The first and easiest money in this AI boom was already made... and it was in the obvious places.

The companies selling the chips.
The companies building the memory.
The companies supplying the power.
The companies financing the data centers.

That is where the earnings bridge appeared first. And once the earnings bridge becomes visible, the market knows what to pay for.

But technology revolutions rarely end where the first money is made...

Infrastructure gets built so that something else can happen on top of it.
Chips are not bought for their own sake.
Data centers are not financed so they can sit there consuming electricity.
Compute is not the final product.

Usage is.

Commerce. Payments. Search. Advertising. Logistics. Manufacturing. Robotics. Mobility. Industrial automation. Consumer platforms. Daily economic life.

That is the application layer.

And this is where the AI story becomes more uncomfortable. Because the next opportunity may not sit in the cleanest market, the safest geography, or the most obvious winner. It may sit where investors still have the least trust.

That brings us to China.

Not because China is easy.

It is not.

Property remains a deep wound. Policy trauma is real. Geopolitical risk is unavoidable. And foreign investors have been disappointed too many times to forget it quickly. But serious investing does not begin when every risk disappears. By then, the discount usually disappears too.

The question is whether the market has priced China with discipline... or with despair.

In May’s issue of VMF’s Strategic Asset Allocation, we argued that China does not need to dominate the frontier-model race for the investment case to matter. It may only need to be good enough in models, unusually strong in deployment, and cheap enough for the market’s distrust to become the opportunity.

In the paid issue, we translated that framework into a concrete Model Portfolio action: the vehicle we chose, the exposure we added, the position we used to fund it, and the catalysts we believe could matter next.

The following excerpt develops the investment case behind that decision.

Because the next AI opportunity may not be hiding where the story is cleanest.

It may be hiding where the market still refuses to look.

Now that the market has begun to reward the visible AI bottlenecks, we need to move to the less comfortable part of the opportunity set.

The application layer.

This is where the debate becomes much harder.

The hardware layer is comparatively easy to understand. If AI usage grows, the world needs more compute. If the world needs more compute, it needs more chips, more memory, more data centers, more electricity, more cooling, more infrastructure, and more capital. The chain of logic is not perfectly clean, but it is visible enough for investors to underwrite.

The application layer is different.

Here, the visibility is lower. The dispersion will be wider. The winners and losers will be harder to identify in advance. Some early leaders will fade. Some apparently obvious use cases will disappoint. Some business models will turn out to be little more than temporary interfaces sitting on top of capabilities that commoditize very quickly.

That is why the market has been more reluctant to pay for this part of the AI stack. And yet, this is also where the long-term upside may ultimately be far larger.

Because infrastructure is not built for its own sake. Chips are not bought for their own sake. Data centers are not financed merely to sit in the desert and consume electricity. They are built because intelligence is expected to move into the real economy: into software, commerce, payments, search, advertising, logistics, entertainment, healthcare, manufacturing, mobility, customer service, industrial automation, robotics, and daily life itself.

In other words, the hardware layer is where AI gets built.
The application layer is where AI gets used.

And that distinction brings us to China.
Not because China is easy.
It is not.

And certainly not because the market has been foolish to apply a discount to Chinese assets...

The discount exists for very good reasons. But before we get to those reasons, we need to recognize something that has become too easy to forget after several years of disappointment: China remains one of the most important technology ecosystems in the world.

Not necessarily because it has overtaken the United States at the frontier-model layer. The U.S. still enjoys significant advantages in leading AI labs, hyperscale cloud infrastructure, advanced semiconductors, venture capital depth, and the ability to fund world-class model development at almost unimaginable scale.

But that is not the whole AI story.

Because the next phase of AI will not be judged only by benchmark scores, training budgets, or the elegance of frontier models. It will also be judged by deployment. By commercialization. By how quickly intelligence gets embedded into the real economy. By how effectively it moves through consumer platforms, industrial systems, payment networks, logistics chains, factories, vehicles, robots, and everyday workflows.

And in that world, China is far more formidable than most investors now seem willing to admit.

This is where the conventional Western framing can become too narrow. Investors look at export controls, chips, sanctions, and the frontier-model race, then conclude that China is structurally behind.

At one layer, that is fair. China still faces serious constraints in advanced semiconductors and the highest-end compute stack. But at another layer, the story is very different...

China has already built one of the largest digital economies in the world. Its ecommerce market is larger than America’s. Its internet population is more than three times the size of the U.S. equivalent. Online retail already represents a much larger share of total retail sales than in the United States, yet internet penetration is still meaningfully lower.

That combination is rare: enormous scale, deep digital behavior, and still some room for incremental penetration.

But the more important point is not just size.
It is embeddedness.

China’s technology platforms are not sitting politely beside the real economy. In many cases, they are the interface through which the real economy already operates. Payments, commerce, food delivery, travel, ride hailing, entertainment, healthcare, merchant services, logistics, advertising, cloud, and consumer finance have been pulled into digital ecosystems with a speed that more mature Western markets often struggle to replicate.

That is the leapfrogging effect1.

China did not need to follow the same path from cash to checking to credit cards before reaching mobile payments. It skipped steps. It did not need to build the same big-box retail infrastructure before embracing e-commerce. It skipped steps. It did not need to preserve every legacy interface in transportation, healthcare, and local services before moving activity onto digital platforms.

Again, it skipped steps.

That is important because AI adoption may reward the same instinct.

A society already accustomed to using digital platforms as the operating layer of daily life may be unusually fertile ground for AI applications. The rails are already there. The users are already there. The merchants are already there. The data exhaust is already there. The habit formation is already there.

This is the part of China’s technology story the market may still be underpricing.

A Chinese consumer does not simply shop online. She pays, chats, orders food, books travel, hails cars, watches video, receives recommendations, interacts with merchants, manages services, and moves across platform ecosystems that are already data-rich and transaction-heavy.

A Chinese merchant does not merely advertise online. It lives inside platforms that touch inventory, payments, logistics, consumer targeting, financing, promotions, and customer acquisition.

That is not a narrow internet story.
It is an application-layer story.

And the more we study it, the more important the distinction becomes. Because if the next phase of AI is going to be about deployment rather than merely model training, China deserves to be taken seriously. Not sentimentally. Not ideologically. Not because we want the China story to work. But because the evidence no longer supports the lazy conclusion that China is simply a second-tier participant in this technology race.

And just as importantly, it is our Inve

The frontier-model discussion still deserves attention, but the gap is no longer as static as many investors still assume. The chart below makes that clear:

The United States remains ahead, with Google’s Gemini 3 Pro still setting the pace in the broad model-performance rankings. But the more interesting observation is not simply who leads today. It is how quickly the field is compressing. And on that score, China’s progress has been striking.

In barely two years, Chinese models have moved from near-irrelevance in these rankings to a level that now forces them into the same conversation as the global leaders. Kimi K2 Thinking may still trail Gemini 3 Pro, but it is no longer sitting in another universe.

The direction of travel is what we should be watching.

The same pattern appears when we move from broad benchmarks to something more practical. On the chart tracking the ability to solve real-world GitHub issues, the American leaders still occupy the top spots, but the Chinese challengers are no longer spectators...

Chinese models have entered the competitive set in a meaningful way. Their scores still lag the very best Western models, but they are now solving a material share of real coding problems, not just performing well on laboratory-style tests. That distinction is important because software development remains one of the clearest early use cases for AI in the real economy.

The question is no longer whether China can produce capable models. It clearly can. The question is how quickly those models continue to close the gap.

Reliability, however, is where humility is still needed.

The hallucination-rates chart shows that several Chinese models remain more error prone than the best offerings from Anthropic and OpenAI. That matters because, in AI, intelligence without consistency still leaves a lot of value on the table. But even here, the lesson is not that China is absent from the race. It is that China is already in the race, just not yet leading it.

And for us, that is more than enough.

Our investment case does not require China to dominate the frontier-model layer. It only requires China to be good enough, to improve fast enough, and to sit inside an ecosystem that is unusually well positioned to commercialize AI at scale.

The patent data points in the same direction.

Now, we should be careful here. Patents are not profits. They are not moats. And in China, as elsewhere, they can sometimes tell us more about policy incentives than commercial outcomes.

China has been climbing the innovation food chain for years.

It has broken into the top tier of global innovation rankings, its export complexity has converged meaningfully with the United States, and its share of global AI patents has moved from marginal levels a decade ago to a commanding position today. The same pattern appears in clean-energy patents and even in nuclear science and engineering research, where China has moved from the periphery to the center of the global research base.

That is not the footprint of an economy simply waiting to import the next technological wave.

It is the footprint of a country trying to build the entire stack.

Models. Applications. Robotics. Manufacturing. Power. Transmission. Nuclear. Clean energy. Industrial automation.

Some of this will be wasteful. Some of it will be subsidized. Some of it will produce poor returns on capital. That is what happens when the state pushes aggressively into strategic industries. But some of it will also work. And when it works, it can scale very quickly.

That is the real point.

The application-layer thesis is not just that China has a large digital consumer base. It is that China has the technological capacity to keep improving the models, the industrial capacity to deploy them, and the social infrastructure to absorb them into daily life faster than many investors expect.

Then comes the other bottleneck.

Power.

Because AI is not merely a contest of models and applications. It is also a contest of electricity. The country that wants to push AI into factories, logistics networks, robots, vehicles, cloud platforms, consumer services, payments, and industrial systems needs an enormous amount of reliable power behind it.

And here, China’s position looks very different from America’s.

The U.S. is discovering that power may be one of the hard limits of the AI boom. Data-center demand is rising into an electricity system that was not built for a sudden step-change in load growth. Peak supply needs are expected to keep rising, while anticipated supply is not keeping pace.

The gap is enormous.

China, by contrast, has been increasing electricity generation at a scale that is difficult for the West to comprehend.

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Based on Energy Institute data, Chinese power generation has risen by roughly 2,500 TWh since 2019, compared with only 221 TWh in the United States and a decline of 110 TWh in Europe. And this is not merely a coal story. China has been adding generation across essentially every major source, electrifying final energy consumption faster than any other major economy, and leading the world in the deployment of high-voltage direct-current transmission lines, which are crucial for moving electricity across long distances.

That does not make China’s energy system clean.
It is not.

Coal still plays a major role, and the environmental, efficiency, and capital allocation questions are real. But from the perspective of the AI race, perfection is not the test. The test is whether China has the ability and willingness to build the physical capacity required to support a much more electrified, automated, and AI enabled economy.

And on that question, the evidence is hard to ignore.

This is why the China application-layer thesis is more interesting than a simple “cheap internet stocks” argument.

China has the users.
It has the platforms.
It has the embedded digital behavior.
It has increasingly competitive models.
It has a rapidly expanding patent base.
It has manufacturing depth.
It has robotics ambition.

And it has a far stronger grip on the power side of the equation than the United States currently appears to have.

That combination is rare.

Scale. Embeddedness. Technological capability. Manufacturing depth. Power.

This is why China may be one of the most important application-layer markets in the AI cycle. Not because it is cleaner than the U.S. Not because it is safer. And not because it leads across every part of the stack. But because it may be good enough where it needs to be good, unusually strong where deployment matters most, and far cheaper than a market with these attributes would normally be.

Which brings us to the uncomfortable part of the argument:

If China has so much scale, capability, and strategic relevance, why are Chinese assets still so cheap?

To answer that properly, we need to do what we always try to do before increasing exposure to a hated market:

We need to make the bear case first.

Let’s start with the obvious wound.

Property.

For years, real estate was not merely another sector in China. It was the household savings vehicle, the collateral engine, the confidence anchor, the local-government funding mechanism, and one of the main pillars of the country’s growth model.

So, when that machine began to reverse, the damage was never going to remain neatly contained inside the property market.

It spread into consumption.
It spread into confidence.
It spread into local-government finances.
It spread into the willingness of households to take risk.

And it created a deflationary impulse that is arguably larger, at least in balance sheet and confidence terms, than the U.S. housing shock that preceded the 2008 crisis. The comparison is not perfect. China’s bust is slower, more state-managed, and less brutally marked-to-market.

But property sits much deeper inside Chinese household wealth and economic activity: Brookings notes that average Chinese households hold about 70% of their assets in housing, while housing valuation is roughly five times GDP.

That is the macro scar.
And it is deep.

But it is also forcing a change in policy direction. If the old wealth engine was property, the new one increasingly has to involve financial assets, capital markets, technology, and consumption. Chinese authorities appear to understand this. Regulators have been encouraging insurers, pension funds, and mutual funds to increase equity exposure, while also reducing frictions in the fund industry to promote longer-term investment.

That does not solve the property problem overnight.
But it changes the direction of travel.

The second wound is policy trauma.

Global investors have not forgotten the crackdowns on internet platforms, fintech, private education, gaming, data, and other parts of the private sector. Nor should they. Those interventions did not merely reduce earnings forecasts. They damaged trust. And once trust is damaged, even good news gets discounted.

That is why the hardest question in China is not whether companies can innovate...

They clearly can. The harder question is whether shareholders will be allowed to capture enough of that innovation.

That uncertainty sits at the heart of the China discount.

The third wound is geopolitics.

The U.S.-China rivalry is no longer background noise. It runs directly through semiconductors, AI export controls, tariffs, sanctions, rare earths, capital flows, Taiwan, and investor access. The same technological ambition that makes China interesting also makes it strategically sensitive. And if China becomes more competitive in AI, robotics, clean energy, nuclear engineering, semiconductors, and industrial automation, the rivalry with the United States is unlikely to disappear.

It may intensify.

Then come the other familiar problems: weak private-sector confidence, deflationary pressure, demographics, youth unemployment, uneven corporate governance, currency risk, and years of foreign-capital exhaustion after too many false dawns.

So, we understand the pessimism.

In fact, we would be suspicious of any China thesis that did not begin there.

But this is also why we do not need to spend the rest of this issue re-litigating the entire China bear case. We have been covering it since the inaugural issue of VMF’s Strategic Asset Allocation, precisely because we have been overweight Chinese equities from the beginning. The risks were visible then. They remain visible now.

What has changed is the opportunity set.

Back then, Chinese equities were cheap because investors had lost confidence.

Today, they are still cheap, but the application-layer AI case has become much stronger. At the same time, the obvious AI trades elsewhere have become much more expensive, while our South Korea position has already delivered the kind of return that forces us to ask where the next pocket of asymmetry may be hiding.

That is why we now believe the China opportunity may be even more attractive than South Korea was at the beginning of 2025.

Not because it is safer.
It is not.

Not because the visibility is higher.
It is not.

But because the market may be making a similar mistake in a different layer of the AI stack. In South Korea, it underpriced the hardware and memory opportunity. In China, we believe it may now be underpricing the application layer.

The opportunity is murkier.
The risks are higher.
But the valuation is much cheaper.
And the potential re-rating may be just as powerful.

The question that remains is simple:

How should we increase this exposure... and by how much?

The easiest mistake in investing is to think the hard part is being right. - It is not.

Being right is only the first test.

The harder test comes later, when the market finally agrees with you, the thesis becomes obvious, and a position that once looked misunderstood begins to look crowded.

That is where discipline matters.

For much of the past year, one of our central arguments at VMF Research has been that AI may look like a chatbot on the screen, but underneath it sits one of the largest physical infrastructure buildouts in modern market history.

Behind every model, every AI agent, and every productivity breakthrough sits an enormous demand shock for semiconductors, memory, data centers, electricity, cooling, grid capacity, and capital spending.

That framework led us into some of the cleanest AI bottleneck trades before the market fully appreciated them.

But now the visible side of the AI boom is no longer hidden...
The market can see the need for chips.
It can see the need for memory.
It can see the need for data centers.
It can see the need for power.

And once a bottleneck becomes visible, capital starts to crowd into it. That does not mean the opportunity is over. But it does mean the job changes.

The question is no longer simply:
Where is the AI growth?

The better question is:
Where is the AI growth still underpriced?

That is the transition at the heart of May’s issue of VMF’s Strategic Asset Allocation.

In the paid issue, we use this framework to explain why we are managing some of our strongest AI-related winners with more discipline, why we have already taken partial profits in one of them, and where we believe the next layer of opportunity may now be emerging.

The following excerpt focuses on the first part of that argument: the visible side of the AI boom, the bottlenecks the market has already begun to recognize, and the portfolio-management lesson that follows when a thesis works almost too well.

There are moments in markets when a thesis stops being controversial and starts becoming obvious. That is usually a good thing. But it is also the moment when the job changes...

At the beginning of a trade, the real challenge is conviction. The evidence is incomplete. The narrative is uncomfortable. The market has not yet fully recognized what is happening. That is where the opportunity usually lives. Later, once the market begins to agree, the challenge is no longer to see the opportunity.

It is to manage what happens after everyone else starts seeing it too.

That is exactly where we now find ourselves with two of the cleanest AI-related bottleneck trades in our Model Portfolio: South Korean equities through and uranium miners through.

When we added South Korea in January 2025, the logic was already there.

The AI buildout was going to require an extraordinary amount of compute.

And compute would not only require NVIDIA GPUs. It would require memory... a lot of memory! High-bandwidth memory. Server DRAM. Enterprise SSDs. Advanced packaging. The entire physical stack behind the intelligence layer.

In other words, the market was already learning to worship the AI model makers and the GPU suppliers, but it had not yet fully appreciated the broader supply chain that would have to scale with them.

That was the opportunity.

Because AI may feel weightless to the end user, but it is not weightless at all. It is one of the most capital-intensive technology waves the world has ever seen. Gartner now expects global semiconductor revenue to grow 64% in 2026, with memory revenue expected to triple, DRAM prices rising 125%, NAND prices rising 234%, and AI semiconductors representing roughly 30% of total semiconductor revenue this year.

That is not just an upgrade.
That is an industrial buildout.

And the numbers across the winners now confirm it. NVIDIA’s full-year revenue rose 65%, while its fourth-quarter data-center revenue increased 75% year over year. Broadcom’s AI semiconductor revenue grew 106% year over year. TSMC’s ( ) first-quarter revenue increased 35%, while net income and diluted EPS both rose 58%.

Even ASML (), the irreplaceable lithography supplier to the leading-edge semiconductor ecosystem, raised its 2026 sales outlook as AI demand continued to outstrip chip supply.

These are not normal cyclical numbers...

They are the financial footprints of a capex supercycle.

But our edge was not simply recognizing that AI hardware would benefit. By early 2025, that was already becoming increasingly clear. The harder question was where that visibility had not yet been fully priced.

Our answer was South Korea.

Because if AI compute was going to grow exponentially, memory demand was going to follow. The market could not keep treating memory as just another cyclical commodity if high-bandwidth memory, server DRAM, and enterprise SSD demand were becoming structural bottlenecks. South Korea, through Samsung Electronics and SK Hynix, sat directly in the path of that change.

That is now being confirmed with force.

Samsung’s latest quarter showed a 43% sequential increase in consolidated revenue, with its Device Solutions division posting an 86% sequential sales increase, as the memory business set new quarterly records and management pointed directly to AI-related demand, HBM, DDR5, SOCAMM2, and enterprise SSDs. SK Hynix, meanwhile, delivered record quarterly results, with revenue rising roughly 60% sequentially, operating profit nearly doubling versus the prior quarter, and management explicitly attributing the performance to strong AI infrastructure demand and higher sales of high-value-added products such as HBM, high-capacity server DRAM, and enterprise SSDs.

That is the earnings bridge the market is now paying for.

And, as you can observe in the chart on the previous page, once that reality became impossible to ignore, the market paid for it aggressively.

Since the beginning of 2025, EWY (green line) has not merely kept pace with the semiconductor trade. It has dramatically outperformed two of the most popular semiconductor ETFs, SMH (red line) and SOXX (blue line).

The green line tells the story: South Korea became one of the cleanest expressions of the AI hardware cycle precisely because the market had initially underappreciated how much operating leverage the memory giants could capture once the demand surge became undeniable.

The initial chart makes the portfolio-management lesson even clearer. We recommended EWY ( ) when the opportunity was still underappreciated and, above all, inexpensive.

That is the crucial point.

The sales and earnings momentum now being delivered by Samsung and SK Hynix sits in the same broad neighborhood as some of the most celebrated AI winners in the U.S. and global semiconductor complex. Yet, because the market had not initially priced South Korea as an obvious AI winner, the price response has been far more explosive. EWY has almost doubled the performance of the semiconductor ETFs where those more fashionable AI names command the largest weights.

Still, when the market began to recognize the story, we took our first partial profit at the beginning of November. And now, after another extraordinary move, we have taken a second partial profit, closing half of the remaining position at $162 and locking in a 206.5% total return on that tranche.

That decision is not a rejection of the thesis...

It is the thesis working.

Btw, Have you check our leaderboard section?

It tells the story better than any intro, showing our total returns since publication, including multiple triple-digit winners and a consistent record of beating the market while managing risk.

And just as importantly, it is our Investment Process working. The goal is not to fall in love with a position after the market has finally discovered it. The goal is to keep moving capital toward the best risk/reward opportunities inside the same secular megatrend.

But before we move to the pockets of opportunity that now look more attractive, we need to address our other obvious AI winner. Because the same broad logic we applied to EWY also applies to URNM.

The difference is that this trade is not yet as mature.

For a long time, nuclear energy was discussed mostly as a political, environmental, or commodity story. Today, it is increasingly being pulled into a very different conversation: AI infrastructure.

The reason is simple.

The AI buildout does not only need chips. It needs electricity. Reliable electricity. Scalable electricity. And, most importantly, electricity that can be delivered where and when data centers need it.

That is becoming one of the most important bottlenecks in the entire AI stack.

OpenAI alone has announced partnerships requiring 30.5 GW of new power. To put that figure into context, that is roughly 75% of the peak level of nuclear capacity the U.S. completed over any five-year period during the nuclear buildout era.

And that is just one company in the AI ecosystem...

That comparison should stop us in our tracks.

Because the market has become comfortable talking about AI as if the limiting factors are mostly models, chips, and capital. But the physical world is now pushing back. The entire U.S. added only 25 GW of capacity in 2024 after adjusting for intermittency and reliability, while the projected gap between peak supply needed and peak supply anticipated continues to widen into the next decade.

The AI economy is trying to grow inside an electricity system that was not designed for this kind of step-change in load growth.

That is the gap nuclear is being pulled into.

Not because nuclear is perfect. It is not. New plants are hard to build. Timelines are long. Permitting is painful. Small modular reactors still have a lot to prove. And only a very small number of decommissioned U.S. nuclear plants may realistically be restarted.

But that is precisely why existing nuclear assets, uranium supply, and the broader nuclear fuel cycle have become so interesting.

If power is the bottleneck, and if new nuclear supply cannot be created quickly, then the existing uranium ecosystem becomes far more strategically relevant. Not because it solves the entire data-center power problem tomorrow. It obviously does not. But because it sits inside the narrow group of solutions capable of providing the kind of reliable baseload power the AI economy increasingly needs.

And that changes the nature of the trade.

Uranium is no longer just a commodity story.
It is becoming another AI bottleneck trade.

That is why our URNM position matters. As the chart on page 6 shows, we are already up roughly 70% since adding the position to the Model Portfolio. Like EWY, it has dramatically outperformed our equity benchmark. And like EWY, it reflects the same Investment Process: identify the bottleneck early before the market fully understands how visible the demand is likely to become.

But, again, maturity matters.

EWY has already entered the phase where the market has moved from recognition to something close to euphoria. URNM is not there yet. The position did briefly approach the 100% mark at the beginning of this year, but the move was too quick for us to formally monetize it through our publications. If it reaches that level again, we intend to close half of the position and lock in part of the gain. That is not because the nuclear thesis would be over. Far from it...

It is because our job is not merely to identify the right secular trend. It is to manage the position as the market increasingly discovers it.

This is the key lesson.

The market has now discovered the visible parts of the AI trade. It understands GPUs. It understands semiconductors. It is beginning to understand memory. And it is increasingly being forced to understand power.

Once a bottleneck becomes visible enough, capital begins to crowd into it. That does not mean the opportunity disappears overnight. But it does mean the risk/reward changes. And once that happens, the opportunity can move very quickly from underappreciated to overextended. That is why discipline now matters.

EWY and URNM have done exactly what we hoped they would do. They gave us exposure to the visible hardware and power bottlenecks of the AI buildout before that visibility had been fully priced. They rewarded us handsomely as the market caught up. And in the case of EWY, the move became stretched enough for us to harvest another part of the gain.

That is not a bad problem to have, it is the kind of problem good investing creates.

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AI is making intelligence cheaper. But that does not mean the whole system becomes cheaper.

April was the month when that idea stopped being theoretical for us.

It became a portfolio question.

Most of the market is still asking whether artificial intelligence will be inflationary or deflationary.

We think that question is too crude.

AI may be deflationary in the work it can replicate: drafting, coding, summarising, researching, analysing, automating.

But it may be inflationary in the bottlenecks it cannot replace: power, compute, data centres, memory, legal rights, trusted distribution, regulation, physical infrastructure, and institutional trust.

That was the thread running through everything we published inside VMF Research in April.

When intelligence gets cheaper, what becomes scarce?

That question shaped our asset allocation work.

It shaped our security selection.

And inside Alpha Tier, it led to actual Model Portfolio changes.

That is why we are launching this new monthly note.

Not to give you a simple archive of what we wrote.

But to show you how the VMF Research process works: how one idea moves from macro framework, to sector opportunity, to security-level thesis, to portfolio action.

That sequence is where the value is.

Strategic Asset Allocation: The Great AI Washout

In April’s issue of VMF’s Strategic Asset Allocation, we argued that the market’s first reaction to AI disruption had become too indiscriminate.

AI is real.

The threat is real.

But markets rarely price disruption with a scalpel.

They usually reach for a flamethrower.

That is what happened across software.

The market began selling companies exposed to AI obsolescence fears without carefully distinguishing between fragile point solutions and deeply embedded enterprise systems.

But enterprise software is not a weekend app.

It is often wired into approvals, compliance, client records, reporting systems, workflows, permissions, and years of operational muscle memory.

Replacing it is not like changing a browser plug-in.

It is more like replacing the plumbing in a skyscraper while the building is still occupied.

Our conclusion was simple:

The software panic had gone too far.

Inside the paid issue, subscribers saw how we expressed that view in the Model Portfolio, why we preferred a diversified sector vehicle over a single-stock bet, and how the position fit into our broader asset-allocation framework.

The public point is this:

AI disruption will create losers.

But when the market starts treating an entire sector as if the ending has already been written, investors should pay attention.

That is often where opportunity begins.

From this issue:

Security Selection: The Best Seat in the House

One week later, in VMF’s Security Selection, we moved from software to royalties.

The issue was called The Best Seat in the House.

The idea was simple:

Own the right asset. Collect the stream.

Some of the best businesses in capitalism are not the ones doing the hardest operating work.

They are the ones that own the superior claim.

That is the royalty model.

From this issue:

And in April, we applied that framework to music.

A great song does not deplete when it is consumed.

It can be streamed, licensed, covered, sampled, synchronised, rediscovered, used in advertising, used in games, and potentially licensed into new AI tools.

A mine depletes as it produces.

A great music catalogue can keep earning for decades.

That is why we became interested in Universal Music Group.

The bear case is obvious. Technology has disrupted music before, and AI could do it again.

But our view is different.

When content becomes infinite, authenticated cultural scarcity may become more valuable, not less.

That is what makes UMG interesting.

It is not merely a music company.

It is a rights platform.

And in a world where AI tools may need licensed creative inputs, premium rights may become a more important tollbooth, not a less important one.

Inside the paid issue, subscribers received the full UMG investment case: the sum-of-the-parts valuation, the royalty-like economics, the strategic value of the catalogue, and why Bill Ackman’s public proposal added a live special-situation catalyst to what we already considered a high-quality platform.

That combination matters.

The best investment ideas are rarely just thematic.

They are thematic ideas with a catalyst attached.

Also From this issue:

Alpha Tier: Bottlenecks

Then came Alpha Tier.

This is where April’s work became more aggressive.

The issue was called Bottlenecks.

The central claim was this:

AI is making intelligence cheaper, but shifting scarcity to new bottlenecks.

For a long time, Alpha Tier leaned into classic scarcity: gold, precious-metals miners, energy, metals and mining, and select commodity-sensitive markets.

That positioning worked.

But April forced us to refine the framework.

The old Debasement Trade is not dead.

It is evolving.

If the market has already recognised some of the obvious AI bottlenecks (power, uranium, semiconductors, memory, and data centres) then the more interesting opportunities may now sit one step away from the crowd.

Rights.
Rails.
Licensing.
Regulation.
Compliance.
Local physical assets.
Institutional trust.
That was the shift.

Inside Alpha Tier, we trimmed some broader scarcity exposures after strong gains and redirected capital toward more targeted bottleneck ideas.

One was Universal Music Group ($UMG.AS ), giving us exposure to licensed cultural scarcity.

The other remains reserved for paid subscribers.

But the high-level logic is worth sharing.

It is a company tied to the physical infrastructure beneath the AI buildout.

Because data centers do not build themselves out of software demos.

The intelligence layer may be digital.

But the infrastructure beneath it is stubbornly physical.
Electricity needs grids.
Compute needs buildings.
Buildings need materials.
Materials need logistics.

And local physical networks cannot be replicated as easily as code.

That is where the AI discussion becomes far more interesting.

Not in the obvious names everyone is already chasing.

But in the bottlenecks AI still needs in order to scale.

Inside the paid issue, subscribers received the full implementation: the name, position size, entry price, funding source, portfolio impact, and investment rationale.

That is the difference between reading a framework and following a process.

A framework tells you how to think.

A portfolio shows you what the thinking requires.

From this issue:

The One Idea From April

April’s message was simple:

AI does not eliminate scarcity. It moves it.

That idea connected all three publications.

In VMF’s Strategic Asset Allocation, it led us to the software washout.

In VMF’s Security Selection, it led us to premium music rights.

In Alpha Tier, it led us to refine the portfolio around more targeted bottlenecks.

That is the research architecture we are building at VMF Research.

Not isolated opinions.
Not fashionable themes.
A sequence.
A framework.
A portfolio process.

And a willingness to keep refining the thesis as the facts change.

Final Thought

The first phase of the AI trade was about owning intelligence.

The next phase may be about owning what intelligence still needs.

That means asking harder questions:

Who owns the rights?
Who owns the rails?
Who owns the infrastructure?
Who owns the regulated gateways?
Who owns the trusted data?
Who owns the bottlenecks?

That is where we spent April.

And that is where we believe some of the most interesting opportunities may still be hiding.

The free essays on VMF’s Market View will continue to share parts of our work. But the full research process (including model portfolios, position changes, entry prices, valuation work, security-level analysis, and new recommendations) remains reserved for paid subscribers.

To follow the full process as it unfolds, from framework to portfolio action, become a paid subscriber to VMF Research!

Good investing,

Vasco

The Debasement Trade.

Gold.
Silver.
Miners.
Real assets.
Scarce claims.
Selected geographies and companies positioned to benefit from a world of excess debt, record deficits, fiscal dominance, and monetary fragility.

That trade has worked.

Our updated Leaderboard makes that clear.

Several of our Top 10 positions are still direct expressions of that original thesis. Some have doubled. Some have tripled. Some have been partially monetized. Some remain open because we believe the story is not over.

But here is the more important point:

The Debasement Trade is not dead.
But it is evolving.

Why?

Because another secular force is now colliding with it:

An Abundance Shock.

AI is making intelligence cheaper. It is compressing tasks. It is lowering the cost of software creation, analysis, content, coding, and workflows.

But cheaper intelligence does not make the whole system cheap.

Quite the opposite.

In a world where more things can be copied, automated, and generated at near-zero marginal cost, what is truly scarce becomes even more important.

Power.
Compute.
Copyright.
Licensing.
Regulatory permission.
Trusted data.
Physical infrastructure.
Embedded workflows.
Cultural IP.
Tollbooths.

That was the real theme of our April publishing cycle.

In VMF’s Strategic Asset Allocation, we published “The Great AI Washout” - explaining why the market’s panic in high-quality software had gone too far.

In VMF’s Security Selection, we published “The Best Seat in the House” - presenting a royalty-like business built on scarce music rights.

And in Alpha Tier, we published “Bottlenecks” - tying it all together: the next winners may be the businesses that capture AI-driven abundance while sitting on scarce assets, rights, rails, or permissions.

The Leaderboard shows what has already worked.

But beneath it, we are already planting the next generation of potential outperformers.

Because in markets, yesterday’s winners matter.

But today’s bottlenecks may become tomorrow’s outliers.

Stay tuned.

Vasco

Disclaimer: This is general information, not personalized investment advice. It’s not a recommendation to buy or sell anything. Investing involves risk, and past performance doesn’t guarantee future results. Do your own research and consider speaking with a licensed/authorized professional who understands your objectives and risk profile.”

The first phase of the AI trade was easy to understand.

Own the obvious bottlenecks.

Own the chips.
Own the power.
Own the data centers.
Own the companies supplying the infrastructure behind the boom.

That was the cleanest version of the story. And, in many cases, it worked.

But once a bottleneck becomes obvious, it also becomes crowded. The market can now see the energy problem. It can see the compute problem. It can see the hyperscalers capex boom. Those opportunities may still matter, but they are no longer hidden.

The more interesting question is what comes next.

If intelligence is becoming cheaper, then the real opportunity may not be in “AI” itself. It may be in the scarce complements around it: the rights, permissions, rails, licenses, infrastructure, and regulated tollbooths that allow abundance to move through the real world.

That is where the investment conversation becomes much more interesting.

Because some of these bottlenecks are not just thematic. They are also becoming event-driven.

One of them, in our view, now sits at the intersection of AI, copyright, global distribution, premium intellectual property, and a live corporate catalyst that increasingly resembles a merger-arbitrage-style setup... not in the clean, risk-free sense, but in the sense that the market is being forced to handicap a real transaction, a real premium, and a real path to value recognition.

That specific recommendation remains inside April’s issue of our Alpha Tier. But the framework behind it starts here.

If AI makes intelligence cheaper, where does scarcity move?

And who gets paid when it does?

Here is “Where Scarcity Moves.”

The first thing to say is that some of the new scarcity is already obvious.

Energy is the clearest example. We have been writing about that for quite some time, and we have also been positioned for it. In VMF’s Strategic Asset Allocation, we added Uranium Miners ( back in August 2024, long before the market fully embraced the idea that this AI buildout would require an enormous amount of electricity and a much broader conversation around baseload power.

The same logic extends to parts of the hardware stack tied directly to the hyperscalers1 capex boom. Our geographic tilt toward South Korea EWY is a good illustration.

Samsung and, even more so, SK Hynix sit right in the path of this surge in demand for advanced memory and AI-related infrastructure. Some of the new scarcity has already been telegraphed rather loudly by the market itself.

These were much more interesting trades when we first identified them than they are now...

That does not make them unimportant. It simply means they are no longer especially mysterious.

The market can now see the energy bottleneck.

It can see the hardware bottleneck.

It can see the capex boom.

And when scarcity becomes obvious, it also becomes more crowded.

We have already been paid handsomely in Tier One for spotting some of these bottlenecks early: our uranium miners position through URNM is now up more than 70%, and our geographic tilt toward South Korea through EWY is up more than 170%... returns well in excess of what the main equity indexes have delivered over the same stretch.

That is exactly why these trades matter as evidence, but not necessarily as the most interesting hunting ground from here.

They were far less obvious when we first recommended them. Today, much of that scarcity is already visible to everyone. Which is why we believe the more interesting work now lies elsewhere... in the pockets of scarcity the market still does not fully appreciate.

One of those pockets sits in the uncomfortable gap between visibility and uncertainty.

You see, the hardware layer offers unusually strong visibility. If one believes that AI adoption is real, that the hyperscalers are serious, and that the capex cycle still has several years to run, then the need for power, memory, networking, and datacenter infrastructure is not especially hard to imagine.

The demand path may wobble, but the direction of travel is clear.

The application layer is different.

There, the upside may ultimately prove much larger, but the visibility is far worse.

Which applications will win? Which interfaces will endure? Which products will become indispensable? Which ones are merely the first draft of a market structure that will eventually belong to someone else?

That is where the old saying becomes useful:

Recent history is full of reminders.

Netscape mattered enormously, but Microsoft captured much of the value that followed. MySpace arrived early, but Facebook built the empire. The same pattern can repeat in every great technological wave. The first product to capture attention is not always the one that ends up controlling the economics.

That is why we do have application-layer exposure across our Model Portfolios, and why some of those bets may still prove extremely rewarding. But we also want to be honest about the risks. The application layer is where imagination runs wild, competition is brutal, and permanence is hardest to underwrite.

However, before turning to the positioning of our own Alpha Tier Model Portfolio, we think it is important to pause on a different class of scarcity altogether.

A less glamorous one, perhaps.

But a less risky one too.

History shows that regulation tends to move far more slowly than technological innovation...

That asymmetry matters. Because when the cost of intelligence falls rapidly, but the right to operate in a market remains protected by licenses, approvals, compliance burdens, legal permissions, or other hard barriers to entry, something unusual can happen. Productivity improves. Costs can fall. Output can rise. And yet the competitive moat does not collapse with it. In some cases, it may even strengthen. The business gets the benefit of cheaper intelligence without suffering the full margin compression that usually comes when technology becomes broadly available.

That, in our view, is a very important sweet spot.

And it becomes even more interesting in a Fourth Turning context. If worker displacement becomes politically salient, if inequality worsens, if trust continues to erode, and if governments feel compelled to intervene more aggressively, then some regulated industries may become even more protected, not less.

The state may become more involved. Licensing may matter more. Compliance may matter more. Institutional trust may matter more.

In that kind of environment, businesses already defended by hard regulatory hurdles and uniquely positioned to benefit from AI-driven productivity gains can become much more valuable than the market currently assumes.

That is our claim.

And more than that, it is already where we have been leaning.

Across our Model Portfolios, we have been building exposure not only to the obvious scarcity tied to power and hardware, and not only to the more speculative upside tied to the application layer, but also to businesses whose economics may improve precisely because intelligence is getting cheaper while permission remains scarce.

This month, we are taking that logic one step further.

We are adding two new positions to Alpha Tier that we believe sit right in that sweet spot: businesses positioned to benefit from the abundance wave, but protected by the kind of regulatory and structural barriers that can keep competition at bay even as AI lowers the cost of doing the work.

Let’s now go deeper on what these businesses are, and why we believe they may be among the most attractive scarcity plays in this entire disruption cycle.

Catch up on Part 1:

Further Reading:

Everyone is asking whether AI will be inflationary or deflationary.

We think that is the wrong question.

The better question is this: when intelligence gets cheap, what becomes expensive?

That distinction matters because artificial intelligence is beginning to compress the cost of a vast layer of cognitive work: drafting, coding, summarizing, researching, analyzing, organizing, and automating tasks that once required expensive human time.

In some areas, that will be profoundly deflationary. Certain services will become cheaper. Certain workflows will require fewer people. Certain software layers will become easier to build. And some business models that looked protected may prove far more fragile than investors assumed.

But cheaper intelligence does not mean a cheaper system.

AI still needs power, compute, data, trusted distribution, legal permission, regulatory clearance, and institutional adoption. And if it begins to displace large amounts of white-collar labor, the consequences will not be absorbed quietly. They will be politicized.

That is why the inflation-versus-deflation debate feels too crude. AI may be deflationary in what it replicates, inflationary in what it cannot replace, and politically explosive in how it distributes the gains.

In April’s issue of Alpha Tier, we argued that the old Debasement Trade is not dead. It is evolving. Scarcity is not disappearing. It is moving.

And once scarcity moves, capital must move with it.

Here is “The Abundance Shock.”

Let’s begin by granting the abundance camp its strongest point. Artificial intelligence is not merely improving existing workflows at the margin. It is beginning to compress the cost of cognition itself.

That is the real break with the recent past.

For years, investors were used to thinking about technological progress in fairly familiar (and linear) ways: better software, faster chips, improved interfaces, more data, more automation at the edges. Important, yes... but still incremental enough that the old economic categories remained mostly intact. Labor still looked scarce.

Skilled cognitive work still looked expensive. Certain kinds of analysis, drafting, coding, organizing, and decision support still required a meaningful amount of paid human time.

Now that assumption is starting to wobble.

Because what the latest generation of AI systems is making cheaper is not only software development or internet search or customer support in the narrow sense.

It is a much broader layer of economically useful mental activity. Summarization. Pattern recognition. First-draft writing. Spreadsheet construction. Data cleaning. Research assistance. Code generation. Document review. Workflow orchestration - All tasks that used to sit comfortably inside the wage bill are beginning to migrate toward the compute bill.

That matters enormously. Because once a capability moves from the wage bill to the compute bill, markets begin to reprice the world around it. Some companies start looking less like differentiated franchises and more like expensive wrappers around functions that are becoming cheaper by the month.

Certain white-collar workflows begin to look less labor-intensive than they did before. Some business models begin to lose the comforting illusion of scarcity that protected them until recently. And investors, sensing that shift, do what they always do when the future arrives faster than expected:

They overreact.

That is exactly what we argued in this month’s issue of VMF’s Strategic Asset Allocation. The market’s first response to this new reality was not careful, nuanced, or discriminating. It was blunt. High-quality software was sold alongside lower quality software. Mission-critical systems were punished alongside fragile point solutions. Embedded workflows were treated as if they were just another feature waiting to be commoditized by the next model release.

But even if the market’s first reaction has been too crude, the underlying signal should not be ignored.

The abundance thesis is not fantasy.

Something very real is happening.

If intelligence-like tasks become cheaper, then certain forms of output can rise dramatically without the usual increase in labor input. More can be produced with fewer hours. More workflows can be automated. More organizations can scale with leaner teams. More experimentation can occur because the cost of trying, building, testing, drafting, and iterating falls.

That is why some of the louder voices in technology have started talking in such radical terms. They are not merely predicting better software margins. They are gesturing toward a world in which intelligence itself begins to look less like a scarce factor of production and more like an abundant utility.

And to be fair, one can see why that idea is so seductive.

If a rising share of cognitive labor can be replicated cheaply, then the immediate first-order consequence should be deflationary pressure in the things most exposed to that replication.

Certain services should become cheaper to deliver.

Certain functions should become less labor-hungry.

Certain software layers should become easier to build.

Certain bottlenecks that once supported pricing power may begin to weaken.

In that sense, the abundance camp is not wrong to say that AI carries a strongly deflationary impulse.

That is why Elon Musk is talking this way. His claim is not merely that AI will make the economy more productive. It is that AI and robotics will make abundance so great that work could become “optional,” money could become “irrelevant,” and government-funded “universal high income1” could offset AI-driven unemployment

without igniting inflation.

That is not just a technological forecast.

It is a civilizational claim about abundance. But beneath the utopian phrasing lies a harder implication: “work will be optional” may simply be a euphemism for large-scale labor displacement.

And Musk is not alone. Sam Altman (OpenAI) has long argued that sufficiently powerful AI could drive the price of many kinds of labor toward zero, radically lower the cost of goods and services, and force a redesign of how technological wealth is distributed across society.

His answer has not been a defense of the old wage system, but a search for broader mechanisms of distribution, including an “American Equity Fund2” and annual citizen payouts tied to capital ownership...

This is entirely consistent with what we highlighted in February’s issue of VMF’s Strategic Asset Allocation and revisited again in April: the possibility of a step change in economic growth, with AI-led productivity gains allowing output to rise much faster than labor input, lowering unit costs even as activity expands.

Cathie Wood of ARK Invest now packages this vision as “The Great Acceleration,” arguing that AI and related technologies could drive one of the largest investment booms in history and materially reshape growth by the end of the decade.

More output with fewer inputs. That is the prosperity case.

Dario Amodei (Anthropic) takes the argument further still. He has described the arrival of powerful AI as the entry of “a country of geniuses in a datacenter” into the global economy, and he believes systems of that kind may be much closer than most people think.

Anthropic’s new Mythos model helps explain why the mood around the frontier is becoming more charged by the month...

Presented as a watershed model because of its unusual capabilities in computer security tasks, Mythos is also being released through the tightly controlled Project Glasswing initiative, reflecting concern that it could accelerate the discovery and exploitation of cybersecurity vulnerabilities.

In other words, the people closest to the frontier are not merely selling upside. They are also signaling that the capability jump is becoming unsettling even for them.

We could keep going.

But the broader pattern matters more than the personalities. They are all, in one way or another, talking their own book. That does not make them wrong. But it does mean we should listen with interest, not surrender.

Amara’s Law3 remains useful here: investors tend to overestimate the effect of a new technology in the short run and underestimate it in the long run. That warning cuts both ways. It warns against breathless extrapolation. But it also warns against complacency.

And on that point, we should be clear: we do not believe this abundance wave is merely coming. We believe it is already here.

But this is also where the abundance thesis often becomes lazy. It takes one true statement... that some forms of intelligence are becoming cheaper... and quietly stretches it into a much larger and much weaker conclusion:

that the whole system is heading toward cheapness.

We do not think that leap holds.

A model may draft the memo, summarize the report, clean the dataset, write the code, or assist with the analysis, but none of that means the broader system in which those capabilities operate suddenly becomes frictionless or inexpensive.

The model still needs electricity. It still needs compute. It still needs data. It still needs legal permission to use certain inputs. It still needs to be embedded into a workflow, supervised by someone accountable when the output is wrong, and distributed through a trusted system.

Most importantly, it still needs to operate inside a political order that will decide how the gains are shared and how the disruption is absorbed. That is the distinction the abundance camp too often blurs. Cheaper cognition is not the same thing as a cheaper system. This is also why the more utopian versions of the story deserve to be treated with some caution...

Take the now fashionable claim that work may become “optional.” Even if we grant the direction of travel, the economic question does not disappear. It becomes harder:

• Optional for whom?

• If firms can produce more with fewer workers, do prices simply fall across the economy?

• Do wages keep pace?

• Do displaced workers move smoothly into better and higher-value roles?

• Or does the political system do what political systems usually do when productive change becomes socially destabilizing and intervene?

That last question matters far more than the abundance camp tends to admit.

Technology is never introduced into a vacuum. It arrives inside a social order...

And our social order is not entering this transition from a position of calm. It is entering it with debt burdens already high, fiscal deficits already large, trust already frayed, politics already polarized, and an electorate already highly sensitive to inequality, cost of living, and institutional failure.

That matters even more if, as we have argued, we are now in the final stretch of a Fourth Turning.

Seen through that lens, today’s rhetoric around optional work, universal basic income, wealth transfers, and broad societal redesign may be doing more than advertising technological abundance. It may be offering a first sketch of the order that could follow this crisis. A glimpse, perhaps, of the next First Turning trying to imagine itself before the current Fourth Turning has fully run its course.

But that is precisely the point: before renewal comes resolution, and before the next order is built, this one still has to break.

In that kind of environment, abundance is unlikely to be processed neatly. It is far more likely to be politicized. That means transfers. That means subsidies. That means industrial policy. That means demands for regulation. That means fights over ownership, access, compensation, copyright, and control.

In other words, even if AI lowers the cost of certain tasks, the transition costs may still be socialized. Even if software becomes easier and cheaper to build, the infrastructure required to run the new system may become more expensive. Even if some services become dramatically more efficient, the political response to displacement may involve exactly the kind of redistribution, fiscal support, and institutional improvisation that keeps monetary pressure alive elsewhere.

That is why we are not willing to reduce this moment to a sterile inflation-versus deflation debate.

The better way to think about it is this:

AI is likely to be deflationary in what it replicates, inflationary in what it cannot replace, and politically explosive in how it distributes the gains.

Once you see that, the abundance story looks very different. Not like a clean glide path toward universal cheapness, but like a messy regime shift in which one layer of the economy becomes more abundant while another becomes more bottlenecked, more contested, and, therefore, more valuable.

Some things may indeed get cheaper. But the complements around them may become more important, not less: power, grid capacity, premium compute, trusted data, copyrighted inputs, regulatory clearance, secure distribution, compliance infrastructure, authenticated brands, and deeply embedded systems.

Abundance in one layer, in other words, does not eliminate scarcity in another. It can intensify it.

And that is the point the market still seems reluctant to price properly. When a powerful new technology lowers the cost of one input, capital does not stop caring about scarcity...

It goes looking for the new scarcity.

That, in our view, is exactly what is happening now. So yes, intelligence may be getting cheaper. But that does not mean the Debasement Trade dies here. It means it evolves. And it means the market is asking the wrong question.

The real one is harder, and far more investable: when intelligence gets cheap, what becomes expensive?

In my previous article, I shared “The Privileged Position,” the opening section of April’s issue of VMF’s Security Selection.

That section laid out the framework: why we admire the royalty model so much, and why we keep hunting for it in places most investors are not trained to look.

This next excerpt picks up exactly where that one left off. Because once you accept that music can be a royalty asset, the question is no longer theoretical.

It becomes investable:
What kind of asset are we really dealing with here?

That is where “After Napster, Before AI” comes in.

It sits at the center of the issue, right after the framework and right before the part that matters most to paying subscribers: the actual recommendation, the full investment thesis, and the valuation work that turns an interesting idea into a position.

And that sequence matters.

Because good investing is not about falling in love with a clever narrative. It is about moving, step by step, from the model... to the asset... to the cash flows... to the mispricing.

In this section, we do exactly that.

We take the old bear case against music, the one born in the Napster era, and ask whether investors are still looking at this business through a lens that is now badly out of date. Then we push one step further and ask what happens when the current technological disruption wave, AI, collides with a royalty stream that may be far more resilient, and far more adaptable, than the market appreciates.

That is the work you are about to read.

The actionable conclusion remains where it belongs.

But the framework, and the logic that leads to it, begin here.

Here is “After Napster, Before AI.”

At first glance, music does not look like the kind of asset conservative investors are supposed to admire.

It is intangible. It cannot be touched, stored, or inventoried in the way copper, oil, or gold can. It does not sit behind a fence. It does not require a reserve report. And because it lives so close to culture, many investors instinctively file it away in the mental category reserved for fashion, celebrity, taste, and unpredictability.

That is a mistake.

Because once you strip away the surface noise, music has many of the traits that make for a remarkable royalty asset. It is consumed repeatedly. It crosses borders

effortlessly. It travels through new technologies rather than being confined by them. And, unlike a mine or a well, a great song does not deplete when it is used. It can be replayed, re-licensed, re-packaged, re-discovered, and re-monetized for decades.

The asset is intangible. The cash flows do not have to be.

And that is precisely why we have been studying Universal Music Group ($UMG.AS ) so closely. At bottom, UMG is not merely a record label. It is a global rights platform sitting on top of one of the most productive catalogs in the cultural economy.

But that is easier to say now than it was twenty-five years ago.

Because there was a period when the entire industry looked broken...

Napster arrived in 1999 and made peer-to-peer music sharing feel not just possible, but normal. Digital piracy spread rapidly. The old model, built around selling physical units at attractive economics, suddenly looked fragile in a world where one legal copy of a song could be reproduced and distributed endlessly at virtually zero cost.

Napster triggered a full-blown legal and economic crisis around digital rights, and the broader lesson was brutal for incumbents: they had not lost the value of music, but control over the channel through which that value had been monetized.

That was the bear case.

And, to be fair, it was a serious one.

If music could be copied infinitely and distributed for free, then what exactly was left to own? If technology had turned the product into a commodity, then perhaps the royalty claim sitting on top of it had become structurally impaired. For a time, that looked plausible. The industry was not merely cyclical. It appeared wounded at the root.

That is why music, for years, did not look like a beautiful royalty asset. It looked like a cautionary tale about what technology can do to legacy profit pools.

But that is not where the story ended.

Because the industry did something investors often underestimate: it adapted.

UMG’s 2025 annual report makes the point with unusual clarity. Management writes that trying to smother emerging technology is futile and often counterproductive, and it frames the relationship between music and technology as a century-long pattern of adaptation and growth.

That is a very important admission. It means the winners in this industry were never going to be the companies that tried to freeze the world in place. The winners were going to be the ones that learned how to make new rails work for them.

And that adaptation is now visible in the numbers.

According to IFPI, global recorded-music revenue grew 6.4% in 2025 to $31.7 billion, marking the eleventh consecutive year of growth. Total streaming revenue surpassed $22 billion and accounted for 69.6% of global recorded-music income, while paid subscription streaming alone represented 52.4% of the total. There are now 837 million paid streaming subscription accounts globally. In other words, the product did not die. The revenue model changed. And once the model changed, the industry began to look far healthier than the old panic ever imagined.

But even that understates what is happening.
Because the real opportunity is no longer just streaming.

It is the widening of the royalty surface.

UMG’s annual report shows a business that is being monetized across far more outlets than the old CD-era model ever allowed.

The company now generates revenue not only through global streaming platforms such as Spotify, Apple, YouTube, Amazon, Deezer, Tencent Music, and NetEase, but also through social media and short-form video, through digital-fitness partners, through gaming environments, and through licensing across film, television, advertising, and video games.

This is the key shift. Technology did not simply rescue the industry. It multiplied the number of places where music can earn a toll.

Then there is the direct relationship with the fan. That may prove even more important over time. UMG is now explicitly leaning into what it calls the superfan opportunity. In its 2025 annual report, the company says superfandom has been at the heart of its growth strategy for years and highlights new products and experiences designed to unlock spending beyond the traditional streaming subscription.

It also notes that its global e-commerce infrastructure now supports more than 1,600 owned and operated artist and branded online stores. That is a very different business from the one investors used to dismiss as a label living at the mercy of radio play and retail shelf space. It is a rights platform that is steadily deepening its direct economic relationship with the most passionate end users.

And now the industry is facing another technological wave.

This time, it is AI.

That threat is real. But it is also familiar. The panic sounds new... the underlying question is not. Once again, the issue is whether new technology destroys the economics or broadens them for the owners of the strongest rights.

The bear case is easy to understand. AI can generate music-like content at enormous scale. It can flood platforms with cheap, low-friction output. It can make supply feel infinite. And when supply appears infinite, investors understandably worry that value itself will collapse.

But that conclusion is far too simplistic.

Because abundance does not eliminate the value of scarcity. In many cases, it sharpens it.

When low-value content becomes effectively infinite, what becomes more precious is what cannot be industrially faked so easily: genuine talent, cultural relevance, emotional resonance, fan attachment, and the accumulated intellectual property of artists people already care about. In other words, the more the internet fills with generic output, the more valuable authentic signal may become.

We have seen this movie before in other markets.

When quantity explodes, quality does not disappear. It often becomes easier to identify and more important to own.

That is why we think the right analogy is not “AI will make music worthless.” The better analogy is: AI will force the industry to separate authorized value from unauthorized noise.

And UMG appears to be moving in exactly that direction.

Its 2025 annual report says the company became the first media company to enter AI-related agreements with established platforms such as YouTube, Meta, TikTok, and KDDI, as well as with emerging AI companies. UMG also says those agreements were designed both to protect artists and songwriters and to develop new revenue streams and commercial opportunities.

Just as importantly, management says its artist-centric platform agreements include provisions that prevent AI “slop” from being counted in the same royalty pools as music from real artists and songwriters. That is a crucial point. It suggests the company is not merely defending against dilution. It is trying to shape the rules of the next royalty system.

This is also where the parallel with Adobe ( ) becomes especially interesting.

Adobe’s Firefly models were trained on licensed content such as Adobe Stock and public-domain material where copyright has expired, and Adobe also offers Custom Models that allow users to generate content based on their own assets, styles, and brand language.

That is a very important template. It shows how generative AI can be commercialized in a way that respects ownership, provenance, and licensing rather than simply scraping the open web and hoping the legal system catches up later.

Our view is that music is likely to move in a similar direction over time: not toward open-season expropriation of valuable catalogs, but toward licensed training, authenticated provenance, and monetization frameworks built around the actual owners of the rights.

And if that is the direction of travel, then the conclusion becomes far more interesting than the panic suggests.

AI-generated content may indeed make music supply feel endless. But endless supply is not the same thing as endless value.

The songs that matter will still matter.
The voices that move people will still move people.
The catalogs that already sit at the center of global culture will still occupy privileged positions.

And if those catalogs become the training ground, reference layer, or licensed substrate for new creative tools and new distribution formats, then the owners of those rights may discover that this new technological wave expands the royalty surface rather than destroying it.

That, at least, is our working view.

It is also entirely consistent with what the industry did after Napster and peer-to-peer piracy. It adapted. It changed the rails. It rebuilt the economics. And then it grew again.

Today, UMG is trying to do something similar through what it calls Streaming 2.0, with agreements covering Amazon, Spotify, and YouTube that it says encourage smarter customer segmentation, greater consumer value, ARPU growth, new paid tiers, richer content bundles, and better protections against fraud and irrelevant uploads.

That does not eliminate the risk.

But it does show a company trying to architect the next phase of monetization rather than merely defend the last one.

This is why we think investors should stop looking at music through the old lens. The old lens says the business was disrupted. The better lens says the business was forced to evolve from a narrow distribution model into a much broader royalty machine.

In our view, that distinction is everything. Because once music is understood that way, Universal Music Group starts to look less like a media stock and more like what it really is: one of the most important public claims on a global catalog of premium intellectual property whose monetization surface is still widening.

The most pressing question now is obvious: how should we value it?

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One of the things I find most satisfying about serious research is this: When you do the work properly, top down and bottom up often end up shaking hands.

In VMF’s Strategic Asset Allocation, we start with the regime, the macro backdrop, the big shifts in liquidity, power, scarcity, and innovation. In VMF’s Security Selection, we do the opposite. We build from the bottom up. We study the asset, the economics, the claim, the incentives. And yet, again and again, the work keeps leading us to the same places.

That is exactly what happened in April’s issue of VMF’s Security Selection.

The excerpt below is the first section of that issue, and it offers a glimpse into one of the business models we admire most in all of capitalism: the royalty model.

Why? Because when the underlying asset is right, royalties create the kind of economics investors dream about. You own the superior claim. Someone else does the hard work. You participate in the stream without carrying the full burden of operating risk, capital intensity, or execution strain.

That is the theory.

But the real fun starts when you go looking for that model outside the obvious places.

Most investors can spot a royalty business in mining or energy. Far fewer are trained to recognize the same architecture when it appears in stranger corners of the market. We look for it tirelessly, across sectors, because some of the best seats in the house are hidden in plain sight.

In this first excerpt, you’ll see exactly that. We revisit two royalty businesses already sitting in our Model Portfolios and explain why they continue to prove just how powerful this model can be when attached to the right asset.

The more actionable conclusions, as always, remain where they belong.

But the thinking begins here.

Here is “The Privileged Position.”

Long before royalties were paid to songwriters, they were paid to kings.

That is not a metaphor. It is the literal origin of the word. Royalty first referred to royal power, royal rights, and the prerogatives of the sovereign.

Only later did the meaning broaden into the commercial sense investors know today: a recurring payment made to the owner of a valuable right by someone else who is permitted to exploit it. Britannica’s legal definition is about as clean as it gets: a royalty is a payment made to the owners of certain rights by those allowed to exercise them, whether the underlying right concerns mineral deposits, patents, or copyright.

The asset changes. The logic does not.

And that is precisely why the model has proven so durable.

At its core, a royalty is one of the cleanest economic arrangements ever created. One party owns the right. Another party brings the labor, the capital, the machinery, the distribution, and the day-to-day operating burden required to exploit that right. The owner does not need to run the mine, drill the well, print the book, or perform the song. The owner only needs to hold the superior claim. That is what makes the model so elegant. It separates ownership from operation.

And when that ownership is attached to the right kind of asset, the result can be extraordinary: long-duration cash flows, upside to rising output and rising prices, embedded inflation protection, and exposure to future expansions the royalty holder did not have to finance.

Of course, that last point is everything.

Because a royalty on the wrong asset is just a legal claim with a nice name. The magic appears only when the underlying asset is scarce, valuable, and tied to demand that endures through time.

That is why the model became so powerful first in the natural-resource world...

The landowner or sovereign did not need to assume geological risk, operating risk, labor risk, or capital-allocation risk.

If the operator succeeded... the royalty holder participated.

If the ore body expanded... the royalty holder participated again.

If inflation lifted the value of what came out of the ground... the royalty holder was paid once more.

The operator did the hard work. The royalty owner held the superior economic seat.

Then, over time, the model made one of the most interesting migrations in economic history: from the physical world into the world of ideas.

The key legal hinge was the Statute of Anne in 1710, which marked a milestone in copyright law by recognizing that authors, rather than only printers or distributors, should be the primary beneficiaries of copyright protection.

Once that door opened, the royalty model no longer needed a mine, a field, or a well. It could attach itself to books, inventions, songs, designs, and every other form of intellectual property that could be owned separately from the effort required to commercialize it.

The extractive logic survived. Only the asset changed.

And that is exactly why investors should think about royalties in broader terms than they usually do.

In fact, one of the most famous royalty-like businesses in the modern world is not a miner, an oil company, or a music publisher.

It is McDonald’s.

Most people think of McDonald’s as a restaurant company.

That is understandable... but economically, it is incomplete.

McDonald’s own investor materials describe a business model built around revenues from rent, royalties, and initial fees, alongside the smaller profit contribution from directly operated restaurants.

The company also says that roughly 95% of its restaurants are locally owned and operated, while its annual report explains that conventional franchisees contribute primarily through rent and royalties based upon a percent of sales. In other words, the golden arches may be what the customer sees, but underneath them sits a structure that behaves far more like a royalty-and-rent machine than a pure restaurant operator.

That distinction matters because it reveals the model’s true elegance.

The franchisee takes on much of the operational grind: labor, local execution, staffing, and day-to-day management. McDonald’s, by contrast, occupies the superior economic position. It controls the brand, controls the system, often controls the real estate, and collects a recurring share of the stream. It is not a pure royalty company in the way a mineral owner is. But the family resemblance is unmistakable.

Once again, the model separates ownership from operation... and that separation is where so much of the beauty lies. Because that broader lesson is the one we want you to keep in mind before we go any further:

The royalty model is not confined to one sector, one legal structure, or one corner of the market. It appears wherever a valuable right can be owned separately from the effort required to exploit it.

Sometimes that right sits under the ground.
Sometimes it hangs above a storefront.
And sometimes it lives inside a catalog.

What matters is not the wrapper...
What matters is the quality of the underlying asset and the quality of the claim.

That is also why this framework is already familiar to our long-term subscribers.

We have already seen what a truly exceptional royalty business can do inside our own Model Portfolios, and no holding illustrates that better than Altius Minerals ($ALS.TO).

As of today, our original May 2024 recommendation already shows a 140%total return, and in March we explicitly reminded readers that we still saw meaningful upside ahead.

But the return, impressive as it is, is only half the story.

The more important point is why Altius has performed so well. From the very beginning, we did not treat it as just another royalty company. We treated it as something more unusual... and more valuable.

That is because Altius does something most royalty businesses do not.

It does not merely collect royalties.
It creates them.

That distinction is everything.

In our last issue, we put it as clearly as we ever have: Altius is not merely a passive owner of royalty interests. It is an active creator of them.

Through its Project Generation arm, it identifies geological opportunities early, advances them intelligently, retains royalties and equity where appropriate, and monetizes selectively when timing and price are right. In other words, it is not just collecting optionality. It is constantly manufacturing more of it.

That is why we have always labeled Altius as “Special.”

The existing royalty portfolio is only part of the story. Behind it sits a continuously renewing pipeline of internally generated opportunities, built by a team with an unusual ability to surface value long before the market is prepared to recognize it.

We also own Viper Energy , which sits in a different industry but follows the same essential economic logic.

Viper is not a driller. It does not need to guess which operator will execute best quarter to quarter, nor does it need to fund the endless treadmill of rigs, crews, completions, and service costs.

It owns mineral and royalty interests in the Permian Basin... and that is precisely where you want to hold them.

Why? Because not all barrels are created equal.

If you are going to own a royalty on oil and gas, you want it attached to the rock that serious operators come back to again and again... the basin with the deepest inventory, the strongest economics, the richest infrastructure, and the greatest strategic importance to U.S. production.

You want the place where capital naturally gravitates, even when the industry turns cautious. You want the basin that keeps getting drilled because, in a capital intensive business, the best acreage is the last acreage management teams want to abandon.

That is the Permian.

It is not merely another oil patch.

It is the crown jewel of American shale... the place where geology, scale, infrastructure, and operator attention all converge. Pipelines are there. Processing is there. Service capacity is there. The majors are there. The independents are there. And because the rock is so attractive, the basin keeps pulling capital toward itself.

Viper’s royalty interests are not sitting on marginal acreage that needs heroic assumptions to work. They sit in the part of the oil market where activity tends to be the most durable and where the best operators keep doing the hard work for you.

That is the beauty of the model.

The operators spend the capital. They drill the wells. They bear the execution

burden. They manage the decline curves, the service costs, the logistics, and the headaches that come with extracting hydrocarbons from the ground.

Viper collects a slice of the stream.

Different asset. Different industry. Same superior architecture: own the right claim, let others do the heavy lifting, and participate in the output. So, when we say that the royalty model deserves to be taken seriously, we are not speaking in the abstract.

We have already seen its power in minerals.
We have already seen it in energy.
And we have already seen how extraordinary the economics can become when the underlying asset is truly world-class and the claim sitting on top of it is structurally superior.

But now we want to take that same idea one step further. Because the next royalty business we are about to present does not sit on a metal deposit or an oil basin. It does not depend on drilling schedules or reserve reports.

Its assets are no less real for being intangible... and in some ways, they may be even more remarkable. They live in the culture. And they get consumed every day.

This month, we are going to argue that one of the most compelling royalty assets in the modern world is not buried underground at all.

It gets played.

Continuing in part 2:

“The real key to making money in stocks is not to get scared out of them.”

Peter Lynch

Last week, we asked a simple question: was the rebound a durable move… or just a reflex rally within a fragile structure?

This week, the market has given us an answer.

Take a look at the charts. They tell the story clearly enough. The pattern we have been highlighting (and, yes, predicting) over the past month and a half has now played out.

The S&P 500 not only held the key levels we were monitoring… it has surged back to all-time highs.

The T.A.C.O. hypothesis was not just a narrative framework. It was the mechanism. As financial conditions tightened (equities threatening to break down and long-term yields beginning to push higher) the political response followed.

The ceasefire served as a pressure valve. And once that valve was opened, markets did what they tend to do in these environments: they moved fast… and they moved hard.

But the most important point here is not that the hypothesis we shared across our product roster proved correct.

It is what we did with it. Because throughout the entire duration of this episode... while the risk-off wave intensified, while sentiment deteriorated, and while both professional and retail investors were selling in size... we did the opposite.

We increased our Model Portfolio’s overweight in equities.

And we did so in what were, at the time, deeply contrarian areas of the market. Geographically, we leaned hard into the United States. From a sector perspective, we tilted toward disruptive technology... precisely the long duration assets investors were most afraid of at the time.

That positioning is now beginning to speak for itself. The two positions we added during that period are already delivering returns that would normally take far longer to materialize. One is up approximately 18% from our entry.

The other is now trading roughly 28% above where we initiated the position.

We do not view that as luck. It is what happens when a framework, an investment process, and the fortitude to act are all aligned. Nor do we view this as a victory lap. If anything, it is a reminder of how these opportunities tend to appear: not when the narrative is comfortable… but when the pressure is highest and the consensus is leaning the wrong way.

And that brings us to the current setup.

Because while the index is now making new highs, the underlying environment remains far from resolved. The geopolitical backdrop is still fragile. The structural tensions we have been highlighting have not disappeared. And, as always, markets have a tendency to move ahead of clarity.

Which means the work does not end here.
If anything, it is just beginning.

Stay close to our work.

Good investing!
Vasco Marques de Freitas, CFA, CMT

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Disclaimer

The information provided herein is for general informational purposes only and does not constitute financial advice or a recommendation to buy, sell, or hold any investment. It is not tailored to any specific individual or investor profile. All investments involve risks, and past performance is not indicative of future results. Before making any investment decisions, it is important to consider your own financial situation and risk tolerance. We do not guarantee the accuracy, completeness, or reliability of any information provided, and we disclaim any liability for any loss or damage arising from reliance on the information herein. Readers are advised to consult with an authorized financial intermediary before making any investment decisions.

Being contrarian is easy.

Being contrarian and right is where the money is.

Last Friday, April 10, we made a new recommendation inside VMF’s Strategic Asset Allocation. It is already up more than 15%. But this is not a victory lap. It is something more useful than that.

It is a case study in what happens when the market panics first and thinks later.

What follows is an excerpt from April’s issue of VMF’s Strategic Asset Allocation, titled “The Great AI Washout.” In that issue, we took the other side of one of the market’s laziest new trades: the idea that if AI is getting smarter, then anything that smells like software must be in trouble.

That is how crowds lose money.

They take a real threat, flatten every distinction, and sell the whole complex as if nuance were a luxury they can no longer afford.

We do not see it that way.

We think AI is real. We think the disruption is real. And precisely because we take it seriously, we also think the market’s first reaction has been too blunt. Some businesses deserve to be punished. Others are being thrown overboard with them. That is where the asymmetry lives.

That is what this excerpt is about.

Not denying the panic. Exploiting it.

The specific recommendation, and the broader actionable positioning around it, remain behind the paywall. But the thinking that produced it begins here.

Below is “The AI Panic Trade.”

To understand why fear has ripped through so many corners of the market, you have to stop thinking about AI as a better chatbot.

That framing is already obsolete.

What changed is not merely that the models got smarter. It is that they started to feel less like tools... and more like coworkers. Digital ones. Tireless ones. Ones that do not just answer questions, but begin to do the work. That is the real psychological break the market is trying to process right now. And once investors began to grasp it, the repricing in software and adjacent fields stopped looking like a routine drawdown and started to resemble something closer to a stampede.

Anthropic’s latest releases helped crystallize that fear by pushing the narrative from

“AI is coming” toward something more immediate: AI agents1 that can organize files, send emails, create spreadsheets, analyze reports, review legal documents, patch security issues, and even help generate software itself.

Pause there for a second.

Imagine the scene from the perspective of a corporate decision-maker. You are not being pitched a futuristic science project...

You are being shown a system that can read the contract, summarize the risk, build the model, clean the dataset, write the first draft, flag the anomaly, and maybe even generate the code needed to automate the next step. Not perfectly. Not autonomously in every case. But well enough to make one uncomfortable thought impossible to ignore: how many layers of paid software, outsourced services, and junior knowledge work are really as indispensable as they looked six months ago?

That is where the fear comes from. It is not just about coding. It is about the sudden sensation that a wide range of cognitive workflows may be getting compressed into one accelerating layer.

And that is exactly why the reaction spread beyond software developers.

In law, the fear is obvious. If a model can rapidly review documents, compare clauses, flag inconsistencies, and summarize implications, then the market immediately starts asking hard questions about the value chain around routine legal work.

In finance, the same dynamic appears in a different costume. If a model can ingest a messy dataset, organize it, surface the drivers, build a clean spreadsheet, and draft an initial analytical output in minutes, then data services, workflow tools, and some parts of the research stack suddenly look a lot more vulnerable than they did before.

In cybersecurity, the fear takes yet another form: if models can increasingly identify and patch vulnerabilities, then parts of the market begin to worry that the old labor-intensive security model could also face pressure.

Anthropic’s product evolution helped bring all of those fears into view at once.

That is why we wrote in February that obsolescence is no longer a theory... it is a timeline. When capability jumps, business models do not get repriced gradually.

They get repriced suddenly. The market does not wait around for ten quarters of audited evidence. It front-runs the possibility. It imagines the end-state, panics, and starts liquidating anything that looks even remotely exposed. That is what dispersion looks like in a true acceleration regime: one cluster of companies gets treated as the new infrastructure of the future, while another gets shoved toward the penalty box before the jury has even met.

And let’s be honest: the fear is not irrational

If you were running a software company built on charging customers for repetitive workflow, light analysis, templated documentation, or narrow productivity features, would you really sleep well after watching the frontier models evolve this quickly?

Probably not.

The market is right to be alarmed. It is right to understand that a new technological layer is arriving with the power to collapse feature sets, compress pricing, and pull more value into the hands of the platforms that own the intelligence itself. We are comfortable saying that plainly because we have not been spectators to this disruption.

In our other Tiers, we have been building exposure to one of the grand catalysts behind it. We know this wave is real. We know the threat is real. And we know it reaches far beyond coding.

But that is only half the story. Because when markets get a credible glimpse of the future, they rarely price it with surgical precision. They price it with a flamethrower.

That is what happened here.

The first reaction was not to separate fragile software from mission-critical software... not to distinguish the feature from the platform... not to ask which products are sticky, embedded, and deeply woven into real corporate workflows.

The first reaction was much cruder. If it smelled like software, if it touched data, if it lived anywhere near the cognitive assembly line, it got marked down. That is how you end up with a market that may be directionally right about disruption, but wildly imprecise about who actually gets disrupted first.

And that distinction is everything because the market began to act as if any new plug-in could suddenly take over everything from data analysis to tax planning to legal services.

So yes... the fear is understandable.

You can see it. You can feel it. In fact, it is easy to picture why it became so visceral. A world in which an AI agent sits beside every worker, drafts before you draft, analyzes before you analyze, and automates before you can bill the hour is a world in which many equity stories deserve to be re-examined from the ground up.

The market is not hallucinating that threat. It is staring directly at it.

But markets do have a habit of moving from insight to exaggeration with astonishing speed.

And that is where our interest begins.

Once a market stops distinguishing between the truly vulnerable and the merely exposed, dislocation takes on a different character. The question is no longer whether disruption is real. It clearly is. The question is whether, in its rush to price the future, the market has begun to punish some high-quality businesses as if the verdict were already final.

That is the opportunity we want to explore. Because outside the demo reel, the real economy is much messier than the panic implies.

Enterprise software is not a weekend side project.
It is not a browser plug-in.

And it is certainly not just a shiny interface sitting on top of generic tasks. In the real world, these systems are stitched into the guts of organizations.

They are wired into approvals, permissions, compliance layers, customer records, reporting structures, accounting logic, communications history, and years of operational muscle memory.

Replacing them is not like swapping one app for another. It is more like replacing the plumbing in a skyscraper while the building remains occupied.

You see... the switching costs are high because the embeddedness is high. And the embeddedness is high because the workflow itself has become the product.

Then comes the data problem... which is really a moat problem in technical form.

A frontier model may be brilliant. But brilliance alone does not grant it immediate access to a company’s proprietary records, internal processes, client history, negotiation patterns, billing architecture, security protocols, or institutional memory.

And even when access is granted, someone still has to decide how the model interacts with those assets, where the guardrails sit, who is accountable when errors occur, and how the outputs get folded back into a living business process.

That is why the software layer is not merely “software.” In many cases, it is a system of record, a system of control, or a system of workflow orchestration. And those systems are far harder to rip out than the market’s first reaction tends to assume.

This is also where our own prior work becomes relevant.

In February, we argued that a productivity-led growth boom may be unfolding in real time... that AI is not a one-factor story, but part of a broader stack in which software gets cheaper to build and deploy, work gets automated, labor economics begin to shift, and output can rise without the usual inflationary heat.

We also highlighted the simplest tell of all: hyperscaler capex.2

This is not the spending pattern of an economy preparing for technological stagnation. It is the footprint of an industrial-scale rebuild of the digital economy.

And if that thesis is even roughly right, then the second-order implication is crucial: lower software-production costs do not necessarily mean less software value. In many cases, they can mean more experimentation, more deployment, more automation, more throughput, and more economic activity to coordinate.

Productivity shocks do not simply eliminate tasks... they often expand the system around them. That is exactly why we wrote that stronger activity can arrive with disinflationary pressure when productivity is doing the heavy lifting. Higher output per hour, lower unit costs, faster throughput... that combination is not a recipe for economic shrinkage. It is a recipe for a bigger pie and a fiercer scramble over who gets to own the toll roads around it.

That is one reason recent intervention is so interesting . His point, stripped to its essence, is that the straight-line “AI destroys jobs, therefore demand collapses” narrative is far too simplistic.

In his view, AI can restore a more historically normal rate of productivity progress, expand output, compress prices, and ultimately create more opportunity rather than less.

You do not have to buy every ounce of Silicon Valley optimism to recognize the deeper logic: when productive capacity rises sharply, new categories emerge, old bottlenecks loosen, and the demand for coordination often expands rather than contracts. In other words, the arrival of AI agents can be disruptive at the task level while still being expansionary at the system level.

And if that is even partially true, then the market is making a category mistake when it assumes that automation must be uniformly bearish for the companies sitting inside enterprise workflows.

Bill Ackman’s recent work points in a strikingly similar direction, only from the vantage point of a stock picker rather than a techno-optimist. Pershing’s recent materials make the case that the market has been too quick to punish high-quality businesses over near-term fears tied to AI disruption or AI-related spending. On Meta, for example, Pershing argues that concerns over elevated spending miss AI’s potential to accelerate revenue growth and reinforce long-term competitive moats.

More broadly, Pershing continues to define its ideal investment as a simple, predictable, free-cash-flow-generative business with formidable barriers to entry and a strong financial profile. That is not a framework built for owning fragile feature companies. It is a framework built for identifying businesses whose quality allows them to absorb disruption, monetize it, and come out the other side with stronger economics than the market initially believed possible.

Notice the parallel.

Our February issue argued that productivity and technological acceleration could support stronger economic activity than consensus expects. Andreessen is arguing, in his own far more provocative style, that the employment and output consequences of AI may skew much more positively than the current doom loop implies. Ackman, meanwhile, is acting on a related idea in markets: the crowd is so focused on the immediate fear that it is beginning to misprice the longer-term earnings power of certain high-quality businesses.

Different language. Different angle. But the rhyme is obvious.

Now, let’s be disciplined.

None of this means every software company is safe. Far from it. Some really are in trouble. Some products are little more than expensive wrappers around tasks that frontier models will increasingly commoditize. Some moats will prove imaginary. Some “platforms” will turn out to be features with good branding.

We are not denying that. We are insisting on something subtler... and more useful: the first phase of a disruption wave almost always punishes with a sledgehammer before it rewards with a scalpel.

That is why this moment deserves our attention. The market has become highly effective at pricing the fear... but much less precise in pricing the boundaries of that fear.

It has been quick to imagine what AI agents may replace. It has been less careful in asking which systems remain deeply embedded, which workflows still command proprietary data, which products sit too close to the customer’s operational bloodstream to be casually removed, and which companies may actually benefit as the volume of digital work explodes.

That is the distinction we care about.

Because if we are entering a world of faster output, cheaper software creation, and broader workflow automation, then the winners will not all be the model makers themselves. Some will be the incumbent toll roads, the entrenched systems of record, the deeply embedded orchestration layers, and the quality businesses whose products remain too woven into real enterprise behavior to be wished away by a scary demo.

And that is precisely where the opportunity begins to take a more actionable form.

Because once you accept that the disruption is real, but that the market’s first attempt to price it has likely been too blunt, the next question becomes obvious: how do we express that view intelligently?

That is where we turn next.

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By the time our last Alpha Tier issue was published, on March 27, 2026, the Iran war was already nearly a month old.

That matters.

Because this was not a first-reaction note written in the heat of the initial shock. It was written after the market had already had time to digest the obvious implications: oil, inflation, rates, risk-off. What interested us was the less obvious question.

What if this war mattered not only because of what it might do to commodities, global growth, or monetary policy... but because of what it might reveal about American power?

That is the frame for this final excerpt from March’s Alpha Tier that I’m sharing here. The more actionable portfolio implications, as always, remain with subscribers.

In Part 1 - Howe, Dalio, and the Stress Test, we stress-tested our Fourth Turning framework against Ray Dalio’s Big Cycle.

In Part 2 - Toward the Reset, we pushed further and asked what a late-stage monetary reset might actually look like. Now the lens shifts again. Away from money itself and toward the thing that ultimately sits behind every monetary order: ...imperial credibility.

That is why this section turns to Suez.

In 1956, Britain, France, and Israel moved against Egypt after Nasser nationalized the Suez Canal. Militarily, the operation looked feasible. Strategically, it was a disaster. The crisis exposed a simple but devastating truth: Britain could still project force, but it could no longer sustain imperial freedom of action once financial and geopolitical pressure turned against it. Suez did not merely mark a foreign-policy humiliation. It marked the moment the world saw that the empire’s reach no longer matched its image.

That is what makes the analogy worth studying.

Not because Hormuz is “the new Suez” in some cheap, one-line sense. And not because history repeats on command. But because chokepoints have a way of exposing realities that markets, politicians, and empires would rather postpone.

The excerpt below asks whether Hormuz may be that kind of test for the United States and for Trump in particular.

The portfolio actions remain where they belong. But the framework for understanding the stakes begins here.

Here is “Hormuz and the Imperial Test.”

If the previous section was about the form a late-stage monetary transition might take, this one is about the kind of event that can force that transition to accelerate.

And that is why the war with Iran matters...

Not simply because oil is rising.
Not simply because markets are volatile.
And not simply because another war has erupted in an already unstable world.

It matters because the Strait of Hormuz is not just another patch of water on the geopolitical map. It is one of the great arteries of the global system. Roughly a fifth of global oil and LNG flows normally move through it, and the conflict has already disrupted those flows materially enough to push crude back toward or above $100, force central banks to revisit inflation assumptions, and rattle markets well beyond the Middle East.

That is what makes this episode qualitatively different from a standard geopolitical flare-up.

A normal regional war can move oil for a few days and then fade into the background. A war that places Hormuz at the center of the contest does something else entirely...

It tests the credibility of the hegemon.

It tests the willingness of allies to align. It tests whether the old order can still secure the trade routes on which it rests. And, by doing so, it begins to test not just military power, but monetary confidence.

This is precisely why the Suez analogy1 is worth taking seriously.

Not because history repeats neatly. And not because we can already declare this America’s Suez. That would be premature. But because Suez captured a very specific type of imperial revelation: a moment when the world begins to understand, all at once, that the incumbent power may no longer possess the freedom of action, coalition power, or financial autonomy that its reputation had previously implied.

That is the deeper significance of 1956.

Britain and France did not simply fail to get their way in Egypt. They launched military action to retake control of a strategic waterway, only to be forced into retreat under intense political and financial pressure. Britannica’s summary is blunt: Britain and France lost most of their influence in the Middle East as a result of the episode.

The monetary dimension matters just as much.

IMF research describes Suez not only as a geopolitical crisis but as a financial one for Britain, with sterling coming under speculative pressure, reserves being drained, and Britain’s need for U.S. and IMF support giving Washington enormous leverage over the outcome.

In other words, Suez was not merely a battlefield embarrassment. It was a public demonstration that Britain’s imperial reach was already dependent on a financial architecture it no longer fully controlled.

That is the risk here...

If the United States ultimately fails to guarantee secure passage through Hormuz in a durable way, this war could come to be seen not merely as another costly conflict, but as a visible test of imperial limitation. And when such tests go badly, the consequences extend far beyond the battlefield. Allies begin to reassess. Rivals grow bolder. Capital starts to ask harder questions. And the old order, while not collapsing overnight, begins to look less unquestionable than it did before.

That is a very Dalio-style danger.
And it also fits the logic of the Fourth Turning rather well.

Because in Howe’s framework, the climax of a crisis era is not simply a matter of social stress at home. It is the point at which institutional fragility, political fracture, external conflict, and hard choices begin to converge.

The war in Iran may be one of those convergence points. In Dalio’s language, it looks like exactly the kind of late-Stage-5 shock that can push a system closer to Stage 6. In Fourth Turning language, it looks like the sort of event that can accelerate the final innings of the crisis.

That is why so much is at stake here. Not only for the great generational and civilizational cycles we have been discussing... but also for the much shorter economic cycle now trying to survive inside them.

The first-order consequences are easy enough to see.

Higher oil and gas prices act like a tax on growth. They squeeze households, pressure margins, lift transport and input costs, and complicate central-bank decision making. The ECB has already raised its inflation forecast because of the energy shock while also cutting growth assumptions, and the Bank of England has signaled that war-related energy pressures could push inflation materially higher in the coming quarters.

That alone is serious.

But the second-order consequences are where the real danger begins.

A sustained Hormuz disruption does not simply raise energy prices. It starts to alter behavior across the entire system. Firms delay investment. Consumers retrench. Freight and insurance costs rise. Credit spreads can widen as recession risk increases. Rate expectations become harder to anchor because central banks are forced to choose between tolerating inflation and protecting growth. And if energy stays high long enough, the initial inflation shock can morph into something more stagflationary and more corrosive to confidence.

This is also where the long-term rates picture becomes critical.

Because the real macro danger is not merely that inflation moves up. It is that long duration borrowing costs start to reprice a more disorderly combination of inflation, fiscal strain, and geopolitical risk. Reuters has already framed the conflict as trapping Treasury investors in a “stagflationary oil dilemma,” noting that oil in the $100–$130 range for any length of time could wipe out the case for rate cuts and even reopen the door to rate hikes.

Since the conflict escalated, the 10-year Treasury yield has moved materially higher, mortgage rates have climbed to their highest since October, and concern has grown that any decisive breakout in the long end would tighten financial conditions across housing, corporate borrowing, private credit, and government finance all at once.

That is not a trivial risk.

A breakout in long-term yields would not simply be a bond-market story. It would be a system-wide repricing. Housing would feel it immediately. Leveraged borrowers would feel it quickly. Equity valuations (especially for long-duration growth assets) would come under renewed pressure. Private-credit stress could worsen. Fiscal arithmetic would deteriorate further as governments refinance at more punitive levels.

And perhaps most importantly, policymakers would find themselves cornered: unable to celebrate higher yields because of the growth damage, but unable to ease policy cleanly because the very shock pushing yields higher would also be keeping inflation elevated.

Then come the unintended consequences.

These are often the most important ones in moments like this...

A prolonged disruption to Hormuz can force changes that outlast the war itself. It can accelerate reserve diversification at the margin. It can deepen distrust between allies. It can push governments toward industrial policy, energy nationalism, and more overt economic coercion. It can strengthen the political case for financial repression, for larger fiscal deficits, for balance-sheet intervention, and for emergency state involvement in strategic sectors.

It can also, paradoxically, increase the appeal of both old hard-money hedges and new digital rails at the same time...

That last point matters.

Because if the old order begins to look more brittle, the world does not need to leap immediately into a new reserve currency for the monetary debate to change. It only needs to become more interested in alternatives at the margin... more gold, more local-currency settlement, more bilateral workarounds, more stablecoin usage, more tokenized collateral, more experimentation with digital forms of trust transmission. In other words, a shock like this does not have to overthrow the dollar to start reshaping the architecture around it.

That is why this war may prove so consequential for the shape of the reset.

A successful reassertion of control by the United States would reinforce one version of the world: a stressed but still durable American order, one in which the dollar remains central and adaptation occurs through extension, improvisation, and renewal. A failure, by contrast, would strengthen a different version: one in which allies hedge more aggressively, rivals press harder, and confidence in the old architecture weakens more visibly.

Either way, the event matters. And it matters not only for the long cycle, but for the shorter one too.

If the shock fades quickly, markets may be forced to reprice how pessimistic they became, particularly if policymakers lean toward renewed liquidity support to prevent a broader financial tightening. But if the disruption proves more persistent, the growth hit, inflation pressure, bond-market stress, and confidence effects could become mutually reinforcing.

That is the real message. The Iran war is not just a headline risk. It is not just an oil shock. And it is not just another excuse for volatile markets...

It is a live test of whether the existing order can still absorb a strategic chokepoint

shock without revealing deeper weakness.

And that is why it belongs in this issue. Because if both the Fourth Turning framework and Dalio’s Big Cycle are even directionally right, this is exactly the kind of event we should expect near the climax: an event in which external conflict, monetary fragility, inflationary pressure, alliance credibility, and questions about the future shape of trust all begin to collide.

Now... what does this mean for our Model Portfolio? Subscribe our premium tiers at VMF Research to get the answer.

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“In war, the first casualty is truth.”

Aeschylus

We waited one more day to publish this update.

And the reason is simple. We wanted more clarity… more color… on the evolving negotiations between American and Iranian authorities regarding the current conflict. In moments like this, the sequence of events matters just as much as the events themselves.

As you know, over the past few weeks we have been expecting some form of a TACO from the Trump administration. And, to a certain extent, it has already been delivered.

The two-week ceasefire, announced just as the S&P 500 was threatening to break down and long-term U.S. government bond yields were beginning to push higher, was not coincidental.

It was a response to tightening financial conditions and rising political pressure.

And markets reacted accordingly. Risk assets rebounded sharply, volatility compressed, and the immediate downside pressure eased.

That is what a TACO looks like in practice.

But as we sit here on Sunday afternoon, the situation remains unresolved.

There is still no durable agreement on the table.

And the Strait of Hormuz remains the central issue, not just for energy markets, but for the global economy more broadly. In other words, while the initial pressure valve has been released… the system has not yet been stabilized.

Which brings us to the current setup.

We believe this week’s price action will be particularly telling. Markets now have to digest a more complex reality: a partial de-escalation without a lasting resolution...

The key question is whether the recent rebound was the beginning of a more durable move… or simply a reflex rally within a still fragile structure. From a technical perspective, the levels just below the S&P 500 now matter even more. If they hold, the market may continue to build on the recent recovery. If they fail, we could quickly find ourselves back in a much more disorderly environment.

We are watching this closely.

Because beyond the short-term volatility, what matters most is that our pipeline of opportunities continues to deepen. Dislocations like these rarely come alone. They tend to cluster. And when they do, they create the kind of setups that define entire vintages of returns.

Stay close to our work.

Good investing!

Vasco Marques de Freitas, CFA, CMT

Disclaimer

The information provided herein is for general informational purposes only and does not constitute financial advice or a recommendation to buy, sell, or hold any investment. It is not tailored to any specific individual or investor profile. All investments involve risks, and past performance is not indicative of future results. Before making any investment decisions, it is important to consider your own financial situation and risk tolerance. We do not guarantee the accuracy, completeness, or reliability of any information provided, and we disclaim any liability for any loss or damage arising from reliance on the information herein. Readers are advised to consult with an authorized financial intermediary before making any investment decisions.

If Dalio helps us understand why systems become vulnerable, his staging of the Big Cycle helps us understand something else that is equally important: where we are inside the process.

That matters enormously.

Because one of the easiest ways to misunderstand long-cycle frameworks is to treat them as interesting historical abstractions rather than as attempts to identify a position in time. And although Dalio is somewhat more cryptic about the actual shape of monetary resets, he is much clearer about the sequencing of the Big Cycle’s stages.

In his own six-stage telling of the Big Cycle, the progression is roughly as follows:

• Stage 1 begins with the creation of a new order after the breakdown of the old one;

• Stage 2 is the period of productive building, rising competitiveness, and institutional strengthening;

• Stage 3 is the phase of prosperity, confidence, and broadening wealth;

• Stage 4 is when excesses begin to accumulate, debt rises, imbalances widen, and the system starts to live increasingly off financial engineering rather than productive strength;

• Stage 5 is the dangerous pre-breakdown phase, when debt burdens, internal conflict, and geopolitical rivalry intensify sharply;

• and Stage 6 is the actual breakdown phase, the period of great disorder in which the old rules no longer hold and a new order struggles to emerge. Dalio has now said plainly that we are in Stage 5.

That should sound very familiar to readers of this publication. Because while Dalio does not use Howe’s language, his diagnosis places us in much the same historical neighborhood: not in the early or middle innings of the cycle, but in its dangerous closing stretch. A stretch in which the old order is visibly weakening, the next one is not yet formed, and the monetary architecture itself becomes increasingly contestable.

And that brings us directly to the real debate.

What matters now is to understand what typically happens as systems move from Stage 5 into Stage 6... or, in the language of the Fourth Turning framework, what tends to happen in the final innings of a crisis era.

That is the transition that concerns us.

Not the comfortable middle of the cycle. Not the phase in which imbalances are still manageable and policymakers can plausibly pretend that the old rules remain intact. But the phase in which those imbalances begin to force resolution... one way or another.

If we are indeed in this late-stage transition then the important question is no longer whether the old system is under pressure.

It is: what happens next? And, more specifically for the Market View we are testing: what form does the monetary reset now take?

The instinctive answer, for many investors, is to look backward.
And fair enough. History offers a familiar playbook.

As systems move from late-stage strain into outright breakdown, the menu of outcomes tends to narrow. Policymakers and states usually fall back on some combination of:

• currency debasement,

• financial repression

• debt restructuring,

• and, at times, a return (partial or explicit) to harder anchors such as gold.

These mechanisms are not theoretical curiosities. They are the classic tools through which overextended systems attempt to survive.

Currency debasement reduces the real burden of debt by allowing inflation to outrun nominal obligations. Financial repression channels savings into government liabilities, often through regulation, captive domestic investor bases, or persistently negative real rates. Debt restructuring, whether overt or disguised, redistributes losses across creditors, debtors, and institutions. And gold, historically, has served as the external anchor of last resort... the asset to which systems return, explicitly or implicitly, when fiat credibility has been stretched too far.

In other words, when Stage 6 arrives, the issue is rarely whether the system changes.

It is how disorderly the adjustment becomes... and which mechanisms are used to restore trust, discipline, or merely temporary stability.

There is no reason to believe these tools will disappear.

In fact, we have been seeing signs of several of them for years. Real interest rates remain politically sensitive. Fiscal dominance is no longer a fringe concept. And the boundary between monetary and fiscal policy continues to blur.

But there is a risk in relying too heavily on this historical template alone. Because this time, the system is not evolving in a vacuum. It is evolving alongside two realities that make this transition meaningfully different from prior ones.

The first is that we are living through a period of remarkable technological innovation. The second is that the world appears to be becoming more fragmented, more contested, and more multipolar.

Both matter.

For the first time in modern monetary history, the infrastructure through which money moves is itself being reinvented in real time.

We believe that is a gamechanger.

Because if previous monetary transitions were largely about changing the unit (gold to fiat, fixed to floating, domestic to global reserve) this one may also be about changing the rails.

Consider what is already emerging:

• Central bank digital currencies (CBDCs), designed to give states more direct control over money and payments;

• Stablecoins, which extend sovereign currencies into digital ecosystems with near-instant settlement;

• Tokenized assets, which allow traditional financial instruments to move across blockchain-based infrastructure;

• and crypto-native assets, most notably Bitcoin, which exist outside the traditional system altogether and offer an alternative store of value.

These are not theoretical constructs.
They are active, competing architectures.
And importantly, they are not mutually exclusive.

This is where the debate becomes more nuanced.
The next monetary adaptation is unlikely to be a clean break in which one system replaces another overnight.
More likely, it will be layered.

The existing fiat system may persist, but with greater reliance on financial repression and balance-sheet expansion. Gold may reassert itself as a neutral reserve asset, particularly in a more fragmented geopolitical landscape. CBDCs may emerge as tools of state control and efficiency, especially in domestic contexts. And at the same time, stablecoins and blockchain rails may extend the reach of existing currencies (particularly the dollar) into new domains of economic activity.

,In that sense, the next system may not be defined by a single dominant form. It may be defined by the interaction between multiple layers of money and multiple layers of infrastructure.

And this is precisely where our thesis begins to diverge from the conventional narrative...

The dominant expectation today tends to frame the future in binary terms:

• either the dollar system persists... or it is replaced.

• either gold returns... or fiat systems collapse.

• either crypto disrupts the system... or it remains marginal.

We think this framing is too simplistic. Because it ignores a more subtle, and, in our view, more likely path.

A path in which the existing system adapts not by rejecting new technologies, but by absorbing them.

A path in which the dollar does not disappear, but is increasingly extended through new digital infrastructure.

A path in which blockchain-based systems do not replace sovereign currencies, but become part of the way those currencies move, settle, and scale.

And that point becomes even more important once we confront the question of multipolarity...

Yes, the world is clearly becoming more contested. Yes, rival power centers are asserting themselves more openly. Yes, the unipolar moment is fading. But from that, many investors jump too quickly to the conclusion that the dollar must therefore be on the verge of losing its hegemonic role.

We think that leap is premature.

Because whatever its vulnerabilities, the dollar still sits at the center of the system.

And if you look at what stands in second place, the contrast is hard to ignore.

The euro remains the nearest challenger by share, but does anyone seriously believe it is better positioned than the dollar to anchor the next era of global trust?

A currency backed by a politically fragmented monetary union, with no single fiscal sovereign behind it, hardly looks like the obvious successor.

And the yuan? It may belong to a rising power, but it remains embedded in a system of capital controls, opaque policymaking, and limited convertibility. That is not the natural foundation of a truly global reserve alternative either.

No... our view is more subtle than that.

We do believe multipolarity is increasing. We do believe the world is fragmenting. We do believe that reserve diversification is likely to continue at the margin. But we do not think that automatically implies a clean post-dollar world.

What we think it implies is something more complex... and, perhaps, more interesting.

A world in which the dollar likely remains hegemonic for longer than many now assume... but one in which the architecture surrounding that hegemony changes materially. A world in which technological innovation plays a much larger role in how trust is transmitted. A world that becomes more digital, more programmable, more tokenized, and, in that sense, more crypto-friendly than the conventional reserve-currency debate usually allows.

In other words, the monetary reset (if that is indeed what we are approaching) may be less about replacing the dollar than about rebuilding the infrastructure through which dollar-based trust operates.

That distinction is critical.

Because it shifts the focus of the debate.

From: what is the next form of money?
To: how is trust going to move?

And once you ask that question, a different set of implications begins to emerge.

The winners are not necessarily those assets that simply benefit from monetary disorder. They are those positioned at the intersection of:

• trust,

• custody,

• compliance,

• distribution,

• and settlement infrastructure.

They are the tollbooths, not just the commodities.

But before we translate that into Model Portfolio positioning, we need to confront the present. Because these transitions do not occur in clean, theoretical environments.

They occur in moments of stress.

Moments in which geopolitical conflict, economic fragility, and policy constraints collide in ways that force the system to reveal its underlying weaknesses.

And that is exactly what we may be witnessing now.

The question, then, is whether the current Iran shock is simply another episode of geopolitical volatility... or whether it is something more consequential: a live test of the very fractures that define this stage of the cycle.

That is where we now turn.

Are you ready for a reset ? To see our full, position-by-position trading plans and the technical triggers we’re watching for the next rotation, subscribe to VMF Research today.

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What if it is about credibility?

Over the last few months, across VMF Research, we have been building a large thesis. In VMF’s Strategic Asset Allocation, we argued that this Fourth Turning may culminate in a monetary reset. In VMF’s Security Selection, we pushed that thesis down to the company level and asked who gets paid as trust moves through new rails. And now, in March’s issue of Alpha Tier, “The Monetary Endgame,” we take the next step: we stress-test the framework itself against Ray Dalio’s Big Cycle and then force it through the hardest proving ground of all, a live geopolitical rupture.

This post unfolds as a three part series.

Part 1 is “Howe, Dalio, and the Stress Test”
Part 2 is “Toward the Reset”
Part 3 is “Hormuz and the Imperial Test”

That structure matters because what is at stake here is not just another market squall or another “oil up, stocks down” episode. In the issue itself, we argue that the war with Iran may be something much bigger: a live test of imperial credibility, monetary confidence, geopolitical power, and the fragility of the postwar order. In plain English, this may turn out to matter not only for markets, but for the United States, for Trump, and for the credibility of the system he is trying to command in the middle of a late-stage crisis era.

And yes, we go there.

Because one of the central historical analogies in this issue is the Suez Crisis of 1956. Not because history repeats neatly. And not because we are lazily declaring this “America’s Suez.” But because Suez captured something far more important than a failed military episode. It captured a moment when the world suddenly understood that an incumbent imperial power no longer had the freedom of action, coalition strength, or financial autonomy its reputation still implied. In the issue, we make the case that Hormuz may pose that kind of test for Washington now.

That is the frame for what follows.

This series is about the stress test. First of the theory. Then of the monetary order. And finally of the empire itself.
Here is Part 1.

By now, you are already familiar with the core hypothesis we have been developing across our entire product roster.

That roughly every 70 to 90 years (approximately the span of an average human life) during what Neil Howe and William Strauss describe as Fourth Turnings, the system is not merely stressed... it is renegotiated. Institutions lose credibility. Political cohesion weakens. Old assumptions break down. And, crucially for our purposes, the monetary order itself becomes vulnerable.

History does not repeat mechanically. But the pattern is difficult to ignore.

As you saw in this month’s issue of VMF’s Strategic Asset Allocation, the Revolutionary crisis gave us the collapse of the Continental and the birth of a more credible federal financial architecture. The Civil War suspended specie, introduced the Greenback, and accelerated the centralization of the monetary system. The Great Depression and World War II shattered the old gold constraint and gave rise to Bretton Woods and the dollar-centered global order.

Different contexts. Different mechanisms. Same underlying dynamic:
When the system breaks, the money changes with it.

That is the hypothesis.

But now comes the real question.

Is this pattern robust... or are we simply imposing narrative structure on a set of historical coincidences?

That objection should be taken seriously. After all, this is a small sample from which to infer what comes next. And even if we widened the lens (going back another two or three Fourth Turnings or casting a broader global net across other great crisis eras) we would still run into the same methodological problem. The parallels may remain striking, but the sample would still be limited, and limited samples are a fragile basis on which to draw definitive conclusions about recurring historical patterns.

It is precisely this fragility that we will now put to the test.

If this thesis is going to do more than sound elegant... if it is going to keep shaping how we allocate capital across our Model Portfolios... then it has to survive a more demanding test than historical analogy alone.

That is why we now turn to

Dalio does not speak the language of generations.

He does not frame history in terms of archetypes, social moods, or civic cycles. Instead, he builds from something more mechanical... and, in many ways, more unforgiving: debt, money, power, and incentives.

At the core of his work sits what he calls the Big Cycle, a multi-decade to multicentury process that governs the rise and decline of nations, empires, and monetary systems. In his more recent public framing, Dalio has described that Big Cycle as typically lasting about 80 years, give or take 25 years... in other words, roughly one lifetime. That timing alone makes the comparison with the Fourth Turning framework hard to ignore.

And within that Big Cycle, one dynamic stands above all others:
...the long-term debt cycle.

It begins, as these things often do, in strength.

A rising power establishes internal order, builds credible institutions, and develops a productive economy. Its currency becomes trusted. Its debt is considered safe. Capital flows toward it. Innovation flourishes. Military strength grows. The system reinforces itself.

Credit expands, but in the early stages, that expansion is productive. Debt finances growth. Growth supports income. Income supports debt service. The cycle feeds on itself in a virtuous loop.

Over time, however, the character of that debt changes.

What begins as productive borrowing gradually becomes consumptive borrowing. Debt grows faster than income. Financial assets inflate. Inequality widens. The gap between the real economy and the financial economy expands. And increasingly, stability itself becomes dependent on the continuation of credit growth.

This is where the system becomes fragile.

Because once debt levels reach a certain threshold, the rules change.

The system can no longer tolerate normal economic downturns without intervention. Central banks are forced to cut rates more aggressively. When rates approach zero, they are forced to print money. When money printing becomes politically sensitive, fiscal policy takes over. And gradually, the boundary between monetary and fiscal policy dissolves.

Dalio calls this phase monetization. In short, the system begins to sustain itself by creating money to support debt that cannot be supported organically.

And importantly, Dalio does not treat this as some distant theoretical endpoint. In How Countries Go Broke, he writes that the monetary-policy regime in which central banks create money and buy debt to compensate for insufficient private demand began in 2008, the first time such a policy had been used since 1933, and he is explicit that these moves are “symptomatic of being in the late phase of the long-term debt cycle.”

By Dalio’s own framework, this cycle did not merely begin to look tired recently... it entered a recognizably late-stage phase nearly two decades ago.

His more recent public comments have pushed the point even further. Earlier this month, Dalio said that we are now in Stage 5 of the Big Cycle, the stage immediately preceding breakdown and great disorder.

Whether one agrees with that exact staging or not, the message is unmistakable: in Dalio’s view, we are no longer in the benign middle innings of the cycle. We are in its dangerous closing stretch.

That interpretation is also consistent with the harder cross-country indicators he has published. In his 2024 Great Powers Index, Dalio ranked the United States indebtedness position 33rd out of 35 countries, noting that, based on current debt levels and debt-service costs, the country has very little room to lever up further.

That is not how he describes a system early in its debt cycle... it is how he describes one already deep into its mature and vulnerable phase.

At this stage, the problem is no longer cyclical.
It is structural.

And this is where Dalio’s framework begins to intersect, in a very direct way, with the world we have been describing.

Because as debt burdens rise and monetary tools become exhausted, the system is pulled into a series of increasingly difficult trade-offs:

• between inflation and growth,

• between creditors and debtors,

• between financial stability and political stability,

• between internal cohesion and external projection of power.

These tensions do not remain confined to balance sheets.
They spill into politics.

This is where Dalio introduces the second critical dimension of his framework: The internal political cycle.

As wealth gaps widen and economic conditions become more uneven, societies tend to move from cooperation toward conflict. Political polarization increases.

Populism rises. Institutional trust erodes. And the willingness to accept painful adjustments declines. In other words, the system becomes less capable of reforming itself through orderly means.

Sound familiar?
It should.

Because this is precisely the terrain Fourth Turning theory describes, albeit through a different lens. Howe sees generational dynamics driving institutional breakdown.

Dalio sees financial imbalances and inequality driving political conflict.

Different starting points.
Same destination.

But Dalio does not stop there.

Because internal disorder rarely remains internal. As domestic pressures rise, countries become more sensitive to external threats and more willing to project power abroad. At the same time, rival powers (often with stronger balance sheets, rising productivity, and growing geopolitical ambition) begin to challenge the incumbent.

This is the third pillar of the Big Cycle: The external geopolitical cycle.

Historically, this is the phase where rising powers test declining ones, often over control of trade routes, resources, or spheres of influence. These confrontations do not always begin as full-scale wars. They often start as economic competition, trade disputes, regional conflicts, or coercive probes designed to test the resolve of the incumbent and the credibility of the order it oversees.

That is the broader context in which so many of today’s flashpoints need to be

understood...

The Russia-Ukraine war is not merely a brutal regional conflict. The long-running violence around Israel and Gaza is not merely a local tragedy. The renewed strategic focus on Venezuela, the pressure politics surrounding Greenland, and now the war with Iran all belong, in different ways and with different intensities, to a world in which geography, resources, chokepoints, spheres of influence, and alliance credibility are once again being contested more openly.

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They are not identical episodes, of course. But they are increasingly legible as symptoms of the same larger transition: a world order moving from presumed stability toward explicit tests of power.

And under the surface, something more fundamental is at stake: who sets the rules of the system, and whose money those rules are denominated in.

Because embedded within all of this is Dalio’s fourth and perhaps most important layer: The reserve currency cycle.

A currency does not become dominant by decree.
It becomes dominant because it is trusted.

And it is trusted because it is backed (explicitly or implicitly) by:

• a strong economy,

• deep and liquid capital markets,

• a credible legal system,

• and, crucially, the geopolitical and military power to enforce the system in which it operates.

But as the long-term debt cycle matures, and as internal and external pressures mount, that trust can begin to erode.

Not all at once.
Not in a single dramatic collapse.
But gradually, then suddenly.

Foreign holders begin to question whether their claims will be honored in real terms. Domestic policymakers become more willing to prioritize internal stability over external credibility. Money creation accelerates. Real returns compress. And alternative stores of value (historically gold, but potentially other assets like Bitcoin) begin to attract attention.

Dalio has been very explicit about this.

When a dominant power becomes overextended financially and then reveals weakness (militarily, economically, or politically) confidence can shift.

And when confidence shifts, so do capital flows.
And when capital flows shift, so does the monetary order.

Now step back and look at the structure we have just outlined:

• A long-term debt buildup.

• Followed by monetary accommodation and eventual monetization.

• Accompanied by rising internal conflict.

• Intersecting with external geopolitical rivalry.

• And culminating in pressure on the reserve currency system itself.

This is not a generational theory.
This is a macro/financial one.

And yet, the overlap with the Fourth Turning framework is striking. Because what Howe describes as a crisis of institutions, Dalio describes as the inevitable consequence of accumulated financial imbalances. What Howe frames as a generational climax, Dalio frames as the late stage of a long-term debt and power cycle. And what Howe observes historically as periods of rupture, Dalio explains mechanistically as periods when the system can no longer sustain itself under the existing rules.

Two different lenses.

One from sociology.
One from macroeconomics.

But both pointing to the same uncomfortable conclusion: there are periods in history when the existing order becomes unsustainable.

So, where does that leave our hypothesis?

Does Dalio confirm that Fourth Turnings coincide with monetary resets?

Not exactly...

But before answering that question too quickly, it is worth pausing on just how much overlap we have already uncovered.

As we have now seen, Dalio’s framework and the Fourth Turning framework share some striking similarities, even if they approach long cycles from very different angles. Howe begins with generations, social mood, and institutional legitimacy. Dalio begins with debt, incentives, capital flows, and power. One is more sociological. The other is more macro.

But that difference, in our view, makes the overlap more compelling, not less. If two very different lenses point toward a similar underlying rhythm, the thesis becomes harder to dismiss as mere narrative convenience.

And the similarities are not trivial ones.

Both frameworks, for instance, point to a cycle lasting roughly 80 years... approximately the span of an average human life.

That detail matters more than it may seem. Because for societies to make the same broad mistakes again, it helps if the people who vividly remember the last disaster are no longer in positions of power... or are no longer alive at all. That is part of what gives these long cycles their recurring character. They are not mechanical in the sense of a clock. They are human. They capture the way memory, complacency, overreach, and institutional decay unfold across generations.

There is another important overlap.

Both frameworks also point, in different ways, to 2008 as the beginning of the current system’s terminal phase. For Howe’s framework, the Global Financial Crisis (GFC) marked a decisive escalation in institutional breakdown and public distrust. For Dalio, it marked the point at which the old playbook ceased to function cleanly and the authorities were forced into a new regime of extraordinary intervention: zero rates, balance-sheet expansion, and large-scale money creation. In other words, both frameworks see the post-2008 era not as a normal continuation of the old order, but as the beginning of its more unstable and improvisational endgame.

That is a remarkable convergence.

What, then, does Dalio actually confirm?

He does not confirm our hypothesis (that Fourth Turnings climax with monetary resets) in any neat or formulaic sense. But he does something just as important.

He provides a framework that explains why monetary systems become vulnerable in the first place. He shows how debt, money creation, political conflict, and geopolitical rivalry interact to push systems toward a breaking point. He shows why the old rules stop working. And he shows why, at certain moments, policymakers are forced to choose between outcomes that all carry significant costs.

What he does not do, at least not explicitly, is tell us what the next system must look like.

His work points toward outcomes such as:

• currency debasement,

• financial repression,

• debt restructuring,

• and, at times, a return to harder anchors such as gold.

But it leaves open a critical question. A question that, in our view, may define this Fourth Turning more than any previous one: through what infrastructure will the next system of trust actually operate?

Because if Dalio helps us understand when systems break... and why they break... he is far less explicit about how they are rebuilt.

And that is precisely where we now turn.

Read Part 2:

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Risk assets have been hit hard, macro narratives have been thrown back into flux, and investors are being reminded, yet again, that in true crisis eras, history does not move in a straight line. It lurches. It shocks. And when it does, assumptions that had seemed stable only days earlier suddenly begin to look fragile, conditional, and open to renegotiations.

Throughout March, across the entire product roster at VMF Research, we have done far more than simply react to the headlines.

We have gone deep into this event, not just its immediate market implications, but its possible historical meaning.

In Alpha Tier, for instance, we asked whether this war in Iran could become America’s Suez Crisis of 1956. Not because history repeats mechanically. Not because every geopolitical rupture deserves imperial comparisons.

But because moments like these can reveal far more than investors first assume. They can expose strategic weakness, test credibility, and accelerate transitions that were already underway.

And, as always, we did not stop at analysis.

We adjusted our Model Portfolios.

In VMF’s Strategic Asset Allocation, we added a new position with heavy exposure to the Fourth Turning dynamics we have been writing about so extensively. In VMF’s Security Selection, we added a crypto Tollbooth specifically designed to benefit from what we believe may be the anatomy of this big cycle’s monetary reset. In other words, we are not merely trying to describe the world that may be emerging. We are trying to position for it.

That brings us to the Leaderboard.

At first glance, the Top 10 has not changed much.

The same broad themes remain in command. The same core positions continue to tell the story.

But just below that Top 10, some of our energy-linked positions are now vying for a place among the leaders. That is worth watching closely. In an environment increasingly shaped by war, chokepoints, and strategic scarcity, energy can very quickly move from supporting actor to central character.

At the same time, the drawdowns we are seeing in several other positions are a useful reminder that rigorous risk management is not optional.

It never is.

We have positions that have left the Top 10, such as ARK Innovation - , but where the process still worked exactly as intended because we exited half the position when it doubled.

That matters. In a volatile market, the path of a position matters almost as much as the final outcome. Booking gains, cutting exposure, and refusing to let a winning trade become a round trip is part of the discipline required to navigate a true crisis era.

But perhaps most important of all is this: in the middle of so much pessimism, we are finding unique pockets of opportunity.

Particularly in the Quality Risk Factor.

That is where some of the most interesting mispricings now appear to be emerging. High-quality franchises, with durable economics and strong competitive positions, are increasingly being priced like average businesses... or in some cases, even worse. That is the sort of setup serious investors should pay attention to. Because when fear becomes indiscriminate, quality often goes on sale with everything else.

There is, quite simply, a lot going on.

A serious geopolitical rupture. A market under pressure. A Leaderboard that remains more resilient than many might expect. Energy-linked positions pressing upward. Fresh evidence of why risk management matters.

And, beneath the surface, some very interesting opportunities beginning to emerge.

Stay tuned.

Vasco

Disclaimer: This is general information, not personalized investment advice. It’s not a recommendation to buy or sell anything. Investing involves risk, and past performance doesn’t guarantee future results. Do your own research and consider speaking with a licensed/authorized professional who understands your objectives and risk profile.”

What if this is just Groundhog Day all over again?

Over the past week, investors have continued to grapple with the same central fear we highlighted in our last note: that the war with Iran, through its impact on energy prices, could tighten financial conditions further, deepen the ongoing risk-off wave, and push the market into a much more disorderly correction.

That risk remains very much alive.

In fact, despite some attempts by the Trump administration to soften its tone and scale down the conflict, Iran has shown little willingness to cooperate.

So, while we still believe the political incentive for another T.A.C.O. remains strong, the path toward de-escalation is proving neither immediate nor straightforward.

Still, the most important chart to watch right now may not be oil... but the S&P 500 itself. Take a look at the chart below.

As you can see, the current pattern is beginning to look eerily similar to the Liberation Day debacle of 2025, the episode that ultimately culminated in a major Trump pivot from Main Street to Wall Street.

Back then, the market rolled over, lost momentum, broke down, and eventually forced a response.

Today, we see a very similar setup unfolding once again: the index has broken below its 200-day moving average, momentum has deteriorated materially, and RSI is now revisiting oversold territory. In other words, we may once again be approaching the zone where financial markets begin to exert much greater political pressure.

And that level matters.

Historically, once the S&P 500 loses its 200-day moving average, the character of the tape can change quickly.

Volatility tends to expand, downside moves can accelerate, and investors begin to pay much closer attention to all the things they were previously willing to ignore. Add to that the fact that we are in a midterm year, which historically has not been especially kind to equity returns, and we believe it is highly unlikely that the Trump administration is oblivious to the risks associated with allowing this drawdown to deepen much further. So, our working hypothesis remains intact:

Trump will continue to press for a T.A.C.O.

In fact, if anything, the bar is now even lower for positive surprises. Sentiment is poor, the technical damage is mounting, and the market is increasingly leaning toward a more persistent and economically damaging conflict.

That is precisely why any credible signal of de-escalation, however imperfect, could still have an outsized impact on asset prices. We may be early once again... but for now, we continue to believe that the political, market, and economic incentives are all pushing in the same direction.

Meanwhile, while the broader risk-off wave continues to ravage financial markets, several pockets of relative and absolute strength remain visible in our Model Portfolios. Most notably, our overweight in energy equities continues to behave exactly as one would expect in this new market regime... as a true diversifier.

We will enter our Spring publishing break next week, and you can expect your next monthly issue on April 10.

Good investing!

Vasco Marques de Freitas, CFA, CMT

Disclaimer

The information provided herein is for general informational purposes only and does not constitute financial advice or a recommendation to buy, sell, or hold any investment. It is not tailored to any specific individual or investor profile. All investments involve risks, and past performance is not indicative of future results. Before making any investment decisions, it is important to consider your own financial situation and risk tolerance. We do not guarantee the accuracy, completeness, or reliability of any information provided, and we disclaim any liability for any loss or damage arising from reliance on the information herein. Readers are advised to consult with an authorized financial intermediary before making any investment decisions.

Over the last few weeks, if you’ve been reading this Substack, you’ve already seen the pieces of the puzzle:

In “A Fourth Turning Goes On-Chain,” I argued that the next monetary reset may not begin with gold, devaluation, or some theatrical Bretton Woods sequel. It may begin by changing how trust moves.

In “The Agentic Era,” I argued that once software becomes a doer, blockchains stop looking like a speculative sideshow and start looking like the rails for identity, settlement, and machine-to-machine commerce.

Fine.

But ideas, by themselves, do not pay.

Businesses do.

That is why this new post matters.

What follows is an excerpt from March’s issue of VMF’s Security Selection, and in my view it is one of the most actionable pieces of work we’ve published in some time.

Because it takes the broader research we’ve been building across our entire product roster and pushes it one level deeper, from macro thesis to business model, from “how trust moves” to the only question investors should really care about:

Who gets paid?

Our answer is

Yes, Coinbase.

Most investors still look at it and see what they expect to see: a cyclical crypto exchange tied to sentiment, speculation, and trading volumes. We think that lens is already stale.

The market is still pricing the old business while the company is trying to become something much larger: the regulated financial operating system of the on-chain dollar, what Brian Armstrong calls the “Everything Exchange.”1

That is the leap this thesis makes.

If our work across VMF’s Strategic Asset Allocation, VMF’s Security Selection, and Alpha Tier is right, then Coinbase is not merely a levered bet on the next crypto bounce. It may be one of the clearest public-market tollbooths on a much bigger shift: Treasury-backed stablecoin rails, blockchain-based settlement, and even the early financial plumbing of the agentic internet.

That is not the consensus view.

But then again, that is usually where the asymmetry is greatest.

Here’s the excerpt:

If last week’s Tier One issue was about how trust moves, this one is about who gets paid when it does.

That is the leap.

Because once you accept the possibility that this Fourth Turning’s monetary reset may not arrive first through a dramatic devaluation, a new gold peg, or some new Bretton Woods-style conference... but instead through new rails for moving, settling, and extending trust... the exercise changes.

You stop asking which token might catch the next speculative wave.

You stop asking which blockchain narrative sounds the most futuristic.

And you begin asking a much more practical question... and, in our view, a much more investable one: which businesses are positioned to own, monetize, and defend the new infrastructure of trust?

We believe Coinbase - may be one of the clearest answers in the entire public market.

That may sound odd at first, because most investors still look at Coinbase and see what they expect to see: a crypto exchange. A cyclical, sentiment-driven business that booms when trading is hot and suffers when speculation cools.

That caricature is not entirely wrong. But it is increasingly incomplete.

The market is still looking at Coinbase through the old lens. But make no mistake, even if Coinbase were nothing more than a crypto exchange, it would still be an increasingly bigger and more dominant one.

The chart above makes that hard to ignore. Total trading volume has surged from $522 billion in 2023 to $2.0 trillion in 2024 and then to $5.2 trillion in 2025.

At the same time, Coinbase’s crypto trading volume market share has climbed from 1.8% to 3.2% and now to 6.4%. So yes, the market may still be looking at Coinbase through the old lens... but even that “old” business is scaling fast and taking meaningful share.

Still, we think the more important perspective is this: Coinbase is trying to become the regulated financial operating system of the on-chain dollar.

Or, in own words, the “Everything Exchange.”

That ambition matters enormously.

Because the Everything Exchange is not simply a bigger crypto venue. It is an attempt to make Coinbase the single trusted place where customers can hold assets, move money, earn yield, access custody, use stablecoins, trade spot and derivatives, and, over time, manage a much broader universe of financial products inside one compliant ecosystem.

Coinbase has explicitly made the expansion of the Everything Exchange one of its three key priorities for 2026, alongside scaling stablecoins and payments and bringing more of the world on-chain through Base, a topic we will address in just a moment.

And it is precisely here that we believe the market’s business-model misdiagnosis is most severe.

An exchange, at least in the way the consensus still thinks about Coinbase, earns fees when people trade. A financial operating system does something far more valuable than that.

It becomes the place where assets sit, where trust accumulates, and where more and more economic activity can be layered on top. It provides custody. It embeds compliance. It distributes stablecoins. It connects wallets. It settles transactions. It monetizes balances. It finances activity. It gives institutions, merchants, developers, and increasingly even AI agents2 a way to interact with the system without needing to understand every layer beneath it.

That is a far better description of what Coinbase is trying to build. And if that is the right description, then the stock belongs in a very different category from the one the market still seems intent on assigning to it.

The clearest proof that the Everything Exchange is more than a clever slogan is the sheer amount of gravity Coinbase has already built.

Even after a brutal drawdown in crypto prices, with Bitcoin roughly halved from its late 2025 peak and many other major assets hit even harder, Coinbase still finished the fourth quarter with assets on platform close to $380 billion. And that figure is not just large in absolute terms.

It is enormous in relative terms...

As the company’s own chart makes clear, Coinbase now holds roughly twice the assets of the next-largest rival. Management went even further on the latest earnings call, stating that Coinbase stores more crypto than any other company in the world, roughly 12% of all crypto globally, and more than the next four competitors combined.

That is not what a commodity exchange looks like. That is what platform-level trust looks like.

And this is precisely why the flywheel matters so much.

Coinbase is not simply trying to maximize fee extraction on trading volume. It is trying to become the place where assets accumulate first, because once the assets are there, everything else becomes easier. Trading becomes easier. Borrowing against those assets becomes easier. Staking becomes easier. Stablecoin usage becomes easier. Payments, subscriptions, custody, derivatives, and, over time, a much broader universe of financial services all become easier.

That is the real strategic logic behind what management calls the “asset accumulation flywheel.” Build trust. Attract assets. Layer more utility on top of those assets. Reinvest the economics into better products, broader distribution, and even more trust.

It is a simple idea. But when it works, it is extremely powerful.

This is where Base becomes so important.

Base is Coinbase’s Layer 2 network built on top of Ethereum. That means it is designed to inherit Ethereum’s security and settlement layer while making onchain activity faster, cheaper, and easier to use. That matters because if Ethereum is the base monetary and settlement substrate, then Base is Coinbase’s attempt to sit much closer to the application layer where real usage, developer activity, and customer relationships actually form.

The latest shareholder letter makes clear that Base is not a side project. Coinbase says it has made the network materially faster and cheaper, with roughly 200 millisecond block times and very low median fees, while also bundling wallet, onchain trading, payments, messaging, identity, mini apps, and Base Pay inside the broader Base App experience.

That is not what a company does when it wants to remain “just an exchange.” That is what a company does when it wants to own more of the stack.

And once you see Base in that context, the whole business starts to look different.

The market still tends to think about Coinbase as if the company’s economic relevance begins and ends with trading activity. But Base levels the company up into something much broader. It gives Coinbase a direct claim on the growth of onchain applications, onchain payments, embedded wallets, consumer identity, developer tooling, and the wider ecosystem forming on top of Ethereum.

In other words, it turns Coinbase from a venue into a platform. And platforms, when they work, are worth much more than venues.

You can also see this evolution in the number of products Coinbase now sells and the pace at which they are growing.

The company says it now has 12 products generating more than $100 million in annualized revenue, half of which generate more than $250 million, and two above $1 billion.

Read that again.

This is not a single-product company riding one cyclical tailwind. It is a business whose product surface is widening quickly across spot trading, derivatives, custody, subscriptions, staking, financing, USDC, payments infrastructure, and developer rails.

The same letter also highlights nearly 1 million paid Coinbase One subscribers, a growing Prime financing business, all-time highs in derivatives volume, strong growth in BTC-backed borrowing, and deeper integrations of USDC across both consumer and institutional products. That breadth matters because it makes the business more resilient and more valuable. A customer holding assets on Coinbase is no longer just a potential trader. That customer is increasingly a candidate for multiple layers of monetization.

One emerging area we find especially promising is AI agents.

We wrote in the last issue of Alpha Tier that the agentic era strengthens the blockchain case... and Coinbase is one of the few public companies already building for that world in a serious way.

The basic logic is straightforward:

If AI agents become real economic actors, and we believe they will, they will need a native way to hold value, make payments, and interact with services across the internet. They will not open traditional bank accounts.

But they can have crypto wallets.

And Coinbase is already building exactly that infrastructure. Through products such as Agentic Wallets, AgentKit, Payments MCP, and x402, the company is explicitly trying to give software agents the ability to hold stablecoins, pay for services, and transact programmatically over crypto rails. x402, in particular, is designed to let both humans and machines make instant stablecoin payments directly over HTTP. That is not a science-fiction use case.

That is the early financial plumbing of the agentic internet.

And that matters because, over time, there may be far more AI agents than humans online.

If that happens, then the world will need a machine-native payment and settlement layer.

We are still early, and we should not pretend that this is yet a major line item in Coinbase’s income statement. But that is not the point. The point is that the company is already building for a future in which autonomous software becomes a genuine economic participant. That gives Coinbase a valuable growth option embedded inside a business that is already getting stronger on much more traditional metrics.

Of course, there are other promising avenues we could spend time on here. Tokenization is one. Prediction markets are another. We could easily go there. And we probably will in future issues. But not today.

For now, we think the cleanest and most useful way to see Coinbase is this: not as a “crypto stock,” and certainly not as a mere exchange, but as a platform. Better yet, as a tollbooth on crypto adoption.

That is the key.

Because if we are right in our broader macro thesis that this Fourth Turning’s monetary reset ultimately proves supportive for crypto, then Coinbase stands out as one of the clearest corporate beneficiaries of that secular trend. It would benefit not only from higher prices and stronger activity, but from deeper stablecoin penetration, broader onchain payments usage, more tokenized assets, richer developer ecosystems, and a more central role in the financial rails of the next regime.

But... and that is exactly what makes this thesis so robust...

...even if our big macro call proves too early, too partial, or simply wrong, we still believe Coinbase can continue to grow in an increasingly digital world where AI agents play a larger role, financial activity keeps migrating online, and customers increasingly gravitate toward asset platforms that combine trust, custody, utility, and product breadth inside a single ecosystem.

In other words, the macro thesis makes the upside bigger.

But it is not the only thing holding the thesis together.

And now, as if to underline that point, the incumbents are beginning to validate the category for us.

The parent of the New York Stock Exchange, ICE, has just taken a minority stake in OKX at a $25 billion valuation and paired that investment with a strategic partnership around crypto futures, tokenized equities, custody, and wallet architecture.

Nasdaq has also partnered with Kraken to develop tokenization infrastructure for blockchain-based equities.

Those are not fringe actors chasing a fad...

They are two of the most important market-infrastructure franchises in the world, and both are now moving toward crypto-native platforms. That is a meaningful tell. It suggests that what we are describing here is not some niche corner of the market, but the early construction of a new financial architecture.

So yes, Coinbase still gets mislabeled as an exchange. And yes, the cycle will still matter. But the more we study the business, the harder it becomes to see it through that narrow lens alone.

The better way to think about it is this: Coinbase is building a flywheel in which trust attracts assets, assets attract utility, utility attracts more activity, and that activity funds the continued expansion of the platform.

If that flywheel keeps turning, then what looks today like a cyclical crypto business may, in hindsight, prove to have been one of the most important public tollbooths sitting on top of crypto adoption all along.

We believe we have made our view quite clear.

We like Coinbase’s competitive position. We like its value proposition. And above all, we like Brian Armstrong’s vision for what the business can become.

Therefore, at this stage, only two questions remain:

• How much are we paying for that business?

• And is the stock suffering from the same kind of market myopia we are seeing in so many other names we like right now... poor price action despite a business that keeps getting stronger?

Let’s begin with valuation.

We have just spent the last several pages arguing that Coinbase is not simply an exchange. It is something much broader and much more strategically important than that. Still, the cleanest place to start is to compare it with the incumbents the market already knows how to value.

And frankly, Coinbase shines against all of them.

Start with the top line.

As the table above shows, Coinbase is growing much faster than the traditional exchanges. A lot faster!

Over the last three years, revenue has compounded at roughly 31% annually, versus about 10% for Nasdaq, 9.5% for ICE, 9.1% for CME, and just 6% for Cboe. No wonder ICE, the parent company of the New York Stock Exchange, has now decided to invest in OKX. And look at the valuation it was willing to bless. Based on the reported $25 billion valuation and the best available outside estimate of roughly $1.5 billion of revenue, the market is implicitly valuing OKX at around 16.7x sales.

Now here is where it gets even more interesting.

For those same growing sales, the market is actually paying a discount for Coinbase versus most of the traditional exchanges. Coinbase currently trades at roughly 7.3x EV/Sales, compared with about 11.2x for ICE, 11.0x for Nasdaq, and more than 17x for CME. Only Cboe screens lower.

In other words, the market is giving the slow-growing incumbents more credit for each dollar of revenue than it is giving Coinbase. That strikes us as a strange conclusion for a business growing at several times their pace and operating much closer to some of the most important financial trends of the coming decade.

When we turn to profitability, measured here through EBITDA, the picture looks different. Coinbase screens more expensive, trading around 29.9x EV/EBITDA, versus roughly 18x to 24x for the traditional exchange group.

But we believe it deserves that premium...

And the reason is not hard to understand. Coinbase is spending heavily to build the Everything Exchange, expand stablecoins and payments, scale Base, deepen regulation and compliance, and widen the product surface of the platform.

Many of those investments run straight through the income statement as operating expenses, which depresses near-term EBITDA even as they may be strengthening the long-term economics of the business. In other words, current profitability understates the embedded optionality.

In the context of everything we have laid out, and the growth runway we still see ahead, we find the valuation attractive.

And we suspect that premium could crystallize much faster if and when the CLARITY Act passes, because that would make it far easier for the market to value Coinbase as a regulated financial platform rather than as a volatile crypto proxy.

Even today, with the legislation still stalled, that reframing is already beginning.

Now let’s talk about the stock.

Regarding the technical action of Coinbase’s chart... well, the chart speaks for itself.

The stock is still trading at less than half the intraday high it reached on the day of its April 2021 direct listing, when shares opened at $381 and rose as high as $429.54 in first-day trading.

Today, Coinbase is still only around the low $200s. So... despite all the progress the business has made since then, the stock is still carrying the emotional scar tissue of the previous crypto bull market.

And... let’s be frank, the stock has not exactly covered itself in glory relative to the broader opportunity set. It has lagged the great benchmark indices (like the S&P500 or the Nasdaq) and, more importantly for our purposes, it has lagged the major Bitcoin, Ethereum or Solana).

Coinbase has underperformed... that’s a fact, but that is also the point. The business has grown massively across multiple fronts, but the stock has not yet fully reflected that transformation.

That disconnect is exactly what interests us.

Because when the crypto cycle turns, and when today’s depressed sentiment eventually gives way to something more constructive, we believe the market will be forced to look at Coinbase very differently.

Not as a one-dimensional trading venue.
Not as a speculative sidecar to Bitcoin.

But as a platform with custody, stablecoins, subscriptions, derivatives, developer rails, Base, and an increasingly important role in the financial architecture of the next monetary regime. And if that is the lens through which the market eventually learns to see Coinbase, then we believe the stock can reach levels well above the old highs from April 2021.

We will be here to be accountable for that claim.

• Let’s allocate 2% of the Quality Model Portfolio to Coinbase @ $197.50.

You might also like reading:

What a difference a month makes.

Not long ago, investors were still debating whether the next move would be a soft-landing melt-up or a healthy consolidation.

Now the market is gripped by something far uglier: a vicious risk-off wave triggered by the latest Trumpquake... the war with Iran.

Over the past week, the conflict has escalated materially, energy infrastructure has taken fresh hits, additional U.S. forces are heading into the region, and the market has moved from assuming a short, containable episode to pricing a more protracted confrontation.

Oil, of course, is reacting accordingly.

But the important point is that this is not just a front-month panic.

The message from both Brent and WTI is that the market is increasingly pricing a disruption that will not be solved in a matter of days.

Brent settled this week at $112.19, its highest since July 2022, while WTI’s active contract closed near $98.23... traders, strategists, and even the IMF are all now framing the duration of the conflict as the key variable for growth, inflation, and monetary policy.

Pictet’s own framework is consistent with that interpretation: the initial pattern is an inflation shock followed by broad portfolio de-risking, while its economists note that a permanent 30% oil shock would lift inflation and drag GDP.

And that is precisely why equity markets are responding so negatively.

Higher oil prices are now large enough to revive a very old fear: that what starts as an energy shock quickly mutates into a growth shock.

JPMorgan has warned that stock/oil correlations typically turn much more negative after roughly a 30% oil spike, and that four of the last five major oil shocks since the 1970s were followed by recession.

But here is where we begin to diverge from the consensus.

As you know, we have been arguing that the bar is now low for positive surprises... in growth, in liquidity, and yes, even in inflation.

These weekly notes are not the place for a full exposition, we will save that for the monthly issues.

But consider the setup.

Gasoline prices have surged by nearly $1 per gallon since the war began. 55% of Americans already say rising fuel costs are hurting their household finances.

Trump’s approval on the cost of living has fallen to 29%, and only 35% approve of his handling of the economy. Meanwhile, the administration is already reaching for relief valves: it has lent 45.2 million barrels from the SPR and is discussing more measures to cap the damage.

The market is also flashing stress: the S&P 500 has now logged a fourth straight weekly decline, hit a six month low, and slipped below its 200-day moving average for the first time since the Liberation Day debacle.

So, here is our working hypothesis: we believe we are moving toward another T.A.C.O. moment... another moment when Trump, faced with rising gasoline prices, rising yields, a stumbling stock market, and a politically costly war, tries to frame the mission as accomplished and pivot toward de-escalation.

Reuters reports that some White House aides are already urging him to find an “off-ramp,” and that declaring victory and walking away is one of the live options now on the table.

That is a contrarian call, because sentiment is already deeply pessimistic and the market is increasingly leaning the other way. We may be early... but we will be here to stand accountable for the claim.

Good investing!

Vasco Marques de Freitas, CFA, CMT

Disclaimer

The information provided herein is for general informational purposes only and does not constitute financial advice or a recommendation to buy, sell, or hold any investment. It is not tailored to any specific individual or investor profile. All investments involve risks, and past performance is not indicative of future results. Before making any investment decisions, it is important to consider your own financial situation and risk tolerance. We do not guarantee the accuracy, completeness, or reliability of any information provided, and we disclaim any liability for any loss or damage arising from reliance on the information herein. Readers are advised to consult with an authorized financial intermediary before making any investment decisions.

In Part 1 - When the money breaks, we made the historical case.

America’s great Crisis eras have a habit of ending the same way: the monetary order comes under stress, the old constraints break, and a new architecture of trust takes shape.

Now comes the more controversial part.

What if this time the reset does not begin by changing the dollar itself, or even what stands behind it, but by changing the rails through which it moves?

That is the argument we make in March’s issue of VMF’s Strategic Asset Allocation, and it is why this second installment matters so much.

Most investors still imagine the next monetary reset in the old language of gold, devaluation, and dollar decline.

We are asking a different question. What if this Fourth Turning’s reset turns out to be digital, blockchain based, and crypto in nature? What if the next phase of monetary power is not built against the dollar, but around new rails that extend its reach?

And what if Washington is already telling you exactly that?

After all, they called it the GENIUS Act.

Almost as if the name itself were trying to give the game away.

Because that law may prove to be far more than a crypto footnote. It may one day be remembered as an early blueprint for the next monetary architecture: private-sector innovation, Treasury-backed stablecoin rails, and a distinctly American attempt to digitize trust without surrendering control.

Yes, that is a contrarian claim.

But then again, so was our view that energy stocks were the quintessential contrarian trade at the start of 2026. We’re comfortable standing apart from consensus, because that is usually where the asymmetry is greatest and where the crowd is slowest to look.

That is what this section explores.

In “A Fourth Turning Goes On-Chain,” we move from historical pattern to modern mechanism. From the recurring logic of monetary reset to the possibility that this one may unfold through stablecoins, blockchain rails, and a new infrastructure of trust.

Here is Part 2.

If the previous Fourth Turnings rewrote what stood behind the money, this one may end up rewriting how the money moves.

That is the leap.

And yes... it is a big one.

Most investors still imagine monetary resets the old way. They think in terms of devaluations, gold confiscations, fixed exchange rates, reserve-asset changes, and central-bank decrees. Fair enough. History gives them plenty of reasons to think that way.

But what if this time the reset does not begin by changing the unit of money... but by changing the rails through which that money circulates, settles, and extends its reach?

That, in essence, is our working hypothesis.

Last summer, in Alpha Tier, we argued that the GENIUS Act1 was historic.

We said it wasn’t “just about crypto.” We said it was about money. About power. About the United States choosing, quite deliberately, to bless private-sector stablecoin innovation rather than pursue a central bank digital currency.

At the time, that argument may have sounded bold. Today, with the benefit of a few more months of reflection, we believe it may have been even more important than we first realized.

Because in the context of this issue’s broader theme, the GENIUS Act looks less like a niche digital-asset bill... and more like a possible early blueprint for this Fourth Turning’s monetary reset.

That’s a serious claim. So, let’s build it carefully.

To begin with, what exactly is the United States doing here?

It is not launching a CBDC. It is not asking the Federal Reserve to issue retail digital dollars directly to the public. And it is not trying to smother blockchain-based money under an impossible regulatory burden. Quite the opposite.

Washington has chosen a distinctly American path: let the private sector build the infrastructure... but make sure the infrastructure is dollar-based, Treasury-backed, regulated, auditable, and fully embedded within the broader U.S. financial system.

That choice matters enormously.

It means the United States is not trying to defeat private digital money. It is trying to harness it.

This chart, taken directly from ARK Invest’s Big Ideas 2026 presentation, highlights some of the most important milestones in the adoption of stablecoins:

And that is where Brent Johnson’s recent work becomes so interesting.

Johnson has made the case that stablecoins may prove as transformative as Bretton Woods or the move off the gold standard. In his framing, the market still treats stablecoins as a crypto side-show, when in reality they may be the embryo of an entirely new monetary architecture.

His key insight is that the private market has already built a parallel system... and now the state is moving to co-opt it, regulate it, and scale it.

In other words, what began as an experiment outside the walls of the traditional system may now become one of the mechanisms through which the traditional system renews itself.

That is a very Fourth Turning idea.

A crisis era does not merely destroy institutions. It forces them to adapt, absorb, and reassert themselves through new forms. Johnson’s stablecoin thesis fits that pattern perfectly. In his view, stablecoins are not just a source of incremental Treasury demand.

They are potentially the next rail of dollar hegemony: a digital infrastructure layer through which the dollar can travel faster, farther, and more efficiently than before... perhaps even reaching millions of people globally who cannot access traditional dollar banking today.

Pause there for a second.

If that sounds like a kind of monetary renewal, that’s because it is.

But Johnson is not the only one thinking in these terms.

Michael Every2 takes the same core phenomenon and pushes it into even more explicitly geopolitical territory. His argument is not simply that stablecoins are new plumbing. It is that they may become a new instrument of economic statecraft. He does not see the endgame as a CBDC either.

In fact, he sees something closer to a revival of private money operating under the shadow and protection of imperial power... much like the old sterling-bill system before the First World War.

In that world, private instruments carried the monetary influence of the hegemon far beyond its borders, lubricating trade and finance while reinforcing the center’s dominance.

That analogy is powerful.

Because if Every is even partially right, then dollar stablecoins are not just about modernizing payments. They are about extending the dollar zone itself.

A user in an unstable local currency converts savings into a dollar stablecoin. The issuer of that coin, in turn, holds cash and short-dated U.S. government paper. The demand for the coin becomes demand for dollars. The demand for dollars becomes demand for Treasury bills. And the rails carrying those claims increasingly sit within a U.S.- regulated architecture.

That is more than innovation.

That is monetary gravity.

And this is why we keep returning to the phrase Infrastructure of Trust.

In every previous reset we examined, the ultimate question was some variation of: what stands behind the money? In the Continental era, the answer was too little. In the Greenback era, the answer became the fiscal and military capacity of the Union state.

In the Bretton Woods era, the answer was the full weight of American balance-sheet strength, gold reserves, and postwar power.

Now the question may be evolving.

Not away from trust... but toward a different layer of it.

In this Fourth Turning, the decisive question may become: through what infrastructure does trust move?

And that is exactly where stablecoins enter the picture.

They do not replace the dollar. They may re-embed it.

They do not overthrow the existing order. They may help extend it.

They do not necessarily create a new reserve asset. They may create a new settlement architecture for the existing one.

That distinction is everything.

Because if this thesis is right, the next monetary reset may not come through the “death of the dollar,” as so many now assume... but through the digitization of its distribution.

A new layer gets built on top of the existing system. A parallel set of rails emerges. At first it looks niche. Then useful. Then strategic. Then indispensable...

And by the time the crowd recognizes what has happened, the architecture of the system has already changed.

Now, let’s be clear: none of this means we should abandon nuance. There are legitimate objections, and we should state them plainly. Jim Bianco, for example, has pushed back against the simplistic version of the stablecoin story.

He is right to do so. Not every dollar that migrates into a stablecoin is necessarily “new” demand for Treasuries in a meaningful macro sense. Some of it may simply be money rotating out of banks or money market funds and into another wrapper.

He is also right to note that stablecoins, today, still serve a heavily crypto-native function.

Much of the activity remains tied to trading and on-chain liquidity rather than mainstream payments.

Fair points.

But those objections, in our view, refine the thesis more than they destroy it.

Because great monetary transitions never arrive all at once, fully formed, with unanimous approval and perfect adoption metrics. They begin at the margin. They grow through utility. They attract regulation once they become too important to ignore. And then, under the pressure of a larger crisis, they can be elevated from innovation to necessity.

That is precisely why this story belongs in a Fourth Turning framework.

The GENIUS Act did not guarantee that stablecoins become the next monetary order. But it did something very important: it moved them out of the realm of regulatory ambiguity and into the realm of national strategy. It effectively said: if these rails are going to exist, they will exist in a form that serves the dollar, supports Treasury demand, and keeps the United States at the center of the system rather than at its margins.

That is not a trivial choice.

That is a strategic one.

And it is made even more interesting by contrast. Europe’s instinct is more centralized, more bureaucratic, more defensive of the official currency. China’s instinct is more overtly state-led, more surveilled, more command-and-control. The American instinct, by contrast, is to unleash private builders... but direct them toward public objectives.

That may prove to be an extraordinarily powerful model in a world where speed, innovation, and geopolitical leverage increasingly matter more than technocratic neatness.

So, where does that leave us?

Our working hypothesis is this: the monetary reset of this Fourth Turning may not arrive first through a formal devaluation, a new gold peg, or a Bretton Woods style conference. It may arrive through the rise of regulated, Treasury-backed stablecoin rails that extend the reach of the U.S. dollar into the next era of global finance.

If that happens, the implications are profound...

It would mean that the United States had found a way, once again, to turn crisis into renewal.

It would mean that in an era of rising debt and open challenges to dollar hegemony, Washington had engineered a market-friendly mechanism to create more demand for its own liabilities while strengthening the global utility of its currency.

It would mean that blockchain technology, far from overthrowing the system, had been partially absorbed into the service of the system.

And it would mean that this Fourth Turning, like those before it, was once again pushing monetary authority toward a more centralized and more expansive form... only now through digital rails rather than through bank charters, gold decrees, or treaty conferences.

That is why we believe this topic matters so much.

Not because stablecoins are fashionable.
Not because “crypto” sounds exciting.
And not because every tokenized dream will come true.

But because, for the first time, we can plausibly sketch the anatomy of what a modern monetary reset might actually look like.

And it doesn’t look like the last one.

It looks digital.
It looks private-sector driven.
It looks Treasury-backed.
It looks blockchain-native.

And, above all, it still looks unmistakably... American.

That, of course, is not the consensus view.

The consensus today is furiously rotating out of U.S. assets, questioning dollar hegemony with renewed conviction, and crowding ever more aggressively into the obvious beneficiaries of monetary disorder... precious metals above all. We understand why. Gold has earned that respect.

Our own Model Portfolio has benefited handsomely from that reality.

But if our working hypothesis is correct, then the next monetary reset may not be nearly as straightforward as the crowd imagines. It may not weaken the United States as decisively as many now expect. And it may not bypass crypto rails in favor of a purely analog hard-money response.

That is what makes this view so contrarian.

It is contrarian because it suggests that, in the middle of a broad consensus turn against the U.S., America may yet find a way to reassert monetary power through a new architecture of trust.

It is contrarian because it suggests that blockchain rails (far from being peripheral to the next reset) may sit close to its very core.

And it is contrarian because it asks investors to look past today’s crowded winners and consider whether some of tomorrow’s biggest beneficiaries still sit in the less loved corners of the field.

As you know, we have seen this movie before. Many of the theses we have shared in these pages over the past two years looked premature, eccentric, or outright unfashionable when first introduced... only to be validated later, and often violently, by the market itself.

So, what does this mean for our Model Portfolio?

Above all, it reads as a powerful endorsement of crypto rails... and of Ethereum in particular. Just revisit the charts from the last few pages, especially the one on page 10: the message is hard to miss. When it comes to stablecoins, the market is already showing a clear preference for Ethereum’s ecosystem.

And that matters enormously. If stablecoins are indeed emerging as the compliant, Treasury-backed bridge between the old financial system and the next one, then the networks most leveraged to tokenization, settlement, and on-chain financial infrastructure deserve renewed attention.

In that sense, this is not just a macro thesis. It is also a thesis update on one of the few pockets of relative weakness in our current Model Portfolio. And let us be very clear: our conviction there has not faded. If anything, after the work we have just done in this issue, it is stronger than ever.

But this section was never only about crypto.

It was also about extending our understanding of where we stand in the Fourth Turning itself. Because if we are right that this Crisis era is moving closer to its monetary climax, then there may also be room to align the Model Portfolio even more precisely with the phase of the generational cycle we are now entering.

In other words, the implications of this framework do not end with stablecoins, Ethereum, or blockchain rails. They stretch into the broader question of how a portfolio should be positioned when history begins to accelerate, when old anchors weaken, and when the infrastructure of trust starts to shift beneath our feet.

And that is exactly what we will do next.

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Looking for where to start?

Have you noticed what the dollar is doing?

Just when the consensus was lining up for its decline, the greenback has been strengthening again, the latest twist in a year that keeps punishing comfortable narratives. That, of course, is exactly the kind of setup that interests us.

This post comes from March’s issue of VMF’s Strategic Asset Allocation, and it is the first installment of a two part series. In that issue, we make what may be our most contrarian monetary claim yet: that this Fourth Turning’s reset may be digital, blockchain based, and crypto in nature, not as a rebellion against the system, but as the next infrastructure through which trust, settlement, and monetary power move.

But before making that leap, we need to start where serious investors always should: with history.

Because Fourth Turnings are not just eras of political rupture, war, and institutional stress. They are also, again and again, eras of monetary reset. The Continental collapse. The Greenback experiment. The break with gold and the birth of Bretton Woods. In every great American Crisis era, the money eventually breaks, and then gets redesigned.

That is what this first part is about.

Below, in “When the Money Breaks,” we go deep on the historical precedent for Fourth Turnings as monetary turning points, because if you want to understand what may come next, you first need to understand the pattern America keeps repeating.”

There is a pattern in American history that most investors never stop to notice.

In every great national crisis, the fighting is never confined to battlefields, cabinets, or elections. Sooner or later, it reaches the money. The old unit of trust gets stretched, discredited, devalued, or redesigned. And by the time the crisis has run its course, the country is no longer operating under the same monetary logic it had before. That is one of the great recurring signatures of a Fourth Turning: when the nation breaks, the money changes with it.

Start with the Revolutionary War1 (1775-1783).

Most people know the phrase “not worth a Continental,” but too few appreciate how violent the underlying monetary collapse really was. To finance the war, the Continental Congress relied heavily on paper emissions because it lacked a strong independent taxing power.

In the first years of the conflict, Continental dollars funded the overwhelming bulk of Congressional spending. As more notes were issued and confidence in future redemption weakened, their value eroded badly. By the end of 1777, the specie value of the Continental had fallen to about 20% of its original value, and by the end of 1779, one paper dollar was worth only about one or two cents in silver...

That is not mere inflation.

That is a collapse in monetary credibility so severe that the currency’s name entered the language as a synonym for near-worthlessness.

There is one important nuance here: the Continental was not originally conceived as a pure fiat note in the modern sense, but as something closer to a zero-interest bearer bond whose present value would naturally trade below face value ahead of final redemption. In other words, some early decline reflected discounting, not outright debasement.

But that nuance does not rescue the larger picture... once Congress altered redemption terms in 1779 and 1780 in ways that were not fiscally credible, the public’s trust snapped. Depreciation turned into collapse.

The Revolutionary monetary reset, then, was brutal but foundational: the old wartime paper failed, and the lesson it burned into the American mind was that durable money required a stronger fiscal state and a more credible public-credit architecture.

That is why the true reset after the Revolution was not simply that the Continental disappeared. It was that the United States gradually moved toward a more coherent national financial order, one built on stronger federal institutions and, eventually, Hamiltonian public credit2. The crisis destroyed one form of money and forced the young republic to think much more seriously about what had to stand behind the next one. The phrase “not worth a Continental” was the scar tissue left behind by that lesson.

Then, roughly eight decades later, the country broke again.

The Civil War (1861-1865) created another moment when the old monetary discipline could not survive the emergency.

In late 1861, specie payments were suspended... that is, the financial system stopped honoring the old hard-money promise that paper could be turned into gold or silver coin on demand. From that moment on, the dollar was no longer anchored in the same way to precious metals.

The public could still hold paper, spend paper, and receive paper... but the right to walk in and demand redemption in specie had effectively been cut off. Then came the Legal Tender Act3 of 1862, which authorized $150 million in non-interest-bearing notes that became known as greenbacks.

By the end of the war, the stock of greenbacks had risen sharply, and the federal

government had crossed a line it had long been reluctant to cross: issuing a true

national paper currency that was not convertible into gold or silver on demand. The

old money had not merely been supplemented. Its discipline had been suspended.

And what happened to purchasing power?

It eroded... fast. As the war progressed, prices expressed in greenbacks roughly doubled, meaning the paper in people’s hands was steadily losing real value.

But this was not a simple one-way story of wartime inflation. The greenback also became a live barometer of the war itself, rising and falling with battlefield developments and with the market’s confidence in the Union cause. Once redemption in gold had been cut off, the dollar no longer rested on metal alone. It rested on politics, military success, and the perceived survival of the state.

The greenback was emergency money for an emergency state, and it traded like one. But here again, the debasement story is only half the story. The deeper reset was institutional. The Civil War did not just cheapen the old money... it helped create a new monetary architecture. The National Banking Acts of 1863 and 1864 established a federally chartered banking system and pushed the country toward a more uniform national currency, reducing reliance on the chaotic patchwork of state-bank notes that had defined much of antebellum America. Then, after years of political conflict, the U.S. resumed specie payments in 1879.

So, once again, the sequence was unmistakable: suspend convertibility, issue emergency paper, suffer inflation and depreciation, then rebuild around a stronger and more centralized national monetary order.

That was the Civil War reset.

Then, roughly another three quarters of a century later, America and the world entered the next great crisis era: the Great Depression and World War II (1929-1945). Here the pattern changes form, but not substance.

In the early 1930s, the problem was not that the dollar was collapsing in domestic purchasing power. Quite the opposite: the gold standard’s rigidity was helping produce a deflationary squeeze. Banks failed, output imploded, and adherence to

gold acted less like a badge of discipline than a monetary straitjacket. Roosevelt’s response in 1933 was to break that constraint. In April of that year, he formally suspended the gold standard, halted gold exports, and stopped the Treasury and financial institutions from converting currency and deposits into gold.

Then came the decisive monetary rewrite.

The Gold Reserve Act of 1934 transferred ownership of monetary gold to the U.S. Treasury, prohibited redemption of dollars for gold, and redefined the official gold price from $20.67 per ounce to $35.

That amounted to a roughly 41% devaluation of the dollar against gold.

So, while the Depression-era reset did not begin as a classic inflationary debasement like the Continental or the Greenback episodes, it still involved a deliberate reduction in what the dollar promised in gold terms. The old gold-backed dollar lost value against its anchor, and that was no accident. It was policy.

And yet even that was not the final settlement.

As with the earlier Fourth Turnings, the true reset only became clear after the broader crisis reached its climax. Near the end of WWII, in 1944, delegates from 44 nations met at Bretton Woods4 and designed a new international monetary order.

Under that system, other currencies were fixed to the U.S. dollar within narrow bands, while the dollar itself was fixed to gold at $35 per ounce.

The old interwar gold regime had failed. In its place emerged something very different: a dollar-centered system in which American state power, American finance, and American gold reserves together formed the new infrastructure of global monetary trust.

That was the postwar reset.

Now step back and look at the pattern:

The Revolutionary crisis.

Then the Civil War.

Then the Great Depression and World War II.

Each separated by roughly the span of a long human life. Each coinciding with profound institutional stress. Each forcing the United States to confront the same question in a different form: what exactly stands behind the money? And each, in its own way, ending with a different answer than the one that existed before the crisis began.

But there is an even bigger pattern hiding inside that one. Each reset did not merely wound the old money. It also concentrated monetary power more heavily than before.

After the Revolutionary debacle, the answer was a stronger federal fiscal state.After the Civil War, it was a more centralized national banking and currency architecture. After the Depression and World War II, it became something larger still: a dollar-centered international order, with the United States sitting at the core of the world’s new monetary system. In other words, the arc was not just from one kind of money to another. It was from weaker and more fragmented monetary authority to stronger and more centralized monetary authority... first nationally, and ultimately globally.

That is the real claim we want to make in this issue.

Not that history repeats mechanically.

Not that every crisis produces the same monetary outcome.

And not that Fourth Turnings are “about money” in some narrow sense.

The deeper point is more powerful than that. When the nation enters an existential crisis, the monetary order becomes vulnerable. Old anchors fail. Emergency measures appear. Political imagination expands. And what once seemed permanent suddenly becomes negotiable.

But the resolution of the crisis tends to move in a recognizable direction: toward a new monetary settlement backed by greater state capacity, greater centralization, and, at times, a wider sphere of power than the one that existed before. That was true of the Continental. It was true of the Greenback. It was true of gold backing and Bretton Woods.

And it may prove true again. Which brings us to the most important leap of this issue: our working hypothesis for the possible anatomy of this Fourth Turning’s monetary reset... arriving, not coincidentally, roughly 80 years after the last one.

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