AI has a dirty little secret...

The smarter the digital world becomes, the more brutally physical its requirements become.

Models can improve in weeks. Software can be copied in seconds. Inference costs can collapse almost overnight.

Power plants cannot.

Transmission lines cannot.

And uranium mines certainly cannot.

That contradiction may become one of the defining investment problems of the next decade.

The market has spent enormous amounts of time debating which model will win, which chip will dominate and which platform will capture the next wave of AI spending.

But every one of those arguments eventually runs into the same wall:

Electricity.

A data centre does not care how elegant the software is if the grid cannot keep it running.

And for workloads that must operate around the clock, availability is not a secondary feature. It is the product.

That is where nuclear power enters the story.

Not because AI suddenly created the uranium bull case.

It did not.

The uranium market was already tightening. The existing global reactor fleet consumes more uranium than primary mines produce each year. Reactor life extensions, restarts and new construction were already pushing future requirements higher. Utilities had already spent years contracting less material than they would ultimately need.

AI simply makes the bottleneck harder to ignore.

It adds a new source of electricity demand to a fuel market that was already living on borrowed inventory.

And something more interesting is beginning to happen.

Governments and state-linked buyers are moving before the demand arrives.

India is securing uranium for a nuclear fleet it has not yet built. China has become a major force in long-term contracting. Kazakhstan, despite being the world’s largest producer, has moved towards building strategic inventories of its own.

The buyers are moving before the miners.

That changes the character of the commodity.

When countries begin accepting the carrying cost of securing future supply because the consequences of not having it are far greater, uranium stops behaving like an ordinary industrial input.

It starts behaving like a strategic reserve asset.

That matters because the supply response is painfully slow.

The uranium price can move in an afternoon.

A mine may take a decade.

Permitting, geology, financing, processing infrastructure and politics all stand between a higher commodity price and an additional pound of production. Even established mines routinely disappoint.

Meanwhile, reactors do not casually switch themselves off because fuel became more expensive.

Uranium represents only a fraction of the economics of operating a nuclear plant. Once the reactor exists, security of supply matters far more than saving a few dollars on the fuel bill.

That asymmetry is what makes the setup interesting.

Supply is slow.

Demand is stubborn.

Inventories are finite.

And the world is asking nuclear power to do more.

July’s Alpha Tier followed our AI thesis out of the digital economy and into the physical constraint underneath it. The same abundance of intelligence that may pressure one scarcity can intensify another.

More useful intelligence can mean more automation.
More automation can mean more computation.
More computation means more electricity.
And electricity needs fuel.

The excerpt below was first published for paid Alpha Tier subscribers on 24 July 2026 at 9:00 p.m. Eastern Daylight Time. It examines why uranium is becoming strategically more important, why years of deferred procurement matter, and why a market capable of repricing in weeks is dependent on supply that may require years to respond.

The AI boom may ultimately be remembered for algorithms.

But algorithms do not keep the lights on.

The fuel beneath the boom does.

Good reading.

Reliable power has a fuel.

Nuclear generation operates continuously, at scale and with far less dependence on weather than most competing sources of electricity. US energy data place its capacity factor at approximately 91% (materially above natural gas, coal, hydro, wind or solar). For data centres running valuable workloads around the clock, availability is not an attractive feature.

It is the product.

The demand side is becoming more visible. Global data-centre electricity consumption could rise approximately two-and-a-half times by 2030, while AI related power use may grow even faster. Renewables will supply part of that increase, but intermittency, grid congestion and the slow expansion of storage leave an essential role for dependable generation. Nuclear power is increasingly being treated as one of the few scalable answers.

Yet the uranium case does not require every AI electricity forecast to prove correct.

The existing reactor fleet already consumes more uranium than the world mines each year. Life extensions, restarts and new construction were strengthening demand before the latest data-centre boom. Artificial intelligence adds urgency, political support and a new class of well-capitalised power buyers.

It accelerates a shortage that was already forming.

The clearest recent evidence comes from India. The country currently operates approximately 8 gigawatts of nuclear capacity but is targeting 100 gigawatts by 2047. Reaching that objective will require sustained construction over two decades... and considerably more fuel than India can produce domestically.

Its procurement strategy is moving ahead of the reactors. A recently agreed contract with Canada’s Cameco covers almost 22 million pounds of uranium from 2027 through 2035. India and Australia have also completed the administrative arrangements required to permit Australian uranium exports under their existing nuclear-cooperation framework. Australia controls roughly 28% of known global uranium reserves, opening another potentially important supply route to one of the world’s largest future consumers.

So, India is securing fuel for generating capacity that has not yet been built.
It is not alone.

China represented roughly half of global long-term uranium contracting in 2024. Kazakhstan, already the world’s largest producer, has moved towards building strategic inventories of its own. Sovereign and state-linked buyers are increasingly competing with utilities for material whose future availability can no longer be assumed.

The buyers are moving before the miners.

The important signal is the order of events. Fuel is being secured before the corresponding demand fully reaches the market. Buyers are accepting the carrying cost of early procurement because the potential cost of insufficient supply is far greater than the benefit of waiting for a slightly lower price.

Uranium is beginning to behave less like an ordinary industrial input and more like a strategic reserve asset.

The market is poorly positioned for that transition.

World mine production already falls short of the annual requirements of the existing reactor fleet. For decades, the difference was supplied through inventories, military stockpiles, reprocessing, underfeeding and other secondary sources created partly by the enormous overproduction of the Cold War.

Those buffers postponed the adjustment. They did not create new mines.

The world now has 440 operational reactors, with 79 under construction and another 120 planned. The World Nuclear Associations' reference scenario projects annual reactor requirements rising from approximately 175 million pounds of U₃O₈ equivalent in 2024 to 391 million pounds by 2040.

Not every proposed reactor will be completed. Timetables will slip, policies will change and construction costs will remain difficult to control. The broad direction is nevertheless clear. Demand is expected to rise from a market that cannot fully supply today’s consumption from primary production.

Supply-and-demand scenarios compiled from UxC and Cameco data indicate a cumulative uranium shortfall of approximately 1.4 billion pounds through 2045 under the base case. If the international objective of tripling global nuclear capacity by 2050 were achieved, the projected deficit would exceed 3 billion pounds.

These forecasts are not promises. Their value lies in illustrating the scale of the response required. Balancing the market would demand a combination of substantially higher mine output, additional secondary supply, lower reactor growth or sustained inventory depletion. None is assured.

The textbook answer to a commodity shortage is higher prices. Higher prices encourage production, attract capital and eventually restore equilibrium.

Uranium complicates that sequence.

The commodity price can move in weeks. A mine may require a decade. Resources must be discovered, delineated and permitted. Processing infrastructure must be financed and constructed. Governments can alter royalties, taxes or operating conditions. Technical studies must survive contact with geology, and projects that appear economic on paper can suffer delays, dilution and cost overruns before producing a single pound.

Even established operations regularly miss production targets.
Supply therefore responds slowly and unpredictably.

Demand is less flexible.

Uranium represents a relatively small share of the total cost of operating a nuclear power plant. Once the facility exists, its owner has little economic incentive to interrupt generation in order to save a few dollars on fuel. The reliability of supply is considerably more important than obtaining every pound at the lowest available price.

Utilities have nevertheless spent much of the past decade postponing the decision. Long-term contracting has remained below replacement requirements for 13 consecutive years. Approximately 116 million pounds were contracted in both 2024 and 2025, materially below annual reactor demand. Even the apparent return to replacement-level contracting in 2023 was distorted by a large one-off purchase from Ukraine.

The market has deferred the purchases.
It has not removed the reactors.

As older contracts expire and available inventories decline, utilities must eventually return to the term market (or accept greater exposure to a comparatively thin spot market that may not contain the volumes they require).

The geographical structure of supply makes waiting more dangerous. Kazakhstan accounted for approximately 39% of world uranium production in 2024. Canada represented another 24%, while Namibia, Uzbekistan and Australia provided much of the remainder. A portion of Kazakhstan’s output is transported through Russia, which also remains central to conversion and enrichment (the specialised stages required to transform mined uranium into usable reactor fuel).

Ore in the ground is not the same as assured fuel in a reactor. Mining, conversion, enrichment, fabrication and transport form one interconnected supply chain. A bottleneck at any point can tighten the entire system, raise inventory requirements and increase the strategic value of supply from politically dependable jurisdictions.

The uranium cycle is therefore being shaped by two forces at once:

• More reactors require more fuel.

• Deglobalisation requires more secure fuel.

Western governments are attempting to rebuild capabilities that were allowed to erode during decades of inexpensive imports. The United States, once the world’s dominant uranium enricher, now has no domestically owned commercial enrichment capacity and has lost almost all of its mine production. Reconstructing that chain will require capital, time and prices capable of rewarding the investment.

The path from a tighter uranium market to uranium equities is not automatic.

It is, however, powerful when it works.

Higher long-term prices expand the margins of existing producers. Better cash flow supports mine extensions, restarts and brownfield development. Resources that were uneconomic at lower prices become more valuable, improving financing conditions for developers and raising the strategic value of credible projects.

The operating leverage can be substantial.

Capturing it without allowing one mine, jurisdiction or management team to determine the outcome is the more difficult part.

Physical uranium offers the cleanest exposure to the commodity, but none of the earnings leverage available to producers. A single miner can deliver far greater upside, yet the result may depend on one asset performing exactly as expected. Junior miners increase the optionality further, together with the financing, dilution and execution risks attached to projects that may remain years from production.

URNM ( ) offers the more balanced expression.

The Sprott Uranium Miners ETF (URNM) combines established producers, developers and physical uranium within one vehicle. As of 30 June, it held 26 issuers, with approximately 82% invested in uranium-related equities and 18% in physical uranium. The structure therefore captures higher fuel prices, expanding producer margins and the increasing strategic value of credible future supply without making the thesis dependent on one mine succeeding.

Tier One subscribers will already know the position. URNM has been held in VMF’s Strategic Asset Allocation Model Portfolio since August 2024. The original recommendation performed strongly before the ETF surrendered a substantial part of its advance over the past several months.

That correction has not weakened the uranium thesis.
But it has restored the asymmetry.

Since inception, URNM has maintained a constructive relative-strength trend against the broader US equity market. That long-term market confirmation lends credibility to the fundamental case, even though the path has been exceptionally volatile. The short-term picture is now far less comfortable. Since the beginning of the year, the ETF has fallen almost 45%, broken below its shorter-term trend measures and returned to an important support zone.

Momentum is weak. But the long-term structure remains intact. That combination is precisely why the position now merits consideration inside Alpha Tier.

There is an obvious parallel with our China exposure. In both cases, the fundamentals are improving faster than the price action. But the portfolio implications are different. China already represents a meaningful overweight, and adding more would deepen an existing concentration before the broad technical evidence confirms the thesis.

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URNM diversifies the portfolio into another sector, another set of earnings drivers and another physical bottleneck. Its short-term technicals are equally damaged, but its long-term relative-strength structure remains considerably more constructive.

We are therefore willing to act before a full technical recovery.

• Initiate a 2% position in the Sprott Uranium Miners ETF at the closing price on the publication reference date.

• Reduce USFR by two percentage points.

USFR is the appropriate funding source. It allows us to convert part of the portfolio’s cash-like allocation into a differentiated energy position without reducing XLE while renewed geopolitical risk in Iran continues to threaten global oil supply. As we will see in Position Progress, URNM also complements Altius Minerals, giving the portfolio two distinct claims on the growing value of dependable power.

Important Disclosure

This article contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It reproduces an excerpt from the July 2026 issue of Alpha Tier. The research, views and Alpha Tier Model Portfolio information are stated as of 24 July 2026, 4:00 p.m. Eastern Daylight Time, unless another date is expressly identified. The original issue was first disseminated to paid subscribers on 24 July 2026 at 9:00 p.m. Eastern Daylight Time.

The publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances, knowledge, experience, liquidity requirements or tolerance for risk. The Alpha Tier Model Portfolio is an illustrative research portfolio and does not represent client assets, transactions executed by VMF Research or the performance of an investable fund or managed account.

The analysis combines thematic, fundamental, commodity, energy-market, geopolitical, technical and portfolio-construction research within a medium- to long-term investment framework. Statements concerning future electricity demand, nuclear-power deployment, uranium requirements, contracting activity, mine supply and the potential effects of artificial intelligence on energy consumption are analytical judgments and scenario-based estimates rather than assurances. Forecasts may change materially as reactor construction, energy policy, commodity markets and supply conditions evolve.

Investments in uranium, uranium-mining companies and related exchange-traded funds involve substantial risks, including commodity-price volatility, mine-development and operating risk, permitting delays, cost inflation, financing and dilution risk, political and jurisdictional uncertainty, changes in nuclear-energy policy, reactor delays or cancellations, supply responses, inventory releases, geopolitical disruption, currency movements, liquidity constraints and the possible loss of capital. Higher uranium prices or greater nuclear-power demand may not translate into higher earnings or investment returns for uranium-related companies or funds. Technical and relative-strength signals may also fail.

Neither VMF Research nor the author received compensation from any issuer or other entity discussed in connection with the preparation of this research, and no issuer reviewed, approved or amended its conclusions before first dissemination.

Past performance, Model Portfolio performance and commodity-price history are not indicative of future results. Readers should conduct their own analysis and, where appropriate, consult an authorised financial intermediary or adviser before making an investment decision.

The strongest thesis in the room can still be the wrong place for the next dollar.

That is one of the least intuitive lessons in investing.

New evidence arrives. Confidence rises. The temptation is obvious: buy more.

But portfolios are not conviction contests.

In The Boom Changes Owners, we argued that China’s rapidly improving AI ecosystem is making our broader thesis harder to dismiss. Capable intelligence is becoming cheaper. Competition is widening. And as models become more interchangeable, value should increasingly migrate towards the businesses controlling customers, proprietary data, distribution and commercial workflows.

The evidence has strengthened.

So has our conviction.

Yet Alpha Tier is not increasing its China weight.

Why?

Because a good portfolio decision cannot be made by asking whether a thesis looks better than it did yesterday. The relevant question is whether the next unit of capital offers better asymmetry there than anywhere else.

That distinction matters enormously.

Existing exposures can overlap. Diversified vehicles can conceal substantial company-level concentration. And a position that was attractive at one weight can become much less attractive when the portfolio already carries enough of the underlying economic bet.

There is another constraint too.

Price.

China’s technological progress is moving faster than the market’s willingness to reward it. Some of the fundamental evidence is increasingly persuasive. The broader technical confirmation remains incomplete.

That leaves us in an unusual position:

We are more confident in the thesis than we were before.
We are not yet more confident in adding capital to it.

This is where investment research becomes portfolio management.

Finding a good idea is only the beginning. The harder discipline is deciding how much of it deserves to be owned, what evidence should justify adding more, and when the next dollar belongs somewhere else.

Or, as we wrote in July’s Alpha Tier:

Fundamentals tell us what deserves to be owned.
Price tells us when conviction deserves more capital.

The excerpt below was first published for paid Alpha Tier Subscribers on 24 July 2026 at 9:00 p.m. Eastern Daylight Time. It explains why stronger evidence behind our China thesis led us to maintain, rather than increase, the existing allocation.

Sometimes conviction tells you to buy.

Sometimes it tells you to wait.

The difference is what separates an idea from a portfolio.

Good reading.

The obvious response to stronger evidence would be to buy more China.

We are not going to do that.

The Alpha Tier Model Portfolio already owns MCHI, KWEB, Alibaba and PDD. That headline understates the degree of concentration. Alibaba is also one of the two largest constituents in both MCHI and KWEB, meaning our economic exposure to the company is materially greater than the direct BABA allocation alone suggests.

That concentration is deliberate.

Alibaba is one of the few businesses capable of participating across almost the entire Chinese AI value chain. It develops the Qwen model family, operates one of the country’s leading cloud platforms and controls the commercial ecosystems through which cheaper intelligence can be deployed at scale. Its commerce, advertising, logistics and enterprise businesses provide customers, proprietary data and economically valuable workflows that independent model laboratories must still find elsewhere.

The latest evidence therefore strengthens an important conclusion.

Alibaba does not need Qwen to win every benchmark.

It needs the supply of capable intelligence to expand, the cost of deploying it to decline and Chinese enterprises to integrate it into a wider range of commercial activity. Alibaba can benefit by developing its own models, distributing third-party models, selling the computing capacity required to run them and applying AI throughout businesses that already serve hundreds of millions of users.

Few companies occupy so many favourable positions at once.

That is why our true exposure to Alibaba matters more than the direct position shown in the Model Portfolio table.

The look-through analysis will make two things clear.

First, Alibaba is already one of the Model Portfolio’s more consequential individual company exposures.

Second, any decision to increase MCHI or KWEB would also increase our effective Alibaba position, even if the trade were presented as an addition to diversified Chinese equities.

We must therefore distinguish between two separate questions.

Has the latest evidence increased our confidence in Alibaba and the wider Chinese application-layer thesis?

Yes.

Has it improved the asymmetry enough to justify greater concentration before the market confirms that interpretation?

Not yet.

The fundamental case nevertheless deserves to be understood because Alibaba’s position inside the Chinese AI ecosystem is unusually difficult to replicate. Independent laboratories may produce exceptional models. DeepSeek has established extraordinary cost efficiency. Kimi and GLM have pushed closer to the frontier in coding and agentic tasks. MiniMax has developed competitive multimodal capabilities. Tencent owns enormous distribution through Weixin and a substantial cloud and gaming ecosystem.

But most of these businesses occupy only part of the chain.

Alibaba is closer to a full-stack expression.

It owns models, cloud infrastructure, enterprise relationships, consumer distribution, transactional data and the applications capable of converting better intelligence into commercial output. It can participate when Chinese companies train more models, when they consume more inference, when enterprises move workloads into the cloud and when AI improves search, advertising, merchant productivity, customer service and logistics inside Alibaba’s own ecosystem.

The distinction is especially important in a market where model leadership may prove temporary.

A laboratory can lose its benchmark advantage with the next release. A model that appears differentiated today can face pricing pressure within months.

The competitive position of a platform controlling customers, data and distribution is more durable.

Alibaba does not need to forecast the permanent winner at the model layer. It can help distribute the winner.

This does not mean Alibaba dominates every category.

DeepSeek may retain superior cost economics. GLM and Kimi may outperform Qwen on individual workloads. Tencent may possess stronger distribution in social media. Independent laboratories may innovate faster because they are less constrained by the priorities of a large organisation.

The investment case does not require otherwise.

It rests on Alibaba’s ability to monetise an expanding ecosystem rather than own every technical breakthrough within it. As intelligence becomes cheaper and model choice proliferates, the capacity to route workloads, serve enterprises and embed AI into established commercial systems may prove more valuable than temporarily controlling the highest score on one leaderboard.

The infrastructure data reinforce that opportunity.

China’s leading cloud platforms are still investing at a fraction of the scale of their American counterparts. Goldman Sachs estimates that capital expenditure by major US cloud providers rose from approximately $254 billion in 2024 to $443 billion in 2025 and could exceed $1 trillion in 2027. Equivalent spending by the principal Chinese platforms increased from approximately $36 billion to $57 billion and is projected to reach $123 billion over the same horizon.

The difference is enormous.

But it should not be interpreted simplistically.

China’s smaller capital programme partly reflects a less developed cloud market and constrained access to the most advanced computing hardware. Spending less is not automatically evidence of superior efficiency, particularly if the smaller investment base limits training capacity or slows the deployment of new infrastructure.

Yet the comparison also reveals how much runway remains.

Chinese model capability has advanced rapidly before domestic AI investment has approached anything resembling Western scale. If improving models stimulate cloud adoption, enterprise deployment and agentic workloads, Alibaba and its peers retain considerable room to expand their infrastructure.

The financing comparison is as important as the absolute spending.

Capital expenditure by the principal US hyperscalers is estimated to approach or exceed their combined operating cash flow during the most intensive years of the buildout. The equivalent ratio for Chinese platforms is also rising, but from a much lower starting point and towards a less extreme peak.

That difference does not eliminate risk. Chinese companies can misallocate capital, invest too aggressively or fail to convert infrastructure into attractive returns. But it suggests that China’s leading platforms can increase AI investment without placing the same immediate pressure on cash generation as several US hyperscalers.

China therefore appears to possess both runway and financial capacity.

The harder question is whether Alibaba can transform that investment into revenue.

That is the real execution test.

Capital expenditure alone does not create shareholder value. It creates the capacity from which value may eventually emerge. Alibaba must demonstrate that additional infrastructure attracts enterprise workloads, supports greater model consumption and produces enough incremental cloud revenue to justify the investment.

The forecasts are encouraging.

Goldman Sachs expects Alibaba’s rolling incremental cloud revenue relative to lagged capital expenditure to improve steadily through 2027. By the end of the forecast period, the company’s conversion ratio could exceed the comparable estimates for Google and Amazon.

That would represent a material change. Alibaba’s cloud business has historically possessed the infrastructure, customer relationships and strategic relevance without consistently delivering the operating leverage investors expected. A sustained improvement in capital-to-revenue conversion would indicate that the AI cycle is beginning to translate technical progress into financial output.

The wider capital-expenditure outlook suggests that this transition may extend beyond Alibaba.

Chinese internet investment is expected to continue rising through 2028, but at a progressively slower rate after the initial surge.

That is not a weakness in the thesis.
It is what the next phase should look like.

The industry is moving from building capacity towards proving what that capacity can earn.

Alibaba is the Model Portfolio’s most integrated expression of that transition. It develops models, owns cloud infrastructure and controls the commercial ecosystems through which cheaper intelligence can be converted into revenue. But it is not our only exposure to the application layer.

PDD Holdings offers a purer downstream expression. It does not need to train the leading Chinese foundation model or finance a hyperscale computing platform. It needs access to increasingly capable intelligence at a declining cost and the ability to deploy it across assets that remain scarce.

Those assets already exist. PDD controls merchant relationships, transaction data, consumer distribution and an expanding international network. Cheaper intelligence can improve product discovery, advertising conversion, fraud detection, translation, customer service, inventory management and logistics without requiring PDD to own the model producing every improvement.

The model may become commoditised. PDD’s network does not.

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KWEB provides the broader internet exposure. Its constituents span commerce, cloud computing, digital advertising, software, entertainment and online services, allowing the portfolio to participate if the benefits of cheaper intelligence diffuse across the Chinese platform economy. That breadth is useful, but it is not as diversifying as the ETF label initially suggests. KWEB contains several companies we also own directly (most importantly Alibaba).

MCHI serves a different purpose. It captures the possibility that technological progress contributes to a wider reassessment of Chinese assets through stronger investment, productivity and confidence. Yet it, too, adds to our effective Alibaba exposure.

The roles are therefore distinct. Alibaba is the integrated model, cloud and commercial-platform position. PDD is the downstream application. KWEB captures the wider internet ecosystem. MCHI expresses the possibility that technological progress contributes to a broader Chinese-equity recovery.

The latest evidence increases the expected value of all four.

It does not automatically increase their appropriate Model Portfolio weights.

That distinction is especially important because our direct positions understate the concentration already embedded in the portfolio. Alibaba is also one of the largest holdings inside MCHI and KWEB. Additional capital allocated to either ETF would therefore reinforce an existing company-level exposure rather than create an entirely new source of return.

A stronger thesis can make what we already own more valuable without making further concentration desirable. The next unit of capital must compete against every opportunity available to the Model Portfolio... not merely against an increasingly attractive China narrative.

Fundamentals tell us what deserves to be owned.
Price tells us when conviction deserves more capital.

The charts are not there yet.

The MCHI chart on the previous page shows the scale of the damage that still needs to be repaired. Chinese equities have underperformed the broader US market dramatically for more than a decade, and the ETF remains only modestly above the levels reached in 2015.

Few charts provide a clearer record of the collapse in investor confidence. The decline from the 2021 peak to the 2022 low was particularly consequential. It broke the previous rising structure and reset the market onto a far lower trajectory. That collapse absorbed a remarkable concentration of negative forces: the consequences of prolonged Covid lockdowns, real-estate deflation, balance-sheet stress, regulatory intervention and the widening geopolitical conflict between China and the United States.

Investors have not lacked reasons to avoid the market. MCHI is the cumulative record of them. A new upward channel may now be developing from the 2022 low, but the recovery remains incomplete. The ETF has retreated towards the lower half of that structure and remains below its 50-week moving average.

The market has begun to stabilise. But it has not yet demonstrated that improving technology fundamentals can overcome the wider macroeconomic and geopolitical discount.

KWEB tells the same story in a more concentrated form.

Its collapse after 2021 represented far more than the unwinding of an expensive valuation. It reflected a wholesale reassessment of regulation, profitability, capital allocation, geopolitical risk and the investability of China’s digital economy.

The recovery remains uneven. KWEB is still far below its previous highs, beneath a declining 50-week moving average and close to its 200-week moving average. Its relative performance against the broader US equity market continues to weaken.

That is what makes the present divergence so compelling. Chinese model capability is improving. Intelligence costs are falling. Cloud platforms retain substantial room to invest. Yet the market continues to price much of the internet complex as though those developments will produce little durable value.

The mismatch is unusually large.
But valuation is not timing.

Markets can resist better fundamentals for far longer than expected when regulation, geopolitics and confidence remain unresolved. KWEB still requires evidence that buyers are prepared to move beyond isolated opportunities and reengage with the wider sector.

Alibaba is beginning to provide some of that evidence.

The stock has outperformed KWEB since 2024, helping validate our decision to own Alibaba directly rather than rely exclusively on the diversified ETF.

That relative strength is consistent with the analysis developed throughout this issue. Investors are beginning to distinguish Alibaba’s combination of models, cloud infrastructure, financial resources, distribution and commercially valuable workflows from the broader Chinese internet complex.

The chart is more constructive than KWEB’s, but it is not fully resolved.

Alibaba remains inside its rising channel and above its 200-week moving average. The recent correction, however, has taken the stock below its shorter-term trend measures and interrupted the previous momentum.

The market is rewarding Alibaba’s superior positioning.

But it is not yet doing so decisively enough to justify increasing an exposure that is already substantial on a look-through basis.

PDD is approaching a different kind of decision.

Its valuation may be even less demanding, but the chart will attract few momentum investors.

PDD remains below its 20- and 50-week moving averages and beneath the descending trendline extending from the all-time high. At the same time, it is approaching long-term support from the rising trendline drawn from the 2022 low.

Those two forces are converging. The result is a compressed technical structure between persistent overhead resistance and a rising long-term floor. An upside resolution would suggest that the market is beginning to recognise PDD’s ability to translate cheaper intelligence into better search, advertising, merchant services, localisation and logistics. A break of support would indicate that the fundamental case still lacks sufficient sponsorship.

We believe the setup can ultimately resolve higher as the wider Chinese equity complex is reassessed.

But the chart is approaching confirmation.
It has not delivered it.

That is the conclusion across the Model Portfolio’s four China positions. The fundamental evidence has strengthened, but price confirmation remains narrow and inconsistent. Alibaba is the strongest expression. MCHI is attempting to establish a new recovery. KWEB still reflects profound scepticism. PDD remains inside a consequential decision zone.

That is sufficient reason to maintain the overweight.
It is not sufficient reason to increase it.

We will therefore keep MCHI, KWEB, Alibaba and PDD at their current weights.

Additional capital should require evidence that the improvement is broadening beyond one company and becoming visible across the market itself.

At a minimum, we want renewed strength from either MCHI or KWEB, supported by confirmation from at least one direct holding. Until then, the Model Portfolio already owns enough of the upside... and enough of the risk.

The analysis does, however, point towards another opportunity.

If cheaper and more capable models lower the cost of useful automation, the number of tasks attempted across the economy should expand. Each individual task may require less computation, but the growth in models, agents and automated workflows could more than offset those efficiency gains.

More intelligence means more computation.
More computation means more data centres, networks and cooling.
Above all, it means more electricity.

We are therefore not allocating the next unit of capital to an existing China overweight before the market confirms the thesis. We are following the same economic transition into the physical system required to sustain it... towards a constraint that better software cannot remove and supply cannot expand quickly.

Intelligence is becoming more abundant.
Reliable power is not.

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Important Disclosure

This article contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It reproduces an excerpt from the July 2026 issue of Alpha Tier. The research, views and Alpha Tier Model Portfolio information are stated as of 24 July 2026, 4:00 p.m. Eastern Daylight Time, unless another date is expressly identified. The original issue was first disseminated to paid subscribers on 24 July 2026 at 9:00 p.m. Eastern Daylight Time.

The publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances, knowledge, experience, liquidity requirements or tolerance for risk. The Alpha Tier Model Portfolio is an illustrative research portfolio and does not represent client assets, transactions executed by VMF Research or the performance of an investable fund or managed account.

The analysis combines thematic, fundamental, technological, valuation, technical and portfolio-construction research within a medium- to long-term investment framework. Statements concerning Chinese artificial intelligence, declining intelligence costs, cloud investment, application-layer economics and future monetisation are analytical judgments rather than assurances. Model Portfolio weights also reflect concentration, overlapping exposures, diversification, price behaviour and relative opportunity. A strengthening investment thesis does not necessarily imply that a larger allocation is appropriate.

Investments in Chinese equities, emerging markets, internet and technology companies and China-focused exchange-traded funds involve substantial risks, including government intervention, regulatory change, geopolitical tensions, restrictions on technology or capital flows, governance concerns, currency movements, intense competition, technological disruption, valuation compression, liquidity constraints and the possible loss of capital. Improvements in AI capability or adoption may not translate into higher revenues, earnings or investment returns. Technical and valuation signals may also fail.

Disclosure of interests: as of the research cut-off, neither VMF Research, Lda. nor Vasco Marques de Freitas personally held any financial instrument included in the Alpha Tier Model Portfolio. A legal entity controlled by Vasco Marques de Freitas held a long position in the KraneShares CSI China Internet ETF (KWEB), which is discussed in this article. This interest may create an actual, potential or perceived conflict of interest and should be considered when evaluating the analysis. The existence of an interest does not validate a recommendation, diminish its risks or imply that it is suitable for any reader.

Neither VMF Research nor the author received compensation from any issuer covered in connection with the preparation of this research, and no issuer reviewed, approved or amended its conclusions before first dissemination.

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

The market has mistaken an arms race for a monopoly.

For much of the past two years, artificial intelligence has been priced as though control of capable intelligence would remain concentrated inside a small group of Western laboratories and the companies supplying their infrastructure.

Capital followed that assumption.

Processors became strategic assets. Memory became a bottleneck. Data centres became factories for a new industrial revolution. The businesses receiving the cheques enjoyed immediate revenues, expanding margins and the comfort of visible demand.

China is now making that certainty dangerous.

DeepSeek opened the door. GLM-5.2 showed that the capability gap was narrowing. Kimi K3 has delivered a more unsettling message: Chinese models may be moving beyond cost leadership and beginning to compete for the economics attached to more valuable work.

Kimi is not the cheapest Chinese model.
That is what makes it important.

Customers appear willing to pay more when a system writes better code, completes longer workflows or produces a reliable result with fewer attempts. Yet Kimi remains less expensive than the leading Western frontier alternatives.

This is no longer a story about cheap imitation.
It is a market-structure story.

China is beginning to compete across both ends of the intelligence market: driving down the price of capabilities that are becoming common, while moving closer to the frontier where newly unlocked capabilities can still command a premium.

And it is no longer dependent on one exceptional laboratory.

DeepSeek, Kimi, GLM, Qwen and MiniMax now represent different organisations, architectures and commercial strategies. Individual leaders will change. Benchmarks will be overtaken. Some technically impressive model developers may still become terrible investments.

The broader signal is harder to dismiss.

China is building a competitive ecosystem capable of making useful intelligence better, cheaper and more widely available.

That gives new weight to a thesis we have been developing across VMF’s Market View.

In The Abundance Shock, we argued that falling intelligence costs would not eliminate scarcity. They would move it. The China AI Reset then examined the force accelerating that transition. The Intelligence Dividend followed cheaper intelligence into healthcare, where proprietary biological evidence remains difficult to reproduce. The Discovery Machine and The Royalty Engine showed how the same framework can ultimately reach individual businesses.

A common thread runs through all of them:

When intelligence becomes easier to obtain, the assets around it become more important.

Customers.
Distribution.
Proprietary data.
Embedded workflows.
Trusted relationships.
Physical infrastructure.

Those assets cannot be downloaded alongside a new model.

That is why the China technology thesis is gaining traction where a serious thesis should gain traction first: inside the mechanism.

The leading Chinese platforms do not need permanent control of the smartest model. They need a growing supply of capable intelligence, falling deployment costs and more businesses willing to integrate AI into commercial activity.

A platform controlling merchants, consumers, cloud infrastructure, transaction data and established workflows can benefit from a model it develops itself, a model supplied by a competitor or a model whose present benchmark advantage disappears six months later.

The model may become interchangeable.
The commercial ecosystem does not.

The market is still watching the leaderboards. We are watching who owns the customer when intelligence becomes cheap.

That distinction could reshape the AI profit pool. Infrastructure demand may remain enormous, particularly if lower costs generate an explosion in usage. But strong demand does not guarantee that every supplier preserves its scarcity premium, margins or valuation.

The companies financing the infrastructure may eventually capture more of the value it produces. Beyond them sit businesses capable of turning cheaper intelligence into better products, lower costs and deeper customer relationships.

That is how a boom changes owners.

The excerpt below was first published for paid Alpha Tier subscribers on 24 July 2026. It examines the latest Chinese model releases, the widening competitive ecosystem and why the application-layer thesis is becoming more credible even before the broader Chinese equity market has fully confirmed it.

The full issue then confronts the harder Alpha Tier question.

What should the next unit of capital own when the thesis is strengthening, but the portfolio already carries meaningful exposure to it?

This article begins with the evidence.
The boom is still growing.
The ownership of its profits may be changing.

Good reading.

The latest evidence arrived almost before the ink was dry.

When July’s VMF’s Strategic Asset Allocation was completed, GLM-5.2 provided some of the clearest evidence yet that China could perform a growing share of economically valuable work without reproducing the cost structure of the leading Western systems.

Days later, Kimi K3 moved the argument forward again.

The new open-weight model appears to compete near the global frontier in coding and selected agentic tasks. More importantly, it is priced materially above several leading Chinese alternatives while remaining cheaper than the principal Western frontier systems.

That combination matters more than another temporary position on a leaderboard.

DeepSeek showed that China could make intelligence cheaper. GLM-5.2 showed that the capability gap was narrowing. Kimi K3 may now be showing that the strongest Chinese models can command higher prices when they perform more valuable work.

China is therefore no longer competing only by offering yesterday’s intelligence at a discount. It is beginning to compete for the frontier itself... and for the economics attached to it.

Kimi is not the cheapest model in the comparison.

That is precisely the point.

Once a capability becomes common, its price should fall. Routine summarisation, translation, content generation and basic coding assistance will become increasingly difficult to differentiate. Providers competing only on capabilities that are already widely available will eventually be forced to compete on cost.

But intelligence is not one uniform product.

A model capable of completing more difficult work, producing more reliable code or navigating a longer agentic workflow can still command a premium. Customers do not ultimately purchase tokens.

They purchase useful results.

A cheap model that fails repeatedly can prove expensive. A more capable model that completes an economically valuable task on the first attempt may justify a higher advertised price while still producing a lower total cost.

The likely outcome is therefore not the complete commoditisation of every model. It is a more competitive market in which established capabilities become cheaper, while temporary pricing power migrates towards newly unlocked and commercially valuable work.

China is beginning to compete at both ends of that market.

The broader intelligence rankings reinforce the conclusion.

Kimi is not sitting alone at the edge of an otherwise Western frontier. GLM, Qwen, DeepSeek, MiniMax and other Chinese model families increasingly occupy positions throughout the leading group.

One strong release can be dismissed as an anomaly. A growing cohort is more difficult to ignore.

The significance of the chart above is not simply that China now has more models on global leaderboards. It is that the improvement extends across general text, coding and agentic workloads.

That breadth changes the probability distribution.

Individual laboratories will still rise and fall. Leadership can disappear with the next release, and competition may make many model developers poor investments even as their technology improves. Lower-end API pricing remains under pressure, training costs continue to rise and the capital required to remain near the frontier is substantial.

But the application-layer thesis does not require one permanent winner at the model layer. It requires competition to make capable intelligence better, cheaper and more widely available.

Deployment can reinforce that process. The research underlying this issue suggests that AI-generated code may already account for a very high proportion of output inside selected Chinese mega-cap technology companies. Those figures should not be treated as representative of the entire Chinese economy, but they illustrate the potential feedback loop: usage produces real-world successes, failures and corrections → that information improves post-training → better models then encourage more usage.

China may therefore be moving beyond the phase in which its laboratories learn principally from a frontier established elsewhere.

Increasingly, they can learn from their own users.

Its emerging domestic technology stack adds another layer of resilience.

Meituan’s LongCat 2.0 was reportedly trained and deployed on a large domestic computing cluster. This does not establish that Chinese accelerators have surpassed the leading American hardware. The more relevant point is that China appears increasingly capable of combining domestic silicon, sparse architectures, software optimisation and enormous engineering scale into a viable end-to-end system.

Export restrictions remain a constraint. But they are also influencing the form of Chinese progress, forcing laboratories to optimise models, hardware and deployment as an integrated system rather than rely indefinitely on access to one irreplaceable foreign component.

Constraints have not stopped progress.

They have shaped it.

That provides further evidence for a working hypothesis we have been developing for several months.

In The Abundance Shock, we argued that artificial intelligence would progressively reduce the cost of economically useful cognition. If that happened, value would begin moving away from the model itself and towards the assets required to apply it: proprietary data, distribution, trusted relationships, embedded workflows and physical infrastructure.

You might also like reading the Abundance Shock:

China’s latest advances do not prove that framework. They are, however, producing several of the outcomes it anticipated.

Models are improving faster than their prices are rising. Businesses can increasingly match different systems to different tasks instead of relying on one provider.

Attention is shifting away from the advertised cost of a token and towards the total cost of completing useful work. Capabilities previously limited to the largest and best-capitalised organisations are becoming accessible to a much broader market.

The economics should follow.
This transition also explains the progression of our own research.

During the first phase of the AI boom, value accrued to the bottlenecks that could not be expanded quickly enough. Advanced processors and high-bandwidth memory were scarce, and our South Korean equity exposure belonged to that phase.

EWY offered underappreciated exposure to an emerging memory shortage before the importance of memory to AI infrastructure became central to the market narrative.

Recognition eventually arrived. Earnings accelerated, South Korean equities appreciated dramatically and an underappreciated expression of the AI buildout became a consensus winner. Tier One harvested the gains and ultimately closed the position... not because the AI opportunity had ended, but because recognition had changed the asymmetry.

We were already turning towards the application layer. Not because physical infrastructure had ceased to matter, but because the next phase of value creation was likely to emerge somewhere less visible.

The first phase possessed a direct earnings bridge. Hyperscalers increased capital expenditure → semiconductor, memory and infrastructure suppliers converted those cheques into revenue. The application layer depended on a different mechanism: lower intelligence costs had to stimulate enough new usage to transform the economics of the companies applying it.

That mechanism is becoming easier to see.

Goldman Sachs estimates that consumer and enterprise agents could drive global token consumption to more than 24 times today’s estimated capacity by 2030.

Most of the projected growth comes not from conventional AI workloads but from agents performing longer, more frequent and increasingly autonomous sequences of work. The exact magnitude is necessarily uncertain. Adoption could proceed more slowly, model efficiency could improve faster than expected and many proposed applications may never become economical.

The composition of the estimate is more important than the precise total.

Non-agent workloads contribute only a small share of the expected increase. Most of the growth comes from consumer and enterprise applications: agents searching, organising and transacting for individuals, and systems operating across software development, customer service, research, administration and commercial workflows.

That is the application-layer thesis in one chart.

As intelligence becomes cheaper, it can be deployed more frequently, across a wider range of problems and at progressively lower levels of economic value. The cost of each task may fall, yet the number of tasks attempted can rise much faster.

Abundance does not merely reduce expenditure.

It can create demand.

China’s latest advances strengthen that possibility. More capable open-weight models, lower task-completion costs and a broader competitive ecosystem should make agentic applications cheaper to build, easier to deploy and less dependent on any single provider.

The result could be an economy that consumes substantially more intelligence even as the cost of each individual unit declines.

That has important consequences for market leadership.

For most of the recent cycle, the companies receiving AI capital-expenditure cheques have materially outperformed those paying them. Hardware suppliers enjoyed immediate demand, expanding margins and visible order books. The platforms absorbed enormous expenditure while the return on that investment remained difficult to measure.

We do not believe that the hardware opportunity has disappeared. If token consumption develops remotely along the path Goldman Sachs envisages, the world will continue to require substantial investment in processors, memory, networking and data centres.

But the distribution of returns can still change.

China’s progress challenges the assumption that the economics of AI will remain concentrated around permanently scarce and increasingly expensive computation. If equivalent or superior output can be produced at a lower cost, the scarcity premium enjoyed by parts of the hardware stack becomes harder to sustain.

At the same time, the platforms financing that infrastructure gain access to cheaper models and a larger opportunity to monetise the capacity they have already built.

The first phase rewarded the recipients of AI spending.

The next may increasingly reward its payers... and, beyond them, the companies capable of converting cheaper intelligence into better products, lower costs and stronger customer relationships.

China’s latest breakthroughs could become an important catalyst for that rotation.

They provide further evidence that capable intelligence is becoming more abundant, that application economics are improving and that the beneficiaries of the AI boom may not remain the same as its earliest winners.

That expectation has significant implications for the Alpha Tier Model Portfolio. Several positions already sit on the favourable side of this transition. The latest evidence strengthens the case for them, although it does not necessarily mean that the next unit of capital should be allocated to the same theme.

That is where we turn next.

Important Disclosure

This article contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It reproduces an excerpt from the July 2026 issue of Alpha Tier. The research, views and Alpha Tier Model Portfolio information are stated as of 24 July 2026, 4:00 p.m. Eastern Daylight Time. The original issue was first disseminated to paid subscribers on 24 July 2026 at 9:00 p.m. Eastern Daylight Time.

The publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances, knowledge, experience, liquidity requirements or tolerance for risk. The Alpha Tier Model Portfolio is an illustrative research portfolio and does not represent client assets, transactions executed by VMF Research or the performance of an investable fund or managed account.

The analysis combines thematic, fundamental, technological, portfolio-construction and market research within a medium- to long-term investment framework. Statements concerning the development of artificial intelligence, the economics of Chinese models, future token consumption and the possible migration of value towards the application layer are analytical judgments and conditional hypotheses, not assurances. Sources and the basis for material factual claims are identified throughout the article. Views and Model Portfolio conclusions may change as technological, competitive, regulatory, geopolitical, corporate or market evidence evolves and are reviewed through VMF Research’s monthly publications, with weekly or ad hoc updates where appropriate.

Investments in China, emerging markets, technology companies and AI-related financial instruments involve substantial risks, including regulatory intervention, geopolitical tension, restrictions on capital or technology flows, currency movements, governance concerns, market-access limitations, intense competition, pricing pressure, technological obsolescence, capital misallocation, valuation compression, liquidity constraints and the possible loss of capital. Improvements in model capability or lower intelligence costs may not produce higher corporate earnings, successful commercial adoption or superior investment returns. Technical and relative-strength signals may also fail.

Any references to historical recommendations or Model Portfolio performance relate to published reference prices and illustrative research portfolios. They do not represent returns earned by clients and should not be interpreted as a complete measure of investment performance.

Disclosure of interests: as of the research cut-off, neither VMF Research, Lda. nor Vasco Marques de Freitas personally held any financial instrument included in the Alpha Tier Model Portfolio. A legal entity controlled by Vasco Marques de Freitas held a long position in the KraneShares CSI China Internet ETF (KWEB), which provides exposure to several Chinese internet and application-layer companies relevant to the themes discussed in this article. This holding constitutes a financial interest and potential conflict of interest and should be considered when evaluating the analysis. Its existence does not validate the conclusions, reduce the risks described or imply that any financial instrument is suitable for a particular reader.

Neither VMF Research nor the author received compensation from any issuer discussed in connection with the preparation of this research, and no issuer reviewed, approved or amended its conclusions before first dissemination.

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

Discovery captures the imagination.

Royalties capture the cash flow.

Our previous article, The Discovery Machine, introduced the first of two biotechnology companies added to the Quality Model Portfolio in July’s issue of VMF’s Security Selection.

Regeneron represented the scientific engine: proprietary human data, experimental infrastructure and the ability to turn biological evidence into medicine. Cheaper intelligence may make that system more productive, but the company must still identify the right target, develop the treatment and survive the clinical process.

The second company offers a very different route into the same opportunity.

Halozyme does not need to discover the next blockbuster.

It gets paid when somebody else already has.

Its core technology allows certain medicines that would ordinarily require intravenous infusion to be administered beneath the skin, often in a fraction of the time. Pharmaceutical partners remain responsible for development, regulatory approval and commercialisation. Halozyme provides the delivery layer and receives fees, enzyme sales and, most importantly, royalties linked to partner revenues.

The attraction lies in the separation between participation and responsibility.

Halozyme participates in the success of multiple medicines without financing the full cost of creating each one. Its partners carry most of the scientific, clinical and commercial burden. Halozyme collects a percentage when the products sell.

That economic architecture should be familiar to long-time VMF Research readers.

We admire royalty businesses, and several appear across our Model Portfolios. The best combine exposure to valuable assets with limited requirements for incremental capital. A mineral royalty company does not need to construct and operate every mine. A music-rights company can continue collecting from a successful catalogue without financing every new recording. A biotechnology royalty company does not need to discover and commercialise every medicine on which it earns.

The assets differ. The principle does not.

Allow somebody else to commit much of the capital and assume much of the execution risk, while retaining a contractual claim on the upside.

Halozyme’s financial statements are beginning to reveal the power of that model. Royalty revenue is growing faster than the wider business, cash conversion is unusually strong and each additional dollar of partner sales requires relatively little incremental expenditure. The company is not merely expanding. Its revenue mix is becoming more valuable as it grows.

The risks remain real. Halozyme depends on medicines and commercial decisions it does not control. Its intellectual property has finite lives, competing delivery technologies exist, and its balance sheet carries more leverage than we would ideally prefer.

Royalty economics reduce certain risks.

They do not eliminate them...

The July issue was first published for paid subscribers on 17 July 2026. At the time of writing on 29 July, both biotechnology additions were trading above their initial reference prices. That early response is encouraging, although a matter of days cannot validate a medium- to long-term thesis.

Together, the two setups express the same biotech opportunity through complementary business models.

Regeneron must improve the probability of discovery.

Halozyme participates after much of the hardest scientific work has already succeeded.

The previous article introduced the discovery machine.

This one introduces the tollbooth.

Good reading.

Regeneron must discover the next medicine.

Halozyme ( ) gets paid when somebody else already has.

Its core ENHANZE technology uses a patented recombinant enzyme, rHuPH20, to temporarily break down hyaluronan beneath the skin. That creates space for large volume medicines that would otherwise require intravenous infusion to be administered subcutaneously, often more quickly, conveniently and potentially outside a hospital setting.

Halozyme licenses the technology to pharmaceutical partners, which remain responsible for developing, obtaining approval for and commercialising the resulting medicines. In return, Halozyme receives upfront fees, milestones, sales of the enzyme and, most importantly, royalties averaging a mid-single-digit percentage of partner sales.

The parallel with Altius Minerals is difficult to miss.

You might like also like reading

Altius does not need to build and operate every mine on which it owns a royalty. Its partners commit the capital, manage the assets and assume most of the operational risk. Altius participates when production succeeds.

Halozyme applies a similar economic architecture to biotechnology. Its partners bear most of the clinical, regulatory and commercial burden, while Halozyme collects a percentage of sales from medicines whose delivery its technology improves.

The underlying assets are different (mineral rights in one case, intellectual property and licensing agreements in the other) but the attraction is the same:

Participate in the upside of multiple assets without financing the full cost of creating each one.

That model is becoming increasingly visible in the financial statements.

Between 2021 and 2025, Halozyme’s revenue rose from $443 million to almost $1.4 billion. Royalty revenue increased even faster, from $204 million to $868 million, expanding from 46% to more than 62% of the total. Free cash flow rose from approximately $298 million to $645 million, equivalent to 46% of 2025 revenue. First-quarter 2026 revenue increased 42% to $377 million, while royalty revenue rose 43% to $241 million.

These are unusually attractive economics.

As partner medicines grow, Halozyme collects additional royalties without carrying a proportionate increase in research, manufacturing or commercial expenditure. The principal current drivers include subcutaneous versions of DARZALEX, Phesgo and VYVGART, while recently signed agreements with GSK, Vertex and Oruka expand the opportunity across ENHANZE and the newer Hypercon platform.

Management expects 2026 revenue of $1.71–$1.81 billion, royalty revenue of $1.13– $1.17 billion and adjusted EBITDA of $1.13–$1.21 billion.

Halozyme is therefore not merely growing.

Its revenue mix is improving as it grows.

Royalties are replacing less valuable and less predictable sources of revenue, expanding cash conversion and reducing the amount of incremental capital required to support each additional dollar of sales. The company has used that cash aggressively on behalf of shareholders. Diluted shares declined from approximately 147 million in 2021 to 124 million in 2025.

Halozyme repurchased 19.1 million shares under its previous programme at an average price of $39.31, followed by approximately seven million additional shares during 2025 at an average price close to $52.

The board has now authorised another $1 billion of repurchases through 2028, with at least $400 million expected during 2026.

At the approximately $77.50 price shown in the chart above, the shares trade near ten times the midpoint of management’s 2026 non-GAAP earnings guidance. That is an undemanding valuation for a company growing royalty revenue above 30%, converting a large proportion of revenue into cash and reducing its share count materially.

But the model is not without vulnerabilities.

Halozyme still depends on the success of medicines it does not control.

Partners determine development priorities, clinical investment, pricing and commercial execution. A trial failure, weak launch or strategic decision to abandon a programme can eliminate an expected royalty stream without Halozyme being able to change the outcome.

Revenue also remains concentrated in several important partner products, while alternative subcutaneous-delivery technologies, including Alteogen’s competing hyaluronidase platform, could weaken Halozyme’s negotiating power or reduce future adoption. ENHANZE patent expirations and contractual royalty reductions must also be considered product by product and country by country.

The balance sheet is the element we like least.

At the end of March, Halozyme carried approximately $2.14 billion of debt, against only $319 million of cash and marketable securities, leaving net debt close to $1.83 billion.

Much of that debt consists of low-coupon convertible notes maturing in 2031 and 2032, and the company’s powerful cash generation makes the burden manageable.

At the midpoint of 2026 adjusted EBITDA guidance, net leverage would fall towards 1.6 times. Nevertheless, acquisitions, continued repurchases and financial leverage now compete for the same cash.

The company has less room for strategic error than the income statement alone might suggest.

The bear case is therefore straightforward.

Partner-product growth could slow, competition could erode the value of ENHANZE,

newer platforms may fail to reproduce its success, and management could continue deploying borrowed capital into acquisitions or repurchases at unattractive prices. Halozyme’s economic model substantially reduces individual drug-development risk; it does not eliminate partner concentration, intellectual-property risk or capital allocation risk.

We believe those risks are adequately compensated.

Management’s 2026 outlook makes the strength of the underlying model unusually clear. Total revenue is expected to reach $1.71–$1.81 billion, implying growth of 22%–30%, while royalty revenue is projected to increase 30%–35% to as much as $1.17 billion. More strikingly, adjusted EBITDA is guided to $1.13–$1.21 billion, an increase of 71%–83%, with non-GAAP diluted earnings per share expected to rise 87%–99%.

Those figures require careful interpretation. Management’s adjusted EBITDA excludes several items captured in the GAAP financials, including acquisition related charges and intangible amortisation, and is therefore not directly comparable with the GAAP EBITDA published before.

We prefer to underwrite the business using the more conservative measure and treat the company’s guidance as evidence of operating momentum rather than a substitute for our own valuation work.

Even on that basis, the outlook is powerful.

Halozyme’s highest-quality revenue stream is becoming a larger share of the business, partner medicines continue to expand, and the incremental economics remain exceptionally attractive. The company does not need to identify the next blockbuster itself. It needs a growing portfolio of established partners to keep selling medicines that physicians and patients already value, while Halozyme collects a royalty each time they do.

The long-term chart reflects that quality.

For more than two decades, the shares have advanced within a remarkable rising channel, repeatedly absorbing volatility without breaking the broader trend. Price remains above rising long-term moving averages, while relative strength against the wider equity market remains firmly positive. We see no reason to conclude that this pattern has reached its end.

Against the backdrop of the healthcare and biotechnology opportunity we have developed across VMF Research, Halozyme represents a compelling addition to our roster: a capital-light royalty engine, growing profitability, powerful cash conversion and exposure to multiple successful medicines without assuming the full clinical and commercial risk of creating each one.

Regeneron gives us ownership of the discovery machine.
Halozyme gives us the tollbooth.

You might also like reading, The Intelligence Dividend - How cheaper AI cuts biopharma’s $300B failure risk and drives the next healthcare rotation:

Important Disclosure

This article contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It reproduces an excerpt from the July 2026 issue of VMF’s Security Selection. The research, views, valuation analysis and Quality Model Portfolio information are stated as of 17 July 2026, 4:00 p.m. Eastern Daylight Time, unless another date is expressly identified. The original issue was first disseminated to paid subscribers on 17 July 2026 at 9:00 p.m. Eastern Daylight Time. Any reference to subsequent price performance is stated separately and does not amend the original analysis.

The publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances, knowledge, experience, liquidity requirements or tolerance for risk. VMF Research’s Model Portfolios are illustrative research portfolios and do not represent client assets or transactions executed by VMF Research.

The analysis combines thematic, fundamental, scientific, valuation and market research within a medium- to long-term investment framework. Forecasts, valuation assessments and statements concerning future revenue, royalties, cash generation or operating leverage are analytical judgments rather than assurances. Sources and the basis for material factual claims are identified throughout the article. Views and conclusions may change as scientific, clinical, regulatory, competitive, corporate or market evidence evolves and are reviewed through VMF Research’s monthly publications, with weekly or ad hoc updates where appropriate.

Investments in Halozyme Therapeutics and other biotechnology companies involve substantial risks, including dependence on partner products and commercial decisions, clinical or regulatory setbacks affecting licensed medicines, product and customer concentration, loss or expiry of intellectual-property protection, competing drug-delivery technologies, pricing and reimbursement pressure, leverage, acquisition and capital-allocation risk, market volatility and the possible loss of capital. A royalty-based business model may reduce some development and commercial risks without eliminating them. Historical growth, cash conversion and technical trends may not persist, and valuation assumptions may fail.

Disclosure of interests: as of the research cut-off, neither VMF Research, Lda., Vasco Marques de Freitas personally, nor any legal entity controlled by him held a position in Halozyme Therapeutics or Regeneron Pharmaceuticals. A legal entity controlled by Vasco Marques de Freitas held a long position in Altius Minerals Corporation, which is referenced in the article as a comparative example of a royalty-based business model. This constitutes a financial interest and potential conflict of interest and should be considered when evaluating the comparison. Neither VMF Research nor the author received compensation from any issuer discussed in connection with the preparation of this research, and no issuer reviewed or approved its conclusions before first dissemination.

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

The most valuable AI asset in biotechnology cannot be downloaded.

It has to be found in millions of human bodies, validated in laboratories and proved in patients.

That distinction sits at the centre of the research we have been publishing throughout July.

In The China AI Reset, we examined how improving Chinese models are driving down the cost of capable intelligence. In The Intelligence Dividend, we followed that supply shock into healthcare, an industry where better decisions can save years of work and hundreds of millions of dollars. Most recently, When Cheap Starts to Lead showed that the opportunity is no longer supported by valuation alone. Healthcare earnings expectations are improving, participation is widening and biotechnology has begun to behave like an emerging market leader.

The next step is the one VMF’s Security Selection was created to perform.

Move from the sector to the business.

More capable algorithms will eventually become available to every large pharmaceutical company. The same cannot be said for proprietary human data, experimental infrastructure or the ability to turn a biological insight into an approved medicine.

Regeneron has spent decades assembling that chain.

Its Genetics Centre has already sequenced more than three million samples and connected genetic information with clinical, proteomic and other molecular data. Additional agreements could expand the evidence base dramatically. The advantage lies not only in the size of the dataset, but in what surrounds it: human genetics identifies a possibility, laboratory biology tests the mechanism, proprietary technologies design the treatment and clinical development determines whether it works.

Artificial intelligence can make every stage more productive.

It can search relationships across genetic variants, proteins, diagnoses and treatment outcomes at a scale conventional research cannot match. It may improve target selection, patient stratification and trial design. It may help scientists recognise promising signals earlier.

Its greatest contribution could be more prosaic.

It may help Regeneron abandon the wrong programmes sooner.

That matters enormously when annual research spending exceeds $6 billion. A weak target killed one year earlier can create substantial value without producing a single new medicine. In biotechnology, avoiding an expensive failure can be almost as important as accelerating a success.

The share price tells a less ambitious story.

Investors see an ageing ophthalmology blockbuster, rising biosimilar competition, slowing reported growth and an increasingly expensive research organisation. Those concerns are real. EYLEA remains under pressure, Dupixent must continue carrying more of the economic burden, and biology has already demonstrated that even excellent science can fail in a pivotal trial.

That tension is what makes the company interesting.

Regeneron is being judged increasingly through the decline of the medicine that financed its expansion. Our research asks whether the more appropriate unit of analysis is the scientific institution built with those profits: a company with proprietary biological evidence, integrated discovery capabilities, substantial current cash flow and the financial strength to keep compounding its research advantage.

This is not an argument that AI will make biology predictable.

Biology rarely grants investors that luxury.

It is an argument that cheaper intelligence may increase the value of scarce evidence, improve the allocation of research capital and strengthen the economics of the organisations best equipped to convert data into medicine.

That is how the work fits together across VMF Research.

VMF’s Strategic Asset Allocation identifies where economic value and market leadership may be migrating. VMF’s Security Selection underwrites the individual companies positioned to capture that change. Alpha Tier considers how quality, asymmetry, conviction and risk budget should coexist inside a more concentrated Model Portfolio.

The research below was first published for paid subscribers on 17 July 2026 at 9:00 p.m. Eastern Daylight Time. Regeneron was one of two new additions to the Quality Model Portfolio in July’s issue of VMF’s Security Selection.

Later this week, we will publish the second.

Regeneron must discover the next medicine.

The other company gets paid when somebody else already has.

One owns the discovery machine.

The other owns the tollbooth.

Today, we open the first.

-

Good reading.

The most valuable dataset in artificial intelligence may not be written in words.

It may be written in the differences between millions of human bodies: why one person develops a disease while another remains protected; why a medicine works in one patient and fails in the next; which molecular changes precede illness; and which merely accompany it.

Regeneron ( ) has spent years assembling that evidence.

Its Genetics Centre has already sequenced more than three million samples and connected genetic information with clinical, proteomic and other molecular data. A separate agreement with Truveta could extend the work to as many as ten million consented participants. More recently, Regeneron secured access to de-identified health records covering approximately 300 million people through TriNetX, with the explicit objective of linking those clinical histories to its molecular datasets and using the resulting information in drug discovery, product development and the training of AI systems.

That is what makes Regeneron particularly relevant to the thesis we developed last week in VMF’s Strategic Asset Allocation.

The model is becoming cheaper.
The evidence required to make it useful is not.

Every large pharmaceutical company will gain access to more capable algorithms.

Far fewer possess proprietary human data at sufficient scale, the experimental infrastructure required to validate what those algorithms discover and the ability to transform the result into an approved medicine.

Regeneron owns all three.

It is a fully integrated biotechnology company: it identifies biological targets, engineers potential treatments, conducts clinical trials, manufactures medicines and commercialises them directly or through selected partners. Its internal research has produced 15 approved or authorised medicines over roughly fifteen years, while nearly 50 candidates are now moving through clinical development across six therapeutic areas. Most originated inside Regeneron’s own laboratories.

Yet the market is increasingly valuing the company through a different lens.

It sees the decline of EYLEA, the ophthalmology medicine that financed much of Regeneron’s expansion. That concern is legitimate. The original product is losing share to competition and biosimilars, while EYLEA HD has not yet grown quickly enough to stabilise the combined franchise.

Our thesis is not that the market has misunderstood the EYLEA problem.
It is that it may be using the wrong unit of analysis.

Regeneron is being treated increasingly as the owner of an ageing blockbuster.

We believe it should be valued as a scientific institution that has repeatedly created blockbusters... and whose proprietary data may become more valuable as the cost of intelligence falls. That distinction is the investment opportunity.

From Biological Evidence to Medicine

Most drug discovery begins with a hypothesis (the same starting point as any rigorous investment process).

Regeneron is attempting to begin with human evidence. Its Genetics Centre searches for naturally occurring genetic variations associated with disease, protection or treatment response. A harmful mutation may reveal a biological pathway that should be blocked. A protective mutation can be even more valuable: it may show that reducing the activity of a particular gene is both beneficial and tolerated by the human body.

That does not make the resulting target automatically investable. Correlation is not causation, and a statistically compelling relationship may still prove impossible to manipulate safely.

Regeneron’s advantage is that the dataset sits inside a wider experimental system. The company can test genetic associations using organoids, RNA interference, CRISPR models and humanised mice. Its proprietary VelociSuite technologies can then generate fully human antibodies, bispecific antibodies, T-cell receptors and other therapeutic formats designed to act on the validated target. Regeneron can subsequently carry the candidate through clinical development, manufacturing and commercialisation.

The economic chain is therefore unusually complete:

Human genetics identifies the possibility. Biology tests the mechanism. Technology designs the medicine. Clinical evidence determines whether it works.

Artificial intelligence can improve several decisions inside that chain. It can search relationships across genetic variants, proteins, diagnoses, treatments and longitudinal outcomes at a scale beyond conventional human analysis. It may help identify causal pathways, prioritise targets, discover biomarkers, select the patients most likely to respond and design trials capable of producing clearer evidence.

But the most valuable contribution may be less glamorous.

AI may help Regeneron decide what not to develop.

The company expects to spend between $6.45 billion and $6.68 billion on research and development during 2026. When the annual research budget is that large, terminating an unpromising programme one year earlier can create substantial value... even if the technology never discovers a blockbuster by itself.

That is how we think investors should frame the opportunity.

The near-term AI benefit is unlikely to appear as a separate revenue line. It may first appear through better capital allocation inside the laboratory: fewer weak targets reaching expensive trials, more precise patient selection and a higher proportion of resources directed towards programmes with strong human evidence.

A modest improvement in those decisions would matter.

A revolutionary improvement is not required.

The Current Business Still Has to Carry the Thesis

Regeneron’s AI and data advantage is compelling, but it cannot substitute for the economics of the existing business.

Those economics are strong...

The five-year financial record tells a more nuanced story than the share-price decline suggests. Reported revenue fell sharply from $16.1 billion in 2021 to $12.2 billion in 2022, before recovering to $13.1 billion in 2023, $14.2 billion in 2024 and $14.3 billion last year. The recovery has therefore been persistent, but modest: revenue has grown at roughly 5.6% annually since 2022, with growth slowing to only 1% in 2025.

The original decline was less alarming than it appears.

Approximately $6.2 billion of Regeneron’s 2021 revenue came from REGEN-COV, its Covid-19 antibody treatment. The emergence of Omicron rendered the medicine largely ineffective, causing the FDA to restrict its use and eliminating what had always been an exceptional source of revenue. Excluding Regeneron’s Covid antibodies, underlying revenue increased 17% in 2022.

The apparent collapse was therefore primarily the disappearance of a pandemic windfall... not the deterioration of the core business.

What followed, however, has been less impressive.

Profitable Enough to Keep Investing

Regeneron remains extraordinarily well financed.

At the end of March, the company held approximately $18.5 billion of cash and marketable securities, against less than $2 billion of long-term debt and approximately $720 million of finance-lease liabilities. Net financial resources consequently approached $15.8 billion, equivalent to almost one-quarter of the company’s current market capitalisation ($73 billion).

The operating business also continues to generate substantial cash. In 2025, Regeneron produced approximately $5.0 billion of operating cash flow. After nearly $900 million of capital expenditure, free cash flow reached roughly $4.1 billion, equivalent to almost 29% of revenue. The company used that capacity to repurchase $3.4 billion of shares, pay its first $370 million of dividends and continue expanding its scientific and manufacturing infrastructure.

It did so while spending $5.85 billion on research and development, or almost 41% of revenue. During the first quarter of 2026, R&D rose another 16% to $1.54 billion (nearly 43% of quarterly revenue).

That combination is rare:

Regeneron can invest more than $6 billion annually in future medicines while continuing to generate billions in free cash flow and maintaining a fortress balance sheet.

The financial capacity is not in question.
The productivity of the capital being deployed is.

Why Revenue Is Languishing

Regeneron’s present revenue profile is being pulled in opposite directions. Net product sales fell from $7.6 billion in 2024 to $6.3 billion in 2025. Over the same period, collaboration revenue increased from $6.1 billion to $7.3 billion. Most of that increase came from Regeneron’s share of the rapidly expanding profits generated by Dupixent, while most of the pressure came from the declining EYLEA ophthalmology franchise.

In simple terms: Dupixent is replacing what EYLEA is losing but not yet creating enough incremental growth to make the broader company accelerate.

The accounting also obscures some of the underlying progress. Sanofi records Dupixent’s global product sales. Regeneron reports its contractual share of the collaboration profits rather than the entire revenue generated by the medicine. Dupixent and Kevzara sales reached $18.4 billion in 2025, up from $14.6 billion, while Regeneron’s Sanofi collaboration revenue increased by approximately $1.35 billion.

The growth is real.
It simply appears differently in Regeneron’s financial statements.

EYLEA is the mirror image. Regeneron records US sales directly, so the decline is fully visible in reported product revenue. Competitive treatments, falling net prices, the migration of patients to EYLEA HD and the arrival of biosimilars have all pressured the original formulation. More biosimilar launches are expected during the second half of 2026.

The company is therefore undergoing a difficult but understandable transition:

• From pandemic revenue to recurring franchises.

• From legacy EYLEA towards EYLEA HD.

• From direct product revenue towards greater Dupixent collaboration economics.

• From a smaller research organisation towards a much broader (and more expensive) development platform.

The business is not stagnant.
Its strongest growth engine is being offset by its most visible decline.

Where the Next Revenue Can Come From

The bull case does not depend on one heroic breakthrough. It depends on several existing and emerging engines beginning to work at the same time.

Dupixent remains the clearest source of growth.

Its underlying biology extends across multiple Type 2 inflammatory diseases, giving Regeneron and Sanofi room to expand the franchise through new indications, geographies and age groups. Regeneron’s share of commercial profits rose strongly again in the first quarter, while the remaining historical development balance owed to Sanofi is nearing repayment. Once cleared, a larger share of future collaboration economics should flow through to Regeneron. Dupixent will inevitably slow as the base becomes larger, but it does not need to sustain 30%-plus growth. Continued double-digit expansion would be enough to support the company while the rest of the portfolio matures.

Libtayo is also becoming economically relevant. First-quarter sales rose 54% to $438 million, with Regeneron retaining greater control over the economics than it does with Dupixent. Lynozyfic broadens the oncology portfolio, while garetosmab and cemdisiran could receive regulatory decisions before year-end. None is likely to replace EYLEA alone. Together, they reduce the need for any single asset to do so.

The longer-duration opportunity lies in Factor XI inhibition, metabolic disease, oncology, genetic medicines and additional immunology programmes. This is where Regeneron’s data and AI advantage matters most. The immediate benefit is unlikely to appear as a separate revenue line. It should appear first in better decisions over which programmes deserve capital (and which should be abandoned earlier). When annual R&D spending exceeds $6 billion, avoiding one costly failure can be almost as valuable as accelerating one success.

EYLEA HD is the bridge between the current business and that future.

The product is gaining traction. As the chart on page 10 makes clear, EYLEA HD is gaining share within the franchise, but not yet quickly enough to offset the decline of legacy EYLEA. Some of the recent weakness reflects seasonality and inventory normalisation... but the more important pressure is structural, driven by lower pricing, competing therapies and biosimilars.

EYLEA HD nevertheless has credible advantages: longer and more flexible dosing intervals, an expanded label, a potential pre-filled syringe and deep relationships with retinal specialists. Those strengths may eventually return the franchise to modest growth.

But that is not our base case.

The hurdle is lower... and more realistic:

EYLEA HD does not need to recreate peak EYLEA. It needs to slow the decline enough for Dupixent, Libtayo and the pipeline to become visible through it.

A return to growth would be meaningful upside. Stability would already be enough to support the thesis.

What Can Go Wrong

The principal risk is that Regeneron’s transition takes longer than the market or the financial model can tolerate.

Dupixent and the US EYLEA franchise together still accounted for approximately 71% of first-quarter revenue. Diversification across nearly 50 clinical programmes is not the same as diversification of present cash flow.

• Several developments could weaken the thesis materially:

• EYLEA biosimilars could accelerate pricing and volume erosion before EYLEA HD reaches sufficient scale.

• Dupixent growth could normalise more quickly than expected, leaving the company dependent on products that are not yet economically material.

• The pipeline could produce medically useful but commercially modest medicines.

• R&D expenditure could continue rising while operating margins compress.

• The company’s large securities portfolio can make GAAP net income more volatile and less representative of underlying operating earnings.

• AI may improve discovery efficiency without solving the late-stage clinical failures that consume the greatest amount of capital.

• Pharmaceutical pricing policy could constrain returns on future innovation.

The fianlimab1 melanoma failure is a useful reminder. Regeneron can possess world class scientists, proprietary data and sophisticated technology—and biology can still refuse to cooperate.

The quality of the platform reduces risk.

It does not remove it.

Valuation: What Has to Happen by 2028

Regeneron is not priced as a distressed biotechnology company. Nor does the current valuation require flawless execution.

At approximately $678 per share, the equity is valued at roughly $73.0 billion. Against trailing sales of approximately $14.9 billion, free cash flow of $4.1 billion and EBITDA of about $4.2 billion, the shares trade at 17.7 times free cash flow. The company’s $15.8 billion net-cash position reduces enterprise value to approximately $57.1 billion, equivalent to 3.8 times sales and 13.7 times EBITDA.

Those multiples are not obviously cheap in isolation. They become more interesting when attached to a net-cash company that continues to generate substantial free cash flow while investing more than 40% of revenue in research.

We value Regeneron primarily on 2028 free cash flow. That gives Dupixent time to expand, EYLEA HD time to moderate ophthalmology erosion and the emerging portfolio time to become more economically visible. Importantly, our base case assigns no explicit revenue to AI. Better intelligence is reflected only through the possibility of more productive research allocation and greater durability beyond the forecast period.

The bear case does not require a scientific collapse. EYLEA erosion simply remains severe, Dupixent slows before new products become material and elevated R&D spending prevents margins from recovering. Regeneron remains profitable, but the market values it increasingly like a mature pharmaceutical company.

The base case assumes something less dramatic: Dupixent continues growing at a sustainable rate, EYLEA HD bends the ophthalmology decline, Libtayo and the emerging portfolio broaden the revenue base and free-cash-flow margins remain close to recent levels. A modest rerating to 19 times FCF would produce approximately 33% market-cap appreciation.

The bull case requires the engines to align. Ophthalmology stabilises, Dupixent remains unusually durable, several new medicines become commercially meaningful and investors begin assigning greater value to Regeneron’s scientific platform and proprietary data. Under those conditions, double-digit sales growth and operating leverage could nearly double the company’s equity value.

Applying probabilities of 20% to the bear case, 55% to the base case and 25% to the bull case produces a probability-weighted market capitalisation of approximately $99 billion, or roughly 35% above the current level, before dividends and share repurchases.

That is attractive, but not riskless.

The valuation offers a reasonable price for exceptional scientific and financial quality... not a margin of safety large enough to ignore product concentration or clinical attrition. It supports a 2% position, while preserving room to increase the allocation if the financial and technical evidence continues to improve.

The Chart Is Reaching a Decision

The technical setup is becoming more constructive, but confirmation has not yet arrived.

After falling sharply from its 2024 peak, Regeneron has spent more than a year compressing inside a large triangle. Descending resistance from the previous high is converging with rising support from the 2025 low, leaving the shares close to a visible decision point.

Price remains marginally below the 20- and 50-week moving averages and well beneath the 200-week average. Those levels still represent resistance.

Encouragingly, however, the shares are challenging the shorter-term averages as relative strength against the broader equity market begins to improve after a prolonged period of underperformance.

The triangle can still resolve in either direction.

A breakout above descending resistance and the nearby moving averages would indicate that investors are beginning to look beyond EYLEA’s erosion and towards Dupixent’s durability, the pipeline and Regeneron’s longer-term scientific optionality. A break below rising support would undermine the recovery and expose meaningful downside.

Our constructive view on healthcare (and biotechnology in particular) raises the probability, in our judgement, of an upside resolution. Sector participation is broadening, biotech has already broken out and Regeneron is approaching resistance as its relative performance begins to repair.

This has been a lengthy thesis because the opportunity demands it. The upside is substantial, but so are the product-concentration, clinical and execution risks; the downside must be underwritten rather than dismissed. Our next recommendation will require less extensive treatment because, in our assessment, its fundamental risk profile is materially lower.

You might also like reading, The Intelligence Dividend - How cheaper AI cuts biopharma’s $300B failure risk and drives the next healthcare rotation:

Important Disclosure

This article contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It reproduces an excerpt from the July 2026 issue of VMF’s Security Selection. The research, views, valuation analysis and Quality Model Portfolio information are stated as of 17 July 2026, 4:00 p.m. Eastern Daylight Time, unless another date is expressly identified. The original issue was first disseminated to paid subscribers on 17 July 2026 at 9:00 p.m. Eastern Daylight Time.

The publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances, knowledge, experience, liquidity requirements or tolerance for risk. VMF Research’s Model Portfolios are illustrative research portfolios and do not represent client assets or transactions executed by VMF Research.

The analysis combines thematic, fundamental, scientific, valuation and market research within a medium- to long-term investment framework. Forecasts, scenario outcomes, valuation estimates and statements concerning the potential effects of artificial intelligence are analytical judgments rather than assurances. Sources and the basis for material factual claims are identified throughout the article. Views and conclusions may change as scientific, clinical, regulatory, corporate or market evidence evolves and are reviewed through VMF Research’s monthly publications, with weekly or ad hoc updates where appropriate.

Investments in Regeneron Pharmaceuticals and other biotechnology companies involve substantial risks, including clinical-trial failure, regulatory rejection, product concentration, loss of intellectual-property protection, biosimilar and competitive pressure, adverse pricing or reimbursement decisions, rising research expenditure, scientific uncertainty, market volatility and the possible loss of capital. Artificial intelligence may improve research productivity without producing successful medicines, higher earnings or superior investment returns. Technical patterns and valuation assumptions may also fail.

Disclosure of interests: as of the research cut-off, neither VMF Research, Lda., Vasco Marques de Freitas personally, nor any legal entity controlled by him held a position in Regeneron Pharmaceuticals or in any other financial instrument mentioned in this article. Neither VMF Research nor the author received compensation from any issuer discussed in connection with the preparation of this research, and no issuer reviewed or approved its conclusions before first dissemination.

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

This Weekly Update was first published for Tier One subscribers on 25 July 2026.

We argued that long-term Treasury yields had entered what we call the TACO Zone: the point at which tighter financial conditions begin imposing enough political and economic pain to make a Trump retreat increasingly likely.

Two days later, President Trump has paused US strikes on Iran. Oil is falling sharply, Treasury yields are easing and risk assets are rallying.

The TACO may now be unfolding in real time.
Here is what we wrote before the turn.

Long-term yields have pushed into the zone we label the TACO Zone.

The 30-year Treasury yield is testing and probing the 5.16–5.20 area after a sustained rise along its multi-year uptrend. We call it the TACO Zone for a reason. At these levels, higher long-end yields begin to impose visible political and economic costs: rising mortgage rates, pressure on equity valuations, and tighter financial conditions that become harder to ignore.

History suggests this is precisely the point at which incentives can shift. For an administration focused on growth and market stability, the political calculus of allowing yields to break decisively higher starts to deteriorate.

The temptation to deliver a TACO (Trump Always Chickens Out), to soften rhetoric, adjust policy signals, or create conditions that ease the long end, tends to rise once the pain threshold is reached.

A decisive break above the zone would tighten financial conditions further and raise the bar for our Dovish Shock confirmation ladder.

The market is still priced for a restrictive path; this is the latest reminder that monetary evidence must still improve before the thesis can move from alive to confirmed.

At the same time, the very existence of the TACO Zone highlights the growing tension between market pricing and political incentives.

Energy has been the clearest beneficiary of the renewed geopolitical risk premium. XLE has broken higher from a textbook flag consolidation, accompanied by improving relative strength versus the broader US equity market.

The existing allocation is responding exactly as the Iran related supply fears and oil volatility would suggest. Absolute momentum and relative performance are both moving in the same direction for the first time in several months.

At the same time, another important dynamic is gripping markets: growing uncertainty about the ultimate size, speed and profitability of the AI capex boom.

Recent breakthroughs from Chinese laboratories continue to change the calculation, both the size of the overall pie and how the slices may ultimately be distributed. Capability is improving while costs compress.

That combination pressures the scarcity premiums that defined the first phase of the trade.

Our recent exit from the remaining EWY position after a 238% total return therefore appears increasingly timely.

We entered when memory was still an underappreciated bottleneck. We left once the market had fully discovered the thesis and price had begun to embed an unusually optimistic future. The subsequent reversal in South Korean equities simply reinforces the value of harvesting when recognition arrives.

No change to the Model Portfolio is required.

The existing exposures already capture the energy risk premium and the downstream application-layer opportunity. Higher long-end yields remain a headwind that the Dovish Shock thesis must still overcome, even as political incentives to deliver a TACO begin to build. We will continue to measure price against process rather than force activity for its own sake.

Good investing!

Vasco Marques de Freitas, CFA, CMT

Important Disclosure

This article reproduces general investment research first published for Tier One subscribers on 25 July 2026. It was produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. The original analysis, charts and conclusions reflect the information available at the time of publication and have not been revised with hindsight. Any introductory comments concerning subsequent events were added when the article was republished and should be distinguished from the original research.

The publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s objectives, financial circumstances, knowledge, experience or tolerance for risk. The Tier One Model Portfolio is an illustrative research portfolio and does not represent client assets or transactions executed by VMF Research.

The analysis combines macroeconomic, political, fundamental and technical evidence within a medium- to long-term framework. The “TACO Zone” and “Dovish Shock” are conditional analytical hypotheses, not forecasts or assurances. Subsequent developments may appear consistent with the analysis without confirming its broader conclusions, and market conditions can reverse rapidly. Views are reviewed through VMF Research’s monthly publications and may be updated through weekly or ad hoc research.

Equities, energy-sector instruments, sovereign bonds and interest-rate-sensitive assets involve substantial market, geopolitical, policy, liquidity and valuation risks. Technical signals may fail, political decisions may change without warning and investors may incur material losses.

Disclosure of interests: as of the original publication date, neither VMF Research, Lda. nor Vasco Marques de Freitas personally held any financial instrument mentioned in this article.

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

The market does not reward you for finding something cheap. It rewards you when everyone else is forced to stop ignoring it.

That distinction has shaped our healthcare work from the beginning.

When we first made the case in October 2025, the opportunity was visible in valuation but absent from the tape. Healthcare companies were producing attractive returns on capital, yet many traded at unusually depressed multiples. Biotechnology looked even more extreme. The sector was strategically indispensable, financially neglected and deeply unpopular.

That was enough to establish asymmetry. It was not enough to establish timing.

UnitedHealth tested that distinction almost immediately. The position moved against us as medical costs rose, management turmoil intensified and political hostility towards managed care deepened. The headlines became worse. The valuation became more compelling. The easy response would have been to confuse discomfort with disproof.

We did not.

UnitedHealth has since delivered a total return above 20% from our reference price and outperformed the S&P 500 over the same period. The result matters. The sequence matters more.

We did not discover healthcare after it began working. We remained with the thesis long enough for the evidence to change.

That change is now spreading well beyond one company.

Recent articles on VMF’s Market View have followed the same development from several angles. The China AI Reset examined how rapidly falling intelligence costs could move the AI boom downstream.

The Intelligence Dividend argued that the greatest economic benefit may appear in industries where mistakes consume the most capital. Our latest public Leaderboard showed that healthcare and biotechnology were already contributing several of the strongest results in the VMF Research record.

The separate threads are beginning to converge.

Analysts are raising healthcare earnings expectations after a prolonged period of deterioration. Valuations remain subdued. Global healthcare equities are pressing towards new highs. Biotechnology has already broken out in both absolute and relative terms. Leadership is appearing across managed care, pharmaceuticals, diversified healthcare and individual drug-development platforms.

This no longer looks like one oversold stock catching a bid. It looks increasingly like capital beginning to rotate.

Artificial intelligence is not the only explanation. Patent expirations are forcing large pharmaceutical companies to replenish their pipelines. Biopharma acquisitions are accelerating. Financing conditions are improving. Clinical progress still matters more than any fashionable narrative.

But cheaper intelligence could make the entire system more productive.

Markets will not wait for that productivity to appear neatly in reported earnings. They will begin capitalising it when confidence improves that proprietary biological data, better target selection, more precise patient stratification and stronger trial design can alter the probability of success.

That repricing may already have started.

The section below was first published for paid subscribers in the July issue of VMF’s Security Selection on 17 July 2026 at 9:00 p.m. Eastern Daylight Time. It revisits the original healthcare valuation thesis, examines the improving fundamental and technical evidence, and explains why biotechnology may be moving from neglected optionality to emerging leadership.

The full issue then performs the work Tier Two was built to do. It moves from an attractive sector to the specific businesses we believe are best positioned to capture its changing economics, adding two complementary biotechnology companies to the Quality Model Portfolio.

One owns the discovery machine.

The other owns the tollbooth.

Before we get there, the sector itself must pass a more basic test.

Cheap has to start leading.

Here is the evidence.

Good reading.

Cheap is not a catalyst.

When we first laid out the healthcare opportunity in October 2025, we were explicit about its limitation: valuation revealed the asymmetry, not the timing. The sector already displayed the kind of dislocation that attracts us most: durable economics, high returns on capital and undeniable strategic relevance, obscured by weak sentiment, policy uncertainty and a market still directing the marginal dollar towards technology.

The evidence was unusually broad.

Most healthcare industries were trading at or below the broader market multiple, often near the lower end of valuation ranges extending back more than three decades. Biotechnology stood out. Its historical range was wider than that of almost any other healthcare industry, but the prevailing multiple sat near its lower boundary and well below the S&P 500.

Low multiples alone did not explain the opportunity.

The more revealing comparison placed valuation beside profitability. Investors were paying the highest price-to-book multiples for semiconductors, information technology and software (industries whose strong returns on equity appeared to justify a premium).

Yet pharma and biotechnology were producing similarly attractive returns on shareholders’ capital while trading on the inexpensive side of the market relationship.

The sector was not cheap because its economics were uniformly poor.

It was cheap despite some unusually strong economics.

That work led directly to the addition of UnitedHealth Group ( ) to the Quality Model Portfolio.

The timing was uncomfortable.

The position initially moved against us as medical-cost pressure, management upheaval, regulatory scrutiny and hostility towards the managed-care industry intensified. But a difficult entry does not invalidate a sound thesis. It tests whether the investor understands what is temporary, what is structural and what the price already discounts.

UnitedHealth has since produced a total return above 20% from our reference price and has outperformed the S&P 500 over the same period.

The result matters, but the more important point is what it illustrates. We did not arrive at healthcare after the charts improved. We identified the valuation opportunity while the sector was still being used as a source of funds. Today, the original valuation case remains largely intact, but the surrounding evidence is becoming more supportive.

The opportunity is beginning to acquire a catalyst.

Expectations Are Turning First...

The shift is also visible in Europe.

After more than fifteen months of deteriorating estimates, analysts have started upgrading healthcare earnings expectations. The forward earnings of the Stoxx 600 Health Care Index are now improving relative to those of the broader Stoxx 600, even as the sector’s relative price remains close to the depressed levels from which previous rotations began.

The two series do not need to turn simultaneously.

Earnings revisions often improve before price leadership becomes obvious.

The important development is that the fundamental line is beginning to rise while the relative-price line has stopped collapsing. The gap between them creates the possibility that price will eventually follow expectations higher.

The sector is also beginning that repair from a favourable valuation base.

European healthcare remains below its historical average on both measures shown in the next exhibit. Its forward P/E is still inexpensive in absolute terms, while its relative P/E versus the wider European market remains deeply discounted. Recent price stabilisation has therefore not eliminated the valuation opportunity. It has merely reduced the probability that the market will continue ignoring it indefinitely.

This is where our interpretation begins to diverge from the emerging analyst consensus.

The analyst community is increasingly prepared to acknowledge that artificial intelligence may provide structural support to healthcare. Yet the prevailing assumption remains that any meaningful impact will be distant... that the technology may ultimately improve drug discovery, clinical development and operational efficiency, but will contribute little to the sector’s near-term investment case.

That conclusion may prove too conservative.

The full effect on revenues, margins and reported earnings will take time. Biological research cannot be reduced to a software-release cycle. Molecules still need to survive clinical trials. Regulators still need evidence. Manufacturing and commercialisation still matter.

But share prices do not wait for the full economic benefit to appear in the financial statements.

Markets capitalise changing expectations.

If investors become more confident that cheaper intelligence can improve target selection, molecular design, patient stratification, trial enrolment and the utilisation of proprietary biological data, the valuation of the companies controlling those assets can change long before the resulting medicines reach patients. The immediate catalyst does not need to be a visible AI revenue line. It can be a change in how the market values scientific productivity, data ownership and the probability of future success.

That repricing may already be beginning.

The Rotation Is Broadening

The Relative Rotation Graph below compares every major S&P 500 sector ETF against the broader market. We have also added the healthcare exposures most relevant to our own research: IXJ ( ) and SBIO ( ) , held through Tier One and Alpha Tier, together with Roivant Sciences, Novo Nordisk and UnitedHealth Group.

The result is unusually revealing.

Roivant ( ), UnitedHealth ( ) and SBIO all sit in the Leading quadrant.

That placement means they combine above-market relative strength with positive relative momentum. They are not merely rebounding from depressed levels. They are already behaving as leaders.

Each arrived there through a different route.

Roivant reflects the market’s growing recognition of a differentiated biotechnology platform and a record of disciplined asset monetisation. UnitedHealth reflects the recovery of a deeply impaired but highly valuable healthcare franchise. SBIO captures the improving appetite for smaller biotechnology companies with clinical, regulatory and strategic optionality.

Three distinct expressions of the same broad opportunity are now leading the market.

The Improving quadrant is equally important.

XLV, IXJ and Novo Nordisk sit there with strengthening relative momentum, even though their relative-strength ratios have not yet crossed into leadership. On a conventional price chart, that may look like incomplete confirmation. On an RRG, it is precisely the transition investors should watch.

Markets rotate.

Leadership rarely appears fully formed. Securities ordinarily move from Lagging into Improving before crossing into Leading. The direction and persistence of the tails therefore matter as much as the current quadrant. XLV, IXJ and Novo Nordisk are not guaranteed to complete that rotation, but the probability is rising as their relative momentum continues to strengthen.

The broader message is that healthcare leadership is no longer confined to one stock, one subsector or one investment style.

It is spreading across:

• Managed care.

• Large-cap pharmaceuticals.

• Broad global healthcare.

• Emerging biotechnology.

• Individual drug-development platforms.

The accompanying table reinforces the distinction. UnitedHealth, Roivant and SBIO have produced particularly strong recent advances, but the improvement extends further. Novo Nordisk, XLV and IXJ are also moving higher, consistent with their progression through the Improving quadrant.

That breadth matters.

A single successful clinical trial can propel one biotechnology company. A relief rally can temporarily lift one damaged insurer. A genuine sector rotation looks different. It begins to appear across instruments with separate business models, market capitalisations and fundamental drivers.

We are increasingly seeing the latter.

Biotech Breaks First

The final chart in this section brings the argument into sharper focus.

SBIO has broken above a resistance area that had contained the ETF since the previous biotechnology cycle. Price is now comfortably above its rising medium- and long-term moving averages, while relative strength against the broader equity market has completed a prolonged base and is moving higher.

The technical structure is important for two reasons.

First, biotechnology often acts as the higher-beta expression of the broader healthcare complex. When investors become more willing to underwrite scientific, clinical and financing risk, biotechnology can begin outperforming before the more defensive parts of the sector achieve full leadership.

Second, the breakout suggests that the market is beginning to look beyond the sector’s familiar objections. Policy uncertainty, capital scarcity, clinical failure and long development timelines have not disappeared. What has changed is the price investors appear willing to pay for the opportunity on the other side of those risks.

When we first wrote about healthcare in October 2025, the thesis rested predominantly on valuation. The sector was cheap, profitable and strategically indispensable... but the tape was still hostile.

Today, the setup is more complete.

The valuation case remains intact, earnings expectations are improving, AI is introducing a potentially important new source of productivity, and the market is confirming the change through widening participation and outright biotechnology leadership.

The opportunity is there.
Increasingly, so is the timing.

That brings us to the work Tier Two is designed to perform: moving from an attractive sector to the specific businesses best positioned to capture its changing economics.

This month, we are adding two of them.

You might also like reading, The Intelligence Dividend - How cheaper AI cuts biopharma’s $300B failure risk and drives the next healthcare rotation:

Important Disclosure

This article contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It reproduces an excerpt from the July 2026 issue of VMF’s Security Selection. The research, views and references to VMF Research’s Model Portfolios are stated as of 17 July 2026, 4:00 p.m. Eastern Daylight Time, unless another date is expressly identified. The original issue was first disseminated to paid subscribers on 17 July 2026 at 9:00 p.m. Eastern Daylight Time.

The publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances, knowledge, experience, liquidity requirements or tolerance for risk. VMF Research’s Model Portfolios are illustrative research portfolios and do not represent client assets or transactions executed by VMF Research.

The analysis combines thematic, fundamental, valuation and market research within a medium- to long-term investment framework. Sources and the basis for material factual claims are identified throughout the article. Views, assumptions and portfolio conclusions may change as scientific, clinical, regulatory, corporate, macroeconomic or market evidence evolves. They are reviewed through VMF Research’s monthly publications and may be updated through weekly or ad hoc research.

Performance figures and relative-return comparisons use the reference prices and periods identified in the article. Returns on open positions have changed since the research cut-off. The figures relate to VMF Research recommendations or illustrative Model Portfolio allocations, do not represent returns earned by clients and should not be interpreted as a complete measure of the performance of VMF Research or any Model Portfolio.

Healthcare and biotechnology investments involve substantial risks, including clinical-trial failure, regulatory rejection, loss of intellectual-property protection, financing and dilution risk, adverse pricing or reimbursement decisions, political intervention, scientific uncertainty, product concentration, market volatility and the possible loss of capital. Artificial intelligence may improve research productivity without producing commercially successful medicines, higher corporate earnings or superior investment returns. Technical and relative-strength signals may also fail.

Disclosure of interests: as of the research cut-off, neither VMF Research, Lda. nor Vasco Marques de Freitas personally held a position in any financial instrument mentioned in this article. Legal entities controlled by Vasco Marques de Freitas held long positions in UnitedHealth Group Incorporated and Novo Nordisk A/S, both of which are discussed in the article. These holdings may benefit from increases in their respective market values and constitute financial interests and potential conflicts of interest. Readers should consider them when evaluating the analysis. Their existence does not validate any conclusion, reduce the risks described or imply that either investment is suitable for a particular reader.

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

Intelligence is getting cheaper. Failure remains brutally expensive.

That gap may determine where the next fortunes in AI are made.

In The China AI Reset, we examined the supply shock now unfolding across artificial intelligence. Chinese laboratories are approaching the technological frontier while offering capable models at prices that could accelerate adoption across the global economy.

This article follows the money.

A cheaper model creates limited value where the underlying task was already inexpensive. The economics become far more powerful where a poor decision can waste years, destroy hundreds of millions of dollars or prevent a valuable product from ever reaching the market.

Healthcare lives inside that world.

Drug discovery is an enormous search problem conducted under punishing uncertainty. Researchers must select the right biological target, design the right molecule, identify toxicity early, choose the patients most likely to respond and construct a clinical trial capable of demonstrating the effect. A mistake at any stage can consume vast amounts of capital before the company learns that it followed the wrong path.

Better intelligence does not need to eliminate failure to transform those economics.

It only needs to improve the odds.

A slightly better target. An earlier warning. A more precise patient population. A weak programme abandoned before another expensive trial. Small improvements compound quickly when the existing failure rate is so high and the potential value of success is so large.

That is the Intelligence Dividend.

It will not accrue evenly across the economy. It should be largest where knowledge is scarce, proprietary data are valuable, experimentation is costly and better judgment changes the distribution of outcomes.

Biotechnology sits near the centre of that opportunity.

The theme is already appearing in our public record.

In the Leaderboard article, using market prices as of 2 July 2026, biotechnology occupied three places in VMF Research’s Top 10: Grail with a closed 272% gain, Roivant Sciences at 215% while still open, and SBIO at 79%. Returns on the open positions have moved since that snapshot, but the broader signal remains important: strength was no longer confined to one company or one corner of the sector.

It was spreading across diagnostics, an individual biotechnology business and a diversified sector vehicle.

That breadth is emerging alongside a powerful fundamental backdrop. Large pharmaceutical companies need to replenish ageing pipelines, biopharma M&A is accelerating, financing conditions are improving, and AI is becoming more useful across target selection, molecular design, clinical development and patient identification.

The pieces are beginning to connect.

Our previous article examined why capable intelligence may become dramatically cheaper. The excerpt below turns to one of the sectors where that lower cost could create the greatest economic value.

Healthcare contains some of the world’s most expensive decisions.
Biotechnology contains some of its most expensive failures.
That is precisely why the Intelligence Dividend could be so large.

The excerpt below comes from July’s issue of VMF’s Strategic Asset Allocation. It examines why healthcare may be moving from defensive laggard to emerging market leader, why biotechnology is already further ahead in that rotation, and how AI, the pharmaceutical patent cliff and recovering M&A could reinforce one another.

The full July issue connects that sector rotation with China’s cost advantage, the evolving liquidity backdrop and the positioning of the Tier One Model Portfolio.

The first wave of AI spending built the machine.

The Intelligence Dividend will belong to those who use it to make better decisions where being wrong costs the most.

Here is the excerpt.
Good reading.

Healthcare is where the downstream AI thesis stops being abstract.

The sector contains some of the most valuable workflows in the economy. It also contains some of the most expensive mistakes. A failed drug programme can destroy hundreds of millions of dollars. A poorly designed clinical trial can waste years. A missed diagnosis can change a life. An inefficient hospital workflow can absorb labour, capital and time at enormous scale.

The opportunity is not that AI makes healthcare easy.
Nothing makes healthcare easy...

The opportunity is that better intelligence may begin to reduce the cost of failure in a system where failure is unusually expensive. That distinction is important for the Model Portfolio.

In China internet, the thesis is developing faster than the chart. KWEB ( ) may be stabilising. The relative-strength setup is improving. But the market has not yet delivered a clean confirmation.

Healthcare is different...

The Relative Rotation Graph 1 (RRG) for US sectors already shows healthcare (XLV) moving into the improving quadrant. That does not yet make the sector a dominant market leader, but it does show a change in character. Healthcare is no longer simply acting as a defensive place to hide. It is beginning to behave like a sector that investors may soon need to own.

Our global expression of that view is IXJ ( )

IXJ is already breaking out in price. The ETF is pushing to new highs, confirming that buyers are returning to global healthcare. The missing piece is relative strength versus the broader equity market. The relative-strength line in the upper panel has not yet broken out decisively, but it is now challenging a long downtrend.

That is exactly the sequence we want to see before a leadership transition becomes obvious.

First, the sector stops going down on a relative basis. Then it begins to stabilise. Then price confirms. Then relative strength catches up. The RRG evidence from XLV suggests the broad healthcare move is already forming. IXJ suggests the global healthcare allocation is beginning to participate.

Biotech is further ahead.

SBIO ( ) is not waiting for the broader healthcare complex to finish the rotation.

The price breakout is already visible. The relative-strength breakout is already visible. Therefore, this is the cleaner confirmation inside the sector. Biotech has moved from “potential recovery” to “emerging leadership.”

That is not accidental.

When the Model Portfolio added SBIO, the thesis was not simply that biotech was cheap. It was that biotech could become a quintessential AI trade.

Not because every biotech company suddenly becomes an AI company.
Not because clinical risk disappears.
Not because biology turns into software.

The thesis was that biotech sits inside one of the most attractive economic setups for artificial intelligence: a field with massive proprietary data, slow feedback loops, high failure rates, expensive experiments, difficult prediction problems and extraordinary upside from getting the answer right.

AI is valuable where intelligence is scarce.
In biotech, intelligence is scarce everywhere...

It is scarce when selecting targets. It is scarce when designing molecules. It is scarce when predicting toxicity. It is scarce when identifying patient subgroups. It is scarce when deciding which trial design gives a programme the best chance of showing a real effect. It is scarce when separating a beautiful scientific hypothesis from a commercially viable therapy.

The traditional biotech model is brutally inefficient because biology is brutally complex. Most programmes fail. Many failures arrive late. Capital is consumed before revenue appears. The market frequently discounts entire platforms because investors cannot confidently distinguish a temporary financing problem from a scientific dead end.

AI may not eliminate that uncertainty.
But it can change the distribution of outcomes.

The potential gain is not magic. It is better iteration. More hypotheses tested before expensive wet-lab work. Better prioritisation of targets. Earlier detection of failure modes. More intelligent trial design. More precise patient selection. Faster interpretation of biological signals. Better use of existing literature, internal datasets and experimental history.

In software, faster iteration improves products.
In biotech, faster iteration may improve the probability of survival.

A small improvement in success rates can be enormous when the starting point is so low. A slightly better clinical trial, a slightly better patient population, a slightly better molecule, or a slightly earlier decision to kill a weak programme can change the economics of a company and, in aggregate, the economics of a sector.

Dario Amodei is now making the same argument from the frontier of AI.

The Anthropic co-founder and CEO recently said he was positive on biotech and expected a renaissance ultimately driven by AI, highlighting peptide-based therapies and cell-based treatments such as CAR-T as areas where AI-assisted design could expand medical innovation.

That comment deserves more attention than a normal bullish soundbite.

Amodei is not merely saying AI will help doctors write notes faster. He is pointing to biology as one of the deepest design spaces in the economy. In his own essay, Machines of Loving Grace, he argued that AI-enabled biology and medicine could compress decades of biological progress into a much shorter period. His core idea is that powerful AI may allow researchers to search biological possibility far more efficiently than human-led trial and error alone.

That is the crucial insight for investors.

Biotech is not only a healthcare sector.
It is a search problem.

The industry is searching for targets, mechanisms, molecules, combinations, delivery systems, biomarkers, trial populations and commercial applications. The search space is vast. The cost of wrong turns is punishing. The payoff from better search is immense.

That makes biotech one of the most natural places for AI to create economic value. The timing also looks unusually favourable.

Large pharmaceutical companies need growth. Their existing franchises are ageing. The patent cliff is no longer a distant risk buried in sell-side slides. Evaluate estimates that more than $300 billion2 of prescription-drug revenue will lose exclusivity between 2025 and 2030.

A patent cliff changes corporate behaviour.

When a blockbuster loses exclusivity, management cannot replace the revenue with hope. It needs new assets. Internal research can help, but internal research is rarely enough. Large pharmaceutical companies have the balance sheets, commercial infrastructure and regulatory expertise. Smaller biotech companies often own the optionality: late-stage assets, novel platforms, targeted oncology programmes, rare-disease pipelines, immunology assets and emerging modalities.

That gap creates transactions.

Biopharma M&A is already accelerating. Reuters reported that if the current pace continues, 2026 biopharma M&A could exceed $250 billion, making it one of the strongest years since the 2019 megadeal cycle.

The deals themselves are telling.

Vertex agreed to acquire Crinetics for roughly $10 billion, adding rare endocrine-disease assets and paying a substantial premium to gain another growth vertical beyond its historical cystic fibrosis franchise. Novartis agreed to acquire Myricx Bio for up to $1,5 billion, strengthening its oncology pipeline through a next-generation antibody-drug-conjugate payload platform.

These transactions are not isolated events. They are symptoms of a broader pressure system. Big Pharma has revenue to defend. Biotech has science to monetise. The patent cliff creates urgency. AI increases the perceived value of better discovery platforms. Improving capital markets make deals easier to finance.

A sector that was starved of liquidity can re-rate quickly once acquirers, investors and strategics begin competing for credible assets again.

This is where IXJ and SBIO play different roles.

IXJ gives the Model Portfolio broad exposure to global healthcare. It captures the sector-level rotation without forcing the entire thesis into one scientific modality, one clinical programme or one financing window. It owns the more diversified expression: pharma, devices, diagnostics, healthcare services and life-sciences infrastructure.

SBIO is the asymmetric expression. It concentrates exposure in smaller biotechnology companies, where the upside from renewed M&A, better financing conditions, rising risk appetite and AI-assisted discovery can be much larger. It also carries more volatility. Clinical failures will continue. Regulatory risk will continue. Financing risk will continue. AI will not turn every experimental asset into a commercial product.

But markets do not need perfection to reprice a sector.
They need improving odds.

Biotech entered this period with depressed expectations, damaged investor psychology and years of capital scarcity behind it. Now the chart is breaking out, relative strength is improving, Big Pharma is buying, the patent cliff is approaching, and one of the most important figures in AI is explicitly framing biotech as a potential renaissance.

That is a rare combination.

The Model Portfolio owns IXJ because healthcare is beginning to improve. It owns SBIO because biotech may be where the improvement becomes explosive.

The distinction with KWEB is useful. In China internet, the thesis is compelling but relative strength is still early. In healthcare, relative improvement is becoming clearer. In biotech, the tape has already started to validate the argument.

The market is not waiting for every AI-driven breakthrough to arrive.
It is beginning to price the possibility that the cost of failure may fall.

That may sound subtle. It is not.

A lower cost of failure can reopen financing windows. It can make more assets worth funding. It can make more platforms strategically valuable. It can make acquirers more aggressive. It can make investors willing to pay for optionality again. And in biotech, optionality is the product.

The first part of this issue argued that the AI boom may be moving downstream. China showed us what happens when intelligence becomes cheaper. Healthcare and biotech show us where cheaper intelligence may change outcomes.

But there is one final constraint.

Biotech does not run on science alone. It runs on capital. Discount rates, liquidity, risk appetite and M&A confidence shape the sector almost as much as clinical data. A better molecule still needs a financing window. A better platform still needs buyers. A better probability distribution still needs investors willing to underwrite uncertainty. That brings the argument back to macro...

If the AI trade is moving downstream, liquidity will decide how far the market is willing to follow it.

The next section returns to the Dovish Shock.

You might also like reading:

2

The estimate comes from Evaluate’s October 2025 report, Portfolio Tactics to Scale the $300bn Patent Cliff*. It calculates that more than $300 billion of prescription-drug revenue will lose exclusivity between 2025 and 2030, increasing pressure on large pharmaceutical companies to replenish growth through internal development, partnerships and acquisitions.*

Important Disclosure

This article contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It reproduces an excerpt from the July 2026 issue of VMF’s Strategic Asset Allocation. The research, views and Tier One Model Portfolio information are stated as of 10 July 2026, 4:00 p.m. Eastern Time, unless another date is expressly identified.

The publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances, knowledge, experience or tolerance for risk. The Tier One Model Portfolio is an illustrative research portfolio and does not represent client assets or transactions executed by VMF Research.

The analysis combines thematic, fundamental and market research within a medium- to long-term investment framework. Sources and the basis for material factual claims are identified throughout the article. Views, assumptions and portfolio conclusions may change as scientific, regulatory, corporate, macroeconomic or market evidence evolves. They are reviewed through VMF Research’s monthly publications and may be updated through weekly or ad hoc research.

The Leaderboard returns cited in the introduction were calculated using market prices as of 2 July 2026. Open-position returns have changed since that date. The figures relate to VMF Research recommendations or illustrative Model Portfolio allocations, do not represent returns earned by clients and should not be interpreted as a complete measure of investment performance. Healthcare and biotechnology investments, including IXJ, SBIO and individual biotechnology companies, involve substantial risks. These include clinical-trial failure, regulatory rejection, loss of intellectual-property protection, financing and dilution risk, political intervention, adverse reimbursement decisions, scientific uncertainty, market volatility and the possible loss of capital. Artificial intelligence may improve research productivity without producing commercially successful medicines or superior investment returns.

Disclosure of interests: as of the research cut-off, neither VMF Research, Lda., Vasco Marques de Freitas personally, nor any legal entity controlled by him held a position in any financial instrument mentioned in this article, including IXJ, SBIO, Grail or Roivant Sciences. Past performance is not indicative of future results. Readers should conduct their own analysis and, where appropriate, consult an authorised financial adviser before making an investment decision.

This week gave crypto investors almost every excuse to sell.

Renewed tensions with Iran weighed on risk appetite. Oil moved higher. The latest Federal Reserve communications reinforced the market’s hawkish interpretation of monetary policy. And liquidity conditions remain far from the kind of backdrop that normally allows crypto-assets to thrive.

Yet something important happened. They refused to break.

Bitcoin continues to test one of the most consequential long-term support zones on its chart. This is the area where the secular breakout, major retracement levels, and long-term trend references converge.

And the week provided a powerful excuse to lose it. It did not. That does not mean the correction is over. Bitcoin remains more than 50% below its peak, the intermediate trend is damaged, and sentiment across the crypto complex remains deeply depressed. But important market signals often begin with what fails to happen.

The bears received the geopolitical shock.

They received the hawkish Fed. They received the tightening narrative. And Bitcoin still held support.

Ethereum is sending an equally interesting message. Its longer-term structure remains less constructive than Bitcoin’s, and we should not pretend otherwise. But ETH has not made a lower low. More importantly, it is beginning to gain relative strength against Bitcoin. That is precisely the kind of change we want to see. Bitcoin typically provides the first line of defence when liquidity is scarce. Ethereum and the broader crypto ecosystem tend to respond later, once investors become more willing to accept risk. If ETH is beginning to outperform before the macro backdrop has clearly improved, the market may be looking beyond today’s headlines.

Solana is showing a similar pattern.

Its chart remains volatile and technically damaged, but it too is refusing to make a lower low while beginning to improve against Bitcoin at the margin.

None of these signals is decisive in isolation.
Together, they deserve attention.

Bitcoin is holding major support. Ethereum is beginning to outperform it. Solana is attempting to do the same. So, what is the market starting to discount?

Perhaps investors are looking ahead to renewed progress on the CLARITY Act and a more durable regulatory framework for digital assets. Perhaps they are beginning to anticipate an improvement in global liquidity. Or perhaps the selling pressure has simply become exhausted after one of the deepest sentiment resets in crypto’s history.

Our working hypothesis remains unchanged. Markets are still positioned for a more hawkish monetary regime than the evidence may ultimately justify. If inflation pressures ease, geopolitical risk fades, and policymakers move from threatening additional tightening toward an extended hold, the resulting repricing could become the Dovish Shock we have been testing across our publications.

And if that thesis proves correct, crypto is precisely the asset class we would expect to respond most forcefully.

Stay tuned.

Good investing!

Vasco Marques de Freitas, CFA, CMT

Important Disclosure

This Article reproduces general investment research from the Alpha Tier Weekly Update published on 11 July 2026, produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. The analysis and charts reflect the information available at the research cut-off. Cryptoassets trade continuously, and prices may have changed materially since publication.

This publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s objectives, financial circumstances, knowledge, experience or tolerance for risk.

The analysis combines macroeconomic, liquidity, technical and relative-strength evidence within a medium- to long-term framework. References to technical support, improving relative strength, regulatory developments or a possible “Dovish Shock” are conditional analytical judgments, not assurances of future performance. Views may change as the evidence evolves and are reviewed through VMF Research’s monthly, weekly or ad hoc publications. Bitcoin, Ethereum, Solana and related financial instruments are highly volatile and speculative. They involve substantial market, liquidity, regulatory, technological, cybersecurity and custody risks and may result in a total loss of capital.

Disclosure of interests: legal entities controlled by Vasco Marques de Freitas hold long positions in listed ETFs providing economic exposure to Bitcoin, Ethereum and Solana. These holdings may benefit from increases in the value of the crypto-assets discussed and constitute financial interests and potential conflicts of interest.

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

The next AI shock may arrive as a price war.

Artificial intelligence is still framed as a contest for supremacy: who owns the largest compute cluster, trains the smartest model and raises the most capital. That contest has already created extraordinary fortunes.

It may now be obscuring the more consequential shift.

China is collapsing the cost of capable intelligence.

Its leading models are approaching the performance of the strongest American systems while charging a fraction of the price. UBS estimates that the capability gap has narrowed from roughly 40 percentage points in 2023 to around 10 today, while Chinese frontier-model prices can average less than one-fifth of those charged for comparable Western alternatives.

The numbers will change. The economics already have.

Most businesses do not need the best model ever built. They need one that completes the work reliably, securely and cheaply enough to produce an economic return.

A retailer does not need frontier-level reasoning to improve product search. A logistics company does not need the world’s smartest model to optimise routes. Thousands of routine coding, translation, customer-service and document-processing tasks can be handled by systems that are simply good enough at the right price.

A model does not need to win every benchmark.
It needs to win the workflow.
That is where China is becoming dangerous.

Restricted access to advanced chips, smaller research budgets and less abundant capital forced Chinese laboratories to solve a different problem from their American counterparts. They had to extract more capability from every unit of compute, every dollar of investment and every watt of electricity.

Constraint became an advantage.

Focused training, efficient architectures, open-weight collaboration and aggressive optimisation are allowing Chinese developers to compete near the frontier with a radically different cost structure. Early usage data suggest that customers are beginning to notice.

Chinese models are gaining traction on platforms where developers compare real systems for real work. Capability is converging. Prices are disruptive. Usage is following.

That combination can reset the economics of the entire industry.

Model providers face greater pricing pressure. Infrastructure companies may eventually need to defend scarcity premiums the market currently treats as permanent. Capital spending will face more scrutiny as customers focus less on token consumption and more on the return generated by each task completed.

The most obvious beneficiaries may sit further downstream.

Cheaper intelligence gives businesses with customers, proprietary data, distribution and specialised workflows access to a more powerful input at a lower cost. It can improve advertising conversion, product discovery, logistics, software development, customer service and operating margins.

The value does not disappear when intelligence becomes cheaper.
It migrates towards those best positioned to use it.

That is the thread connecting much of our recent work at VMF Research. The Abundance Shock began with the idea that falling intelligence costs would rearrange scarcity and profitability across the economy. We then followed the AI trade from physical bottlenecks towards the application layer.

July reveals the force accelerating that migration.

China does not need to overtake the United States across every dimension of artificial intelligence. It does not need to dominate the American market or win the final increment of capability. It can transform the global profit pool by becoming the preferred cost-performance supplier for the enormous number of tasks where the absolute frontier adds little economic value.

That is a far more useful investment question than another argument about which country is “winning AI”.

The excerpt below examines China’s narrowing capability gap, the architecture behind its cost advantage and the evidence that users are already directing real workloads towards Chinese models.

It also raises the question at the heart of July’s research:

What happens when the next generation of AI winners are the companies buying intelligence rather than selling it?

The full issue translates that reset into the Tier One Model Portfolio’s exposure across Chinese equities, China’s internet and application layer, and US software.

The first phase of the boom rewarded control of scarce intelligence infrastructure.

The next may reward access to intelligence at commodity prices.

Good reading.

The most important AI release of June did not come from Silicon Valley.

It came from Beijing.

GLM-5.2, the latest open-weight model from Z.ai, is forcing investors to confront an uncomfortable possibility. China may not need to build the world’s most capable model to change the economics of artificial intelligence. It may only need to build one that is capable enough, dramatically cheaper and improving faster than the market expects.

That threshold is beginning to look much closer.

On selected coding and long-horizon knowledge-work benchmarks, GLM-5.2 is competing near the leading proprietary systems. Its weights can be downloaded, adapted and deployed beyond infrastructure controlled by its developer. And its listed price is a fraction of what users pay for the most advanced Western alternatives.

None of this establishes that China has overtaken the United States. The leading American laboratories continue to define the overall frontier, benchmarks measure specific capabilities rather than universal intelligence, and newly released models have not yet been tested across the full range of commercial workloads.

But the direction matters more than the ranking on any particular day.

Chinese laboratories are no longer competing merely by offering yesterday’s intelligence at a discount. They are approaching the frontier while preserving a radically different cost structure.

The chart above reframes the investment question while adding further support to our overweight in Chinese technology equities.

The conventional debate focuses on which country owns the smartest model. That matters for scientific leadership, national security and the small number of tasks requiring the absolute frontier.

It matters less for much of the commercial economy. Most businesses do not need the most intelligent model ever created. They need one capable of completing a particular task reliably, securely and at a price that leaves room for an economic return.

A retailer improving product search does not require the same system as a laboratory conducting advanced scientific research. A company processing documents, translating communications or writing routine code may gain little from paying several times more for the final increment of capability.

UBS estimates that the leading Chinese models now deliver around 90% of the intelligence of the best US systems, compared with approximately 60% in 2023.

Several Chinese models are already competitive in coding, video generation and other multimodal applications. Four of the five highest-ranked video-generation models in the analysis included in our research deck were developed in China.

The exact percentage will change as laboratories release new systems. But the investment conclusion is more durable.

A growing share of economically useful intelligence is no longer confined to the most expensive American models.

And China is offering it at a fraction of the price.

China is not becoming more competitive simply because its models are cheap.

After all, cheap intelligence with poor outcomes has limited value.

The quality available for each dollar spent is improving at an extraordinary pace. That advantage appears to be the product of an ecosystem rather than one exceptional release.

The US AI industry has developed in an environment of abundant capital, advanced semiconductors and investors willing to fund increasingly expensive attempts to move the frontier. China has faced smaller research budgets, restricted access to the most advanced chips and less capable domestic hardware.

Those constraints have not prevented progress. They have shaped it.

Chinese laboratories appear to concentrate more resources on focused training, post-training optimisation and architectures designed to reduce the amount of compute and memory required for each request. Mixture-of-experts systems activate only part of a model for a particular task. Sparse attention reduces the computational burden of long inputs. Cache compression lowers one of the largest costs in agentic workloads. Lower-precision computation improves throughput, while better batching and scheduling allow more requests to pass through the same infrastructure.

The open-weight ecosystem compounds those advantages.

Once one laboratory validates a useful technique, other developers can adapt it without repeating the full research process independently.

UBS also identifies lower electricity and GPU-hour costs as part of the advantage. Its analysis suggests that comparable infrastructure costs in China can be materially lower than in the United States, reinforcing the gains produced by better model architecture and deployment efficiency.

The spending comparisons are equally striking.

UBS estimates that Zhipu and MiniMax spent approximately $500 million and $300 million respectively on research and development during 2025, only a fraction of the amounts attributed to the largest US frontier laboratories. The same analysis estimates that Chinese frontier-model API prices average below 20% of comparable Western systems, while gross margins for major Chinese model providers may still fall broadly between 20% and 40%.

These comparisons are imperfect. The companies use different accounting methods, infrastructure arrangements and business models. Some may subsidise adoption for strategic reasons, and the disclosure surrounding private AI laboratories remains limited.

Even with those caveats, the signal is difficult to dismiss.

China may be converting each dollar of capital and each unit of compute into more commercially useful intelligence.

The market’s first response has been to declare the model layer a commodity.

That conclusion is directionally correct and analytically incomplete.

Once several models can perform a routine task adequately, customers compare price, speed and reliability. Basic chat, translation, summarisation, conventional content generation and standard coding assistance should continue to become cheaper.

But intelligence is not one uniform product.

A business may use a low-cost system for thousands of routine interactions while reserving a more capable model for the smaller number of tasks where reliability, reasoning depth or successful completion carries much greater economic value.

Mature capabilities deflate as they become widely available. Newly unlocked capabilities can still command a premium when they reduce errors, complete harder work or replace expensive human effort.

GLM-5.2 illustrates both sides of that market. It remains dramatically cheaper than leading Western frontier models, yet Z.ai appears to have raised its realised pricing relative to the previous GLM generation because the new system can perform more valuable work.

The relevant question is therefore no longer simply how much a token costs.

It is how much it costs to complete the task.

A cheaper model that repeatedly fails can prove expensive. A more capable one that completes a high-value workflow may produce an exceptional return. Providers that stop improving will face commoditization. Those that continue to unlock harder tasks may preserve pricing power even as the cost of established intelligence keeps falling.

This increasingly tiered market also makes it less likely that one model will dominate every workload.

Businesses can route tasks between different providers according to capability, price, latency, reliability, security and privacy. A frontier system may handle advanced research or difficult software architecture. A cheaper model may process documents, write routine code or answer a large volume of customer enquiries. An open-weight system may be deployed internally when sensitive data cannot leave the organisation.

That should favour model-routing platforms and cloud ecosystems capable of matching each task with the most efficient provider. It also allows a Chinese system to capture meaningful demand without becoming the principal model for every customer.

The shift from maximizing token usage towards measuring return on that expenditure should make cost-performance more important.

As enterprises become less impressed by AI consumption itself and more demanding about the productivity it produces, Chinese models appear well positioned for high-volume workloads where the absolute frontier is unnecessary.

That opportunity extends well beyond the United States.

Europe, the Middle East, Latin America and much of Asia lack a local foundation-model ecosystem with comparable breadth. Some governments and businesses will continue to favour American providers because of trust, security or geopolitical alignment. Others will prioritise price, local deployment and the freedom to adapt the model.

China does not need to displace the leading US systems in their home market to become a major global supplier. It can gain substantial share by becoming the preferred cost-performance option across the rest of the world.

The implications stretch across the AI value chain.

Model providers face more competition. Hardware companies face greater scrutiny over how long their scarcity premiums can endure. Data-centre economics become more sensitive to utilisation and rental prices.

At the same time, cheaper intelligence should stimulate far more consumption.

Lower computing costs did not reduce the use of computers. Lower bandwidth costs did not reduce internet traffic. Falling storage prices did not persuade companies to store less data. Each decline in cost made new applications viable.

Artificial intelligence may follow the same path. Efficiency can reduce the resources required for each task while causing the number of tasks to expand much faster.

For semiconductor and infrastructure companies, the crucial question is whether that expansion is sufficient to preserve aggregate demand. For model laboratories, it is whether higher volumes compensate for declining unit prices.

For the applications and companies using AI, however, cheaper intelligence is more clearly an opportunity.

Businesses with customers, proprietary data and specialised workflows acquire a more powerful input at a lower cost. The benefit may appear through faster product development, lower customer-service expenses, better advertising conversion, more efficient logistics or stronger operating margins.

That is how the boom moves downstream.

The next question is whether this downstream shift is already visible in usage data.

That is why the chart below matters.

OpenRouter is not the entire AI economy. It does not capture every enterprise contract, every internal model call, or every private deployment inside the largest technology companies. Its data should therefore be treated as a proxy for marginal demand rather than a complete map of AI usage.

But that is precisely why it is useful.

Model-routing platforms sit close to the economic decision. They are where users, developers and applications compare models not in theory, but in practice: which system is good enough, fast enough, reliable enough and cheap enough to complete the task?

On that measure, Chinese models are no longer just improving on paper. They are gaining traction in the market. OpenRouter data compiled by FT Research show weekly usage of leading Chinese models rising sharply through 2026, while comparable usage of leading US models has grown far more modestly.

That does not prove China has won the AI race.
It proves something more relevant for investors.

China is beginning to compete for the workflow.

That is the bridge from capability to demand. Benchmark convergence tells us that Chinese models are becoming good enough. Price convergence tells us that they are becoming disruptive. Usage growth tells us that some users are already acting on both.

At that point, this stops being a laboratory story.
It becomes an investment story.

And it helps explain why the Model Portfolio has been changing its exposure inside the AI trade. EWY ( ) was our first-phase expression. The hidden bottleneck was memory. The market eventually discovered it. The position worked, and we harvested it.

KWEB ( ) is a very different setup.

It is not an obvious winner. It is not crowded.

It does not carry the comfort of recent outperformance. In fact, its chart still reflects years of disappointment, policy risk and macro distrust. But that is also why the asymmetry is more interesting...

While EWY now appears to be losing relative strength after an exceptional advance, KWEB may be starting to stabilize after a long period of underperformance.

That is the kind of rotation we want to study before it becomes obvious.

Our broader China overweight expresses the same view at two levels. MCHI gives the Model Portfolio diversified exposure to Chinese equities. KWEB gives it a more concentrated exposure to the Chinese internet and application layer, where cheaper intelligence can become better advertising, better search, better commerce, better logistics, better customer service and stronger operating leverage.

IGV ( ) is the US expression of the same rotation.

Software was treated too simplistically as an AI victim. Some companies will be disrupted. But others may become more valuable as AI lowers development costs, improves functionality, automates workflows and turns static software into intelligent systems.

That is why our application-layer thesis is not only a China thesis. It is a broader view that the next AI winners may be the companies that deploy intelligence well, not only the companies that manufacture or host it.

China, however, remains the higher-convexity expression.

The valuation starting point is lower. The scepticism is higher. The scars from the last cycle are still visible. Policy and geopolitical risks remain real. And the US still commands a much larger pool of private AI investment, an important constraint on any overly simplistic China-bullish narrative.

But the market already knows the US advantage.
It does not yet fully price the China option.

That is why the Model Portfolio maintains its China overweight and its exposure to the application layer through KWEB, MCHI and IGV.

So...

We are not abandoning AI. We are moving further downstream. We are not chasing the most visible winners after the crowd has arrived. We are looking for the next place where cheaper intelligence can change the economics before the market has fully adjusted.

The first AI trade was about scarcity.
The next may be about adoption.

And if usage is now beginning to follow cost and capability, the most interesting opportunities may not be in the companies everyone already recognizes as AI winners. They may be in the places where intelligence is becoming cheap enough to change the economics, but where the market still refuses to pay for it.

That brings us to the next question.

If cheaper intelligence is moving downstream, which sectors combine some of the most expensive workflows in the economy, deep pools of proprietary data, high failure rates from discovery to commercialization, and the greatest economic upside from better decisions?

Healthcare sits near the top of that list.
Biotech may sit at the very centre of it.

That is where we go next.

You might also like reading:

Important Disclosure

This article contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. It reproduces an excerpt from the July 2026 issue of VMF’s Strategic Asset Allocation. The analysis and Tier One Model Portfolio information are stated as of 10 July 2026, 4:00 p.m. Eastern Time.

The publication contains information recommending or suggesting an investment strategy. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances, knowledge, experience or tolerance for risk. The Tier One Model Portfolio is an illustrative research portfolio and does not represent client assets or transactions executed by VMF Research.

The analysis combines thematic, fundamental and market research within a medium- to long-term investment framework. Sources are identified throughout the article. Views are reviewed through VMF Research’s monthly publications and may be updated through weekly or ad hoc publications as evidence and market conditions evolve. Investments in China, emerging markets, technology companies and AI-related financial instruments involve substantial market, regulatory, geopolitical, governance, currency, liquidity and valuation risks. Investors may incur material losses.

Disclosure of interests: as of the research cut-off, neither VMF Research, Lda. nor the author personally held any financial instrument included in the Tier One Model Portfolio. A legal entity controlled by the author held a long position in the KraneShares CSI China Internet ETF (KWEB). This interest may create a potential conflict and should be considered when evaluating the analysis.

Past performance is not indicative of future results.

“I think biotech is about to have a renaissance… ultimately driven by AI.”

Dario Amodei

For most of this cycle, market leadership has been extraordinarily narrow. Technology (XLK) remains the only major sector firmly established in the leading quadrant of the Relative Rotation Graph above.

And even within technology, much of the strength has remained concentrated in semiconductors and the physical infrastructure required to build artificial intelligence.

But something important may now be changing.

Healthcare (XLV in the chart above) is beginning to gain relative momentum. The sector remains in the lagging quadrant, so we should not declare a new leadership regime prematurely.

Still, the direction of travel matters.

After years of persistent underperformance, healthcare is beginning to rotate higher precisely as investors search beyond the most crowded AI winners.

The chart provides the first confirmation.

Our global healthcare position has broken above a major resistance zone and is now attempting to escape a long relative-strength downtrend against the broader equity market. The move has also occurred with price comfortably above its rising 20- and 50-week moving averages.

That is constructive.

It suggests that healthcare may be doing more than participating in a temporary defensive rotation. The sector could be entering a more durable period of market recognition after years of depressed valuations, political uncertainty, and capital scarcity.

There are several reasons why.

Large pharmaceutical companies face an increasingly urgent need to replace revenues threatened by patent expirations. Biotechnology financing and M&A activity are recovering. And perhaps most importantly, artificial intelligence is moving from an abstract promise toward a practical tool for drug discovery, molecular design, patient selection, clinical development, and operational efficiency.

That last point has always been central to our thesis. The first phase of the AI boom rewarded those building the intelligence layer: semiconductors, memory, data centers, and power infrastructure.

The next phase may reward the sectors capable of applying that intelligence to some of the world’s most complex and valuable problems.

Healthcare sits near the top of that list.

Dario Amodei, the CEO of Anthropic, recently argued that biotechnology may be entering a renaissance ultimately driven by AI. His point was not that algorithms will magically eliminate the physical realities of clinical trials, regulation, manufacturing, and commercialization.

It was that biology contains design spaces far too complex for human intelligence to navigate efficiently on its own.

AI may change that. And the market appears to be reaching a similar conclusion.

Our biotech position through has already broken decisively to multi-year highs.

Relative strength is improving, and the position is now approaching an 80% gain from our original recommended price.

This is important for two reasons.

First, biotechnology often acts as the higher-beta expression of a broader healthcare rotation. Its outperformance suggests that investors are not merely hiding in defensive pharmaceutical companies. They are beginning to reward innovation.

Second, it confirms that the opportunity we identified while the sector remained deeply unpopular is becoming increasingly consensual. That does not mean the trade is over.

Quite the opposite.

SBIO may be showing us where IXJ is heading. We will still demand confirmation. Breakouts can fail, and healthcare carries regulatory, scientific, and political risks that should never be minimized.

But the message from the charts is becoming harder to ignore.

Healthcare is gaining relative strength.
IXJ is breaking out.
SBIO is already leading.

The AI trade may be beginning to migrate from chips to cures.

-

Good investing!

Vasco Marques de Freitas, CFA, CMT

Important Disclosure

This publication contains general investment research produced by Vasco Marques de Freitas, CFA, CMT, Founder and CEO of VMF Research, Lda. The analysis and market data are stated as of 2 July 2026, 4:00 p.m. Eastern Time, the latest New York market close before publication on 4 July 2026. It is not personalised investment advice and does not consider any reader’s individual objectives, financial circumstances or tolerance for risk.

The Tier One Model Portfolio is an illustrative research portfolio and does not represent client assets or transactions executed by VMF Research. References to portfolio positions relate solely to that Model Portfolio.

Healthcare and biotechnology instruments, including IXJ and SBIO, involve substantial market, scientific, clinical, regulatory and political risks. Technical breakouts may fail, and investors may incur material losses. Views are reviewed through VMF Research’s monthly publications and may be updated weekly or ad hoc.

Disclosure of interests: as of the research cut-off, neither VMF Research, the author personally, nor legal entities controlled by the author held positions in IXJ or SBIO.

Past performance is not indicative of future results.

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

For a while, that was the right playbook.

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

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

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

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

That is the possibility we explored throughout June.

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

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

This note is the map.

Start with the setup

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

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

Instead, markets sold off.

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

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

Our question was whether that interpretation was too crude.

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

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

That distinction shaped the rest of the month.

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

Then follow the liquidity trail into crypto

The second stop is The Invisible AI Rails.

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

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

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

But that is not the whole story.

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

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

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

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

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

That was the point of the second excerpt.

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

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

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

The third stop is The Hike That May Never Come.

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

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

That combination was important.

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

That is why the title mattered.

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

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

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

Gold is the monetary anchor of that world.

Silver is the accelerator.

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

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

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

The public excerpt explained the macro door opening.

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

Finally, widen the lens to trust

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

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

Where should investors place their trust?

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

Gold and Bitcoin ask something different.

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

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

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

We think that is the wrong argument.

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

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

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

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

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

Sometimes preparation justifies action.

Sometimes it justifies patience.

Paid subscribers received the full portfolio decision.

The June reading order

Read June in this order:

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

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

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

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

That was June inside VMF Research.

A live hypothesis.

A sequence of tests.

And a portfolio question.

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

Free readers can follow that trail through the public excerpts.

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

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

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

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

We started preparing for a different one.

Important Disclosure

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

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

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

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

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

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

Markets change. Leadership rotates. And the strongest investment processes should capture that evolution before it becomes obvious.

That is precisely what is happening inside VMF Research’s latest Top 10 Positions, with prices as of July 2, 2026.

Our precious-metals complex has lost momentum.

Physical gold has now dropped out of the Top 10, while junior gold miners, senior miners, and silver have moved lower in the ranking.

That does not invalidate the original thesis.

Far from it.

The Debasement Trade has already rewarded us handsomely, and we crystallized partial profits after several of those positions doubled.

But while precious metals have been consolidating, other parts of our Model Portfolios have been gaining material traction.

And the strength is increasingly broad-based.

Royalties.

Disruptive innovation.

Spin-offs.

Healthcare.

Biotechnology.

One sector, however, is beginning to stand out.

Biotech is moving to the front

Biotechnology now occupies three very different places in our public Leaderboard.

Grail remains in first place, with a closed return of 272%.

Roivant Sciences has climbed to third place and is now showing a 215% return while the position remains open.

And SBIO, our broader exposure to medical-breakthrough companies, has entered the Top 10 with a 79% gain.

One individual winner could be dismissed as fortunate security selection.

Two would be harder to ignore.

But strength across a closed position, a live individual stock, and a diversified sector vehicle begins to look like something more meaningful:

A theme gaining traction.

That is why we took particular interest in recent comments from Dario Amodei, Anthropic’s founder and CEO.

Amodei described himself as positive on biotechnology and argued that the sector may be approaching an AI-driven renaissance. His conviction is that artificial intelligence could accelerate progress across areas such as peptide therapies, cell-based treatments, and programmable biology.

That is remarkably close to the premise behind our original investment thesis.

Biology is extraordinarily complex.

Drug discovery is expensive, slow, and burdened by enormous failure rates.

But it is also unusually well suited to the capabilities AI is beginning to develop: pattern recognition, simulation, optimization, adaptive experimentation, and the ability to search vast design spaces far more efficiently than human researchers can manage alone.

We have believed for some time that biotechnology could become one of the most important application layers of artificial intelligence.

So, naturally, we welcome validation from one of the most important figures in the AI landscape.

But intellectual validation is not the kind that matters most in markets.

Price validation is.

And that is increasingly what our Model Portfolios are receiving.

Grail has already delivered an exceptional realized return.

Roivant continues to compound as an open position.

And the broader biotech complex is now demonstrating that the strength extends beyond a single company.

This is exactly how an investment theme should begin to reveal itself.

First through the thesis.

Then through individual securities.

And finally through broader market participation.

Biotech may be entering that third stage.

But it is far from the only area showing strength beneath the surface.

Our roster of possible new Leaderboard entrants remains extensive and spans several very different themes, sectors, geographies, and investment structures.

Some are already approaching the threshold.

Others remain earlier in their development.

The public leaderboard shows what has already worked.

Subscribers see what may be coming next, and, more importantly, the research explaining why we believe the asymmetry exists before the market fully recognizes it.

There is a great deal happening beneath the Top 10.

Stay tuned.

Good Investing!

New From Market View:

Important Disclosure

This article contains general investment research and commentary 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. References to previous recommendations do not constitute a new recommendation to buy, sell or hold any financial instrument. The underlying views are reviewed through VMF Research’s monthly, weekly or ad hoc publications and may change as market conditions and evidence evolve.

The Leaderboard is a selective presentation of VMF Research’s ten highest-returning recommendations and is not a complete record of its recommendations or Model Portfolio performance. The information and prices are stated as of 2 July 2026, at the close of the relevant markets. Returns are measured from the original published reference price to the applicable exit price for closed positions or the relevant market price for open positions. The status column identifies positions that remain open, have been closed or have been partially realised. Displayed returns should not be interpreted as the realised or net return earned by any investor and do not reflect individual execution, fees, taxes or financial circumstances. The Model Portfolios are illustrative research portfolios and do not represent client assets or transactions executed by VMF Research. Equities, biotechnology, sector ETFs, commodity-related securities and innovation-focused investments can be highly volatile and may result in substantial losses. Past performance is not indicative of future results. Readers should conduct their own analysis and, where appropriate, consult an authorised financial adviser.

Disclosure of interests: as of publication, legal entities controlled by the author hold long positions in Altius Minerals Corporation and ARK Innovation ETF. These interests may create potential conflicts and should be considered when evaluating the analysis.

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.

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