The recent pullback in technology stocks doesn't signal the end of the AI narrative, but it does reveal a market undergoing a profound shift in how it prices growth.

With Nvidia falling for seven consecutive sessions and the Nasdaq under pressure, Rich Privorotsky, head of Goldman Sachs' One-Delta trading desk, attributes the selloff not to a single trigger, but to a confluence of credit market strain, supply chain expectations, and policy headwinds. He cautions that the credit market is now "raising questions that equity markets have largely ignored."

This repricing has already left a clear mark on valuations. The Philadelphia Semiconductor Index's 24-month forward price-to-earnings ratio has compressed from roughly 21-22 times to about 15 times, widening the range of potential outcomes. The central question has also shifted — from "what AI can do" to "who will pay for its operation."

Credit Markets Sound the First Alarm

While equity investors have long remained optimistic about the sustainability of AI infrastructure spending, sentiment in credit markets is quietly shifting.

Privorotsky notes that credit default swap (CDS) spreads for Nvidia and Broadcom have widened considerably, with focus turning to backstop arrangements and off-balance-sheet commitments tied to AI financing. This concern is now rippling through the broader semiconductor sector, becoming a key driver of the current valuation reset.

The logic in credit markets differs fundamentally from equities: the former prioritizes cash flow certainty and debt repayment capacity over long-term growth potential. When CDS spreads widen, it signals that bond investors are beginning to doubt the return path on AI capital expenditure — a signal that often arrives earlier and proves harder to ignore than equity market cues.

Inventory Build-Up Concerns Emerge

Meanwhile, conversations across the semiconductor supply chain are taking a subtle turn, with market participants starting to discuss inventory accumulation risks in certain segments.

Industry data cited by Privorotsky shows that order backlogs for wafer fabrication equipment (WFE) manufacturers have climbed sharply, driven by persistently strong capital expenditure intentions from semiconductor makers. Relevant agencies have raised their global WFE market forecasts: 2026 market size is projected at $150.3 billion, up 36% year-over-year; 2027 at $217.5 billion, up 45%; and 2028 expanding further to $280.9 billion, a 29% increase.

However, while these figures highlight the intensity of AI capital spending, they also carry an underlying risk — as front-end equipment orders continue to pile up, any deviation between end-market demand and expectations could quickly expose supply chain inventory pressure. This is precisely why market dialogue has begun to pivot.

Power Bottlenecks and Policy Friction Slow Construction

Beyond funding and supply chain pressures, the physical rollout of data centers is encountering real-world frictions.

Privorotsky points to power constraints and growing policy resistance around data center construction as sources of uncertainty for both the pace and ultimate scale of AI infrastructure expansion. This tension is also visible at the political level: candidates from both parties are increasingly recognizing that AI's benefits are national, diffuse, and often take years to materialize, while construction costs are local, concentrated, and immediate.

This structural mismatch between delayed benefits and upfront costs is making local governments and communities more cautious toward data center projects, thereby weighing on overall construction timelines.

AI Hardware Demand May Shift Rather Than Disappear

Despite these pressures being real, Privorotsky does not believe the AI investment story is over — but he emphasizes that the range of potential outcomes is widening.

On the hardware demand front, he highlights a potential structural variable: next-generation AI models, exemplified by Safe Superintelligence Inc. (SSI, founded by Ilya Sutskever), are exploring continuous learning architectures based on test-time training (TTT). Unlike traditional pre-training paradigms, these systems allow models to update their parameters in real time during inference, turning each interaction into a genuine learning experience.

If continuous learning technology proves viable, it could reduce reliance on massive pre-training clusters. However, Privorotsky remains skeptical of the "declining total compute demand" thesis. He believes the more likely outcome is a reallocation of compute consumption — shifting from upfront large-scale training toward ongoing inference-side computation, rather than an overall contraction in demand.

For near-term market direction, he notes that catalysts such as Nvidia's earnings, PCE data, and speeches from Federal Reserve officials could provide support. Yet he concedes that the current moment remains slightly premature for a full-scale re-entry. His baseline view: AI valuations will undergo a period of compression, after which the market will resume its chase as earnings estimates roll forward to fiscal 2027-2028 — at which point valuation justification becomes easier to sustain.