The investment question has changed with it. The first phase of AI investing was led by hyperscalers, advanced semiconductors, and large language model developers. The current phase has been driven by infrastructure buildout, and markets have already recognized much of that trade. Over the next few years, we expect the most attractive opportunities to center on more persistent bottlenecks, particularly around power, and on companies that control workflow, own the customer relationship, bring domain expertise, and turn AI output into business action. That includes the software infrastructure and applications that will shape how AI is deployed and managed. As companies integrate AI into workflows, adopters across industries should benefit. We expect much of the next phase of value creation to come from emerging AI-native companies and disruptive business models, though at this early stage, even today’s disruptors may be displaced.

In this edition of VantagePoint, we focus on three questions:

• Which bottlenecks are durable?
• Can rising revenues justify the capital required to sustain leadership?
• Where can value persist as AI becomes cheaper, more capable, and more widely available?

The broader consequences for labor and society may prove profound, but they remain harder to observe clearly and are progressing slower than the technology itself. Regulation and sovereignty risk considerations clearly matter, as does society’s willingness to absorb the pace of change and its consequences. Those forces will shape AI’s development, but they are not the focus of this paper. We focus instead on where the evidence is strongest today and where the investment implications are becoming harder to ignore.

Tech is still early, moving fast

AI capabilities continue to improve at breakneck speed. The leading labs remain in a tight race, and each major release raises the bar while increasing disruption risk for incumbents and start-ups alike. Leadership among US models continues to oscillate. Chinese models have narrowed the gap on several technical measures despite US export controls on advanced chips. The open-model ecosystem, much of it coming from China, has also become a credible lower-cost option for use cases outside of mission-critical workflows that still demand premium US tools.

Frontier intelligence is becoming more capable, more available, and less exclusive. That should expand adoption, but it also makes raw model access a less reliable source of durable advantage and shifts value toward the assets and capabilities that make intelligence useful, governable, and hard to replace.

AI is shifting from a tool that generates responses to a tool that performs useful work. Recent model releases have improved reasoning, reliability, memory, and the ability to work across different types of data. Anthropic’s AI model, Claude, helped accelerate the move from coding assistant to autonomous coding agent through Claude Code, then extended that logic into broader knowledge work. While this is a major disruptive theme and a central focus of this paper, it is only one part of a broader transformation. Specialist models in mathematics, biology, and other fields are proliferating, while researchers continue to experiment with non-transformer architectures such as world models designed for physical AI. Smaller models are improving as well, pushing more inference to run on local systems instead of keeping them entirely in centralized cloud environments.

These advances are beginning to make disruption more visible. In software, faster model improvement is compressing product cycles, narrowing functional differentiation, and pressuring application-level moats that once appeared durable. In labor-intensive workflows such as customer service, the effects are already showing up in shorter handle times, lower staffing needs, and greater pressure to automate routine work. Much of the broader disruption still lies ahead, but the direction is clearer.

Enterprise AI adoption is rising, but scaled deployment remains limited

The pace of enterprise adoption is becoming increasingly apparent, even if scaled deployment remains limited and difficult to track in real time. McKinsey survey data captures the key point for investors: adoption is rising, but much of it still reflects experimentation, piloting, and limited deployment rather than full integration across core workflows.

Coding remains the clearest use case, though productivity gains are still early and uneven. Deployment inside real organizations will be challenging, requiring integration, oversight, and governance as much as good model performance. Usage will evolve as service models change. For example, firms are still enjoying subsidized model pricing, internal controls are weak, and many firms are still learning to use these tools effectively while keeping token costs under control.

As AI advances rapidly, the need for stronger corporate governance is becoming more urgent, even if progress is moving at a more human pace. Some early adopters are pulling ahead in part because they addressed governance, access controls, and oversight sooner, making scaled deployment in sensitive workflows easier. Others may appear to be moving faster precisely because they are deferring those disciplines and accumulating a governance backlog that has not yet surfaced in operating results. Recent events also show how quickly regulatory and security concerns can affect commercialization. Anthropic’s temporary withdrawal of Fable 5 and Mythos 5 following a US government directive serves as a reminder that deployment risk may increasingly hinge on security, liability, and policy judgments. Public backlash is also growing, which could make regulation more political over time. As AI use broadens, questions of data provenance, auditability, security, and liability are likely to matter even more.

This shift from experimentation to scaled use is changing the investment question. Technical progress and broader adoption are making AI more commercially relevant, but they do not by themselves determine where durable returns will accrue. That depends increasingly on the economics of deployment and on which firms can turn AI capability into repeatable business action.

How AI economics are evolving

In our last edition of VantagePoint: The Rearview Mirror Problem, we argued that investors often mistake recent winners for future return drivers. That risk is especially acute in AI. The first-wave beneficiaries are well known, and the infrastructure buildout has become the market’s central focus. The harder question now is how the economics are evolving beneath that narrative, and which parts of the opportunity set can still deliver durable returns. We think investors should focus on three underwriting questions: Which bottlenecks are durable? Can rising revenues justify the capital required to sustain leadership? Where can value persist as AI becomes cheaper, more capable, and more widely available?

The economics are changing along with the technology. Constraints have shifted from training toward inference, usage, and deployment, and value capture is broadening with them. The relevant opportunity set now extends beyond frontier-training hardware to a broader mix of inference and data infrastructure, deployment software, workflow and permission layers, governance tools, and applications that shape how AI is embedded in business processes.

Earnings have begun to catch up with enthusiasm in parts of the AI ecosystem. Recent gains in AI-linked equities no longer rest on expectations alone. Several leading firms have reported strong revenue growth tied to AI demand, especially in semiconductors, cloud, and selected infrastructure segments. Private model providers, such as Anthropic, appear to be seeing similar momentum, though their economics remain less transparent. Stronger fundamentals validate part of the move. They do not settle the harder question of whether revenue growth will prove durable enough to justify the operating and capital costs required to sustain it.

Which bottlenecks are durable?

This first question deals with whether bottlenecks are persistent or simply reflect temporary undersupply. Early in the cycle, scarcity centered on training compute and raw GPU capacity. That is no longer the full story. As AI deployment scales, the tighter constraints are becoming more physical. Data center infrastructure, memory, and advanced packaging remain important, but power is emerging as the clearest hurdle. Reliable electricity, cooling, transmission, and the ability to bring new capacity online increasingly matter as much as access to chips. ¹

Falling prices in one layer of the stack do not remove constraints in another. Listed token prices have fallen sharply, which should broaden adoption. But lower prices do not mean lower compute demand. As models have become more capable and AI systems take on more complex tasks, the compute required to complete useful work has continued to rise. More autonomous and always-on systems will reinforce that trend by increasing token consumption and placing greater strain on the physical stack.

Power deserves particular attention because it is increasingly one of the hardest constraints to relieve. Utilities, grid equipment, cooling, and related enabling infrastructure can be difficult to replicate quickly because they depend on permitting, transmission access, engineering capacity, and time to build. Those are more durable barriers than the temporary scarcity that can emerge in parts of the hardware stack early in a buildout.

Memory and advanced packaging also remain important choke points, supporting stronger pricing and earnings across parts of the semiconductor ecosystem. The key investment question, however, is not whether these areas are constrained today, but whether those rents are likely to persist. Some supply bottlenecks may prove temporary as capacity expands. Others may reflect capabilities that are harder to replicate quickly.

In some parts of the market, security, compliance, and regulatory approval may also function as bottlenecks. Where customers need trusted systems for sensitive workflows, firms that can meet higher standards for resilience, auditability, and control may accrue durable advantage.

Not every bottleneck supports durable economics. Some stem from short-lived pricing power. Others are tied to assets, regulation, siting, expertise, or customer relationships that are harder to reproduce. Open and lower-cost models reinforce that point. They may broaden adoption and accelerate experimentation, but they also challenge the idea that frontier capabilities alone guarantee durable pricing power. In areas where customers do not require frontier performance or tightly integrated proprietary systems, improving open models are compressing economics at both the model and software layer.

Can rising revenues justify the capital required to sustain leadership?

The second question asks whether rising revenues and earnings can justify the capital required to sustain AI leadership. That issue is now most visible in the capex cycle. For current spending to earn attractive returns, revenue growth must continue to catch up with investment, usage must remain high, enterprise monetization must deepen, and margins must hold up despite a much larger capital base.

Consensus expectations for the five major hyperscalers call for combined revenue to increase 54% while EBITDA is expected to increase about 111% from year-end 2025 through 2028. Over the same period, depreciation is expected to rise much faster. Depending on assumed asset lives, it could increase by roughly 175% to more than 340%. That drag is large enough to matter. At the low end of those estimates, 2028 depreciation would come close to the group’s 2025 net income of $405 billion.

AI is making important parts of technology more capital intensive. Some parts of the market may still be valued as if AI were reinforcing capital-light software economics, when in fact it is making important parts of the stack more asset-heavy and operationally demanding. Investors should place more weight on depreciation, reinvestment needs, financing conditions, and the durability of pricing power on these more asset-heavy companies.

Memory is not a direct analogue for hyperscalers, and the current AI cycle has different drivers. Still, its history is a useful reminder that periods of tight supply, strong pricing, and high margins can look more durable than they prove to be once capacity expands. Shortages have repeatedly lifted margins and encouraged new investment, only to erode those same margins as supply caught up. AI may not follow that path exactly, but the lesson is familiar. Strong demand does not by itself protect returns when supply can respond and pricing power is not well defended.

Indeed, Micron and SK Hynix—two of the memory companies most directly exposed to advanced AI demand—have increased capex by a combined 70% in each of the last two years, and consensus expects another roughly 55% increase in 2026. Investors should be careful not to assume that today’s strong pricing and profitability will persist unchanged as capital spending rises and supply responds.

The quality of demand matters as well. Investors should distinguish between durable end demand and demand supported by ecosystem-linked commercial arrangements, strategic subsidy, or circular deal structures that make near-term economics look stronger than they are. As the system matures, leverage and structured financing also deserve more attention. Risk rises when capital assumptions become aggressive ahead of proven cash flows. After rising by roughly $900 billion since the start of this year, gross supply of investment-grade credit is expected to increase by about 17% over the full year to a record $2.1 trillion, with much of the increase coming from hyperscalers and related infrastructure. Structured credit markets are expected to see data center securitizations rise by nearly 50% in 2026 to $30 billion.

Stronger fundamentals have made the buildout more credible, but not necessarily more durable. High depreciation expense creates a demanding hurdle for hyperscalers that are increasingly competing with one another for business. Capital-intensive businesses facing rising competition may struggle even if the addressable market continues to grow. Not all participants will fare well. Semiconductors and advanced memory should benefit from tight supply and, in some cases, multi-year contracts, but supply is likely to catch up over time as capacity expands and technology becomes more efficient. Investors should be cautious about extrapolating today’s pricing and profitability too far into the future.

Where can value persist as AI becomes cheaper, more capable, and more widely available?

The final question considers where durable value can persist. Access to models alone will not remain enough. As intelligence diffuses, we expect more defensible positions to belong to firms that control how it is used: who owns the workflow, governs permissions, controls distribution, and connects output to execution. Hyperscalers and other large platforms are trying to capture value across multiple layers of the stack through vertical integration, from compute and cloud infrastructure to model access, routing, deployment, and enterprise tooling.

The most important shift is in what software and adjacent systems actually do. As agentic systems begin to perform economically meaningful work rather than simply assist users, part of the addressable market shifts from software budgets to labor budgets, which are much larger. That may expand revenue pools, deepen integration, and create stronger business models. It may also intensify disruption across software, services, and selected consumer sectors.

Software and adjacent control layers may still be where much of the value ultimately accrues, but they also pose the hardest underwriting questions. AI may expand revenue pools even as it weakens traditional moats. Customers may expect broader functionality without proportional price increases, while model, compute, orchestration, and support costs remain material. We expect the stronger positions belong to firms that are deeply embedded in a workflow, possess privileged task-specific context, and can convert AI output into completed work rather than simply sell access to a feature.

That distinction matters because control over the workflow is different from access to the model. A firm that helps generate an answer may be easy to displace. In an agent-driven environment, durable advantage should rest increasingly on control over permissions, approvals, and execution rather than on data alone. Systems that determine what autonomous software can access, trigger, and complete could have a competitive upper hand over systems of record alone.

This logic extends beyond enterprise software. In consumer markets, such as commerce, education, and travel, AI is likely to reshape discovery, service, and execution. New products and business models should emerge. But these same layers may also face the greatest pressure from improving models and larger platforms, especially where functionality is easy to replicate, customer relationships are weak, or distribution is controlled by someone else.

The same logic also shapes investment underwriting. Strong adoption and fast top-line growth may not be enough if a company with limited bargaining powers depends heavily on a single model provider, hyperscaler, or distribution platform. Downstream growth may prove real without translating into durable economics. Strategic acquisition may become a common end state for promising firms, supporting investment outcomes alongside a select group of independent long-duration compounders. For private equity and venture investors, underwriting should place more weight on customer ownership, monetization after model and compute costs, governance quality, likely end states, and the durability of economics if acquisition interest fades.

Investment implications

AI should be treated as a system-wide set of exposures rather than a narrow thematic trade. We see the stronger opportunities ahead in harder-to-relieve bottlenecks—especially power and related infrastructure—alongside the software and application layers that govern deployment in real workflows and emerging AI-native businesses that can reshape industry economics. The same shift should benefit adopters that use AI to improve their own economics while increasing disruption risk for incumbents that fail to adapt or are displaced by new business models.

This shift argues for more caution toward parts of the AI ecosystem where expectations, capital spending, and competition have all risen sharply at once. Large hyperscalers remain central to the buildout and may continue to benefit from scale, distribution, and enterprise integration. But they are also engaged in an increasingly costly race to secure compute, power, and physical infrastructure, and the associated depreciation burden is becoming harder to ignore. We therefore lean away from the most crowded first-wave winners, particularly where valuations still leave limited room for disappointment. The same caution applies to parts of semiconductors and memory, where recent earnings strength has been real, but history suggests investors should be careful not to mistake tight supply and current pricing power for durable advantage.

By contrast, we are more constructive on select infrastructure and real assets tied to harder-to-relieve constraints, particularly electricity infrastructure, grid access, and related enabling assets. These areas appear better positioned to benefit as AI deployment scales and physical bottlenecks become more binding.

The widening opportunity set also creates room for emerging disruptors, many of which were inconceivable before recent AI advances. As AI becomes cheaper, more capable, and more widely available, value will migrate toward firms that control workflows, permissions, customer relationships, and operational integration rather than those relying on thin wrappers or temporary model arbitrage. In software, the more durable positions are likely to belong to companies that can embed AI into economically meaningful tasks, govern it effectively, and monetize completed work rather than simple access. Private equity may benefit where businesses need capital, operational support, and technology investment to adapt successfully to AI-driven changes in cost structure and competition.

Venture capital remains the key channel for accessing emerging AI-native companies and disruptive business models, and investors seeking that upside likely need some participation in private markets. But this technology cycle is still early and, as in past cycles, a few winners are likely to emerge alongside many losers as innovation advances faster than commercial adoption. In that environment, exposure to AI is not the same as access to strong investment returns. Because private commitments are long-lived, pacing matters as much as manager selection. Investors should continue to allocate selectively, with discipline on timing and valuation rather than rushing to add exposure simply because the theme is compelling. As a new wave of highly anticipated technology IPOs comes to market this year, investors should be thoughtful about redeploying capital to venture capital, balancing those opportunities against other market segments with more attractive valuations and differentiated return potential.

The same framework should also be applied defensively. AI is not only a source of new opportunity. It is also a source of disruption risk in existing holdings. Businesses with weak differentiation, labor-intensive models, or information-heavy processes may be more vulnerable than they appear, even if they sit outside any obvious AI category. Equity long/short hedge funds may be well positioned to benefit from rising dispersion as AI creates clearer winners and losers across software, services, and other information-intensive industries. As disrupted companies—especially ones that took on private debt during the period of zero interest rates and high valuation from 2021 to early 2022—struggle to refinance over the next few years, stressed and distressed opportunities may emerge.

AI remains an important area of exposure. From here, we expect the best opportunities to come from identifying durable bottlenecks, defensible control points, and the businesses most likely to benefit from disruption rather than suffer from it. Active management across public and private markets will be central to success.

Graham Landrith and Justin Hopfer also contributed to this publication.

Index Disclosure

MSCI All Country World Index (ACWI)
The MSCI ACWI captures large- and mid-cap representation across 23 developed markets (DM) and 24 emerging markets (EM) countries. With 2,558 constituents, the index covers approximately 85% of the global investable equity opportunity set. DM countries include Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Hong Kong, Ireland, Israel, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom, and the United States. EM countries include Brazil, Chile, China, Colombia, Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Korea, Kuwait, Malaysia, Mexico, Peru, the Philippines, Poland, Qatar, Saudi Arabia, South Africa, Taiwan, Thailand, Turkey, and the United Arab Emirates.

Footnotes

1. The situation is somewhat flipped in China, where access to the most advanced chips remains a constraint, while electricity is more available. Notably, China controls refinement of most critical material, such as rare earths.

The post VantagePoint: Artificial Intelligence Investing After the First Wave appeared first on Cambridge Associates.

Multiple structural forces are converging: AI-driven electricity demand, the electrification of transport, industry, heating, and other applications, and the integration of distributed renewable generation into a grid designed for a different era. On top of those forces, high and volatile fossil fuel prices because of the Iran War may further accelerate electrification. European electric vehicle (EV) sales jumped 51% in March 2026, and China’s “new three” exports (solar, batteries, and EVs) rose 70% year-over-year, according to Ember and Chinese customs data.

The combination of demand pressures is significant. Hyperscalers are racing to build AI compute capacity, and new data centers require more power and often new transmission connections as well. Electrification is adding load from EVs, heat pumps, and industrial processes, while the shift to distributed, intermittent renewable generation requires storage, load balancing, demand response, and smart grid technologies that the existing system was not designed to accommodate.

The numbers are stark.

Similarly, 40% of European distribution grids are more than 40 years old. According to the International Energy Agency, while investment in renewables has doubled since 2010, grid capex has remained largely flat, creating a choke point for electrons in developed economies. Order backlogs for transformers, cables, and switchgear are growing, and lead times for large power transformers now span three to five years in North America and Europe.

The opportunity spans asset classes. In public equities, large industrial companies supplying grid equipment—including transformers, cables, and switchgear—have seen significant re-ratings. The more attractive opportunities are likely to be companies with multi-year order backlogs and demonstrable pricing power, rather than those primarily riding the thematic wave on sentiment. Private infrastructure funds can offer exposure to grid assets with long-duration, inflation-linked cash flows. Growth equity and venture capital can provide access to grid-enhancing technologies, including demand response platforms, energy storage software, and grid optimization tools, increasingly enabled by AI. What distinguishes strong managers in this space is the combination of engineering expertise, understanding of industry-specific sales cycles, and the ability to navigate highly localized regulatory complexities.

The context is different in many low- and middle-income countries, where the challenge is often not modernizing an aging grid but expanding energy access for the first time. Solar costs have fallen more than 90% since 2010, and the combination of rooftop solar, mini-grids, and battery storage now offers a faster, cheaper, and more resilient path to electrification than extending the traditional grid. This is the energy leapfrog, analogous to how mobile phones bypassed fixed-line telecommunications across many emerging markets. Here, the opportunity set is more distinct and often centers on distributed energy, last-mile distribution platforms, productive-use appliance financing, and the digital infrastructure—including metering, payments, and demand forecasting—that makes distributed energy commercially viable at scale.

Footnotes

1. The situation is somewhat flipped in China, where access to the most advanced chips remains a constraint, while electricity is more available. Notably, China controls refinement of most critical material, such as rare earths.

The post Underinvestment in the Electric Grid Has Created Opportunity Across Transmission, Distribution, and Grid-Enabling Technologies appeared first on Cambridge Associates.

Tariffs are one reason the economics are shifting. When duties raise the cost of virgin steel, aluminum, or plastics, the reuse of materials becomes more competitive. Recycling, remanufacturing, and recovery can reduce reliance on imported goods that may be disrupted by trade disputes, export controls, or freight bottlenecks.

AI is increasing demand for critical minerals and rare earth elements used in the infrastructure that powers data centers. Companies that build circular systems through recovery of end-of-life batteries, electronics, and industrial equipment can reduce sourcing inputs from geographically concentrated and politically sensitive regions. This lowers exposure to external shocks and improves long-term supply resilience. Additionally, waste-to-energy systems are finding new demand with the growth in site-specific energy needs.

Plastic offers one of the clearest near-term business cases. Virgin plastic production is tied to fossil fuel feedstocks with oil price spikes quickly flowing into resin costs. Recycled plastic is not immune to volatility, but it is less dependent on virgin hydrocarbon extraction, which makes recycled content supply chains economically more attractive in times of oil price volatility.

The broader inflationary environment adds to the investment case. Newly extracted inputs are significantly exposed to inflation in energy, transport, labor, and trade. Using recycled inputs, extending product life, and recovering components can function as direct margin protection when prices rise.

Regulation is reinforcing the economic opportunity within the circular economy, and recycled content mandates are creating demand floors for secondary materials.

That number will rise incrementally to 60% by 2028. The EU’s packaging rules are also tightening recycled content requirements. With regulatory compliance for recycled content increasing in a growing number of jurisdictions, a first-mover advantage is emerging for companies that secure feedstock.

Translating the circular economy investment thesis into portfolio action requires a deliberate approach across asset classes. Many institutional portfolios already have meaningful exposure to sectors where circularity is becoming a competitive differentiator, including industrials, materials, consumer staples, technology hardware, and logistics. Investors should better understand how managers are evaluating companies’ waste reduction and reuse strategies. Companies genuinely innovating on circularity—rather than merely reporting on it—may exhibit lower input cost sensitivity and more durable margins over time. Private equity and growth equity managers with dedicated circularity mandates offer access to advanced recycling platforms and the software systems that make reverse supply chains commercially competitive. Real assets managers with operational expertise in supply chain logistics are well positioned to develop and own the physical infrastructure required to support circularity at scale.

Circularity is increasingly competing on price, resilience, and operational relevance. In a more disrupted world, the circular economy is becoming a more practical business and investment consideration.

Footnotes

1. The situation is somewhat flipped in China, where access to the most advanced chips remains a constraint, while electricity is more available. Notably, China controls refinement of most critical material, such as rare earths.

The post Circular Economy Models Can Improve Supply Chain Resilience appeared first on Cambridge Associates.

The business case begins with continuity. Water scarcity has been linked to weaker economic growth and higher inflation. Because water is expensive to move relative to its value, local treatment, recycling, storage, and efficient allocation can often generate better long-term returns than securing additional supply.

Trade and geopolitical conflict reinforce the value of secure water resources. Regions that can offer dependable water access may be better positioned to attract manufacturing, food production, and digital infrastructure, which can help insulate from tariff fluctuations and reduce dependence on other jurisdictions. Geopolitical conflict adds another layer of risk and value. The Strait of Hormuz is not just an energy choke point; it is a reminder that water infrastructure can be strategically vulnerable, particularly in desalination-dependent economies in the Gulf region. More broadly, governments are increasingly treating water as a strategic resource rather than only a utility issue.

The AI buildout has heightened the urgency of this theme. Large data centers can consume enormous amounts of water for cooling. In the United States, an average 100-megawatt data center consumes water equivalent to roughly 6,500 households.

The AI companies that use recycled-water infrastructure may benefit from better positioning with regulators and communities.

Inflation strengthens the investment case further. Water has been underpriced in many regions for years, but utilities and regulators are facing rising costs tied to aging infrastructure, tighter standards, and climate adaptation efforts. This points toward structurally higher water costs over time, especially in stressed basins. Companies that invest early in water efficiency are locking in lower operating costs before the full impacts of repricing.

The investable opportunity spans public and private markets with business models that: reduce water use through analytics, metering, leak detection, and water-efficient industrial systems; reuse water through advanced treatment, recycling, and closed-loop infrastructure; replace fresh water demand through desalination and brackish-water; or deliver water more effectively through utility concessions and water-as-a-service models.

Investors should consider water to be a portfolio issue and stress test holdings for water intensity and resilience. Managers should demonstrate how they incorporate water-related risk factors into investment decisions. This applies to both equities and credit. According to Moody’s, nearly $2 trillion in corporate debt is highly exposed to water management issues. The common thread is simple: businesses that secure supply, improve productivity, and reduce exposure to future price shocks may become more valuable as water scarcity becomes more visible. Managers proactive in managing risk and leaning into companies that provide water solutions should be well positioned.

Footnotes

1. The situation is somewhat flipped in China, where access to the most advanced chips remains a constraint, while electricity is more available. Notably, China controls refinement of most critical material, such as rare earths.

The post The Importance of Water Reliability Is Growing, as Is the Investment Opportunity appeared first on Cambridge Associates.

Today’s market leaders remain strong businesses, but they are also priced for a continuation of unusually favorable conditions. Meanwhile, valuation gaps across regions and styles are wide; inflation risks are less settled; and the geopolitical, policy, and fiscal backdrops are less benign than investors had come to expect. In this edition of VantagePoint, we explain why investors should be wary of relying on rearview mirror assumptions, where current concentrations create the greatest vulnerability, and why more attractive opportunities now lie beyond the market’s recent winners. The implication is not to react abruptly, but to act strategically.

Investors are not prepared

At the end of every bull market, investors tend to be overallocated to whatever worked best. Today, that means not only US equities and US dollar assets, but also a US market that has become unusually concentrated. The United States now represents roughly 64% of the MSCI All Country World Index, up from about 42% in 2010, and the top 10 US companies—most of them technology related—account for 24%. The information technology sector is near its 36% share of the US market reached earlier this year, exceeding the concentration seen in the late 1990s, and that doesn’t even include Amazon, Alphabet, Meta, and Tesla, which are classified as consumer discretionary and communication services stocks. Foreign capital has been attracted to US assets, supporting the US dollar. As was the case in the 1990s, once the enthusiasm for US equities fades, the US dollar will likely fall as well.

US household equity ownership has also risen to unusually high levels relative to net worth, with equities exceeding real estate by the widest margin in the post-World War II era. Previous episodes in which a narrow group of companies came to dominate market returns—including the Nifty Fifty era in the late 1960s/early 1970s and the dot-com era of the 1990s—proved poor moments to abandon diversification. The point is not that these exposures are about to collapse. It is that many portfolios now rest on assumptions that have become less reliable.

Most investors have correspondingly little direct exposure to tangible assets with real-world scarcity value. That mix made sense in a world shaped by falling inflation, expanding globalization, and stable supply chains. It makes less sense in a world where supply constraints, geopolitical friction, and higher capital intensity are becoming more persistent features of the investment landscape.

The supply side of the economy has changed in ways that look more structural than cyclical. In just five years, investors have had to absorb a series of major shocks: the COVID-19 pandemic, Russia’s invasion of Ukraine, US tariffs, and now the Iran War. Each has reinforced the same lesson. Inflation is not simply a demand-management problem, and geopolitical risk is not a remote tail event. It is a recurring influence on growth, prices, capital flows, and investment priorities. Geopolitical rivalry, defense modernization, artificial intelligence (AI) adoption, energy security, and climate change mitigation and adaptation are increasingly moving together, with important consequences for capital spending, inflation, and asset returns.

This is the rearview mirror problem. Investors are still using the conditions that shaped the past 15 years as a guide for what comes next, even as that backdrop becomes less dependable. We made a similar point in early 2000, when we warned that apparent diversification had been undermined by a shared dependence on US technology exposure. The parallel is not exact, but the lesson is familiar: portfolios built around what worked in one regime can become more fragile than they appear when conditions change.

AI is eating the world, at a cost

AI is the most powerful expression yet of a much longer cycle of US tech leadership. What began as a market preference for scale, duration, and capital-light growth, evident in the earlier dominance of the FANGs (the popular acronym for Facebook, Amazon, Netflix, and Google first coined in 2013) has evolved into a dependence on a small group of companies tied, directly or indirectly, to the AI buildout. Their influence now extends well beyond equity benchmarks. AI-related spending is helping to drive corporate capex, support economic growth, shape investment-grade debt issuance, and increase demand for electricity generation, transmission, and grid resilience. As in past booms, a single theme has grown large enough to shape multiple parts of the investment landscape at once.

That broader reach is one reason historical parallels are useful. Major technological revolutions have repeatedly followed a familiar path. Railroads, electrification, and the internet all produced real economic transformation. They also produced overbuilding, excessive optimism, and eventually some form of bubble dynamic. That is not a contradiction. It is often how transformative technologies are financed. Capital rushes toward the most visible opportunities, investors assume early leaders will capture most of the value, and markets discount a future that proves harder, more competitive, and more capital-intensive than expected. There is little reason to expect AI to be different.

The late 1990s offer a particularly useful comparison. The problem then was not simply speculation. It was that market leadership, capital spending, financing activity, and investor expectations became tightly bound to a single secular narrative. Many dot-com companies were highly speculative and unprofitable, with Pets.com serving as a useful poster child for the era. But companies like Cisco, Microsoft, and Oracle were real companies with strong businesses and central roles in the digital economy. The lesson is that even genuine technological leaders can become poor investments when valuations imply too smooth a path from innovation to durable returns. The same risk exists today.

Recent market weakness has taken some of the air out of the most stretched valuations, although much of the improvement has been retraced in April’s sharp market rally. Forward earnings multiples for the Magnificent 7 are meaningfully below their peaks. And on sales-based measures, the derating has been less pronounced, suggesting that investors are still paying demanding prices for a narrow set of companies expected to deliver an unusually large share of future growth. Some froth has come out of the market, but it remains heavily dependent on continued execution from the same leadership cohort.

The financing side of the story deserves at least as much attention as valuation. Hyperscaler capex has risen sharply even as free cash flow has started to decline from 2024 peaks, narrowing the gap between internally generated cash and the spending required to maintain leadership. Indeed, these companies are expected to spend $700 billion on capex in 2026—a 70% increase over 2025 spending, which will eat into free cash flows even as operating cash flow is still rising.

As that gap closes, debt financing—including off-balance-sheet structures—is becoming more important. The breadth of the theme is visible in private markets as well. AI and machine-learning deals accounted for roughly half the value of global venture capital investment in 2025 and are expected to account for an even larger share in 2026, up from about one-fifth in 2020 and almost nothing in 2010. That is another sign that AI is no longer simply one promising area of innovation. It is increasingly the organizing principle for capital formation across the growth ecosystem. That may create important opportunities, but it also reinforces the need for discipline. When one theme absorbs such a large share of capital, the line between durable advantage and speculative excess becomes harder to draw.

Just as important, the market may still be valuing some of these companies as if they retained the economics of capital-light software businesses. History suggests caution. Firms that grow assets aggressively have tended to underperform more capital-efficient peers, a pattern consistent enough to be embedded in academic factor models. If AI leadership increasingly requires sustained investment in data centers, chips, power, and networks, some of today’s leaders may deserve a different valuation framework than the one investors have become accustomed to applying. Consider that if current analyst estimates are correct, the hyperscalers will have accumulated $2 trillion in AI-related assets by 2030. Assuming an average life of five years, or 20% depreciation, would result in $400 billion in annual depreciation charges, roughly equaling their combined profits of $405 billion in 2025. The AI cycle is moving from enthusiasm to financing, and markets may still be underestimating the eventual cost of leadership.

The investment risk, then, is not simply that AI enthusiasm has gone too far. It is that many portfolios are more dependent on a single theme than they appear. When one secular story drives equity concentration, capital spending, credit issuance, infrastructure demand, and venture enthusiasm all at once, the case for diversification becomes stronger, not weaker. Investors do not need to reject AI to recognize that the better long-term opportunity may lie in markets where expectations are lower, valuations are less demanding, and portfolios are less dependent on a single story.

Credit as an early warning system

Credit deserves attention because it often reveals fragility before equity markets do. The signal today is mixed. Traditional default rates in public credit have eased, and reported defaults in private credit appear to have eased as well. But broader measures that include distressed exchanges, liability-management exercises designed to avoid default, and payment-in-kind restructurings paint a less comfortable picture. Headline default rates still look manageable, but they may understate where strain is building. That is especially true in private markets, where quarterly marks, amend-and-extend activity, and abundant capital can delay recognition of weakening credit conditions.

AI adds another layer to this picture. As the financing cycle has progressed, more of the capital required to support AI-related investment has moved beyond equity enthusiasm and into credit markets. Investment-grade credit is expected to see gross supply rise about 25% this year to a record $2.25 trillion, with a 10x increase to an estimated $400 billion coming from hyperscalers and related infrastructure. Structured credit markets are expected to see data center securitizations rise by nearly 50% to $30 billion. That includes financing for data centers, infrastructure, and businesses whose economics remain unsettled. It also matters for software-heavy loan books, where some of the most aggressively structured deals were made in businesses that now face greater competitive pressure or pricing uncertainty as AI diffuses. Parts of the market are now being tested against assumptions formed in a more benign period.

Credit market structure has also changed materially over the past decade, with important implications for who provides financing, where leverage sits, and how stress could spread through the system. Private credit is now a much larger and more influential part of the financing ecosystem than it was a decade ago. Much of that growth reflects tighter bank capital regulation after the Global Financial Crisis (GFC), which made some forms of lending less attractive for banks and created room for private lenders to expand. Investor capital followed, drawn by higher yields and the promise of illiquidity premia. This shift brought real benefits, including broader access to capital and more flexible financing for some borrowers. But it also intensified competition, particularly in direct lending, where spreads are tight and lender protections have weakened in more crowded parts of the market.

The same shift was reinforced by the long period of near-zero interest rates that followed the GFC. Zero Interest Rate Policy (ZIRP) did not just lift asset prices. It also encouraged financing structures and underwriting assumptions that were easier to sustain when capital was cheap, and refinancing was routine. Highly levered companies, aggressive growth strategies, and buyouts struck at elevated multiples all looked more manageable in that environment. Many businesses will prove less resilient in a higher-rate world, especially where earnings growth is slowing, equity cushions are thinner, and valuations remain anchored to a more forgiving era. The clearest pressure point is the cohort of loans originated in 2021, when financing terms were exceptionally easy, equity valuations were overstated, and leverage was often pushed to levels that are harder to refinance today. That matters for private equity as well as private credit and secondary funds that are picking up these companies as they come to market. Some of the most vulnerable credits are direct loans tied to sponsor-backed transactions completed when financing was abundant.

The main risk is not necessarily systemic in the way investors associate with 2008. But the warning is still meaningful. Pockets of strain in credit reinforce the broader message of this note: portfolios built around a narrow set of favorable assumptions may be more fragile than they appear. A slowdown in private credit lending would still amount to a form of credit contraction, potentially weighing on growth and certainly increasing the cost of the AI buildout. Risk to the banking system is limited for now, although bank exposure to the private-credit ecosystem has grown and available data almost certainly understate the full extent of those linkages. Insurance is another area to watch, particularly, if private-letter credit ratings overstate underlying credit quality. In such circumstances, highly levered, thinly capitalized insurance companies that have accumulated too many direct loans may come under pressure. Recent concern has also centered on semiliquid vehicles that offer more liquidity than the underlying private credit assets they hold. These funds have seen increased retail outflows, raising the possibility that stress may emerge through gating, valuation uncertainty, or a more selective and uneven availability of credit rather than through a single market-wide break.

For investors, the takeaway is not to avoid credit, but to recognize that portfolios built with disciplined manager selection, underwriting, and fund structuring during more exuberant periods should be better positioned to navigate market shifts. Diversification across sub-strategies should also provide ballast to portfolios.

Where to look for diversification

Investors should respond by trimming crowded exposures and rebuilding diversification. The most compelling opportunities now lie in areas where valuations are lower, expectations are less demanding, and return drivers are less tied to the same crowded narrative. That points first to non-US equities, value, small-cap equities, active strategies including hedge funds, and real assets tied to a more capital-intensive and electrified economy.

The strongest public market opportunity is outside the United States. Non-US equities offer lower valuations, less concentration, and greater exposure to sectors and styles left behind during the long period of US large-cap dominance. A weaker dollar, which we expect, would provide an additional tailwind for non-US equity exposure. The combination of valuation support and currency tailwind has historically been a powerful setup for extended periods of outperformance. Global ex US equities are also tilted to traditionally value-oriented sectors, which adds to their appeal in the current environment. The broader capex cycle now underway, while still tied to AI, also incorporates more geographically dispersed themes of energy security, grid resilience, and defense, providing additional support to non-US markets with deeper exposure to industrials, utilities, and related cyclicals.

Small-cap equities also look more attractive than they have for some time. Relative to large caps, valuations are modest, and earnings expectations are beginning to improve as growth broadens beyond technology-related winners. Small-cap equities, especially in the United States, have been held back by higher financing costs, weaker balance sheets, and the market’s overwhelming preference for scale. But that is also why they offer greater upside if capital becomes more selective and market leadership broadens.

Near-term risks remain. Global ex US equities and global small caps are more exposed than US large caps to economic disruption tied to conflict in the Middle East, and the dollar could strengthen further in the short run. But the situation is too fluid to time tactically. Investors are better served by rebuilding diversification and using periods of renewed dollar strength and US equity outperformance to add to non-US positions and reduce US dollar exposure.

A less concentrated market would also improve the opportunity set for active management. The dominance of large-cap equity performance has created a powerful headwind for active managers. Broadening market leadership and ebbing concentration would change that. Within hedge funds, the case is strongest for strategies that benefit from greater dispersion rather than broad market direction. Equity long-short managers should have a better opportunity set in a world where valuation matters more and returns become less concentrated. This may be particularly fruitful in less efficient markets outside the United States. At the same time, less directional hedge funds, such as arbitrage, global macro, and trend-following strategies can help diversify portfolios when stock-bond relationships become less reliable and macro shocks reverberate across markets in less predictable ways. Other diversifying strategies, such as insurance-linked securities and asset-backed credit can also provide diversification. These are not perfect hedges, but they are better suited to a more fractured environment than portfolios that rely on cash and sovereign bond duration as their only ballast.

Real assets should also play a larger role, especially the resources and infrastructure needed to support a more capital-intensive and electrified economy. Electricity infrastructure and grid modernization are among the most compelling structural opportunities in real assets. The International Energy Agency estimates that $600 billion per year in grid investment is needed by 2030, roughly double current levels. The AI buildout, reshoring, and the energy transition are all increasing demand for generation, transmission, storage, and grid efficiency. The Iran War reinforces the strategic value of energy security, resource independence, and AI-related capabilities, all of which are increasingly being treated as matters of national security. These are long-duration needs with real economic importance and, in some cases, attractive supply/demand characteristics. For portfolios that have become too dependent on financial assets and intangible growth, this is one of the clearest ways to rebuild exposure to scarcity value and real-economy investment.

Commodities and natural resources offer some inflation sensitivity and exposure to supply constraints, though timing has been difficult, particularly given weakness in the Chinese economy and property sector. Years of underinvestment in mining and extraction have created meaningful supply deficits in copper, lithium, nickel, and uranium. The structural case is strong, but current pricing already reflects some of that scarcity even as Chinese demand remains soft. Should the tail risk scenario of the Iran War push the global economy into recession, valuations in parts of the complex would likely become more attractive, creating better entry points and reducing the timing risk that has frustrated investors in this space. Investors should approach this opportunistically, building positions when prices allow rather than chasing what has already moved.

Private markets also require more discrimination. Venture capital is likely to produce some of the future winners in AI, and investors who want exposure to that upside will need some exposure to the private ecosystem. But this is still an early-stage technology cycle, and the eventual winners are far from settled. Investors should be careful not to confuse access to the theme with access to the returns. Meanwhile, many existing technology investments, especially enterprise software deals struck when rates were near zero and valuations were generous, are being tested in a more demanding environment of higher rates and AI disruption. Because private commitments are long-lived, pacing matters as much as manager selection. Investors should continue to allocate to private investments selectively, with patience about timing and valuation rather than rushing to add exposure simply because a theme is compelling. If a new wave of eagerly anticipated technology initial public offerings (IPOs) comes to market this year (e.g., SpaceX, Anthropic), investors should be measured in redeploying capital to private investments, balancing those opportunities against other parts of the market offering more attractive valuations and differentiated return streams. Especially in AI- and software-focused private equity, investors should favor managers with valuation discipline, rigorous underwriting, and the ability to distinguish durable advantage from enthusiasm financed on easy terms.

In short, portfolios built with too much concentration in prior winners are increasingly fragile. We cannot know precisely when leadership will change, but we do know that portfolios are more resilient when they are not built on the assumption that the recent past will continue indefinitely. We believe the strategic response is clear: trim crowded exposures and rebuild diversification through non-US equities, value, small-cap equities, real assets, and active strategies that can benefit from greater dispersion. Investors do not need to abandon the market’s recent winners. They do need to stop treating them as the only place to look for durable long-term returns.

Drew Boyer and Justin Hopfer also contributed to this publication.

Footnotes

1. The situation is somewhat flipped in China, where access to the most advanced chips remains a constraint, while electricity is more available. Notably, China controls refinement of most critical material, such as rare earths.

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The coordinated attacks on Iran by the United States and Israel, which began on Saturday, February 28, have jolted markets, with the clearest effects showing up first in energy. Tanker traffic through the Strait of Hormuz, a critical passage for about 20% of global oil and liquefied natural gas (LNG) supply, has dropped sharply. At the same time, production across the region, including in Iran, Kuwait, Iraq, Saudi Arabia, the United Arab Emirates, and Qatar, has also been disrupted. Because oil demand is relatively insensitive to price in the short run, even modest supply losses can push crude prices meaningfully higher. That dynamic helped drive front-month ICE Brent futures up 46% from when the conflict began to $106 per barrel in trading today, prompting G7 countries to consider releasing petroleum from their strategic reserves and renewing concerns about inflation.

Many economists estimate that a $10 per barrel increase in oil prices would add roughly 15 to 30 basis points to US headline inflation. On that basis, a sustained 50% rise in oil prices could add about 0.5 to 1.0 percentage point (ppt) over the following year. The incremental inflation pass-through from further oil price increases may also diminish at higher price levels and over time as demand weakens. The impact on core inflation, which excludes food and energy prices and matters more for monetary policy and asset prices, would likely be much smaller. This is because energy is often only a modest input into the cost of goods and services relative to labor and other expenses, and firms may absorb part of the increase in margins rather than pass it through fully to consumers. The broader impact is also likely to be more limited than in the 1970s, when economies were far more energy intensive. In fact, in many advanced economies, energy use per unit of output has fallen by more than half since then as efficiency has improved.

Still, the inflationary impact will not be uniform across countries and regions. Economies that are net importers of oil, LNG, and other affected goods such as fertilizer are more exposed. In Europe, for example, prices for a key natural gas benchmark rose 67% last week, compared with an 11% increase in the United States, even though supplies from the Middle East account for only about 5% of the EU’s combined LNG and pipeline gas imports. Similar dynamics have played out in parts of Asia. For many non-US energy importers, the challenge could be compounded by the tendency of the dollar to strengthen during periods of market stress, which raises the local currency cost of dollar-priced commodities such as oil and LNG. Taken together, the hit to headline inflation in some non-US economies could be meaningfully larger, perhaps twice that of the United States. But, as in the United States, the effect on core inflation would likely be more limited for the same reasons.

Of course, the impact of the conflict on inflation will depend largely on its duration and scope. President Trump has sent mixed signals on how long it could last, at times suggesting it may end within weeks and at others that it will continue as long as necessary, likely as part of a pressure campaign aimed at securing a deal. Even so, he appears to prefer a short conflict. He has long criticized the protracted wars in Iraq and Afghanistan, and a prolonged campaign would raise the risk of greater US casualties, backlash from some Middle East allies, and higher inflation, all of which could weigh on political support at home ahead of the November congressional elections. That helps explain the administration’s move to support the war risk insurance market, which could limit further disruption to shipping flows if the conflict remains contained. Longer-dated oil & gas prices in both the United States and Europe likewise suggest investors expect the conflict to subside rather than become prolonged.

Even if the conflict were to last longer than most expect, the inflation backdrop would still differ markedly from the one that produced the pandemic-era surge. That episode reflected an extraordinary combination of fiscal and monetary stimulus and severe supply constraints, especially labor shortages. In the United States, annual inflation rose by 8.8 ppts, from 0.2% in May 2020 to 9.0% in June 2022, an increase comparable in scale to the major inflation episodes that peaked in 1974 and 1980. By contrast, today’s inflation risk is more concentrated, with higher energy prices rather than a broad-based demand and supply shock serving as the main transmission channel.

The conflict is likely to reinforce this year’s existing rotation within equity markets. Energy and industrial equities could benefit further as investors place a higher premium on sectors tied to commodity supply, defense, and industrial capacity, while the risk of firmer inflation may limit central banks’ willingness to cut rates, creating a less supportive backdrop for rate-sensitive growth sectors such as technology. We expect this dynamic to continue supporting our July 2025 recommendation to tactically overweight Latin American equities within emerging market portfolios, given the region’s significant valuation discount and more moderate, though still present, exposure to geopolitical risk. Across geographies, US equities may continue to benefit in the near term from safe-haven demand. Over time, however, the broader aftermath of the conflict could support greater marginal flows to ex US assets, consistent with the trend evident earlier this year, as some investors reconsider the risks of concentrated exposure to US assets and the dollar amid greater policy uncertainty and elevated valuations.

More broadly, periods of heightened geopolitical risk are a reminder of the value of diversification and discipline. As we noted in our 2026 Outlook, investors that have allowed their equity allocations to drift higher over the last decade or two should evaluate increasing their policy exposure to diversifying strategies such as hedge funds, given the broader shift in the risk-reward profile across asset classes. While markets often recover quickly from geopolitical shocks, the case for diversifying strategies is particularly compelling today relative to broad equities, which remain expensive, unusually concentrated in a small number of names, and less geographically diversified than is typical. Put differently, today’s environment calls for portfolios built to withstand a wide range of outcomes.

Footnotes

1. The situation is somewhat flipped in China, where access to the most advanced chips remains a constraint, while electricity is more available. Notably, China controls refinement of most critical material, such as rare earths.

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The US residential real estate sector remains a weak spot in an otherwise resilient economy, with residential investment contracting at a 4.4% annualized rate through the first three quarters of 2025. Affordability has worsened due to high home prices, elevated mortgage rates, and tight credit, all of which have dampened activity. Recent Federal Reserve easing has helped stabilize the market, lowering the 30-year mortgage rate by about 170 basis points since late 2023. However, a sustained recovery is likely to require a sharper decline in rates or a significant increase in supply, both of which are unlikely. Supply-side reforms are difficult to implement, and a substantial drop in rates does not appear likely, given the current macro environment. Mortgage rates are closely tied to long-term Treasury yields, which have limited room to fall unless the Fed eases more than expected or growth expectations weaken. Additionally, the spread between mortgage rates and Treasury yields has moved back near their historical average, leaving little room for further compression.

In response, the Trump administration has proposed measures with the goal of improving housing affordability, such as restricting institutional ownership of single-family homes and directing Fannie Mae and Freddie Mac to acquire $200 billion in mortgages. Several other potential ideas were floated as well. However, these policy interventions are unlikely to overcome the sector’s headwinds in the near term. For example, a ban on institutional investors would face legal challenges and have little immediate effect on supply, as they own less than 1% of single-family homes. The planned $200 billion in mortgage purchases may exert some downward pressure on rates, but the scale is modest compared to the Fed’s $2.3 trillion in mortgage-backed securities (MBS) purchases across the three previous quantitative easing programs. With the Fed still unwinding its MBS holdings and mortgage spreads near their historical average, these factors may further limit the impact of government-sponsored enterprise (GSE) purchases.

For investors, the proposed policies are unlikely to materially change the outlook for most asset classes tied to US residential real estate. Homebuilder and home improvement stocks, after trailing the broader market by more than 18% in 2025, have recently rebounded, with the S&P Homebuilders Select Industry Index up 15.8% year-to-date versus -0.1% for the S&P 500 Index. Still, the outlook remains challenged. These stocks are highly sensitive to mortgage rates, and with affordability still stretched, sales volumes are flat, and price appreciation is fading. Revenue growth is under pressure, with 12-month forward earnings per share for homebuilders at -1.6% versus 15.1% for the S&P 500. Elevated input costs, driven by tariffs and labor shortages, have further compressed margins. Despite these headwinds, valuations are not particularly cheap, suggesting limited scope for sustained outperformance.

Within credit, residential agency MBS remain among the most attractive segments in the investment-grade universe. In 2025, agency MBS returned 8.6% versus 7.3% for the Bloomberg Aggregate Index, benefiting from tightening spreads and lower volatility. While upside is now more limited, newly issued agency MBS still offer both competitive yields and superior risk-adjusted returns compared to corporates, given their higher credit quality. Although the ultimate impact of new policies remains uncertain, the administration’s affordability push and planned GSE mortgage purchases may provide a modest backstop for spreads and help reduce downside risk. With corporate bond spreads historically tight, agency MBS stand out as a high-quality alternative should credit spreads widen.

Among other segments, REITs and private funds focused on single-family rentals face the most direct policy risk, but most have already shifted to build-to-rent strategies, reducing their exposure to restrictions on institutional buying. The affordable and apartment sectors are two areas we continue to favor, given limited policy risk and supportive structural tailwinds, including persistent demand for affordable housing and the elevated cost of owning versus renting.

In sum, we do not expect the Trump administration’s affordability push to meaningfully improve conditions in the US residential real estate market. Given this view, we believe the best tactical opportunity is in current-coupon agency MBS, while private residential real estate segments like affordable and apartment properties will continue to benefit from strong structural supports.

Footnotes

1. The situation is somewhat flipped in China, where access to the most advanced chips remains a constraint, while electricity is more available. Notably, China controls refinement of most critical material, such as rare earths.

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by Sean Duffin

Hedge funds remain a vital part of diversified portfolios, and building resilience requires a thoughtful mix of strategies. In today’s environment—marked by elevated dispersion, low correlations, and ongoing policy uncertainty—equity long/short (ELS) managers are especially well positioned. Advances in AI and persistent tariff-related disruptions have driven pronounced outperformance in select sectors, notably technology and communication services, resulting in significant gaps between winners and laggards. Skilled ELS managers can exploit these market inefficiencies and others through both long and short positions, offering the potential for attractive risk-adjusted returns. Given these considerations, investors should consider leaning more than typical into ELS—either through portfolio rebalancing or by adding a new position—as part of a well-diversified hedge fund strategy mix.

The investment landscape is being shaped by a complex interplay of macroeconomic and geopolitical forces, including tariff-related uncertainty, sticky inflation, and evolving labor market dynamics. These crosscurrents are creating opportunities for nimble hedge fund managers. Global macro and other absolute return strategies are well positioned to navigate these challenges, given their flexibility across asset classes and regions. Yet, what distinguishes the current environment is the pronounced sector dispersion and volatility driven by technological innovation and policy shifts—conditions that are particularly favorable for ELS managers, who can capitalize on both broad market trends and stock-specific inefficiencies.

Hedge fund strategies offer distinct trade-offs for investors navigating the uncertainties of 2026. While a recession is not our base case scenario, the potential for episodic volatility and policy-driven market disruptions remains elevated. ELS approaches provide a practical way to position for this environment, offering reasonable defensiveness without sacrificing significant growth potential. Over the last 20 years, ELS strategies have captured about 70% of the equity market’s total gain but have lost roughly half as much as broader equity markets during major drawdowns. In contrast, more defensive hedge fund strategies such as trend-following and global macro have excelled during sustained market stress, providing diversification and crisis alpha, though these strategies have significantly lagged equity markets over the long term. By combining ELS with these and other defensively oriented strategies in a diversified hedge fund allocation, investors can position portfolios to participate in market upside, while maintaining robust protection against extended periods of volatility or unexpected downturns.

Another supportive factor for hedge funds that short securities is the current level of interest rates, which has increased the short rebate—the interest earned on cash collateral from short sales. While this is a structural feature of the strategy rather than a source of manager alpha, it does provide a tailwind for funds employing short positions, boosting baseline returns compared to the low or negative rate environment of 2010–21. As long as rates remain elevated, this dynamic should continue to benefit hedge funds with meaningful short exposure.

While we recommend leaning into ELS strategies, given current market dynamics, it remains essential to prioritize manager quality and ensure each allocation fits within the broader portfolio. Investors should avoid over-concentration in any single strategy or style and align allocations with overall portfolio risk and objectives—whether adding risk or protecting capital. For taxable clients, selecting managers that actively consider tax implications and demonstrate a track record of tax-aware trading can further enhance after-tax outcomes.

Leaning into hedge fund strategies in 2026 is prudent for investors seeking both performance and protection. ELS strategies are especially well positioned, given current market dynamics, but a diversified approach that includes other defensive hedge fund strategies remains critical for portfolio resilience. By focusing on high-quality managers and strategic fit, investors can harness the diversification benefits that hedge funds provide—helping portfolios remain resilient and adaptable amid today’s market uncertainties.

Investors should lean into real asset secular themes in 2026

by Wade O’Brien

In 2026, investors should favor real assets that benefit from secular themes like digitalization, decarbonization, and demographics. However, as competition for these assets has driven up pricing, choosing skilled value-add managers who can develop projects and look beyond traditional plays is essential to unlocking high returns. Secondaries funds in both infrastructure and real estate are also attractive given access to high-quality assets at often favorable pricing.

Recent returns for infrastructure funds underline the dual role they can play both in generating absolute returns as well as protecting against inflation. Private infrastructure funds have generated annualized internal rates of return (IRRs) of around 11% over the last five and ten years. Returns have been even higher for skilled managers who capitalized on these secular themes, with value-add funds investing in areas like energy transition and data centers often outperforming generalist infrastructure funds and even some buyout strategies.

Infrastructure valuations have risen for many assets, reflecting demand that has exceeded forecasts. For example, last year Grid Strategies predicted that US power demand could increase by 8% over the next five years, given surging data center demand, almost 3x the pace it had modeled just two years prior. Even when strong demand growth is well telegraphed, supply can struggle to respond. An aging US population will require between 35,000 and 45,000 new senior living units per year, but supply has fallen well short in recent years, given rising labor and financing costs.

Rising price tags for certain infrastructure assets favor funds developing new projects over those acquiring existing assets, though in some markets—such as US renewables—distressed sales will present opportunity. As partners in developing new projects, investors should carefully search for managers that bring specialized toolkits to the table. Developing complex assets like data centers requires navigating challenges like permitting, power supply, cooling, and scaling traditional designs to meet today’s massive compute needs. Underwriting tenant risk is also important, as long-term contracts with deep-pocketed hyperscalers may prove more secure than short-term rentals with more speculative players. Diversified private infrastructure funds also can have an edge in identifying related plays, for example in the case of data centers identifying companies that generate and store power or help upgrade grids to connect these assets.

Global infrastructure funds are an attractive choice, given the diverse opportunities and varying valuations across markets. For example, publicly traded utilities in the United States fetch higher valuations than those in other markets, reducing their attractiveness as take-private candidates. Also, while data center capacity is expected to experience almost uniformly rapid growth across the United States, Europe, and Asia in future years, renewable growth in the United States may be slower due to recent policy shifts.

In real estate, as in infrastructure, we favor value-add managers focused on secular themes. Elevated valuations for core real estate assets limit the potential for price appreciation and reduce the appeal to lock-up capital. Instead, value-add strategies targeting themes such as demographics (e.g., senior housing) and digitalization (e.g., cell towers) are more compelling.

For investors seeking to accelerate portfolio deployment, secondary funds are worth considering, though the rationale for doing so varies across asset classes. Infrastructure secondaries can provide immediate access to cash-flowing assets, though at modest discounts. In contrast, real estate secondary stakes can offer substantial discounts, offering a margin of safety for assets with deteriorating fundamentals.

Looking ahead to 2026, real asset investors should stick with secular winners. While valuations have risen for some of these assets, partnering with private infrastructure and real estate funds that add value through design and operation can enhance return potential. Should economic growth disappoint or inflation surprise to the upside, these strategies should be supported by strong long-term fundamentals.

Investors should overweight California Carbon Allowances in 2026

by Celia Dallas and Justin Hopfer

California’s Carbon Allowances (CCAs)—permits issued under the state’s cap-and-invest program—present an attractive investment opportunity relative to global equities. CCAs offer an asymmetric return profile: the program’s price floor limits downside risk, while tightening supply, linkage with Washington state, and regulatory changes create significant upside potential. As the market transitions from annual supply surpluses to persistent deficits, we believe CCA prices are poised for accelerated appreciation. Current pricing offers an attractive entry point, with prices near the price floor, whereas global equities remain constrained by elevated valuations and index concentration.

California’s cap-and-invest program, run by the California Air Resources Board (CARB), requires entities to surrender allowances equal to their emissions in three-year compliance cycles. Allowances are distributed through free allocation and quarterly auctions, with auction prices supported by a price floor and the Allowance Price Containment Reserve (APCR). The cap, a state-set limit on emissions, declines each year to meet climate targets by reducing free and auctioned allowances. Once prices reach containment tiers, CARB releases additional allowances from the APCR at set prices. After APCR units are depleted, CCAs can rise to the price ceiling. Price tiers rise annually by inflation plus 5%. Entities may “bank” allowances for future use and use carbon offsets, credits earned from emission-reduction projects, to meet part of their compliance.

Since 2019, the cap has decreased by 4% annually, while emissions have declined by 2%–3%, tightening supply relative to demand. Prices remain subdued, given the large bank of allowances, but as the cap tightens and these are depleted—projected by the early 2030s—prices should rise sharply. CARB’s proposal to accelerate the annual cap decline would remove 118 million allowances from 2027 to 2030. This would likely drive the market into persistent annual deficits starting in 2027, ultimately exhausting banked supply by 2031 and supporting higher prices. Even without accelerated cap declines, deficits are projected to emerge by 2034.

Asset manager Aetos’ base case scenario, with 118 million allowances removed through 2030, indicates the program could hit the first containment tier in 2031 and the second in 2032. Current CCA prices are $30, with Tier 1 and Tier 2 prices estimated at $96 and $134 in those years, implying annualized returns of 24% over the next six to seven years. Across four managers, expectations range from banked allowances being depleted from 2031 and 2034, with projected IRRs of 24% to 14%, respectively. Furthermore, the anticipated linkage with Washington state’s program, expected by 2027, would likely drive price convergence and support higher prices. Even in bearish scenarios, returns remain positive, as the price floor rises annually. This underscores CCAs’ attractive, asymmetric risk/reward profile, especially compared to global equities, which face subdued return expectations as outlined earlier. For US taxable investors, CCAs also benefit from long-term capital gains treatment, enhancing after-tax return potential.

Despite compelling return potential, the thesis faces regulatory, political, and market volatility risks. An immediate concern is further implementation delay, especially after the program extension to 2045 took longer than expected. Next steps—Initial Statement of Reasons (ISOR) publication and rulemaking—must be completed before the October 2026 issuance of free allowances to enable accelerated allowance removals and deplete banks allowances. Recent federal executive orders have also prompted legal challenges, creating ongoing litigation and regulatory uncertainty as a tail risk. Nevertheless, the program has withstood past legal challenges and enjoys strong state support, reinforced by its fiscal contributions—$33.7 billion since inception.

The investment case for overweighting CCAs remains strong as the market shift from surplus to persistent deficit. Prudent position sizing is essential, given political and regulatory risks, lower liquidity, and event-driven volatility. Overall, CCAs offer differentiated return and diversification potential, with significant upside relative to global equities if anticipated catalysts are realized.

Footnotes

1. The situation is somewhat flipped in China, where access to the most advanced chips remains a constraint, while electricity is more available. Notably, China controls refinement of most critical material, such as rare earths.

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