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.

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.

Their discussion spans both the need to upgrade existing infrastructure including roads, rail and airports and the investment opportunity in the infrastructure of tomorrow, from EV charging and microgrids to waste and water systems.

Josh explains that sustainable real assets can serve a range of portfolio objectives. For some investors, core infrastructure and operational assets such as solar plants can provide steady, income-generating exposure. For others, assets like agriculture and timber can offer diversification alongside public equities and fixed income. And for clients seeking more growth-oriented or impact-driven opportunities, backing newer technologies and greenfield projects may offer greater upside, albeit with higher risk.

Anne also discusses how manager selection is evolving, with energy security, AI and geopolitics reshaping the investment landscape. Together, they reflect on a maturing opportunity set that increasingly allows investors to align portfolio construction with both financial goals and broader sustainability priorities.

Watch the video below to hear Josh and Anne’s insights on investment opportunities within sustainable real assets:

Sustainable and impact investing at Cambridge Associates focuses on helping clients invest in ways that support positive social and environmental outcomes alongside financial returns. Sustainable Investing in Focus is designed to make these topics accessible to everyone by explaining key concepts in a clear and simple way. By sharing practical examples and insights, the series helps viewers understand how sustainable investing works, why it matters and how it’s changing.

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 Sustainable Investing in Focus: The Role of Infrastructure appeared first on Cambridge Associates.

The implications for public equity indexes are meaningful. Together, these companies are expected to list at a combined valuation approaching $4 trillion, more than the total amount raised in all IPOs during the entire dot-com era. While the initial free float for each is likely to be far smaller, ² index requirements have either already been waived, or are likely to be waived, so these firms could enter major indexes relatively quickly. Over time, as floats and index weights increase, OpenAI and Anthropic would further expand the large weight of technology in public benchmarks, while SpaceX could blur traditional sector lines across industrials, communications, and technology. Their addition could also make expensive parts of the market look richer still. None of the three companies are yet profitable, and SpaceX’s targeted $1.75 trillion valuation would equate to roughly 100x its 2025 revenues.

Public market investors will gain access to the leading artificial intelligence (AI) franchises and the dominant private space and satellite communications platform. But by the time these companies list, much of the earliest and most explosive upside may already have accrued to private investors. That points to how the financing model for innovative companies has changed. Previous generations of high-growth firms often went public relatively early to fund expansion; today’s largest private companies can remain private much longer because they have access to enormous pools of capital. OpenAI raised more than $120 billion in a private round earlier this year, and Anthropic is reportedly raising about $30 billion privately. As a result, these IPOs are not just about raising capital but also about providing liquidity, establishing transparent price discovery, and broadening the shareholder base. Public markets are no longer the first engine of scale for companies like these, but they remain the main venue for liquidity, governance visibility, and wide ownership.

These offerings could affect the broader IPO market by drawing capital away from other new issues. While Anthropic has not yet filed, SpaceX and OpenAI are each rumored to be seeking at least $60 billion in IPO proceeds, more than double the previous US record set by Alibaba in 2014. In any issuance window, investors have finite risk budgets, portfolio capacity, and attention. Offerings of this size could dominate the calendar and lead investors to fund participation by trimming allocations elsewhere. Even so, Goldman Sachs expects total equity issuance in 2026 to be around $600 billion, including IPOs, which would still amount to less than 1% of US equity market capitalization. That suggests the broader market should absorb these deals without major dislocation, even if they temporarily crowd out smaller offerings.

For venture investors, the implications are more nuanced. Successful IPOs could boost fund-level marks and eventually help convert paper gains into realized distributions, though liquidity may be more gradual because initial floats are small and, at least in SpaceX’s case, tiered lock-ups would make some future sales partly dependent on stock performance. Because funding in these companies has been so concentrated, their IPOs could further widen the gap between top- and bottom-performing venture funds, reinforcing the importance of manager selection. They may also influence future capital deployment by requiring some funds to cast a wider net. Five companies, including OpenAI and Anthropic, accounted for more than 80% of US venture funding in first quarter 2026. Subject to tax and cost considerations, some investors may wish to consider hedging arrangements for what may end up being outsized single positions. For taxable investors specifically, there may be additional options to diversify risk and offset or defer taxes, including direct indexing or extension strategies.

Overall, these mega-IPOs matter less because of the immediate size of their public floats than because of what they signal about valuation, liquidity, and capital formation. Their small initial floats should limit near-term market impact, but their rich valuations raise the risk that public investors gain access only after much of the upside has been captured privately. And while the offerings are likely to be digested by the overall market, their prominence may crowd out other issuers in the near term. For venture investors, that creates both tailwinds and headwinds: stronger marks, realizations, and distributions for the best-positioned funds, but also greater concentration risk and a tougher environment for other portfolio companies seeking to go public.

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.
2. For example, SpaceX is expected to raise less than 5% of a targeted $1.75 trillion market cap.

The post Do Mega-IPOs From Companies Like SpaceX, OpenAI, and Anthropic Mark a Changing Relationship Between Public and Private Markets? appeared first on Cambridge Associates.

In this presentation from the Investment Leaders Exchange, Kevin Rosenbaum, Head of Global Capital Markets Research & Investment Communications at Cambridge Associates, outlines five forward-looking views that may have important implications for long-term portfolio construction. Kevin examines why diversification may matter more in a world of higher geopolitical risk and lower expected equity returns, why the U.S. dollar may weaken from here, and why ex-U.S. equities could outperform U.S. markets over the next cycle.

The presentation also highlights the opportunity for hedge funds to add more value in a more dispersed market environment and explores how AI may create both investment opportunity and misallocation risk across public and private markets. Together, these views frame a broader shift in how investors may think about return sources, regional allocations, active strategies, and technology exposure in the years ahead.

Click the download button to access the presentation.

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.
2. For example, SpaceX is expected to raise less than 5% of a targeted $1.75 trillion market cap.

The post Five Views That Matter for the Next Five Years | Presentation appeared first on Cambridge Associates.

Those concerns have grown in Trump’s second term amid repeated calls for lower rates and efforts to remove Fed officials. Even so, we see limited risk of a meaningful erosion in Fed independence. Legal and institutional safeguards still constrain political influence, and policy should remain driven mainly by inflation, labor market, and growth data. Continuity on the current board, whose members have a long record of voting independently, further limits tail risk, especially with Powell expected to temporarily remain a governor after his term as chair ends.

Warsh’s appointment is unlikely to drive a major near-term policy shift. In Senate testimony, he said he is “not pre-committed” to any course of action, and the Iran War has reinforced a wait-and-see stance by adding uncertainty around energy prices and inflation. Markets have also sharply repriced the Fed outlook for 2026, moving from near certainty of at least one cut and high odds of two to no cuts priced at all, with investors now split between the Fed staying on hold or hiking once. The latest dot plot still points to an easing bias, with two cuts penciled in through the end of 2027, but three dissents highlighted growing disagreement over the path forward and the chair’s role in forging consensus.

The bigger question is how far Warsh reshapes Fed strategy over time. He has called for changes to forward guidance, balance sheet policy, and the inflation framework. Some of these views diverge from the Fed’s current direction, particularly as policymakers appear inclined to slow and eventually end quantitative tightening as reserve balances approach ample levels, in part to reduce the risk of renewed funding-market stress, as seen in 2018–19. Warsh has also pointed to more stable inflation measures and artificial intelligence–driven productivity gains as reasons price pressures may prove less persistent than headline data suggest. Still, any major shift would require broad committee support and depend on the economy he inherits.

Market reaction so far has been limited, as the appointment has been overshadowed by broader macroeconomic and geopolitical developments. The bigger risk is not an outright loss of Fed independence, but that investors begin to demand a higher premium for policy uncertainty or price in greater tolerance for inflation at the margin. Over time, that could lift inflation expectations modestly, steepen the yield curve, and weigh on the US dollar, strengthening the case for diversifying away from concentrated US dollar and equity exposure.

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.
2. For example, SpaceX is expected to raise less than 5% of a targeted $1.75 trillion market cap.

The post The Economy, Not Kevin Warsh, Will Drive Fed Policy appeared first on Cambridge Associates.

Betting markets see Starmer as being very likely (between 70% and 80% odds of being gone by year end) to lose any such leadership contest. However, any successor would likely still have limited political capital to pursue regulatory, public sector, or welfare reforms that could materially improve growth. As such, the weakness in gilts earlier this week reflected expectations that a new Labour Party leader may lean towards some degree of fiscal easing. Expectations differ by possible candidate, with Angela Rayner and Andy Burnham seen as most dovish, while Wes Streeting is viewed as less likely to deviate from current policy. Whoever wins, one indication of their fiscal intent will be whether current Chancellor Rachel Reeves retains her post, given she is now seen as a defender of current fiscal rules. The timing of any resolution remains uncertain. If Starmer were to resign and support quickly coalesced around a single candidate, the process could conclude relatively swiftly. A multi-candidate contest, however, could run until September, ahead of the Labour Party conference.

Certainly, the longer the contest drags on, the more disruptive it will be for the economy and markets. The uncertainty by itself may be enough to keep some upward pressure on UK bond yields. Nonetheless, we do not think that these risks materially alter the bigger-picture relative value proposition that has emerged in gilts for UK-based investors. In the first instance, substantial fiscal easing looks unlikely, in part because of the discipline the market has imposed on the government since the Truss/Kwarteng budget. Any major fiscal changes are more likely to be redistributive than expansionary. Even prior to this episode and the current energy price spike, gilts were trading materially cheap in our fair-value model based on economic fundamentals, with a much greater risk premium priced in than peers. Furthermore, as has been the case over the past two months, energy price dynamics will continue to dominate the short-run direction of travel for yields. We expect weaker domestic activity and labour market dynamics to limit the extent of inflationary pressures broadening beyond energy and food, which takes some hiking pressure off the BOE. While a decisive shift towards fiscal easing, such as abandoning current fiscal rules, is a risk, that is not our base case. Instead, we continue to expect gilts to outperform peers over a three-year horizon.

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.
2. For example, SpaceX is expected to raise less than 5% of a targeted $1.75 trillion market cap.

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That concentration risk is visible in the structure of the global equity market. US equities now account for roughly 64% of the MSCI All Country World Index, up from 42% in 2010, while the top ten US companies make up about 25% of the benchmark. Information technology holds a peak 37% share of the S&P 500, above late-1990s levels. These figures show that market leadership remains unusually concentrated, even as leadership has spread to a wider set of AI-linked beneficiaries.

The earliest winners, semiconductors and hyperscalers, were joined in 2024 by memory producers, data center providers, digital infrastructure firms, electrical equipment manufacturers, industrial companies tied to power build-out, and some utilities. Recent results underscore this expansion. Both Samsung and SK Hynix benefited from rising demand for high-bandwidth memory and related AI-linked memory products in first quarter 2026, and electrical equipment suppliers reported strong order growth linked in part to data center demand. Utilities and power-related firms have also participated as investors seek exposure to AI-driven electricity demand. AI remains a capital-intensive build-out that ties a growing share of market leadership to the same investment cycle.

The hyperscalers remain central to that cycle and may be more vulnerable than markets assume. Markets have not fully rerated hyperscalers for a business model that is becoming far more capital intensive. Combined capex for Alphabet, Amazon, Meta, Microsoft, and Oracle is estimated at roughly $760 billion in 2026, with cumulative property, plant, and equipment (PP&E) potentially approaching $2 trillion by 2030. On a five-year depreciation schedule, that translates to about $400 billion of annual depreciation expense, roughly equal to their combined 2025 profits. Markets may be underestimating the earnings growth and monetization required to justify such capital intensity, particularly in a market segment where enthusiasm and fear of missing out have often pushed equity prices ahead of fundamentals. Leadership that depends on companies becoming more asset-heavy, financing-dependent, and execution-sensitive is less secure than it appears.

If AI economics disappoint or investment slows, the impact could ripple across multiple market segments that now appear diversified. Essentially, hyperscaler capex is driving much of the market earnings and price momentum in ways that make the entire market edifice dependent on spending by five hyperscalers. For example, Empirical Research Partners finds that a basket of 48 large-cap stocks benefiting from AI spending has outperformed the broad market by 174 percentage points since the start of 2024. Spanning semiconductors and related equipment, capital equipment, metals, utilities, and tech hardware, the basket has accounted for 42% of market returns over the last 12 months and is expected to contribute nearly half the market’s earnings growth this year. While leadership has broadened in one sense, continued strong performance is dependent on the capital spending of a narrow set of companies. And if their capex should continue at such a frenetic pace, it becomes more challenging for these companies to earn the high returns on invested capital (ROICs) that market valuation multiples demand.

There is also an important distinction within the expanding beneficiary set. Some adjacent beneficiaries, especially at the intersection of AI and electrification, may prove more resilient than pure AI plays. Grid modernization, transmission, electrical equipment, and certain utilities enjoy support from multiple demand drivers, including industrial electrification, energy security, and broader infrastructure needs. AI helps these areas, but it is not their sole source of support, making their return outlook less tightly bound to AI monetization than that of hyperscalers or direct AI plays.

The practical implication is that investors should focus on how many truly distinct drivers of return a portfolio contains. When a single secular story drives equity concentration, capital spending, credit issuance, infrastructure demand, and venture enthusiasm at the same time, the case for diversification becomes stronger, not weaker. Investors need not reject AI, but they should 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. AI has made concentration more diffuse and stealthier, but it has not made it less risky.

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.
2. For example, SpaceX is expected to raise less than 5% of a targeted $1.75 trillion market cap.

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