H1 2026 Market Commentary

As we enter the second half of 2026, the investment landscape remains constructive, but more complex. Economic growth remains positive, corporate earnings are expanding, and the long-term opportunity set tied to artificial intelligence, data infrastructure, automation, energy, and security remains intact. At the same time, markets have become more concentrated, inflation has proven more persistent, and the path of monetary policy has become less certain.

Our view is straightforward: we remain constructive, but selective. The first half of the year rewarded a narrow group of AI infrastructure winners. We believe the next phase of the cycle will require a broader lens, with greater focus on companies that can convert AI investment into revenue growth, productivity gains, margin improvement, and measurable earnings power, while maintaining our focus on quality, durability, pricing power, and free cash flow.

In our Q4 2025 Viewpoint, we emphasized balance: supportive fundamentals, but elevated expectations; positive growth, but less margin for error; attractive opportunities, but a continued need for selectivity, diversification, and downside resilience. That framework remains appropriate today.

The first half of 2026 was defined by the AI infrastructure trade. Semiconductors, memory, hardware, data centers, power, and related infrastructure companies drove a disproportionate share of market returns. According to 3Fourteen Research, semiconductors accounted for 71% of the S&P 500’s year-to-date return through June 22 while representing only 18% of index market capitalization. Their attribution work shows semiconductors contributing 6.88 percentage points of the S&P 500’s 9.80% year-to-date return, with energy contributing 0.61 percentage points and the rest of the index contributing only 2.31 percentage points. Their work also shows that semiconductors and technology hardware accounted for the vast majority of recent margin expansion and more than half of the increase in forward earnings estimates.

Thus far, markets have avoided a 10% correction, although the roughly 7% pullback in April felt more severe. Our portfolios participated in the AI theme, but we did not seek to match the benchmark’s concentrated exposure to the hardware and memory layer of the market. That created a relative headwind during a period when those companies drove a disproportionate share of index returns. We view that as a deliberate portfolio-construction choice rather than a change in our long-term view: we want exposure to the AI cycle, but not dependence on a single phase of it.

We believe AI is a multi-year capital cycle that is likely to evolve across layers: first infrastructure, then platforms, then applications, and ultimately broader enterprise adoption. Hyperscalers are not spending this level of capital without expecting a return. Morgan Stanley Research estimates that nearly $3 trillion of AI-related infrastructure investment will flow through the global economy by 2028, with more than 80% of that spending still ahead. The same research notes that AI adopters delivering measurable results are already seeing cash-flow margin expansion at roughly twice the global average.

That is the key point for portfolios. The first phase of the AI cycle rewarded scarcity in compute, chips, memory, networking, and power. We believe the next phase may increasingly reward companies that can use that infrastructure to create revenue, reduce costs, improve workflows, deepen customer relationships, and expand margins. Our focus remains on quality, durability, pricing power, free cash flow, and companies that can convert AI from a capital-spending story into an earnings story. Goldman Sachs Research similarly argues that attention is beginning to shift beyond infrastructure toward AI platform stocks and productivity beneficiaries, while its software research notes that investors may be applying AI disruption concerns too broadly across software.

The macro backdrop remains positive, but less uniform. The economy is not moving in one clean direction. AI-related capital spending, infrastructure investment, energy demand, and national security priorities continue to support growth. At the same time, housing, lower-income consumers, and other rate-sensitive parts of the economy remain under pressure.

KKR describes this environment as the Divergence Conundrum: certain areas of the economy, including AI, productivity, high-end services, and national security, continue to attract capital and drive growth, while housing, lower-income consumers, old-economy capex, and other rate-sensitive sectors remain constrained. Their conclusion is not that investors should avoid risk, but that they should be more disciplined in how they own it. We agree.

AI is now large enough to influence both growth and inflation. 3Fourteen notes that hyperscaler capex budgets have surged to approximately 2.5% of GDP, making the economy more resilient while also contributing to the reheat that has complicated the Fed’s job. Their conclusion is that AI and Fed policy are the two issues likely to define the second half of 2026. Morgan Stanley frames this similarly, describing AI as a macro variable rather than simply a technology theme. Its research estimates roughly $2.9 trillion of global data-center construction cost alone through 2028 and argues that AI-related investment now looks more like an industrial buildout than speculative technology spending.

Inflation has moderated from its post-pandemic peak, but it has not returned to the pre-2020 regime. Tariffs, energy volatility, supply-chain redundancy, power demand, and geopolitical risks are keeping a firmer floor under prices. As a result, the Fed has less flexibility than investors expected earlier in the year. The market has shifted from expecting a straightforward easing cycle to debating whether policy may need to remain restrictive for longer.

Equity markets remained resilient in the first half of 2026, but leadership was unusually narrow. As noted above, the AI investment cycle continued to dominate market returns, with the strongest performance concentrated in semiconductors, memory chips, hardware, data-center infrastructure, and other beneficiaries of accelerated compute demand.

This has been a powerful and legitimate theme. The buildout of AI infrastructure is real, capital spending remains substantial, and 3Fourteen’s work shows little evidence in GPU availability, token pricing, or data-center backlog that the AI mega-theme is losing steam. At the same time, the degree of semiconductor outperformance argues for discipline. Morgan Stanley’s Global Investment Committee recently noted that semiconductor-linked enthusiasm has become crowded, while the broader AI opportunity may be shifting toward a more selective phase in which investors reassess where durable cash flows actually accrue across the AI value chain.

The question now is where value accrues over the full cycle. We believe the first phase of the AI trade rewarded scarcity in compute. The next phase may place greater emphasis on returns from AI investment. Hyperscalers are not spending hundreds of billions of dollars to build unused capacity; they are trying to defend and expand their most valuable profit pools across cloud, advertising, commerce, productivity software, data, security, developer tools, and enterprise workflows. The companies that can translate AI infrastructure into revenue growth, customer retention, workflow automation, cost savings, and margin expansion should become increasingly important as the cycle matures.

This informs how we think about portfolio construction. We want exposure to the infrastructure layer, but we do not want portfolios to become dependent on a single hardware cycle or a narrow group of memory and semiconductor winners. We want exposure across the full AI stack: infrastructure, hyperscale platforms, software, cybersecurity, digital finance, automation, healthcare innovation, energy and power infrastructure, and high-quality companies using AI to improve productivity. We complement this exposure with dividend growth companies across various sectors. We expect them to accelerate dividend growth by passing along efficiencies from AI implementation.

We are also looking for opportunities outside the AI infrastructure trade. Netflix, which has been down meaningfully year to date, is a useful example of how we think about quality when market leadership becomes overly concentrated. Names like NFLX and other high-quality companies have been used as a source of funds for the blockbuster IPOs this year, including SPCX, OpenAI, and Anthropic. It is not an AI hardware company, but it is a high-quality global franchise with scale, recurring revenue, pricing power, a deep data advantage, and operating leverage. Businesses like this can temporarily lag when market leadership is dominated by a narrow momentum trade. That does not necessarily mean their quality has deteriorated; it may simply mean the market’s attention has narrowed. We view these dislocations as potential setups for future re-rating opportunities, particularly when earnings durability, free cash flow, and renewed investor interest begin to improve.

A re-rating occurs when the market assigns a higher or lower valuation multiple to a company because investors reassess the quality, growth, durability, or risk profile of its earnings. A useful way to think about it is that price follows earnings multiplied by the market’s confidence in those earnings. A stock can move quickly when both sides of that equation improve at the same time.

For example, a business earning $10 per share at 15x earnings is valued at $150. If investors gain confidence that earnings can grow to $11 and the appropriate multiple expands to 18x, the implied value becomes $198, a 32% increase. The business did not need to triple; the market simply reassessed the durability and growth of the cash flows.

The catalysts for a re-rating are usually identifiable: an earnings inflection, margin acceleration, improving free cash flow, evidence that a feared disruption is manageable, a credible new revenue stream, better capital allocation, or a shift from under-ownership to renewed sponsorship. This is why we are willing to own high-quality companies during periods of temporary underperformance. We are evaluating the conditions that could support a future re-rating, not simply reacting to recent price action.

Fixed income continues to play an important role in diversified portfolios, but the nature of that role has evolved. Starting yields remain attractive, and high-quality bonds can provide meaningful income. However, in a world of higher deficits, stickier inflation, and more frequent geopolitical shocks, investors should be careful about relying too heavily on long-duration Treasuries as the only portfolio stabilizer.

KKR highlights that the relationship between stocks and bonds is changing. In prior cycles, long-duration Treasuries often provided a strong hedge when equities declined. In the current regime, that hedging benefit may be less dependable, particularly if equity weakness is tied to inflation, higher rates, or geopolitical shocks rather than a traditional growth scare.

The AI buildout also has implications for credit markets. Morgan Stanley notes that AI’s scale means balance sheets matter again, with secured, unsecured, structured, securitized, public, and private credit all expected to play a role in financing AI-related infrastructure.

We continue to favor high-quality fixed income, including short- and intermediate-duration bonds, investment-grade credit, and tax-aware municipal strategies where appropriate. In credit, spreads are not broadly distressed, so underwriting discipline matters. We believe investors should emphasize structure, collateral, documentation, and downside protection rather than simply reaching for yield.

We also see a growing role for collateral-based cash flows across public and private credit markets. Asset-based finance, real estate credit, infrastructure debt, and other structured solutions may offer differentiated income streams tied to essential activity in the real economy. These exposures do not eliminate risk, but they may provide more defined sources of repayment when supported by strong collateral, seniority, and disciplined underwriting.

The Age of Intelligence remains a central investment theme. Artificial intelligence represents a paradigm shift that is accelerating creative destruction: it is reshaping business models, cost structures, and the sources of competitive advantage. As the cost of intelligence declines and more cognitive work becomes automated, companies built around legacy workflows may face pressure, while businesses with proprietary data, distribution advantages, domain expertise, and the ability to embed AI into products and operations should be better positioned to create revenue growth, productivity gains, and margin expansion.

The first half of 2026 showed how powerful the AI infrastructure phase can be, but we believe the full opportunity is broader. Portfolios should maintain some exposure to the infrastructure layer, while also broadening toward hyperscale platforms, software, application-layer companies, cybersecurity, digital workflows, productivity beneficiaries, and high-quality businesses that can convert AI investment into measurable returns. This aligns with our continued focus on quality, earnings durability, diversification, and downside resilience.

The investment environment remains supportive, but less forgiving. Elevated valuations, sticky inflation, narrow leadership, policy uncertainty, and geopolitical risk argue for humility and balance. We believe the right approach is to remain constructive, but selective: participate in the themes that matter, broaden AI exposure beyond the first phase of infrastructure winners, use private markets to plant capital for future harvests, and continue emphasizing quality, income, diversification, operational value creation, and downside resilience.

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