Nvidia's Real Risk: Hardware That Ages Too Fast?

Core Viewpoint - Michael Burry is betting against the AI sector, particularly Nvidia and its chip valuations, viewing the current market as a bubble driven by accounting practices rather than genuine profitability [2][4]. Group 1: Nvidia's Financial Metrics - Nvidia reported third-quarter revenue of $57 billion, reflecting a 62% year-on-year increase [2]. - Major tech companies like Microsoft, Google, and Meta have extended the useful life of their server hardware from approximately four years to nearly six years, which reduces annual expenses and enhances net income [4]. Group 2: Accounting Adjustments and Implications - The accounting strategy of extending depreciation periods raises concerns about the long-term profitability of AI investments, as faster obsolescence could lead to significant write-downs [4][6]. - Nvidia's GPUs, which are critical for AI workloads, may not endure the extended depreciation timeline due to physical breakdowns from thermal cycling, potentially leading to a loss of reported value [5][6]. Group 3: Market Dynamics and Future Risks - Hyperscalers are expected to increase capital expenditures to around $460 billion in the next year, but if they perceive that GPUs have a limited effective lifespan, future capital expenditures may slow down [7]. - The rapid pace of innovation in AI chips could render existing hardware economically worthless, leading to billions in write-downs and impacting the perceived profitability of the AI sector [6][7]. Group 4: Counterarguments and Market Support - Proponents of Nvidia argue that demand for computational power will continue to grow, and older chips can still be repurposed for various applications, thus maintaining their value [8][9]. - The potential for a cascading demand for older GPUs in different sectors could mitigate the risks associated with rapid technological advancements [9]. Group 5: Triggers for Depreciation Adjustments - Key events that could prompt hyperscalers to adjust their depreciation timelines include competitive pressure, auditor scrutiny, and public acknowledgment from CEOs regarding the rapid pace of AI innovation [14][15].