科技巨头AI竞赛转向表外融资，万亿债务定时炸弹成危机前兆？

Core Viewpoint - Technology companies are increasingly turning to Special Purpose Vehicles (SPVs) and off-balance-sheet financing methods to raise substantial funds for AI development, raising concerns about hidden debts and financial risks [1][3][4]. Group 1: Financing Methods - Major tech firms are utilizing SPVs and joint ventures to place hundreds of billions of dollars in debt off their balance sheets, allowing them to meet massive funding needs without directly impacting their financial status or credit ratings [3][4]. - Meta has secured approximately $600 billion for data center construction, with $300 billion raised through off-balance-sheet transactions facilitated by Morgan Stanley [3][4]. - Elon Musk's xAI is seeking $20 billion through SPVs to lease NVIDIA chips, limiting its financial exposure to a five-year lease agreement [5][6]. Group 2: Historical Context and Risks - The resurgence of off-balance-sheet financing tools is reminiscent of past financial scandals, such as Enron's bankruptcy in 2001, which was linked to hidden debts [8][10]. - Analysts express concerns about the potential for hidden liabilities and the ability to track commitments associated with these financial engineering practices [10][12]. - The rapid obsolescence of technology poses risks, as the effective lifespan of chips may be shorter than anticipated, potentially leading to significant financial implications for companies [11][12]. Group 3: Market Dynamics - The market value and strength of hyperscalers like Meta, valued at $2 trillion, enable them to explore financing options that exceed previous project funding amounts [5]. - The demand for debt financing in the AI sector is significant, with estimates suggesting that the entire AI ecosystem requires approximately $1.5 trillion in external funding, with over $1.15 trillion expected to come from debt [14][15][16]. - Private credit funds are playing a crucial role in this financing landscape, attracting billions from institutional investors seeking high-yield opportunities in AI infrastructure [13].