AI under pressure, no recession alarm

Why the sell-off is hitting high expectations the hardest, and what signals the bond market is sending

The recent market turmoil is a stress test for AI euphoria. Following the US midterm elections, technology and semiconductor stocks in particular came under pressure. Behind this are two interconnected questions: Will the billions invested in data centers generate sufficient returns? Could new political hurdles delay or increase the cost of expansion?

So far, the US bond market has not signaled a comparably pronounced flight into a recession scenario. The correction therefore points more toward a reassessment of ambitious growth expectations than toward an already broadly priced-in economic downturn.

Market developments in the week through November 6, 2026

• Nasdaq 100: −6.2 percent
• US technology sector: −9.5 percent
• 10-year US Treasury yield: −10 basis points

Source: Bloomberg market overview provided

The sell-off is hitting the AI winners the hardest

The S&P 500 lost around 4.4 percent in yesterday’s trading session. The decline in the technology sector was considerably steeper. The Nasdaq 100 fell 6.2 percent over the week, and the US technology sector dropped 9.5 percent. Losses at chipmakers Nvidia and AMD were even more pronounced. Asian markets also came under pressure, as numerous chip and hardware manufacturers depend on the continued expansion of US computing infrastructure.

While the price decline is broadly visible, its epicenter lies among companies whose valuations depend especially heavily on future earnings from AI expansion. This is what currently distinguishes the move from a classic recession shock.

A sharp price decline is not automatically evidence of weak business performance. For highly valued growth companies, a large part of today’s enterprise value rests on profits expected only years from now. If discount rates rise or assumptions about growth, margins, and long-term returns on capital are lowered, fair value can fall significantly even if revenues and profits continue to grow in the near term.

Concentrated positioning, weakening technical trends, and rising risk aversion may also have amplified the move. The extent to which systematic strategies, derivatives positions, or forced risk reductions contributed to the sell-off cannot be determined without reliable, up-to-date positioning data.

From investment promises to measurable returns

The major US technology companies are investing in chips, data centers, networks, and power supply on an unprecedented scale. Based on current plans and estimates, capital expenditure by Microsoft, Alphabet, Amazon, Meta, and Oracle could reach around 803 billion US dollars in 2026. These figures reflect management plans and estimates, not spending already realized for the full year.

For semiconductor, server, and infrastructure manufacturers, these investments are already translating into revenue today. For the buyers, however, the result is assets that must generate sufficient income over many years to cover acquisition, operation, financing, and economic depreciation. High chip sales are therefore not equivalent to a guaranteed return for the AI ecosystem as a whole.

This shifts the central valuation question. So far, the market has mainly rewarded rising investment budgets and the rapid buildout of computing capacity. Going forward, what is likely to matter more is whether this translates into corresponding revenues, free cash flow, and attractive returns on capital.

This development fits the concept of an S-curve. After a phase of accelerated growth, investment and profits can continue to rise, but at declining growth rates. That would not yet constitute a contraction. For highly valued stocks, however, such a slowdown can still have significant consequences if prices have already priced in continued acceleration.

Parts of the AI ecosystem may therefore well show signs of excess. These include the extrapolation of exceptionally high growth rates, concentrated positioning, and dependence on continuously rising investment. However, this does not mean that artificial intelligence is merely a speculative bubble or that the entire technology sector is overvalued. The leading technology companies are profitable, generate high cash flows, and largely have solid balance sheets. A fundamental technological innovation and the overvaluation of individual companies can coexist.

Productivity: initial effects, but no AI boom yet

US productivity data provide initial evidence that digitalization, automation, and possibly artificial intelligence are boosting economy-wide efficiency. Non-farm output rose by 2.3 percent in the third quarter of 2026, while hours worked increased by only 0.2 percent. Output per hour worked thus rose by 2.1 percent. Nearly all of the output growth was therefore driven by higher labor productivity rather than by additional labor input.

This is economically significant. The US economy is producing more without expanding labor input to a comparable degree. However, the aggregated data do not allow the specific contribution of artificial intelligence to be isolated. Conventional software, automation, higher capital intensity, changes in employment structure, and cyclical factors can also affect productivity.

Moreover, the quarterly trend shows no further acceleration. Productivity growth slowed from 2.8 percent in the first quarter to 2.3 percent in the second and 2.1 percent in the third quarter. The data are more consistent with an early diffusion phase, in which initial efficiency gains become visible, than with an already pronounced AI-driven productivity boom.

Higher productivity is at least easing labor cost pressure. Compensation per hour worked rose by 4.5 percent in the third quarter, while unit labor costs increased by only 2.4 percent. Outside of labor costs, however, pressure remains high. Non-labor payments per unit of output rose by 6.0 percent. The buildout of computing infrastructure could be contributing to this through depreciation, financing, and energy costs. How large this effect actually is cannot be determined from the aggregated data.

AI could therefore already be improving labor productivity while simultaneously driving up capital and infrastructure costs. For companies and investors, what ultimately matters is whether long-term gains in productivity and earnings outweigh the full cost of capital deployed.

Political uncertainty raises the risk

The Democrats’ success in the midterm elections raises the prospect of stronger parliamentary oversight and stricter requirements for the AI industry. Of particular relevance are possible hurdles to building new data centers: permitting processes, power and water consumption, grid connections, and resistance at the state and local level. Such requirements could delay projects and increase their costs.

However, political uncertainty is not yet enacted regulation. Starting in January 2027, a Democratic-controlled Congress will face a Republican president. Federal legislation is therefore likely to remain contested. For individual projects, decisions by states and municipalities could have a more immediate impact.

The elections were thus a plausible catalyst, but not the sole cause of the sell-off. The market is reacting to a higher probability of delays and rising costs, not to already enacted restrictions on AI expansion.

The bond market is not yet signaling a recession

In a broad economic shock, investors would typically not only sell stocks but also expect significantly stronger key rate cuts. This reaction is barely visible so far. Yields on two-year US Treasuries fell by 12 basis points, 10-year yields by 10, and 30-year yields by 6 basis points. The move is noticeable but moderate relative to the equity sell-off. The 10-year US Treasury yield most recently stood at around 4.83 percent.

The futures market also currently shows no sign of imminent monetary easing. For the meeting on December 9, model-based estimates put the probability of a 25 basis point rate hike at around 66 percent. Such probabilities can change quickly. However, the current reading argues against the notion that the market is already pricing in a significant economic downturn or rapid monetary easing (source: Bloomberg).

A broader recession signal would require additional developments: markedly rising credit risk premiums outside the AI sector, a pronounced weakening of the labor market and consumer spending, and widespread downward revisions to earnings expectations in sectors without an AI focus.

Implications for equity allocation

Our tactical allocation at the start of the month (before the price declines) already reflects a reduction in risk. The broad US equity position was scaled back from around 67 to 43 percent. Within the US, selective positions in Quality, Information Technology, Health Care, and High Dividend remain in place. This preserves exposure to structural growth while placing greater emphasis on profitable companies, robust balance sheets, and actual free cash flow generation.

Broader regional diversification across Europe, Japan, Asia ex-Japan, and Latin America reduces reliance on US mega-cap technology. The position in global metals and mining companies adds a real-economy investment component to the portfolio. This diversification does not protect against a global rise in risk aversion, but it does reduce concentration risk should the correction primarily affect highly valued US growth stocks.

 

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