The most important hypothesis we are testing right now is also one of the most consequential for portfolios: the market may be preparing for the wrong monetary shock.

Investors have spent the past several months leaning into a more hawkish interpretation of the Federal Reserve. Higher yields, a stronger dollar, tighter liquidity, and renewed fears of rate hikes have punished the assets most sensitive to that view.

We recently updated our thesis in The Hike That May Never Come, where we examined why the expected tightening could disappear before it ever arrives.

This week, we turn to two of the assets with the most to gain if that happens.

Gold and Bitcoin.

The market often presents them as rivals.

One is ancient, physical, and embedded in the world’s monetary history. The other is digital, portable, and enforced through code. Their loudest supporters argue as if investors must choose between them.

We see the opposite.

Gold and Bitcoin solve different trust problems. They draw demand from different owners. They respond to liquidity through different channels. And precisely because their drivers are not identical, they can play highly complementary roles inside a well-built portfolio.

Both have also been severely tested by the recent hawkish repricing.

Gold has suffered as real yields and the dollar moved higher. Bitcoin has absorbed the same pressure through weaker liquidity, ETF outflows, deleveraging, and fading speculative demand.

If the tightening impulse continues, both may face further pressure.

But if our Dovish Shock thesis is right, these are also two of the assets best placed to respond.

The excerpt below comes from June’s issue of Alpha Tier. It examines why the traditional 60/40 portfolio may now be incomplete, why Gold and Bitcoin belong together rather than in opposition, and what evidence would need to appear before a higher Alpha Tier Model Portfolio allocation would be supported.

The full issue goes further, showing how Gold and Bitcoin fit alongside Silver, miners, cryptoassets, and the liquidity reserve across the broader portfolio.

Here is the excerpt.

Good reading.

The traditional 60/40 portfolio has survived for a reason. Equities provided growth, while bonds supplied income and protection. For much of the past four decades, falling inflation, declining interest rates, and central-bank credibility allowed government debt to generate returns while diversifying equity risk.

That framework was highly effective in the environment for which it was designed.

But the environment has changed.

Inflation can now damage stocks and bonds simultaneously. Fiscal expansion can push yields higher even as economic growth weakens. Large public-debt burdens restrict the freedom of central banks and make financial markets increasingly dependent on accommodative conditions.

The 60/40 portfolio is not dead.
But it is incomplete.

BlackRock ( ) is now making a version of the same argument. That is significant for a firm whose iShares platform helped make low-cost equity and bond exposure the foundation of modern portfolio construction.

Its recent research compares the relatively benign regime between 2012 and 2022 with the period that followed. BlackRock’s illustrative 60/40 portfolio generated an annualized return of approximately 11.1% with volatility of 7.9% during the earlier period. Between 2022 and 2025, its return fell to roughly 6.7%, while volatility increased to 11.6%.

The portfolio delivered less return while exposing investors to more risk.

BlackRock attributes that deterioration to more frequent supply shocks, less predictable inflation, tighter monetary constraints, weaker diversification from bonds, and equity performance concentrated in a smaller number of companies.

We place the beginning of this regime change in 2020.

The response to the first Covid lockdown combined monetary and fiscal intervention on an extraordinary scale. Central banks created liquidity while governments transferred purchasing power directly into the economy. Those measures prevented a deeper immediate collapse, but they also changed the inflationary landscape and strengthened the connection between fiscal policy, central-bank decisions, and asset prices.

Inflation became more volatile. Bonds lost part of their defensive reliability. Governments became more dependent on favorable financing conditions, while central banks found themselves balancing price stability against increasingly fragile debt structures.

Assets linked to scarcity, liquidity, and monetary debasement consequently acquired a larger portfolio role. Our Model Portfolios were built for that environment. Both Tier One and Alpha Tier began with material allocations to Alternative Assets, particularly precious metals and crypto assets, to provide exposure to risks that a conventional stock-and-bond portfolio was poorly equipped to absorb.

The latest In Gold We Trust Report (IGWT)1 reaches a similar conclusion through a different framework. Its proposed “New 60/40 Portfolio” retains equities and bonds as the largest components but reduces their combined weight to 60%. The remaining allocation is divided among physical Gold, performance-oriented precious metals, commodities, and Bitcoin.

Those weights should not be copied mechanically. Portfolio construction must reflect the investor’s mandate, time horizon, valuation discipline, and tolerance for drawdowns.

The broader message is more useful: Gold and Bitcoin are increasingly being treated as structural portfolio components rather than speculative positions placed at the margins.

The historical work presented in IGWT also finds that adding Gold improved risk adjusted returns, with the strongest outcomes appearing at allocations considerably larger than the negligible weights found in many conventional portfolios. The precise optimum changes with the period and methodology, but zero was rarely the most efficient allocation.

Bitcoin extends the diversification case into a younger and more volatile monetary network. Because its ownership base, adoption curve, and market structure differ from Gold’s, the correlation between the two has remained low and unstable.

That instability is useful.

Portfolio diversification does not require assets never to rise together... it requires them not to depend entirely on the same drivers. Shared monetary forces can lift Gold and Bitcoin simultaneously, while differences in demand allow one to progress when the other is temporarily held back.

The past eighteen months illustrate the point. Gold substantially outperformed Bitcoin through much of 2025 and early 2026.

More recently, both have weakened.

The strategic question has already been answered: each deserves a place in the Model Portfolio. The immediate task is to determine whether their corrections are approaching an end and what evidence would justify allocating more capital.

Both assets were hurt by the market’s hawkish repricing. Higher nominal and real yields strengthened the dollar, tightened financial conditions, and increased the return available from conventional fixed income.

Gold entered that environment after an exceptional advance.

PHYS remains one of Alpha Tier’s strongest positions, while Silver and the Junior Miners have generated even larger gains from their original reference prices. Momentum had become extended, however, and the monetary thesis was increasingly popular.

Once rates became less supportive, the market was vulnerable to profit-taking.

Bitcoin behaved more directly as liquidity beta. ETF outflows, deleveraging, weaker sentiment across the crypto complex, and the loss of marginal demand amplified the decline.

Their technical structures tell us where confirmation must appear.

Gold retains the stronger long-term structure.

PHYS broke out from a base that developed over more than a decade, and the scale of the subsequent advance reflected the importance of that move. Its 20-month exponential moving average (green line) and 50-month simple moving average (blue line) continue to rise, while the price remains well above the original secular breakout area.

Relative strength against equities also formed a prolonged base before beginning to improve. The recent decline has interrupted that progress but has not yet reversed the longer-term relative trend.

The monthly chart therefore continues to describe a secular bull market undergoing its first meaningful test since the advance accelerated. PHYS is approaching its rising 20-month average, with broader support around the consolidation and breakout shelf formed during 2025.

Because the current monthly candle remains incomplete, an intramonth move below support would not be decisive. A recovery from this area would favor the interpretation that the decline is correcting an extended trend. A sustained monthly close beneath it would increase the probability of a deeper retest.

The weekly chart is less constructive. PHYS has fallen below its 20- and 50-week moving averages, while the rising relative-strength channel against equities has broken.

Gold has lost intermediate momentum and market leadership.

The area between approximately 32.5 and 34 now combines former price support with the two most relevant intermediate moving averages. Through reverse polarity, a zone that previously attracted buyers is likely to attract sellers as investors who failed to exit during the breakdown use a recovery to reduce exposure. Reclaiming that cluster would provide the first convincing evidence that the correction is repairing itself.

The conclusion is straightforward: the secular trend remains bullish, but the intermediate trend has not yet turned.

Bitcoin faces a more demanding repair.

The monthly chart shows Bitcoin returning to major support after losing the momentum that carried it to new highs. Price has fallen below its 20-month exponential moving average (green line) and is testing a zone containing the rising 50-month simple moving average (blue line) and the breakout area from the previous cycle.

Relative strength against equities has also broken beneath the channel that supported much of the advance, confirming that Bitcoin has lost leadership as well as price momentum.

A successful monthly defense of this area would preserve the interpretation of a severe correction within a continuing secular advance. A decisive close beneath the 50-month average and surrounding support would expose the asset to a deeper adjustment.

The weekly chart reinforces the importance of the current zone.

Bitcoin has underperformed equities since July 2025 and remains beneath declining 20- and 50-week moving averages, both of which now represent overhead resistance.

At the same time, price is testing its rising 200-week moving average (red line) near the lower boundary of support. That average has mattered across several Bitcoin cycles, although the brief break beneath it in 2022 reminds us that no technical level is inviolable. Its present significance comes from confluence with the monthly support zone and a region of substantial prior trading activity.

Holding this area would preserve the long-term thesis. A sustained weekly break would materially weaken it.

The first evidence of repair would be a recovery above the 20-week exponential moving average (green line) in the low-$70,000 region. Reclaiming the higher 50- week average (blue line) would provide stronger confirmation that the intermediate downtrend had ended.

Gold’s secular structure therefore remains clearly bullish despite the deterioration in its weekly trend. Bitcoin has suffered greater damage across both timeframes, but it is testing identifiable long-term support rather than trading in technically uncharted territory.

How those tests are resolved will depend heavily on the same macro forces that caused the damage.

Rising yields strengthened dollar-denominated assets, lifted real rates, and drained liquidity from markets most dependent on easier financial conditions. Gold absorbed that pressure through a higher opportunity cost and weaker Western flows. Bitcoin absorbed it through falling marginal demand, ETF redemptions, and deleveraging across the crypto complex.

The dollar and the long end of the Treasury market will now tell us whether that tightening impulse is approaching an end.

The dollar formed a significant reversal candle near its early-2026 low and has recovered toward broad resistance. Its 20-week moving average (green line) has turned higher, while the 200-week average (red line) near 103 lies directly ahead. The upper portion of the range that has contained the index since 2022 and the descending trendline from that cycle’s peak provide additional resistance above it.

A confirmed breakout has not yet occurred, but further appreciation would extend the tightening already visible across global markets.

Dollar strength attracts capital toward the United States, increases the burden of dollar-denominated debt, and restricts liquidity elsewhere.

Persistent appreciation also raises the local-currency cost of Gold for international buyers and increases the relative appeal of US fixed income. Bitcoin is even more sensitive because its strongest advances have generally coincided with easier dollar liquidity and a greater willingness to accept risk.

The present resistance zone therefore provides a useful decision point. A move through the 200-week average and the upper boundary of the range would prolong the tightening impulse. A rejection would remove an important obstacle, especially if long-term yields weakened at the same time.

The ten-year yield is compressing between descending resistance from its 2023 peak and a rising support line connecting the major lows that followed. At approximately 4.5%, it is again testing the upper boundary of that structure.

An upside resolution would tighten financial conditions even without another increase in the Fed Funds rate. Mortgage costs, corporate financing rates, and equity discount rates would remain elevated, while the government’s refinancing burden would continue to rise.

If nominal yields increased faster than inflation expectations, real yields would remain restrictive... the combination that has weighed most directly on Gold. For Bitcoin, higher long-term yields increase the return available from safer assets, raise the cost of leverage, and reduce the capital willing to pursue distant or uncertain payoffs.

A rejection from resistance would suggest that the hawkish repricing was losing momentum and that one of the principal sources of pressure on both markets was approaching an inflection point.

The thirty-year yield raises the stakes because it is where a market problem can become a policy problem. It is once again approaching the 5.1%–5.2% region that we have labelled the “TACO Zone.”

The name is deliberately provocative. The level itself is best understood as a policy-pressure zone rather than a guaranteed ceiling.

A sustained breakout would feed directly into mortgage rates, long-duration corporate financing, infrastructure investment, equity valuations, and federal interest expense. It would also signal that investors were demanding greater compensation for inflation, duration, or fiscal risk.

At those levels, the incentives of the Trump Administration, the Treasury, and the Federal Reserve increasingly converge.

The Trump Administration has a political interest in preventing financing costs from damaging housing, investment, and asset prices. The Treasury must preserve orderly market functioning and refinance a large debt stock. The Fed cannot ignore a severe tightening in long-term financial conditions merely because the overnight policy rate has not changed.

Possible responses include less inflationary fiscal or trade policy, adjustments to the maturity composition of Treasury issuance, more active use of buybacks, lower policy rates, or liquidity support if market functioning deteriorates.

The exact instrument matters less than the pressure behind it. The economic and political cost of maintaining a hawkish stance rises sharply when long-term yields threaten growth, financial stability, and debt sustainability simultaneously.

This concern is no longer confined to traditional fiscal bears. Some of the most influential figures in finance have begun warning about the same relationship between debt issuance, interest expense, and the stability of the Treasury market.

The feedback loop lies at the heart of Fiscal Dominance. Higher yields increase government interest expense. Greater interest expense expands future financing requirements. Additional issuance can then push yields higher still, eventually forcing a choice between monetary restraint and system stability.

The Fourth Turning framework gives that choice historical context. Governments rarely remain passive while institutional stress compounds. Political involvement expands, the boundary between fiscal and monetary policy becomes less distinct, and system preservation gradually takes precedence over earlier conventions.

Yet one element of the hawkish narrative is already beginning to soften...

Market-based inflation compensation has moved lower across several horizons. The five-year breakeven captures average inflation compensation over the next five years and is the most sensitive to current energy prices, tariffs, and cyclical conditions. The ten-year measure covers the coming decade, while the five-year, five-year forward rate isolates the five-year period beginning five years from now and therefore provides a cleaner signal of longer-term inflation credibility. The recent decline across all three suggests that investors are becoming less concerned about persistent inflation, even as nominal Treasury yields remain elevated.

Breakevens are not pure inflation forecasts. They also contain inflation-risk and liquidity premia. Their direction nevertheless creates a tension within the prevailing market narrative: investors continue to price restrictive interest-rate conditions while the compensation demanded for future inflation is beginning to recede.

That tension strengthens the asymmetry behind our Dovish Shock thesis.

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The catalyst could be softer inflation, weaker employment, lower oil prices, deteriorating credit conditions, or policy action intended to prevent long-term yields from breaking decisively higher. Markets may identify the catalyst only after prices begin to respond, but the transmission would be familiar: lower expectations for further tightening, falling real yields, weaker support for the dollar, and improved global-liquidity conditions.

Those changes would address the forces behind the recent weakness in Gold and Bitcoin.

Gold should respond most directly to lower real yields and a softer dollar. Silver and the miners would offer greater torque if the move broadened across the precious metals complex.

Bitcoin would benefit from better liquidity conditions, although a durable recovery

should also be confirmed through stronger ETF flows, improving relative strength, and broader participation across Ethereum, Solana, and the rest of the crypto market. Having already endured substantial deleveraging and sentiment damage, the sector could respond powerfully once those conditions align.

A Dovish Shock would not create the secular case for either monetary universe. That case rests on sovereign-debt accumulation, Fiscal Dominance, monetary debasement, reserve diversification, digital adoption, and the recurring need for liquidity in an increasingly leveraged financial system.

But it could remove the obstacles currently preventing those forces from reasserting themselves in price.

As the evidence we presented suggests, those conditions remain prospective rather than confirmed.

The Alpha Tier Model Portfolio already carries substantial exposure to Gold, Silver, Junior Miners, Bitcoin, Ethereum, and Solana. There is no need to increase risk merely to demonstrate conviction. The 5% allocation to short-duration Treasuries preserves optionality and pays us while we wait.

Gold must defend its long-term structure and reclaim the former-support and moving-average cluster on the weekly chart. Bitcoin must hold the convergence around its 200-week and 50-month moving averages, then recover its 20-week trend with improving relative strength.

At the macro level, a rejection in the dollar and long-term Treasury yields would strengthen the case that the Dovish Shock was beginning to appear in market prices.

Simultaneous breakouts in both would argue for continued restraint.

Until then, patience is the position.

That discipline completes the first side of The Architecture of Trust. Gold and Bitcoin are distinctive because neither depends on a sovereign or corporate issuer honoring a promise. Their scarcity exists independently of a government balance sheet, a management team’s judgment, or a board’s willingness to treat shareholders fairly. That characteristic becomes more valuable when confidence in institutions, currencies, and fiscal discipline begins to weaken.

The operating businesses through which we express parts of these themes are different. Miners, exchanges, custodians, and other listed companies return capital to the hands of executives, boards, founders, and controlling shareholders.

Scarcity is no longer enough.
Ownership, incentives, governance, and capital allocation become decisive.

The next question is therefore not what we trust.
It is whom.

Important Disclosure

This article contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances or tolerance for risk. The analysis and Alpha Tier Model Portfolio information are stated as of 26 June 2026, 4:00 p.m. Eastern Time. The Model Portfolio is an illustrative research portfolio and does not represent client assets or transactions executed by VMF Research. Gold, Bitcoin and related financial instruments are volatile and may generate substantial losses. The views expressed may change as market conditions and evidence evolve and are reviewed through VMF Research’s monthly, weekly or ad hoc publications.

Disclosure of interests: legal entities controlled by the author hold long positions in Coinbase Global, Inc. and in a listed Bitcoin ETP. These interests may create potential conflicts and should be considered when evaluating the analysis.

Past performance is not indicative of future results. Readers should conduct their own analysis and, where appropriate, consult an authorised financial adviser.

A contrarian thesis is easy to admire before the market tests it.

The real work begins when the tape turns hostile.

Over the past two weeks, readers of VMF’s Market View have watched our Dovish Shock thesis develop in public.

It began with the labor market. Payrolls surprised to the upside, wage pressure remained relatively contained, and investors treated economic resilience as an inflation problem.

Then came The Setup for a Dovish Shock, where we argued that improving productivity, technological investment, and changing supply dynamics could eventually force the market to reconsider the policy path.

In The Invisible AI Rails, we showed why that debate reaches far beyond the bond market. Crypto, precious metals, software, China internet, and other liquidity-sensitive assets are already trading inside it.

Then Kevin Warsh chaired his first Federal Reserve meeting.

The market’s verdict was immediate.

Hawkish.

Rates remained unchanged, but almost everything surrounding the decision pointed toward tighter policy. Forward guidance became thinner. Price stability took center stage. The new projections leaned toward rate increases. Traders rapidly pushed the probability of a hike even higher.

On the surface, our thesis had just met its contradiction.

Good.

That is when a thesis becomes useful.

The first reading of the meeting was clearly hawkish. The second was more interesting.

The Committee was divided almost perfectly between higher rates and no change. Warsh declined to submit his own projection. He described the forecasts as being written in pencil, with large erasers. And one of his first acts as Chair was to reopen the Fed’s frameworks for productivity, inflation, employment, economic data, communications, and the balance sheet.

Meanwhile, the energy shock that helped drive the inflation scare began to reverse.

Oil surrendered much of its war premium as the preliminary agreement between the United States and Iran reopened the Strait of Hormuz, improved the outlook for supply, and weakened one of the strongest arguments for renewed tightening.

The market is now pricing a hike just as one of the forces behind that hike may be disappearing.

That does not guarantee a dovish outcome.

It creates asymmetry.

A Dovish Shock does not need to begin with an emergency cut or a dramatic surrender from the Fed. It can begin much more quietly, with investors discovering that the expected hike is never coming.

When the market moves from pricing one or two increases to accepting that rates may simply remain unchanged, financial conditions can ease before the Fed cuts a single basis point.

That is the setup explored in the excerpt below.

It is our post-mortem on Warsh’s first meeting, the split inside the Committee, the reversal in oil, and the market reaction that may matter more than the headline itself.

Read it with one question in mind:

What happens when the most widely anticipated rate hike of the year quietly disappears?

The full June issue of VMF’s Security Selection goes one decisive step further. We identify the stock-level expression we believe offers exceptional torque if this repricing begins, build the complete bear, base, and bull valuation, and add the position to one of our Model Portfolios.

This free excerpt gives you the macro stress test.

Paid subscribers received the investment decision.

Good reading.

If you have already read the June issue of VMF’s Strategic Asset Allocation, you know where we stand: we believe the market remains vulnerable to a dovish shock.

Not merely one interest-rate cut appearing slightly earlier than expected. We are referring to something broader: a potential reset in the way investors think about inflation, economic growth, and monetary policy

The market has spent much of the past several months returning to a familiar equation:

• Stronger growth means higher inflation.

• Higher inflation means a more hawkish Fed.

• And a more hawkish Fed means higher rates for longer... or even renewed rate increases.

Our working hypothesis is that this equation may be incomplete.

If economic growth is increasingly being driven by productivity, technological investment, and improved operating efficiency, then stronger growth does not necessarily need to produce stronger inflation. The economy may be capable of growing faster without generating the same degree of wage pressure and pricing power that investors have learned to fear.

We will not repeat that entire thesis here.
That was Tier One’s job.

But only one week has passed since we published it, and enough has already happened to put the hypothesis through a serious test.

Kevin Warsh has now chaired his first Federal Open Market Committee meeting.

And the market’s verdict was immediate:

Hawkish.

The Fed kept interest rates unchanged at 3.50%–3.75%, which surprised no one.

Everything surrounding that decision did. The statement placed far greater emphasis on price stability, while conventional forward guidance disappeared.

The projections were equally striking... but less one-sided than the headline suggested. Nine policymakers expected at least one interest-rate increase before the end of 2026, with six anticipating more than one. Yet eight projected no change, while only one expected a cut.

Warsh repeatedly stressed that inflation remains too high and that the Committee will deliver price stability.

• The dollar jumped.

• Treasury yields moved higher.

• Equities fell.

• Gold, silver, Bitcoin, and Ethereum were all sold.

At first glance, this appears to be the exact opposite of the Dovish Shock we described last week.

And we need to acknowledge that, honestly.

The Dovish Shock did not arrive at Warsh’s first meeting. Quite the opposite. At the very least, a mini hawkish shock did. But that is not the end of the analysis.

It may only be the beginning.

Gold, silver, Bitcoin, and Ethereum are among the market’s most sensitive barometers of real rates, dollar liquidity, monetary expectations, and risk appetite.

If the Fed had delivered an unequivocally hawkish regime change (and if investors had accepted that a durable tightening cycle was now beginning) we would have expected these assets to break decisively lower.

They did not.

All four sold off after the meeting. But, as of this writing, none has made a fresh lower low. Each remains above an important area of support, and the broader technical structures remain constructive.

That is not proof that our thesis is correct.

Markets can pause before falling again.

Support can break.

And one day of resilience should never be mistaken for final confirmation.

But the failure to make new lows matters because the meeting produced nearly every headline a precious-metals or crypto investor should have feared: a stronger dollar, higher yields, renewed hike projections, no forward guidance, and a chair determined to re-establish the Fed’s inflation-fighting credibility.

The assets bent. They did not break.

That suggests at least part of the hawkish message may already have been reflected in positioning and prices. It also suggests that investors may have heard more in Warsh’s press conference than the headline summary captures. Because although the immediate policy message sounded hawkish, the institutional message was far more nuanced.

Warsh confirmed something we have been expecting:

A new era at the Federal Reserve has begun.

Powell’s Fed spent years guiding markets toward its next decision. Officials communicated frequently. Policy changes were prepared through speeches, interviews, carefully adjusted statements, and a dot plot that investors often treated as a roadmap.

Warsh appears to have a very different philosophy.

The Fed may no longer hold the market’s hand.

Forward guidance, in its existing form, is gone.

Warsh refused to offer a view on the next meeting. He did not submit his own interest-rate projection. He said he did not regard the dot plot1 as particularly helpful and heard little conviction behind the forecasts his colleagues submitted.

That last point deserves attention.

The headline was that nine officials projected at least one hike. But eight projected no change, one projected a cut, and the chair himself declined to participate. That is not a Committee united behind a tightening cycle. It is a Committee divided almost perfectly in half, with an unusually consequential missing vote.

And Warsh described the forecasts as being written in pencil. With large erasers.

The dot plot delivered a hawkish shock, but even the new chair warned investors not to mistake it for a commitment.

Warsh also announced five major reviews covering the Fed’s communications, its balance sheet, the economic data it uses, its productivity and employment framework, and its understanding of inflation.

That list could hardly be more relevant to the thesis we presented last week.

One of those reviews will specifically examine the reach of artificial intelligence and other general-purpose technologies across productivity and employment.

That is a direct validation of our working hypothesis.

We have argued that the Fed and the market may be relying on economic relationships that no longer describe the economy particularly well. Payroll growth does not automatically imply wage inflation. Stronger output does not automatically imply overheating. And technological diffusion may be changing both the productive capacity of the economy and the bargaining power of labor.

Warsh is not yet using that argument to justify lower rates.

We should not pretend otherwise.

At his first meeting, he emphasized inflation, not productivity, as the immediate policy priority.

But the fact that the new chair is reopening the Fed’s productivity, jobs, inflation, data, communications, and balance-sheet frameworks simultaneously tells us that he does not believe the old framework is adequate.

That is important.

The policy may look hawkish today.

But the analytical machinery beneath it is being rebuilt. And because the Fed will now provide markets with less guidance, future changes in that framework may not be introduced gradually.

Under Powell, the Fed usually prepared investors before it changed direction. Under Warsh, investors may be forced to discover that change through the data, and through the decision itself.

That will probably mean more volatility.

But more volatility also means more opportunity.

If markets receive less information, expectations can move further away from reality. Positions can become more crowded. Wrong assumptions can survive for longer. And when the underlying facts finally force a reassessment, the adjustment can become far more violent.

The absence of forward guidance cuts both ways.

The first surprise was hawkish.
The next one does not have to be.

And almost on cue, the first major challenge to the Fed’s newly hawkish narrative has already arrived. Because, just as policymakers marked up their inflation and interest-rate projections, the energy shock underpinning much of that concern began to reverse.

The war with Iran has reached its most significant diplomatic milestone so far.

The United States and Iran have signed a memorandum of understanding intended to end the conflict, reopen the Strait of Hormuz, restore maritime traffic, permit renewed Iranian oil exports, and create a 60-day window for negotiating a broader agreement.

This is not yet peace.

Either side can still walk away. Implementation disputes remain. Israel and Lebanon could complicate the ceasefire. Shipping may take time to normalize. And President Trump has made clear that military action could resume if the agreement fails.

But markets rarely wait for certainty. They react to changes in direction.

And the direction has changed materially.

As the previous chart shows, oil prices have continued to retreat and are now testing major technical support, including the 200-day moving average, represented by the red line.

Brent, in particular, has surrendered most of its war premium and returned toward the zone from which the conflict-driven advance began. At the same time, the fundamental backdrop is softening: Iraqi production is recovering, tankers are repositioning toward the Gulf, Iranian exports may resume, and the IEA now expects global oil consumption to fall far more sharply than it previously projected, while warning that a successful peace agreement could contribute to renewed oversupply in 2027.

And here lies the tension at the heart of the current setup: the Fed’s hawkish shift was heavily influenced by the energy shock.

The new projections were formed in a world of disrupted Gulf supply, a closed Strait of Hormuz, oil above $100, rising headline inflation, and fears that higher energy costs would spread through the rest of the economy.

That world may already be changing.

The Fed itself acknowledged that part of the recent inflation increase reflected sector-specific supply shocks, including energy. If oil continues to fall, the market will eventually have to answer an uncomfortable question:

Why should a temporary supply shock produce a durable tightening cycle after the shock itself has begun to reverse?

That is where the Dovish Shock setup we presented last week becomes more, not less, interesting.

The market has now moved beyond merely fearing higher rates. As the table below shows, it is pricing a hike.

And that is what makes the setup increasingly asymmetric.

The hurdle for another hawkish surprise has risen.
The hurdle for a dovish one has fallen.

Remember, the first stage of the Dovish Shock we anticipate does not require the Fed to begin cutting rates tomorrow. It requires only a change in the direction of expectations.

If the market moves from believing that the Fed’s next move will be a hike to accepting that policymakers are likely to remain on hold, financial conditions can begin easing long before the first rate cut arrives. And if inflation continues to cool, that pause could eventually give way to outright easing.

That is how a Dovish Shock can begin without the Fed cutting a single basis point.

The latest In Gold We Trust Report captures the broader mechanism particularly well. As the chart on the previous page shows, the sequence is often straightforward:

• An energy shock lifts inflation.

• Higher inflation tightens financial conditions.

• Bond and credit markets come under pressure.

• Investors sell liquid assets.

• And, eventually, policymakers are forced to respond.

We are not claiming that every stage must unfold exactly as it did in 2008 or 2020. But the broader lesson is important: the assets that suffer during the tightening phase can become the largest beneficiaries when the policy response changes.

We already own several positions that should benefit disproportionately if our Dovish Shock thesis proves correct.

Coinbase gives us leverage to a recovery in crypto liquidity and trading activity.

Bitmine offers even more direct exposure to Ethereum. And Altius Minerals remains our preferred high-quality, royalty-based expression of the long-term precious metals and real-assets cycle.

But this time, we want something different... We want silver torque.

We want a company whose earnings, cash flow, and equity value can respond far more aggressively than the metal itself if silver resumes its advance.

The top-down setup may be approaching a turn.
The bottom-up case is already in place.

And when both begin pointing in the same direction, that is when VMF’s Security Selection should act.

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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.

“There is nothing more deceptive than an obvious fact.”

Arthur Conan Doyle

Kevin Warsh’s first FOMC meeting appeared, at first glance, to challenge one of our highest-conviction working hypotheses: That markets are vulnerable to a Dovish Shock.

The Federal Reserve left interest rates unchanged, but the tone was unmistakably different. Forward guidance disappeared. Price stability moved to the center of the message. And the latest projections suggested that a meaningful portion of the Committee now sees higher rates before the end of the year.

The market’s verdict was immediate.

Warsh had introduced a hawkish bias. But is that really the whole story?

Warsh launched five reviews covering communications, the balance sheet, data, productivity and employment, and inflation.

Two are especially relevant to our thesis. He argued that AI-driven productivity could allow strong growth, low inflation, and high employment to coexist... precisely the framework we outlined in June.

He also questioned the Fed’s reliance on lagging, heavily revised statistics, favoring more real-time private-sector data and AI-enabled analysis.

A Fed looking through the windshield may reach very different conclusions from one staring into the rear-view mirror.

This does not make Warsh dovish yet. But the market may be extrapolating too much from one press conference.

Elsewhere, Asia produced a striking divergence.

Chinese technology stocks continue to fall. KWEB has broken below its rising channel, sentiment remains exceptionally poor, and the weekly RSI is deeply oversold.

The prevailing consensus is clear: China is uninvestable.

We disagree.

Chinese technology remains one of the cheapest, most unloved, and potentially powerful expressions of the AI application layer.

The technical picture is weak, but that weakness is precisely what creates the asymmetry.

Japan sits at the opposite extreme. The yen keeps depreciating. Sovereign yields keep rising. Yet Japanese equities continue making higher highs. Our exposure through EWJ remains one of the Model Portfolio’s strongest pockets of relative performance.

While investors debate the macro contradictions, price keeps rewarding Japanese corporate reform and improving profitability.

Finally, both senior and junior gold miners ended the week with powerful reversal candles. GDX briefly broke below its widely watched 50-week moving average, rejected those lower prices, and closed back above support.

That is the kind of weekly hammer we notice.

It is not confirmation that the correction is over. But it may signal that sellers are exhausting themselves precisely where pessimism has become extreme.

And... it may be an early indication that our Dovish Shock thesis remains alive.

Warsh’s first appearance sounded hawkish. But his emphasis on productivity, real-time data, and the compatibility of strong growth with lower inflation suggests his reaction function may ultimately prove far more dovish than the market anticipates.

Stay tuned.

Good investing!

Vasco Marques de Freitas, CFA, CMT

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.

Crypto has spent the past few months doing what it does best when confidence breaks: punishing believers, rewarding cynics, and making every long-term thesis look foolish.

That is exactly why this moment deserves serious work.

The easy response is ideological. The bulls dismiss the damage. The bears declare the entire cycle finished. Neither approach is useful when capital is at risk.

So, in this excerpt from June’s issue of VMF’s Strategic Asset Allocation, we begin with the evidence against us.

The bear case is deep.

A hawkish repricing of the Fed. ETF outflows. Retail capitulation. Speculative deleveraging. Technical breakdowns. Capital being drained toward the most visible AI trades and mega IPOs. New questions around Bitcoin treasury companies. Protocol-security failures. The long shadow of quantum computing.

These are not imaginary risks, and they should not be waved away by anyone serious about investing in the asset class.

But price damage and thesis damage are not always the same thing.

Crypto is still being priced largely as a volatile liquidity trade. When yields rise, liquidity expectations weaken, or investors need cash, it gets sold quickly and indiscriminately.

Beneath that liquidation, however, a more important use case may be taking shape.

AI Agents will need to do more than generate answers. They will need to authenticate, hold value, make payments, settle transactions, access paid services, prove authorization, and interact economically with other machines.

That requires rails.

Wallets. Stablecoins. Digital identity. Programmable payments. Verifiable settlement. Blockchains capable of operating at software speed.

The market currently sees speculative wreckage.

We see parts of a financial stack that may become increasingly important to the agentic economy.

That does not mean every token wins, every network matters, or every decline should be bought. It means the long-term utility of the infrastructure may be improving while the short-term price action is deteriorating.

That tension is where important bottoms are often formed.

We may now be approaching the kind of bottom that investors can actually monetize, rather than recognize comfortably months later. Confirmation still matters. Position sizing still matters. The chart still matters. But the asymmetry is becoming increasingly difficult to ignore.

The analysis that follows presents the full bear case before making the bull case.

Because conviction that cannot survive hostile evidence is not conviction.

It is marketing.

Good reading.

Crypto does not look like an AI trade right now.

That is precisely why we need to study it.

The market already knows how to price the visible side of AI. It can see the chips, the memory, the data centers, the grid constraints, the power demand, and the physical infrastructure behind the intelligence layer. That was the first trade. And as we have explained, we owned that trade through less obvious expressions before the market fully caught up.

EWY ( ) was the cleanest example.

We did not own South Korea as a generic country allocation. We owned it because the market had not yet understood the importance of memory inside the AI buildout. High-bandwidth memory, DRAM, NAND, advanced packaging, Samsung Electronics, SK Hynix... these were not side characters. They were part of the physical stack required to make AI scale.

Then the market learned.

The position delivered extraordinary returns.

And we closed it.

Not because the thesis failed.

Because the thesis worked.

That is the lifecycle we want. Find the hidden bottleneck, let the market discover it, and harvest when the asymmetry changes.

Since then, we have been moving toward the less visible AI trades.

Software was one.

When we added IGV ( ), the market was still treating software as an obvious AI loser. That was too blunt. Some software businesses will absolutely be disrupted. But others may become more valuable as AI improves their products, lowers development costs, automates workflows, and turns static software into intelligent software.

China’s application layer was another.

Through KWEB, and then more directly through PDD Holdings in VMF’s Security Selection and Alpha Tier, we moved toward platforms where AI can become monetization: better discovery, better pricing, better advertising, better merchant tools, better logistics, better fraud detection, and better matching between consumers and supply.

Revisiting the structural case for PDD:

Now comes the next question.

If the first AI trade was about building the machine. And the second AI trade is about using the machine. What is the third?

Our answer may sound strange at first: The rails that allow autonomous software to transact.

That brings us to crypto.

But before we make the bull case, we need to begin with the bear case. Because the bear case is real.

Why Crypto is Falling.

Bitcoin is not falling randomly.

The broader crypto universe is not under pressure because investors suddenly forgot the long-term promise of digital assets. Several forces have converged at once, and together they have created one of the most important tests for the asset class since the launch of the spot Bitcoin ETFs.

The first force is macro.

Crypto remains one of the purest liquidity assets in the world. It may have a long-term monetary role. It may have a long-term technological role. It may eventually become part of the infrastructure of digital settlement, tokenization, and agentic commerce.

But today, in public markets, it still trades like high-beta liquidity.

When investors expect easier money, Bitcoin usually benefits. When investors price a more hawkish Fed, Bitcoin usually suffers.

That is exactly what happened after the latest employment report.

Stronger payrolls pushed yields higher, increased the probability of rate hikes, and forced investors to reprice the liquidity path. Bitcoin, Ethereum, Solana, crypto equities, and smaller tokens all felt the pressure.

The market did not need to decide that crypto was broken. It only needed to decide that liquidity was less friendly than it thought.

That was enough.

The second force is flows.

Recent ETF data show that investors have been pulling capital from Bitcoin products at a significant pace. This is important because the ETF structure changed the market. It made Bitcoin easier to own. It brought new capital into the asset. It legitimized the allocation for a wider investor base.

But that channel works both ways.

The same wrapper that makes it easier to buy also makes it easier to sell.

When macro pressure rises, when sentiment deteriorates, or when investors need liquidity, ETF flows can quickly become a pressure valve. In that sense, Bitcoin’s institutionalization is not only a source of upside. It is also a source of faster, more visible outflows during stress.

The third force is sentiment.

Retail sentiment is in the dust.

The speculative energy that surrounded crypto earlier in the cycle has faded dramatically. Funding rates have weakened. Outflows have intensified. The broader crypto universe has underperformed. And the psychology has shifted from excitement to exhaustion.

That matters because crypto is still heavily influenced by marginal retail buyers.

In equities, a deep institutional base can sometimes absorb retail fatigue. In crypto, retail and speculative capital still matter enormously at the margin. When those buyers disappear, prices can fall faster than fundamentals change.

The fourth force is speculative deleveraging.

Crypto sits inside a broader risk-taking ecosystem. It overlaps with leveraged ETFs, 0DTE options, meme stocks, high-beta growth, disruptive technology, altcoins, crypto treasury companies, and retail momentum.

When that ecosystem comes under pressure, crypto usually feels it quickly.

The market does not deleverage politely. It sells what is liquid. It sells what has volatility. It sells what can be turned into cash. It sells the assets with the most emotional ownership.

Bitcoin may be the institutional anchor of crypto, but the broader universe is still full of leveraged beta. Ethereum, Solana, smaller tokens, miners, treasury companies, and crypto-linked equities can all behave like amplified expressions of the same liquidity cycle.

That is why crypto selloffs often feel disorderly. They are not only fundamental repricings... they are positioning events.

The fifth force is liquidity absorption from the obvious AI trade.

This may be the most interesting pressure point of all.

The market is preparing for enormous capital events tied to the most visible parts of the technology boom: mega-IPOs, AI infrastructure financings, data-center-related equity raises, and the public-market arrival of companies investors already associate with the future.

Capital is not infinite.

If investors want to buy the next obvious AI story, they need cash. And when they need cash, they sell what they can.

Crypto is liquid. It trades constantly. It is volatile. It is widely owned by speculative capital. It is easy to use as a source of funds.

That gives us the irony at the heart of this issue:

The market may be selling one of the invisible AI trades to fund the obvious one.

The sixth force is Strategy .

This point requires nuance.

Strategy’s recent Bitcoin sale was tiny relative to its total holdings. Financially, it was almost irrelevant. Psychologically, it mattered much more...

For years, the company’s Bitcoin strategy was built around a simple message: accumulate, hold, never sell. So, when even a small sale occurs, investors begin to ask questions that did not seem urgent before.

• Could Bitcoin treasury companies become marginal sellers?

• Could preferred dividends, leverage, capital-structure complexity, and market discounts create new pressure points?

• Could the financialization of Bitcoin introduce reflexive downside channels that did not exist in earlier cycles?

This does not invalidate Bitcoin. But it does remind us that every form of institutional adoption brings new market plumbing. And market plumbing can transmit stress in both directions.

The seventh force is protocol-security risk.

The recent Zcash episode was not a Bitcoin event. It was not even a crypto-wide protocol failure. But markets are not always careful during periods of stress. A critical vulnerability in a major privacy chain reminded investors that complex cryptographic systems can contain hidden risks, even after years of operation and audits.

That is especially relevant because many of the most exciting crypto use cases depend on technical complexity: zero-knowledge proofs, privacy pools, bridges, smart contracts, rollups, custody systems, interoperability layers, and programmable settlement.

Complexity creates power.

It also creates attack surfaces.

This does not mean the entire sector is unsafe. It means the sector is still young enough that security, verification, governance, and upgradeability remain central investment risks.

The eighth force is quantum computing.

This is not an immediate Bitcoin death sentence. It is often exaggerated by critics and misunderstood by casual observers. But it is not irrelevant. Bitcoin’s proof-of-work mining mechanism is far less vulnerable to practical quantum attack than many headlines imply. The more serious long-term concern is signature security: whether sufficiently powerful quantum computers could eventually threaten the elliptic-curve cryptography used in many digital-asset systems.

That risk is not here today.
But markets discount the future.

As quantum progress continues, institutions will increasingly ask whether digitalasset networks have credible paths toward quantum-resistant upgrades. The answer may ultimately be yes. Open-source networks can adapt. Cryptography evolves. Post-quantum standards are being developed. But the transition will require governance, coordination, incentives, and time.

So, quantum risk should not be treated as a reason to dismiss crypto. It should be treated as a reason to demand maturity from it.

Finally, there is the chart.

Bitcoin is testing a major long-term support zone, but the weekly and daily charts have already suffered meaningful damage. The broader crypto universe remains weak. Several support levels have broken. Momentum has deteriorated. And the burden of proof has shifted back to the bulls.

That is the bear case.

1. Macro pressure.

2. ETF outflows.

3. Retail capitulation.

4. Speculative deleveraging.

5. AI-related liquidity absorption.

6. Strategy (MSTR) narrative damage.

7. Protocol-security concerns.

8. Quantum overhang.

9. Technical weakness.

None of this should be dismissed.
But none of it necessarily destroys the long-term thesis either.

And that is where the opportunity begins.

The Bull Case Begins Where the Bear Case Ends

The strongest bull markets do not usually begin when the headlines look clean. They begin when the market has good reasons to be worried, but the price has already absorbed too much of the worry.

We strongly believe that may be the situation today.

Crypto is being treated as broken liquidity beta at the exact moment when its long-term relevance may be increasing. The market is looking at Bitcoin and seeing a risk asset that sold off when yields rose. It is looking at altcoins and seeing speculative wreckage. It is looking at ETF outflows and seeing disappointment. It is looking at Strategy and seeing a crack in the “never sell” story.

All of that is visible. What may be less visible is the infrastructure being built beneath the surface. Because AI is changing the question.

For most of the internet’s history, the problem was information.

• How do we create it?

• How do we organize it?

• How do we distribute it?

• How do we search it?

• How do we monetize attention?

AI changes the problem.

When intelligence becomes abundant, the next bottleneck shifts toward action. A chatbot answers. An agent acts. An agent can search, compare, negotiate, book, pay, settle, verify, authenticate, and interact with other software systems. It can make decisions inside defined limits. It can use tools. It can transact on behalf of users or organizations.

Once that happens, the internet needs more than information rails. It needs economic rails.

That is where crypto becomes interesting again. Not the casino.

The rails.

AI agents need ways to hold value, send value, receive value, verify identity, prove authorization, access paid APIs, settle microtransactions, use digital services, and interact across platforms without a human manually approving every step.

The legacy financial system was not built for that. Banks require human identity. Card networks require human approval flows. Payment processors are optimized for merchants, not swarms of autonomous software agents. Cross-border settlement remains slow. Micropayments remain awkward. Machine-to-machine commerce remains underdeveloped.

Crypto was built around a different primitive.

• A wallet.

• A signature.

• A transaction.

• A settlement layer.

• A verifiable record.

That primitive may be far more important in an agentic economy than the market currently appreciates. Stablecoins are the clearest example. They already allow digital dollars to move globally, programmatically, and with far less friction than many legacy payment systems. If AI agents need money that is always on, programmable, global, and capable of settling at software speed, stablecoins are an obvious candidate.

That is why recent infrastructure developments are so important.

Agentic payment systems are beginning to move from concept to implementation.

The relevant point is not that the current infrastructure is already mature... it plainly is not. The point is that major cloud, payments, and crypto-infrastructure companies are now building the plumbing through which AI agents can discover services, authenticate wallets, execute payments, settle in stablecoins, and receive verifiable proof that a transaction has occurred.

That matters because it hints at the emergence of a new financial layer for the internet. It will not arrive fully formed. It will be early, imperfect, and full of difficult questions around security, compliance, identity, fraud prevention, user permissions, spending limits, audit trails, and protocol design. But that is often what important infrastructure looks like before the market understands it. The first versions are usually clunky, the killer use case is rarely obvious in real time, and the investment opportunity often appears before the vocabulary around it has fully settled.

Then, gradually, the pieces begin to connect. APIs become paid resources. Agents purchase data. Models pay for tools. Software transacts with other software. Stablecoins become settlement rails. Wallets become agent accounts. Programmable blockchains become execution environments. Tokenized assets become machine-readable collateral and ownership records.

At that point, crypto stops looking only like a speculative corner of the market and starts looking like one possible financial operating system for autonomous digital activity.

That is the bull case.

Not that every token wins. Most will not. Not that every blockchain becomes systemically important. Most will not. And not that Bitcoin, Ethereum, Solana, Coinbase, stablecoins, tokenization, DeFi, and agentic payments are all expressions of the same trade. They are not.

Bitcoin is the monetary asset. Stablecoins are the payment layer. Programmable blockchains are the execution layer. Exchanges, custodians, wallets, and compliance systems are the tollbooths. Tokenized assets are the ownership layer. Together, they form a potential financial stack for a more automated internet.

The market is not pricing that today. It is pricing fear, outflows, Fed risk, technical damage, and the exhaustion of retail speculation. That is exactly why the setup deserves attention.

The market is selling crypto because it looks like liquidity beta. We are studying it because parts of the crypto stack may be becoming financial infrastructure for the agentic-AI economy.

That does not mean we buy blindly. A strong thesis does not cancel a weak chart, and a powerful long-term story does not remove the need for discipline, position sizing, and confirmation. But it does tell us where to focus our attention.

The issue, then, is not whether crypto is comfortable. It is not. The issue is whether the market is allowing near-term liquidation to obscure an emerging use case at precisely the wrong time.

That is the question we now need to answer as investors. Because identifying the invisible AI trade is only the first step. The next step is deciding what to do with it.

You might also like reading: - why the index can be expensive while the opportunity set remains attractive.

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.

“Bull markets are born on pessimism, grow on skepticism, mature on

optimism, and die on euphoria.”

John Templeton

In June’s issue of VMF’s Strategic Asset Allocation, we devoted significant attention to one of the most important divergences in markets today. AI-related assets have gone parabolic.

Cryptoassets have gone in the opposite direction.

That contrast is striking. It is also exactly why the opportunity may be so interesting. As we explained in the monthly issue, we do not see crypto as separate from the AI story. Over time, we believe it may prove to be one of its most important extensions, especially as autonomous agents, digital payments, tokenized assets, programmable money, and machine-to-machine transactions become increasingly relevant.

For now, however, the market does not see it that way.

Bitcoin remains under pressure and sentiment across the crypto complex is deeply depressed. But price is now testing a major long-term support zone. And this is not just any support zone. It brings together several important technical references: key moving averages, Fibonacci retracement levels, and the prior multi-year breakout area.

Take your pick...

There is a lot of support here, which is precisely what makes this level so important. Our view remains that it will hold. That does not mean the process will be comfortable. Important lows rarely are.

But the combination of extreme pessimism, a 50% drawdown, a major support test, and an emerging bullish momentum divergence is exactly the kind of setup we want to study closely, not abandon emotionally.

The same message is beginning to appear elsewhere in our Alternatives book. Take silver. As you can observe in the chart, PSLV just printed a large weekly hammer almost exactly where it needed to: right above the support provided by the 50-week moving average.

This is happening after a sharp correction, with momentum washed out and retail sentiment toward precious metals sitting at deeply pessimistic levels.

That matters. When an asset refuses to break down at a widely watched support level, precisely when investors are least interested in owning it, the message deserves attention. Silver is not yet out of the woods, but the risk-reward profile is improving.

Meanwhile, in our Equities book, Altius Minerals continues to shine. This remains one of our highest-conviction positions across Alpha Tier’s Model Portfolio, and the price action continues to validate that conviction.

While many cyclical stocks have struggled through the recent risk-off environment, Altius has continued to move higher and now sits close to new highs.

That is not normal.
But Altius is not a normal natural resources company. It is a royalty and streaming business with exposure to long-duration resource optionality, inflation-linked assets, and structural demand themes across the commodity complex. The market is finally beginning to understand that distinction...

We recognized it early. Now, price is doing the talking.

So yes, Alternatives have been under pressure. Crypto has tested investor’s patience. Precious metals have corrected. And leadership has remained concentrated elsewhere.

But the setup is changing.
Bitcoin is testing major support. Silver is refusing to break lower. Altius continues to defy the broader cyclical weakness. And across the Alternatives bucket, the gap between sentiment and potential opportunity is becoming increasingly interesting.

That is exactly why we are paying attention.

Good investing!

Vasco Marques de Freitas, CFA, CMT

From Market View:

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.

Headlines are a poor compass.

They tell you where attention has already gone. They rarely tell you where the next mispricing sits.

A new monthly publishing cycle begins at VMF Research with SpaceX dominating the market conversation. That matters to us because both VMF's Security Selection and Alpha Tier have carried exposure to SpaceX since the start of 2026, through a listed vehicle that also provided access to private-market leaders such as Anthropic, Revolut and others.

That is the process in one sentence: identify the theme before it becomes consensus, find the investable vehicle, size the exposure, and manage it inside a portfolio.

June’s issue of VMF’s Strategic Asset Allocation begins somewhere else.

The Fed.

Every great growth story eventually meets the discount rate. SpaceX, Anthropic, software, China internet, crypto, precious metals, long-duration equities, and private-market vehicles all depend, in different ways, on liquidity, inflation expectations, and monetary policy.

And right now, we believe the market may be preparing for the wrong Fed surprise.

That is the point of the excerpt below.

Markets recently treated stronger growth as a problem. Payrolls surprised to the upside, the economy looked resilient, and investors immediately reached for the post-pandemic playbook: stronger growth means more inflation risk, more inflation risk means a more hawkish Fed, and a more hawkish Fed means pressure on risk assets.

That reflex may be stale...

If AI, software, automation, capital spending, and operating leverage are beginning to lift productivity, then stronger growth could carry a very different macro signal. It could give the Fed more room, not less.

That is the setup for a dovish shock.

This also connects directly with the Market View we have been developing over the past few months: AI as an abundance shock, scarcity moving across the stack, visible bottlenecks becoming crowded, and the next opportunity shifting toward less obvious parts of the market.

June’s Tier One issue takes that framework into macro.

The excerpt that follows lays out the evidence, the policy logic, and the assets most exposed if the market has priced the wrong path.

Read it as a macro stress test:

What happens if the next major Fed surprise is a dovish repricing rather than another tightening scare?

Good reading.

Last week, the market gave us one of those moments that reveals far more than a hundred calm trading sessions ever could.

The latest payrolls report came in stronger than expected. On the surface, this should have been good news. A stronger labor market means greater economic resilience, stronger income formation, and a higher probability that the expansion remains intact. That is how markets used to interpret this kind of data.

The reaction was not surprising to anyone fluent in the market’s current policy reflex, but it was revealing enough to prompt Donald Trump to comment publicly...

And yet... markets fell sharply.

Equities sold off. Technology shares were hit particularly hard. Treasury yields rose. Oil climbed. Bitcoin weakened. Precious metals came under pressure. Risk assets moved into a clear defensive posture. What should have looked like evidence of economic strength was instead treated as a threat.

That is the starting point of this month’s issue.

Because when markets react perversely to seemingly good news, the key question is not simply what happened.

The key question is: what framework are investors using to interpret what happened?

The market’s logic was straightforward:
Stronger payrolls imply stronger growth.
Stronger growth implies more inflation risk.
More inflation risk implies a more hawkish Fed.
A more hawkish Fed implies higher yields.
Higher yields imply lower valuations, tighter financial conditions, and pressure on liquidity-sensitive assets.

That was the sequence.

To be fair, it was not irrational. It was the logical extension of the regime investors have been living through since the post-pandemic inflation shock. For the last few years, markets have learned to fear strength. Good news became bad news because strong data threatened to keep inflation alive and force the Fed’s hand.

That framework is now deeply embedded in prices. It explains why strong payrolls triggered a vicious risk-off move. The market did not sell off because growth was weak. It sold off because growth was interpreted as inflationary.

But what if that interpretation is too simple?
What if we are dealing with a different kind of growth impulse?

Over the past few months, we have repeatedly argued that the economy may be entering a phase that strongly resembles the Great Acceleration framework laid out by Cathie Wood and ARK Invest.

For the full context on this framework, read:

This is not a generic bullish slogan. It is a specific macro thesis. The idea is that the U.S. economy may be moving into a phase where growth accelerates not because of a classic overheating cycle, but because productivity is beginning to improve meaningfully. AI, software, automation, capital spending, and operating leverage may be combining to create a different kind of expansion than the one markets instinctively fear.

Not all growth is created equal. Some growth is inflationary. It comes from excess demand, rising wages, supply bottlenecks, and companies passing higher costs through to consumers. But some growth is productivity-driven. It comes from better tools, better systems, better software, better processes, and the ability to generate more output with fewer incremental inputs.

The first kind of growth forces the Fed to tighten.
The second kind can relieve inflationary pressure.

That is the core of the disinflationary growth thesis. And if we are right, the market may be using an old framework to interpret a new regime.

The previous chart is crucial.

As you can see, labor productivity has turned higher again. The series is volatile, of course, and no serious investor should pretend otherwise. But the broader message is increasingly difficult to ignore: productivity is no longer behaving like the stagnant variable many macro narratives still assume.

The market’s reflex remains deeply tied to a Phillips Curve1 worldview... a worldview in which stronger labor markets automatically create inflationary pressure through wages.

But wages are only part of the equation. The inflationary problem is not simply whether workers earn more. The real problem is whether compensation is rising faster than output. If productivity rises alongside compensation, unit labor costs can remain contained even in a stronger labor market.

That changes the meaning of growth.

A stronger economy with stagnant productivity can become inflationary.
A stronger economy with improving productivity is something very different.

And there is more evidence that this is not just a statistical curiosity.

The Bureau of Labor Statistics’ state-level release reinforces the point.

The breadth of the improvement is striking: output increased across the entire country, while private nonfarm labor productivity rose in 42 states. Some of the gains were unusually strong, with the District of Columbia up 5.2%, Arizona up 4.4%, and California up 4.2%.

That last number deserves special attention.

California is not a small regional footnote. It is one of the largest economic engines in the world. When a state of that size delivers productivity growth above 4%, it should not be dismissed as noise.

This does not prove that we are already in a full-blown productivity boom. The data are backward-looking. They are volatile. They can be revised. And productivity cycles are notoriously difficult to identify in real time.

But the evidence is beginning to accumulate.

Productivity is improving in the national data. It is broadening across states. And it is doing so at the same time that AI adoption, software investment, automation, and capital spending are moving from narrative to implementation.

That is no longer merely a story about what might happen someday.
It is beginning to show up in the data.

Now add the economic growth backdrop.

The Atlanta Fed’s GDPNow estimate is pointing to real GDP growth well above consensus expectations. The economy is not merely avoiding recession. It is outperforming what most forecasters had penciled in.

Here is where the market begins to panic. If growth is accelerating while the economy is already near full employment, the standard interpretation is obvious: this must be inflationary.

That fear is not unreasonable.

A stronger growth impulse can absolutely become inflationary if it is driven by excessive demand, wage pressure, and tightening capacity constraints. But that is the analytical question, not the conclusion.

Is this growth impulse demand-driven overheating? Or is part of it coming from a supply-side improvement... higher productivity, stronger capital spending, better technology adoption, and greater operating efficiency?

Those two scenarios lead to very different inflation outcomes.

They also lead to very different Fed outcomes.

Which brings us to inflation itself.

If one looks only at the official Consumer Price Index (CPI) narrative, that concern is understandable. The BLS measure is still printing at a level that keeps investors on edge.

But note the divergence with Truflation2.

The Truflation index is running meaningfully below the official BLS CPI. That discrepancy deserves attention.

The two indicators are built differently. The BLS CPI is the official benchmark. It is methodologically structured, widely followed, and crucial for policy, contracts, and market expectations. But it is also slower-moving by design. It relies on survey based collection, fixed methodology, and lagged components (especially shelter, which can keep official inflation elevated even after real-time housing pressures have started to soften).

Truflation is different. It updates more dynamically and attempts to capture price changes closer to real time. It is not a replacement for official statistics, and we should not pretend otherwise. But it can be useful when trying to detect whether inflation pressure is already fading beneath the surface.

That is why this divergence is important. If Truflation is closer to the real-time direction of travel, inflation could surprise on the downside even as growth remains stronger than expected.

That would be a major macro surprise.

Because the market is currently positioned for the opposite.

The implied probabilities for Fed hikes into year-end have increased materially. Investors are no longer merely debating whether the Fed stays higher for longer. They are increasingly entertaining the possibility that the next move could be up.

The following chart captures the market’s fear very clearly.

That is why markets sold off.

Not because growth was bad. Because growth was interpreted as inflationary. And inflationary growth was interpreted as a reason for tighter policy. That is the market’s chain of thought.

But if the growth impulse is more productivity-led than investors believe. And if inflation surprises on the downside rather than the upside… Then the market may be positioned for exactly the wrong policy path.

That brings us to Kevin Warsh.

The consensus view on Kevin Warsh is that he is hawkish. That is certainly how the market is treating him. At first glance, the assumption makes sense. He has spent years warning about inflation credibility, criticizing monetary excess, and sounding more orthodox than many investors would prefer.

But markets have a habit of flattening people into caricatures.

The deeper question is not whether Warsh has hawkish instincts.
The deeper question is how he would respond to a world in which growth is improving because productivity is improving.

That is a very different setup from a classic inflationary overheating cycle.

If stronger growth is accompanied by higher productivity, better capital allocation, and contained inflation pressure, then the case for further tightening becomes much weaker. In fact, under that scenario, the real policy surprise may not be hikes at all.

It may be cuts.

That sounds highly contrarian today. Which is precisely why it deserves attention: if the market is positioned for a hawkish Fed and instead receives a more dovish policy path, the repricing could be violent.

Risk assets would not need perfection.

They would only need the market to discover it had become too pessimistic. And perhaps our view is simply paying closer attention than consensus to what Warsh has been signaling all along: the quality of growth matters, the supply side matters, and productivity matters.

The White House is not being subtle about this. It is openly arguing that higher productivity and capital spending (particularly those associated with AI) can reduce inflationary pressure and give the Fed room to cut.

That is no longer a fringe interpretation... it is being stated explicitly. Of course, the White House has political incentives. We understand that. No serious investor should confuse political messaging with objective truth. But in a politically charged Fourth Turning environment, it would be equally foolish to ignore the direction of policy pressure.

Markets have an old adage:

Don’t fight the Fed. Don’t fight the tape.

In this cycle, we would add a third:

Don’t fight the White House.

If the administration wants lower rates, if it sees AI and productivity as disinflationary, and if the data begin to support that interpretation, the market may be setting itself up for a major upside surprise.

Because the current consensus is still anchored to the old inflationary-growth framework.

If our thesis holds, the implications are significant.

A market that has rapidly repriced toward higher rates would have to reconsider the policy path. Investors who have been selling duration, software, China internet, precious metals, and other liquidity-sensitive assets would have to ask whether they reacted to the right data with the wrong framework. And the parts of the market now being punished for their sensitivity to rates and liquidity could suddenly become the greatest beneficiaries of a macro surprise.

That is where the opportunity sits.

Not necessarily in the most obvious AI winners, where the earnings bridge is already visible, positioning is increasingly crowded, and the market has learned to pay for the story. The more interesting setup may now lie in the assets still carrying the scars of the old inflationary-growth framework: long-duration growth, where lower rate expectations can change the discount-rate math; software, where AI may turn from perceived threat into operating leverage; China internet, where the application layer remains deeply discounted; and precious metals, where a softer real-yield path could revive the monetary thesis.

But there is one area where the disconnect may be even more striking.

Because if the market is wrong about the nature of this growth impulse, if the Fed surprise is dovish rather than hawkish, and if the next AI opportunity is less visible than the last one, then the major opportunity may not be in the AI trade investors have already discovered.

It may be in the one they are currently selling.

Crypto.

—

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.

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And from VMF’s Strategic Asset Allocation:

One of the strongest pillars supporting our Great Acceleration thesis has always been the labor market. And this week, it delivered another important piece of evidence.

The latest employment report came in far stronger than expected, with nonfarm payrolls rising by 172,000 versus consensus expectations of just 88,000.

Better still, previous months were revised higher and wage growth remained relatively contained. In other words, the economy appears to be accelerating without the kind of wage pressures typically associated with an inflationary boom.

Yet the market’s reaction was anything but constructive. Virtually every major asset class sold off as investors rushed to price in a more hawkish Federal Reserve. Treasury yields jumped, equities fell, precious metals weakened, and Bitcoin, one of the hardest-hit assets of the day, retreated sharply as traders suddenly began entertaining the possibility of additional rate hikes later this year.

The reaction was severe enough to draw a response from President Trump himself.

And while we rarely focus on political commentary, his remarks reveal that he understands exactly what happened.

Markets were not reacting to stronger growth. They were reacting to the belief that stronger growth would force the Fed to tighten policy once again.

Here, however, we continue to disagree with the market.

Our view remains that incoming Fed Chair Kevin Warsh is likely to prove considerably more dovish than investors currently expect. More importantly, we believe the market may still be struggling to distinguish between inflationary growth and productivity-driven growth.

Rising employment, subdued labor costs, improving productivity, and the accelerating adoption of AI are not necessarily ingredients for higher inflation. They may instead be laying the groundwork for stronger growth alongside lower inflation pressures than most investors currently anticipate.

Meanwhile, sentiment has deteriorated rapidly. Bitcoin, in particular, now finds itself testing an important support zone precisely as pessimism approaches levels typically associated with better long-term opportunities than risks.

Taken together, the latest economic data, the market’s reaction, and the growing divergence between fundamentals and sentiment are creating one of the most fascinating macro environments we’ve seen in years.

Which is why next week’s issue of VMF’s Strategic Asset Allocation will be particularly important. We’ll revisit some of our highest-conviction theses and assess whether recent developments are strengthening (or challenging) the framework we’ve been building for months.

Stay tuned.

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.

You might also like reading:

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.

VMF Research - Top 10 Positions - May 2026

Strong enough, in fact, to make us act.

In May, in VMF’s Strategic Asset Allocation, we closed the remaining position in our South Korean equities tilt through .

Final result: +238%.

That is not a typo.

A position that more than tripled in less than 18 months has now been fully exited.

And the reason is no mystery.

We have been writing about it both in front of and behind the paywall: the visible side of the AI Trade is getting crowded.

Semiconductors have gone parabolic.

EWY’s heavy exposure to South Korea’s AI-linked hardware champions (including SK Hynix and Samsung) has been an extraordinary tailwind.

But markets have a way of changing character...

What begins as a differentiated thesis can eventually become consensus.
What begins as a powerful trend can turn into a crowded trade.
And what begins as opportunity can, at the wrong price and sentiment extreme, become risk.

Today, South Korean retail sentiment is showing the kind of euphoric, bubble-like behaviour that serious investors should not ignore.

So, we did not ignore it.
We closed the position.

To us, this was a textbook exit: a great thesis, a parabolic move, euphoric sentiment, and a return that had already exceeded even our more optimistic expectations.

But here is the important part:

We are not abandoning AI.

Far from it.

We are moving deeper into what we have been calling the invisible AI Trade.

The visible side of the AI Trade is easy to understand: chips, semiconductors, data centres, hardware, capex beneficiaries.

That trade has worked spectacularly.

But the market is now beginning to look beyond the buildout.

It is starting to ask a more important question:

Who actually benefits from the application of this technology?

That is where our focus has been shifting across VMF Research.

In April, we added exposure to the software sector through , arguing that the market had gone too far in punishing high-quality software and workflow franchises.

A little more than a month and a half later, IGV is already up more than 30%.

And yet... it still has not made it into our public Leaderboard.

That tells you something.

The Leaderboard is not the research process.

It is the visible evidence of what the research process has already produced.

Beneath it, the next generation of potential outperformers is already being seeded.

Another fast-approaching contender is a special investment vehicle we selected in January to gain exposure to the potential IPOs of SpaceX and Anthropic.

Back then, this was definitely not the fashionable trade.

Gold, miners, commodities, and the Debasement Trade were stealing most of the attention.

How times have changed.

The market now seems increasingly eager for these IPOs.

And this still-unnamed position may soon force its way into our very public Leaderboard.

That is the point.

A lot is happening inside our Model Portfolios.

We are taking profits where trades have become crowded.
We are pressing where asymmetry is improving.

And we are continuing to search for the most attractive risk/reward opportunities tied to the AI megatrend... especially where the market is starting to move beyond the obvious capex winners and toward the companies that may actually capture the value created by AI’s application layer.

The Leaderboard shows what has worked.

But the next outliers are usually born before they appear on any public table.

Stay tuned.

Vasco.

From Market View:

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 great obstacle to discovery is not ignorance... it is the illusion of

knowledge.” Daniel J. Boorstin

Before our Easter break, we devoted an entire issue of VMF’s Strategic Asset Allocation to a simple idea:

The market had become too blunt.

As AI capabilities accelerated, investors began treating large parts of the software sector as if obsolescence were already a foregone conclusion.

The market stopped distinguishing between fragile software businesses and deeply embedded enterprise platforms. If a company sold software, it was increasingly viewed as an AI loser.

That was the opportunity we identified.

And so far, the market is validating that view with force.

The daily chart below shows IGV ( ) , the vehicle we added to express this thesis. Our recommendation arrived almost exactly where the selloff exhausted itself.

What initially looked like a breakdown increasingly appears to have been a classic bear trap.

More importantly, IGV has now broken decisively above the resistance level we highlighted in April.

Our Model Portfolio position is already up roughly 36%.

And remember: this was a 4% allocation.

So, this was not a footnote. It was a meaningful Model Portfolio decision made precisely when the prevailing narrative around software was at its most pessimistic.

The weekly chart gives the move even more weight.

It places the reversal inside the timeframe that actually governs our investment horizon. And on that higher-level view, the message is cleaner still: IGV has reclaimed important ground, relative strength is turning higher, and the breakout is now visible across multiple timeframes.

That is exactly the kind of confirmation we want to see.

But the most interesting validation may not come from the chart at all. It comes from an AI-powered portfolio.

This week, the Claude Portfolio account highlighted ServiceNow as one of Claude’s highest-conviction positions.

Pause there for a second.

ServiceNow is precisely the kind of company many investors initially assumed AI would threaten. Yet here we have an advanced AI model effectively making the opposite argument.

And that is the whole point.

Building intelligent models is one challenge. Deploying them inside real organizations is another.

Enterprises still need workflow management, governance, permissions, audit trails, compliance controls, data integration, and operational orchestration.

In fact, as AI spreads through the enterprise, they may need more of those systems, not less.

That was the core of our April thesis.

The market focused almost entirely on who AI might replace. It paid far less attention to who would help companies deploy, manage, govern, and monetize AI at scale.

That distinction is now becoming visible in price. The software-is-dead panic looks increasingly premature. And as the charts in this update suggest, the market may finally be beginning to agree.

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.

You might also like reading

Markets leave fingerprints.

The names change, the technologies change, and every generation convinces itself that this time the script is different. But the same clues keep appearing before major turning points: capital rushes in, expectations climb, leadership narrows, and investors begin treating acceleration as permanence.

That is why pattern recognition matters.

It is not a crystal ball. It is a discipline. In life, it helps us recognize an old mistake wearing a new face. In markets, it helps us notice when a powerful truth is being priced as if nothing can interrupt it.

That is the spirit of this excerpt from May’s issue of Alpha Tier.

The question is not whether today’s boom is real. The question is whether parts of it have become too obvious, too crowded, and too dependent on uninterrupted acceleration.

To answer that, we study three patterns.

The first is the capital-spending cycle. Today’s AI buildout is already comparable with some of the greatest infrastructure booms in modern history. That does not make it bearish. Transformational technologies require transformational investment. But the railroad and internet cycles both offer the same warning: even when the technology changes the world, capital can still arrive too quickly, expectations can still run too far, and equities can still peak before the physical buildout does.

The second is market concentration. The current rally is being carried by a narrow group of winners whose earnings bridge to AI infrastructure is already visible. That explains the strength. It also creates fragility, because a market carried by fewer names becomes more sensitive to any disappointment in the story those names represent.

The third is the Presidential Cycle, especially the midterm-year window. Seasonality alone is not enough to build a thesis. But when a historically fragile part of the political cycle overlaps with narrow leadership, crowded positioning, speculative enthusiasm, and a politically charged environment, it becomes worth respecting.

None of this gives us certainty.

It gives us context.

And context is what investors need most when the story has become easy to believe.

In the full May issue of Alpha Tier, we translated this framework into portfolio action: what we trimmed, where we rotated, where we increased liquidity, and which signals would make us more defensive or more constructive from here.

This free excerpt gives you the pattern-recognition exercise.

Paid subscribers received the risk-budget decision.

Good reading.

In this month’s issue of VMF’s Strategic Asset Allocation, we published the chart below from J.P. Morgan’s research. It is one of the cleanest ways to understand the scale of what is now happening.

The conclusion is striking.

Measured as a share of GDP, today’s technology capex boom already exceeds the peak annual spending of some of the most famous infrastructure and innovation projects in modern American history: the Manhattan Project, the Apollo Project, the Interstate Highway buildout, the electrification wave captured in 1949, and even the broadband capex cycle of 2000.

That last comparison is especially important.

The broadband buildout was the physical layer behind the internet revolution. It was real. It was necessary. It helped create the digital world we now inhabit. But it also became one of the great capital-allocation mistakes of the late 1990s and early 2000s. Too much money chased the right idea too quickly, at the wrong price, with too little discipline...

The current AI buildout is already operating at a larger annual share of the economy than that cycle.

And when we broaden the lens, the comparison becomes even more interesting.

This second chart tracks private fixed investment in information processing equipment and software as a share of potential GDP. This broader measure captures more than just the narrow data-center buildout. It reflects the deeper capital cycle behind the digital economy: hardware, software, systems, and the infrastructure required to process information at scale.

On this measure, the current cycle has now surpassed the late-1990s and early- 2000s peak.

That moves the AI buildout from narrative to macro variable.

It means the AI-related capital cycle is no longer just something we can observe in stock prices. It is already visible in the structure of the economy.

But there is an important difference versus 2000.

The late-1990s boom was heavily populated by fragile companies, speculative business models, and young unprofitable technology firms trying to finance the future with cheap equity capital.

Today’s boom is being led by some of the most extraordinary companies ever created: Microsoft (MSFT), Amazon (AMZN), Alphabet (GOOG), Meta (META), and the broader ecosystem around them.

These are not garage-stage dreamers with no earnings. They are cash-generating machines with dominant market positions, enormous balance sheets, and strategic reasons to keep spending even when near-term returns are difficult to measure.

In our view, that makes this boom more durable.
It also makes it more dangerous to dismiss too early.

In fact, if we go further back in history, the railway manias remind us that truly transformative infrastructure booms can command an even larger share of the economy. In the British railway cycle, investment rose to more than 7% of GDP at its peak.

That is a much bigger number than today’s AI capex share.

The railway comparison does not give us a simple bearish answer.
It points instead to a useful tension.

On one hand, it suggests the AI capex boom may still have further to run. If AI is even remotely as transformative as we believe (and potentially more consequential than railroads, electricity, or the internet) then it is not absurd to imagine this buildout absorbing an even larger share of economic output before the cycle matures.

On the other hand, the railway boom also gives us the key pattern we need to monitor. Stock prices led the physical buildout.

As the chart shows, railway stocks rose before construction peaked. They also rolled over before the actual construction cycle did. The market did not wait for the final mile of track to be laid. It repriced the future first.

That is the crucial lesson.

In capital-spending booms, equities do not trade on realized capex alone. They trade on the expected path of future capex, future revenues, future margins, and future returns on capital.

Which brings us back to AI.

Our hypothesis is not that the AI capex boom must end tomorrow. It is not even that actual AI spending needs to decline for the market to suffer. The more important variable is the rate of acceleration in expected capex.

As long as expectations keep rising, the obvious winners can keep rising with them.

If expectations stop improving, the burden of proof changes. And if expectations begin to fall, the market may no longer be willing to pay a higher multiple for the same story, the same earnings beat, or the same capex guidance.

That is why the next charts are so important.

Goldman’s late-2025 chart shows that consensus AI hyperscaler capex estimates for 2026 were still being revised meaningfully higher, reaching roughly $527 billion by November.

More recent estimates from late April 2026 point in the same direction. The company universes are not perfectly identical across estimates, but the message is consistent: expected AI-related capex is still moving higher.

The expected spending curve is still bending upward.

So... for now, the capex clock still belongs to the bulls.

The next pattern is more nuanced.

The AI rally is narrow... but not in a random way.

It is narrow because the earnings upgrades are narrow.

As the Goldman Sachs chart below shows, AI infrastructure stocks have driven the largest upward revisions to 2027 S&P 500 earnings estimates. Since the start of the year, consensus EPS estimates for AI infrastructure stocks have risen by roughly 32%.

Energy has also seen strong upward revisions, helped by the broader geopolitical and commodity backdrop.

The S&P 500 as a whole has seen estimates rise by around 8%. But the most important line is the grey one. Excluding AI infrastructure and Energy, S&P 500 earnings estimates have gone nowhere.

That tells us something important about the anatomy of this rally. The market is not yet paying for a broad, economy-wide AI productivity boom.

It is paying for the companies that are making money from the buildout today: chips, memory, hardware, capital goods, power, grid infrastructure, data-center suppliers. That is where the earnings revisions are visible. And markets always have a bias toward what they can see.

The below Goldman chart adds an important caveat. EPS revision breadth has been positive across every S&P 500 sector, so this is not a market where everything outside AI is deteriorating.

But the magnitude of the upgrade is still highly concentrated. The average sector may be seeing some improvement... the AI infrastructure complex is seeing the real acceleration.

This is where the concentration evidence becomes important.

The same narrowness visible in earnings revisions is also visible in market performance. Goldman’s work shows that the S&P 500’s gains have been overwhelmingly driven by the AI-linked complex.

TMT, plus Amazon and Tesla, accounted for most of the index’s year-to-date return, while the S&P 500 excluding TMT was up only modestly. Nvidia alone contributed a disproportionate share of the advance. Since late February, the divergence became even clearer: semiconductors surged, the Mag 7 rose, the cap-weighted S&P 500 advanced, but the equal weighted S&P 500 declined.

So, the pattern is clear.

The earnings revisions are concentrated.
The index returns are concentrated.
And the leadership is concentrated around the companies most directly exposed to AI infrastructure spending.

That is not automatically bearish.

In fact, it partly explains why the rally has been so powerful. The market is rewarding the companies where the earnings bridge is already visible.

But it also tells us what kind of AI rally this is.

This is not yet a broad, full-spectrum AI mania where investors reward every company with a plausible AI story. During the internet bubble, thematic participation became much wider. The market rewarded telecom companies, software firms, portals, e-commerce platforms, incubators, concept stocks, and even companies whose main asset was the ability to attach themselves to the “new economy” narrative.

Today’s AI rally is different.

The market is mostly rewarding the companies with a visible earnings bridge to AI capex. It has not yet fully rewarded the broader application layer... the companies that may use AI to improve software development, customer acquisition, commerce, advertising, pricing, logistics, financial rails, research productivity, operating leverage, and decision-making.

That is why we must separate two questions.

First: who is benefiting from AI today?

Second: who may benefit from AI tomorrow?

Today, the answer to the first question is clear: the benefits are concentrated in the capex chain.The answer to the second question is still open.

And that is precisely why the broader AI cycle may still have further to run.

The market has not yet paid for every AI use case. It has not yet paid for every software winner. It has not yet paid for every marketplace, financial platform, commerce platform, or productivity beneficiary that may ultimately embed AI into its operating model.

But inside the first AI trade (the visible capex-beneficiary trade) the picture is different. There, the market has already found its winners...

Goldman’s momentum work reinforces the point. The current Momentum Factor rally is tightly linked to AI, especially memory and semiconductors.

Its long leg is tilted toward semiconductors, tech hardware, and capital goods tied to the AI infrastructure buildout, while its short leg includes software and commercial services companies caught in the market’s fear of AI disruption.

That is the whole market psychology in one chart.

The market is paying for AI capex today.
It is still discounting much of what AI may enable tomorrow.

That is why we closed our entire position in EWY in VMF’s Strategic Asset Allocation. EWY, the South Korean equities ETF, is heavily influenced by Samsung and SK Hynix.

We bought it because the market was underpricing the memory bottleneck inside the AI infrastructure buildout. That bottleneck is no longer hidden.

In less than a year and a half, the position delivered more than 230% in total returns.

EWY did not fail our thesis. It fulfilled it.

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The same logic explains why we have been adding exposure across the AI application layer throughout VMF Research: IGV, KWEB, Coinbase, Scottish Mortgage Investment Trust, Universal Music Group, Amrize and, more recently, PDD in VMF’s Security Selection.

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This is not a retreat from AI.
It is a rotation inside AI.

The first trade was about the buildout.
The next trade may be about monetization.

Therefore, the conclusion is not that the AI boom is over. The conclusion is more precise: the first AI trade has become crowded, while the second AI trade is still being formed.

That is why our response is rotation, not retreat.

We are not selling the future.
We are selling the part of the future the market has already discovered.

This brings us to the final pattern-recognition test: timing.

The capex data says expectations are still rising.
The earnings data says the visible AI infrastructure winners are still receiving the strongest upgrades.

The concentration data says the rally is narrow, but not yet a full-spectrum AI mania.

That leaves price and time.

We begin with the long-term chart we first published in the September 2025 issue of Alpha Tier.

This chart has been central to our roadmap because it captures one of the most important features of market history: secular bear markets are rarely single events.

They are long structural ranges.

The crash is only the beginning.
The repair takes years.
The new secular bull does not truly declare itself at the low. It declares itself when the market finally breaks out of the range that contained the prior bear market.

That distinction is crucial.

The 1942 low was generational, but the 1950 breakout confirmed the new secular bull. The 1974 low was generational, but the early-1980s breakout confirmed the next regime. The 2009 low was generational, but the 2013 breakout above the long 2000–2013 range confirmed the current secular bull.

This is why Bank of America’s long-term roadmap is so interesting.

BofA reaches a very similar conclusion. Its chart also treats 2013, not 2009, as the beginning of the current secular bull market. That is unconventional, but technically sound. Secular bulls do not begin when prices stop falling.

They begin when prices escape the old structure.

In our view, that is the right way to think about this market.

It also fits the broader Fourth Turning framework we have been developing since Alpha Tier’s early issues. Generational lows tend to emerge during periods of institutional stress, political disorder, policy experimentation, and social fracture.

But markets do not immediately leave those regimes behind. They first repair. Then they break out. Only later does the new secular bull become obvious.

That is exactly what happened after 2009.

From this perspective, the current bull market may still have further to run. If we date the cycle from the 2013 breakout, the AI capex boom could still represent the final, powerful phase of a secular advance rather than its immediate end. That would also fit our Fourth Turning roadmap: a period of crisis, fiscal dominance, technological acceleration, geopolitical tension, and speculative capital pushing toward a dramatic climax before the next regime begins.

The long-term chart, then, does not force a bearish conclusion.

It leaves room for a melt-up.

But it also tells us the bull market is no longer young.

BofA’s chart adds one more pattern worth noting: prior secular bulls suffered major cyclical bear markets around their seventh year. The 1957 decline appeared in the seventh year after the 1950 breakout. The 1987 crash came in the seventh year after the 1980 breakout. And if we use 2013 as the starting point for the current secular bull, the Covid crash arrived in the seventh year as well.

We should not turn this into a law. The sample is tiny. Three observations do not make a trading system.

But as pattern recognition, it matters.

The point is not that every seventh year must produce a crash. The point is that secular bulls can absorb violent cyclical declines without ending. That is why the 2020 crash did not invalidate the secular bull. It may have confirmed its structure.

So, the long-term message is balanced: the secular bull may still have further to run, but the cost of complacency is rising.

The shorter-term pattern is less forgiving.

We are now in a midterm election year. And as the table below shows, the April to-October window in midterm years has historically been one of the more difficult stretches for U.S. equities.

Using the long-term monthly S&P dataset, midterm years from 1930 to 2022 show an average April-to-October return of -1.94%, with only 45.83% of observations positive.

The shorter windows are less dramatic: May-to-October is only modestly negative on average, while June-to-October and July-to-October are close to flat.

This is not a mechanical sell signal.
But the left tail matters

The worst outcomes are not random...

Many of the deepest losses occurred inside, or near, the same secular repair regimes we identified in the long-term chart: 1930 and 1934 during the post-1929 structural bear market; 1946 during the long post-war transition; 1966 and 1974 around the inflationary secular bear; and 2002 during the post-dot-com secular bear. More recently, 2022 also fits the profile of a politically charged, inflationary, liquidity-sensitive regime.

That is where this seasonal work becomes more useful.

Midterm weakness is not equally important in every environment. It matters more when it overlaps with regime stress: inflation pressure, political volatility, populist policy impulses, narrowing leadership, and investor crowding.

That is why the 1970s remain an important analogue. They were not just weak midterm years. They were weak midterm years inside a populist, inflationary, institutionally strained period. As we have argued in prior issues, today’s environment increasingly shares some of those features: fiscal dominance, industrial policy, tariffs, geopolitical fragmentation, redistribution pressure, and a political system increasingly willing to use economic policy as an electoral weapon.

That does not mean 2026 must behave like 1974.
But it does mean the current window deserves respect.

Put the pieces together...

The long-term chart still allows for a final secular-bull advance. The AI capex cycle is still accelerating. The application layer has not yet fully participated. And if the AI boom broadens, the upside could remain powerful.

But the short-term setup is less benign. We are entering the weakest seasonal stretch of a midterm year while the market is increasingly dependent on AI capex expectations, narrow leadership, crowded Momentum, and parabolic winners.

That is why this issue began with a pause.

Not because the future has stopped being exciting.
Because it has become too easy to believe.

And when a mature secular bull, a transformative technology, a narrow market, and a fragile seasonal window begin to overlap, the right response is not panic.

It is judgment.

The future has become too easy to believe.
That is when markets get dangerous.

The worst speculative mistakes rarely begin with stupidity. More often, they begin with a truth: a real breakthrough, a real scarcity, a real acceleration in earnings, a real story that early investors understood before the crowd.

Then the crowd arrives.

Price becomes proof. Skepticism becomes embarrassment. The story stops being analyzed and starts being worshipped.

That is the moment this excerpt is about.

In May’s issue of Alpha Tier, we called it The Tulip Test. Not because the current boom is fake, but because every great boom eventually faces the same question: are investors still exercising judgment, or are they simply extrapolating?

The most dangerous bubbles are not always built on fiction.

Sometimes they are built on truth.

Railroads were real. The internet was real. Broadband was real. Each changed the world. Each also produced moments when investors paid too much, too late, for a future that had already become obvious.

This issue asks a harder question than whether the revolution is real. It asks what happens after that question has already been answered.

After the obvious winners have been rewarded. After capital has flooded in. After the easy money has been made. After the investor’s edge moves from information to judgment.

Because in an age of answer inflation, judgment becomes the scarce asset.

In the full May issue of Alpha Tier, we turned this framework into Model Portfolio action: what we trimmed, where we rotated, where we increased liquidity, and which signals we are now watching.

This free excerpt gives you the framework.

Paid subscribers received the risk-budget decision.

Before the portfolio answer, comes the test:

What is the price of believing an obvious future too late?

There is a reason Tulip Mania still refuses to die.

Nearly four centuries later, the story remains irresistible. A flower becomes an asset. A luxury becomes a market. A market becomes a fever. And for a brief moment in the Dutch Golden Age, the price of certain tulip bulbs appears to detach so completely from ordinary reality that the episode becomes the first great parable of financial madness.

The version most investors know is simple.

Too simple, in fact.

According to the popular story, all of Holland lost its mind. Sailors, chimney-sweeps, merchants, aristocrats, servants, farmers, and speculators abandoned productive work to trade flowers. A single bulb changed hands for the price of a house. Then, suddenly, the bubble burst, fortunes vanished, and the Dutch economy was left in ruins.

It is a wonderful story.

It is also not quite true.

Modern historians have done much to correct the caricature. Tulip Mania was not a nationwide collapse. It did not engulf the whole Dutch population. It did not destroy the Dutch economy. And the best archival work suggests that the most serious participants were not the mythical “lowest dregs” of society, but a relatively narrow group of wealthy merchants, brewers, cloth traders, collectors, growers, and a small number of local elites.

Anne Goldgar, whose archival history has done much to demythologize the episode, shows that the tulip trade moved less like a national frenzy and more like a concentrated social market, carried through networks of reputation, credit, status, and trust.

Still, that narrower reality makes the episode more useful, not less.

The real lesson of Tulip Mania is not that an entire nation went mad over flowers. It is that a small, wealthy, networked, intelligent group of people can still produce a speculative fever when novelty, scarcity, social proof, financialization, and price momentum begin to reinforce one another.

Its usefulness, then, does not come from comparing a seventeenth-century flower bulb with a twenty-first-century general-purpose technology.

That comparison would fail almost immediately.

It comes from something more durable: the way markets behave when a scarce object captures imagination, when rising prices begin to validate the story, and when participation itself becomes a signal of sophistication.

Because the psychological machinery of speculation has not changed as much as we like to think...

Tulips entered the Dutch Republic as exotic luxury goods from the East. By the early seventeenth century, the Netherlands was becoming one of the richest and most globally connected societies in the world. Dutch merchants were global leaders in trade, finance, shipping, and capital formation. Wealth was rising. Gardens were fashionable. Collecting was fashionable. Rarity was fashionable. And tulips, especially the most unusual varieties, became a form of social display.

They were beautiful. They were foreign. They were scarce. And they were difficult to reproduce quickly.

That combination matters.

The most prized tulips were not ordinary flowers. They were “broken” tulips, whose dramatic streaks and flame-like patterns were caused by what we now know to be a virus. To the seventeenth-century collector, however, these patterns were mysterious, rare, and extraordinarily desirable. A rare bulb was not merely a plant. It was an object of taste, status, knowledge, and distinction.

In other words, tulips began with a real foundation.

They had aesthetic value.
They had scarcity value.
They had status value.
They had collector value.

And for professional growers, they even had economic value as breeding stock. That is where every dangerous boom begins. Not with pure fantasy, but with a plausible foundation.

The first buyers are often right. They identify something scarce before others do. They understand why the object is desirable. They see the imbalance between limited supply and rising demand. And, for a while, the market rewards them.

Then price begins to do something psychologically powerful. It validates the thesis.

A rising price tells the early buyer that he was not merely lucky, but perceptive. It tells the next buyer that someone else has already done the work. It tells the skeptic that perhaps he is the one missing the point. And it tells the entire community that this is no longer just a curiosity. It is an opportunity.

That is when the emotional cycle begins.

At first, there is curiosity. Then optimism. Then excitement. Then confidence. Then enthusiasm. Then euphoria. Along the way, judgment is quietly replaced by reinforcement.

Confirmation bias makes every new price increase feel like proof.
Recency bias makes the recent acceleration feel permanent.
Social proof makes participation feel safer because other intelligent people are already involved.
FOMO turns caution into emotional discomfort.

And overconfidence convinces investors that they will be able to leave before the music stops.

This is why speculative manias are so difficult to recognize from the inside.
They do not feel like madness at first.
They feel like discovery.
Then they feel like validation.
Then they feel like inevitability.

By late 1636 and early 1637, the tulip trade had reached that dangerous final stage. Prices were no longer rising in a calm, linear fashion. They were accelerating. Historical reconstructions of the period show a near-vertical move in tulip contract prices into early 1637, followed by an abrupt collapse.

That chart is the real lesson. Not because every data point from the seventeenth century should be treated with modern precision. The historical record is incomplete, and some of the most dramatic prices came from transactions and auction records that deserve caution.

But the shape of the move is unmistakable.

Gradual.
Then fast.
Then vertical.
Then broken.

That is the anatomy of speculative acceleration.
And acceleration is the key word.

Bubbles do not usually require the story to become false. They require the rate of belief to slow. A market built on acceleration becomes vulnerable the moment the next buyer is no longer willing to extrapolate at the same speed.

The tulip did not need to become worthless for prices to collapse.
The next buyer simply had to disappear.

That is the greater-fool problem. In the early stages, investors buy because the asset is scarce, misunderstood, or underappreciated. In the later stages, they buy because someone else has been willing to pay more. The object may still have value, but the price increasingly depends on the existence of a future buyer with even greater confidence.

Once that chain breaks, the psychology reverses.

Euphoria becomes anxiety.
Anxiety becomes denial.
Denial becomes fear.
Fear becomes urgency.
And urgency becomes liquidation.

This is the point where the tulip story becomes useful for our own market.
Again, not because AI is a flower bulb.

The comparison is not about utility. It is about expectations.

AI has real economic value. More than that, it may become the most important general-purpose technology since electricity... and perhaps the most powerful economic force ever introduced into markets.

That is precisely why the analogy matters. The greatest bubbles do not form around trivial ideas. They form when a transformative truth becomes impossible to ignore, then impossible to price with discipline.

That is the real danger.
Not technological failure.
Extrapolation.

The tulip buyer did not need to discover that tulips had no beauty. He only needed to discover that the next buyer was no longer willing to pay a higher price. The same logic applies to every great boom once it becomes crowded enough.

The AI investor does not need to discover that AI has no value. He only needs to discover that the next buyer is no longer willing to pay a higher multiple for the same story, the same capex guidance, or the same earnings beat.

That is how expectations reset.

Slowly at first.
Then all at once.

Because the most dangerous moment in a boom often arrives after the thesis has been proven. Proof attracts capital. Capital creates extrapolation. Extrapolation creates fragility. And fragility reveals itself when acceleration slows.

That is the Tulip Test.

Not whether the innovation is real. Whether the expectations attached to it have become too dependent on uninterrupted acceleration... and on the permanent presence of a greater fool.

And that brings us to the better historical parallels. Tulips reveal the psychology. But railroads, broadband, and the internet reveal the capital cycle. Those were not decorative bubbles. They were infrastructure booms built around real technologies that changed the world. And they still ended in brutal market resets.

That is where we go next.

Stay tuned.

Vasco

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And about when the world’s most uncomfortable market gives you an asset-light intelligence engine at 5x free cash flow:

“What the market overlooks today becomes consensus tomorrow.”

Michael Mauboussin

Welcome to our weekly update - Week 22.

One of the most important developments in our Model Portfolio over the past few weeks has been the rapid confirmation of our newest addition:

,

IGV … our preferred way to gain exposure to what we have repeatedly described as the AI application layer in the United States.

The position is already up more than 25% in less than a month.

That kind of move tells you something important.

These leading software companies were far more punished, hated, and oversold than most investors realized. In fact, if you look closely at the chart above, you’ll notice something even more interesting about the timing of our entry.

We added IGV to the Model Portfolio precisely as it was breaking below what many technicians would consider a critical support zone: the 200-week moving average.

That level had held throughout virtually the entire cycle.

Normally, breaking such a widely watched long-term trend gauge would be interpreted as bearish. But we believed this was a false break. And the reason we had conviction in that view is because we never rely on technical analysis alone.

We reconcile technicals with fundamentals, macro, liquidity, and sentiment... the other pillars of our Investment Process.

Sentiment around software had become excessively pessimistic just as the AI narrative was becoming too narrowly concentrated around semiconductors and infrastructure.

Meanwhile, the application layer, the companies that will actually monetize AI productivity gains across the economy, was being discarded.

That disconnect created opportunity, and we believe the market is only beginning to recognize it.

Another recent addition to the Model Portfolio is also starting to show encouraging signs, although it has not yet delivered the type of relative strength we ultimately expect. We are referring to , our play on global healthcare equities.

Much like software, healthcare has lagged while capital crowded into the obvious AI winners.

But we continue to believe healthcare could become one of the largest beneficiaries of the AI productivity boom.

The ability of AI to improve diagnostics, drug development, operational efficiency, and administrative workflows could fundamentally reshape the economics of the sector over time.

And technically, the setup is becoming increasingly constructive.

Now, in our alternatives book, the picture has admittedly been less exciting.

Both our precious metals complex and our crypto exposure have lagged in recent months.

Bitcoin, in particular had started to show improving relative strength, but as the chart below illustrates, that momentum has recently faded again.

For now.

Because while the price action has cooled, the underlying backdrop continues to improve.

Regulatory clarity in the United States is slowly taking shape through initiatives like the Clarity Act.

Institutional support continues to broaden.

And perhaps most importantly, we believe the emergence of the Agentic AI economy could prove transformational for the crypto ecosystem.

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Why?

Because truly autonomous AI agents will require native digital payment rails, programmable value transfer systems, and machine-to machine economic coordination.

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In our view, that dramatically increases the long-term relevance of blockchain-based infrastructure.

While crypto has garnered far less attention over the past few months than many expected, we increasingly believe the foundations for the next major move are quietly being built beneath the surface.

And as always, we’ll be here to keep you posted.

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.

For the past few months, we have been approaching China from the top down.

We have studied the macro scars, the property downturn, the policy trauma, the geopolitical discount, and the possibility that China’s real AI opportunity may not sit only at the frontier-model layer, but also in the application layer, where intelligence gets embedded into industry, robotics, commerce, payments, logistics, advertising, consumer behaviour, and daily economic life.

That work led us to a broad conclusion: China remains one of the most uncomfortable markets in the world to own, but that discomfort is now part of the price.

This post is where we move from the country to the company.

In May’s issue of VMF’s Security Selection, we identified what we believe is one of the more interesting stock-level expressions of that China application-layer thesis: PDD Holdings, a controversial, cash-rich marketplace platform with a powerful domestic profit engine and a global option through Temu.

The market is not stupid. PDD is cheap for reasons that deserve to be taken seriously.

China risk is real.

The VIE structure matters.

Temu faces increasing regulatory scrutiny.

Disclosure is imperfect.

Competition is brutal.

And global investors have good reasons to demand a discount.

But investing is rarely about finding situations without problems. It is about finding situations where the problems are known, the price is depressed, and the underlying value may be much greater than the market is willing to recognize.

That is the tension we see in PDD.

In this excerpt, we are disclosing the actual recommendation from May’s issue: the target weight, the full thesis, the bear case, the valuation work, the role of Temu, the balance-sheet analysis, and why we believe the risk/reward was compelling enough to add PDD to our Value Model Portfolio.

This is a longer post than usual because that is what the opportunity requires. When a stock is controversial, cheap, and sitting at the intersection of China, AI, e-commerce, and global regulation, the work cannot be reduced to a slogan.

Good reading.

The company is PDD Holdings .
Not NVIDIA.
Not a South Korean memory champion.
Not a uranium miner.
Not a data-center landlord.

Not one of the obvious names investors now reflexively associate with artificial intelligence. And that is precisely why it belongs in this issue.

The first phase of the AI trade was easy to understand. More AI required more compute. More compute required more chips, more memory, more power, and more data-center infrastructure. The market could see the earnings bridge.
So it paid for it.

The next phase is less obvious. It is not only about building the AI machine. It is about using that machine to improve real economic activity:
Commerce.
Search.
Discovery.
Pricing.
Advertising.
Logistics.
Merchant tools.
Consumer behavior.
Transaction flows.

That is where AI stops being a demo and becomes monetization.

And that is where PDD sits.

The market still sees PDD mostly as a Chinese e-commerce company, with all the usual baggage: weak consumer confidence, geopolitical tension, regulatory uncertainty, and growing scrutiny around Temu. That framing is understandable.

But it is incomplete.

PDD is not entirely new to VMF Research.

In VMF’s Strategic Asset Allocation, we recently increased our position in KWEB, the KraneShares CSI China Internet ETF. PDD is one of KWEB’s largest holdings, at roughly 8.5%. That made sense for Tier One. KWEB gives us a diversified way to own China’s internet platforms without pretending we can know, in advance, which company will capture the largest share of the application-layer opportunity.

But VMF’s Security Selection has a different job. Here, we are not building a broad geographical or sector tilt. We are asking a more specific question:

Is there one company inside that basket where the risk/reward is strong enough to own directly?

That is the question we want to answer this month.

PDD also appears inside another VMF holding: Scottish Mortgage Investment Trust.

That is a different kind of signal.

We own Scottish Mortgage in VMF’s Security Selection and Alpha Tier because it remains one of the market’s most interesting vehicles for long-duration disruptive growth. Its portfolio is filled with companies trying to reshape large markets: SpaceX, TSMC, NVIDIA, ByteDance, Amazon, Stripe, Revolut, MercadoLibre, ASML, Anthropic and others.

And there, too, we find PDD.

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As of Scottish Mortgage’s March 2026 portfolio valuation, PDD represented 2.0% of the trust. Scottish Mortgage’s own description of the company is also revealing: it calls PDD an innovative e-commerce platform built around value-for-money merchandise and interactive shopping experiences.

That is the first interesting point.

PDD is not only a China internet holding inside KWEB. It is also held by a manager whose mandate is to find exceptional growth companies capable of shaping the future economy. Scottish Mortgage is not buying PDD because it needs to hug an index. It is buying PDD because it sees something more durable than the market currently wants to recognize.

But the story becomes even more interesting when we look at who else owns it, because PDD is not only attracting growth investors. It is also attracting some of the most respected value-oriented investors with deep knowledge of China.

Li Lu’s Himalaya Capital is one example. Li Lu1 is one of the most respected investors in the Buffett-Munger tradition, and Charlie Munger famously entrusted a meaningful portion of his own family capital to him. His disclosed U.S.-listed portfolio is highly concentrated, and as of the latest 13F data, PDD represented 14.64% of Himalaya Capital’s holdings.

Duan Yongping2 is another.

Duan is one of China’s most successful entrepreneurs and investors, with a reputation built over decades through businesses, capital allocation, and long-term ownership. Through H&H International Investment, his disclosed U.S. portfolio also includes a major PDD position. As of the latest filing, PDD represented 7.48% of the portfolio, making it one of his five largest disclosed holdings.

So, PDD sits in a very unusual place.

It is innovative enough to remain inside Scottish Mortgage.
It is cheap enough to attract world-class value investors.
And it is controversial enough for the broader market to still keep its distance.

That is the combination we find interesting.

We are not buying PDD because other investors own it.

That would not be research. It would be imitation. But when a company appeals simultaneously to long-duration growth investors and deeply selective value investors, we pay attention.

Especially when the valuation looks like this.

PDD currently trades at roughly 10x trailing earnings, 8x forward earnings, just over 2x sales, and less than 9x free cash flow.

On an enterprise-value basis, the valuation becomes even more striking, with the stock trading around 6x EBITDA and roughly 5x free cash flow.

And that is before fully appreciating the strength of the balance sheet.

At the end of 2025, PDD held RMB422.3 billion in cash, cash equivalents, and short term investments, equivalent to roughly $60.4 billion. If we subtract debt-like obligations, mainly lease liabilities, net cash still sits close to $60 billion.

A very large part of PDD’s market value ($136.3 billion) is backed by cash and liquid investments.

That is not how the market usually values a dominant, profitable, asset-light technology platform with exposure to one of the largest consumer markets in the world.

And the operating scale is just as important. In 2025, PDD generated RMB431.8 billion in revenue, or roughly $61.8 billion, representing 10% year-over-year growth, despite a year in which investors remained deeply skeptical of Chinese equities, consumer confidence, and Temu’s international expansion.

That leaves us with a paradox.

PDD is large.
It is profitable.
It is cash-rich.
It sits directly in the application layer of the AI economy.
It is held by sophisticated growth investors.

It is held by highly respected value investors.

And yet the market values it like a business carrying a very large warning label. The obvious question is:

Why?

Let’s begin where we should always begin when a stock looks this cheap... with the bear case.

The first layer is the country.

China remains one of the most difficult markets in the world for foreign investors to underwrite.

We do not need to repeat the full discussion from this month’s VMF’s Strategic Asset Allocation, but the concerns are real: weak consumer confidence, the property downturn, regulatory scars, geopolitical friction, governance concerns, and the persistent fear that Chinese companies may create value without allowing outside shareholders to capture enough of it.

From VMF’s Strategic Asset Allocation:

Then there is the VIE structure.

As we explained in detail when we recommended Alibaba back in July 2024, many Chinese internet companies listed offshore do not give foreign investors direct ownership of the operating business inside China. PDD follows that same broad architecture.

Investors in PDD’s ADSs own shares in an offshore Cayman Islands holding company, not direct equity in the Chinese VIEs and their subsidiaries. Their economic exposure to the operating business is created through contractual arrangements rather than ordinary share ownership in the underlying operating assets.

That structure has worked for decades, but it is not the same thing as owning a conventional U.S. or European equity. It adds a layer of legal, regulatory, and political uncertainty that foreign investors are right to discount.

So, the China discount is not random.

It exists for a reason.

But in VMF’s Security Selection, we cannot stop at the country label. Our job is to ask whether the country risk has overwhelmed the company-specific reality.

And with PDD, the company-specific concerns are also serious.

The first is Temu.

Temu has been one of the most aggressive e-commerce expansions of the past several years. It transformed PDD from a mostly domestic Chinese e-commerce story into a global discount-commerce force almost overnight.

That created enormous optionality, but also a much harder underwriting problem.

International expansion is expensive.
Customer acquisition is expensive.
Logistics are complex.
Regulatory scrutiny is increasing.

And the model’s early advantage depended partly on cross-border shipping economics that governments are now challenging...

The United States has already moved against the de minimis framework that helped low-value direct-from-China e-commerce packages enter the country at favorable economics.

Reuters reported that Temu’s daily U.S. users fell sharply after the end of that loophole, a reminder of how sensitive parts of the model may be to policy changes.

Europe is moving in the same direction. EU finance ministers agreed to accelerate customs duties on low-value parcels, a change expected to hit Chinese online retailers such as Shein and Temu.

More recently, the EU also agreed to overhaul its customs system, with tougher enforcement and potential fines for platforms selling illegal or unsafe products into the bloc.

That is a real risk.

Temu may need more local warehousing, more compliance spending, more merchant support, more customer-service infrastructure, and more regulatory adaptation than early bulls expected. The international business may still be valuable, but it is unlikely to be frictionless.

The second concern is profitability.

PDD is highly profitable at the group level, but investors still struggle to understand how durable those profits really are.

How much comes from the core China platform?
How much is being reinvested into Temu?
How much margin pressure should we expect as the company supports merchants, localizes operations, and defends its international model?

That uncertainty is part of the discount. A low multiple is less useful if the “E” in the P/E ratio becomes difficult to trust.

The third risk is competition.

In China, PDD competes with Alibaba, JD.com, Douyin, Kuaishou, and other platforms fighting for consumers, merchants, advertising budgets, and transaction volume. The market is enormous, but it is also ruthless.

Promotional intensity can rise. Merchant subsidies can increase. Delivery expectations can tighten. Consumer weakness can push platforms into more aggressive price competition.

Internationally, Temu competes not only with Shein, Amazon, AliExpress, and local retailers, but also with governments, regulators, and increasingly hostile political narratives around Chinese imports.

This is not a clean battlefield.

The fourth risk is legal and regulatory scrutiny.

Temu’s rise has attracted attention everywhere. Reuters recently reported that Shein accused Temu of “industrial scale” copyright infringement in a U.K. legal battle, while Temu denied the allegations and accused Shein of trying to suppress competition.

The details of that dispute are less important than the broader message: as Temu scales globally, it will be scrutinized like a major platform business.

That means more legal costs.
More compliance costs.
More product-safety obligations.
More platform policing.
More headline risk.

The fifth risk is disclosure and capital allocation.

PDD does not communicate like a Western blue-chip compounder. It provides less granular segment disclosure than many investors would like, especially around Temu. It does not yet return capital in the way global shareholders might prefer, despite its enormous cash pile. And because the company is exposed to China, investors apply an additional discount to almost everything on the balance sheet.

Again, this is not irrational.

When a company holds tens of billions of dollars in cash but shareholders are unsure how much of that cash will ultimately be returned or reinvested well, the market will not value it like cash in a U.S. large-cap compounder.

So, let’s be clear.

The bear case is real.

That is why PDD trades where it does.

But the existence of risk does not automatically mean the absence of opportunity.

In fact, the best investments often begin where the market has identified real risks, then priced the business as if those risks are the only facts that matter.

Which brings us to the other side of the question.

What exactly is the market missing?

And above all, what are elite growth and value investors seeing that the broader market still refuses to recognize?

In our view, the answer begins with a simple distinction:

PDD is not a retailer.
Not really...

A retailer buys inventory, marks it up, manages stores or warehouses, carries working-capital risk, and hopes the customer shows up. That is not the right mental model here.

PDD is closer to a marketplace intelligence engine.

Its real asset is not inventory. It is the system sitting between consumers, merchants, products, prices, advertising, demand signals, logistics partners, and transaction flows. The company’s own description points in that direction.

Pinduoduo pioneered a team-purchase model, uses social sharing to aggregate demand, connects merchants and buyers, and helps merchants offer more competitive prices and customized products.

Temu follows the same broad operational logic internationally, bringing together buyers, merchants, manufacturers, brands, logistics vendors, and fulfillment partners.

That is why the application-layer framing matters so much. PDD does not need to build the world’s best frontier model to benefit from AI. It only needs to keep deploying intelligence into commerce more effectively than its competitors.

Better product discovery.
Better pricing.
Better ad targeting.
Better merchant ranking.
Better demand forecasting.
Better logistics coordination.
Better fraud detection.
Better software development.

Better matching between what consumers want and what merchants can supply.

That is not AI as a press release.
That is AI as operating leverage.

And this is where we think the market is being too narrow. It sees a controversial Chinese discount-commerce company. We see a cash-rich, asset-light platform that has already proven it can change consumer behavior in China, and that now owns a global option through Temu.

The core of the thesis, however, is not Temu alone.

That point is important.

Temu is the upside option. It is also where much of the controversy lives. But our base case does not require it to become the “next Amazon,” or even close. We do not need every regulatory, tariff, logistics, and profitability question around the international business to resolve perfectly.

The domestic Chinese operation already gives us enough to work with.

That is the foundation.3

PDD has a scaled marketplace in one of the largest consumer markets in the world. It generates substantial free cash flow. It has a fortress balance sheet. And as we noted earlier, even after a more difficult year, revenue still grew 10% in 2025 to RMB431.8 billion, or roughly $61.8 billion. We also highlighted the balance-sheet strength: at year-end, the company held RMB422.3 billion in cash, cash equivalents, and short-term investments, equivalent to roughly $60.4 billion.

We should not value that cash the same way we would value cash sitting inside a U.S. blue-chip.

That deserves to be said clearly.

There is less visibility around how it will be reinvested, whether it will eventually be returned to shareholders, and how much of it should be discounted because of China, the VIE structure, currency controls, and capital-allocation uncertainty.

So, we do not give it full credit.

In our valuation model, we apply a significant haircut to the cash balance. Even then, it remains a major source of margin of safety.

That is the beauty of the setup...

As the valuation work on these pages shows, the investment case does not depend on a perfect story or heroic assumptions.

We are paying a discounted price for a strong domestic profit engine, receiving only partial credit for a very large cash balance, and getting Temu as a valuable option rather than as the entire thesis.

Our base case is intentionally conservative.

We assume revenue growth in the mid-single to high-single-digit range over the next five years, far below the explosive growth PDD delivered earlier in its life. We also assume only gradual margin improvement, despite the company’s asset-light structure and the operating leverage that could emerge if Temu’s losses moderate or the domestic business continues to scale efficiently.

We also use a demanding cost of capital. Our base case applies a 12% WACC, which is much higher than what we would use for a comparable U.S. platform business.

That is deliberate. PDD deserves a China discount. It deserves a governance discount. It deserves a Temu uncertainty discount. We are not trying to make the valuation work by pretending those risks do not exist.

Even with those assumptions, our base case reaches roughly $148 per ADS, implying just over 50% upside from our reference price of $98.50.

That is the central point. The stock does not need everything to go right...

It just needs the market to stop treating every risk as if it will resolve in the worst possible direction.

Our bear case is also useful. There, we give Temu no value, haircut the cash muchmore aggressively, and penalize the free-cash-flow forecast. Under that scenario,

we arrive at roughly $78 per ADS, or around 20% downside from today’s price.

That is not trivial.

But it is acceptable for a 2% position when the base and bull cases offer substantially better upside. In the bull case, the numbers become much more interesting. There, the domestic business compounds better, the market gives more credit to the cash balance, and Temu proves it is more than a temporary play on tax, shipping, and regulatory “arbitrage.” It becomes a durable global commerce platform.

Under that scenario, our valuation reaches roughly $250 per ADS, or more than 150% upside.

But again, that is not our base case.

The bull case requires Temu to matter.
The base case does not require Temu to become a global winner. It only assumes that Temu has some option value, while the domestic platform and the balance sheet carry most of the valuation.

That is exactly how we want to underwrite this kind of position.

Core business first.
Cash with a haircut.
Temu as an option.
No fantasy.
No blind faith.
No assumption that China risk suddenly disappears.

The sensitivity tables reinforce the same conclusion.

Even when we flex the key variables (discount rate, terminal growth, cash haircut, and Temu option value) the valuation work still points to a wide range of outcomes above today’s share price.

That is important because it tells us the thesis is not dependent on one fragile assumption. The stock does not need a perfect WACC. It does not need full credit for the cash. And it does not need Temu to be valued like a proven global winner.

Across reasonable scenarios, the current price appears to discount a great deal of failure already.

What the market may be missing is that PDD does not need to become a clean, Western-style compounder to be mispriced. It only needs to remain a highly profitable, cash-generative, asset-light marketplace while proving that Temu is not a permanent value destroyer.

That is a much lower bar than the market seems to be setting...

And it explains why sophisticated investors from very different schools can end up in the same place.

A growth investor can look at PDD and see a platform with enormous application layer optionality.

A value investor can look at PDD and see a cheap, cash-rich marketplace with a strong domestic profit engine.

We look at it and see both.

That does not make the investment easy.
But it does make it interesting.
And at this price, it makes it investable.

You might also like reading: - why the index can be expensive while the opportunity set remains attractive.

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.

“Bull markets narrow before they end.”

Bob Farrell

The market’s rebound over the past two months has been extraordinary.

But what may be even more extraordinary is just how concentrated it has become. Take a look at the sector charts above. Out of all eleven major S&P 500 sectors, only one has managed to break out to new highs:

Technology.

Not industrials.
Not financials.
Not consumer discretionary.
Not communication services.

Just tech.

Tech is doing almost all the heavy lifting behind the major U.S. equity indexes now trading at all-time highs.

And the concentration becomes even narrower once we look beneath the surface. Because it is not “technology” broadly speaking that is driving this move.

It is semiconductors.

More specifically, it is the small group of companies at the epicenter of the historic AI capex boom currently underway.

The market has become obsessed with the picks-and-shovels layer of the AI trade: GPUs, memory, foundries, networking infrastructure, and hyperscaler capex.

That is precisely why we have spent so much time discussing the AI theme across our entire product roster.

But importantly, we have not approached it through the obvious consensus trades. We have been much more contrarian.

Take our latest addition to the Model Portfolio: , the software ETF.

We added it almost precisely when sentiment around software had collapsed and the group was being labeled an “AI loser” trade.

Meanwhile, capital was crowding aggressively into the already obvious semiconductor winners. But markets evolve. And increasingly, we believe we are beginning to witness an important rotation within technology itself: from the infrastructure layer of the AI boom toward the application layer.

From semiconductors… to software. You can already observe that shift emerging in IGV’s relative strength and price action. Interestingly, even elite investors are beginning to move in that direction. Bill Ackman just disclosed a position in Microsoft ( ) with a thesis that strongly rhymes with what we have been publishing for months:

That the real monetization opportunity in AI may increasingly accrue to dominant software ecosystems rather than only to the hardware suppliers enabling the compute buildout.

But if there is a new opportunity emerging inside technology, we must also pay attention to the other side of the trade.

Because some of the obvious AI winners are now becoming extremely extended. And nowhere is that more visible than in our South Korea position through - one of the best-performing trades of this cycle.

The move has been extraordinary.

Our initial recommendation is now up approximately 238%. But sentiment has also become extraordinarily euphoric, particularly among South Korean retail investors.

At the same time, major bearish divergences are beginning to develop across momentum indicators, even as price continues to press higher. That combination tends to appear late in powerful advances.

So, for risk management purposes, we have now closed the remaining EWY position in our Model Portfolio.

This does not mean the long-term AI story is over. Far from it.

It means the original thesis worked.

We wanted exposure to one of the less obvious bottlenecks in the AI buildout: South Korea’s memory and semiconductor ecosystem. The market eventually caught up. Then sentiment became euphoric. And when a position moves from misunderstood to widely celebrated, the job changes.

At that point, discipline matters just as much as conviction.

This is the kind of portfolio-management work we do every week inside VMF Research: not just identifying themes, but sizing them, tracking them, taking profits when risk/reward changes, and reallocating capital as the opportunity set evolves.

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 May’s issue of VMF’s Security Selection, we argued that this may be one of the most interesting moments for stock pickers in years.

Not because the index is cheap.

It is not.

The main benchmarks are near record highs. Market concentration remains extreme. The largest AI winners have already been rewarded. And the most obvious conclusion is tempting:

Stocks are expensive.

But that conclusion is too blunt.

The index is expensive.

The opportunity set is not.

That distinction matters because the more capital crowds into the same obvious winners, the more likely it becomes that high-quality businesses outside the spotlight are ignored, misunderstood, or left behind.

That is where security selection begins.

This week, we are publishing two excerpts from May’s issue.

Today’s excerpt explains why an expensive index can still contain mispriced individual businesses, and why market concentration may be creating opportunities rather than eliminating them.

In the next post, we will go one step further and disclose one of our newest stock recommendations: a controversial, cash-rich technology platform sitting directly inside the AI application layer.

But first, the broader setup.

Why the index is expensive...

And why the opportunity set is not

The U.S. market is not cheap.

As Goldman Sachs Asset Management shows, the valuation premium of the largest U.S. companies relative to the rest of the market has moved far above its long-term average.

The biggest names are not only large. They are also expensive relative to everything beneath them.

That matters because valuation is not just a number on a spreadsheet. It is a measure of how much future perfection investors are already willing to pay for today. And right now, a great deal of perfection is being priced into a very small group of companies.

This does not mean the winners are bad businesses. Far from it. Many of them are extraordinary. They have dominant platforms, fortress balance sheets, powerful AI exposure, and some of the best economics ever seen in public markets.

But even wonderful businesses can become dangerous investments when the price of consensus gets too high.

That is the first point.

The second point is even more important.

The index is no longer a neutral picture of “the market.” It has become a much narrower bet than many investors realize. As the next chart shows, the top 10 stocks now account for an unusually high share of the U.S. equity market.

This is not merely a valuation issue. It is a concentration issue.

When the top of the market becomes this large, investors start making a dangerous mental shortcut.

They look at the index.
They see high valuations.
They conclude that stocks are expensive.

But the index is not the opportunity set.

The S&P 500 is market-cap weighted. And today, that means it is heavily influenced by a small group of mega-cap winners.

That is why we believe this environment is so interesting for stock pickers.

Because the more the index becomes dominated by the same obvious companies, the more likely it becomes that other high-quality businesses are ignored, misunderstood, or left behind.

In other words, market concentration does not eliminate opportunity... It changes where the opportunity hides.

The opportunity is less likely to be in the most admired businesses already priced for near-perfection. It is more likely to be in the companies that still require explanation. The ones with visible problems. The ones investors distrust. The ones sitting outside the comfort zone of benchmark buyers.

That is where the real work begins.

And this is where the chart below becomes especially useful.

J.P. Morgan’s work shows that the U.S. equity market has become increasingly dominated by technology and by the United States itself within global equities. It also shows that the Magnificent Seven1 have been delivering much stronger earnings growth than the rest of the S&P 500.

So, let’s be clear.

This concentration did not come out of nowhere. The winners have earned a great deal of their leadership. Their earnings growth has been superior. Their business models are exceptional. Their exposure to AI is real.

But this chart does more than explain the past.
It also hints at the next phase.

The earnings-growth gap is expected to narrow.

In fact, by the fourth quarter of 2026, FactSet’s estimates show the Magnificent Seven’s earnings growth slowing materially, while the rest of the S&P 500 is expected to keep accelerating. If those forecasts are even directionally right, the market’s leadership may already be preparing to broaden.

That does not mean the mega-caps will collapse.
It does not mean the AI leaders are finished.

And it certainly does not mean investors should blindly rotate out of them... But it does suggest that the thesis we have been building across VMF Research is now beginning to show up in the numbers.

The first phase of the AI trade rewarded the companies building the infrastructure.

The next phase may reward the businesses that can turn that infrastructure into better economics: higher revenue, wider margins, faster productivity, deeper customer engagement, smarter logistics, cheaper software development, stronger distribution, and superior capital efficiency.

You might also like reading:

And remember: the chart only shows the S&P 500.

Our opportunity set is much broader.

It includes international markets. It includes emerging markets. It includes individual companies outside the benchmark comfort zone. It includes businesses investors still distrust because of geography, regulation, complexity, size or recent disappointment.

That is why this is becoming a stock picker’s market.

Not because the index is cheap.
It is not.
Not because the macro backdrop is simple.
It is not.
And not because every beaten-down stock is suddenly a bargain.
Most are not.

It is a stock picker’s market because the gap between what is loved and what is ignored remains extreme... while the earnings-growth gap that justified some of that concentration may already be starting to close.

When that happens, careful security selection becomes more valuable, not less.

That is why Bill Ackman’s recent comments caught our attention.

In late March, Ackman argued that this is one of the best times in a long time to buy quality stocks. He urged investors to look past macro fears and focus on deeply discounted opportunities, even as markets were still wrestling with sticky inflation, rising energy prices, and geopolitical stress.

Coming from Ackman, that is not generic bullishness. His entire investment career has been built around a very specific discipline: concentrated ownership of durable, cash-generative businesses when the market is temporarily unwilling to value them properly.

That is also the logic behind Pershing Square USA Ltd2 ( )

PSUS is Ackman’s new U.S.-listed closed-end fund. In broad terms, it gives American investors access to the same style of concentrated, quality-focused investing that sits behind Pershing Square Holdings ($PSHD.L), the London-listed vehicle we already own in our Model Portfolios.

But the structure was more ambitious than a simple fund launch.

The transaction also included a second layer: Pershing Square Inc. (PS), the management company itself. As part of the offering, investors who bought PSUS received shares in Pershing Square Inc. at no additional cost.

In other words, the asset-management company was used as a sweetener for the fund IPO. Buyers were not only getting access to Ackman’s concentrated investment portfolio through PSUS... they were also receiving a direct interest in the economics of the Pershing Square platform itself. That made the listing a broader attempt to bring the entire Pershing Square ecosystem to public investors: the fund, the permanent-capital structure, and the management company behind it.

On paper, it was a major event.

PSUS raised $5 billion, making it one of the largest closed-end fund IPOs in history. Ackman and his employees also committed roughly $500 million to the offering, a meaningful signal of alignment. But the market reception was underwhelming. The fund traded sharply below its IPO price on the first day, and Ackman later blamed part of the weakness on retail investors who had overcommitted to the deal.

We see a useful lesson in that contrast.

Ackman is telling investors that high-quality stocks are unusually attractive. At the same time, his own new vehicle for concentrated quality investing struggled to generate the kind of enthusiasm once imagined.

That gap is revealing.

Investors like the idea of quality. But in practice, many still prefer the familiar comfort of index exposure, mega-cap leadership, and the obvious AI winners that have already worked.

In other words, the market is still rewarding what is easy to own.

Our job in VMF’s Security Selection is different.

We are not trying to time the exact moment when market leadership broadens. That is a more natural discussion for Alpha Tier. Here, we care about something narrower and more practical: individual securities where the risk/reward already looks compelling before the crowd is willing to agree.

And that brings us to this month’s company.

The broader market is near all-time highs. By several measures, it looks historically expensive. But beneath that surface, we continue to find businesses where quality, growth and optionality do not line up with the prevailing pessimism.

This one sits directly inside the application layer of the AI trade we discussed in May’s Strategic Asset Allocation issue.

It is not part of the most obvious infrastructure story.
It is not in the geography investors feel most comfortable owning.

It is in China, where sentiment remains depressed, valuations remain compressed, and many investors still struggle to separate country-level discomfort from company-level opportunity.

That is what makes the setup interesting.

We are moving from the index to the stock.
From infrastructure to application.
From crowded consensus to a business many investors still refuse to trust.

Let’s turn to the company itself.

Tomorrow, we will reveal the name right here.

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.

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.”