2026-11-14 22:16

Introduction: Capital expenditure peak reached, market shifts from shovel play to cash flow. Recent bullish and bearish signals in the AI sector have significantly amplified market anxiety.

A cluster of bullish and bearish signals in the AI sector has recently emerged, greatly intensifying market concerns.

Goldman Sachs’ latest 2027 Global Market Outlook report warns that the surge in AI capital expenditure is likely to peak around early 2027, shifting market dynamics from a one-way upward trend to a two-way consolidation phase, and recommends increasing diversification and hedging strategies;

The Federal Reserve explicitly highlighted artificial intelligence in its most recent Financial Stability Report, cautioning about its potential threat to asset valuations and financial stability.

Meanwhile, prominent short seller Michael Burry has made his position clear by doubling his Nasdaq-100 put options while simultaneously re-establishing short positions in semiconductor ETFs, declaring this a “bull trap,” particularly drawing parallels to the dot-com bubble peak in 2000, with Huang Renxun’s departure further deepening his skepticism toward the entire AI supply chain.

01

Specifically, multiple reports point to several hard constraints currently facing the AI narrative: capital expenditure, high interest rates, Democratic regulatory constraints, and trust issues within the supply chain.

These factors are precisely what underlie recent market anxieties.

The Goldman Sachs report indicates that AI investment growth as a share of GDP will peak around early 2027, though it does not imply an outright reversal in capital spending—only a slowdown in the rate of increase.

Stock market pricing inherently focuses on marginal changes; the previous bull run in upstream compute segments was driven by sustained acceleration in capital expenditure growth. Once this momentum stalls, the core support for high valuations will weaken.

According to Goldman Sachs’ estimates, cloud vendors’ capital expenditure has a clear correlation with S&P 500 EPS, contributing 2 percentage points in 2024, 5 points in 2025, peaking at 12 points in 2026, declining to 9 points in 2027, and turning into a 5-point drag on earnings by 2028.

Thus, 2026 represents essentially the final window for capital expenditure to drive earnings growth.

The step-down in earnings growth reflects this shift—S&P earnings expectations have dropped from 36% to 11%, with similar weakening observed across European, Japanese, and Asian markets.

Objectively speaking, 11% growth is not poor by historical standards. The pain lies in the steep decline from 36% to 11%.

Previously, investors benefited from a double-barreled Davyss effect—both earnings and valuation rising together. Going forward, the market is likely to see only gradual earnings support, with persistent downward pressure on valuations, making it nearly impossible to replicate the past two years of effortless gains.

The second unavoidable constraint is the high-interest-rate environment.

A high-rate environment exerts a dual pressure on the AI sector.

AI is a quintessential long-duration asset, with most returns materializing in the distant future. Rising long-end interest rates directly increase discount rates, inherently compressing valuations.

Cloud giants like Microsoft and Google, with deep balance sheets and strong cash flows, are relatively insulated. The most vulnerable are mid-tier GPU cloud service providers such as CoreWeave and Nebius, which are major customers of NVIDIA and rely heavily on large-scale GPU leasing and procurement for rapid expansion—making them highly dependent on external financing.

When interest rates rise, their borrowing costs spike immediately, forcing contraction in expansion plans. Meanwhile, their valuations were priced based on extremely high growth assumptions, leading to significant valuation gaps when reality fails to meet expectations.

The Fed’s warning about circular trading risks also deserves attention.

Technology firms and startups invest in, purchase from, and endorse each other, creating a web of interdependencies. In times of abundant liquidity, these relationships can inflate reported revenues and boost valuations. But when financing tightens, they become a chain of interconnected risk exposures—where cash flow issues at one node can propagate through the network. This isn’t merely a valuation issue but a credit risk concern.

The biggest recent negative catalyst has been the Huang Renxun incident, which has shaken the pricing anchor of the entire chain.

It remains too early to determine the long-term impact on NVIDIA’s products and order book.

Over the past few years, many companies' order forecasts and valuation models have been predicated on NVIDIA’s dominant position. With that anchor now wobbling, everyone must reassess the value of their holdings.

Burry clearly seized this window, but his strategy has evolved.

Previously, he favored direct bets on individual stocks like NVIDIA and Palantir. Now, his focus has shifted to indices and semiconductor ETFs—doubling his Nasdaq-100 put options while simultaneously buying back short positions in VanEck Semiconductor ETFs, with Taiwan Semiconductor (TSMC) and NVIDIA being the heaviest holdings in that ETF.

He compares the Nasdaq-100’s peak in early October to the dot-com bubble top in 2000, shouting “bull trap,” targeting whether NVIDIA’s influence is gradually weakening.

Certainly, his strategy may not be correct—this analogy differs significantly.

In the 2000 bubble, many companies had no cash flow and were burdened with debt; today’s U.S. tech giants have robust balance sheets, and the valuation premium of the five largest tech stocks over others has narrowed to historically low levels, far from the frenzy of the late 1990s.

Moreover, this options bet carries a high barrier: the index must fall more than 20% before expiration to generate profit—requiring precision in timing, magnitude, and direction, none of which can afford error.

An often-overlooked pressure comes from regulatory tightening.

With Democrats in power, the U.S. stance toward AI infrastructure is likely to shift toward regulation. Energy consumption, land use, and power infrastructure for compute clusters and large data centers will face increasingly stringent constraints.

This won’t mean killing AI outright, but it will disrupt the unchecked expansion of compute capacity, lengthen deployment cycles, and raise construction costs.

Looking back at the capital expenditure curve, this variable will further flatten the growth slope. What was once expected to be explosive, continuous growth in compute capacity now faces more approval hurdles, energy caps, and compliance demands—causing the peak to arrive earlier and the realization pace to slow down.

Four layers of pressure stacking up inevitably squeeze the valuation bubble built on optimistic narratives.

02

But we must be clear: the bursting of the AI valuation bubble does not mean the AI industry is in decline—the underlying demand driving AI remains intact.

There’s also a telling signal repeatedly mentioned by Goldman Sachs.

U.S. cloud giants’ forward P/E ratio stands at just 19.7x, below the 20-year historical lower bound and even lower than that of the traditionally defensive consumer staples sector.

What many consider high-growth, high-volatility stocks now trade at valuations beneath those of defensive assets—indicating that pessimistic expectations have already been largely priced in, and not everyone is blindly optimistic.

Long-term regulatory tightening isn’t entirely negative. An end to the uncontrolled compute arms race forces companies to conduct a cost-benefit analysis: Can the invested compute capacity generate stable revenue and profits?

Only when business models prove viable—without endless burning of capital to expand GPU inventory—will the AI story become more credible. Short-term pain leads to long-term purification.

After the bubble deflates, market opportunity structures will shift to a new logic.

Two years ago, the simplest strategy was to go all-in on upstream “shovels”—chips, optical modules, memory—proximity to compute meant higher returns. That logic depended on continuously accelerating capital expenditure and is unlikely to repeat.

The market’s evaluation criteria will shift from “who is expanding” to “who is generating real, tangible profits.”

The first group worth tracking is cloud giants whose valuations have corrected sufficiently. Slowing capital expenditure growth actually means reduced investment and improved free cash flow. With solid balance sheets and diversified revenue streams, they’re transitioning from high-volatility growth plays into defensive assets with growth characteristics—making them increasingly attractive in volatile markets.

The second group comprises truly deployed AI application companies. For years, market focus was overwhelmingly on compute, obscuring the value of applications.

Going forward, the key question will be whether AI can help clients cut costs and increase revenue, and whether there’s sustainable payment and renewal behavior. Only companies that deliver verifiable productivity gains and clear ROI will be able to lead the next phase.

This is exactly what Goldman Sachs refers to as the “succession pipeline”—how smoothly this transition occurs will determine whether this correction results in a soft landing or continued selling pressure.

The third group consists of cyclical sectors already pricing in heavy pessimism, such as South Korea’s memory market. Stock prices already reflect a projected 70% earnings drop by 2027, yet order backlogs extend to 2029, and PB ratios have fallen to historic lows. The implied odds look favorable, but risks remain substantial, with extreme volatility.

Goldman Sachs doesn’t blindly favor Asia; its core concern is excessive concentration—TSMC, Samsung, SK Hynix dominate the space, and if the narrative reverses, drawdowns could be devastating.

Conversely, many names should be actively avoided. Mid-sized GPU cloud providers highly dependent on NVIDIA orders, lacking stable cash flows, and reliant on financing to survive; pure burn-rate startups without product delivery—are the most vulnerable in this round of stress testing.

03

Overall, the current AI narrative indeed faces significant headwinds. Under these market conditions, persisting with the mindset of the past few years—overweighting upstream compute and continuing to pay for “ever-faster growth”—is a dangerous habit prone to losses.

A safer approach is to reduce concentration in single sectors and moderately diversify positions.

The market has now transitioned from a one-sided bull run to a two-way range-bound phase, where hedging and diversified asset allocation will carry greater value than in prior years.

The test this AI narrative faces isn’t whether AI can transform the world—but whether these investments can convert into stable returns. The deflation process will undoubtedly be painful, with prolonged volatility. But after market clearing, only assets capable of withstanding scrutiny and backed by solid cash flows will be poised for the next leg of sustainable, long-term growth.

During this period, staying calm, enduring pressure, and patiently waiting for the dawn is the best course.

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