think about, consider this: paid subscriptions don’t just keep this blog alive – they help me produce the best-possible content I can think of. No ads, no fluff. Just high-quality, independent research for long-term thinkers. Your support helps me go deeper, write better, and serve smarter investors like you. If you value clarity in a noisy world, subscribe.

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Defining “Tech” – And Why It’s Getting Complicated

“Tech” has always been a loose label. Investors have long lumped everything from chipmakers and cloud platforms to consumer hardware and SaaS billing software into one oversized bucket. Consider my portfolio’s sector exposure shared above. Arguably, my entire portfolio consists of “tech,” but only 7.7% of my holdings are labeled as “Information Technology” companies – are Meta, IBKR, Evolution Gaming, and Wise not “tech” names? I certainly think of them as “tech.”

So arguably, the term has been more of a shorthand than a precise definition – and for a while, that ambiguity didn’t really matter. Whatever sat under the “tech” umbrella generally shared a familiar profile: scalability, high margins (or high margin potential), low incremental costs, asset-light business models, sometimes platform potential, and optionality baked into the narrative. It was less about the sector classification and more about the economics – tech was where operating leverage lived.

But in today’s AI-driven market, that old shorthand is starting to break down. The differences between so-called tech companies are becoming more important than their similarities, as the risks and pace of AI-driven disruption vary widely across business models and sectors.

Apple and Paycom aren’t facing the same threats. Amazon Web Services and a niche vertical SaaS provider don’t enjoy the same moats. NVIDIA and Salesforce may both be “tech,” but the underlying economics, risk profiles, and defensibility of their models couldn’t be further apart.

And now, generative AI is amplifying those differences. It’s pushing us to rethink what “technology exposure” even means from a portfolio perspective.

Take software. For the past decade, it was widely viewed as the holy grail of business models. Recurring revenue. High gross margins. Low incremental cost. “Software is eating the world” became gospel – and rightly so, in that era.

But what happens when software itself becomes easier to generate, easier to replicate, and, in many cases, easier to give away?

The more AI levels the playing field, the more exposed many “tech” companies become. It’s no longer enough to simply be digital, cloud-based, or subscription-driven. The market doesn’t care that your UI is slick or that you’ve got an elegant backend. If someone can prompt-engineer a clone of your product in a few days and distribute it for free or at a significantly lower price, your clients may switch and your economic castle may no longer have any form of “moat.”

This is especially true for companies operating at the shallow end of the complexity pool – low-touch SaaS tools, lightweight consumer apps, etc. These are increasingly becoming commodities. And in a world where code is free, where hosting is cheap, and where AI can write 80% of what used to take teams of developers, software as a category is losing its privileged position.

To be clear: not all tech companies are equally vulnerable. But that’s precisely the point! The broad-strokes label of “tech” is no longer useful. Investors have to go granular – to look not just at sector, but each company’s business model, at management execution speed, general product defensibility features, market structure, and how deeply embedded a company is in its customers’ workflows or supply chains (consider my Monday.com deep dive as a case study).

That’s where the real differentiation is now. Not in the label, but in the specifics.

Comparing Disruption Epochs: Dotcom vs AI

Every generation of investors faces at least one defining technological revolution. For many seasoned investors, the prospect and rise of the Internet, which resulted in the dotcom era, was the first.

It was chaotic, irrational, and – in hindsight – fairly straightforward. Companies with zero profits and barely any revenue traded at multibillion-dollar valuations. Pets.com was a punchline, and yet Cisco and Amazon were already laying the foundations for what would become truly durable franchises.

But here’s what’s important: in 2000, the real disruption risk was relatively low. The Internet was still clunky. Broadband penetration was limited. E-commerce was a novelty. Even Google was still a startup (founded in 1998). The story of the web was just beginning, businesses had time to adapt, and most of the losses investors suffered came not from obsolescence or competitive failure, but from simple overvaluation.

The ideas were right – the timing and prices were wrong.

Compare that to 2025. The AI revolution feels less like the Internet circa 1998 and more like the Internet plus cloud plus mobile – all happening at once, but on fast-forward.

The level of penetration, the speed of adoption (by both businesses and consumers), and the volume of capital flowing into the space are all orders of magnitude higher. We’re not talking about a few startups raising Series A rounds. We’re talking about trillion-dollar incumbents pouring billions into GPU clusters, model training, and full-stack integration – all at once.

And the uncertainty isn’t just about adoption curves. It’s about whether anyone can maintain an edge for long. In the early 2000s, the foundational tech players weren’t really being disrupted. In fact, companies like Microsoft and Oracle were still solidifying their dominance (again, if we leave valuation aside here).

But today? Even Google – arguably the most defensible business model of the past decade – is under siege. AI hasn’t just introduced new competition; it’s fundamentally challenged what search even is. I wrote about this here:

If we take a step back, this shift starts to resemble a true regime change. In the 2010s, following the global financial crisis, the cloud-mobile-AWS era gave birth to the greatest collection of business models the world had ever seen.

Software went from product to platform. Data turned into network effects. Subscription models led to highly valuable recurring revenues. Horowitz’s now-famous 2011 essay, Why Software Is Eating the World, wasn’t just an observation – it was a prophecy that played out over a decade of monster returns, particularly in the Nasdaq. Back in late 2021, the 10-year compounded price return of the Nasdaq Composite was >20%!

But here in 2025, we may be seeing that cycle turn? I don’t know. Of course, I don’t know. No one does. I’m just thinking out loud.

Again, view this post as some food for thought, and keep in mind that I arguably own only “tech” names and have no intention of selling them.

But one hypothesis is that “software is NO LONGER eating the world.” It’s being digested, reconstituted, and in some cases, commoditized by AI.

The returns of the past were driven by new forms of leverage: global scale, high gross margins, and resulting operating leverage dynamics, automation, code, high switching costs, and (global) network effects.

In a future world of cheap AI agents, those levers are seemingly more accessible; not all of them, and certain companies possess multiple moats, making them more defensible, but ultimately, I expect more and faster disruption going forward.

That’s the difference. Dotcom was a hype bubble. This? This might be something much deeper, a more structural change, possibly changing the world of work AND the “clue of societies” forever.

Moats Aren’t What They Used to Be – Especially the Tech-Enabled Ones

If you’ve invested in tech over the past decade, chances are the word moat has played a central role in your thesis. Recurring revenue, high switching costs, network effects, proprietary data, distribution advantages, brand – the tech sector gave us a masterclass in how software could embed itself into the daily life of both consumers and businesses. You didn’t just buy a product. You often got locked into an ecosystem, you often subscribed to a service and paid businesses upfront (creating negative working capital dynamics, essentially funding the company’s growth), you accepted price hikes without much churn, your usage generated data that improved the product for everyone, and your entire workflow or tech stack increasingly depended on integrations with that single vendor.

But that playbook might be showing serious cracks in the age of AI. AI is coming for you, tech companies … Better watch out!

The core issue is this: most tech moats were built on friction – on the assumption that it’s hard to build (talent was scarce), even harder to scale, often hard to switch, and taking all of this together … hard to replicate.

AI is now actively erasing all three.

Consider network effects – long hailed as one of the strongest forms of competitive advantage in tech. AI is putting pressure on some network effect types (on some more than others). In lightweight, tool-based products – think early note-taking apps or LLM-based CRMs – the “network” often consists of usage data, shared templates, or open communities. These are relatively shallow and easy to replicate. When dozens of alternatives pop up overnight with similar capabilities, more attractive UIs, agents trained to create the best-possible marketing campaign (at virtually no cost), trained on broadly available datasets, the switching costs are minimal. Familiarity is high. Loyalty is low. And differentiation fades fast.

But that’s not true of all network effects. Products with direct user-to-user interactions, like Instagram or WhatsApp, are, I believe, far more defensible. Their value grows exponentially with each additional user because of the actual connections, not just the usage data. Same for marketplaces like Airbnb or platforms like YouTube, where liquidity or content density becomes self-reinforcing. These kinds of networks don’t unravel easily. But once an unraveling process starts, it often picks up pace quickly!

What AI seems to threaten most are the weaker, data-driven network effects – the kind that sounded good in a pitch deck but never built true user interdependence. In those cases, the speed and replicability of AI might compress the edge down to months, not years.

Switching costs? Many SaaS companies have spent years cultivating these – think CRM systems, HR platforms, financial reporting tools. But in a world where generative AI relies on robust datasets, can auto-translate interfaces, port data, and customize workflows with natural language, switching is no longer the pain it used to be.

A good chunk of “stickiness” might have been just legacy UX inertia – and that’s starting to disappear.

The counterargument would be that many companies are moving slowly and are reluctant to switch. Distribution and trust also play critical roles. For now, at least, the "risk" of switching may still seem too high.

So, there’s nuance. Not all businesses are equally vulnerable.

Mark Leonard, at the 2025 Constellation Software AGM, made a sharp distinction between pure software products and software+services bundles. He suggested that AI is far more disruptive to companies that are low-touch, standardized, and run purely through the web. But in industries where software is deeply embedded in workflows (consider Computer Modelling Group as an example) – and combined with services, training, and customization – AI may become an enhancer, not a threat.

“Where it is more of a threat or more of an opportunity depends to some extent on the kind of business you're talking about. So if you're talking about a SaaS business with very few people with software that is sold through the Internet that has very little training, very little support, that isn't automated, then I would think AI would be a pretty significant threat. The other end of the spectrum, and Dexter gave the example of pulp and paper is where we deploy a solution that looks like an awful lot of people who spent an awful lot of time around pulp and paper and software. So we're providing advice, support, customization, integration and intimate knowledge of the pulp and paper industry. And so at that end of the spectrum where it's a bundle of services and software, I think AI is much less of a threat and much more of an opportunity where we can deliver what we're learning about AI to our end customers.“ – Mark Leonard

That’s a critical insight. AI doesn’t destroy all moats. It just forces a re-evaluation of where the value really lives. If your edge is code and interface, you’re exposed. If your edge is deep domain expertise, integration, or customer intimacy, you might actually be able to use AI to strengthen your advantage.

But even that comes with a caveat: you have to move fast. Because, as explained, the window of defensibility is shrinking, and the slope of change is steep.

Even proprietary data, once considered a very defensible moat, is facing real challenges. Models are becoming good enough with generalized datasets. And for many applications, the marginal advantage of a unique data asset isn’t enough to outweigh the cost and effort of managing it. If a competitor starts coming along, providing a similar value proposition at 5% of the current cost, managers might think about this offer twice.

On top of this, the rise of AI may bring along a completely new major computing platform, making “apps” as we know them largely redundant. Such a transition to a new computing platform (AR glasses) may also challenge the more defensible network effects mentioned above, as social media apps, for instance, will have to reinvest themselves to feel “natural” on the new platform. But arguably, this shift, I believe, is first and foremost a major threat to mobile apps and software run on desktops.

To quote from my Monday.com deep dive:

Let’s address the elephant in the room when assessing various risk scenarios related to Monday:

How Real Is the AI Risk to Monday.com?

One of the more unsettling long-term risks for Monday.com – and frankly for many SaaS platforms, or more broadly, virtually every software company across the globe – comes not from a traditional competitor, but from a fundamental shift in how we interact with technology.

As generative AI accelerates, the very concept of using "apps" could become outdated.

Meta’s CTO Andrew Bosworth recently outlined a provocative thesis: in a world where users simply express intentions in natural language, the app layer disappears. You won’t open a calendar app to schedule a meeting or a CRM to update a sales lead – you’ll just tell your AI assistant what you want, and it will coordinate all the necessary actions behind the scenes.“

That’s not just a UI redesign. That’s a redefinition of computing.

OpenAI, with its plans for agent-based operating systems, is already nudging in this direction. The endgame? A computing paradigm where the OS, apps, and interfaces dissolve into one seamless AI-native layer.

If that vision plays out, Monday.com could face an existential threat. After all, it’s a visually structured, user-driven platform. Teams log in, click through boards, customize workflows, and manually assign tasks. In a world where natural language is the interface and agents handle execution, the entire idea of logging into a dashboard might seem archaic.

The effect of all this is a massive moat compression across large swaths of the software and tech-enabled landscape. Time itself is contracting. What used to be a multi-year advantage might now last just for a few more months, maybe 1-2 years.

The pace at which AI is progressing is hard to fathom and businesses have to adapt by moving fast. The “move fast and break things”-motto, coined by Mark Zuckerberg, needs to be embraced by all businesses.

So, as put it in his piece “Clouded Judgement 5.30.25 - Moats in the Age of AI”:

In today’s environment, being able to execute fast, ship fast, learn fast, and adapt faster than your peers might be the only edge left?

That doesn’t mean all moats are dead. But the durable, “tech-enabled” moats that defined the 2010s – many of them are now time-bound bridges (to stick with Jamin’s analogy above). Temporary. Situational. And unless companies can use that time to leap to a new defensible position, they’re just standing on a bridge waiting to be overrun. That’s why I’m still bullish on Meta – Zuck just gets things done fast – he still does –, never tired, always hungry.

Ironically, many of the most defensible business models now might be those with non-tech moats layered with tech – think vertical integration, tech + heavy infrastructure plays, domain expertise, regulatory complexity, or high-touch services enhanced by software. These don’t scale as fast, scaling them may be capital intensive, but they’re much harder to disrupt (quickly).

AI hasn’t killed moats. But it has changed where they live.

Portfolio Risk Reconsidered – Fund Managers vs Private Investors

Risk means different things to different investors.

For private investors like you and me, risk should primarily be just about permanent capital loss. Period.

Does the company have low debt levels or could leverage kill the business if it hits a rough patch? Can this business defend its economics and continue to grow and earn above-average (or at least above cost of capital) returns on capital over time? Or can competitors eat their lunch, and one might see operating leverage effects in reverse?

Those are the kind of questions long-term investors should obsess over – and rightly so. The stomach for short-term volatility is what buys us entry into those long-duration compounding stories.

But for fund managers, particularly those managing other people’s money, risk (often) takes on a more complex shape. Volatility itself becomes dangerous – not because it affects intrinsic value, but because it affects their investors’ behavior and how much trust they continue to place in their money managers. Clients pull capital. Allocators lose confidence. Public performance rankings shift. You’re not just fighting market cycles – you’re fighting redemptions and narrative whiplash.

Just think of what happened during the 2022 tech drawdown. Rob Vinall, one of the most thoughtful long-term managers out there, saw brutal drawdowns as tech & “growth” multiples compressed across the board. He had the benefit of a like-minded investor base – people who understood the process and stuck with him, and he luckily quickly recovered temporary paper losses (the chart below is cropped off in 2022 to illustrate how rough the drawdown was; but now, Rob is back to new ATHs!). But many others weren’t so lucky. And the pressure to de-risk or rotate out of positions just to placate skittish investors can push managers into decisions that have nothing to do with business fundamentals.

That’s where AI complicates things even further. For professional investors, AI-induced volatility isn’t just market noise – it may represent a career risk. Take Dennis Hong’s recent post on Japanese software companies. His enthusiasm was evident, but so was his hesitation. He laid out the multi-layered risks of investing in unfamiliar markets with structural and cultural headwinds – not just valuation risk, but liquidity, currency, alignment, and exit risk. And that was before even layering on the uncertainty of how AI might impact those companies’ moats.

For individual investors, those same dynamics matter a little less. We don’t need to worry about quarterly redemption notices. But we do need to worry about something even harder to model: structural disruption. AI might not tank a stock tomorrow, but it could fundamentally erode a business’s economics over a multi-year period (or, as outlined, sometimes the disruption may happen in a matter of months) – especially if we’re anchoring to the same frameworks that worked in the last cycle.

The real challenge is that most portfolio construction still assumes some level of continuity. But what if tech, as we know/knew it, is becoming a structurally worse place to invest – not because the products are worse, but because the economics and visibility are? What if the risk is not a 30% drawdown, but owning companies whose moats evaporate before our thesis has time to play out?

Volatility is survivable. Fragility isn’t.

Big Tech’s Uncertain Bets and the Limits of Scale

If anyone should be well-positioned to dominate the AI age, it’s the tech giants. Apple, Microsoft, Google, Amazon, Meta, NVIDIA – these companies have the scale, the talent, the data, and the distribution. And yet, when you zoom out, the picture gets murkier – not clearer.

The numbers are staggering. In 2024, the Big Six increased CapEx by 63% year-over-year, committing more than $212 billion. That’s not a typo. It’s one of the largest capital spending booms in tech history.

Here’s another whopping statistic I came across: "Between 2022 and 2026, [hyperscalers] will have made a whopping $1.14 trillion in capital expenditures, or three times what they did in the preceding five years."

But here’s the kicker: no one knows what the return on that capital will be. Not even the companies themselves.

And that’s the problem.

When the largest and most sophisticated businesses in the world all pile into the same space, competing on the same axis (compute, model quality, distribution), it might a zero-sum arms race.

What feels like reinvestment might end up being overinvestment.

What sounds like a “moat-expanding initiative” might be a desperation move to stay relevant.

Google is the clearest example. For over a decade, it was widely regarded as the best business on the planet. A distribution monopoly with a 50% margin, cash-minting search engine, and an ad machine that scaled like magic. But now? AI has put its core product – search – directly in the crosshairs. Googling is replaced by ChatGPTing – especially among younger demographics. All of a sudden, the thing that looked like an impenetrable fortress is being poked at from every angle. Not because competitors have better branding or lower prices – but because AI has fundamentally changed what users want (instant answers to search results or entirely new, more creative queries).

Investors should pay close attention here. The rise of AI has triggered massive reinvestment – but to come back to the concept we brought up earlier in the post, the magnitude and durability of returns of those reinvestments is anything but clear – even for Big Tech.

High ROICs aren’t guaranteed. And short-term high ROICs alone aren’t enough. They have to be durable to be valuable. And it’s precisely that combination – longevity and (seemingly infinite) reinvestment opportunities – that powered tech’s dominance during the 2010s.

This wouldn’t be quite as concerning if valuations were pricing in uncertainty. But they’re not (Google’s the exception here). As of 2025, the IT sector is trading in the 82nd percentile of its absolute price-to-earnings ratio over the last decade – and in the 76th percentile over the past 30 years.

In other words, we’re paying near-peak prices for businesses facing peak uncertainty.

That’s a tough setup. Especially in a sector where capital intensity is surging, moats are compressing, and reinvestment returns are increasingly hard to forecast.

But today, those pillars are wobbling. If you're investing in a business that reinvests 50% of its profits at 20% ROIC (resulting in 10% growth), but the durability of that return profile is questionable beyond a few quarters… what exactly are you underwriting? What price can you pay for that asset?

This is also where Buffett’s allergy to tech starts to make more sense. It’s not that he doesn’t understand the internet or software. It’s that he doesn’t trust what he can’t see – and right now, the long-term economics of AI are almost impossible to forecast. When the landscape is this unpredictable, even great execution might not deliver great outcomes.

To be clear, these giants aren’t going away. They’ll likely continue to grow. But growth is not the same as value creation. Especially not when massive spend is being funneled into hyper-competitive, fast-shifting areas where edge collapses quickly and regulation hasn’t even caught up.

This might not be the end of Big Tech. But it could be the end of Big Tech as we knew it.

Is AI Just a Feature? Or a Paradigm Shift?

One of the most important – and underappreciated – debates in investing right now is whether AI is a true paradigm shift or simply a powerful new feature that existing platforms will absorb – a feature that will arguably make them better, stickier, (much) more profitable.

For instance, Microsoft just laid off 3% of its workforce, seemingly because AI can replace many of the jobs formerly completed by humans.

Your answer to that question has massive implications for how you build a portfolio, value companies, and assess risk.

Some argue that AI is just another layer – like mobile, like cloud. They believe it will enhance existing products, improve margins, streamline workflows. In this view, the winners are already picked. Microsoft, Meta, Google, Amazon – they own the users, the data, and the compute. AI is simply the next thing they bolt on.

And the hyperscalers will keep fueling the boom from the infrastructure layer – selling the picks and shovels, and watching their cloud businesses become more profitable every quarter. Easy mode.

The value accrues to scale and distribution. End of story.

But I don’t think it’s that simple. It’s not that I strongly disagree – I really don’t know, and I do own Meta (which says something as well) –, but I think your assessment requires a lot of nuance.

So the alternative view – the one I find more compelling, at least for now – is that AI is a paradigmatic change. Not just because of what it does, but because of how it erodes value. It accelerates commoditization. It collapses differentiation in many ways. It dramatically lowers the cost of replication. And it breaks many of the levers incumbents – and now I’m talking about “Tech” in general and not “Big Tech” exclusively – relied on to maintain dominance: product moats, organizational inertia, even talent.

We’ve already seen it play out in niche verticals. The first LLM-based CRMs, note-taking tools, code assistants – all of them were cloned within weeks. The tools that initially felt groundbreaking quickly became interchangeable. The winners weren’t necessarily those with the best product. They were the ones who could ship faster, adapt quicker, and build more efficiently. In a world where everything is open source and one blog post away from being copied, speed is no longer a tactic – it’s the moat.

And again, I cannot emphasize this enough, things are moving incredibly fast. Blink and you might miss the next top-ranked LLM model.

Eric Schmidt recently predicted (watch it here) that within a few years, we’ll have “AI mathematicians” and “super-programmers” – agents that can independently experiment, test, and refine ideas millions of times faster than any human. If he’s even halfway right, software becomes scale-free. The entire product development loop – from idea to deployment – gets compressed into minutes.

What does that mean for software companies built on slow iteration cycles? I don’t know. But that’s just one tiny domain Eric Schmidt’s prediction will affect.

Its implications are far-reaching and touch on everything: education, medicine, finance, law, logistics, agriculture, energy, defense, cybersecurity, entertainment, gaming, architecture, climate, biotech, retail, customer service, journalism, governance, scientific research, therapy, translation, music, insurance, ethics, recruiting, negotiation, testing, and diplomacy.

Contemplating its scope is akin to contemplating the scope of the universe – fascinating, terrifying, and deeply unsettling all at once because our tiny human brains were never built to grasp something so vast and recursive, so far beyond our intuitive sense of scale and time.

And if AI really is a paradigm shift – not just for tech companies but for entire economies and for societies – then we’re likely heading into uncharted territory in more ways than one. Imagine a world where AI drives annual GDP growth of 10%, yet unemployment climbs to 10% or even 20%. That’s not science fiction. That’s a plausible near-future scenario. As Marc Andreessen recently put it, “AI will make everything so cheap, it’ll break the economy.” Productivity may explode, but labor displacement could outpace our social, political, and economic systems’ ability to adapt.

Investors often model cash flows – few model second-order effects on labor markets, policy responses, or societal volatility. But they matter. Because if AI breaks how value is distributed, it may also break how it's captured.

The Physical Economy Still Exists

It’s easy to get swept up in the speed and spectacle of what’s happening in tech right now. AI is captivating. It’s intellectually thrilling. And it genuinely feels like the start of something epoch-defining.

But while everyone’s busy re-engineering the future in Python and GPU clusters, it’s worth remembering a simple truth: we still live in a physical world. Mark Zuckerberg’s metaverse vision still seems quite far off.

Buildings still need to be built. Water still has to be pumped. Crops still have to be harvested, transported, processed, and sold. Industrial supply chains still exist. Healthcare still relies on physical diagnostics, drug manufacturing, and trained practitioners. Food, energy, shelter, logistics – none of it gets magically replaced by a chatbot.

That doesn’t mean AI can’t or won’t enhance parts of the real economy. It already is. Take Munich-based startup Fernride, for instance. They're revolutionizing logistics by deploying autonomous electric trucks in industrial yards and ports, utilizing a "human-assisted autonomy" model where remote operators oversee multiple vehicles, ensuring both efficiency and safety. This approach not only addresses the critical driver shortage but also enhances sustainability and productivity in logistics operations.

And that’s just one very unique example. Then there’s predictive maintenance, robotic process automation, etc. – there’s huge potential here.

But the keyword is enhance, not replace. Most of these industries are not pure code. They are complex hybrids of physical infrastructure, regulation, human labor, and legacy systems. AI will change how they operate – but it’s not going to collapse their value the way it might for some software niches.

And yet, if you look at most investor portfolios – especially those with a “quality” bias – they’re still overweight tech. The assumption has long been that the best businesses live in software. Maybe that was true in 2015. Maybe even in 2020. But in 2025, that assumption needs to be re-examined.

Because here’s the irony: the same AI revolution that’s threatening software moats might actually amplify the value of the defensibility of high-quality businesses in the real economy. Especially those that are hard to digitize, hard to copy, and deeply embedded in the physical world. These companies don’t move as fast – but they don’t break as easily either.

There are businesses out there producing predictable cash flows, with strong balance sheets, conservative management, and competitive positions that have lasted for decades. If you haven’t heard of the Lindy effect, go ahead and open Google (or I bet, many of you will use a chatbot ;-)) to learn more about this mental model!

Many of them aren’t flashy. They don’t get X threads written about them. But they might be exactly the kind of ballast investors need as tech volatility ramps up.

It’s not about abandoning innovation or ignoring the potential of AI. It’s about acknowledging that there’s a world beyond tech – and in a market obsessed with speed, efficiency, and disruption, slower-moving industries might offer a very different kind of edge: durability. That’s what Buffett has always preferred and why he “missed” the tech run.

Conclusion: Build Like It’s 2025, Not 2015

So, are we living through the most risky time for tech investors in modern history?

I think the answer is yes – but not for the reasons that usually dominate headlines.

It’s not just valuations, though those are lofty. It’s not just competitive pressure, though that’s real. It’s the deeper, structural shift in what creates – and sustains – advantage in a world where technology moves faster than business models can adapt. AI isn’t just changing the playing field. It’s shortening the game clock.

What made tech great in the past may not make it great going forward. Many of the moats we once considered enduring – network effects, switching costs, proprietary data – are being compressed, cloned, and commoditized. Meanwhile, the giants of the industry are engaged in a furious, capital-intensive arms race, the outcome of which is wildly uncertain.

That doesn’t mean tech is dead. It’s not. But investing in it now requires a different mindset. A new level of scrutiny. A willingness to question old assumptions. You have to ask not just what a company does, but how it defends its advantage when everything is open source, instantly replicable, and moving at the speed of compute.

At the same time, it might be time to give more attention to the parts of the market that haven’t been fashionable – sectors where the rules are slower, stickier, and maybe more boring. Because boring isn’t bad. In a world chasing novelty, durability is the ultimate edge.

Portfolio construction isn’t about picking winners – it’s about managing uncertainty. And the biggest risk right now isn’t missing the next OpenAI or Anthropic (and we still have to see whether these venture bets will have a big payoff anyway). It’s anchoring your portfolio to a version of tech that no longer exists.

AI will change a lot. But it won’t change everything. And in the end, the best portfolios won’t be the ones that predicted every twist in the hype cycle – they’ll be the ones built on a foundation that still holds when the hype fades.

So yes – build for the future. Embrace what’s coming. But do it like it’s 2025. Not 2015.

His latest dispatch, reflecting a multi-stop journey through China, Japan, and the US, is a window into business dynamics, geopolitical shifts, and cultural undercurrents that cannot show up in financial statements or spreadsheets – but matter a great deal to investors.

Vinall doesn’t write like a pundit – he writes like a thoughtful capital allocator with boots on the ground. His reflections are more than travel musings. They often foreshadow where the puck might be going.

So below, I’ll unpack seven of the most compelling insights from his latest post, adding some context with charts, etc., and occasionally some thoughts of my own.

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1. Trade Wars Are Back – and They’re Dumber Than Ever

The return of tariffs in 2025 – triggered by what Trump called “Liberation Day” – has reignited uncertainty across global supply chains.

Vinall’s conversations with John Deere executives, an American agricultural and construction equipment manufacturer, highlight a deep contradiction: policies meant to support US industry are leading to investment paralysis.

Multinational businesses can’t plan – and without visibility, they won’t deploy capital.

Vinall puts it bluntly: the idea that we can weaponize trade without collateral damage is flawed. The last time we tried this on a global scale – in the 1930s – it didn’t end well.

Beyond the historical analogy, there's a more immediate risk: the assumption that the US can win a trade war simply because of its economic might underestimates how much leverage the rest of the world still holds.

Many American firms rely on imports for components, talent, and growth. If tariffs are designed to protect them, the actual effect may be to weaken their global competitiveness long term.

“The US has many of the world’s most successful international companies, so they have the most to lose from de-globalisation. And if US companies are insulated from global competitors, they will inevitably lose competitiveness over time.”

China, ironically, looks like a relative winner. Despite initial concerns, the country appears to have digested the first wave of tariffs, adjusted its fiscal and business strategy, and is emerging with more confidence. It is even taking up the mantle of free trade advocate, not out of altruism, but because it has learned to compete globally – and win.

Europe and Japan, meanwhile, find themselves uncomfortably reminded of their dependence on the US (more on this further down below). The mood in both regions – especially Japan – is one of quiet concern about a future where American support can no longer be taken for granted.

The investment takeaway? The trade war isn’t just a headline risk – it’s redrawing the lines of industrial policy and economic alignment. And the US may not be on the right side of this equation.

“If the US perhaps overestimated the extent of its leverage over China, my sense is that the opposite may be the case in the rest of the world.”

2. China’s Post-Covid Pivot Is Surprising – and Making China Investable Again?

Vinall’s commentary on China challenges the dominant narrative. While Western headlines often depict an increasingly closed and hostile China (which Vinall himself described in previous “postcards from China”), his 2025 experience paints a more nuanced picture. After years of draconian Covid controls and economic stagnation, China appears to be pivoting toward a more open and investor-friendly stance.

This shift isn’t just rhetorical. From President Xi’s meetings with business leaders to stories of foreign filmmakers being granted near-total creative freedom, the signs are there.

What’s behind it? Possibly a recognition that economic strength – especially innovation and entrepreneurship – is China’s most credible response to external pressure.

For investors, the implication is critical: the risk-reward tradeoff in China may be far more favorable than perceived. With Western sentiment still skeptical, this is fertile ground for contrarian bets.

“The newfound enthusiasm for business is likely due to the realisation that innovative companies and a thriving domestic economy are the best defence in a trade war. It is too early to say whether this change in direction is cyclical or secular. However, cycles can be self-reinforcing, and I hope the trend towards liberalisation continues.”

Before we dive back in, a quick note…

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3. No One’s Boycotting Disney – The Chinese Consumer Isn’t Turning Inward

Vinall’s visits to KFC and Disney Shanghai weren’t just family outings – they were boots-on-the-ground research. Despite political friction, American consumer brands in China seem to be thriving. This runs counter to the narrative that Chinese nationalism would drive a mass consumer rejection of Western products.

This insight matters. In a world obsessed with decoupling, real-world behavior tells a different story. Love for Western brands appears sticky – especially when those brands deliver joy, status, or convenience. That makes a difference when valuing companies like many of the fashion luxury houses, Coca-Cola, or Disney.

Western investors often forget: geopolitical friction doesn’t always translate into grassroots backlash. Chinese consumers aren’t boycotting American culture – they’re standing in line for it.

“Consumers in Canada and Europe reacted to the tariffs by cancelling travel to the US and boycotting US goods. How did Chinese consumers respond? […] They looked more like super fans to me.”

4. The West Is Losing the EV War – and It's Not Just About Subsidies

The most sobering part of Vinall’s trip might be what he saw on Chinese roads. EVs with green license plates vastly outnumber combustion engine vehicles. Didi rides nearly always arrived in EVs. What’s more, the innovation cycle is faster – with Chinese firms treating vehicles like consumer electronics, not traditional industrial products.

This reframing is crucial. It explains why companies like BYD, Nio, and Xiaomi are outpacing legacy Western automakers. They aren’t just swapping out powertrains – they’re rethinking the product from the ground up, using a smartphone mindset.

Tesla, once the disruptor, now risks falling behind – not because it’s failing, but because the pace of change has accelerated.

“Tesla… still sells, by and large, the same models as several years back. Its lineup looks increasingly tired compared to Chinese cars.”

And traditional OEMs? They might not survive the shift at all, especially without software DNA.

5. Japan Is Worried, Europe Should Be Too

Japan came across as quietly anxious in Vinall’s conversations. The trade war, and the broader strategic decoupling between China and the US, has exposed just how dependent Japan remains on the US – not just for exports, but for regional security.

The dynamic isn’t unique to Japan. Many countries in Europe are equally exposed. As Vinall notes, the reality dawning on policymakers is simple: if the US chooses to weaponize its alliances or demand more from its partners, the leverage is there. But that very act of leveraging it may accelerate the search for independence.

The implication for investors is that the post-WWII playbook – where the US underwrites the global order – might no longer hold. And that will ripple through trade flows, currency alignments, and capital markets.

“Overall, the main message I took away was that the trade war brought home to many Japanese how dependent they are on the US and how vulnerable they will be if US support is withdrawn. I suspect most European countries have drawn a similar conclusion.”

6. AI Is Booming in China – and Export Controls Might Be Backfiring

If the US hoped to slow China’s progress in AI by restricting chip exports, the effect may be the opposite. As Vinall writes, scarcity is forcing creativity. Chinese firms are adapting to limited hardware by improving software and optimizing existing architectures. That’s led to breakthroughs, such as Deepseek’s LLM innovations.

This mirrors an earlier dynamic: China’s Great Firewall in the 2000s, meant to limit Western influence, inadvertently created a thriving domestic internet economy. Now, export controls may be triggering the birth of a fully indigenous AI ecosystem.

“Today, the US is building a firewall around China to prevent the diffusion of AI and inadvertently fostering a domestic AI industry.”

This presents a double-edged sword for investors. On one hand, it means China’s AI momentum is real – and possibly investable. On the other hand, it implies that current US firms face long-term competitive risks that are still being priced as geopolitical tail risks rather than operational ones.

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7. Chinese Companies Are Just… Better Now

Vinall’s visit to Xiaomi encapsulates something investors are still struggling to grasp: Chinese firms aren’t just cheaper – they’re better, more ambitious, and more vertically integrated – and fwiw this was also always my key takeaway from previous postcards from China written by Rob.

According to Vinall, Xiaomi, for example, now makes over 1,000 SKUs, including high-end home appliances and even EVs, sold through 2,000+ retail stores that resemble Apple Stores on steroids.

This is not about dumping overcapacity. It’s about business excellence – fast product cycles, relentless iteration, and an unmatched work ethic.

And while Western firms rely on regulatory protection (I’m looking at you EU legislators … ;-)) or legacy brand equity, Chinese firms are winning with execution.

That’s not to say Western firms are doomed. But as Vinall warns, absent customer lock-in or true IP, they’re likely to be outcompeted. Investors need to stop seeing China as a low-end manufacturing hub and start seeing it as a high-end, high-stakes rival.

“I am increasingly sceptical that high-end manufacturers in the West can compete with their Chinese counterparts unless they have significant customer lock-in. Many will go the same way as low-end manufacturers in the 2000s and fall by the wayside. One investor friend told me that the Head of BMW China spends most of his time trying to find a Chinese JV partner, tacitly acknowledging this reality.

This is not the end of the world for Western companies. There are plenty of areas where Western companies excel and Chinese companies do not. My lawyer friend told me that Western companies in China and Chinese companies overseas prefer to use international law firms due to the shortcomings of the Chinese legal system. Companies like Disney, LVMH and KFC are also thriving. Disney could and should triple the size of its theme park in Shanghai.”

Final Thoughts – See the World, See the Truth

Vinall closes with a powerful message: “The best investment opportunities arise when companies with great prospects are perceived as having poor ones. It is difficult to think of a place where this is so obviously the case as China today.”

That line stuck with me.

It’s easy to dismiss China, or to focus on the macro noise. But that’s not how fortunes are made. They’re made by digging into reality – and finding mispriced truths.

For me, these postcards are part field report, part forward-looking perspectives by an investor that thinks similarly to the way I do. And this one, in particular, suggests that the edge may lie exactly where most investors refuse to look.

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From the start of the year through early April, the major indices experienced significant declines. The Nasdaq Composite fell approximately 23% from its February high, the S&P 500 dropped around 19% from its previous high, and the Russell 2000 declined by about 24%.

But markets are nothing if not unpredictable. In a stunning reversal, the Nasdaq has surged 25%-ish from its lows, the S&P 500 has climbed 17%, and the Russell 2000 has rebounded by 19%. These gains were largely concentrated in brief, explosive sessions – Citi noted that owning the S&P 500 for just 60 minutes in the last month would have captured most of the recovery, highlighting the outsized impact of policy announcements on following market movements.

Adding to the bullish momentum, the S&P 500 recorded eight consecutive days of gains, marking its longest winning streak since August.

This rapid rebound has left many investors on the sidelines, grappling with the emotional toll of missed opportunities.

If you find yourself in this position, know that you're not alone. In the sections that follow, I'll explore the psychological challenges of investing, the importance of patience, and strategies for navigating volatile markets.

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The Emotional Tax of Missing the Move

There’s something uniquely gutting about watching the market surge while you’re still sitting on the sidelines, paralyzed by indecision or clinging to the hope of even lower prices.

It’s not just about the money you didn’t make – it’s the psychological weight of watching others win while you hesitate.

This latest rally was particularly brutal in that regard. The rebound happened fast – almost too fast.

In just a few sessions, prices ripped higher, driven not by improving fundamentals or earnings revisions, but by narrative shifts and sudden macro / policy reversals.

It was almost theatrical. The administration’s walk-back of the “Liberation Day” tariffs was the spark, but it was the implied end of the drama – the “deal” with China – that truly set off the short covering and momentum-driven buying.

If you didn’t pull the trigger when stocks were down, you probably didn’t even get a chance to ease into positions before they were already up 10–15%. It was like showing up to a fire sale only to find the shelves already half-empty and prices creeping back to full.

To make matters worse, if you’re anywhere near FinTwit, you were likely bombarded with screenshots of green portfolios, victory laps, and subtle (or not-so-subtle) digs at those who didn’t buy the dip. As the account aptly put it:

It’s hard not to feel like you missed something – like you failed in some way. And that feeling can eat at you if you let it, leaving you paralyzed.

But here’s the thing: that gut punch? That’s part of the game.

This isn’t a personal shortcoming. It’s just a consequence of how markets – and emotions – work. I’ll get into that next.

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Regret Is Built into the Game – Deal With It!

Regret is baked into the investor’s experience. Whether you’ve been investing for six months or two decades, the feeling never entirely goes away. Sometimes it's a faint whisper; other times, it’s a full-throated scream. But it’s always there – lingering.

In fact, social media may amplify those emotions and you need to learn how to deal with them.

Regret is built into the game of investing as investing forces you to make decisions with incomplete information. You never have perfect foresight. You’ll sell too early, you’ll buy too late, you’ll hold something that tanks, or you’ll miss out on something that rips. And even if you get the general trend right – say, identifying a market bottom – you might still fumble the execution (e.g. not loading up big enough).

So yes, you'll always feel regret in investing. The key is learning how to live with it and, more importantly, how to not let it dictate your process.

I wrote about this dynamic in my earlier piece, “Everyone Thinks the Market Is Doomed! But The Data Says Otherwise…” In that post, I laid out why – even amidst the chaos – there were strong signals pointing toward a potential bottom in early April. Breadth thrusts, sentiment extremes, capitulation-like volumes. The market was flashing classic recovery signs. But the truth is: acting on that insight still wasn’t easy. You had to choose between buying quality names at fair – but not fire-sale – prices, or sitting on your hands and waiting for something closer to an actual “no-brainer.”

Personally, I chose the latter. Not because I thought the market wouldn’t recover, but because I didn’t see anything that truly screamed, buy me now. I’m not in the business of chasing 20% drawdowns that leave me feeling lukewarm about the business.

And I’ll be honest: I have to disagree with people like WastelandCapital, who tweeted:

“What an incredible gift that sell-off was. Imagine being able to buy quality compounders at sudden 20–50% discounts, just like that. Crazy, huh!? Except it happens over and over again, like clockwork.”

I don’t think that’s true. Sure, prices came down. But discounts aren’t the same thing as bargains. I think wrote in a WhatsApp chat recently that a $5 apple in a basket of more expensive apples remains expensive.

Similarly, this time around, most of what I saw felt like discounted multiples, not discounted businesses.

That’s a big difference.

For me, it comes back to how rarely truly compelling opportunities come around. I transact infrequently. And that’s by design. I believe patience adds more alpha than action does, especially if you zoom out. Most stocks aren’t true bargains even when they’re down. And when they are, it’s usually obvious – not in price action, but in the sheer disconnect between business value and market value.

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Warren Buffett once put this beautifully:

“Understandably, really outstanding businesses are very seldom offered in their entirety, but small fractions of these gems can be purchased Monday through Friday on Wall Street and, very occasionally, they sell at bargain prices. We are impartial in our choice of equity vehicles, investing in either variety based upon where we can best deploy your (and my family's) savings. Often, nothing looks compelling; very infrequently, we find ourselves knee-deep in opportunities.”

And at this year’s Berkshire Hathaway Annual Meeting – his last as CEO – Buffett elaborated on that mindset in what I thought was one of the most instructive things he’s ever said. I’ve included the full quote below, because it captures not only his approach, but also why inaction is often a form of strategy in this game:

“When something is offered that makes sense to us and that we understand and offers good value, and where we don't worry about losing. And the one problem with the investment business is that things don't come along in an orderly fashion, and they never will. I mean, it isn't like every day. The long-term record is sensational, but that is not a product. And I've been… see, I've had 200 trading days times 80 years – 16,000 trading days.

It would be nice if every day you got four opportunities, or something like that. And, you know, they were expected to be equally attractive. If I was running a numbers racket, you know, every day would have the same expectancy, that I would keep 40% of whatever the handle was. And so the only question would be, how much was transacted? But we're not running that kind of business.

We're running a business which is very, very, very opportunistic. And Charlie always thought I did too many things. He thought if we did about five things in our lifetime, we’d end up doing better than if we did fifty.”

That’s how I try to operate, too. I’m not here to do fifty things a year. I’m here to do five great ones (or two, or just one for that matter!) when the stars align.

And if that means sitting tight through a rebound I didn’t fully participate in? So be it. I honestly don’t care.

The market isn’t a game of precision. It’s a game of process.

Obviously, it also makes a big difference whether you’re sitting on 10% in cash or 90% (but that would be another discussion).

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More from the GOAT

There’s something uniquely calming about hearing Warren Buffett talk about market chaos – because he almost never sounds surprised. That’s not because he can predict the future. It’s because he doesn’t try. His worldview is built around the idea that chaos, volatility, and confusion aren’t exceptions in investing – they’re the norm.

This year’s Berkshire Hathaway Annual Meeting was a powerful reminder of that. It marked Buffett’s final meeting as CEO, and while there were plenty of reflective moments, it was his commentary on the recent tariff turmoil that stuck with me most. As markets were swinging back and forth in response to policy headlines, Buffett essentially shrugged and said: “Nothing new here.”

Here’s what he said, verbatim:

“What has happened in the last 30, 45 days, 100 days, whatever you want to pick… whatever this period has been, it’s really nothing. There’ve been three times since we acquired Berkshire that Berkshire has gone down 50%, and over a very short period of time… nothing was fundamentally wrong with the company at any time.
But this? This is not a huge move. The Dow Jones Average hit 381 in September of 1929. It got down to 42.”

“It’s really nothing“ … Just think about that for a moment. Buffett is telling us – again – that volatility is just noise unless the underlying business fundamentals are permanently impaired. Berkshire has dropped 50% three times, and in none of those instances was anything actually wrong. The same logic applies to the broader market.

When the Nasdaq shed 11% in two days earlier this year, it felt dramatic. And sure, in the moment, it was. But the violent recovery that followed – a 25% surge – shows how often these selloffs are more about sentiment than substance.

The market didn’t find its footing because earnings suddenly improved or economic growth reaccelerated. It bounced because the White House started reversing its own self-inflicted wounds. Tariff threats got watered down. A “deal” with China was floated.

Suddenly, risk-on was/is back in fashion.

And yet, if you had asked Buffett whether any of that changed the long-term outlook for businesses he admired, you already know what he would’ve said.

Markets move in fits and starts. Headlines come and go. Administrations flip their stances. Algorithms chase momentum. But the underlying principles of investing – buying great businesses at reasonable prices and holding them long enough for the market to catch up – don’t change.

So when I look back on this recent episode, I don’t see a failure to act. I see a reminder. A reminder that you don’t need to respond to every dip. That you don’t need to treat every swing like a signal. And that, most of all, you don’t need to predict what happens next.

You just need to be ready when it actually matters.

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So What Now? It Depends Who You Are

When the dust settles after a sharp market rebound, the natural question becomes: what now? What should you do if you're still sitting on cash? Should you chase? Should you wait? Should you panic, pivot, double down, or disconnect entirely?

I understand that market movements can sometimes feel overwhelming.

Well – as unsatisfying as this may sound – it depends. Specifically, it depends on what kind of investor you are.

If You’re Passive

If you're a passive investor, your job is relatively simple: stick to the plan. Keep dollar-cost averaging. Keep allocating. Maybe increase your contributions when the market dips, but don’t try to time it. That’s the beauty of passive investing – your main advantage is consistency. You remove discretion from the equation, which also removes a lot of the potential for emotional error.

If you’re passive and still sitting on a large cash pile, that’s trickier. Research overwhelmingly shows that lump-sum investing tends to outperform dollar-cost averaging on average, over long time horizons. But unless you’re making that decision 500 times in your life (which you’re not), your personal sample size is 1 – and that means the trade-off between regret minimization and expected return becomes very personal. Personally, as expressed in previous posts, I also wouldn’t feel particularly comfortable putting a big lump-sum into a basket of US equities provided by ETF providers.

If you wonder why, read my article “Three Probability Lenses on America’s Market Outlook“ next.

So while the “textbook answer” might be to put that cash to work immediately, the more realistic answer is this: if you can’t stomach the emotional side of lump-summing right after a sharp rally, don’t force it. Invest part of it. Build a structured deployment plan. Just don’t freeze.

If You’re Active

If you're an active investor like me – especially one managing a concentrated portfolio – this environment feels different. You have choices. Lots of them. And that changes the entire calculus.

The first rule here is simple: don’t be a Johnny-come-lately as my friend Tiho Brkan recently put it in a privat interaction.

These are the fund managers and private investors who sold near the bottom – rattled by the volatility, overwhelmed by the headlines – and are now waking up to the fact that they’re lagging the S&P by hundreds of basis points. So what do they do? They rush back in. They chase the rebound. They “re-risk” at higher prices, just to avoid tracking error.

That’s not a strategy. That’s capitulation in reverse.

And it almost never ends well.

The second rule is a little harder to internalize: cash still has value – even now, after the rebound; maybe even more so.

The problem is that cash doesn’t scream its usefulness in real time. There’s no line item on your portfolio statement that says “optional future alpha stored here.”

There’s no dopamine hit. In fact, if anything, it feels like dead weight. And yet, I’ve found that some of the best returns I’ve made came not from what I bought – but from when I waited.

Let me give you a concrete example.

Back in late summer 2020, I bought Nintendo. The setup made sense. The outcome was decent. I made money. But in hindsight? If I had just sat on my cash and waited two more years, I could’ve bought it at a lower price.

That’s the lesson: sometimes, not deploying is the better move. Because patience isn’t about perfection. It’s about recognizing that you don’t need to swing at every pitch right away.

A successful investment that doubles over just 12 months can easily make up for sitting on cash for two years prior to that investment. Your compounded return over this 3-year period was still exceptional.

Especially if your portfolio is concentrated, you only need a few ideas every few years to make it work. You don’t need to be fully invested all the time. You just need to be ready when your pitch comes.

And it will come.

“Very occasionally, but it will happen again, [...] it could be next week, it could be five years off, but it won't be 50 years off, we will be bombarded with offerings that we'll be glad we have the cash for." "It'd be a lot more fun if it'd happen tomorrow but it is very unlikely to happen tomorrow. [...] The probabilities get higher as you get along.“ – Buffett (2025)

There will be another selloff. There always is. Maybe it’ll be deeper, broader, more panic-inducing. Maybe it’ll hit the specific sectors you love. Maybe it’ll knock the wind out of names you’ve been stalking for years (or those you already own). Maybe it won’t – not soon, anyway. But it will come. And if you have cash then, you’ll be in a position to act while others are licking their wounds.

Because as hard as it is to see the value of cash today – it becomes obvious the moment the market serves up a real fat pitch.

Closing Thoughts: Don’t Confuse Discount with Bargain

The phrase “the spring sale is over” has been thrown around a lot lately. And yes – prices did come down. For a brief moment, stocks looked cheapER.

But here’s the part I want to leave you with:

Discount ≠ Bargain.

Just because a stock is down 20%, 30%, even 50% – that doesn’t automatically make it a good buy. A lower multiple doesn’t mean a better business. And in this latest selloff, I didn’t see the kind of widespread mispricing that screams “load up.”

I saw markdowns – not misjudgments. Corrections – not capitulation.

That’s why I stayed patient. Not because I’m infallible. Not because I wasn’t tempted. But because I didn’t see my pitch. And when you’re playing a long game, when you’re trying to compound capital over decades – it’s not about how often you swing. It’s about how selective you are when you do.

As an aside, here is Dev Kantesaria on NOT investing in - the selectivity of Dev’s philosophy is inspiring:

So if you’re feeling frustrated right now – because you missed the bounce, because you were cautious, because others are flexing 30-day P&Ls like it means something …

… breathe.

This isn’t a race. It’s a craft. And like any craft, it rewards patience and process.

The truth is, markets will continue to offer moments like this. You’ll miss some. You’ll catch others. That’s okay. Regret is inevitable. But if you keep your head, stick to your principles, and refuse to let FOMO steer the wheel – you’ll do just fine.

You don’t need to be early. You don’t even need to be fast.

You just need to be right when it matters.

And in this game, that’s more than enough.

Bank of America recently published a table showing that the index is overvalued on 19 out of 20 valuation indicators compared to long-term averages, many of which span back over a century. That’s not nothing.

And yet… something doesn’t quite add up.

While the story being told is one of fragility and risk, the market’s recent behavior paints a more nuanced – even contradictory – picture. Beneath the surface, powerful sentiment indicators are flashing signals that historically point to a different outcome.

Since mid-April, we’ve seen unusually strong buying pressure, some of the rarest short-term rallies in decades, and, most notably, a confirmed breadth thrust – one of the most powerful, historically reliable indicators of future market strength.

The S&P ended April marginally positive after a deep -13.4% intra-month drawdown.

That’s one of the biggest intra-month comebacks ever recorded and shows a lot of resilience considering what’s unfolding on the macro stage.

It’s a strange place to be in as an investor. On the one hand, valuation metrics say “proceed with caution.” On the other, sentiment and breadth data – the kind of stuff that often matters at turning points – are telling a different story: that the market may have already bottomed, and we’re entering the early innings of a new bull trend.

So which is it?

This post is my attempt to explore that question in depth, not with gut feeling, but with data, history, and a bit of behavioral finance. I’ll break down what’s happening under the hood of this market, explain why some of these sentiment signals matter more than they seem, and lay out the two main paths I believe investors can take in the face of today’s uncertainty.

Because while the market doesn’t care how we feel, it does reward those who think probabilistically, weigh signals rationally, and act with intention – even when it feels uncomfortable.

Part 1: The Valuation Trap: Yes, the Market Is Expensive

Let’s get this out of the way: valuations are high. No one’s disputing that.

Bank of America’s now widely shared research piece laid it bare: on 19 out of 20 traditional valuation metrics, the S&P 500 is more expensive than its long-term average. These include forward and trailing P/E, CAPE (Cyclically Adjusted Price-to-Earnings), EV-to-sales, price-to-book, and others – many based on data going back 100 years or more.

And they are not just slightly elevated either; by some measures, we’re in historically rarefied air, only comparable to the dot-com bubble or post-COVID melt-up.

So yes, if you're purely valuation-focused, it’s tempting to look at today’s market and conclude: this can’t end well.

But here’s the trap – and it’s one I’ve seen investors fall into time and time again, myself included (!): high valuations do not automatically mean poor forward returns. We do not always require outright panic, for markets do bottom. And high valuation multiples certainly don’t imply an imminent crash.

Markets don’t move in straight lines, nor do they follow valuation logic in the short term. In fact, many of the best-performing periods in market history began from elevated valuations, because sentiment and positioning were bombed out.

Let’s remember: market valuation approaches (unlike valuation tools to value individual stocks) are blunt tools. They are powerful predictors over long time frames (7 to 10 years or more), but they’re notoriously unreliable over 3-month, 12-month or even 3-year horizons.

Why? Because valuation is just one input among many. Liquidity, sentiment, positioning, macro catalysts, and technicals often overwhelm pure valuation in the short term.

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High Valuation ≠ No Upside

Consider these historical truths:

• The S&P 500’s long-term average P/E is around 16–17. But it has traded above that level for most of the last 30 years.

(The S&P 500's price-to-earnings ratio including its rolling 20Y average)

• In 1995, the S&P 500 was trading at a high P/E and then the index doubled over the next five years before the actual crash arrived.

What investors often miss is that markets don’t need to be cheap to go up. They just need to be less bad than feared, or simply positioned in such a way that even neutral news creates positive reactions. When everyone is on one side of the boat – say, expecting a crash – even a lack of bad news can trigger an upside repricing (and sometimes even bad news can lead to positive stock reactions; e.g. higher than expected unemployment rates could have investors anticipate a (more significant) rate cut).

This is what Sir John Templeton meant when he said:

“Bull-markets are born on pessimism, grow on skepticism, mature on optimism and die on euphoria.”

By that logic, you have to ask yourself: where are we now? Because it sure doesn’t feel euphoric. If anything, it feels like skepticism at best and deep concern at worst.

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Valuations as a Psychological Anchor

One reason the valuation trap is so potent is because it appeals to our innate desire for numerical clarity. “It’s expensive” feels like a definitive statement. But in reality, valuations can stay elevated for years, and bull markets can persist well beyond what feels rational.

In fact, there’s a behavioral element here: we tend to anchor our expectations to valuation-based reasoning because it feels more objective, more quantifiable. But markets are pricing mechanisms, not fairness meters. They often respond to marginal changes in expectations, not absolute notions of cheap or expensive.

That doesn’t mean valuations should be a concern for long-term investors – they always should be! But they aren’t the full story. In fact, when everyone is leaning into the “valuation is too high” narrative, that’s often when markets do something unexpected.

So rather than anchoring exclusively to price multiples, I believe it’s more productive to ask:

That’s where I’ll turn next because despite all this talk about valuations, there’s a powerful signal flashing beneath the surface that suggests the tide may already have turned.

Part 2: Signals That Suggest the Bottom May Be In

The market doesn’t shout its turning points. It whispers them – in the form of subtle behavioral shifts, rare statistical anomalies, and technical patterns that often go unnoticed in the noise of macro headlines.

Over the past few weeks, I believe we might have seen exactly that: the quiet but powerful emergence of signals that suggest we may have already bottomed.

These aren’t predictions. They’re probabilities.

And taken together, they form a compelling case that investors may be underestimating what’s happening under the hood of this market.

A Market Rebound Hiding in Plain Sight

Let’s go back to April 2 – “Liberation Day.”

We got two of the worst consecutive trading days for the S&P 500 in recent memory. Volatility was (and still is) high. Investors were retreating. But then something shifted.

Between April 22 and 24, the S&P put together three consecutive days of 1.5%+ gains – a rare event in any environment, and especially in one that had been weighed down by bearishness. We didn’t get a fourth, but if we had, it would’ve been the first such streak since 1982. That’s how uncommon this level of broad-based buying pressure is.

And it wasn’t just large-cap tech dragging the index higher. It was the breadth of the move – the number of stocks participating – that stood out.

Which brings me to one of the most powerful, yet underappreciated indicators in technical analysis: the Zweig Breadth Thrust.

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What is a Breadth Thrust, and Why Should You Care?

The Zweig Breadth Thrust was developed by legendary technician Martin Zweig, and it measures the degree of participation in a market rally. Specifically, it looks at the number of advancing stocks as a percentage of advancing + declining stocks, smoothed over a 10-day moving average.

When that 10-day average moves from below 40% to above 61.5% within 10 trading days, Zweig considered it a “breadth thrust” – a powerful signal that momentum had shifted from bearish to bullish, and that a major low was likely in.

Since 1943, whenever a true breadth thrust occurred, the S&P 500 was higher 12 months later — 19 out of 19 times (see chart below). That’s a 100% success rate with an average return of over 23.6%!

It’s not just a bullish signal — it’s one of the most consistently accurate signals in market history.

There’s a caveat, of course. Many of these studies start post-WWII, conveniently leaving out the volatile 1930s. If you go back to 1929, the signal isn’t quite flawless, which is worth noting for transparency. But since WWII, when markets became more structured and liquid, the record is perfect.

That doesn’t guarantee anything, but it’s not something I’m willing to ignore.

And yes, we just triggered a Zweig Breadth Thrust in April.

So while the prevailing sentiment may still feel bearish or uncertain, the market internals are saying something very different.

Sentiment Indicators Are Flashing Contrarian Buy Signals

Here’s where things get really interesting. It’s not just market breadth suggesting a change in tone: sentiment indicators across the board are still flashing deep pessimism.

And in markets, as Templeton suggested, pessimism is often the soil in which bull markets grow.

Let’s look at a few of the most telling data points:

1) The Affect Heuristic: Why Feelings Fool Investors

Humans are wired to act on emotion. Psychologists call this the “affect heuristic.” We substitute feelings for facts because it’s easier, faster, and less cognitively taxing. In investing, that’s dangerous. We think we’re making informed decisions, but often we’re just reacting to how things feel.

Right now, it doesn’t feel like a new bull market. There’s no euphoria. No Reddit mania. No irrational buying. Which, ironically, is exactly why it might be the beginning of one.

2) Retail Investors (and Everyone Else) Still Don’t Believe

Multiple survey-based indicators support this view:

• AAII Bulls vs. Bears: The American Association of Individual Investors survey continues to show muted bullish sentiment; far below levels typically seen at market tops.

• Business & Consumer Confidence: Still deeply depressed relative to historical averages.

3) Analysts and Earnings Revisions

One of the more subtle but powerful sentiment shifts happens in analyst earnings revisions. We’re now seeing more and more downward revisions. As analysts should be considered “dumb money” this may be another signal indicating the bottom is near.

4) The Put/Call Ratio: A Gauge of Fear

The put-call ratio – which measures the volume of bearish option bets (puts) versus bullish ones (calls) – has spiked to elevated levels. A high ratio typically signals fear, hedging, and downside protection… not greed or complacency.

And when fear persists while the market climbs, that’s what technicians call a “wall of worry.” Bull markets love to climb those.

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Part 3: Two Ways to Play This Market?

Assuming you're sitting on some cash right now, you've essentially got two main options I believe.

You can either start putting money to work, selectively and rationally, or you can sit tight and wait for opportunities that feel more obvious.

There’s no universal right answer here. Each approach comes with trade-offs, and the best choice depends on your temperament, time horizon, and how confident you are in interpreting the current signals. But here’s how I think about the decision:

Option 1: Selectively Buy High-Quality Stocks at Reasonable Prices

The idea isn’t to buy indiscriminately, but to find companies with strong fundamentals, pricing power, and durable business models that can compound value over the long run – and that are currently trading at reasonable (not bargain) valuations.

Think of it this way: you’re paying a fair price for a great business, betting that the next 6 to 12 months will bring a modest increase in earnings, and potentially a modest expansion of the multiple (as the market as a whole gets a boost); two small but compounding tailwinds.

Take Meta, for example. Their latest earnings were a reminder of how robust their underlying business is, even in a tough macro. Earnings per share grew 37% despite the challenging macroeconomic backdrop.

Google, Amazon, and S&P Global all fall into a similar category – dominant franchises, sticky revenue, strong balance sheets. Even something like Medpace, which flies a little more under the radar, has a long runway for growth and trades at a valuation that doesn’t assume perfection.

These aren’t screaming buys in the way that deep-value plays are, but they don’t need to be. If you’re buying businesses that continue to execute, a modest re-rating plus some EPS growth can get you a solid 10–15% (maybe more) return over the next 12 months without taking on much existential risk. That’s not flashy, but it may be a rational and data-driven approach.

Of course, the risk is that valuations are too high, that something breaks in the macro, or that the market’s strength is just a bear market rally. But by focusing on high-quality businesses with durable economics, you give yourself a buffer. You’re not betting on a macro call. You’re betting on long-term resilience.

On the other hand, stocks like Fair Isaac and Company which undeniably are of the highest of quality but for the first time in four years reported a decline in its annual recurring revenue…

… and are still trading at 80x earnings, do not fall into the bucket of stocks I’m considering.

Option 2: Sit Still and Wait for the Fat Pitch

Then there’s the second option — do nothing. Stay in cash. Wait until valuations are screamingly obvious. Only swing when you see the fat pitch.

This is the camp that says: yes, sentiment is interesting, but it’s too dangerous to act on it when valuations are this high and uncertainty is this elevated. Why risk being early – especially if you think earnings estimates are still too optimistic, or that something unexpected could hit the system?

I have respect for this view. There’s wisdom in sitting out when you don’t feel you have a real edge. Warren Buffett has said repeatedly that you don’t need to swing at every pitch. And in a market that looks fully priced, it’s easy to imagine scenarios where even quality companies see multiple compression.

But here’s the catch: if you wait for perfect clarity, you’ll almost always be late. Fat pitches come rarely, and when they do, you have to be willing to act decisively, which is easier said than done, especially when those pitches usually coincide with widespread panic. Think March 2020. Think October 2022. They felt terrible in the moment.

Also, if you're holding cash and worried about downside, ask yourself: what would actually cause a large drawdown from here? Valuations alone aren’t enough. You need a catalyst – and most of the likely ones (recession, rates, geopolitical shock) are already well known. Markets don't typically crash on the known knowns. You need some sort of black swan event.

So while there’s no shame in waiting, the bar for that strategy to outperform gets higher the longer markets drift upward.

My Leaning?

Personally, I’m honestly unsure which option to lean towards. On the one hand, I absolutely do NOT think valuations are cheap, but on the other hand, the weight of evidence from breadth, sentiment, and positioning suggests the downside may be more limited than people think.

I might be adding selectively and in small-ish allocations. I want businesses with strong competitive advantages, capable management, pricing power, and a track record of navigating uncertainty. And I’m not going to pay 25x+ FCF for that. I’m looking for reasonable prices, not bargains. And I’m always thinking in probabilities, not certainties.

There’s no glory in being early if you get crushed. But there’s also little reward for waiting forever if the market has already turned and you’re anchored to a narrative that no longer fits.

This is the investor’s dilemma: act too soon and risk a drawdown. Wait too long and miss the meat of the move.

That’s why you need more than just price. You need context. You need signal.

You need to weigh, not feel.

Conclusion: The Market Doesn’t Care How You Feel

If there's one overarching lesson I keep coming back to, it's this: markets don’t reward how you feel. They reward how you think and assess the current setup.

Right now, it’s easy to feel like we’re skating on thin ice. Valuations are high. Geopolitics are unstable. Trump is unpredictable.

Hence, most investors are either nervous or disengaged. That’s a perfectly human response. But investing isn’t about being human in the emotional sense, it’s about being deliberate.

Look at the data, and you start to see a different story emerge. Breadth thrusts don’t happen in weak markets. Sentiment doesn’t stay this skeptical forever. Insider buying doesn’t ramp up (albeit just slightly) when they don’t see value.

In other words, the emotional fog many investors are stuck in doesn’t match what’s happening beneath the surface.

That’s not to say we’re off to the races. There’s always the chance that this is a bear market rally in disguise, or that some left-field risk derails everything. But if you're constantly waiting for clarity, you’ll find yourself investing only when it feels good – which is often when the best opportunities are already gone.

There’s wisdom in restraint. There’s power in patience. But there’s also danger in becoming paralyzed by narratives and forgetting to weigh evidence.

The market doesn’t price in how loud the warnings are. It prices in how surprised people will be when the outcome diverges from their expectations.

Personally, I still have to make up my mind. But I’m not letting market valuation ratios alone dictate inaction when so many historical and technical signals suggest something more bullish is at play.

You might disagree with my conclusion, and that’s fine. The goal here isn’t to convince you to buy or sell. It’s to encourage you to think. To sift signal from noise. To remember that feeling is not a strategy, and that the most powerful moves in markets often come when conviction is scarce, not abundant.

This could be one of those moments.