Every great investment theme eventually reaches an uncomfortable point.

At first, the market does not believe it. Then it questions it. Then it slowly accepts it. And finally, after prices have already moved, the same idea that once required imagination starts to feel obvious.

That is when the real work begins.

Because the goal is not to be early forever. Nor is it to remain loyal to the first expression of a thesis simply because it worked. The goal is to keep asking where the mispricing still lives.

That was the question behind almost everything we published in May.

We did not become bearish on AI. Quite the opposite. We continue to believe artificial intelligence is one of the defining investment themes of this cycle. But we also believe the market has become much better at pricing the first layer of that theme: the hardware, memory, data-center, power, and infrastructure bottlenecks that make the AI buildout possible.

Those bottlenecks were once underappreciated. They no longer are.

That does not mean they cannot keep going. It does mean the easy money, if there ever was such a thing, has already been made in some of the most visible parts of the trade.

So May became a month of rotation...

Not a retreat from AI.

A rotation inside AI.

From the part of the trade the market can now see clearly to the parts it still finds harder to underwrite.

That is the thread connecting the May issues of VMF’s Strategic Asset Allocation, VMF’s Security Selection, and Alpha Tier. And it is also the best way to read the free excerpts we published on Substack during the month.

Think of this note as the map.

Begin with the bottlenecks

The first stop is The Visible Side of the AI Boom.

This excerpt came from May’s issue of VMF’s Strategic Asset Allocation, where we revisited one of the biggest winners in our Model Portfolio: South Korea.

When we recommended South Korean equities, the idea was not to make a generic country call. It was to own one of the less obvious beneficiaries of the AI buildout. Everyone understood Nvidia. Fewer investors fully appreciated what the explosion in AI compute would mean for memory: high-bandwidth memory, server DRAM, enterprise SSDs, and the broader supply chain around Samsung and SK Hynix.

AI feels weightless to the end user. Behind the curtain, it is one of the most physical, capital-intensive technology waves the world has ever seen.

That was the opportunity.

The market eventually caught up, and South Korea moved from being an underappreciated AI bottleneck trade to one of the clearest examples of the thesis working. That created the kind of problem every investor wants to have: what do you do when you are right?

The answer is not always “hold forever.”

Sometimes the right answer is to harvest, resize, and redeploy.

That was the first lesson of May. Once the market starts agreeing with you, the job changes. Conviction got you into the trade. Discipline has to manage what happens next.

Then follow the trail to the application layer

The second stop is The Forgotten Application Layer.

If the first AI trade was about building the machine, the next one may be about using it.

That distinction shaped the entire month.

The hardware layer is easier for the market to price. More AI usage means more compute. More compute means more chips, more memory, more data centers, more power, more cooling, and more grid infrastructure. The earnings bridge is visible.

The application layer is less tidy. It is harder to know in advance which companies will turn AI into better economics. Some will use it to improve pricing, logistics, advertising, software development, customer acquisition, fraud detection, personalization, and operating leverage. Others will merely attach themselves to the narrative.

That uncertainty is precisely why the opportunity may still exist.

Markets usually pay first for what is obvious. They pay later for what still requires judgment.

In May’s Strategic Asset Allocation issue, that led us to China’s internet ecosystem. Not because China is clean or comfortable. It is neither. The property bust, regulatory scars, weak confidence, geopolitical tension, VIE structures, governance discounts, and years of foreign-capital exhaustion are all real.

But investing is not about pretending problems do not exist. It is about asking whether the market has already priced them... or over-priced them.

China may not lead the frontier-model race against the United States. But it remains one of the most important markets in the world for AI deployment. Commerce, payments, logistics, advertising, consumer platforms, manufacturing, robotics, mobility, industrial automation... these are not abstract use cases. They are exactly where AI may move from technological promise to economic value.

That was why we increased exposure to China internet in Tier One.

The free excerpt explained the thesis. The full paid issue went further: vehicle, sizing, funding source, target weight, technical setup, catalysts, and the Model Portfolio implications.

From the basket to the business

The third stop is The Index Is Expensive. The Opportunity Set Is Not.

This excerpt came from May’s issue of VMF’s Security Selection, and it addressed a mistake we think many investors are making right now.

They look at the index and conclude that stocks are expensive.

That is partly true. The major U.S. indexes are highly concentrated. The largest companies are expensive relative to the rest of the market. The most obvious AI winners have already attracted enormous amounts of capital.

But the index is not the opportunity set.

A market can be expensive and still contain mispriced businesses. In fact, the more concentrated the market becomes, the more likely it is that capital keeps crowding into what is easy to own while more complicated opportunities are left behind.

That is where security selection matters.

Not because every cheap stock deserves attention. Most cheap stocks are cheap for a reason. But when a business is controversial, cash-rich, strategically positioned, and priced as if too many things will go wrong, the work becomes worth doing.

That brought us to PDD: China Price, Temu Optionality.

PDD is not the kind of company investors instinctively place inside the AI conversation. It does not make chips. It does not own data centers. It is not a frontier-model lab.

But it sits much closer to where AI may actually be monetized: commerce, pricing, product discovery, advertising efficiency, logistics, merchant tools, demand forecasting, and transaction flows.

The market mostly sees a controversial Chinese e-commerce company with Temu risk attached. That concern is understandable. We began the paid issue with the bear case because any thesis that ignores why a stock is cheap is not analysis. It is hope.

But after confronting the risks, we asked the more useful question: what if the market is confusing discomfort with impairment?

Our view is that PDD is better understood as a marketplace intelligence engine. It sits between consumers, merchants, products, prices, advertising, logistics partners, and transactions. That is exactly the kind of system where better intelligence can become better economics.

The public excerpt gave readers the core idea. The paid issue gave subscribers the full underwriting work: bear case, base case, bull case, valuation model, cash haircut, Temu optionality, sensitivity analysis, and position sizing inside the Value Model Portfolio.

That is the difference between seeing an idea and underwriting an investment.

Finally, the Tulip Test

The last part of the month belonged to Alpha Tier.

The public trail begins with The Price of the Obvious and continues with A Pattern Recognition Exercise.

The title of the paid issue was The Tulip Test, but the point was not that AI is tulips. That would be a lazy argument.

Tulips did not change the world. AI probably will.

The better historical lesson is that real revolutions can still become dangerous when investors pay too much for the future. Railroads were real. The internet was real. Broadband was real. Each changed the world. Each also produced moments when capital rushed into the right idea at the wrong price.

That is what we wanted to test.

Not whether AI is important.

Whether parts of the AI trade have become too dependent on uninterrupted acceleration.

The answer was not simple, which is usually a good sign. AI capex expectations are still rising, which means the boom may have further to run. The application layer has not fully participated. The secular bull market may still have another powerful phase ahead.

But the first AI trade has become crowded. Earnings revisions are concentrated. Momentum is concentrated. Leadership is concentrated. And the seasonal window has become less forgiving.

So Alpha Tier did what Alpha Tier is supposed to do. It translated pattern recognition into portfolio action.

The conclusion was rotation, not retreat.

We were not selling the future. We were reducing exposure to the parts of the future the market has already discovered, adding to the application layer, and increasing liquidity because the risk/reward had changed.

The public excerpts walked through the framework. Paid subscribers received the actual tactical changes: what we trimmed, what we added, how much liquidity we increased, and what we will monitor from here.

The May reading order

Read May in this order:

1. The Visible Side of the AI Boom
The market has discovered the cleanest AI bottlenecks.

2. The Forgotten Application Layer
The next opportunity may sit where AI gets used, not where it gets built.

3. The Index Is Expensive. The Opportunity Set Is Not.
A concentrated index can hide mispriced businesses.

4. PDD: China Price, Temu Optionality
Our stock-level expression of the China application-layer thesis.

5. The Price of the Obvious
Real revolutions can still become dangerous when the price gets too obvious.

6. A Pattern Recognition Exercise
The first AI trade is crowded, but the second AI trade may still be forming.

That was May inside VMF Research.

Tier One gave us the map.

Tier Two took us from the basket to the business.

Alpha Tier made the risk-budget decision.

The month was not a collection of disconnected articles. It was a sequence: identify the visible winners, recognize where the market has already paid us, move toward the underpriced application layer, select a direct stock-level expression, and adjust the tactical portfolio.

That is what we are trying to build at VMF Research.

Not more market noise.

A process.

One that connects macro regimes with asset allocation, investment themes with individual securities, and conviction with position sizing.

Because in a market flooded with information, the scarce asset is not another opinion.

It is judgment.

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.

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