Air Liquide is one of the world’s leading players in the industrial and medical gases sector. Founded in 1902 and headquartered in Paris, the company provides gases, technologies, and services to industrial and healthcare customers across Europe, the Americas, Asia-Pacific, the Middle East, and Africa.

Its portfolio includes industrial gases such as oxygen, nitrogen, argon, hydrogen, carbon monoxide, and helium, as well as ultra-high-purity gases and specialty molecules used in advanced technological applications, including semiconductor manufacturing.

However, Air Liquide’s business model extends well beyond the simple production and sale of gases. In many applications, particularly within Large Industries and Electronics, Air Liquide designs, finances, builds, owns, and directly operates the production facilities required by its customers. These assets may be installed directly at the customer’s production site or connected to multiple customers through extensive pipeline networks.

This characteristic makes Air Liquide, in many cases, more akin to a provider of essential industrial infrastructure than to a traditional commodity producer. A refinery, steel mill, chemical plant, or semiconductor fab cannot operate normally without a continuous and reliable supply of certain gases. Once the infrastructure has been installed and integrated into the customer’s production process, the commercial relationship therefore tends to be particularly long-lasting and characterized by high switching costs.

In the first half of 2026, Air Liquide generated €13.83 billion in revenue, of which €13.41 billion, approximately 97% of the total, came from Gas & Services and €420 million from Engineering & Technologies. The consolidated revenue mix consisted of approximately 44% Industrial Merchant, 27% Large Industries, 16% Healthcare, 10% Electronics, and 3% Engineering & Technologies.

This diversification is one of the distinguishing features of Air Liquide’s business model.

Large Industries provides high visibility through dedicated infrastructure and long-term customer relationships; Industrial Merchant adds diversification, distribution density, and pricing power; Healthcare reduces exposure to the industrial cycle; while Electronics adds an important structural growth component tied to investment in advanced semiconductors and artificial intelligence infrastructure.

Organizational Breakdown and Profitability

From a financial reporting perspective, it is important to distinguish between business lines and profitability reporting segments.

Air Liquide reports revenue for Large Industries, Industrial Merchant, Healthcare, and Electronics, but does not disclose Operating Income Recurring for the individual business lines. Within Gas & Services, OIR is instead reported on a geographic basis.

In the first half of 2026, the Americas generated €5.23 billion in revenue, with €1.29 billion in OIR and a margin of 24.7%, while EMEA reported €5.41 billion in revenue, €1.16 billion in OIR, and a margin of 21.5%. Asia-Pacific contributed €2.77 billion in revenue, €598 million in OIR, and a margin of 21.6%. Overall, Gas & Services generated €13.41 billion in revenue, €3.05 billion in OIR, and a margin of 22.8%, while Engineering & Technologies generated €420 million in revenue, €57 million in OIR, and a margin of 13.6%.

At the consolidated level, after corporate costs and reconciliations, Air Liquide generated €2.89 billion in Operating Income Recurring, corresponding to an OIR margin of 20.9%.

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After Prologis, Equinix and Digital Realty, we return to analysing a Real Estate Investment Trust, but this time with a profoundly different exposure. In the three previous articles, the data center sector featured, to varying degrees, among the drivers of the investment thesis. With Ventas (NYSE: VTR), however, we move into healthcare real estate and, above all, senior housing, a sector whose main drivers are linked to population ageing and the growing demand for residential and care facilities dedicated to older adults.

Ventas is an S&P 500 company with a portfolio comprising senior housing communities, outpatient medical buildings, research centers, hospitals and other healthcare facilities located in the United States, Canada and the United Kingdom. As of December 31, 2025, the company owned or held investments in 1,409 properties, of which 1,374 belonged to its operating segments and 35 were held through unconsolidated real estate entities.

Ventas has elected to be taxed as a REIT since 1999. Subject to compliance with the requirements of U.S. law, this structure generally allows the company to avoid paying federal income tax on taxable income distributed to shareholders. As with other REITs, the business model therefore revolves around the ownership of real estate assets capable of generating recurring cash flows, but in Ventas’s case, the way those cash flows are generated varies significantly depending on the type of property.

The company operates through three main segments: Senior Housing Operating Portfolio, Outpatient Medical and Research, and Triple-Net Leased Properties. These are complemented by certain activities classified as non-segment, mainly comprising financial investments, loans receivable, interests in unconsolidated entities and activities related to the Ventas Investment Management platform.

To analyse the relative weight of the different divisions, we use full-year 2025 data. Ventas does not use EBITDA or EBIT to assess the performance of its individual segments.

Debra A. Cafaro, Chairman and CEO of Ventas, also serves as Chief Operating Decision Maker and assesses divisional performance and resource allocation based on NOI, or Net Operating Income. The company defines NOI as revenues less operating expenses directly attributable to the properties and expenses related to the third-party capital management platform. Interest expense, depreciation and amortisation, corporate administrative costs, taxes and other items not directly attributable to the properties are not allocated to the individual segments.

In 2025, Ventas generated $5.834 billion in revenue and $2.393 billion in NOI. SHOP was by far the largest source of revenue, while from an NOI perspective the contribution was more evenly distributed across the three businesses.

Business Lines

Senior Housing Operating Portfolio (SHOP) - (73.3% of revenue | 49.5% of consolidated NOI)

In 2025, the segment comprised 752 properties, generated $4.276 billion in revenue and $1.184 billion in NOI, equal to 49.5% of Ventas’s total NOI. SHOP also accounted for approximately 73.3% of consolidated revenue.

The Senior Housing Operating Portfolio, or SHOP, represents the operational core of Ventas and the main engine of the company’s current strategy.

In this segment, Ventas directly owns and invests in senior housing communities and participates in their operating performance. Day-to-day management is generally entrusted to specialised third-party operators, professional companies that actually operate the communities on Ventas’s behalf, but Ventas bears the costs and economic risks associated with the properties and directly benefits from changes in their revenue and profitability.

This is a fundamental distinction compared with a REIT whose business model is primarily based on collecting rental income. If the number of residents increases, if the communities are able to charge higher rates, or if operating efficiency improves, the benefit flows directly through to Ventas’s NOI.

The portfolio includes several types of communities. Independent living communities cater to older adults who are still largely independent, but provide services such as dining, housekeeping, transportation and recreational activities. Assisted living communities add support services for needs such as bathing, dressing or taking medication. Memory care communities are dedicated to residents with cognitive impairments, including Alzheimer’s disease and other forms of dementia, while continuing care retirement communities can combine independent living, assisted living, memory care and, in some cases, skilled nursing care within the same community.

Segment revenue comes from resident fees and services. These include not only accommodation, but also the care and ancillary services provided by the communities. On the cost side, Ventas incurs expenses related to staff, food, utilities, property taxes, insurance, maintenance, marketing and fees paid to operators.

The company also supports operators through Ventas OI, Ventas Operational Insights, a proprietary data and analytics platform used to support decisions related to pricing, sales, marketing, operating benchmarking, investments and capital expenditure. Ventas therefore does not directly operate most of the communities, but seeks to leverage scale, data and asset management capabilities to improve their performance.

Outpatient Medical and Research (OM&R) — 15.4% of revenue | 24.7% of consolidated NOI

In 2025, the segment comprised 409 properties, generated $897.9 million in revenue and $590.2 million in NOI, equal to 24.7% of total NOI. It accounted for approximately 15.4% of consolidated revenue.

Through Outpatient Medical and Research, or OM&R, Ventas primarily acquires, owns, develops, leases and manages outpatient medical buildings and research centers.

Outpatient medical buildings are predominantly located on or in close proximity to hospital campuses and are leased to health systems, physician groups and other operators. The spaces are generally tailored to healthcare needs and may include examination rooms, diagnostic imaging, outpatient surgery and other medical activities.

Research centers, meanwhile, include laboratories and offices leased primarily to universities, academic medical centers, technology and biotech companies, pharmaceutical companies, medical device manufacturers and other organisations active in research. These properties generally require more sophisticated infrastructure than conventional office buildings, including advanced electrical, mechanical and ventilation systems, emergency generators, laboratory fume hoods and other specialised equipment.

The business model differs significantly from SHOP. In OM&R, Ventas operates primarily as the owner and landlord of the properties. Tenants, meaning the lessees, pay rent and, depending on the lease agreement, may also bear or reimburse a portion of the property expenses, including utilities, property taxes, insurance and maintenance.

Ventas therefore does not directly participate in the profitability of the healthcare or research activities carried out within the properties. The main economic drivers instead become the ability to maintain high occupancy levels, the quality and location of the properties, and the financial strength of the tenants.

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Over recent months, one of the primary drivers of financial market pressure has come from the long end of the Treasury curve. The core issue has not merely been the level of the Fed Funds rate. While the Federal Reserve directly controls ultra-short-term borrowing costs, yields on 10-, 20-, and 30-year Treasuries respond to a far broader set of variables: growth and inflation expectations, the scale of fiscal deficits, the sheer volume of debt the market must absorb, and the compensation required by investors to lock up capital over extended horizons.

It is precisely this final element, the term premium, that has taken center stage. When Washington expands the deficit and needs to issue growing amounts of debt, the market is under no obligation to absorb it at any price. If duration supply outpaces demand, investors demand higher compensation. Bond prices fall, yields rise; this is the exact mechanism through which the bond market enforces fiscal discipline.

In recent days, however, the dynamic has shifted. Treasury Secretary Scott Bessent announced that the Treasury will significantly scale up its buybacks of longer-dated government securities, at least doubling its scheduled purchases.

Bloomberg characterized the decision as an unusually direct intervention, a clear signal of Washington’s mounting concern over rising borrowing costs.

This is not a new round of Quantitative Easing. The Treasury is neither creating money nor erasing federal debt. Instead, it is attempting to alter the amount of duration risk the private sector is required to absorb.

And that is arguably the most critical takeaway from the entire move.

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The true objective is not debt reduction, but duration reduction.

To understand Washington’s strategy, we must distinguish between the overall volume of debt and its structural composition. A Treasury bill maturing in a few months carries negligible duration risk. By contrast, a 30-year Treasury exposes the investor to decades of shifts in inflation, real rates, monetary policy, and fiscal sustainability. The longer the maturity, the greater the bond’s price sensitivity to yield movements.

When the U.S. government issues large volumes of 10-, 20-, or 30-year paper, it offloads a significant amount of duration onto the private sector. Inducing investors to absorb that supply often demands offering higher yields.

The buyback program targets precisely this pressure point. By repurchasing outstanding long-dated bonds, the Treasury reduces the supply available to market participants. Marginal demand rises while the net supply of available duration shrinks, providing support to bond prices and keeping yields in check.

Naturally, the government’s underlying funding requirement remains. The deficit does not disappear, nor is aggregate debt magically erased. The main outcome is a shift in the debt’s maturity structure: leaving less long-duration exposure in private hands while shifting financing to other parts of the curve.

In simple terms, Washington is swapping lower duration risk for the private sector for higher rollover risk for the Treasury. It is a significant trade-off. Relying more heavily on short-dated funding means debt must be refinanced more frequently, making annual interest expenditure far more sensitive to the future trajectory of policy rates.

There is no free lunch. Over the near term, however, the goal is clear: limit the volume of duration the market must digest and prevent a disorderly expansion in the term premium.

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Curtiss-Wright is a high-spec engineering firm specialising in mission-critical applications across aerospace and defence, naval defence, commercial nuclear power, process engineering, and select industrial markets. The company shouldn’t be viewed as a traditional defence prime contractor; rather, its role is that of a high-value-add supplier of components, technologies, and subsystems deeply embedded within platforms where reliability, qualification, safety, lifecycle support, and switching costs are paramount.

As management highlighted in their latest earnings call, Curtiss-Wright operates primarily as a Tier 2/Tier 3 supplier. This places it upstream from end-platform manufacturers, concentrating on highly specialised technology that is exceptionally hard to replace once qualified for a programme.

The group’s scale reflects the expanding reach of this technology platform. Curtiss-Wright currently employs around 9,200 people and, following an upgraded guidance after second-quarter results, projects 2026 revenues between $3.768 billion and $3.813 billion, representing 8–9% growth over the $3.498 billion recorded in 2025.

Roughly 70% of expected revenues stem from Aerospace & Defence markets, with the remaining 30% attributed to Commercial markets. The group retains significant exposure to the structural growth in global military spending, whilst simultaneously benefiting from the recovery in commercial aircraft production, the strengthening nuclear cycle, and select industrial markets.

The portfolio is structured across three segments, each defined by distinct financial profiles.

Naval & Power is the largest segment, accounting for roughly 43% of 2025 revenues and around 35% of adjusted segment operating income. Defence Electronics represents approximately 29% of revenues but generates about 40% of segment operating income, reflecting structurally higher margins. Meanwhile, Aerospace & Industrial contributes around 28% of sales and roughly 25% of adjusted operating income.

The portfolio structure effectively combines the long-term visibility of naval and nuclear activities with the high profitability of Defence Electronics, alongside Aerospace & Industrial’s exposure to the growth in commercial aerospace and select defence and industrial applications.

Business Lines

The three segments exhibit markedly different growth and profitability profiles. Over the last ten quarters, Naval & Power has recorded the largest expansion in absolute terms, underpinned by the acceleration of naval and nuclear programmes, whilst Aerospace & Industrial has demonstrated a steady strengthening of its top line. In contrast, Defence Electronics displays more lumpy revenue growth, largely down to programme timing, yet maintains significantly superior financial characteristics compared to the rest of the portfolio.

Aerospace & Industrial (FY2025: 28% of revenue; ~25% of adjusted segment operating income)

Aerospace & Industrial supplies sensors, controls, electromechanical actuation components, power-management electronics, traction inverters, transmission shifters, control systems, and surface treatment services, including shot peening, laser peening, and engineered coatings. As such, the segment combines exposure to commercial aerospace, aerospace defence, ground defence, and specialised industrial markets, frequently leveraging technologies originally developed for commercial applications across military programmes.

In commercial aerospace, Curtiss-Wright benefits primarily from ramp-ups in Boeing and Airbus build rates. Around 90% of the group’s commercial aerospace exposure is tied to original equipment manufacturers (OEMs), split roughly 60% narrow-body and 40% wide-body. In Q2 2026, commercial aerospace sales rose by 11% year-on-year, backed by higher OEM output. Meanwhile, the segment’s defence activities profited from increased sensor and actuation equipment sales across domestic and international fighter jet programmes, as well as stepped-up deliveries of electromechanical actuation systems.

The segment is also demonstrating a notable improvement in operating leverage. In Q2 2026, revenues advanced 12% to $268 million, whilst adjusted operating income surged 25% to $49 million, pushing margins up from 16.6% to 18.4%. This margin expansion was underpinned by fixed-cost absorption on higher volumes, a favourable sales mix, and benefits from restructuring initiatives, despite increased R&D spend.

Following Q2, management upgraded full-year 2026 guidance for the segment: revenues are now projected to grow by 8–10% (up from 6–8%), with operating margin expected to hit 18.5–18.7%, representing a 110–130 basis point expansion over 2025. The core growth drivers remain the commercial aerospace production ramp-up, expanding actuation equipment sales across Aerospace and Ground Defence, and a gradual recovery in the industrial vehicle market.

Defence Electronics (FY2025: 29% of revenue; ~40% of adjusted segment operating income)

Defence Electronics is the highest-margin business within the Curtiss-Wright portfolio. The division supplies commercial off-the-shelf (COTS) embedded computing modules, integrated subsystems, tactical communications, turret drive stabilisation systems, avionics, flight-test equipment, and aircraft data-management solutions, primarily tailored for aerospace and ground defence applications.

The segment’s strategic moat lies in the fact that Curtiss-Wright does not deal in commodity electronics. Instead, it provides systems engineered for harsh military environments where ruggedisation, qualification, platform integration, size-weight-and-power (SWaP) constraints, and reliability are mission-critical. Once selected and qualified for a platform, these systems command high switching costs and exceptional longevity across programme lifecycles.

Furthermore, the proliferation of sensors, processing power, secure communications, and autonomous systems is steadily expanding the electronic content per military platform. In Q2 2026, Curtiss-Wright saw higher embedded computing sales across domestic fighter jet and UAV programmes, whereas Ground Defence was temporarily weighted down by the timing of tactical communications deliveries. As a result, segment revenues slipped 3% year-on-year to $246 million. However, operating margin expanded by 120 basis points to 28.0%, propelled by a richer embedded computing mix and stringent cost control, easily offsetting higher R&D expenditure.

This quarterly top-line soft patch is by no means a sign of weakening demand. In Q2, Defence Electronics orders jumped roughly 47% year-on-year to a record high, and are up by more than 30% year-to-date. Management highlighted that the moderation in Ground Defence primarily reflects order-to-revenue conversion timing rather than underlying demand.

For full-year 2026, Curtiss-Wright maintains its forecast of 4–6% revenue growth and has raised its operating margin guidance to 27.5–27.7% (compared to 27.3% adjusted in 2025).

The primary growth engine remains Aerospace Defence, driven by aircraft modernisation, UAVs, next-generation programmes, and early opportunities tied to Golden Dome. Meanwhile, Ground Defence is set to benefit over the medium term from turret stabilisation, tactical communications, Indirect Fire Protection Capability (IFPC), and surging international demand.

For 2026, Curtiss-Wright continues to project 4–6% revenue growth and has raised its operating margin guidance to 27.5–27.7%, up from 27.3% adjusted in 2025.

The primary growth engine remains Aerospace Defence, underpinned by aircraft modernisation, UAVs, next-generation programmes, and early opportunities associated with Golden Dome. Meanwhile, Ground Defence is set to benefit over the medium term from turret stabilisation, tactical communications, IFPC, and surging international demand.

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The late-July FOMC meeting marked the primary inflection point of the month for financial markets. The Federal Reserve held the fed funds target range steady at 3.50-3.75% in a 9-to-3 vote, even though a contingent within the Committee had expressed a preference for an immediate hike.

Ultimately, the decision leaned less hawkish than investors had feared. Heading into the meeting, markets were still pricing in a meaningful probability of further tightening, driven by the enduring strength of the US economy and inflationary pressures that are not yet fully consistent with the 2% target.

Kevin Warsh reaffirmed the central bank’s commitment to price stability, yet signaled a greater willingness to let real yields and broader market-driven financial conditions do some of the tightening work. The Fed therefore appears intent on dialing back its reliance on forward guidance, effectively putting the onus back on investors to price in the rate trajectory and risk premiums.

This shift should not be conflated with monetary easing. Rather, it indicates that the central bank believes it can contain inflation without immediate adjustments to the policy rate, relying instead on the restrictive effects of real borrowing costs, credit selectivity, and higher yields at the long end of the curve.

In other words, monetary risk hasn’t dissipated, it has simply migrated from the policy rate to the long end of the yield curve.

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The Fed De-risks the Front End, but Relinquishes Control Over the Broader Curve

The immediate market reaction centered on the front end, which is highly sensitive to Federal Reserve policy. The premium for an imminent rate hike was swiftly priced out, driving a decline in short-term yields.

The most consequential price action, however, unfolded at the long end of the yield curve.

Long-dated Treasuries saw no relief from the central bank’s pause. Conversely, their yields continued to grind higher, signaling that investors did not frame the decision as the prelude to an aggressive easing cycle.

The market began demanding higher compensation for holding long-duration bonds, reflecting a confluence of still-resilient real growth, sticky inflation risks, and heightened uncertainty surrounding the future path of monetary policy.

The outcome was a pronounced steepening of the yield curve: the front end priced out the probability of an immediate hike, while the long end baked in a higher term premium.

This distinction is crucial. While the Fed neutralized a near-term tail risk for financial assets, it did not necessarily lower the broader cost of capital. The tightening burden has effectively shifted from the policy rate to long-end yields, with direct knock-on effects for fixed income, mortgages, corporate funding, and long-duration equities.

Furthermore, a central bank less reliant on proactive forward guidance grants the market more leeway to price in underlying fundamentals. With growth proving more resilient than anticipated and inflation stuck above target, this market autonomy naturally translates into elevated volatility and a higher duration premium.

Ultimately, the Fed’s pause was not construed as an all-clear on monetary restriction. The market merely rolled the tightening constraint from the front end to the back end of the curve.

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SLB, historically known as Schlumberger, is one of the world’s leading companies in technologies and services for the energy industry. Founded in 1926 and listed on the NYSE under the ticker SLB, the company operates globally and maintains its principal operating offices in Paris, Houston, and The Hague. In recent years, the company has also undergone an identity transformation: the brand became SLB in 2022, while the legal name of the listed holding company was changed in 2025.

SLB is not a traditional oil company. It does not explore for and produce oil and gas to sell them directly on the market, as an integrated major or an E&P company would. Its role is different: SLB provides technologies, services, software, equipment, and engineering solutions to energy companies that need to discover resources, drill wells, increase reservoir productivity, and manage complex assets throughout their life cycle.

To understand where SLB fits within the industry, it is useful to clarify the oil and gas value chain. Upstream is the “front end” of the chain: exploration, subsurface analysis, drilling, well completion, and production. Midstream covers transportation, storage, and infrastructure, such as oil pipelines, gas pipelines, and terminals. Downstream is the “back end”: refining, petrochemicals, distribution, and the final sale of energy products. SLB is primarily an upstream company, although some Production Systems activities extend to the surface, pipelines, and connections with processing and refining facilities.

In other words, SLB operates at the technological core of the energy upstream sector. It helps customers better understand the subsurface, build more efficient wells, intervene in existing reservoirs, extend the productive life of assets, and digitalize extremely complex industrial processes. Its main customers are NOCs, or National Oil Companies—national oil companies often controlled or partly owned by governments—as well as large integrated companies and independent operators. No single customer accounted for more than 10% of consolidated revenue in 2025, 2024, or 2023.

This distinction is important. The investment thesis for SLB is not simply a direct bet on the price of oil, but rather on the company’s ability to monetize a structural need within the industry: producing energy more efficiently, increasing recovery from existing reservoirs, reducing operating costs, and integrating software, automation, and artificial intelligence into activities where operational errors can have very significant consequences.

In 2025, SLB generated $35.71 billion in revenue and $3.37 billion in net income. These figures should be viewed within a challenging industry environment, marked by lower upstream spending, commodity volatility, and geopolitical uncertainty. Despite this, SLB continued to accelerate its strategic evolution, placing greater emphasis on production and recovery—the technologies and services that allow customers to optimize oil and gas production from existing reservoirs and increase the recovery of hydrocarbons already present underground. This is complemented by AI-driven digital transformation and the development of the Data Center Solutions business.

From a strategic perspective, the group is currently moving along three main directions. The first is the traditional upstream core, represented by Reservoir Performance, Well Construction, and Production Systems. The second is Digital, encompassing software, data, cloud, automation, and AI. The third includes several adjacent areas, including Data Center Solutions, carbon capture, meaning CO₂ capture, and new energy technologies. This evolution does not eliminate the cyclicality of the business, but it makes SLB a broader and more technology-driven company than its former image as a simple oilfield services provider.

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Over the past two years, the AI–infrastructure equation has become one of the main lenses through which to assess these companies’ results. Investments in data centers, accelerators, proprietary chips, and compute capacity have reached unprecedented levels, but the latest earnings reports are making one thing increasingly clear: what matters is not only how much capital is being invested, but how quickly that capacity can translate into utilization, revenue, margins, and cash generation.

We start with Microsoft, Alphabet, and Amazon, the three companies most directly exposed to competition in the cloud. Azure, Google Cloud, and AWS are all going through a period of strong acceleration, supported by growing demand for AI-related compute capacity.

The chart clearly shows just how pronounced this acceleration has become. In the most recent quarter, AWS grew 37%, Azure 43%, and Google Cloud 82%, although the latter figure was partly boosted by the first sales of TPU systems directly to customers and is therefore not perfectly comparable with the other two. The direction, however, is the same: demand for cloud and AI infrastructure continues to grow rapidly and, in many cases, the main constraint on expansion is no longer finding customers, but building enough capacity to meet demand.

This is precisely where the differences among the three companies become particularly interesting. Amazon, Alphabet, and Microsoft are all investing heavily, but with very different effects on free cash flow, margins, and capital structure. For now, Microsoft continues to fund its expansion while maintaining strong cash generation; Amazon is front-loading very large investments that are temporarily compressing free cash flow; Alphabet, despite Google Cloud’s exceptional growth, has begun relying more significantly on both debt and equity to sustain the pace of expansion.

After these three companies, we will turn to Meta and Apple, two cases that are less directly comparable with the hyperscalers but are equally important for understanding the sector’s current phase. Meta is investing enormous amounts in AI infrastructure without yet having a major cloud business through which to sell that new capacity directly, while Apple is pursuing a completely different model, much more focused on on-device processing and on integrating artificial intelligence into the iPhone and its broader ecosystem.

The purpose of this update is therefore not simply to determine which company reported the “best” quarter, but to capture where each Big Tech company stands today: where growth is coming from, how much it costs to sustain, which investments are already generating visible returns, and which will require more time before they can be properly assessed.

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Microsoft: the most complete monetization model

Microsoft has provided one of the most compelling demonstrations that the current cycle of investment in artificial intelligence can translate into tangible growth, recurring monetization, and cash generation. The market reacted strongly: in the session following the earnings release, the stock gained around 15% at the Wall Street open, marking its largest intraday rise in the past six years.

The reaction is particularly significant because Microsoft was rewarded despite an exceptionally high level of investment: investors recognized that the new infrastructure capacity is being absorbed rapidly and is already generating incremental revenue.

Azure grew 43% in the fiscal fourth quarter, accelerating from 40% in the previous period and exceeding the roughly 40% growth rate expected by the market. Management expects further acceleration to around 45% in the current quarter, while demand continues to exceed available capacity. For the full fiscal year, Azure surpassed $100 billion in revenue for the first time, while Microsoft Cloud reached $214 billion for the year and $59.3 billion in the quarter alone, up 27%.

This point is crucial: the main constraint on growth is not a lack of customers, but the amount of compute capacity Microsoft can bring online.

CFO Amy Hood emphasized that the capacity additions delivered during the quarter were monetized almost immediately, confirming that the company is not building infrastructure ahead of demand, but rather trying to close an existing gap between available supply and customer demand.

During the quarter alone, Microsoft brought 31 new data centers online across five continents, bringing the total number of new facilities opened during the fiscal year to 88. The company also added approximately one gigawatt of capacity during the period and remains on track to roughly double its overall capacity over a two-year period. At the same time, in its most important regions, the time required to move new GPUs from delivery to actual production deployment has been reduced by nearly 50%.

These improvements have a direct economic impact. In an environment where demand exceeds supply, reducing deployment times means bringing forward revenue recognition, increasing asset utilization, and shortening the time required to earn a return on invested capital.

Microsoft is also increasing the returns generated by its existing infrastructure. Since the beginning of the year, throughput for Copilot workloads has quadrupled, allowing the company to process a much larger volume of requests on the same infrastructure base. Its proprietary Maia 200 chip also delivers 30% better performance per dollar than previous-generation hardware in the fleet and, when used with Microsoft’s proprietary MAI models, provides a 40% improvement in performance per watt.

Returns on CapEx therefore do not depend solely on volume growth or potential price increases. Microsoft can also improve ROIC through greater utilization of existing capacity, the adoption of proprietary chips, the use of lower-cost models, better efficiency per token, and shorter deployment times for new infrastructure. These factors help reduce the unit cost of inference and provide meaningful support for margins over time.

Monetization is also not limited to the infrastructure layer. Microsoft 365 Copilot has surpassed 30 million paid licenses, up from around 20 million just three months earlier, with net new activations more than doubling sequentially.

Adoption is also becoming deeper, not just broader. The number of customers with more than 50,000 Copilot licenses has increased more than sevenfold year over year, while the number of companies that have deployed the product to the majority of their information workers has grown by around 75% from the previous quarter. The time required to reach high levels of usage has fallen from several months to just a few days, while the number of conversations per user has nearly doubled year over year. According to management, average weekly engagement is now comparable with established products such as Outlook and Teams.

Microsoft is therefore able to monetize artificial intelligence across several complementary layers:

• Azure capacity consumption;

• recurring software licenses;

• per-user billing;

• models based on usage intensity;

• increasing the average value of enterprise suites through premium products such as Copilot, E5, and E7.

The commercial model is in fact evolving from a pure per-user licensing structure to a combination of licensing and consumption-based billing. This allows the group to monetize both the expansion of its customer base and the increase in usage intensity. The shift is already visible in GitHub Copilot, whose revenue growth accelerated by more than 60% sequentially following the introduction of pricing more closely linked to consumption and value generated.

The quality of demand also appears particularly strong. Commercial Remaining Performance Obligations reached $678 billion, up 84%, and increased 25% even excluding OpenAI. All of the sequential increase in RPO was generated by customers other than the leading frontier model developers, while nearly 90% of Microsoft Cloud’s annual revenue comes from customers outside this category.

This reduces the risk that growth is overly dependent on a small number of large AI labs or on potentially circular commercial relationships between hyperscalers and model developers. Instead, demand is spreading across different industries, geographies, and customer types, strengthening visibility into future revenue.

Quarterly CapEx increased 70% to $41 billion, but Microsoft continues to fund most of its expansion program internally. During the quarter, operating cash flow rose 30% to $55.4 billion, allowing the company to generate $19.6 billion of free cash flow despite the exceptional level of investment. Consolidated operating margin remained around 45%, while Microsoft Cloud gross margin, despite the greater weight of Azure and the costs associated with AI infrastructure, came in better than expected at 65%.

Around two-thirds of CapEx was allocated to assets with relatively short economic lives, primarily CPUs and GPUs, while the remaining share was directed toward longer-lived assets such as land, buildings, and data center infrastructure. This mix gives the company a meaningful degree of flexibility: if demand were to slow, Microsoft could reduce or defer processor purchases, which represent the largest and most adjustable component of the program, without giving up the strategic investments already made in real estate and network infrastructure.

Beginning in fiscal 2027, Microsoft will also extend the estimated useful accounting life of data centers and office buildings from 15 to 25 years, reflecting the observed and expected period of use for these assets. According to management, the benefit to 2027 operating income will be minimal; the more significant effect will instead relate to lease classification.

A larger share of future data center contracts will be accounted for as operating leases rather than finance leases. Because finance leases are included in reported CapEx while operating leases are excluded, this change has brought the company’s projected calendar-year 2026 CapEx to approximately $175 billion. Microsoft has nevertheless clarified that, excluding the accounting reclassification, expectations for actual economic investment remain broadly unchanged.

A future slowdown in reported CapEx should therefore not automatically be interpreted as a reduction in infrastructure expansion. Part of the change will simply reflect the shift from finance leases to operating leases, with a larger share of the cost progressively appearing in operating cash flows and future contractual obligations.

Microsoft therefore represents the case in which the economic chain of AI investment currently appears the most complete:

CapEx → capacity brought online → Azure consumption → software licenses and usage-based revenue → positive free cash flow.

The company is not simply building infrastructure; it is reducing the time required to make that infrastructure productive, increasing the efficiency of existing capacity, and introducing commercial models that allow it to monetize both the number of users and the intensity of usage. The growth in backlog outside the major model developers also shows that demand is broadening across the enterprise market.

As long as Microsoft continues to fund its expansion primarily through its own cash generation while preserving high margins and positive free cash flow, its CapEx program should continue to be viewed by the market as a productive investment already in the process of being monetized, rather than simply a promise of future returns.

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Alphabet: exceptional cloud growth, but a more aggressive financing strategy

Alphabet delivered exceptionally strong operating results. Consolidated revenue increased 24% to $119.8 billion, while Google Cloud accelerated by 82%, reaching $24.8 billion.

More importantly, that growth was accompanied by a significant improvement in profitability. Google Cloud operating income rose from $2.8 billion to $8.8 billion, bringing the segment’s operating margin close to 36%. The increase was driven by demand for AI infrastructure, enterprise solutions, and core Google Cloud Platform services.

Part of the quarter’s growth came from the first sales of TPU systems installed directly in customers’ data centers. Management nevertheless clarified that Google Cloud would have posted a significant acceleration even excluding this contribution, while GCP grew at a faster rate than the overall Cloud segment. TPU sales therefore expand Alphabet’s addressable market, but they make the comparison with Azure and AWS slightly less like-for-like. The majority of the revenue related to agreements already signed is also expected to be recognized in 2027 rather than in the quarter just ended.

Visibility into future revenue also improved significantly. Google Cloud backlog increased by more than $50 billion sequentially to $514 billion, and management expects just over half of that amount to be converted into revenue over the next 24 months. Most of the backlog relates to standard GCP contracts signed with a broad customer base rather than to TPU system sales alone. The pace of new customer acquisition has more than doubled year over year, while existing customers are exceeding their contractual commitments by more than 50%. Transactions through Google Cloud Marketplace have also increased more than sevenfold.

Enterprise adoption further confirms that demand is not concentrated solely among a small number of large model developers. Nearly 90% of Fortune 100 companies use Gemini Enterprise, while around 500 Cloud customers have each processed more than one trillion tokens over the past 12 months and more than 2,000 companies have exceeded 100 billion tokens. Overall, Gemini model APIs are now processing around 22 billion tokens per minute, up from 16 billion in the previous quarter.

The increase in consumption, combined with the fact that Alphabet remains constrained by available capacity, indicates that infrastructure expansion is responding to already observable usage rather than to a purely speculative forecast of future demand.

The traditional advertising business also maintained solid momentum: Search grew 17%, YouTube Ads 13%, and the overall Google Services segment 15%. Gemini-powered features are therefore beginning to support both the cloud business and engagement across consumer products.

The call also provided important signals regarding the risk that artificial intelligence could cannibalize the traditional search business. AI Mode has surpassed 1 billion monthly active users and is driving a net increase in the overall number of queries, while Alphabet continues to report encouraging results in monetizing searches that include AI Overviews. At the same time, the cost of generating a response through AI Mode has fallen to its lowest level since launch, despite the introduction of more advanced features.

Advertiser effectiveness is improving as well. Around 500,000 advertisers have already adopted AI Max and, according to Alphabet, campaigns using AI Max or Performance Max generate on average 15% more conversions or value at the same return on ad spend.

Gemini also makes it possible to understand and monetize longer, more complex queries, including searches that were previously difficult to match with a relevant ad. In other words, AI is not only defending Search against competition: it is expanding the pool of monetizable queries and improving the precision of ad matching.

The issue is not demand, but rather the financial intensity required to meet it.

During the quarter, Alphabet generated $39.1 billion in operating cash flow but invested $44.9 billion in property, plant, and equipment, resulting in negative quarterly free cash flow of $5.9 billion. Although free cash flow remained positive at $53.3 billion over the last 12 months, the figure shows how quickly the infrastructure buildout is absorbing the cash generated by the business.

Pressure on cash flow is set to continue. Alphabet raised its 2026 CapEx guidance from $180–190 billion to $195–205 billion and expects another significant increase in 2027.

In the second quarter, around 60% of technical infrastructure investment was allocated to servers, while the remaining 40% went to data centers and networking equipment. The expansion in capacity will also lead to a gradual increase in depreciation, power consumption, and the operating costs associated with data centers.

In the near term, however, internal capacity will still not be sufficient to fully meet demand. Alphabet will therefore rely more heavily on third-party infrastructure as a bridge solution, accepting some moderate pressure on Google Cloud margins. Management explained that, for certain large customers, it can make economic sense to absorb higher costs for six months if doing so helps preserve multi-year contracts with very attractive overall returns. Short-term margin pressure would therefore not necessarily imply a deterioration in ROIC over the full duration of the customer relationship.

Alphabet has also adopted a particularly aggressive financing strategy. In the second quarter, it raised:

• $49.6 billion through common stock and mandatorily convertible preferred shares;

• $20.3 billion through senior unsecured notes;

• established an ATM program of up to $40 billion, which had not yet been used by the end of the quarter and is intended primarily to cover tax obligations associated with employee stock-based compensation.

During the call, the CFO clarified that Alphabet evaluates its funding sources in a specific order: operating cash flow, debt, and, finally, equity. The use of equity therefore does not reflect a depletion of liquidity—the group ended the quarter with $242.5 billion in cash and marketable securities—but rather a desire to preserve a resilient balance sheet throughout a multi-year investment cycle.

Debt increased from around $16 billion to nearly $100 billion over a 12-month period, making it less attractive to finance the entire funding requirement solely through additional bond issuance. The equity issuance therefore allowed Alphabet to spread the risk across multiple funding sources and preserve greater financial flexibility. At present, management does not expect any further equity issuance beyond the ATM program, which is primarily linked to tax obligations arising from employee stock-based compensation.

This approach protects the group’s liquidity and diversifies its funding sources, but it introduces two costs that the market cannot ignore: shareholder dilution and higher interest expense.

The stock fell by around 7% in the session following the earnings release, despite Google Cloud’s exceptional growth. The reaction did not represent a rejection of Alphabet’s AI strategy, but primarily reflected market concerns over the growing financial intensity of the investment cycle and the need to demonstrate that, over time, a greater share of the expansion can once again be funded through internally generated cash flow.

A superficial reading of the $9.11 GAAP EPS should also be avoided: the figure was heavily influenced by approximately $99 billion in gains on the equity investment portfolio, largely unrealized. Operating performance should therefore be assessed primarily through revenue, margins, and cash flow.

The more accurate interpretation is therefore not that Alphabet still needs to prove it can monetize artificial intelligence. Google Cloud is already converting infrastructure investment into revenue growth, strong margin expansion, and a multi-year backlog, while Search is using Gemini to increase query volumes, improve advertising effectiveness, and expand the pool of monetizable searches.

The real source of uncertainty is the speed at which Alphabet must build new capacity to support this demand. Investment is growing faster than operating cash flow, leading the group to combine internally generated cash, debt, equity, and third-party capacity. Unlike Meta, therefore, Alphabet has already demonstrated a direct link between AI infrastructure and incremental revenue; what the market is questioning is the financial cost required to sustain this acceleration, not the existence of demand or the ability to monetize it.

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Amazon: AWS accelerates, free cash flow slows

Amazon delivered one of the most compelling quarters among the Big Tech companies, primarily because the acceleration in investment was accompanied by equally clear signs of monetization. The market reaction was particularly significant: in the session following the earnings release, the stock gained 15.3%, closing at around $272 and moving back close to the highs reached in May.

The move becomes even more significant when viewed in the context of the preceding weeks. After reaching the $275–280 range between May and June, Amazon had gradually corrected to around $235 on the eve of the earnings release. In a single session, the stock therefore recovered much of the previous decline, on volumes well above average.

The market appears to have rewarded, above all, the growing evidence that Amazon’s massive infrastructure investments are already translating into tangible results. AWS accelerated to its fastest growth rate in the past 18 quarters, margins expanded significantly, and backlog reached unprecedented levels, while the company simultaneously raised its CapEx outlook further.

Consolidated revenue increased 20% to $200.6 billion, while operating income rose 43%, from $19.2 billion to $27.5 billion. Net income reached $62.6 billion, up from $18.2 billion a year earlier, but this figure should be interpreted with caution: the quarter included $53.4 billion in pre-tax non-operating income, primarily related to the revaluation of Amazon’s investment in Anthropic. To assess the quarter’s underlying operating quality, it is therefore much more useful to focus on revenue growth, operating income, and, above all, AWS.

AWS generated $42.2 billion in revenue, up 36.7%, accelerating for the fifth consecutive quarter and posting its fastest growth rate in the past 18 quarters. Sequentially, the segment added more than $4.6 billion in revenue, around 80% more than the previous record quarterly increase, bringing its annualized run rate to approximately $169 billion.

More importantly, the growth did not come at the expense of profitability. AWS operating income rose from $10.2 billion to $16.6 billion, up 64%, lifting the operating margin from around 33% to more than 39%. The quarter benefited from approximately $600 million in accounting gains related to the revaluation of certain energy contracts, but even excluding this effect, the improvement in profitability remains highly significant.

This is particularly important in the context of the current investment cycle. During the Q&A portion of the second-quarter earnings call, the issue of AI workload profitability was raised explicitly, with some investors and analysts suggesting that returns could initially be lower than those of traditional cloud workloads. For now, Amazon is showing encouraging signs: AWS is accelerating while profitability is improving, despite rising depreciation and the massive expansion of infrastructure capacity.

Visibility into future demand is equally significant. During the call, Andy Jassy indicated a $496 billion backlog, growing at a triple-digit rate year over year. Data reported in the 10-Q also show that contractual commitments for future services not yet recognized as revenue, predominantly related to AWS, amounted to approximately $496 billion, with a weighted-average remaining term of 6.4 years.

A significant portion of this growth comes from large model developers. In the first quarter, OpenAI expanded its multi-year agreement with AWS by $100 billion over an eight-year period, while in the second quarter Anthropic increased its commitment by more than $100 billion over ten years. Both agreements include contractual obligations tied to the performance of AWS chips.

A meaningful share of the backlog therefore comes from a small number of exceptionally large contracts. This increases concentration among a limited number of major customers and should be taken into account when interpreting the overall $496 billion figure. At the same time, these agreements strengthen visibility into future demand and make it difficult to argue that Amazon is building capacity without already having customers ready to use it. In fact, the company’s problem remains the opposite: available capacity is not sufficient to meet all customer demand.

AI monetization is already visible across several areas of the AWS offering. The business directly related to artificial intelligence has surpassed an annualized run rate of $25 billion, growing at more than 100% year over year. The proprietary chip business has also exceeded a $25 billion run rate and is growing at a triple-digit pace.

Trainium (Amazon’s proprietary AI chip) is securing multi-year, multi-gigawatt commitments from Anthropic and OpenAI, while Graviton (Amazon’s proprietary CPU chip for cloud workloads) is now used by 98% of the top 1,000 EC2 customers (Amazon Elastic Compute Cloud, one of AWS’s core services). Revenue commitments tied to Graviton have nearly tripled from the previous quarter, and Graviton5 is growing almost twice as fast as the prior generation. At the same time, Amazon continues to use NVIDIA at scale, giving customers the option to choose between third-party accelerators and proprietary hardware.

The role of proprietary chips should therefore not be viewed simply as a diversification strategy away from NVIDIA. Trainium and Graviton allow Amazon to directly influence the unit cost of compute, improving price-performance and therefore the overall economics of the infrastructure. It is the same mechanism seen at Microsoft with Maia and at Alphabet with TPUs: in a business where demand exceeds supply, increasing the performance generated by each dollar invested allows capacity, competitiveness, and returns on capital to expand simultaneously.

Monetization also extends further up the AWS stack. Bedrock (the AWS service that enables companies to access, customize, and use generative AI models) continues to accelerate sharply: hundreds of thousands of customers now use the service, more customers have been added in the past six months than in the first two years since launch, and customer spending in Q2 alone exceeded cumulative spending across all previous quarters.

Amazon is also expanding its offering through AgentCore, Kiro, and other services designed to build, deploy, and use AI agents directly on AWS infrastructure.

In other words, Amazon is not trying to monetize artificial intelligence through a single product. The company controls multiple layers of the value chain: data centers, CPUs, AI accelerators, infrastructure services, third-party models, developer tools, and agentic applications. The more AI usage grows, the greater the number of points at which AWS can capture a share of that spending.

During the call, however, management provided an even more interesting detail, explaining with particular clarity how it assesses the economic return on CapEx. Amazon now expects approximately $220 billion in cash CapEx in 2026, up from roughly $200 billion previously indicated, mainly because of higher memory prices. Most of that spending will continue to be directed toward AWS and artificial intelligence. Despite the increase in investment, the company still believes it will not have enough capacity to fully meet demand in 2026 and expects this situation could extend into 2027. Andy Jassy also emphasized that the demand already visible for 2028 is particularly strong.

The structure of the investment is therefore critical to understanding the current pressure on cash flow. Amazon distinguishes between two very different economic cycles.

Data centers require capital starting roughly two years before servers can be installed and begin generating revenue. The physical structure (building, electrical systems, and cooling infrastructure), once completed, has a long useful life,often spanning several decades, sometimes up to 30 years or more, and can house multiple successive generations of hardware, which instead have a much shorter useful life, typically ranging from 3 to 8 years for traditional servers and even less for the latest-generation GPUs dedicated to AI. The capital spent today to build the physical shell is therefore monetized only in the future, but it can support a long succession of technology cycles, since the hardware is renewed multiple times over the lifespan of the structure.

Servers and networking equipment, by contrast, operate on a much shorter cycle. Amazon generally purchases them only a few months before they are put into service, when it therefore has much greater visibility into actual demand. According to management, these investments reach break-even on average in just under three years, while servers currently have a useful life of at least five or six years. Much of the new AI capacity is also contracted for periods of at least five years.

The economic logic is important: Amazon is currently committing an enormous amount of capital to infrastructure that cannot yet generate revenue, while the hardware component is purchased much closer to actual utilization and is often backed by contract durations longer than the period required to recover the investment.

Once a data center has been built, moreover, subsequent generations of servers should generate better economic returns because they do not require the initial investment in the building and physical infrastructure to be repeated. According to Amazon, a data center with a useful life of more than 30 years should support at least five or six generations of servers.

It is precisely this timing mismatch that explains the deterioration in free cash flow.

Over the last 12 months, Amazon generated $161.4 billion in operating cash flow, up 33%, but purchases of property and equipment, net of proceeds from sales and incentives, reached approximately $169 billion. The intensity of the investment cycle has therefore progressively absorbed cash generation: TTM free cash flow fell from $18.2 billion in Q2 2025 to $14.8 billion in Q3, $11.2 billion in Q4, $1.2 billion in Q1 2026, and finally -$7.6 billion in the most recent quarter. The deterioration therefore does not reflect a reduced ability of the business to generate operating cash flow, which continues to grow rapidly, but rather the fact that CapEx is rising even faster.

At the same time, the 10-Q shows how aggressively AWS is expanding its infrastructure base. In the second quarter alone, net additions to property and equipment attributed to the segment rose from $16.0 billion to $48.6 billion, while depreciation increased from $4.8 billion to $8.1 billion. Despite this sharp rise in infrastructure-related costs, AWS was able to accelerate revenue growth while also expanding its operating margin significantly.

This is probably the most important signal from the entire quarter. Free cash flow is deteriorating because Amazon is building capacity far faster than assets still under construction can currently contribute to revenue, not because the capacity already available is struggling to be utilized or monetized.

The investment cycle is nevertheless also changing the group’s financial profile. Long-term debt increased from $65.6 billion at the end of 2025 to $128.9 billion in June 2026, while in the first six months of the year Amazon raised approximately $67 billion through new long-term debt issuance. The period, however, also included major strategic investments outside ordinary CapEx, including new stakes in OpenAI and Anthropic, meaning that the increase in debt cannot be attributed solely to the buildout of AWS capacity.

Amazon’s financial position nevertheless remains very strong. At the end of June, the company held approximately $123 billion in cash, cash equivalents, and marketable securities, while operating cash flow continues to grow. This is therefore not a question of financial sustainability, but rather a change in how the company is funding an unprecedented phase of infrastructure expansion.

Unlike Microsoft, the current investment program can no longer be fully absorbed by the free cash flow generated by the business. This makes execution increasingly important: the longer CapEx continues to grow faster than operating cash flow, the greater the need for new capacity to come online and begin generating revenue according to the expected timeline.

Meanwhile, AI is beginning to have an impact outside AWS as well. Advertising reached $19.8 billion in revenue, up 26%, while tools such as Ads Agent are reducing costs and improving campaign efficiency. Alexa for Shopping is also linking artificial intelligence directly to purchasing behavior: active users have nearly doubled, while interactions have increased more than fivefold year over year.

In the United States, customers who use Alexa for Shopping spend on average more than 40% more per order than other users, while those who have tried Alexa+ subscribe to Prime at a rate almost 25% higher. These figures do not yet show what the overall economic contribution of AI to retail will ultimately be, but they indicate that Amazon is beginning to use these tools not only as standalone products, but also to increase engagement, conversion, and the value of its existing ecosystem.

Amazon’s position is therefore very different from that of a company asking investors to fund today a possible source of demand in the future. Demand is already visible in the backlog, available capacity remains insufficient, AWS is accelerating, the AI business has already reached meaningful scale, and margins are increasing despite the sharp rise in depreciation.

The real question is instead the timing of the cash return.

Amazon is front-loading enormous investments today in data centers that will progressively come online over the next several years. If, as management argues, revenue growth is eventually able to outpace incremental CapEx growth once this phase is completed, the current compression in free cash flow could primarily represent the upfront cost of an expansion designed to generate returns for many years. Amazon also argues that AI margins and returns are following a trajectory at least comparable to, and for now slightly better than, that seen in traditional cloud computing at the same stage of development.

The economic chain of AI investment therefore already appears fairly clear:

CapEx → new AWS capacity → contracted utilization → revenue growth → high margins → free cash flow deferred over time.

The main difference versus Microsoft lies in the final step. Microsoft is demonstrating that it can sustain exceptionally high levels of investment while already maintaining strong free cash flow generation today. Amazon, by contrast, is temporarily sacrificing cash generation in order to build capacity well ahead of its monetization.

For now, the market appears willing to accept this trade-off, because AWS continues to show that available capacity is being absorbed rapidly and because there is already strong contractual visibility into future demand. The 15% gain in the stock following the earnings release is probably the clearest indication of how investors are evaluating the current CapEx cycle: what matters is not only how much a Big Tech company is spending, but how clearly it can demonstrate that this spending will translate into revenue, margins, and, with a lag of several years, free cash flow.

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Meta: the core business is strong, but the link between CapEx and new revenue remains less visible

Meta presents the clearest contrast between the performance of its existing business and the still-uncertain returns on AI infrastructure.

Second-quarter revenue increased 28% to $60.8 billion, supported by a 14% increase in ad impressions and a 12% rise in the average price per ad. The core advertising business therefore remains extremely strong.

The call, however, clarified an important point: AI investments are already producing tangible benefits within the existing business. Zuckerberg explained that artificial intelligence is improving the relevance of content shown on Instagram and Facebook, increasing user engagement and strengthening Meta’s ability to predict, rank, and optimize advertisements. According to management, Meta’s advertising business is currently growing faster year over year, in revenue terms, than that of any other major player in the industry.

Advertiser adoption of AI tools is also accelerating. Around 9 million small businesses already use at least one of Meta’s AI-powered advertising creative tools, while the new Muse Image and Muse Video models are expected to further expand advertisers’ ability to generate and test creative content more quickly and effectively. This is relevant because it confirms that part of the CapEx is already supporting tangible monetization within the core business, even as the market continues to demand greater visibility into the direct returns on the new infrastructure.

The strength of the core business is also supported by an exceptionally large user base. During the quarter, 3.6 billion people used at least one Meta app every day; Instagram reached 2 billion daily active users, while Threads surpassed 500 million monthly active users. This scale reinforces the company’s competitive advantage both in distributing new products and in advertising monetization, because it allows Meta to apply AI-driven improvements to a massive user base almost immediately.

At the same time, total costs increased 55% to $42 billion, compressing the operating margin from 43% to 31% and reducing operating income by 8%.

The quarter included $2.4 billion in legal charges and $1.18 billion in restructuring costs, but the divergence between revenue growth and cost growth remains significant.

The most concerning signal came from cash generation. Meta generated $31.9 billion in operating cash flow, but CapEx, including finance lease payments, reached $31.1 billion. As a result, free cash flow fell from $8.55 billion a year earlier to just $784 million.

The company also narrowed its 2026 CapEx guidance to $130–145 billion, raising the lower end from the previous range of $125–145 billion.

Meta is already using AI to improve content ranking, engagement, and advertising campaign efficiency. However, unlike Microsoft, Alphabet, and Amazon, it still does not have a major cloud business capable of selling the new compute capacity directly to external customers.

Potential future sources of monetization—personal agents, business agents, APIs, smart glasses, and the sale of excess capacity—remain at an early stage.

The call provided some additional detail on these areas of development. Meta explained that it sees three major areas of opportunity: personal agents, which are expected to become a new category of consumer product; business agents, already used every week by more than 1 million businesses on WhatsApp and Messenger; and a broader enterprise opportunity that includes APIs, services for large customers, and the potential direct sale of compute capacity.

The expected monetization model combines subscriptions, volume-based pricing, and, over time, more outcome-based models similar to the logic of advertising.

The key point, however, is that these business lines remain at an early stage relative to the scale of the infrastructure Meta is building. Unlike Microsoft or Amazon, which can already show a very visible link between additional capacity and cloud growth, Meta is asking investors to focus primarily on the future potential of these products rather than on monetization that is already fully visible in current financial results.

As a result, the market has greater difficulty seeing a direct link between CapEx and incremental revenue.

To reduce the immediate cash outlay, Meta has also begun adopting external financing structures. In the $14 billion joint venture with funds managed by BlackRock, those funds will own 80% of the project, while Meta will retain 20%. The company will be able to use the entire campus through lease agreements without directly financing the full project.

The call also clarified that Meta continues to operate in an environment where internal demand for compute capacity exceeds available supply and expects to have numerous positive-return uses for additional capacity at least through 2026 and 2027, not only in new AI initiatives but also in the core business.

Management explained that beyond 2027 it prefers to preserve flexibility, focusing today primarily on securing access to land and power while postponing decisions on more expensive purchases, such as chips and other critical components.

This leads us to believe that infrastructure pressure is unlikely to ease in the near term: even if the mix of spending evolves, capacity requirements will likely remain elevated for several more quarters.

The structure reduces the initial cash requirement, but it does not eliminate the economic risk: Meta will continue to incur lease payments and has provided guarantees on the residual value of the infrastructure. In substance, part of the CapEx is being transformed into future contractual obligations and potential liabilities rather than disappearing from the company’s financial profile.

The stock fell by approximately 8–10% following the results.

The market reaction therefore does not call into question the strength of the advertising business, nor the fact that AI is already improving engagement, ranking, and ad performance. The issue lies elsewhere: the scale of infrastructure investment is growing faster than visibility into incremental returns outside the core advertising business. Until Meta can demonstrate more convincingly that personal agents, business agents, APIs, and enterprise services can translate into recurring revenue large enough to justify the new level of spending, investors are likely to view CapEx more as a drag on free cash flow than as an immediate driver of multiple expansion.

Our view remains cautious. Meta continues to show an extremely resilient core advertising business, and AI is already helping improve its performance. However, as long as CapEx continues to rise and absorb an increasing share of operating cash flow, without sufficiently strong revenue acceleration to demonstrate unequivocally that infrastructure investment is generating tangible incremental returns, a sustained re-rating of the multiple will remain difficult.

In other words, the market is not questioning the quality of the existing business, but is asking for stronger evidence that the new compute capacity can translate into a second engine of growth and monetization large enough to offset the current cash drain.

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Apple: iPhone accelerates, but the outlook weighs on the stock

Apple reported an exceptionally strong June quarter, but the market looked beyond the results just released and focused primarily on the outlook for the coming months. In the session following the earnings report, the stock fell by around 7%, after dropping as much as 10% intraday, abruptly interrupting the rally that had pushed the shares to recent highs in the preceding weeks.

The quarter’s results are difficult to criticize. Revenue increased 16% to $109.4 billion, while operating income rose from $28.2 billion to $35.7 billion and net income from $23.4 billion to $29.8 billion, up 27%. Diluted EPS increased from $1.57 to $2.02.

Once again, the main driver of growth was the iPhone. Revenue increased 22% to $54.3 billion, representing almost half of total quarterly revenue. Mac also delivered particularly strong performance, with revenue up 29% to $10.4 billion, while Wearables, Home and Accessories grew 6%. The only hardware category to decline was iPad, with revenue down 6%.

Strength was also broad-based geographically. In particular, the Greater China segment (mainland China, Hong Kong, and Taiwan) returned to growth, with revenue increasing 22% to $18.8 billion, driven primarily by the iPhone. Europe and Rest of Asia Pacific grew 22% and 16%, respectively, while the Americas posted an 11% increase.

The iPhone therefore remains Apple’s primary growth engine. Its importance goes beyond its immediate contribution to revenue: every additional device sold expands the installed base and deepens customer integration within the Apple ecosystem, creating further monetization opportunities over time through Services, iCloud, apps, payments, wearables, and future replacement cycles.

The call, however, clarified an important aspect of the guidance. For the September quarter, Apple expects revenue growth of 9% to 11%, compared with the 16% just reported and a consensus of around 12% referenced by analysts during the call. The slowdown does not, however, appear to reflect a deterioration in demand. Management expects an approximately 2.5 percentage-point headwind from foreign exchange and, above all, a significant increase in supply constraints affecting iPhone, Mac, and iPad.

Tim Cook explained that the main bottleneck is the availability of advanced process nodes used for Apple SoCs, but he also made clear that the primary cause is not a supplier issue: iPhone and Mac are simply selling much better than Apple itself had expected. The company is pulling forward supply wherever possible, but the supply chain currently has less flexibility than in the past.

iPhone growth is expected to slow from 22% to around 15% in the September quarter. The key point, however, is that the slowdown does not appear to be driven by weaker demand, but primarily by supply constraints that will prevent Apple from fully meeting customer demand. In other words, the underlying operating signal is stronger than the guidance alone might suggest.

Services also reached a new record, with $30.7 billion in revenue, up 12%. The slowdown versus previous quarters, however, needs to be interpreted carefully. During the call, management explained that foreign exchange was the main factor behind the deceleration, while the App Store was also affected by greater weakness in mobile gaming and changes to its business model in certain markets. Despite these pressures, cloud services, advertising, video, and payment services continued to grow at double-digit rates, and Apple surpassed 1.5 billion paid subscriptions across an installed base of more than 2.5 billion active devices.

A second area of attention is margins. Gross margin rose to 50.1%, but benefited by around two percentage points from tariff refunds. Excluding this effect, the margin would have been around 48.1%. For the September quarter, Apple expects gross margin of 47% to 48%, including approximately one percentage point of benefit from tariff refunds.

The main pressure, however, is not coming from foreign exchange, but from memory costs. During the Q&A, management explained that more than 100% of the underlying margin decline expected between June and September is attributable to higher memory prices, partially offset by lower costs for other components, favorable mix, and the use of inventory purchased at earlier prices. Tim Cook described the current memory pricing environment as a “100-year flood,” adding that Apple has already raised prices on some products, albeit reluctantly.

This is probably one of the most important variables for the coming quarters. Apple believes that market memory prices could continue to rise beyond September, while the benefit from inventory purchased earlier is expected to gradually diminish. The risk therefore concerns not only product availability, but also Apple’s ability to preserve margins without undermining demand through further price increases.

Apple, however, represents a very different case from the hyperscalers discussed earlier. Microsoft, Alphabet, Amazon, and Meta are sustaining exceptionally high levels of CapEx to build data centers and AI compute capacity. Apple’s numbers do not yet show a comparable increase in capital intensity: in the first nine months of the fiscal year, payments for property, plant, and equipment amounted to $6.8 billion, compared with $9.5 billion in the same period a year earlier. Over the same period, operating cash flow increased from $81.8 billion to nearly $117 billion.

This does not mean that Apple is investing little in artificial intelligence. R&D spending increased 32% to $11.7 billion in the quarter, and the 10-Q attributes the increase primarily to higher infrastructure-related costs, including those associated with AI, as well as higher personnel expenses. In the first nine months of the fiscal year, R&D reached $34 billion, compared with $25.7 billion in the same period a year earlier.

Management also clarified why Apple’s model may remain less capital-intensive than that of the hyperscalers. Tim Cook explained that the company uses a hybrid model, combining its own data centers with third-party cloud infrastructure, while some workloads are processed directly on devices. Apple views the ability to perform part of the inference on-device as a strategic advantage because it reduces reliance on the cloud while strengthening privacy, speed, and hardware-software integration.

Siri AI will therefore be particularly important in understanding how this model evolves. Management acknowledged that it is still too early to estimate its compute costs precisely, but it has already pointed to one possible form of monetization: users who make more intensive use of AI features could be encouraged to move to higher-tier iCloud+ plans.

The difference versus Amazon or Microsoft is therefore substantial. Apple does not necessarily need to monetize AI by directly selling compute capacity to external customers. The objective may instead be to use artificial intelligence to increase the perceived value of the iPhone, accelerate replacement cycles, strengthen the ecosystem, and expand Services revenue over time.

For now, however, it is still too early to say that Siri AI is becoming a true driver of the hardware cycle. Management highlighted the very positive feedback received from the beta versions, but did not quantify any expected impact on iPhone sales. In addition, the rollout remains incomplete in some important markets: in Europe, Apple is still working with the Commission to make Siri AI available on iPhone and iPad, while in China only some initial Apple Intelligence features have been approved.

The current picture therefore remains one of an extremely strong operating business, supported by one of the strongest iPhone cycles in recent years, but with growth expected to slow in the near term due primarily to factors external to demand: supply constraints, foreign exchange, and memory cost inflation.

The negative reaction in the stock appears to reflect precisely this contrast. The market did not question the quality of the June quarter, but focused instead on slower expected growth and pressure on margins in the coming months. At the same time, the call suggests that the underlying picture is less weak than it may appear: demand for iPhone and Mac continues to exceed internal expectations and, at least for now, the problem is producing enough devices to meet that demand.

Over the medium term, however, the key variable will be AI. Unlike the hyperscalers, Apple is not yet putting free cash flow under pressure with hundreds of billions of dollars in CapEx: it is seeking to leverage its advantage in on-device processing and a hybrid infrastructure model to integrate AI directly into the product. If Siri AI succeeds in becoming a tangible point of differentiation for the iPhone while also supporting Services monetization, Apple could generate a meaningful return on AI without having to replicate the same capital intensity as its competitors.

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Qualcomm Incorporated is a US-based technology group founded in 1985 and headquartered in San Diego, California, engaged in the development and commercialization of foundational technologies for the global wireless industry. The company is historically one of the main enablers of the evolution of cellular standards, from 3G and 4G up to 5G, and combines semiconductor design activities, system software, advanced connectivity, and intellectual property monetization.

The group formally operates through three segments: Qualcomm CDMA Technologies (or QCT), Qualcomm Technology Licensing (or QTL), and Qualcomm Strategic Initiatives (or QSI). From an economic and operational perspective, however, the analysis of the company remains primarily focused on QCT and QTL.

QCT develops and supplies integrated circuits and system software for mobile devices, automotive, IoT, edge networking, and connected computing platforms. QTL grants licenses on the use of significant portions of the Qualcomm patent portfolio, including essential patents for 3G, 4G, and 5G cellular standards, which are utilized in the manufacturing and sale of wireless devices. QSI, on the other hand, is more financial and strategic in nature: it invests in early-stage companies in areas consistent with the Qualcomm ecosystem, including 5G, AI, automotive, consumer, enterprise, cloud, IoT, and extended reality.

This structure makes Qualcomm different from a standard fabless company. The group does not solely compete on the sale of individual chips, but rather on its ability to develop integrated platforms that combine connectivity, computing, software, and intellectual property. In QCT, the company monetizes the technological content embedded in the end devices; in QTL, it monetizes the role of its patents within global wireless standards. The combination of hardware, software, standards, and licensing provides Qualcomm with a more multifaceted economic profile compared to many other peers in the semiconductor sector.

Alongside its historical smartphone and mobile connectivity end-markets, Qualcomm is progressively expanding its perimeter into automotive, industrial IoT, edge AI, personal AI, government technologies, and data centers. These verticals do not all share the same degree of maturity: Automotive and IoT are already highly visible in QCT revenues, while Data Center, Physical AI, and certain activities tied to the government technology business still represent emerging strategic vectors. These must be evaluated based on management’s ability to convert design wins and partnerships into scalable revenues, margins, and cash flows.

The true economic engine of the group can be understood through three core drivers.

The first is the monetization of dollar content per device: within QCT, Qualcomm drives top-line growth not simply by selling more chips, but by increasing the technological footprint embedded in each device. In smartphones, this translates to the processor, modem, RF, and AI engine; in automotive, it encompasses the digital cockpit, telematics, ADAS, and software; in IoT, it involves edge AI, connectivity, and local computing.

The second driver is standard monetization: QTL allows Qualcomm to collect royalties on the use of its intellectual property within cellular standards, commanding structurally higher margins than the semiconductor business.

The third driver is the leverage derived from Research and Development reusability: the core competencies developed in modems, CPUs, NPUs, RF, AI, and low-power compute can be amortized and reused across multiple end-markets, from smartphones to cars, and from industrial IoT to data centers.

This combination sets Qualcomm apart from a standard fabless company. While QCT exposes the group to semiconductor cyclicality and end-market demand, QTL generates an ultra-high-margin royalty stream.

In Q2 FY2026, Qualcomm reported consolidated revenues of 10.6 billion dollars and non-GAAP EPS of 2.65 dollars, hitting the upper end of management’s guidance. QCT generated 9.1 billion dollars in revenue with an EBT margin of 27%, while QTL delivered 1.4 billion dollars with an EBT margin of 72%.

The underlying economic quality of the group is therefore heavily dependent on its licensing arm. QTL accounts for a minority share of consolidated top-line revenue, but a disproportionately large share of divisional operating profit. This structural dynamic is highly critical for shareholders: QTL acts as an ultra-high-margin source of profitability and free cash flow generation, effectively funding the massive R&D intensity required to sustain QCT’s technological leadership while underwriting a highly aggressive capital return policy.

Qualcomm’s competitive advantage lies primarily in three areas.

• First, the company possesses a highly significant patent portfolio in cellular standards, which fuels QTL.

• Second, Qualcomm leverages historical expertise in designing low-power integrated platforms, a critical element across smartphones, wearables, automotive, and edge devices.

• Third, the integration of hardware, software, and connectivity reduces the time-to-market for OEMs and can increase switching costs, as the customer is not just buying a component, but an entire technology platform.

Alongside these strengths, there are significant structural risks. Qualcomm itself points out that a substantial portion of its revenues is derived from a concentrated number of customers; in FY2025, Apple, Samsung, and Xiaomi each accounted for at least 10% of consolidated revenues.

The group is also exposed to customer vertical integration: Apple, Samsung, and Xiaomi already develop certain chips in-house, and management acknowledges that these customers could increasingly utilize proprietary components in lieu of Qualcomm products in the future.

The Apple issue remains the elephant in the room. Although the intellectual property licensing business is distinct from semiconductor sales, Apple’s progressive shift towards proprietary modems represents the most visible customer concentration risk for QCT Handsets. In other words, the loss of revenues from supplying semiconductors to Apple does not automatically imply the loss of QTL licensing revenues, but it nevertheless reduces the visibility of the smartphone business and contributes to the discount at which the market tends to value Qualcomm.

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Geographic dependence is also material. In FY2025, China (including Hong Kong) accounted for 46% of consolidated revenues, calculated based on the headquarters of the customer or licensee, which is not necessarily the country where the final device is sold.

This exposes Qualcomm to regulatory, tariff, and geopolitical risks, US-China export restrictions, and Chinese technological self-sufficiency policies.

The company profile must therefore be analyzed along two dimensions. On one hand, Qualcomm remains exposed to the smartphone cycle, customer concentration, and the Apple/China risk. On the other hand, the company is actively working to transition into a broader platform for the intelligent edge, automotive, industrial AI, personal AI, robotics, and data centers. The key question for the equity story is whether this diversification will be rapid and profitable enough to offset the structural pressure on handsets.

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Business Lines

Qualcomm reports two primary financial segments: QCT and QTL.

QCT represents the semiconductor and technology platforms segment, while QTL represents the intellectual property licensing business. This distinction is fundamental because the two areas operate under profoundly different economic models: QCT generates revenue through the sale of integrated platforms, semiconductors, modems, connectivity solutions, and system software; QTL, by contrast, monetizes the patent portfolio, specifically standard-essential patents related to cellular standards.

Within QCT, Qualcomm disaggregates revenue across three primary end-markets served: Handsets, Automotive, and IoT. Consequently, these areas should not be interpreted as standalone segments with individual profitability, but rather as the primary commercial channels for QCT technologies. Indeed, the company does not publish separate margins for Handsets, Automotive, and IoT; margins are reported only at the overall QCT level. For this reason, in our analysis of the individual application areas, we highlight their contribution to total revenue, while their contribution to margins remains undisclosed and consolidated within the overall QCT margin.

In Q2 FY2026, QCT generated $9.076 billion, representing approximately 85.6% of consolidated revenues, with an EBT of $2.465 billion and an EBT margin of 27%. QTL, on the other hand, generated $1.382 billion, accounting for approximately 13.0% of consolidated revenues, with an EBT of $994 million and an EBT margin of 72%. On a combined QCT+QTL segment EBT basis, QCT accounts for approximately 71.3% of divisional profits, while QTL represents approximately 28.7%.

This divergence clearly underscores the group’s economic structure: QCT serves as the primary revenue driver, while QTL, despite accounting for a much smaller share of the top line, contributes disproportionately to overall profitability due to the high-margin nature of the licensing model.

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This week’s market narrative revolves around an apparently technical, yet absolutely critical data point: the shape of the yen OIS curve. Before diving into the implications for our portfolios, we need to clarify what an OIS actually is, and why it currently represents the purest, most accurate gauge of Bank of Japan expectations.

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Understanding Swaps and How to Read an OIS Curve

An interest rate swap is a contract between two counterparties to exchange interest rate cash flows based on a specified notional principal (the benchmark amount used solely to calculate interest payments, which is never actually exchanged between the parties). Let’s look at a practical example using a 100 million notional (meaning both parties will calculate their respective interest payments as if they had borrowed or lent 100 million, though this amount remains purely theoretical and never changes hands):

• Party A pays an agreed fixed rate, say 3% per annum. They will pay 3 million at the end of the period, regardless of market fluctuations.

• Party B pays a floating rate. If this rate settles at 2.4% at the end of the period, Party B owes 2.4 million (these rates are purely illustrative).

In practice, the two cash flows are netted out: Party A will make a net payment of 600,000 euros to Party B. The fixed-rate receiver (Party B) profits if the floating rate falls, while the fixed-rate payer profits if the floating rate rises.

An Overnight Indexed Swap (OIS) operates on the exact same mechanism, with one key distinction: the floating rate is not a 3-month Euribor, but rather the official overnight rate (TONAR for Japan), compounded daily over the life of the contract (meaning the overnight rate for each individual day is applied, and the resulting interest accrues alongside previous days’ interest, exactly like daily compound interest, to determine the final total due at maturity).

Consequently, the agreed fixed rate in an OIS contract represents the market’s best estimate of where the average overnight rate will sit over that specific horizon. This is precisely why the OIS curve is the cleanest metric for front-running and anticipating central bank moves.

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The Signal to Watch: Bear Flattening on the Japanese OIS Curve

Over the past 12 months, the Japanese OIS curve has carved out an unmistakable trend: all tenors have moved higher, but through vastly different dynamics. The short-end of the curve (1, 3, and 6-month maturities) surged from roughly 0.5% in August 2025 to the current 1%. Concurrently, the 10-year tenor has literally doubled, ripping from 1.3% to over 2.6%.

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Corning is not simply a glass manufacturer. It is a materials science specialist with a 175-year history, capable of transforming expertise developed in glass manufacturing, ceramics, and optical physics into essential products for some of the major technological innovations of the last century.

Its solutions are often barely visible to the end consumer, but they occupy a critical position within numerous industrial supply chains. Corning produces the high-resistance glass used in smartphones, the precision substrates needed to create increasingly thinner and high-resolution displays, optical materials destined for semiconductor manufacturing, ceramic filters for vehicle emissions control, and products for the solar industry. Above all, it is the company that invented the first low-loss optical fiber in 1970, a technology destined to become one of the pillars of global digital infrastructure.

The business model is founded on the ability to develop proprietary materials, perfect manufacturing processes that are difficult to replicate at scale, and collaborate directly with key industrial customers to solve complex technological problems. Corning therefore does not operate as a simple commodity supplier. It invests in research, builds highly specialized facilities, and seeks to monetize its expertise through products with superior technical characteristics and a growing added-value content.

This setup requires a significant industrial presence. At the end of 2025, the group operated through 128 manufacturing facilities and connected structures distributed across 14 countries. Reported revenue under GAAP accounting principles reached $15.6 billion, representing a 19% increase compared to the previous year.

To analyze the relative weight of individual divisions, however, it is more useful to utilize segment net sales, which stood at $16.4 billion. The difference compared to reported revenues deserves an explanation.

Corning carries out a significant portion of its activities outside the United States and publishes divisional results at constant currency, applying exchange rates defined by management that are consistent with the hedging instruments used by the group. The objective is to isolate the operational performance of the divisions from currency volatility and make the evolution of underlying volumes, prices, and profitability more readable.

The topic is particularly important for the Display business. Corning produces glass substrates for the major Asian manufacturers of LCD and OLED panels, destined primarily for televisions, notebooks, monitors, tablets, and mobile devices. This activity has a significant exposure to the Japanese yen. When the yen weakens against the dollar, revenues and profits realized in local currency translate into a lower dollar amount, even if sold volumes and commercial conditions have not changed. The reported result can therefore worsen due to a currency reason, without necessarily reflecting an industrial deterioration.

To mitigate this effect, in 2025 Corning applied a core rate of 120 yen per dollar, compared to the 107 yen used in 2024. The change was applied prospectively, without reclassifying the comparative data from the previous year. The difference between reported revenues and 2025 segment net sales was $779 million and is primarily attributable to the currency adjustments of the Display division.

In addition to segment net sales, Corning publishes segment net income, the measure used internally by management to evaluate the performance of individual activities and decide how to allocate resources. The company does not communicate EBITDA or EBIT by division. Segment net income is calculated according to managerial criteria and is not perfectly comparable with consolidated net income or metrics used by other companies. It remains, however, useful for understanding the relative contribution of the different business lines to overall profitability.

In the financial statements closed on December 31, 2025, Corning presented five main divisions, to which was added a residual item dedicated to emerging activities.

The most important division was Optical Communications, with $6.274 billion in segment net sales (38.2% of the total) and $1.048 billion in segment net income (38.5% of the overall divisional result).

The business is divided between Carrier Networks and Enterprise Networks. Carrier Networks generated $3.079 billion and includes fibers, cables, and connectivity solutions destined for telecommunications networks, submarine and long-distance connections, metropolitan infrastructures, and fiber-to-the-home networks. Enterprise Networks contributed $3.195 billion and includes products destined for private networks, businesses, and hyperscale data centers.

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To fully grasp the current setup, one must look back at the latest FOMC meeting, the first featuring Kevin Warsh in his new role as Federal Reserve Governor. On that occasion, the Fed’s messaging was interpreted by the market as moderately hawkish: not aggressive enough to trigger an outright capitulation, but restrictive enough to bring duration risk back to the forefront.

The Fed signaled that inflation remains too elevated to warrant rapid monetary easing and that, given still-solid nominal growth, the central bank is under no immediate pressure to cut rates. This shift in tone altered the market equilibrium: investors pivoted from a regime dominated by credit risk compression back to one where the primary concern is the level of real yields.

The chart below clearly illustrates how 2-year Treasury yields (nominal yields) surged higher following Kevin Warsh’s hawkish remarks.

Two-year Treasury yields have begun pricing in a more aggressive Federal Reserve, climbing faster than their 10-year counterparts.

Given that inflation expectations have remained flat following the end of the conflict, the rise in nominal yields has driven a corresponding increase in real rates.

For long-duration assets, and semiconductors in particular, this marked a critical turning point. As real yields rise, the present value of future earnings is discounted, naturally leaving high-multiple sectors more vulnerable. Unsurprisingly, following the FOMC meeting, segments most exposed to long-term growth, such as semiconductors, software, and other high-multiple tech verticals, began showing signs of increased fragility.

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Attributecribing the recent volatility in semiconductors solely to the Fed would be an oversimplification. As we previously highlighted in our earlier macro reports, the second major risk factor was driven by the USD/JPY exchange rate.

In recent weeks, the dollar has strengthened against the yen once again, breaking back above the 160 handle. This level carries significant psychological and political weight; in the past, it served as an informal line in the sand, beyond which Japanese authorities began weighing more aggressive currency market interventions. Indeed, Japan’s 2024 interventions to support the yen occurred right around this 160 area, cementing that threshold as a key focal point for both policymakers and traders.

The issue today, however, is that the market is no longer just looking at 160. The true danger zone appears to have shifted higher, between 163 and 170. This range concentrates a heavy volume of reverse knockout levels tied to FX hedging products used by Japanese corporate importers. Once these barrier levels are triggered, the currency protection lapses, potentially forcing companies in need of dollars to buy them directly on the spot market, thereby fueling further downward pressure on the yen.

This distinction is crucial: the real focal point is no longer just the symbolic 160 threshold, but rather the fact that the market is already looking toward higher levels, where corporate hedges and potential reverse knockouts could trigger an accelerating sell-off in the yen.

Source: Bloomberg, Japan’s Ministry of Finance. The USD/JPY exchange rate is once again trading above 160, but the market's true danger zone is shifting higher, between 163 and 170, where corporate FX hedges and potential reverse knockouts are heavily concentrated.

This is a crucial point: when an exchange rate approaches certain technical thresholds, market dynamics can become highly non-linear. Minor moves can trigger short covering, liquidations, or forced dollar buying, causing the trend to accelerate abruptly. For this reason, the risk associated with the yen is not merely a currency issue, it is technical and systemic.

For semiconductors, this link is particularly relevant. The sector is heavily exposed to Asia, global supply chains, and overall tech risk sentiment. Furthermore, following months of massive outperformance driven by the AI theme, positioning across many names in the space had become extremely crowded. When you combine rising real yields, a strong dollar, a weak yen, and the looming risk of Japanese intervention, the natural outcome is increased pressure on the market’s most congested segments.

To understand why this dynamic is so critical, one must look back at the mechanics of the yen carry trade.

For decades, Japan has been one of the primary sources of cheap funding for global markets. The reason is simple: rock-bottom interest rates, often near zero, as illustrated below by the 1-year government bond yield, or even in negative territory, and a central bank that for a long time maintained a monetary policy stance significantly more accommodative than those of other major developed economies.

The carry trade is born from this exact divergence. An investor borrows yen at a low cost, sells the yen in the FX market, buys dollars or another higher-yielding currency, and invests that capital in more remunerative assets: U.S. Treasuries, credit, U.S. equities, Japanese export-oriented stocks, emerging markets, or other financial instruments.

As long as the yield differential remains favorable and the exchange rate does not move violently against the position, the strategy works. The investor pockets the yield on the purchased asset while carrying a minimal funding cost. The issue, however, is that these trades are often highly leveraged to amplify returns. And when leverage enters the equation, a seemingly manageable currency move can rapidly spiral into a massive loss.

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Let’s look at a simple example using straightforward numbers:

Imagine an investor borrows 15 billion yen when the USD/JPY exchange rate is at 150. At this rate, 15 billion yen equates to roughly $100 million. The investor sells the yen, buys dollars, and deploys that $100 million into U.S. assets.

As long as USD/JPY remains stable or moves higher, the trade is perfectly sustainable. However, if the yen strengthens sharply and the exchange rate drops from 150 to 125, the picture changes dramatically. To pay back that original 15 billion yen debt, the investor now needs roughly $120 million instead of $100 million. The loss is driven by the currency move, not necessarily by the performance of the underlying asset.

If that position was built using leverage, the risk escalates further. The broker may issue a margin call, force position closures, or automatically liquidate the purchased assets. At that point, the investor is forced to sell their dollar-denominated holdings, buy back yen, and repay the debt.

When a vast number of market participants are positioned the exact same way, everyone is forced to execute the same transaction simultaneously.

This is precisely how the carry trade can unravel into a mechanical selling spiral.

This mechanism is far from theoretical. The market witnessed it play out with absolute clarity during the summer of 2024.

During that period, two factors converged with particular volatility. On one hand, U.S. macroeconomic data came in softer than anticipated, headlined by an unemployment print that missed expectations at 4.3% versus the forecasted 4.1%, which fueled expectations of aggressive Federal Reserve rate cuts. On the other hand, the Bank of Japan caught the market off guard with a surprise rate hike, driving a rapid and sharp appreciation of the yen.

The result was a textbook yen carry trade unwind. Investors who were short the yen were forced to cover their positions at a loss. This forced short squeeze in the currency market compelled many participants to simultaneously liquidate risk assets, including Japanese equities and tech names. The Nikkei plunged roughly 12% in a single session, while global volatility spiked, forcing a significant correction in the Nasdaq before it eventually stabilized and recovered over the following weeks.

That episode is critical because it demonstrates that the yen carry trade is not a phenomenon isolated to the currency market. When the positioning is massive, widespread, and highly leveraged, its unwind can trigger rapid ripple effects across equities, credit, global volatility, and liquidity.

Today, we do not believe that this exact dynamic will necessarily repeat itself with the same intensity. The summer 2024 shock was triggered by a surprise rate hike from the BoJ; following that event, the Bank of Japan committed to communicating any future monetary tightening well in advance.

Nevertheless, the parallels are striking: USD/JPY has returned to extremely sensitive thresholds, the Fed has leaned hawkish, the BoJ is under immense pressure, and the global tech sector still exhibits highly crowded pockets.

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There is also an angle that often goes underappreciated: a weak yen is not a structural net positive for the whole of Japan.

It is true that major Japanese exporters tend to benefit from a depreciating currency. They sell abroad, generate revenues in dollars or other hard currencies, and see their top and bottom lines swell once those earnings are converted back into yen. This explains why, during certain phases, the Japanese equity market has capitalised on currency weakness.

However, for many small and medium-sized enterprises, the reality is starkly different. Domestic companies that rely on imports for raw materials, energy, components, or intermediate goods face a direct surge in input costs. Crucially, they often lack the necessary pricing power to pass these hikes onto end consumers, resulting in a severe margin squeeze.

Bloomberg highlighted that the yen’s weakness has contributed to a sharp spike in currency-related corporate bankruptcies. Forty-five companies failed between January and June due to headwinds linked to the weak yen, a more than 30% increase year-over-year, and the highest level since Tokyo Shoko Research began tracking this specific metric in 2022.

The pressure is particularly acute for wholesalers, retailers, and manufacturers with minimal pricing power. Bloomberg cited the case of Merry Time Foods, a seafood importer specializing in crab, shrimp, and tuna, which filed for bankruptcy in May after its profitability eroded due to the weak yen and political instability in its supplier countries.

This underscores that the USD/JPY exchange rate has transformed into a pressing domestic political issue, rather than just a chart to track on Bloomberg terminals. The further the yen weakens, the heavier the economic toll on Japanese households and SMEs. And as this burden grows, so does the probability that Japanese authorities will escalate their rhetoric or deploy direct intervention.

Finance Minister Satsuki Katayama has already stated that Japan stands ready to take appropriate action if necessary, after the yen slipped to its lowest levels against the dollar since 1986. At the same time, Bloomberg notes that repeated verbal interventions have so far done little to arrest the currency’s decline, leaving traders waiting to see if and when the government will transition from words to action.

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Turning our attention to the United States, the latest labor market data has introduced an additional layer of complexity.

In June, the unemployment rate ticked down from 4.3% to 4.2%. On the surface, this print appears positive: a lower-than-expected unemployment rate suggesting that the economy remains resilient. However, this headline reading risks spinning a misleading narrative.

The reason is straightforward: the unemployment rate does not simply measure how many people find jobs; it also accounts for how many individuals remain within the labor force. Technically, the unemployment rate is the ratio of unemployed individuals to the total labor force, which comprises both the employed and the unemployed. If a portion of the population stops looking for work, they exit the labor force and are no longer counted as unemployed.

This is the crux of the matter. In June, the U.S. economy generated very few jobs: nonfarm payrolls grew by a mere 57,000, down sharply from the 129,000 recorded the previous month.

Furthermore, the headline figure slipped below the 100,000 threshold for the first time in four months. Consequently, the dip in the unemployment rate was not a reflection of labor market strength, but primarily the byproduct of a shrinking labor force.

In other words, while the headline print improved, the underlying fundamentals deteriorated. It is a textbook statistical illusion: the top-line number looks better, whereas the quality of the data tells a far more fragile story.

Markets reacted by rotating into more defensive sectors, such as healthcare, consumer staples, utilities, and gold miners, interpreting the report as a clear sign of an economic slowdown. While this type of rotation is understandable, we believe it may prove to be overly mechanical.

Indeed, the most compelling price action did not emerge from equities, but from the bond market.

US Government Bonds 2 YR Yield

Short-end yields fell as investors priced out the probability of a Fed rate hike at the upcoming meeting. This is a logical reaction: if the labor market is softening, the Fed has significantly less leeway to push ahead with monetary tightening.

US Government Bonds 10 YR Yield

At the same time, however, long-end nominal yields did not collapse. In fact, during certain phases, they even ticked slightly higher despite the decline in inflation expectations. This nuance is critical, as it suggests the bond market is not yet pricing in a true recession. Instead, it is signaling that while the Fed may lean less aggressive in the near term, the broader U.S. growth narrative remains intact.

This divergence is fundamental. While equities reacted with a defensive rotation, bonds told a more balanced story: a reduced risk of an immediate Fed hike, but zero capitulation on growth.

For this reason, we believe the defensive rotation could prove transitory. If the market continues to price out the probability of another rate hike in the coming days, without pricing in a severe economic slowdown, we could see capital flows shift back toward growth-oriented sectors: technology, consumer discretionary, and, progressively, semiconductors.

This does not imply that volatility is behind us. On the contrary, it suggests that volatility may remain elevated, though with a direction that is far less linear than the market’s initial knee-jerk reaction would imply.

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The next true litmus test will be the U.S. CPI print on July 14.

In our view, the data could surprise moderately to the upside. The base effects for June appear relatively low, which increases the risk that the year-over-year figure or certain month-over-month components could come in hotter than expected. In a market that remains hyper-sensitive to the trajectory of real yields, even a minor upside surprise could spark market disorder.

In the near term, a hotter CPI could push short-end nominal yields higher (e.g., 3-month and 2-year notes), driving a bear flattening of the yield curve. The most vulnerable segments would once again be those with high multiples and long-duration earnings profiles, namely technology, software, and semiconductors.

However, this does not necessarily alter our core thesis. The decisive factor will not merely be the July 14 CPI print, but how the Fed chooses to interpret it during the late-month FOMC meeting.

Our baseline scenario is that the Fed may turn out less aggressive than the market feared following the previous meeting. If Warsh signals that the bar for an additional rate hike remains high, even in the face of inflation that is not yet perfectly normalized, the market could react positively. In that scenario, duration pressure would ease, providing a more supportive backdrop for growth names.

In other words, July’s trajectory could split into two distinct phases. In the first half, the market may remain neutral (barring short-term bounces following the recent sector rotation), largely trapped in wait-and-see mode ahead of the CPI and amid ongoing USD/JPY cross-currency tensions. In the second half, should the FOMC prove less hawkish than feared, we could see a strong recovery in long-duration assets.

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In summary, the market is facing a particularly delicate confluence of factors.

On one hand, the Fed brought duration risk back to the forefront during the June FOMC meeting; yet at the same time, Kevin Warsh stated last week that inflation risk is slowly receding. Looking closely at Warsh’s remarks, the message he likely intended to convey was that inflation is structurally “transitory,” which will presumably provide a tailwind for the market.

On the other hand, the yen has rebounded to levels that escalate the risk of official intervention, carry trade unwinds, and technical flows linked to knockout barriers. Meanwhile, the U.S. labor market is beginning to flash more ambiguous signals: the headline unemployment rate improved, but underlying job creation was highly tepid.

For this reason, we would not interpret the recent volatility in semiconductors as a structural deterioration of the sector’s core fundamental thesis. Instead, we view it as the byproduct of three temporary yet potent pressures: higher real yields, a strong dollar dragging down the yen, and highly concentrated positioning in AI winners.

Consequently, our view remains constructive but selective. Any corrections in semiconductors driven by a Japanese intervention, a sudden strengthening of the yen, or a temporarily hotter CPI print could generate near-term volatility, but they will not necessarily alter the sector’s long-term fundamental trajectory. On the contrary, should the late-July FOMC confirm a less aggressive stance, long-duration assets could once again capitalize on a more supportive backdrop.

The key will be distinguishing between technical volatility and fundamental deterioration. At present, we see far more of the former than the latter.

All opinions and views expressed in this report constitute personal judgments as of the date of publication and are subject to change at any time without notice. There is no obligation to update or correct the information contained herein. This content is provided solely for informational and educational purposes and does not under any circumstances constitute investment advice, nor an offer or solicitation to buy or sell any financial instruments. The ratings and target prices expressed herein represent personal estimates based on subjective analysis models and in no way constitute a guarantee of return or an indication of future performance. The information contained in this material has been obtained from sources believed to be reliable, but its accuracy, completeness, or timeliness is not guaranteed. It should not be used as the primary basis for investment decisions and does not take into account the specific investment objectives, financial situation, or particular needs of any individual investor. Equity Analysis is not a licensed financial advisor under applicable regulations. Please remember that investing involves risks, including the potential total loss of capital, and that past performance is not indicative of future results. Equity Analysis disclaims any and all liability for any direct or indirect loss or damage arising from the use of the information contained in this document.

Digital Realty Trust is a leading global REIT (Real Estate Investment Trust) specializing in data centers, colocation (leasing physical space and technical infrastructure within a data center), and interconnection (direct connections between customers, networks, clouds, and other digital nodes). Founded in 2004 and publicly traded in the United States, the company now operates one of the world’s most extensive data center platforms. Through Digital Realty Trust, L.P., the partnership through which the group owns, acquires, develops, and operates its assets, Digital Realty provides critical digital infrastructure used by cloud providers, hyperscalers, telecom operators, internet platforms, financial institutions, and large enterprises to host servers, storage systems, network equipment, and increasingly intensive workloads (computing tasks, including applications, data, and processes processed by servers).

In an economy where cloud computing, artificial intelligence, data, and connectivity are becoming central drivers of growth, Digital Realty occupies a unique position: it does not sell software, semiconductors, or cloud services to the end customer, but provides the physical infrastructure that enables them to function on a global scale. Its data centers are the environments where power, cooling, physical security, connectivity, and operational continuity combine to sustain applications critical to the functioning of the digital economy.

Unlike a traditional technology company, Digital Realty has a hybrid nature: on one hand, it is a highly specialized real estate owner and developer; on the other, it is a global infrastructure platform serving the digital economy. This dual nature allows it to combine the typical characteristics of a real estate investor, such as recurring revenues and long-term contracts, with structural growth trends related to cloud, artificial intelligence, and digitization.

The heart of the business model is PlatformDIGITAL, the global platform through which Digital Realty enables customers to deploy, connect, and scale their digital infrastructure. The value proposition is structured around four core needs: capacity, geographic coverage, connectivity, and control. In practice, Digital Realty allows clients to choose where to place their workloads, how much capacity to utilize, which networks and clouds to interconnect with, and with what degree of operational control to manage their infrastructure.

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Since then, this thesis has been strongly validated by the price action. Prysmian has comfortably breached our target price, posting a return of roughly 80% since our last report, while its year-to-date rally exceeds 67%.

Should you wish to delve deeper into the group’s structure, its core business lines, and the strategic pivot initiated by CEO Massimo Battaini, you can revisit our previous note here.

• Link to the previous report

• Link to the initial report

The objective of this update is not to rehash the original thesis. That framework, anchored in Transmission, Power Grid, pricing power, and a multi-year backlog, has already played out. The focal point today is different: the Q1 2026 earnings release and, crucially, the messaging from the earnings call suggest that Prysmian may be on the cusp of a second leg of structural rerating.

The first phase was driven by data center electrification. The second could be driven by their optical infrastructure.

To grasp this transition, we must start with a simple yet often overlooked concept: an AI data center is not merely a massive electricity consumer. It is a highly complex physical infrastructure that must be powered, cooled, connected to the grid, linked to other data centers, and internally cabled to allow thousands of GPUs, switches, and storage systems to communicate with ultra-low latency.

Within this framework, Prysmian is not exposed to just a single node in the value chain. The company is steadily building a cross-sectional footprint across almost the entirety of the data center’s critical infrastructure.

This is the industrial rationale behind the slide presented by management in Q1 2026, where Prysmian positions itself as a “one-stop shop for data centers.”

This expression should not be dismissed as mere marketing. Rather, it represents a material shift in how the group aims to be perceived by the market: no longer solely as a global cable manufacturer, but as an infrastructure platform positioned to simultaneously capture both the power and data demand generated by Artificial Intelligence.

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How Prysmian is Capturing the Data Center Market

The first layer of exposure relates to long-distance power transmission. Large data center campuses require robust, reliable power connections, increasingly linked to new generation sources, grid interconnectors, or renewable energy projects. This is where the Transmission segment, already one of the group’s most strategic businesses, comes into play. Through high-voltage submarine and terrestrial cables, Prysmian is instrumental in building the infrastructure required to bridge power generation, the grid, and massive industrial loads.

This is a crucial point: data center growth translates not only into higher electricity consumption but also into mounting pressure on transmission networks. These new loads cannot simply be “plugged into” the existing grid. They demand dedicated infrastructure, grid upgrades, new interconnectors, and, in many cases, high-capacity links to generation hubs located far from consumption centers.

The second layer involves long-haul telecom connections. With the acquisition of Xtera, Prysmian has bolstered its footprint in submarine telecom links, the optical backbones that enable data centers to communicate across broad geographical footprints. This segment is particularly relevant for hyperscalers, as large cloud and AI platforms do not operate as isolated facilities, but rather as global networks of interconnected data centers.

Put differently, the demand is not isolated to single data centers, but extends to the distributed architecture linking them together. Every new AI campus drives the need for connectivity to other campuses, regions, and networks. This reinforces the structural tailwind for high-capacity optical cables and submarine telecom infrastructure, an arena where Prysmian is actively expanding its reach.

The third layer is Power Grid. As power demand surges, the localized grid requires reinforcement. Data centers demand absolute reliability, redundancy, and robust distribution capacity. This translates into capex directed at power distribution, high-voltage AC, grid tie-ins, transformers, and the upgrading of legacy infrastructure.

Power Grid’s growth in Q1 2026 validates this trend. The segment delivered exceptionally strong organic growth of 16.2%, though margins experienced a temporary squeeze due to the lag in contractual pass-through mechanisms for rising raw material costs. The underlying fundamental read-across, however, remains positive: demand is robust, management sees no pricing pressure, and a margin recovery is anticipated in the coming quarters.

The fourth layer is Electrification, fortified by the acquisition of Encore Wire in the US. This segment physically routes power into the data center. Once the grid delivers power to the site, electricity must be distributed via power cables, industrial wiring, switchgear, transformers, technical rooms, and internal power systems. In this context, the Encore Wire acquisition is highly strategic, as it significantly enhances Prysmian’s North American footprint in copper and aluminum electrical cables.

The US market is particularly pivotal for two reasons. First, the US is the absolute epicenter of new AI data center build-outs. Second, nearshoring and local manufacturing are increasingly critical to serving hyperscaler clients who are focused on de-risking their supply chains, compressing lead times, and securing domestic manufacturing capacity.

The fifth layer is Digital Solutions in its legacy form: Data Center Interconnects (DCI). Prysmian already manufactures the optical fiber and cables used to link buildings, campuses, telecom networks, and data centers over medium-to-long distances. This constitutes the Data Center Interconnect (DCI) market, the infrastructure that allows one data center to communicate with another.

Historically, this was the most intuitive element of the group’s optical exposure: high-density cables, long-haul links, campus-to-campus networks, terrestrial backbones, and submarine telecom.

However, the real takeaway from the earnings call isn’t this...

The game-changer is the sixth layer: Digital Solutions inside-the-building. Here, the narrative shifts entirely. We are no longer talking solely about connecting one data center to another.

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Our approach remains highly pragmatic: before celebrating, we prefer to wait for the official outcome of the negotiations. At the same time, however, we don’t want to be caught off guard. For this reason, at the end of the report, you will find our detailed analysis of a dual macroeconomic scenario.

This perspective will also be essential ahead of the upcoming FOMC meeting: the potential rise of Kevin Warsh is a factor we consider crucial, and one that could completely change the Fed’s regime and narrative.

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The announcement of a tentative agreement between the United States and Iran, aimed at reopening the Strait of Hormuz and resuming energy shipments through the region, has significantly reduced the immediate geopolitical risk premium embedded in oil prices. The market reaction was swift, with Brent retreating as investors priced in a lower probability of a prolonged supply disruption.

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At first glance, the market’s reaction might seem counterintuitive. In fact, the catalyst for the sell-off was an ostensibly positive macroeconomic data point: U.S. Non-Farm Payrolls.

Consensus estimates called for the creation of approximately 85,000 new jobs, whereas the actual print came in at 172,000. Under normal circumstances, a resilient labor market would be interpreted as a sign of economic strength. Today, however, the macro backdrop is fundamentally different.

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Equinix is one of the leading global companies in the digital infrastructure sector. At first glance, it might look like a simple data center operator, but this definition is reductive. The true value of the company does not stem solely from owning or managing buildings full of servers, but from its ability to create a global ecosystem where corporations, cloud providers, telecom operators, digital platforms, and AI providers can place their infrastructure and connect with each other directly, securely, and with low latency.

The simplest way to describe Equinix is to think of it as a neutral digital hub. Companies can install their private infrastructure inside Equinix data centers and, from there, connect directly to AWS, Microsoft Azure, Google Cloud, Oracle Cloud, telecom operators, tech partners, or AI providers. Equinix does not replace these cloud providers; instead, it allows clients to connect to them more efficiently than through the standard public internet.

This function is becoming increasingly important because large corporations no longer use a single IT infrastructure. Many combine private servers, public cloud, SaaS applications, telecom networks, cybersecurity, analytics, and AI models. This architecture is often called hybrid multi-cloud—a combination of private infrastructure and multiple public clouds. In this context, the issue is not just where to store data, but how to connect different environments quickly, securely, and reliably.

Equinix’s business model is mainly based on two pillars: colocation and interconnection. Colocation is the service through which a client installs their own servers, routers, firewalls, storage, or other IT equipment inside a data center managed by Equinix. Generally, the client owns or controls their equipment, while Equinix provides the physical space, electrical power, cooling, security, operational continuity, and the technical environment necessary to run that infrastructure.

This point is crucial: as mentioned, Equinix is not primarily a cloud provider like AWS, Azure, or Google Cloud. In the public cloud, the client utilizes computing power and storage managed by the cloud provider. In colocation, however, the client can maintain control over their own infrastructure but hosts it within a professional, redundant, and highly connected environment.

Interconnection, on the other hand, refers to the ability to directly connect two digital entities—for example, a corporation and a cloud provider, a bank and a payment platform, or a private infrastructure and an AI provider. These connections can be physical, via dedicated cables, or virtual, through platforms like Equinix Fabric. The advantage is that they enable private connections that are more predictable and often faster than the normal public internet. For many critical applications, such as digital payments, financial trading, cybersecurity, supply chain, digital health, or AI inference, connection quality is essential. Latency—the time it takes for data to travel from one point of the network to another—can become a competitive factor.

Equinix therefore generates recurring revenues from physical space, rack cabinets installed inside the data center (standardized metal structures where the client’s servers, network equipment, firewalls, and storage systems are mounted), power, cooling, operational services, private connections, and digital interconnection platforms. This recurring nature is one of the most attractive elements of the business: the company does not rely primarily on one-time sales, but on ongoing contracts with clients using critical infrastructure.

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When we look at the tech arms race defining this decade, everyone’s eyes are glued to the exact same names: the Big Tech titans spending tens of billions in CapEx, and the leading-edge digital foundries fighting over the most microscopic geometric nodes on the planet. But there is a massive miscalculation in this narrative.

The roaring rivers of capital deployed by Western governments through various Chips Acts, combined with the colossal budgets of hyperscale cloud providers, do not stop at advanced digital processors. To make an AI data center function, to connect supercomputers at photonic speeds, manage power efficiency, and bridge the digital world with physical reality, raw digital logic isn’t enough. You need analog.

This staggering wave of investment is creating a massive cascade effect, trickling down to smaller, specialized players that are completely overlooked by the general public but firmly entrenched in high-value, ultra-differentiated market niches.

Take, for instance, one specific player: an independent foundry that has deliberately chosen not to compete in the race for advanced digital scaling. It is not a “mini-behemoth” trying to copy Taiwan’s giants; it is a specialty leader. Its entire value proposition centers not on shrinking transistors, but on deep process know-how applied to analog, mixed-signal, radio frequency (RF), power management, imaging, and, crucially, silicon photonics.

While the broader market recently viewed this company as just another cyclical specialty foundry recovering from a trough, it has just hit a structural inflection point. It is rapidly transforming into a capacity-constrained, critical supplier within the AI optical connectivity value chain.

The latest quarterly data reveals the sheer scale of this shift:

• A Key Segment in Hyper-Growth: RF Infrastructure revenues nearly doubled year-on-year, driven by silicon photonics platforms (up 3x) and silicon germanium, both of which are essential for high-speed optical transceivers in AI data centers.

• Unprecedented Visibility: The company has already locked in $1.3 billion in contracts for 2027 with its largest silicon photonics customers, backed by $290 million in upfront customer prepayments just to reserve factory capacity.

• Explosive Operating Leverage: A 15% increase in top-line revenue translated into a staggering 96% jump in operating profit, nearly doubling operating margins as manufacturing facilities from the US to Japan ramp toward optimal utilization.

This business does not report separate profit pools for its various technologies; it operates as a single, powerful analog ecosystem where profitability is driven by utilization, smart capacity allocation, and aggressive mix-shifting toward high-margin applications.

In the following analysis, we break down the underlying economics of this specialty model, the structural dynamics of the foundry industry, and why this transition from a cyclical manufacturer to a strategic choke point in AI infrastructure represents one of the most asymmetric investment cases in the semiconductor landscape today.

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Few companies better represent the evolution of modern logistics real estate than Prologis. Today, the group is one of the leading global platforms dedicated to logistics, supply chain infrastructure, and real estate logistics, with a model built around properties located in highly strategic areas near major cities, ports, airports, and commercial hubs.

In very simple terms, Prologis owns, develops, and leases logistics properties to corporate clients, including retailers, e-commerce operators, transport companies, manufacturing firms, and distribution players. Furthermore, in recent years, the company has begun expanding its scope into adjacent areas such as data centers, energy infrastructure, and services connected to the modernization of supply chains.

From an accounting and organizational standpoint, Prologis reports two reportable segments: the Real Estate Segment (93.3% of revenues and 95.1% of NOI) and the Strategic Capital Segment (6.7% of revenues and 4.9% of NOI). This point is important because real estate development does not constitute a third, autonomous business line; instead, it is part of the Real Estate Segment, which encompasses both Rental Operations and Development activities.

The Real Estate Segment represents the economic core of the company. This includes property ownership, the generation of rental revenues, operational management of the portfolio, and the development of new assets. In 2025, this segment generated $8.20 billion in revenues, accounting for 93.3% of total Operating Revenue, up from $7.53 billion in 2024. Nearly all of these revenues come from rental revenues, which amounted to $8.159 billion, while development management and other revenues contributed $39 million. After deducting rental expenses (costs related to managing leased properties) and other expenses, the segment produced a Real Estate Segment NOI of $6.188 billion, equal to 95% of total NOI. NOI, or Net Operating Income, is the net operating income generated by properties—a key metric of asset profitability before corporate costs, depreciation, and other items not directly related to property management, which is essential for evaluating companies like Prologis.

These numbers clearly demonstrate the nature of Prologis’s business: the company relies primarily on a large portfolio of logistics assets capable of generating recurring revenues with high visibility and strong operating cash flow capacity. At the same time, the presence of development activity makes the model more dynamic than that of a simple property owner. Indeed, Prologis does not just collect rent; it develops new properties, executes build-to-suit projects for clients, and unlocks value from its land bank (portfolio of strategic land) in areas where the availability of quality logistics space is often limited.

The Strategic Capital Segment is much smaller in terms of revenues but holds significant strategic importance. In 2025, it generated $592 million, representing 6.7% of total Operating Revenue, down from $672 million in 2024. Net of strategic capital expenses (costs related to managing the third-party capital platform), the segment recorded an NOI of approximately $321 million, accounting for 5% of total NOI. Through this segment, Prologis manages third-party capital via joint ventures, private vehicles, and partnerships with institutional investors. This allows the company to expand its investment capacity, generate fee income, and grow more efficiently without having to fund every project entirely through its own balance sheet.

The geographical distribution of revenues shows a global company, but one that remains heavily centered on the United States.

In 2025, the U.S. generated approximately $8.00 billion in Operating Revenue, accounting for 91.0% of the total. This was followed by Europe at $356 million (4.0%), Other Americas at $283 million (3.2%), and Asia at $150 million (1.7%). The U.S. concentration is even more pronounced in the Real Estate Segment, where the U.S. represents approximately 95.2% of segment revenues, compared to 2.4% for Other Americas, 1.7% for Europe, and 0.8% for Asia.

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Cameco Corporation, headquartered in Saskatoon, Canada, and founded in 1987, is one of the world’s leading providers of uranium and nuclear fuel cycle services. The company serves nuclear utilities and institutional clients across the Americas, Europe, and Asia, positioning itself as one of the few Western players with an integrated presence across strategic segments of the nuclear value chain.

Historically established as a uranium producer, the group has progressively expanded its industrial scope in recent years. From a predominantly upstream operator, Cameco has transformed into an integrated nuclear platform, with exposure not only to uranium mining and marketing but also to conversion, fuel fabrication, reactor technology, and recurring services for the installed nuclear fleet.

Cameco represents one of the primary publicly traded Western vehicles for gaining exposure to the uranium cycle and, more broadly, to nuclear supply chain security. While the stock maintains a significant correlation with uranium prices and the geopolitical environment, it distinguishes itself from pure-play mining producers through its Tier-1 asset base, long-term contracts with nuclear utilities, midstream capabilities in the fuel cycle, and a strategic stake in Westinghouse.

The operating structure is divided into three main segments:

1. Uranium, focused on the exploration, mining, processing, purchase, and sale of uranium concentrate;

2. Fuel Services, dedicated to the refining, conversion, and fabrication of nuclear fuel products;

3. Westinghouse, 49% strategic stake in one of the world’s leading providers of nuclear reactor technology, components, and services.

The industrial model can be summarized along a vertical axis:

uranium mining → fuel conversion and fabrication → reactor technology and maintenance

This architecture distinguishes Cameco from a pure-play mining producer. The group maintains strong leverage to uranium prices, but also possesses more infrastructure-like and recurring business activities related to nuclear supply chain security, conversion services, and the maintenance of the global reactor fleet.

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In this analysis, we will not focus on the blockbuster earnings reports from Big Tech, nor on the fact that cloud segments are already demonstrating a concrete conversion of AI demand into top-line revenue. While that narrative is significant, it is not the core of our thesis. The critical point is this: a new infrastructural layer of the digital economy is being forged in the United States. This layer is materializing through data centers, power grids, hardware, energy procurement contracts, transformers, industrial real estate, and non-residential fixed investment.

In other words, AI has evolved beyond a “software narrative” into a hard Capex cycle. It is precisely at this crucial inflection point where theory translates into tangible alpha: indeed, it is no coincidence that many of our recent actionable insights have hit their targets in record time, delivering exceptional performance.

Track Record & Performance Milestones

• AMD: +70% in under 30 days

• Emcor: +26% in just over 40 days

• Nvidia: +13% in under 60 days (currently positioning for a breakout; we anticipate a major surge in 2H)

• Alphabet (Google): +28% in approximately 6 months

• Prysmian: +170% YoY (marking our one-year anniversary on May 17th)

• Teradyne: +240% in 9 months, maintaining momentum despite the recent retracement

• TSMC: +85% in 10 months

• ASML: +95% in approximately 11 months

These results represent just a snapshot of our diversified exposure to the data center ecosystem. Our selection process has been granular: from the architects of next-generation silicon to the lithography leaders essential for chip fabrication, and extending to the critical providers ensuring the energy resiliency of these facilities.

In our view, this trend will persist over the coming months. For Big Tech, the opportunity cost of underinvesting currently outweighs the risk of the investment itself. At this stage of the cycle, any hyperscaler that scales back CapEx risks losing terminal value and market share to its peers.

We are not chasing momentum or speculative euphoria. Instead, we have strategically positioned ourselves at the bedrock of this transformation, methodically navigating the defining macroeconomic wave of this decade: the data center infrastructure.

We have decided to make this analysis public to provide clarity on the current macroeconomic regime. If you find these insights valuable, we invite you to subscribe to our page and support our research.

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A growing segment of the market continues to interpret the Artificial Intelligence cycle exclusively through the lens of a speculative bubble. This perspective is understandable: technology valuations are stretched, the concentration of the S&P 500 remains historically high, and the AI theme now dictates the global investor narrative. However, in our view, this interpretation remains fundamentally incomplete.

The crux of the matter is that AI is not merely driving an expansion of equity multiples. It is catalyzing a real investment cycle.

Our focus, therefore, extends beyond the record-breaking earnings of Big Tech or the growth in cloud segments, where several operators are already demonstrating a clear transmission of AI demand into top-line revenue. While significant, that is only one facet of the story. From a macroeconomic standpoint, the core development is far more structural: a new physical and energy-intensive production infrastructure is being built across the United States to support the AI ecosystem.

Market participants tend to perceive AI as an intangible phenomenon, software, models, and nebulous productivity gains. In reality, its scaling requires an immense material foundation: data centers, power generation, transmission grids, advanced cooling systems, specialized semiconductors, electrical transformers, industrial real estate, utilities, and a surge in non-residential fixed investment and construction spending.

The Data Center Cycle is Already a Capex Cycle

The most compelling data point remains the sheer scale of investment. Leading global entities involved in data center development and management are escalating their CAPEX to levels characteristic of a structural infrastructure cycle. Total capital expenditure for the 14 largest publicly traded operators is projected to reach approximately $725 billion by 2026, a massive surge from just under $450 billion in the prior year.

Crucially, this is not “sunk” or unproductive capital. Current metrics indicate that every dollar of CapEx deployed today generates approximately $1.50 in revenue, with long-term projections trending toward $3.00 for every dollar spent. This transition is a critical validation of the cycle’s fundamental health.

In parallel, global under-construction IT capacity for data centers has already surpassed the 23 GW threshold, with approximately three-quarters of this new capacity concentrated within the United States.

This inflection point is critical. When CapEx reaches this magnitude, the impact is no longer confined to the balance sheets of Big Tech. Instead, it propagates through the entire real economy value chain: industrial construction, electrical components, power generation, grid infrastructure, specialized hardware, private credit, and skilled labor.

It is precisely here that the “bubble” narrative becomes overly simplistic. While a purely financial bubble tends to exist solely within asset valuations, we are currently witnessing a significant allocation of capital toward tangible assets, long-term energy contracts, and future productive capacity. While this does not eliminate the risk of overinvestment in the coming years, it fundamentally alters the structural nature of the cycle.

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And the most compelling aspect concerns the persistent, ongoing requirement for infrastructure investment.

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The Geography of the Boom: The United States at the Epicenter

As of late September 2025, global under-construction IT capacity for data centers reached 23.1 GW, distributed across 831 sites. The Americas region accounted for 17 GW of this total, with the United States alone hosting 15.9 GW of active capacity development. Europe, the Middle East, Africa (EMEA), and the Asia-Pacific (APAC) regions follow at a significant distance, representing approximately 2.9 GW and 3.2 GW, respectively.

New data center construction has accelerated significantly since 2020, with a clear dominance in the Americas region. These figures confirm that the AI cycle is already tangible, manifesting directly through the expansion of physical capacity under development.

The concentration of development within the United States carries significant implications. If AI evolves into a sector defined by high capital, energy, and infrastructure intensity, then competitive advantage will no longer depend solely on model architecture or semiconductor availability. It will hinge on the institutional capacity to finance, permit, power, and interconnect industrial-scale infrastructure.

Consequently, this shifts AI from being a purely technological theme into an energy, industrial, and geopolitical imperative.

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The True Bottleneck: Energy, Not Just Silicon

The energy constraint is arguably the most undervalued component of the AI cycle. In 2025, major data center developers accounted for 72% of corporate clean energy demand via Power Purchase Agreements (PPAs) in the Americas. On a global scale, the leading data center operators represented approximately half of all corporate clean energy PPAs executed throughout 2025.

This is precisely why the current cycle cannot be viewed merely as an expansion of equity multiples. AI demand is already fundamentally reshaping energy load forecasting, utility sector planning, the construction of new grid capacity, and the demand for industrial commodities. Increasingly, the "AI trade" is not just about the silicon; it is about the power socket.

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The GDP Nexus: Non-Residential Fixed Investment

The subsequent analytical step involves linking this cycle to U.S. GDP. The primary transmission channel is through non-residential fixed investment, specifically within the “Information Processing Equipment and Software” category. In the first quarter of 2026, this component reached an annualized $1.565 trillion, a significant increase from $1.466 trillion in Q4 2025 and $1.311 trillion in Q1 2025.

Investments in information processing equipment and software continue to accelerate despite a sustained high cost of capital. This represents one of the most significant indicators of the current technological “super-cycle”: corporations are deploying capital because the expected return on investment from AI is perceived to exceed the prevailing marginal cost of debt.

When this investment trend ascends within a high-interest-rate environment, the macroeconomic signal is unambiguous. It indicates that the private sector is aggressively allocating capital despite financial conditions that are not fully accommodative. This confirms that the AI cycle is not merely a feature of equity narratives, but is deeply embedded in the investment function of the broader economy.

In macroeconomic terms, this provides a structural tailwind to growth via a specific GDP aggregate: non-residential fixed investment. This is precisely why the data center boom should be viewed not merely as a thematic sector play, but as a fundamental pillar contributing to the ongoing resilience of the U.S. economy.

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GDPNow: Beyond the Growth Print - The Importance of Composition

Real-time monitoring of the economic cycle continues to broadcast a signal that is fundamentally at odds with a recessionary narrative. As of May 7, 2026, the Atlanta Fed’s GDPNow model estimated real GDP growth for the second quarter at an annualized 3.7%.

It is critical to note that this is not a discretionary forecast; rather, it is a quantitative nowcast, constructed mechanistically based on high-frequency macroeconomic data releases as they occur. For this reason, GDPNow serves as one of the most effective tools for assessing the current state of the economy, stripped of subjective interpretive bias.

Even more significant than the aggregate figure is its internal decomposition. The subcomponent contributions chart reveals that growth is not being sustained solely by personal consumption; it is also bolstered by a clearly positive contribution from non-residential fixed investment (highlighted in the green block).

This element is particularly salient, as it signals that the current cycle does not rely exclusively on the resilience of the American consumer. Instead, it is increasingly supported by a CapEx component that continues to expand despite a restrictive cost of borrowing. This diversification of growth drivers underscores the structural robustness of the ongoing expansion.

From a macroeconomic standpoint, this constitutes a high-quality signal. When the investment contribution remains positive within a context of restrictive nominal rates, it implies that corporations are deploying capital with high conviction regarding the expected internal rate of return (IRR) on their projects. This is precisely where the data center and AI infrastructure build-out integrates into the broader picture: CAPEX is not merely a market narrative; it is a structural force already embedded in the U.S. growth function.

The lower panel of the chart, which tracks changes in marginal contributions to the GDPNow model, further reinforces this interpretation. Despite standard daily volatility, the contribution from non-residential investment remains a primary driver behind the nowcast’s resilience. In essence, the emerging signal is not that of a fragile economy artificially buoyed by a single variable, but rather one characterized by broad-based cyclical growth, underpinned by resilient consumption and expanding capital investment.

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The consumer remains the pivot of the U.S. economy, but the signal within the data is more nuanced.

While the AI cycle and infrastructure CapEx are currently central components of American resilience, they cannot be analyzed in isolation. The U.S. economy remains structurally consumption-driven; in the first quarter of 2026, Personal Consumption Expenditures (PCE) accounted for 68.1% of U.S. GDP. This implies that assessing the probability of a recession requires looking beyond headline GDP or data center investments. One must monitor the trajectory of the consumer, as that is the ultimate yardstick for the durability of private demand.

Real U.S. GDP grew at an annualized rate of 2.0% in the first quarter of 2026. This growth was supported by personal consumption, investment, exports, and government spending, while imports acted as a drag on the headline figure.

The first point to underscore is that Q1 GDP figures do not describe an economy in contraction. Real GDP expanded at an annualized rate of 2.0%, marking a significant acceleration from the 0.5% print in Q4 2025. More importantly, the composition of growth remains consistent with an expansionary cycle: according to the BEA, the primary positive contributors were investment, exports, consumer spending, and government spending. Conversely, imports, which function as a subtraction in the GDP identity, acted as a net drag on the aggregate figure.

The second point, and more critical to our macro thesis, is that consumption data must be analyzed using more high-frequency metrics than lagged GDP. While GDP is a backward-looking indicator subject to multiple revisions, Personal Consumption Expenditures (PCE) are published monthly, providing a direct gauge of household spending. Consequently, to determine whether the American consumer is deteriorating, PCE remains one of the most salient variables.

As of March 2026, the data does not signal a breakdown in private demand. Personal income rose by $149.2 billion (+0.6% MoM); disposable personal income (DPI) increased by $142.5 billion (also +0.6% MoM); and Personal Consumption Expenditures surged by $195.4 billion, or +0.9%.

This indicates that spending is currently outstripping the growth in disposable income, a trend that confirms consumer resilience but warrants close monitoring, as it inevitably exerts downward pressure on the personal savings rate.

Disposable income continues to rise, but outlays are accelerating at a faster pace. While the consumer remains robust, they are effectively absorbing part of the inflationary shock by reducing their marginal propensity to save.

The correct interpretation is not that “the consumer is invulnerable.” Rather, a more sophisticated analysis is required: the consumer remains resilient enough to sustain nominal growth, but the composition of spending reveals an economy absorbing a fresh inflationary impulse. In March, personal outlays increased by $198.6 billion, while the personal saving rate settled at 3.6% of disposable personal income. While this does not signal an imminent crisis, it is a metric that warrants close monitoring; when spending outpaces income growth, the sustainability of the cycle hinges on the stability of the labor market, wage growth, and credit quality.

The decomposition of monthly spending provides even more compelling insights. Of the $195.4 billion increase in nominal March PCE, $132.6 billion was driven by goods and $62.9 billion by services. A granular look reveals a significant contribution from the energy component, with “gasoline and other energy goods” acting as the primary driver of the increase. This is a critical distinction: a substantial portion of the acceleration in nominal spending does not merely reflect robust real demand, but rather an increase in energy prices (driven by the conflict in Iran).

The origin of the energy shock is geopolitical. In the first quarter of 2026, the prices of crude oil and refined products surged significantly following military escalation in the Middle East and the de facto closure of the Strait of Hormuz. According to the EIA, Brent crude climbed from $61 per barrel at the start of the year to $118 by the end of the quarter; the average U.S. retail gasoline price reached $3.99 per gallon on March 30, the highest real level in over two years.

This distinction is crucial to avoid an interpretive error. The United States is far less vulnerable to a global energy shock than Europe or Asia because it has been a net exporter of total energy since 2019. In 2024, total American energy exports exceeded imports by approximately 9.26 quadrillion BTU, the widest annual margin on record. However, the U.S. is not completely insulated from global crude prices: it remains a net importer of crude oil, and gasoline prices inherently incorporate crude as their primary refining input.

Nevertheless, the disparity between the U.S., Europe, and Japan remains substantial. In the European Union, approximately 57% of energy requirements were met by net imports in 2024. Japan, conversely, exhibits very low energy self-sufficiency and is heavily dependent on imports of oil, LNG, and coal, with a Middle East dependency exceeding 90% for crude imports. Consequently, the same oil shock tends to transmit more aggressively into the industrial margins and trade balances of net-importing economies compared to the United States.

The takeaway, therefore, is not that the United States is immune to energy fluctuations. Rather, the American energy shock is of a different nature: it impacts the consumer through gasoline prices and headline inflation, but simultaneously supports the income, investment, and cash flows of the domestic energy supply chain. In Europe and Japan, the same shock more readily translates into a deterioration of the terms of trade, as it inflates the cost of energy imports without generating a comparable domestic production offset.

PCE inflation is reaccelerating in both its headline and core components. The fact that core PCE (excluding food and energy) remains above 3% signals that price pressures are not driven exclusively by energy costs.

The inflationary component is the most critical focal point for the Fed. In March, the PCE Price Index increased by 0.7% month-over-month and 3.5% year-over-year. However, the more significant data point is that core PCE (excluding food and energy) also rose by 0.3% monthly and 3.2% annually.

This implies that inflation cannot be dismissed as a mere “gasoline shock.” While energy accounts for a portion of the headline acceleration, the core PCE reading indicates that price pressures remain broad-based, even when stripping out the volatile food and energy sectors.

In the first quarter of 2026, the PCE Price Index rose by an annualized 4.5%, while core PCE increased by 4.3%. This signal is consistent with a reflationary regime: sustained positive growth coupled with more persistent inflation.

The quarterly data further corroborates this reading. In the first quarter of 2026, the PCE Price Index surged by an annualized 4.5%, up from 2.9% in Q4 2025; meanwhile, core PCE rose by 4.3%, compared to 2.7% in the previous quarter. In other words, the issue is not collapsing demand, but rather demand that remains robust enough to coexist with a new phase of price pressure.

The macroeconomic conclusion is clear: the American consumer is not yet signaling a recessionary break. Instead, the signal is of an economy that continues to expand, albeit within a context of more sticky inflation.

This is the classic profile of a reflationary regime: resilient consumption, sustained positive investment, an energy-driven impulse on headline figures, and core pressures that remain elevated. For the markets, the primary risk is therefore not an immediate recession, but a more complex combination: robust nominal growth, a Fed with less room to cut rates aggressively, and real assets, commodities included, supported by a mix of demand, geopolitics, and a weaker dollar.

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Delinquencies, the Consumer, and Real Rates: Why 2008 is Not Our Base Case

A further element that leads us to consider the “imminent 2008” narrative premature is the trend in delinquency rates, the percentage of loans with past-due payments. In the specific case of the FRED credit card chart, this metric tracks the share of credit card loans held by commercial banks that exhibit payment delays significant enough to be classified as impaired. This is an extremely useful indicator, as it allows for near real-time monitoring of whether the American consumer is entering a phase of systemic financial stress.

Credit card delinquency rates remain well below the peaks observed during the Great Financial Crisis. The American consumer is undergoing a normalization, not a collapse.

The interpretation of the chart is quite clear. Credit card delinquencies currently stand around 3%, an uptick from the exceptionally compressed lows of the post-pandemic period, but far cry from the levels exceeding 6% observed in 2009-2010. This indicates that while a normalization of consumer credit is certainly underway, we are not witnessing dynamics consistent with a systemic fracture in private demand. In other words, the American consumer currently appears neither “broken” nor insolvent on a scale that would justify a narrative of imminent macroeconomic collapse.

The decisive point emerges from the intersection of this data with the PCE observations mentioned earlier. On one hand, consumer spending continues to grow; on the other, the delinquency rate does not signal a deterioration comparable to that of major recessions. The combination of solid consumption and contained delinquencies reinforces the idea that the U.S. economy is undergoing a phase of selective slowdown and credit normalization, rather than an implosion of demand. We are not suggesting that a recession cannot occur in the future; we are stating something more precise: the data available today does not indicate that such a moment has arrived.

This reading has direct implications for markets. In the absence of a genuine consumer breakdown, the framework remains compatible with a continuation of the bullish trend in U.S. equities over the coming months, especially when viewed through the lens of compressing real rates. This is where monetary policy returns to center stage. Although the latest FOMC meeting did not convey a particularly dovish tone, it is equally true that the Federal Reserve did not raise rates and, crucially, did not explicitly open the door to a new restrictive phase. In a context where nominal rates remain steady while inflation expectations trend upward, the mechanical result is a compression of real rates.

This transition is vital from an asset allocation perspective. If inflation rises again, with oil remaining the primary driver of inflationary transmission, cascading into gasoline, energy, transport, and supply chain costs, while nominal yields remain relatively stable, the real yield offered by Treasuries deteriorates. In such an environment, the relative attractiveness of government bonds diminishes, and capital is naturally pushed toward assets with higher return profiles: equities, gold, and, for a segment of investors, crypto assets.

In other words, the theme is not only macroeconomic but profoundly financial: stagnant nominal yields + rising inflation = lower real yields = greater incentive for yield-seeking. This is precisely the mechanism that, in the absence of a recession, tends to fuel a climate of euphoria in risk assets. The U.S. stock market, in particular, continues to benefit from this dual support: growth that isn’t collapsing on one side, and a real rate structure that does not reward parking capital in government debt on the other.

A possible scenario for the coming quarters adds to this. If the Federal Reserve’s future guidance were to shift toward an approach closer to that of Kevin Warsh, interpreting inflation as a byproduct of excessive government spending and fiscal disorder rather than Fed Funds rates being too low, then the outlook could become even more constructive for risk assets. In this view, AI is seen as a medium-term disinflationary force capable of compressing costs through productivity, efficiency, and automation. In a scenario where the geopolitical tension in the Strait of Hormuz normalizes, thereby easing the energy premium embedded in prices, a Fed with this type of reaction function might feel more empowered to initiate a cutting cycle.

Should this occur, the market effect would be potentially massive: sustained positive growth coupled with falling nominal rates would further amplify support for equities, gold, and the crypto sector. For this reason, our view remains constructive: while macro risks exist, we do not currently see in consumer data or delinquencies the typical signals of deterioration that would justify an extreme defensive posture. Our conclusion is straightforward: as long as the consumer continues to spend, remains solvent, and real rates stay compressed, the path of least resistance for American assets remains skewed to the upside.

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