Investors are increasingly puzzled by a market paradox: AI computing stocks, which should theoretically be insulated from interest rate hikes, have been leading the technology sector's recent declines. This issue has sparked widespread debate, but the answer lies in two distinct layers of market logic that are often conflated.

First, let's examine why AI infrastructure spending remains largely immune to rising rates. The Q2 2026 earnings season provided a clear answer: operating margins for AWS, Google Cloud, and Intelligent Cloud reached 39.4%, 35.6%, and 40.6% respectively, with sequential improvements. The balance sheets of leading cloud providers are equally robust, with Microsoft holding an AAA rating, while the debt-to-EBITDA ratios for Alphabet, Amazon, and Meta range between 0.55 and 0.89 times, well below the 1.0 to 1.5 times downgrade thresholds set by rating agencies. Since the start of 2026, the five hyperscalers have issued approximately $223 billion in new bonds, surpassing the $109 billion total for all of 2025, yet the spreads on 2- to 4-year dollar bonds relative to Treasuries remain narrow, signaling that the market has no concerns about their creditworthiness.

The companies most vulnerable to rate hikes are those that rely on cheap financing to fund cash burn and depend on distant future profitability. In contrast, the key players in AI computing currently possess both profits and order backlogs, making the marginal impact of higher financing costs on their income statements minimal. This situation is analogous to an infrastructure contractor holding ample cash flow—a few percentage points increase in interest rates does not alter the certainty of a project's returns.

So why has the tech sector experienced the steepest declines? The problem lies in pricing logic, not fundamentals. The market is no longer debating whether computing power providers can withstand rate increases, but rather whether the AI investment narrative can sustain its momentum. Previously, the core premise supporting computing stock valuations was the continuous advancement of model capabilities, with the gap between closed-source and open-source models widening, thereby justifying FOMO-driven capital expenditure expansion. Should this premise weaken, AI infrastructure could transition from high-growth technology assets to a pricing model resembling public utilities, inevitably leading to a downward shift in valuation benchmarks.

This explains why recent developments—such as OpenAI's release of GPT-6 Astra focusing on the RSI narrative and Anthropic's introduction of anti-distillation mechanisms—are directly influencing short-term movements in tech stocks. The market is repeatedly testing a single question: can computing power advantages translate into long-term technological moats?

Adding to this is the amplifying effect of shrinking trading volumes. In the first week of September, the average weekly turnover across the entire A-share market stood at 1.97 trillion yuan, a 44.4% decline from the 3.55 trillion yuan recorded at the end of June. In a zero-sum game of capital allocation, funds are forced to make choices; once divergent views emerge regarding the tech sector's long-term narrative, capital shifts toward defensive assets, making the decline appear more concentrated.

In essence, this is not a case of interest rates crushing AI computing stocks, but rather the market using the rate hike window to reassess the future value of the tech sector. The resilience of fundamentals and the correction in valuations are fundamentally two separate matters.