That is understandable. Investors are trained by pain, and the pain of the last few years was inflation. Every strong payroll print, every rise in oil, every upside surprise in wages, every resilient consumer datapoint was filtered through the same reflex: stronger growth means more inflation, more inflation means a more hawkish Fed, and a more hawkish Fed means pressure on risk assets.

For a while, that was the right playbook.

In June, we began testing whether it had become the wrong one.

The live hypothesis inside VMF Research was simple: the market may be vulnerable to a Dovish Shock. Not because growth is collapsing. Not because the economy is obviously breaking. Not because investors should expect a sudden return to the easy-money world of the 2010s. The point is more nuanced than that.

The market may have moved too far in pricing the wrong kind of strength.

If growth is driven by overheating, wage pressure, excess demand, and supply bottlenecks, the Fed has a problem. But if growth is increasingly supported by productivity, technology, automation, operating leverage, and better tools, then the policy equation changes. Stronger growth does not automatically have to mean higher inflation. In the right conditions, it can coexist with a softer inflation path.

That is the possibility we explored throughout June.

And if the market is priced for a hawkish shock just as the conditions for a dovish one are improving, the consequences could be substantial. Not for every asset, and certainly not in a straight line, but for a very specific group of assets that have been under pressure precisely because investors have feared tighter liquidity, higher real yields, and a Fed forced to lean harder against inflation.

That was the thread connecting our June work across VMF’s Strategic Asset Allocation, VMF’s Security Selection, and Alpha Tier.

This note is the map.

Start with the setup

The best place to begin is The Setup for a Dovish Shock.

This excerpt came from June’s issue of VMF’s Strategic Asset Allocation, where we started with the market’s reaction to stronger labor data. Payrolls surprised to the upside, prior months were revised higher, and wage pressure remained relatively contained. In an older market regime, that would probably have been received as a constructive mix: resilient employment without a clear wage spiral.

Instead, markets sold off.

Yields rose. Equities weakened. Precious metals came under pressure. Bitcoin fell. Investors treated stronger growth as an inflation threat and immediately moved toward a more hawkish Fed interpretation.

That reaction told us something important. The market was still using the inflation playbook of the last cycle. Strong data was not being read as economic resilience; it was being read as a policy threat.

Our question was whether that interpretation was too crude.

Productivity matters. If output per hour is improving, the economy can generate more real growth without the same inflationary pressure. Wages alone do not determine the inflation problem... wages relative to productivity do. If compensation rises but productivity rises with it, unit labor costs can remain better behaved than the market fears.

That is why the Dovish Shock thesis is not a fantasy about weak growth forcing the Fed to rescue markets. It is a more interesting possibility: stronger growth may be less inflationary than investors assume.

That distinction shaped the rest of the month.

The free excerpt introduced the framework. The paid issue went further, connecting the productivity evidence, the market’s policy reflex, the Fed’s likely reaction function, and the asset classes most exposed if the hawkish scare begins to fade.

Then follow the liquidity trail into crypto

The second stop is The Invisible AI Rails.

At first glance, crypto did not look like a beneficiary of anything in June. It looked wounded. Bitcoin had sold off sharply. The broader crypto universe had weakened. Flows were poor, technical damage was real, and the asset class was being treated exactly as many skeptics have always described it: speculative liquidity beta.

We did not dismiss that bear case. In fact, the weakness itself was part of the point.

When markets become nervous, crypto is easy to sell. It is liquid, volatile, widely held by speculative capital, and extremely sensitive to liquidity expectations. If investors suddenly believe the Fed may be more hawkish, crypto often gets hit first and hardest.

But that is not the whole story.

The question we asked was whether investors were selling crypto because the long-term thesis had deteriorated, or simply because the asset class was functioning as a pressure valve for tighter liquidity fears.

That distinction matters because the irony may be that investors are selling one of the least visible AI trades to fund the most visible one.

The first AI trade was about the physical buildout: chips, memory, data centers, power, and infrastructure. But an agentic economy will also need financial rails. It will need wallets, stablecoins, programmable payments, settlement layers, verification, identity, tokenized ownership, and machine-readable economic infrastructure.

If software agents are going to transact, pay, verify, settle, authenticate, and coordinate economic activity, then the digital-financial stack becomes more important, not less.

That does not make every token valuable. It does not rescue every bad crypto project. It does not eliminate volatility, regulatory risk, or speculative excess. But it does suggest that parts of the crypto stack may belong in the AI infrastructure conversation in a way the market still does not fully appreciate.

That was the point of the second excerpt.

Crypto was being sold as liquidity beta. We asked whether parts of it may also be invisible AI infrastructure.

Paid subscribers received the full bear case, the technical context, the portfolio implications, and the decision rules for what would make us more constructive or more cautious.

The shock that mattered may not have been the one investors feared

The third stop is The Hike That May Never Come.

This excerpt came after the macro setup became even more interesting.

Oil had started falling after the preliminary deal with Iran, easing one of the most visible inflation pressures in the market. At the same time, investors were still focused on the possibility that the Fed could be pushed toward a more hawkish path.

That combination was important.

A major inflation input was cooling just as markets had become increasingly worried about rate hikes. If oil continued to fall, if inflation data softened, and if productivity kept improving, then the market’s hawkish interpretation could become vulnerable. The surprise might not have to be an immediate rate cut. It might simply be the disappearance of a hike investors had started to fear.

That is why the title mattered.

The hike that may never come can be just as important as a cut if markets have already priced too much tightening.

This is where June’s work moved from macro into opportunity.

If the market is positioned for the wrong shock, the assets most punished by the hawkish scare deserve attention. That includes precious metals, miners, and other parts of the Alternatives bucket that had been under pressure as real yields, the dollar, and liquidity fears moved against them.

Gold is the monetary anchor of that world.

Silver is the accelerator.

And when silver begins to work, the right miner can offer far more torque than the metal itself.

That is where June’s paid Security Selection issue became especially important. We examined a silver miner that we believe offers one of the most compelling risk/reward setups in the precious-metals universe today. It is not the safest corner of the market... silver miners never are. They are volatile, operationally complex, and highly sensitive to the metal itself. That is exactly why they can become so powerful when the cycle turns.

Our filter was strict. We wanted real producing assets, not an exploration dream. We wanted meaningful silver exposure, enough scale and liquidity to matter, a balance sheet that does not force bad decisions during normal commodity volatility, and a technical setup already showing relative strength.

The public excerpt explained the macro door opening.

Paid subscribers received the company, the thesis, the valuation context, the technical setup, the risks, and the portfolio sizing.

Finally, widen the lens to trust

The final stop is The Only Assets That Owe You Nothing.

This excerpt came from June’s Alpha Tier issue, where we stepped back from the immediate macro debate and asked a more fundamental question:

Where should investors place their trust?

Every portfolio is built on promises. Cash depends on the currency preserving enough purchasing power. Bonds depend on issuers repaying us in money that still holds value. Equities require trust in managers, boards, founders, controlling shareholders, incentives, and capital allocation.

Gold and Bitcoin ask something different.

Gold has no management team, no board, no quarterly earnings call, and no promise to pay. Its monetary relevance rests on physical scarcity, durability, history, and the willingness of households, institutions, and central banks to hold an asset that is not another party’s liability.

Bitcoin is younger, more volatile, and more liquidity-sensitive, but it is also a portable, verifiable monetary network with digital scarcity enforced by code and distributed consensus.

The usual debate tries to turn them into rivals. Gold people mock Bitcoin as speculative code. Bitcoin people dismiss gold as an analogue relic.

We think that is the wrong argument.

Portfolio construction is not a search for one monetary religion. It is the combination of assets that can survive and benefit from different regimes.

That is why we have argued from the beginning of VMF Research that both Gold and Bitcoin deserve a role in sophisticated portfolios. They solve different trust problems. They respond to overlapping but not identical drivers. And in a world of fiscal dominance, unstable bond diversification, and recurring monetary stress, assets outside the traditional stock-and-bond framework become more important.

This was the deeper layer of the June Dovish Shock work.

If the market has over-discounted hawkish policy, alternatives can recover. But the reason we own alternatives is bigger than one Fed repricing. The traditional 60/40 portfolio is not dead, but it is incomplete. A world of higher debt, more fiscal intervention, geopolitical fragmentation, and periodic inflation stress requires a broader architecture.

Alpha Tier’s job was not simply to say “buy more gold” or “buy more bitcoin.” The question was more disciplined: does the Model Portfolio already carry enough exposure, is that exposure balanced correctly between resilience and torque, and should the next unit of capital go into physical monetary assets, higher-beta expressions, or short-duration liquidity?

Sometimes preparation justifies action.

Sometimes it justifies patience.

Paid subscribers received the full portfolio decision.

The June reading order

Read June in this order:

1. The Setup for a Dovish Shock
The market may be interpreting stronger growth through the wrong inflation framework.

2. The Invisible AI Rails
Crypto is being sold as liquidity beta, but parts of the stack may become critical infrastructure for the agentic economy.

3. The Hike That May Never Come
The market may have priced too much hawkishness just as oil, productivity, and inflation data could move the other way.

4. The Only Assets That Owe You Nothing
Gold and Bitcoin solve different trust problems inside a portfolio that can no longer rely only on stocks and bonds.

That was June inside VMF Research.

A live hypothesis.

A sequence of tests.

And a portfolio question.

The hypothesis was that the market may be vulnerable to a Dovish Shock. The tests moved through productivity, oil, the Fed, crypto, precious metals, silver, miners, Gold, Bitcoin, and the broader architecture of trust.

Free readers can follow that trail through the public excerpts.

Paid subscribers received the implementation behind it: the full asset-allocation framework, the crypto bear and bull case, the Alternatives stress test, the silver-miner recommendation, the valuation and risk work, the technical confirmation levels, the Alpha Tier model portfolio decision, and the monitoring framework we will use from here.

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

Not a collection of market opinions, but a research process that connects macro regime, asset allocation, security selection, and portfolio construction.

In June, the market was preparing for the wrong shock.

We started preparing for a different one.

Important Disclosure

This article contains general investment research and market commentary produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It is provided for informational purposes only and does not constitute personalised investment advice, portfolio management, or a solicitation to buy, sell, subscribe for, or hold any financial instrument, security, crypto asset, commodity exposure, fund, ETF, ETP or other investment product.

The article summarises themes and research published by VMF Research during June 2026 across VMF’s Strategic Asset Allocation, VMF’s Security Selection and Alpha Tier. References to securities, asset classes, model portfolios, paid research or public Substack excerpts are made to explain VMF Research’s research process and should not be treated as a complete investment thesis or as a substitute for the full paid publications.

The views expressed are stated as of the date of publication and may change without notice. Forward-looking statements, scenarios and hypotheses, including the discussion of a possible “Dovish Shock”, are inherently uncertain and may not materialise. Past performance is not indicative of future results. All investments involve risk, including the possible loss of capital. Cryptoassets, precious metals, mining equities and other alternative assets can be highly volatile.

VMF Research’s Model Portfolios are illustrative research portfolios. They do not represent client assets, personalised mandates or transactions executed on behalf of subscribers. Each reader remains responsible for assessing whether any investment is appropriate in light of their own objectives, financial circumstances, risk tolerance, liquidity needs, tax position and investment horizon.

Disclosure of interests: legal entities controlled by the author hold long positions in Coinbase Global, Inc. and in a listed Bitcoin ETP. VMF Research Model Portfolios may hold positions in securities, funds, crypto asset exposures, precious-metals instruments or other assets discussed or referenced in this article or in the paid publications mentioned above. These interests may create potential conflicts and should be considered when evaluating the analysis.

Readers should conduct their own due diligence and, where appropriate, consult an authorised financial intermediary or other qualified professional before making any investment decision.

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