Goldman Sachs trader: The "core bull market logic" has been challenged over the past two weeks.

Goldman Sachs top trader Mark Wilson warns that core narratives of the equity bull market have all been challenged in the past two weeks. Fundamental concerns combined with overly crowded market positioning have triggered the largest "high beta momentum" pullback since the DeepSeek incident.

Wilson states that the three core narratives of the recent stock market bull trend have all faced skepticism:

The sustainability and pace of AI spending are being questioned, especially as credit financing increases while investment returns remain unclear.

Market confidence regarding a Fed rate cut in December and continued dovish policy into 2026 has been shaken, as several Fed officials made divergent comments, intensifying this uncertainty.

The sustainability of economic activity is also under scrutiny, with deteriorating conditions for low-income consumers and weak employment trends raising concerns about a K-shaped recovery and the outlook for 2026.

These worries are resonating with overly optimistic market positioning and retail investor enthusiasm, resulting in crowded trades in aggressive market segments. Data shows hedge fund exposure correlates with the "momentum" factor at the highest level in five years.

In the week before NVIDIA’s earnings report—and as markets began to focus on locking in year-end performance—high beta momentum stocks suffered their biggest drop since the DeepSeek event.

Wilson said that two weeks ago he predicted that if META shares dropped another 10% and Oracle’s credit default swaps continued to widen, the market would need to reassess its commitment to AI capital spending. NVIDIA’s earnings next week will provide a real-time update on AI investment prospects, but current market sentiment, positioning, and stock performance have changed dramatically compared to two weeks ago.

Wilson expects spending signals will be strong enough to drive AI-related stocks higher again. However, the increasing disadvantages of Western countries’ electricity supplies are coming under the spotlight, which could become a key constraining factor in the AI race.

The issue of power supply bottlenecks has been brewing all year, but direct demand for hydrocarbons and possible shortages have yet to become a focal point. Wilson expects this theme will become more important next year, posing a substantial challenge to AI development.

Limits of Historical Comparisons and Credit Risk

Although the market constantly draws parallels between the current tech cycle and history, Wilson believes these comparisons have obvious shortcomings.

One study suggests that the current AI boom is more like the tech craze of 1997-1998 than the bubble stage of 1999-2000, implying the AI investment craze still has significant room for growth.

However, the data from credit markets is worrisome. This year, 29% of US dollar credit issuance is related to AI, a proportion that raises questions about excessive leverage. Although credit comparisons between dark fiber and inference computing are easily disputed, such market concerns are entirely reasonable.

Economic Data and Fed Policy Outlook

Debate about broader economic conditions will continue as government reopening and the end of quantitative tightening make market visibility more complicated for the coming weeks. But compared with earlier pessimistic expectations about consumer weakness and declining Chipotle sales, market expectations for bad news have already been fully priced in.

Goldman’s newly established layoffs tracker shows that corporate labor changes since the government shutdown provide reasonable confidence for another Fed rate cut in December.

Despite increased layoffs, post-earnings sentiment is rebounding strongly in the third quarter, partly thanks to cost control measures.

The K-shaped recovery pattern is becoming increasingly evident, adding complexity to the market outlook. Wilson concludes that year-end market positioning is becoming extremely interesting, and investors must closely monitor the evolution of multiple uncertainties.

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