CITIC SEC has released a research report stating that the current U.S. AI industry exhibits characteristics of an "inflexible bubble." Its "inflexibility" stems from AI's deep integration into the core of U.S. national strategy and political correctness, enjoying strong policy backing and support from the profitability of tech giants. The "bubble" aspect is reflected in valuations that have already significantly priced in future growth, with massive capital expenditures engaged in a tense race against output efficiency and cash flow generation. The short-term robust reality of AI is being overshadowed by medium-term narratives; a reversal of the bubble narrative requires advancements in model paradigms, breakthroughs in monetization, and the commencement of an interest rate cutting cycle. It is recommended to build a three-tier dynamic asset allocation portfolio when the narrative reverses. The first tier is the "ballast" opportunity represented by internet giants; the second tier is the "picks and shovels" opportunity in the computing power chain; the third tier is the "contrarian" opportunity in the software sector. The main views of CITIC SEC are as follows:

The development of AI has become central to U.S. national strategy and political correctness, receiving strong policy guarantees and fundamental support from major corporations. This constitutes the fundamental source of the current AI boom's "inflexibility." Economic data clearly shows that the proportion of AI-related industries in U.S. real GDP has rapidly increased from 5.35% in Q1 2020 to 7.36% in Q3 2025, with the growth rate still accelerating, indicating it has become an indispensable core pillar of growth. Meanwhile, AI giants, represented by the "Magnificent Seven," continue to account for over 30% of the S&P 500's market capitalization, contributing the majority of the market's growth momentum and forming a "too big to fail" influence in financial markets. More critically, this economic importance has been deeply politicized. The "tech right," represented by figures like Musk and Thiel, has formed a closed loop of "personnel-policy-interests" through massive political donations and direct political participation, binding the current U.S. administration's political survival directly to maintaining the prosperity of the AI industry. To this end, the U.S. government has systematically dismantled previous regulatory frameworks and, through initiatives like the plan to win the AI race, streamlined infrastructure approvals, opened federal lands, and even promoted legislation using federal power to suppress state-level regulations, aiming to remove all obstacles for AI capital expenditure. This elevation of AI competition to a national security narrative akin to a "Digital Age Manhattan Project," combined with the urgent pressure of the 2026 midterm elections, means that for the foreseeable policy cycle, the U.S. government's explicit or implicit "guarantee" for the AI industry possesses strong inherent rigidity, significantly reducing market concerns about short-term fluctuations.

Valuations in the U.S. AI sector are currently at high levels, engaged in a crucial race against future profit growth. The market's rise has a rational profit foundation, and typical signals of systemic overheating have not yet appeared. A core observation is that while valuation levels, represented by the Nasdaq index, are at historically high percentiles, the forward P/E ratio has not soared alongside new index highs but instead shows a pattern of consolidation at high levels. The fundamental reason is the continuous upward revision of corporate profit expectations: the 12-month forward EPS for the Nasdaq index has climbed strongly from around $610 at the end of 2024 to $826. Strong profit growth has effectively "digested" the stock price gains. Furthermore, a key difference from the dot-com bubble era is that current tech giants maintain steady revenue growth, with core cash cow businesses like cloud services and digital advertising providing a solid foundation. Simultaneously, hallmark signs of a bubble peak, such as a frenzy of tech IPOs and large-scale insider selling, have not materialized. However, risks cannot be ignored. Asset valuation metrics like Price-to-Book (PB) and Price-to-Sales (PS) ratios have reached record highs, indicating the market is paying a premium for future growth potential and asset value. Therefore, the market's current health entirely depends on the continued fulfillment of the "growth digesting valuation" narrative. The outcome of this "race" hinges on earnings reports in the coming quarters; any signs of deteriorating return on investment could become a trigger that breaks the current fragile balance.

The continued expansion of the AI industry faces four major constraints from financial, physical, and competitive dimensions. This constitutes the substantive stress test for the "bubble" aspect of the "inflexible bubble." The first constraint is the dangerous "scissors gap" between capital expenditure and output efficiency. Major giants plan to continue increasing capital expenditures, betting on scaling laws. The combined capital expenditure guidance for four major companies for 2026 reaches 640.4 billion yuan, a 55% year-on-year increase. This investment intensity far exceeds current industry revenue; this high-intensity "mortgaging the future" investment relies entirely on an exponential explosion in application-layer revenue in the future. The second constraint is imminent cash flow pressure. Estimates suggest that by 2026, after meeting massive investments, leading companies may face a gap between their free cash flow and commitments to maintain high shareholder returns, forcing the industry to shift from internal funding to reliance on external financing, as evidenced by the rapidly rising industry debt exceeding $150 billion. The third constraint is an unavoidable physical ceiling. Global advanced semiconductor capacity (e.g., TSMC) is nearing short-term bottlenecks, and the U.S. Energy Information Administration (EIA) predicts data center electricity demand could account for 9% of total U.S. electricity consumption by 2030, with the U.S. power grid potentially facing supply shortages starting in 2026. Chip and power shortages together lock in a short-term upper limit for computing power expansion. The fourth constraint is unresolved competition among technological pathways. The gap between OpenAI and Google's Gemini has narrowed, while Google-backed Anthropic has also shone with its models. If Google wins凭借 its complete ecosystem, it would challenge the premium the market is currently paying for "multiple parallel technological paths," potentially triggering a revaluation across the entire industry chain from chips to cloud services. These four constraints are intertwined; a serious disconnect in any one area could escalate structural risk into systemic pressure.

In a narrative-driven "inflexible bubble," alpha generation stems from active timing based on the narrative cycle and scenario-based layered allocation. Given that fundamentals and valuations are likely to be suppressed by grand narratives in the medium term, traditional linear extrapolation investment methods are ineffective. Stock price fluctuations will be primarily driven by the intensity of industry narratives, which ebbs and flows cyclically with events like technological breakthroughs, policy signals, and financing events. Therefore, effective timing involves identifying and utilizing marginal changes in narrative intensity for contrarian operations. Investors should consider buying against the trend when the market becomes overly pessimistic due to events like technological stagnation, downward revisions to capital expenditure guidance, or crises at flagship companies, and remain cautious when narratives become overly optimistic due to model breakthroughs, clearer monetization paths, or improved liquidity. Based on this, CITIC SEC believes the best response is to construct a three-tier dynamic asset allocation portfolio when the bubble narrative reverses. The first tier is the "ballast" opportunity, allocating to internet giants with stable cash flows, which provide downside protection and liquidity support under any scenario. The core of this framework is to abandon betting on a final outcome and instead actively manage uncertainty through strategic layering, thereby capturing the long-term benefits of AI while effectively controlling its unique narrative and fundamental risks. The second tier is the "picks and shovels" opportunity. Regardless of which application ultimately dominates, computing power infrastructure is essential. It benefits from increased AI capital expenditure boosting EPS, and supply chain tightness also brings opportunities for price increases to boost profits. Furthermore, an explosion in AI applications can alleviate concerns about the sustainability of capital expenditure, thereby boosting valuations. The third tier is the "contrarian" opportunity. The market's extreme worry about AI Agents本质上 stems from excessive optimism about AI application prospects combined with concerns about incumbents' interests being eroded. From a medium to long-term perspective, it is expected that models replacing some standardized software functions and software vendors integrating state-of-the-art models to strengthen customer stickiness will proceed in parallel; the core barriers of the industry have not been fundamentally impacted. In this context, it is recommended to seize layout opportunities following deep adjustments in the software sector.

Risk factors include macroeconomic deterioration leading to decreased risk appetite; macroeconomic deterioration leading to decreased user willingness to pay; the Federal Reserve adopting more hawkish policies than expected; risks of持续 tightening policy regulation in the tech sector; U.S. AI companies' debt accumulation pace exceeding expectations; slower-than-expected iteration of foundation models; slower-than-expected commercialization of AI applications; intensification of global geopolitical risks; risks of持续 intensifying industry competition.