Quick Thoughts: The AI Selloff and Portfolio Implications

Last night saw a sharp selloff in the technology sector, leading to sharp falls in the Nasdaq (-5.5%) and S&P 500 (-4.4%) indexes. Ten-year Treasuries and gold prices rallied, while the US dollar remained broadly stable.

The selloff comes one day after an initial sharp market rally in response to the “blue wave” Democratic Congressional sweep in the US midterm elections, wiping out those gains (and more).

Note that the weakness in the technology and artificial intelligence (AI) segments preceded yesterday’s moves, and that AI stocks had also underperformed the immediate post-election rally.

That implies that investors were already growing concerned about valuations, earnings prospects, and credit conditions in the AI space. The market was therefore vulnerable to perceptions (and some rhetoric, for example from Senator Bernie Sanders) that the Democratic majorities in Congress and across key US states (e.g., Texas or Ohio) may pursue policies that would slow the rollout of AI infrastructure, including data centers.

What Are the Market and Portfolio Implications?

• First, volatility is likely to remain high. As noted, AI skepticism was present before the midterm results. While we remain convinced that AI will be a transformative technology, lifting efficiency, productivity and returns on capital across much of the economy, that does not mean that certain segments (neoclouds, software) are not subject to concerns about financing costs (credit spreads), shifts in investor sentiment, and doubts about the speed with which downstream users of AI can provide the revenues to cover the vast costs of building AI infrastructure. The key takeaway is that while AI is fundamentally viable, shakeouts are also very likely. Volatility is likely to be a recurring phenomenon and may have to be risk managed.
• Second, the backlash in the public against AI is real and will be addressed. Concerns about data centers driving up the costs of electricity or disrupting local communities are shared across the US political spectrum. Those concerns will be taken up by politicians armed with a mandate, and the AI sector may have to face new regulations on building data centers and/or will have to bear higher costs for energy, water and other inputs. Writ large, those changes should not significantly impact the profitability story of the biggest players, however.
• Third, the moves overnight remind investors of how vulnerable portfolios are to AI concentration.
  • AI represents macroeconomic concentration risk. Capital expenditures required to expand the US AI infrastructure platform could top US$9 trillion over the next six years, equivalent to 3.2% of annual US gross domestic product. That means that if AI has a more fundamental setback, it could significantly slow growth, with widespread knock-on effects on profitability, credit risk, expectations for Federal Reserve policy, and the shape of the Treasury yield curve.
  • AI represents equity index concentration risk. Hyperscalers account for approximately 18% of S&P 500 market capitalization, while AI-adjacent companies represent another 28%.¹ Together, these businesses comprise roughly 46% of the market capitalization of the S&P500 Index. Companies associated with AI represent approximately US$21.8 trillion, or 19% of global equity market capitalization.² TSMC, Samsung, and SK Hynix collectively represent approximately 30% of the MSCI Emerging Markets Index and 36% of the MSCI Emerging Markets Asia Index.
  • AI represents credit concentration risk. In US investment-grade and high-yield credit markets, AI-related companies have already issued approximately US$330 billion of new bonds during the first half of 2026. AI-related issuers now account for roughly 16% of the total outstanding investment-grade corporate debt.³ In high-yield markets, approximately US$32 billion of AI-related bonds were issued during the first half of 2026, representing roughly 40% of net new high-yield issuance this year.⁴
  • AI is becoming a larger part of private markets. AI-related companies now account for approximately a quarter of all direct lending. Various estimates suggest that over 15% of total private equity investments are in the AI space.

How to Manage AI Risk

For investors, the issue is risk management. Simply reducing AI holdings reduces longer-term opportunities to profit from secular high returns and is therefore not the best approach, in our view.

Diversification can help. Across asset classes, liquid alternatives (including non-directional strategies in alternative risk premia or hedge funds) and bonds can diversify portfolios. Significantly, during the period of AI weakness of the past few weeks, Treasuries have rallied, evidence that bonds can diversify AI concentration risk as well as the risk of potential economic weakness.

Beyond diversification, we think investors should also consider other complementary approaches to concentration risk management.

• Passive index-tracking equity holdings are exposed to AI concentration. To reduce such exposures, investors can shift to equal-weighted strategies. Or, they can actively select companies that are likely to be downstream beneficiaries of AI adoption. Such businesses may achieve efficiency and profitability gains from AI adoption, but without the equity or debt exposure of AI technology and infrastructure sectors. Healthcare or consumer staples are examples.
• Credit portfolios, meanwhile, can be reallocated to styles that emphasize balance-sheet strength, sustainable cash flows, manageable refinancing requirements, and appropriate duration. Higher-quality issuers may also prove more resilient to higher interest rates. Select local currency emerging debt markets (e.g., in Latin America) also offer the prospect of high real yields, underpinned by economic fundamentals not closely linked to the AI story.