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.

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 Scarcity in an Age of Disruption: Five Sustainability Themes for Investors to Embrace 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.

Potential public and private markets opportunities span platforms, services, talent-focused enterprises, and financing solutions tied to workforce development and skilled labor businesses.

The displacement is already visible. In 2025, US employers cited AI as a factor in nearly 55,000 job cuts. More broadly, the World Economic Forum projects that technology disruption, geoeconomic fragmentation, and the energy transition could affect 22% of jobs by 2030, creating 170 million new roles while displacing 92 million. The effects are uneven: among workers ages 22 to 25 in AI-exposed occupations, employment has fallen 6% since late 2022, and employment for the youngest software developers remains roughly 20% below its late-2022 peak. Workers will need to adapt and build highly valued skill sets, both technological and cognitive, to thrive in today’s economy.

At the same time, labor shortages in skilled trades are intensifying. The United States needs roughly 500,000 construction workers and 80,000 electricians each year, along with tens of thousands of plumbers, pipefitters, and HVAC technicians. Demand is rising further as AI infrastructure, the energy transition, and climate adaptation require more physical buildout than the current labor pipeline can supply.

For investors, this creates several potential areas of opportunity across public and private markets:

• Platform and Technology Plays, including learning management systems (LMS), adaptive content platforms, enterprise LMS, and apprenticeship management software that enable enterprises implement workforce development programs
• Services and Consulting Plays, such as enterprise re-skilling programs, workforce transformation consulting, and employer-facing trades staffing and training platforms
• Talent-Forward Enterprises, including large global organizations that are approaching talent development as a mission-critical strategy to build competitive advantage
• Employee Ownership Transition Capital, like private credit financing for employee stock ownership plans (ESOPs) and employee ownership trusts (EOTs) conversions, particularly in businesses dominated by skilled labor

Retention is also a part of the scarcity story. AI innovation risks concentrated value creation at the top of the skills distribution, raising questions around who captures that value. This is not just a social issue; it is a talent strategy question. Employee ownership—through structures such as ESOPs, EOTs, and worker cooperatives—is emerging as a powerful and underutilized innovation for retaining skilled workers across both digital and trades pathways.

There is a small but growing number of private equity and credit strategies dedicated to employee ownership that investors should lean into. Some companies transitioning to employee-owned offer lower-middle-market exposure through often inflation-resilient, real-economy businesses, while aligning tax-advantaged financial returns with worker wealth creation. ESOP companies also tend to pay higher wages, offer more retirement benefits, and show stronger engagement and retention than comparable non-ESOP companies. The same “silver tsunami” of retiring business owners creating the employee ownership investment opportunity is concentrated in construction, electrical, plumbing, and HVAC contracting, which are industries where skilled small- and mid-sized contractors dominate, the businesses are often profitable and growing, and the transition-capital gap is acute.

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 Workforce Development in the Age of AI Is a Large, Undercapitalized Investment Opportunity 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.

Trade conflicts are forcing companies to reconfigure supply chains around security and regional resilience rather than pure efficiency. Companies that have already diversified suppliers, lowered dependence on unstable inputs, and positioned production in locations better suited to a changing climate will be best positioned to gain competitive advantage. In that sense, resilience can become a source of continuity and, in some cases, pricing power.

Adaptation converges with value creation in agricultural production. Industrial farming models remain more exposed to weather variability and fertilizer costs, which have spiked recently with disruptions in the Middle East. Regenerative and climate-adaptive agricultural systems improve soil health and moisture retention, stabilizing production and reducing dependence on synthetic inputs. Economies with more resilience embedded in their agriculture and other productive systems may be less exposed to recurring cost surges and repeated rebuilding expenses from extreme weather events.

The scale is already large enough to matter.

The financing gap is not only a policy issue; it may also signal that private capital has not priced in future demand. However, the cost of inaction continues to escalate as damaged infrastructure creates disrupted service and repair needs, crop failures reduce supply, disrupted logistics increase delayed freight, and energy volatility feeds through to industrial inputs. Insurance markets are the clearest early warning system. Premium increases and selective coverage withdrawal in climate-exposed regions suggest that physical risk is beginning to reprice faster than many investors and corporate planning models.

The investment opportunity spans both pure-play resilience providers and resilience-integrated incumbents. Climate analytics, flood-control systems, cooling technologies, resilient materials, and early warning platforms offer direct exposure as demand broadens. Well-positioned infrastructure operators, building materials companies, and essential service providers offer a second route: steadier cash flows supported by better adaptation of core assets and operations.

Physical climate risk will continue to create additional operational risks across sectors. Recognizing the leaders and laggards in adaptation and resilience may become a more important part of portfolio risk management.

Five Scarcities. Many Intersections.

The conditions have changed. Each of these scarcities was visible years ago, but economic, regulatory, and geopolitical developments are making them even more urgent and investable. The world has been:

For all of these interconnected themes, the investability is scalable across public and private markets. Investors would benefit from assessing existing risk exposures, engaging managers, and investing proactively in a growing set of solutions.

Investors who lean into these themes today would enhance long-term portfolio resilience in a world where both scarcity and disruption are abundant.

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 Physical Climate Risk Is Creating Opportunities in Adaptation and Resilience appeared first on Cambridge Associates.

In recent weeks, UK assets have not been especially sensitive to Westminster drama in isolation; rather, gilts and sterling have been driven more by growth concerns, energy prices, and resultant expectations for Bank of England (BOE) policy. Because Starmer was already widely expected to lose any leadership contest, his resignation is unlikely on its own to trigger a large repricing. The bigger question is whether Burnham has the capability to catalyse a turnaround in the weak UK growth outlook without further deteriorating the country’s fiscal balance.

Burnham is generally seen as more comfortable than Starmer with an activist domestic agenda, particularly around housing, regional investment, infrastructure, and industrial policy. Burnham’s natural inclination is also to spend more, but that would need to be financed primarily by higher taxes, given he has expressed his commitment to abiding by current fiscal rules. Such a redistributive goal could shift the tone of UK policy debate and create more differentiation across domestically exposed sectors. However, it seems inevitable that any new prime minister would remain bounded by the same constraints that have challenged predecessors: limited fiscal room, high borrowing needs, and the prospect of strong market discipline. In other words, a Burnham premiership could change policy emphasis more easily than it changes the underlying UK macro constraints or direction.

This point is especially important in the current environment. We recently argued that markets may be overestimating how much the BOE will need to tighten in response to the energy shock. Indeed, the tentative agreement between the United States and Iran to end the war may eventually put downward pressure on energy prices. In any case, for the United Kingdom, higher energy prices look more likely to weaken growth than to generate broad, persistent inflation pressure. Labour market conditions have softened, wage growth is becoming less threatening, and second-round inflation risks are less likely to emerge as a result. That suggests the main macro story for UK markets remains one of weaker activity and potentially less BOE tightening than currently priced.

If Burnham were to raise doubts about fiscal discipline, gilt yields could come under renewed pressure and sterling could weaken. Absent a clear signal of fiscal slippage, we believe UK bonds still appear cheap compared to the most likely macro paths. This continues to support the case for UK gilts on a relative value basis versus global government bonds for UK-based investors. In equities, any impact is likely to be more sector-specific, with domestic and regulated industries more exposed to changes in policy tone than large multinational businesses.

For investors then, Starmer’s resignation is best understood as the expected formalisation of a transition rather than the start of a new market regime. The key question now is whether a Burnham government would risk undermining the United Kingdom’s budgetary credibility by materially loosening fiscal policy to pursue his economic agenda. We doubt the new government will be so bold, and is unlikely to seriously test the market’s willingness to impose fiscal discipline. Instead, macro and geopolitical forces still look more important than political considerations for the direction of UK assets.

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 Starmer’s Resignation as UK PM Sees Focus Shift to Burnham’s Fiscal Stance appeared first on Cambridge Associates.