A recent report from Zhongtai Securities Co.,Ltd. suggests that following the latest deep correction in global tech stocks, market focus on AI investment is shifting from "scale worship" to a demand for "value verification." The broker outlines four key variables that will determine the path of the tech stocks' recovery.

The core of the debate centers on what is being called the "AI capital expenditure paradox." The same investment in computing power is seen both as a sign of industry health and as a drain on free cash flow. To dissect this, it is crucial to differentiate between two types of buyers: those with strong cash flow from mature businesses, like Google, and those with high leverage, like Oracle. While Oracle's high-leverage model has historical parallels to the telecom expansion of 2000, the presence of giants like Google, Microsoft, and Amazon in the current buyer base, along with the national security implications of AI, provides a strong counter-argument to fears of a simple bubble burst.

Deleveraging and Earnings Season: A Systemic Shift in AI Investment 'Taste'

The market's core aesthetic for AI investment is undergoing a systemic shift from "scale worship" to "value verification." During the second quarter earnings season, Alphabet and Microsoft recorded opposite stock price reactions despite both increasing capital expenditure. This clearly shows that capital is no longer paying for the scale of investment alone; instead, it demands that every dollar of capital expenditure must correspond to verifiable revenue growth and a clear financial justification. Concurrently, hedge funds have been consistently reducing their holdings in US tech stocks, with sector exposure contracting to near three-year lows. This, combined with the forced liquidation of leveraged single-stock ETFs in South Korea, suggests that the impact from the leverage structure has been concentratedly released, and the market is searching for a new equilibrium.

The 'AI Capital Expenditure Paradox': Why the Market is Questioning AI Demand

The core of the controversy lies in the "capital expenditure paradox." The key to resolving this paradox is distinguishing between two types of buyers: demand backed by the strong cash flow of mature businesses, such as Google, and demand driven by high leverage, as seen with Oracle. Oracle's remaining performance obligations reached $638 billion by the end of fiscal 2026, but its free cash flow has turned negative. Whether Oracle can successfully convert its order book into revenue is a critical variable in judging the sustainability of the current investment cycle. While Oracle's high-leverage model bears some resemblance to the telecom operator expansion of 2000, the presence of mature tech giants like Google, Microsoft, and Amazon in the current buyer structure, along with the national security attributes of AI and the ability to reallocate computing power, constitutes a systemic rebuttal to a simple "bubble burst" analogy.

Key Differences Between the 2000 Dot-Com Bubble and the Current AI Cycle

The severity of the 2000 tech bubble burst was fundamentally driven by the collapse of the financing loop. Telecom operators used IPOs, vendor financing, and high-yield bonds to build network capacity far exceeding actual demand. Once the financing window closed, orders built on "expected demand" instantly lost their foundation. There are similarities today, such as long-term contracts and prepayments, which functionally resemble vendor financing. However, the differences are a systemic rebuttal. Today's buyers are primarily giants like Google and Microsoft with mature business cash flow, not loss-making startups. Computing power has a clear capacity ceiling and reallocation capability, a stark contrast to the vast amount of idle fiber optic capacity in 2000. Furthermore, the national security implications of AI imply a policy backstop. While Oracle serves as a high-leverage case to watch, simply applying Cisco's fate to the entire AI industry ignores the fundamental differences in buyer structure and underestimates the real industrial support behind this cycle's demand.

The Reasoning Arms Race: AI Model Layer's Moat and Sustainability

Revenue growth at the AI model layer is showing significant divergence. Leaders have forecast their first profitable quarter, while chasers are still mired in large losses. More critical than the total figures is the shift in revenue structure towards enterprise-level access. AI is transforming from a technological curiosity into a business necessity. While the competitive advantage of model architecture and algorithms is compressing from "years" to "months," the true stickiness lies in the depth of ecosystem integration. Once an AI Agent is deeply embedded in a company's codebase and business processes, the switching cost decouples from the model's performance lead. This competitive landscape makes it unlikely to produce a single winner. As long as the top few companies continue to chase each other, the demand for computing power from the arms race will not systematically stall due to the poor commercialization of a single company.

Four Key Variables Determining the Post-Correction Trajectory

The path forward for tech stocks requires focusing on four key variables. First, whether the impact of Federal Reserve rate hikes has been fully priced in. Following the dissent at the July FOMC meeting and the abandonment of forward guidance, the market has shifted to a data-driven model, and current rate hike pricing is likely overdone. Second, whether the capability gap between frontier and open-source models can be re-established. Regulatory processes add time costs for closed-source models, objectively providing a window for the open-source camp to catch up. Third, whether the deleveraging process is nearing its end. Hedge fund tech holdings have fallen to near three-year lows, and the concentrated liquidation wave of leveraged ETFs in South Korea has been largely released. Fourth, whether the IPO financing window for model companies can open smoothly. If the listings of OpenAI and Anthropic are not as successful as expected, primary market valuations will face a systemic revaluation.

Risk Factors

Risks include the potential for AI commercialization and the penetration of knowledge worker agents to fall short of expectations; significant discrepancies between the disclosed revenue of model companies and their actual financial results; financing difficulties for high-leverage computing cloud and data center projects; overcapacity in GPUs, storage, and other equipment; higher-than-expected depreciation and asset impairment; rising energy prices, power access constraints, and regulatory hurdles; unexpected delays in the release of frontier models due to regulatory reviews; potential reversals in the Federal Reserve's rate hike expectations; and the possibility of significant volatility in related tech stock prices.