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

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

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

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

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

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

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

The post Will the Iran Conflict Trigger a Pandemic-Style Inflation Spike? appeared first on Cambridge Associates.

The election results represent a resounding win for Takaichi, with the LDP alone winning a two-thirds supermajority in the Lower House. Together with their coalition partner, the Japan Innovation Party (JIP), Takaichi now effectively controls 76% of Lower House seats. While the LDP does not have a majority in both houses, the Lower House supermajority enables the LDP/JIP coalition to override any opposition from the Upper House.

Takaichi secured the election by pledging decisive leadership and a vision for a more self-sufficient and assertive Japan, while also addressing the country’s cost of living crisis. Opinion polls consistently indicate that inflation is the most pressing concern among voters. With the electoral mandate, Takaichi will be able to press ahead with planned reductions in consumption taxes, expand household subsidies, and implement strategic investments and reforms in sectors such as semiconductors, shipbuilding, and AI. Additionally, increased defense spending looks likely. All in all, fiscal spending may increase by 2%–3% of GDP.

While fiscal stimulus may boost near-term growth, which has helped Japanese equities outperform global equities by 6 percentage points this year, increased government spending comes with its own risks. Notably, Japanese bond and currency markets were initially spooked in mid-January following the announcement of the snap election, reflecting concerns about debt burdens, political pressure on the Bank of Japan (BOJ), and the prospect of higher inflation.

Fiscal crisis concerns, while relevant, are overblown. Japan’s debt-to-GDP ratio has been declining in recent years, and interest expense as a percentage of GDP is lower than in other developed countries. Additionally, foreign ownership of JGBs is relatively low, reducing the likelihood of a sudden fiscal crisis or a “Liz Truss moment” similar to what the United Kingdom experienced in 2022. The recent rise in Japanese bond yields has been driven by rising inflation in Japan and reduced bond purchases by the BOJ, which has sought to shrink its balance sheet. With core inflation running close to 3%, real interest rates in Japan are still low, which is partly why the yen remains under pressure.

Tackling cost of living concerns ultimately requires a stronger yen, as a weak yen is partly to blame for inflation pressures. The Japanese government has made it clear that it will intervene if the USD/JPY exchange rate approaches the 160 level. But such a level will be hard to defend in the absence of higher interest rates. Given the election all but guarantees increased fiscal stimulus, the BOJ will need to continue hiking rates, otherwise, it risks a further rise in inflation.

Continued BOJ rate hikes, combined with modest rate cuts by the Federal Reserve, would further narrow the yield gap between Japan and the United States, providing support for the yen. Additionally, higher government bond yields in Japan could prompt the repatriation of some Japanese overseas bond holdings, exerting further upward pressure on the yen.

Overall, we see the election outcome as positive for the Japanese economy and, by extension, the yen. To capitalize on this outlook, we favor strategies that are less sensitive to JPY appreciation. Specifically, we like Japanese small-cap equities, which are a significant component of our current tactical recommendation to overweight developed markets small caps, as well as private equity buyouts and activist strategies. These strategies are well-positioned to benefit from stronger domestic growth and the ongoing momentum in corporate governance reforms and merger & acquisition activity within Japan’s market.

The post Japanese Election Result Should Boost the Economy and Ultimately the Japanese Yen appeared first on Cambridge Associates.

by Thomas O’Mahony

Global ex US equities have outperformed US equities by 4.4 percentage points (ppts) in local currency terms so far in 2025 and by 11.2 ppts in USD terms. We believe that conditions are in place to see that outperformance trend continue in 2026 and we recommend that most investors modestly overweight global ex US equities from US equities. This view is founded on attractive relative valuations, improving regional growth catalysts outside the United States, and rising concentration within US equities.

There are two facets to the valuation proposition of overweighting global ex US equities from US equities, the first of which is the still-elevated valuation of the US dollar. As detailed earlier in this outlook, we expect the dollar to decline further in 2026. Despite some depreciation in 2025, the dollar remains 32% above its median real valuation based on current equity weights. While a declining dollar does provide some earnings uplift for US equities via the translation impact on their non-US earnings, the overall net impact should still be a headwind for the performance of USD-denominated assets when translated into other base currencies.

The second leg of the valuation argument rests on the historically rich relative valuation of US equities. As of the end of November, the cyclically adjusted price-to–cash earnings (CAPCE) ratio of the MSCI US Index was 2.2x greater than that of the MSCI ACWI ex US Index, representing a 50% premium above their long-run median relative valuation. Of course, a portion of this is attributable to the greater weight of more profitable tech stocks in the US index, which justifies a higher valuation. However, the broad valuation point remains valid even when looked at from an equal-sector weighted basis, whereupon the relative CAPCE is 25% higher than its median value. Valuations are powerful predictors of returns in the long run. Though their usefulness in forecasting short-run returns is weaker, they nonetheless succinctly express where pockets of both opportunity and risk may lie.

The global ex US category is not, of course, a monolith, but rather a diverse grouping of countries with distinct drivers. In Europe, while underweights to the high-performing IT and communications services sectors were a headwind to performance, financials—the region’s largest weighting—outperformed every sector of the ACWI. This outperformance was significantly aided by rate cuts delivered in the region and the resultant steepening of yield curves. Nonetheless, a significant valuation discount persists versus the United States. With lending data to both the household and corporate sectors picking up, this outperformance should have further to run in 2026. Indeed, the intention of Germany, Europe’s recent laggard, to materially increase fiscal spending should lift all boats to an extent in 2026, even as certain peripheral economies, such as Spain, have performed strongly.

Meanwhile, in Japan a coordinated push for enhanced corporate governance is ongoing, led by the Tokyo Stock Exchange (TSE). Its initiatives emphasize improving capital efficiency, pushing companies with low price-to-book ratios to disclose credible improvement strategies, and unwinding legacy cross-shareholdings. This has prompted an increase in shareholder returns through share buybacks and dividends. Concurrently, the nation appears to be emerging from its multi-decade disinflationary environment, with a virtuous wage/price dynamic gaining traction. This, alongside a tight labor market, is bolstering nominal wage growth, providing a tailwind for domestic consumption and nominal equity prices. Expectations of further fiscal easing from new Prime Minister Sanae Takaichi should also support these themes.

Emerging markets (EM) economies stand to particularly benefit from a continued decline in the dollar. A weaker greenback generally eases EM debt burdens, creating space for governments to use fiscal policy to support growth. Stronger local currencies also curtail imported inflation, allowing domestic central banks to run less restrictive monetary policy. Emerging markets should also benefit from a likely continued, if gradual, decline in trade tensions.

The earnings per share (EPS) growth of the US equity market is on course to thoroughly outstrip that of most other global equity markets in 2025, aided especially by the growth generated by the Magnificent 7 companies. It is perhaps unsurprising that a wide valuation differential can persist in such an environment. However, if the currently forecasted convergence in EPS growth rates across regions occurs in 2026, valuations outside the United States will look much more appealing by comparison and price pressures will emerge to narrow the value gap. EPS growth in 2025 also highlights the potential vulnerability of US equities to weakness in the tech sector (and tech-adjacent industries), with concentration risk having increased significantly, as discussed earlier in this outlook. As a result, earnings disappointment could result if headwinds impact just a handful of firms. Furthermore, the exposure of US equities to a narrow slate of sectoral drivers, particularly the AI story, increases the idiosyncratic vulnerability of the index to a dampening of enthusiasm toward that theme.

Of course there are risks surrounding this view. In the first instance, it could transpire that the US economy proves more resilient than we expect. Secondly, leading US companies in high-growth industries could maintain strong financial and competitive positions, particularly if optimistic projections regarding AI adoption are validated, attracting further investor capital. Nonetheless, we view the cumulative probability of these scenarios as being less likely than the alternatives. As a result, we expect global ex US equities to outperform US equities in 2026.

Investors should overweight developed markets small-cap equities in 2026

by Sean Duffin

Both US and non-US DM small-cap equities are positioned to outperform their larger-cap counterparts in 2026, supported by a convergence of attractive valuations, solid fundamentals, and favorable macro and policy dynamics—though the relative influence of these factors varies between the two blocs.

The outlook for small-cap equities is shaped by several key macroeconomic trends, most notably the ongoing realignment of the international trade order. The US tariff policy introduced in 2025 has primarily affected trade between the United States and other countries, rather than trade among non-US economies. The United States now runs an average effective tariff rate of 17%, compared with a rate around 2% at the start of 2025. With these barriers in place, small-cap companies in non-US markets may be better positioned to grow earnings, as their limited reliance on US consumers makes them less vulnerable to the potential negative effects of US tariffs than their large multinational counterparts.

Recent and ongoing policy actions further bolster the case for small-cap equities. In the United States, anticipated policy rate cuts could loosen credit conditions, benefiting small-cap companies that typically carry more debt than large caps. Newly enacted tax legislation—including more favorable interest expense deductions—could also support small-cap earnings. The 2025 One Big Beautiful Bill Act reverts the interest expense deduction calculation to EBITDA for all companies subject to the interest limitation rule, allowing most US small caps to deduct more interest expense. While the bill also raises the small business exemption threshold, this exemption applies only to the very smallest firms and does not affect the majority of US small-cap stocks, which are much larger by revenue. Efforts to revive domestic production may also favor small caps, depending on the success of these initiatives.

Outside the United States, recent policy initiatives also look supportive. In Europe, fiscal stimulus measures—such as those passed into law by Germany earlier in 2025—are expected to boost domestic demand and support smaller companies. Japan, which has a sizable number of small-cap companies, is implementing major industrial policy initiatives, including the Green Transformation plan and strategic support for semiconductors and supply chain resilience, and could also benefit from its shift to a more pro-business leadership regime.

Small-cap equities in both the United States and developed markets outside the United States are also trading at multi-decade discounts relative to mid- and large-cap peers, based on normalized price-earnings ratios. Despite these steep discounts, small-cap companies have not experienced the kind of fundamental deterioration that would warrant such low valuations. In fact, small caps have delivered resilient earnings growth compared to large caps, particularly outside the United States. Looking ahead, consensus estimates point to a meaningful acceleration in small-cap earnings growth across regions in 2026 and 2027, outpacing their larger-cap counterparts. This robust outlook suggests that current valuation discounts are not justified by fundamentals.

Over the past 25 years, in aggregate, DM small caps have delivered an average annual excess return of 1.7 ppts over large caps, primarily driven by strong performance from the end of the tech bubble in 1999 through 2011. This period was marked by robust performance in the industrials, materials, and financials sectors. After a prolonged era of mega-cap tech dominance, investor appetite could very well broaden. Small caps’ greater representation in sectors, such as industrials and materials, positions them to benefit from trends like reshoring, supply chain diversification, and industrial policy—particularly in Europe and Asia. The higher domestic revenue exposure of small caps, which previously insulated them from global trade frictions and currency volatility, remains a relevant advantage amid ongoing geopolitical uncertainty.

Taken together, the outlook for US and non-US DM small-cap equities is compelling. Wide and unjustified valuation discounts, prospects for stronger earnings growth and multiple expansion, supportive macro and policy tailwinds, and favorable sector dynamics all point to significant outperformance potential in the coming year.

Investors should overweight Latin American equities in 2026

by Capital Markets Research

EM equities have performed strongly in 2025 and are on track to outperform developed markets for the first time in five years. While gains have been broad-based, Latin America (LatAm) stands out, delivering a 53% year-to-date return and outpacing other major EM regions. We expect LatAm offers further outperformance potential, supported by deeply discounted equity and currency valuations, solid momentum, and improving macroeconomic conditions.

The broader EM equity outlook is more constructive than in recent years, driven by two key factors: a weakening US dollar and the Fed’s renewed rate-cutting cycle. Although the US economy is slowing and the labor market has softened, a recession is not our base case. Historically, non-recessionary rate-cutting cycles have provided a favorable backdrop for EM stocks. This environment supports our recommendation to overweight global ex US equities (including emerging markets) relative to the United States, with a particular preference for LatAm within emerging markets.

LatAm has been underappreciated for many years, with absolute valuations near 20-year lows. Relative to broader EM equities, LatAm now trades at a near record 51% discount, largely due to a sharp divergence from Asia, where valuations have climbed significantly. This de-rating in LatAm reflects factors such as currency depreciation, commodity price weakness, slower economic growth, and political volatility. However, improvements in these areas could set the stage for stronger performance in LatAm equities.

LatAm currencies are attractively valued, with real exchange rates versus the US dollar 11% below their 20-year median. This provides a potential tailwind as global capital seeks undervalued assets amid a weakening US dollar. Additionally, technological innovation—particularly the buildout of AI infrastructure—is likely to increase demand for raw materials, benefiting commodity exporters. As the most commodity export–oriented region within emerging markets, we believe LatAm stands to gain from this trend.

Regional policy dynamics further support economic activity. While interest rates remain elevated, inflation is moderating toward central bank targets, and leading indicators point to continued cooling. As the US Fed eases monetary policy, LatAm central banks may soon follow, which could further stimulate growth. Looking ahead, major elections in 2026—most notably Brazil’s presidential race—could bolster fiscal policy, as spending typically rises in election years.

Performance momentum also makes the region’s entry point compelling. Relative equity performance momentum has rebounded from oversold levels in late 2024, and since then, LatAm equities have outperformed broader EM equities by 11 ppts. Historically, in the six previous cycles of LatAm outperformance, the region has exceeded emerging markets by a median of 84 ppts cumulatively, with cycles typically lasting about three years. If this marks the start of a new cycle, further upside may lie ahead.

The balance of risks to the earnings outlook continues to favor LatAm. The region is relatively insulated from US trade policy, benefiting from some of the lowest effective US tariff rates, unlike EM Asia, where large trade surpluses have attracted scrutiny from the Trump administration. Additionally, LatAm companies generate a greater share of revenues from markets outside the United States compared to their Asian peers.

The tariff front-running tailwind that boosted global trade in 2025 is expected to fade, with the World Trade Organization projecting global merchandise volume growth to slow to just 0.5% in 2026. This poses a significant downside risk to Asia’s earnings outlook, where analyst expectations for EPS growth of 19% appear elevated. In contrast, the consensus for LatAm is more measured, with analysts forecasting EPS growth of 5%, compared to a 9% average annualized rate over the past decade. Increasing LatAm exposure within a broader EM allocation can help mitigate Asia’s vulnerability to policy-driven headwinds.

Risks remain, including political uncertainty, fiscal and debt pressures, limited exposure to technology and AI, and the potential for weaker remittances if US growth slows more than expected. However, shifts in some of the structural themes that have hampered LatAm equities in recent years suggest that current valuations offer a compelling margin of safety, already reflecting many of these concerns.

The post 2026 Outlook: Public Equity Views appeared first on Cambridge Associates.

The Fed is weighing whether to lower its policy rate from 4.25%–4.50% at its September meeting, which would mark the first cut since December. After reducing rates by 100 bps in the second half of 2024, the Fed paused to assess the impact, as robust growth, a strong labor market, and sticky inflation limited the case for further cuts. In 2025, US growth has slowed—real GDP rose 1.4% in the first half versus 2.5% in 2024—while core CPI remained elevated at 3.1% in August and higher tariffs threaten to add to inflation. In July, the Fed held rates steady, citing inflation risks. However, newly revised data revealed a much softer labor market—the three-month average pace of job growth fell to 29,000 in August, down from 209,000 when the Fed last cut rates. This shift has increased the likelihood of a rate cut this month.

Looking ahead, markets expect a swift path for policy easing, with 75 bps of total cuts by year end and another 75 bps in 2026. This contrasts sharply with the Fed’s June projections, which indicated three total cuts through 2026. While the Fed’s outlook may have shifted as inflation and employment risks have evolved, market pricing remains notably more aggressive. Based on the Taylor rule, ¹ the current policy rate is only modestly restrictive. For the Fed to deliver the market’s expected cuts, core inflation would likely need to fall below 2% or unemployment rise above 5%—neither outcome appears likely. There is also considerable uncertainty about how restrictive policy truly is, given the resilience of the US economy. Separately, the Fed’s updated long-term framework signals a more proactive approach to fighting inflation, reflecting lessons from the 2021–22 period when policy lagged. All of this suggests the Fed will be cautious in its approach to easing.

While we expect a gradual easing cycle, a sharper downturn in growth or a significant erosion of Fed independence could prompt more aggressive rate cuts. Although US recession risk appears low, slowing growth and rising cost pressures from tariffs or inflation could further squeeze corporate profit margins and consumer spending. The secular trend of AI has helped counterbalance cyclical headwinds, but this may not pan out as expected. Fed independence also faces its greatest challenge in decades, with the Trump administration repeatedly calling for rate cuts and openly discussing replacing Chair Jerome Powell before his term ends in May. The recent attempt to fire Fed Governor Lisa Cook over alleged mortgage fraud is unprecedented. While legal and policy barriers make it difficult for the president to remove a Fed governor or directly influence policy, heightened political pressure alone tends to result in lower rates and higher inflation over time.

Still, we expect US economic growth to remain positive and the Fed to maintain its independence in setting policy. The Fed should be able to lower rates, but likely less than markets anticipate. In this environment, front-end Treasury yields may decline, while long-end yields could stay elevated as inflation, fiscal, and policy uncertainty keep term premiums high. Narrowing short-term interest rate and growth differentials between the United States and other major economies will likely further weaken the US dollar, which has already fallen this year but remains overvalued. Markets have mostly shrugged off political attacks on the Fed, but any significant erosion of its independence—though not our expectation—would likely compound these pressures, steepening the yield curve and adding to dollar weakness.

Given these dynamics, investors should temper expectations for a rapid Fed cutting cycle. We recommend maintaining a neutral duration stance versus policy and modestly tilting toward non-US assets, such as unhedged developed markets ex US government bonds or global ex US equities, which stand to benefit from a weaker dollar.

Footnotes

1. The Taylor rule is an equation that prescribes a value for the federal funds rate based on inflation and the output gap.

The post Is the Projected Path of Fed Easing Too Aggressive? appeared first on Cambridge Associates.

Since early March, DM small-cap stocks outside the United States have delivered strong performance, coinciding with the introduction of new US tariff policies and heightened global trade tensions. The MSCI World ex US Small-Cap Index has returned 23.6% in US dollar terms since March 3, outpacing large caps by 9.6 percentage points (ppts). The greatest contributors to this rally have been the materials, financials, and industrials sectors, which tend to be more domestically or regionally focused and, therefore, less directly exposed to global trade disruptions.

The current global trade environment has been a key catalyst for this outperformance. DM ex US small-cap companies are less vulnerable to the effects of tariffs and trade volatility than their larger, multinational peers, whose operations and earnings are more directly impacted by shifts in trade policy. For example, large DM ex US consumer discretionary and information technology companies have delivered weak performance amid tariff-related uncertainty compared to their smaller-cap counterparts, which are more reliant on local demand and less exposed to cross-border regulatory changes. Multinationals with substantial US dollar revenue may face headwinds if, as we anticipate, the dollar weakens, since this would reduce the profitability of their overseas operations. Notably, DM ex US mid-/large-cap companies derive approximately 22% of their revenue from the United States, compared to just 11% for small-cap companies. As these dynamics persist, we expect small caps to outperform.

Valuations for DM ex US small caps also remain compelling. Currently, these stocks trade at a 10% discount to their mid- and large-cap peers based on normalized earnings ratios. While this discount has narrowed from a trough of 15%, it remains notable, especially since small caps have typically traded at a 16% premium since 1999. This valuation gap suggests that small caps are still attractively priced relative to larger companies, offering investors room for multiple expansion as market conditions improve. Analysts also expect DM ex US small caps to deliver robust EPS growth—12% in 2025 and 17% in 2026—compared to -1% and 10% for large caps, respectively. Although non-US small caps have lagged over the past five years, their long-term compounded returns have exceeded those of large caps by about 3 ppts annually over the last 25 years. This historical outperformance underscores the potential for small caps to deliver superior returns over longer investment horizons, particularly when entry points are favorable.

Looking ahead, international economic growth is expected to improve over the next few years, providing a favorable backdrop for small-cap equities, which tend to be more sensitive to changes in economic growth. Evolving policy dynamics in regions such as Europe and Asia may provide additional tailwinds. For example, Germany’s recent relaxation of its fiscal constraints and the establishment of a defense fund, as well as Japan’s efforts to reshore manufacturing, are likely to stimulate local economies. DM ex US small-cap indexes are heavily weighted toward industrials and materials—sectors that stand to benefit from increased government investment in infrastructure and protectionist trade policies.

In summary, we recommend investors maintain or adopt tactical exposure to DM ex US small-cap stocks from DM ex US large caps. Their relative insulation from trade disruptions, combined with attractive valuations and improving international economic fundamentals, positions them well for continued outperformance. The market structure of the DM ex US small-cap universe also presents unique opportunities, as this segment is often less efficient than the larger-cap space due to lower analyst coverage, limited institutional ownership, and greater return dispersion. These inefficiencies create opportunities for skilled active managers to identify mispriced securities and generate additional value add beyond that provided by a simple tilt to the market.

Footnotes

1. The Taylor rule is an equation that prescribes a value for the federal funds rate based on inflation and the output gap.

The post Is the Outperformance of DM ex US Small Caps a Lasting Trend? appeared first on Cambridge Associates.

LatAm has performed strongly in 2025, returning 25.0% year-to-date in US dollar terms, compared to 18.4% for EM. Two main drivers underpin this outperformance. First, most LatAm countries run trade deficits or modest surpluses with the United States. As a result, they were less affected by the reciprocal tariff policy unveiled in April. In contrast, Asian countries with large trade surpluses have faced more scrutiny from the Trump administration. Second, LatAm markets are rebounding from steep declines in 2024, when a stronger US dollar and political risks dampened investor appetite. Notably, currency depreciation accounted for roughly two-thirds of last year’s decline in US dollar terms.

Beyond recent returns, other factors also support LatAm’s outlook. LatAm equities trade at a 50% valuation discount to other EM stocks—one of the lowest levels on record. Historically, lower relative valuations have often led to stronger subsequent performance, even over shorter time horizons. LatAm currencies are also cheap, with real exchange rates versus the US dollar roughly 15% below their trailing 20-year median. While equity market momentum has improved this year, further upside potential remains: LatAm outperformed EM by an average of more than 70% cumulative during prior upside cycles, which typically lasted around 2.5 years.

The current policy and geopolitical environment should continue to favor LatAm. US tariff policy poses a significant headwind for Asia, where elevated valuations and earnings growth expectations do not reflect the risk of slowing trade volumes. Global investors appear underweight LatAm, where attractive valuations are likely to garner incremental fund flows as the US exceptionalism narrative fades. Additionally, a softer US dollar, escalating geopolitical tensions, and structural themes—including the energy transition and the buildout of artificial intelligence capabilities globally—suggest potential for sustainably higher commodity prices. Combined with the potential inflationary impact of tariffs, these factors would support LatAm outperformance, as periods of rising commodity prices and inflation have historically benefited the region at the expense of net commodity importers in Asia.

US proposals to impose 50% tariffs on copper and Brazilian imports are likely to prove more bark than bite. Brazilian exports to the United States represent just 2% of GDP, a far cry from Asia’s exposure, while Brazil’s deepening trade ties with other partners are likely to accelerate and help mitigate the impact. Chile, Mexico, and Peru—which are among the world’s top copper producers—supply around 70% of US copper imports, where roughly half of annual copper consumption in the United States is imported. As copper demand tends to be inelastic in the short term, this has raised speculation that LatAm producers could receive a reduced tariff rate, exemptions, or implementation delays, similar to other sector-specific tariff actions. Finally, listed equities in LatAm derive only 10% of revenues from the United States, versus roughly 70% from within the region.

LatAm currencies have the potential to add additional value. LatAm interest rates are elevated relative to major EM peers, placing a floor under any depreciation potential, and leading indicators suggest inflation is set to cool in LatAm in the coming months. While Asian currencies are similarly valued against the US dollar, their upside may prove more limited. Significant appreciation would erode export competitiveness, which is economically more significant for Asia. Additionally, various sectors in the region, such as Taiwanese life insurers, are currently sitting on large losses in foreign bond holdings, and further currency appreciation would exacerbate these strains.

While the outlook is positive, risks in LatAm remain. They include political uncertainty with major elections in 2026, fiscal pressures from budget deficits, and an underweight to technology stocks. However, we believe these risks are reflected in prices presently, with valuations offering a compelling margin of safety. Given our preference for a disciplined approach to risk, the sizing of an overweight should remain modest, but we see strong potential for continued outperformance in LatAm.

Footnotes

1. The Taylor rule is an equation that prescribes a value for the federal funds rate based on inflation and the output gap.

The post Can Latin American Equities Continue Outperforming? appeared first on Cambridge Associates.

The Productivity Puzzle

For much of the postwar era, rising productivity fueled economic expansion. However, since the 2000s, productivity growth has been notably weak across many economies. While the causes of this slowdown are complex and debated, the primary factors include the waning impact of the IT and internet revolution, weak investment and slow diffusion of innovation across firms, and demographic headwinds—particularly aging populations and slower growth in the working-age labor force, which can dampen economic dynamism and slow the adoption of new technologies. Although these are the most significant contributors, other factors, such as regulatory barriers and measurement challenges, have likely also played a role.

Amid these headwinds, AI presents a new avenue for boosting productivity. As highlighted in the first piece in this series, AI adoption rates have matched, or even surpassed, those of previous major technology cycles, underscoring the speed and scale of its integration into the economy. Unlike earlier waves of technological change, AI’s capabilities extend well beyond simple automation, enabling innovation in areas such as data extraction, research synthesis, and complex problem solving. Early evidence from industries like finance, logistics, and healthcare suggests that AI-driven tools are already delivering measurable efficiency gains, though it will likely be at least a couple of years before integration is widespread enough to meaningfully improve productivity growth at the economy-wide level.

These advances, however, are not without challenges. As AI automates a growing range of tasks, concerns about potential job displacement—particularly in routine or highly automatable roles—have come to the fore. While new opportunities are likely to emerge as AI creates demand for new skills and industries, the transition may be disruptive for certain segments of the workforce, highlighting the importance of thoughtful policy responses and workforce reskilling initiatives to ensure broad-based benefits. Against this backdrop, some of the most visible applications of large language models (LLMs) today include:

• Customer Service Chatbots: LLMs power conversational agents that handle customer inquiries, troubleshoot issues, and provide support across digital platforms, reducing wait times and freeing up human agents for more complex tasks.
• Content Generation and Copywriting: Businesses use LLMs to draft and optimize marketing copy, blog posts, and product descriptions, streamlining content creation and enabling rapid experimentation.
• Coding Assistance: LLMs assist software developers by generating code snippets, suggesting improvements, and automating routine coding tasks, accelerating development cycles.
• Document Summarization and Search: LLMs extract key information, summarize lengthy documents, and answer user questions, transforming legal research, contract review, and knowledge management.
• Language Translation and Text Correction: LLMs provide real-time translation and advanced grammar correction, enhancing communication and breaking down language barriers.

The Market’s Reaction

The promise of AI has ignited a surge of investment, with capital flowing into infrastructure, model development, and a wide range of applications. Large US technology companies are leading the way, committing record sums to data centers, specialized hardware, cloud platforms, and the research needed to build increasingly sophisticated models. These investments are essential to support the computational demands of modern AI and enable new applications across industries. The scale of these commitments reflects both the strategic importance of AI and the intense competition for leadership in the next wave of technological innovation. As models become more complex and data-hungry, robust infrastructure and ongoing model development have become defining features of this investment cycle.

This wave of investment is not limited to major technology firms. Venture capital (VC) activity in AI and machine learning (ML) has also reached unprecedented levels. In 2024, VC deal-level investment in AI and ML totaled $143 billion, a dramatic increase from just $59 billion in 2019. This surge is not only a function of larger deal sizes but also a growing number of startups and scale-ups focused on AI-driven solutions across industries. The share of VC dollars allocated to AI and ML deals has risen sharply, from 15% in 2019 to 37% in 2024, underscoring the sector’s growing prominence within the broader innovation landscape. Investors are increasingly viewing AI as a foundational technology with the potential to reshape entire industries, driving a virtuous cycle of capital deployment and technological advancement.

The competitive dynamics of the VC market are also evident in deal terms. Over the past five years, both the median capital investment and median pre-money valuation for AI and ML deals have risen, reflecting heightened investor enthusiasm and the perceived value of AI-driven business models. Notably, these metrics have increased in a comparable manner for all VC deals, suggesting that while AI and ML are attracting premium valuations, the broader VC market has also become more capital-intensive. This environment has enabled AI startups to access the resources needed to scale rapidly, invest in talent, and accelerate product development, further fueling the sector’s momentum.

Geographically, the distribution of AI and ML investment has shifted significantly in recent years. According to PitchBook data, Asia—once a rising destination for VC capital in these fields—saw its share drop to just 11% in 2024, while the United States accounted for a commanding 73%. This change reflects concerns over Chinese state involvement in technology and the impact of Western regulations restricting capital flows into sensitive sectors. As a result, the United States has solidified its role as the global investment destination for AI innovation. However, it is important to note that PitchBook’s data may underrepresent the full scope of Chinese activity, as it is often more comprehensive for fund-based VC investments and may not fully capture direct investments by companies or government-backed initiatives in China. The recent release of advanced Chinese models, such as DeepSeek, underscores that China remains a formidable force in AI research and development, and leadership in the field remains contested.

While recent market attention has centered on tariffs and trade tensions, it is AI innovation and its integration into the economy that may ultimately prove far more transformative in the years ahead. Unprecedented investment by leading technology firms, together with strong VC interest in AI startups, is fueling rapid technological advancement and shaping the next wave of economic growth. While some observers have noted elevated valuations and questioned the durability of current trends, the underlying drivers—namely, the promise of productivity gains and the transformative potential of AI—suggest that investment momentum will persist, even if widespread productivity improvements take time to materialize. In the final piece of this series, we will examine how AI’s transformative potential is reshaping asset allocation opportunities across both private and public markets.

Graham Landrith and Mark Sintetos also contributed to this publication.

Footnotes

1. The Taylor rule is an equation that prescribes a value for the federal funds rate based on inflation and the output gap.

The post Navigating the AI Revolution: Unlocking Productivity with AI Investment appeared first on Cambridge Associates.

Investment Implications Through The Tech Cycle

To navigate the AI investment landscape, it is helpful to segment the market into five archetypes that capture the diverse ways in which companies interact with AI:

1. Creators are the pioneers at the frontier of AI innovation—companies developing foundational models, advanced algorithms, the software development toolchain, and specialized hardware that form the core of the technology.
2. Disruptors create a transformative change that goes beyond integrating technology into an existing process, launching new business models that were unimaginable prior to the technological leap (e.g., Uber or Amazon of the internet era).
3. Enablers provide the essential physical infrastructure that makes AI possible, including semiconductors, data centers, and energy solutions.
4. Adaptors are businesses that integrate AI into their operations, harnessing its power to drive efficiency, unlock new business models, expand their market share, and maintain competitive advantage.
5. Finally, the Disrupted are incumbents whose market share or relevance is threatened by the rise of AI-powered competitors.

Each of these archetypes presents distinct investment opportunities and risks across asset classes.

As discussed in Part 1, where we highlighted past technology cycles, this framework echoes the dynamics of the internet era that launched the information age. During that period, Apple, Google, and Microsoft were among the creators, building the platforms and software that defined the new economy. Amazon emerged as a disrupter, fundamentally changing the retail landscape. With the emergence of cloud computing, software-defined infrastructure was developed to manage or enable compute, storage, and networking through software. Companies like Intel and Cisco served as enablers, providing the chips and networking equipment that powered the digital revolution. Today, as AI ushers in another wave of transformation, understanding where companies sit within this cycle is essential for identifying both risks and opportunities across the investment landscape.

Creators and Disruptors

Venture capital (VC) remains a crucial funding source for innovative start-ups engaged in high-risk research and product development. This dynamic drove previous technology waves, such as the internet, mobile, and cloud computing. However, the AI era presents a different landscape. Unlike the cloud era—where established companies were slow to adapt and start-ups captured early gains—many incumbents are now early AI leaders. These companies are cloud-native and deeply integrated into corporate systems. They leverage their scale and distribution to build AI capabilities internally or accelerate innovation by acquiring or investing in VC-backed AI start-ups. Notable examples include Google’s acquisition of DeepMind (which powered Google Brain and Gemini), Microsoft’s early partnership with OpenAI, and Amazon’s partnership with Anthropic. Hyperscalers’ capital expenditures have been extraordinary and are expected to continue as AI technology advances. Key areas of VC investment include large language models (LLMs), supporting software infrastructure, and “applied AI” applications built on this foundation.

As outlined in Part 2, VC investment in AI has reached record highs, with intense enthusiasm and abundant capital pursuing a limited number of high-quality start-ups. Adoption rates have surged across many companies (see Part 1), but much of the early revenue is “experimental,” reflecting trial phases rather than sustainable businesses. This momentum has spurred a wave of new company formations and AI strategy announcements, creating significant “AI noise” in the market. Interest is also growing in “physical AI,” where AI intersects with industries such as manufacturing, construction, healthcare, and aerospace and defense. However, all this frenzy has led to inflated valuations, intense competition, and overfunded segments given its relative infancy. Although AI-first companies have seen rapid revenue growth, its durability is uncertain due to the experimental nature of adoption and the lack of strong competitive moats—even companies with $50 million–$100 million in revenue can be overtaken whereas in prior cycles that typically signaled victory. While a few leaders have already created significant value, many AI start-ups are likely to fail due to oversaturation, poor management, and rapid sector evolution.

Historically, major technology shifts often result in commoditization, and it is rarely clear at the onset which companies will ultimately succeed. The winners are typically those that either build on existing technology through innovation or leapfrog older products and services entirely. For instance, Dell Technologies initially dominated the PC market, EMC led in on-premises enterprise data storage before the transition to cloud solutions, and Cisco was the leader in network hardware before the rise of software-defined networking. AI is likely to follow similar patterns, with rapid change and innovation making it difficult to identify long-term leaders. As open-source competition and verticalized alternatives have driven SaaS commoditization, so too will these forces and the broader open-source community drive further innovation and disruption in AI. Despite these uncertainties, we expect long-term VC returns in AI to remain attractive.

Who will be the winning investors? We recommend diversifying across the AI value chain and managing risks through careful position sizing. Investors should prioritize general partners (GPs) with deep sector expertise, particularly at the foundational and network infrastructure levels, and a proven track record of business building. This expertise—whether within specialist or generalist firms—enables better deal flow, talent identification, and assessment of technical merit. Select specialists for investments where technology risk is high, and generalists for broader investment strategies, leveraging the strengths of both. As AI becomes more widespread and many start-ups incorporate it into their products, investment decisions will increasingly focus on how AI is applied rather than on the technology itself. This trend mirrors previous technology cycles, where, as markets matured, investment success depended more on careful selection and curation than on technical expertise. Many GPs focused on AI are relatively new and still gaining investment experience, given the technology’s rapid rise in prominence. Large generalist firms have captured many early AI successes, often partnering with specialists to combine strengths. These generalists offer larger capital pools, enabling them to support AI start-ups through multiple funding rounds, provide customer access, and offer business-building expertise. Their broad go-to-market and business development capabilities help start-ups as they scale.

Enablers

Enablers are the backbone of the AI revolution, providing the physical infrastructure that supports AI’s rapid expansion. The primary beneficiaries to date have been semiconductor manufacturers (especially those producing AI chips), hyperscale data center operators, and the power and utility companies that support this ecosystem. However, the scale and speed of investment in these areas have raised concerns about sustainability, valuations, and the risk of overbuilding—reminiscent of the internet era’s fiber optic boom and bust.

The rise of generative AI and LLMs has driven unprecedented demand for high-performance chips, particularly GPUs and custom AI accelerators. Companies like Nvidia, AMD, and emerging players such as Cerebras have seen orders and backlogs soar. Supply constraints and technological leadership have enabled leading chipmakers to command premium pricing and margins. Dominant players, especially Nvidia (through its CUDA platform), are building integrated hardware-software ecosystems, creating high switching costs and network effects, but also raising antitrust concerns. Valuations remain high, with Nvidia trading at a forward price-to-earnings (P/E) ratio of 32.3, as of June 30, 2025. While this is below 2024 peaks, it remains vulnerable to correction if AI adoption slows, or competition intensifies. As such, consider modest tilts away from expensive public equity mega-cap tech stocks to reduce valuation risk and enhance portfolio diversification.

Data centers are also major beneficiaries, driven by AI, ongoing cloud adoption, and rising data usage. McKinsey estimates data center capacity demand will grow at an annual rate of about 20% through 2030, with generative AI data centers accounting for a small, but growing share of new demand. Investors should partner with infrastructure and real estate managers with specialized development and operating expertise that are well-positioned to benefit from this supply/demand imbalance. However, transaction multiples have risen materially, averaging 25x EBITDA over the last four years according to Infralogic, compared to a 13.5x average for private infrastructure more broadly. This makes careful underwriting essential for attractive returns. Like other AI infrastructure assets, data centers face risk of overbuilding, as well as regulatory and environmental concerns and constraints such as local opposition and permitting delays. These risks can be mitigated by focusing on managers who can develop assets at lower multiples (e.g., low double-digit EBITDA) and sell into a strong market, often with long-term contracts from investment-grade hyperscalers (e.g., Microsoft, Amazon) seeking development partners. In contrast, speculative and remotely located data centers with more limited utility face heightened risks. From a portfolio construction perspective, data centers offer lower expected returns than private investments in innovative AI firms but can provide returns competitive with broad equities (e.g., 15%–20% target gross IRR) with diversification benefits.

Other enablers, such as utilities and grid infrastructure, have also seen increased demand and capital inflows driven by electrification and digitization trends. McKinsey expects global data center capacity demand between 2025 and 2030 to drive investment in power (including generation and transmission) to total between $200 billion (constrained momentum) to $600 billion dollars (accelerated demand), with $300 billion as their baseline for continued momentum. US on-grid electricity demand is expected to increase 2%–3% per year through 2030 up from virtually flat growth over the last decade, with faster growth in Asia (from a lower base) and slower growth in Europe. While difficult to estimate, rapid AI adoption and potential onshoring in the United States could further boost energy demand. Although AI energy efficiency is expected to improve, associated cost reductions may spur broader adoption, likely resulting in net energy demand growth. Investment in essential electricity infrastructure with inelastic demand is critical. Data centers require reliable power, necessitating redundant infrastructure such as back-up generators and batteries.

All enabler segments have strong growth potential, with chips and data centers experiencing the fastest expansion, but they also trade at heightened valuations and have the greatest exposure to overbuilding. Scale, technological edge, strong customer relationships, and specialized expertise are critical for managing these risks.

Adaptors and the Disrupted

Building on the productivity themes from Part 2, growth equity and private equity-backed companies are increasingly using AI to boost revenue and improve margins. As private entities, they have more flexibility to integrate and scale AI across operations, though successful implementation requires careful execution. While many companies are still experimenting, some are already seeing early benefits in product enhancements and margin gains.

Private equity investors are actively assessing both the opportunities and risks AI brings to their portfolio companies and industries. They look for cost savings through automation (e.g., customer support, onboarding, coding) and revenue growth from AI-driven products (e.g., sales planning, demand forecasting). At the same time, they remain cautious about risks, such as commoditization (e.g., graphic design, digital marketing) and increased competition from low-cost automation (e.g., auditing, document preparation, call centers). Technology-focused managers have an edge due to sector expertise, but both specialist and generalist firms are hiring AI talent to support investment teams and portfolios. The full impact of AI will unfold over time as new use cases and broader adoption and understanding of AI technologies and their impact continue to emerge.

Similarly, public companies must adapt to AI or risk disruption. Investors should focus on active management to distinguish winners from losers and to assess price risk, selecting managers with deep sector expertise. Employ long/short and fundamental strategies to manage risk and exploit valuation dislocations. Public investors face the challenge of avoiding overvalued AI leaders while not overlooking lower-priced companies that may lag behind. Many leading public companies are cloud-native and well-positioned for AI, but investors should consider the entire spectrum of innovators and disruptors. Public market valuations for AI-enabled companies have dropped from their late 2021 peak; forward P/E ratios relative to the S&P 500 Index hit a nine-year low earlier this year, and have since rebounded, but remain below recent historical spikes. This environment favors long/short managers that can identify mispriced companies amid the current AI hype.

As outlined in Part 1, we recognize that non-technological factors—particularly regulatory and policy uncertainty—are increasingly shaping both the AI investment landscape and broader societal outcomes. The concept of Responsible AI (RAI) is gaining more attention as generative AI models and systems grow in complexity and become more deeply embedded across industries. RAI frameworks address the development and deployment of LLMs and broader AI applications, emphasizing principles such as fairness, transparency and explainability, accountability, privacy, safety, and security. From an investment perspective, effective governance is inherently complex, intersecting regulatory, ethical, technological, and human considerations. This complexity necessitates cross-disciplinary collaboration and often involves navigating trade-offs and misaligned incentives. As AI adoption accelerates, reported incidents of ethical misuse have increased in recent years. A recent survey found that only 14% of businesses have dedicated AI governance roles, yet 42% reported improved operations and 34% noted increased customer trust due to RAI policies and investments. ² Companies should proactively assess, and address financially material risks associated with neglecting RAI practices, such as regulatory actions or erosion of their societal license to operate, which could result in negative commercial consequences. Governments worldwide are trying to address complex issues like data privacy, algorithmic transparency, antitrust, and national security, and new regulations could significantly impact sector competition. Investors must also monitor regulatory developments closely, as evolving rules and policies will likely influence long-term value creation and competitive differentiation in the rapidly evolving AI sector.

The “AI noise” phenomenon extends beyond private investments. Most technology companies now market themselves as AI-focused, and those that do not, risk appearing outdated. Enterprise software incumbents with high switching costs, complex technology, and strong innovation pipelines may continue to thrive, while agile start-ups can exploit weaknesses and expand from niche solutions into strategic adjacencies, potentially displacing incumbents. For example, it is unclear whether established security firms will lead in AI security or whether nimble start-ups will secure the AI/ML software supply chain. ServiceNow, a leading enterprise software provider, has thus far demonstrated successful AI adoption by leveraging its integrated suite and existing customer base to pivot toward AI-driven solutions. Given the rapid pace of change, both long-only and long/short hedge funds can find alpha by capitalizing on short-term disruptions and mispriced companies. Valuation-based and fundamental short strategies remain relevant, though it can be difficult to short declining businesses that retain temporary relevance or to identify companies prematurely dismissed as AI losers. Investors should consider managers with crossover expertise—spanning both public and private markets—as they are well-positioned to capitalize on rapidly evolving AI developments by spotting trends in private markets before they are reflected in public market valuations, and can continue to invest post IPO.

AI-related risks and opportunities are increasingly influencing credit markets. Credit managers are financing core infrastructure—such as GPUs, data centers, and energy projects—while also supporting the broader AI ecosystem. Several large managers are establishing dedicated asset-backed finance teams and raising capital specifically to pursue these opportunities. Direct lenders, in particular, have significant exposure to technology and business services, which will need to adapt in response to AI advancements.

More broadly, credit managers must evaluate the adaptability of their portfolio holdings. Many software companies—particularly those with high leverage and business models vulnerable to AI automation (e.g., HR, legal, accounting, and other back-office SaaS providers)—face considerable disruption risk. The past decade’s low-rate environment led to aggressive leverage and high valuations, leaving some companies with thin interest coverage and little margin for error. These firms are especially vulnerable if AI-driven disruption erodes their revenue base. Should AI agents automate or disintermediate core functions, revenue models may be cannibalized, and even modest declines in topline revenue could threaten debt service capacity.

Some credit managers are proactively encouraging portfolio companies to adopt AI, aiming to drive efficiencies and mitigate disruption risk. Lenders are increasingly evaluating management’s AI strategy as part of their underwriting process. Companies that successfully integrate AI may improve margins and creditworthiness, while laggards risk being left behind. As disruption accelerates, a wave of distressed opportunities may emerge among over-levered incumbents unable to adapt to AI-driven change. However, the timing of this transition is highly uncertain: some companies may be “slow melting ice cubes,” experiencing gradual market decline, while others may yet adapt successfully.

Investors should select credit managers who proactively assess AI-related opportunities and risks, including overbuilding in data centers and other infrastructure, while proactively managing exposure to incumbents in sectors vulnerable to AI disruption, such as highly leveraged back-office SaaS providers. Credit opportunity managers may be best positioned to benefit from distressed cycles arising from AI-driven disruption, as these managers can capitalize on market dislocations.

Investors should question managers on their approach to AI, both in terms of portfolio company adaptation and exposure to AI-related risks and opportunities, as part of ongoing due diligence.

Conclusion

AI is fundamentally reshaping the investment landscape, presenting both extraordinary opportunities and new risks across asset classes. The technology’s reach extends from the innovators building core capabilities, to the enablers providing critical infrastructure, to the adaptors and disrupted incumbents navigating a rapidly changing environment. Although substantial investment has already driven rapid growth in AI and its supporting infrastructure, we remain in the early stages of this technological shift, which is expected to evolve over the next decade and beyond. In previous technology cycles, the initial investments and returns from foundational innovation were ultimately surpassed by the gains generated by disruptive companies. These disruptors leverage the established or rebuilt technology infrastructure and benefit from network effects as commercial adoption accelerates, enabling them to redefine industries or create entirely new markets and business models. Attractively valued companies that can leverage AI to improve their profitability should also benefit meaningfully.

Investors should strategically seek opportunities to incorporate AI Creators, Disruptors, Enablers, and Adaptors within their portfolios, all the while maintaining a careful watch on potential disruption risks and the possibility of inflated valuations and overbuilding. Investment success in this new era will require investors to combine deep sector expertise, rigorous due diligence, and a willingness to adapt as the technology and its applications evolve. Investors that partner with managers that can distinguish between hype and enduring value, anticipate regulatory shifts, and identify the true drivers of sustainable growth will be best positioned to capture the far-reaching potential of AI in shaping asset allocation for years to come.

Grayson Kirk, Graham Landrith, and Archie Levis also contributed to this publication.

Footnotes

1. The Taylor rule is an equation that prescribes a value for the federal funds rate based on inflation and the output gap.
2. Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, Tobi Walsh, Armin Hamrah, Lapo Santarlasci, Julia Betts Lotufo, Alexandra Rome, Andrew Shi, Sukrut Oak. “The AI Index 2025 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2025 and McKinsey & Company survey 2024.

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