Investment Rating - The report indicates a cautious outlook on the AI infrastructure investment in the U.S., suggesting a potential mismatch between investment pace and demand [2][20]. Core Insights - Since 2025, the U.S. AI infrastructure has entered a phase of "ultra-high-speed expansion + high-leverage support," with major companies raising approximately USD 93 billion, surpassing the total of the previous three years [2][20]. - The capital expenditure on AI data centers is being revised upward, but the revenue and cash flow from the end market have not yet aligned with this accelerated investment pace, indicating a potential risk of over-investment [2][20]. - The report emphasizes that while the long-term demand for AI as a general-purpose technology is likely to absorb most infrastructure investments, the timing of this demand realization is critical [15][23]. Summary by Sections 1) **Funding Side: Transition from High Profitability to High Capex** - Major tech companies have significantly increased their bond market financing, raising about USD 93 billion since 2025, which is expected to lead to over USD 5 trillion in cumulative capital expenditure on AI-related data centers over the next decade [4][20]. - The shift in funding structure indicates a move from "high profitability + low leverage" to "high Capex + high leverage," with debt financing becoming more prevalent [4][20]. 2) **Short-term Outlook (1-2 years)** - The market shows tolerance for high capital expenditure and rapid leveraging, characterized by front-loaded funding and Capex, while revenue and cash flow lag behind [5][21]. - Early investments are seen as beneficial for securing scarce resources and competitive advantages [5][21]. 3) **Medium-term Outlook (3-5 years)** - If the rollout of high-ARPU scenarios is slower than expected, the earlier intensive investments may lead to pressure on balance sheets, with risks of valuation repricing and asset price corrections [6][22]. - The report warns of potential structural pressures on profitability due to increased price competition and underutilization of resources [6][22]. 4) **Long-term Outlook (5-10 years and beyond)** - The demand for AI is expected to gradually absorb most infrastructure investments, but the mismatch in investment and demand realization could lead to a concentration of returns among a few participants who effectively match investment with demand [7][23]. - The report highlights the importance of companies being able to convert heavy investments into high utilization and stable cash flows to maintain market share and pricing power [7][23]. 5) **Demand Side: Competitive Landscape and Pricing Pressure** - The competitive landscape is characterized by converging differences among AI models, leading to increased price competition and pressure on profit margins [10][11]. - The emergence of low-cost, high-performance models is expected to further compress pricing power for mainstream closed-source models, impacting the overall revenue growth in the AI infrastructure sector [10][11]. 6) **Investment Strategy: Transition from AI Beta to Structural Alpha** - The report suggests that the investment logic in AI-related assets should shift from merely betting on "AI Beta" to focusing on the matching of investment and demand, utilization rates, pricing power, and quality of free cash flow [17]. - The ability to navigate the credit and capital expenditure cycles will be crucial for companies to achieve sustainable returns in the long term [17].