Raisah Rasid
Global Market Strategist
Katrina Chiu
Global Market Strategist
Published:
The structural case for AI remains intact, but index-level exposure concentrates risk in a narrow set of mega-cap names.
In brief
- AI remains a key market theme, but rising index concentration means that broad indices are becoming increasingly directional bets on uninterrupted AI momentum.
- However, market leadership within AI is evolving, shifting from hyperscalers to broader sector beneficiaries like semiconductors, equipment makers, and component suppliers, with end-users in sectors like financials and industrials potentially poised to be the next leg of the opportunity.
- Key risks include over-investment outpacing monetization, alongside regulatory complexity and the potential for earnings misses to trigger broad de-risking. The cost of tokens could also force businesses to ration staff access to AI and the scope of development.
- A broader exposure beyond mega-cap leaders to the wider AI ecosystem and region diversification, as well as private markets, may help in reducing the risks associated with concentrated markets.
Despite the geopolitical headwinds, markets remain sharply focused on artificial intelligence (AI), with U.S. large-cap tech stock performance underpinning robust S&P 500 returns and earnings in recent quarters. While AI disruption may bring future opportunities, investors should be mindful of risks associated with concentrated equity markets and consider geographical diversification and AI-related investment opportunities alongside private market exposure.
Opportunities: Identifying the winners of the evolving AI trend
The initial phase of the AI trend was concentrated in the physical infrastructure buildouts and capital expenditure. With the advent of Generative AI (Gen AI), the structural demand for compute has risen materially. Capital expenditure from the biggest AI hyperscalers is likely to reach USD 700billion this year, up 70% from 2025. Even as the surging AI demand is driving up prices across the supply chain, profit margins of this subset of companies remain steady, as efficiency becomes a priority. Crucially, the recent rally has been supported by strong earnings, with the technology sector still firmly in the driver’s seat and revenues underpinned by cloud sales and agentic AI subscriptions.
As AI drives demand for semiconductor and memory chips, the beneficiaries within the technology sector are evolving, as market leadership moves upstream in the AI supply chain. The year-to-date performance of semiconductors vis-à-vis hyperscalers reinforces the idea that relative winners of the AI trend are evolving. This also benefits parts of Asia's semiconductor supply chain, particularly Korea and Taiwan. Meanwhile, China's comparative advantage in mature semiconductor nodes and its push for domestic self-sufficiency are generating an AI innovation trajectory increasingly independent of the U.S. and broader Asian cycle.
Beyond the technology sector, earnings in other sectors have been strong. Financials have benefited from AI-driven financing demand and capital markets activity, while industrials are buoyed by data center expansion.
We have stressed that AI monetization through broader adoption by consumers and businesses will be key. While AI adoption rates are currently uneven across sectors, there is scope for structured integration with the objective of improving measurable productivity gains. This could partially narrow the margin gap between sectors such as financials, healthcare, industrials and software, which has commanded the highest profit margins in the market.
Risks: When a powerful theme meets fragile market plumbing
The clear risk in today's AI trade is that broad equity benchmarks have quietly become narrow expressions of a single theme. As of June 10, the Magnificent 71 accounts for 34% of the total market value of the S&P 500, turning it into an increasingly directional bet on uninterrupted AI momentum. Asia exhibits a similar dynamic: A handful of South Korean and Taiwanese semiconductor names are the drivers of their local benchmarks. Any revenue miss in technology or communication services could trigger indiscriminate de-risking across the AI-related portfolios. There are also reports that the high token costs are forcing users to set limits on staff access to AI models or the scope of tasks for development. Anticipated initial public offerings (IPOs) of leading AI architects risk further consolidating exposure into a narrow set of high-profile names.
Competitive dynamics are pushing hyperscalers to keep accelerating spending on chips, compute, and power. Free cash flow is expected to begin rising as hyperscaler revenues increase, providing a forward-looking justification for the elevated capex cycle (Exhibit 1). The risk is not that AI demand proves inorganic, but that the pace of investment outruns near-term monetization. If hyperscalers’ earnings disappoint, capital expenditure commitments can be revised quickly, with downstream effects rippling down through the value chain.
The policy environment adds a third layer of complexity that investors may be underpricing at this juncture. Governments are actively building out governance frameworks around AI: the EU through its AI Act and AI Continent Action Plan, and the U.S. through a national legislative framework that addresses societal concerns ranging from child safety and community impact to AI-enabled fraud. These frameworks are not hostile to AI development; however, the expanding body of compliance obligations introduces operational complexity and legal uncertainty that can weigh on deployment timelines, and that cost is not yet fully reflected in valuations.
Investment implications: Participate in AI, but engineer resilience
The structural case for AI remains intact, but index-level exposure concentrates risk in a narrow set of mega-cap names. The most effective approach is to widen the opportunity set through active selection across the broader ecosystem by looking beyond hardware leaders to the infrastructure layer, including data centers, power grids, cooling systems, and enabling equipment, where demand can persist even as leadership rotates. AI adopters in healthcare, financials, and industrials offer productivity-driven margin expansion without the same valuation premium as the largest names. Utilities also benefit from AI-driven electricity demand and grid investment (Exhibit 2). The objective is not to avoid leaders, but to avoid over-reliance on them.
Private markets can complement public AI exposure by offering access to the buildout at earlier stages and in different parts of the capital stack, often with lower correlation to public equities and bonds and less day-to-day mark-to-market volatility. Meanwhile, income-generating exposures can provide ballast against equity drawdowns while keeping portfolios genuinely invested in the AI buildout.