Core Insights - Major companies plan to invest $1 trillion in AI data centers over the next five years, with a focus on depreciation as a key financial consideration [2] - The lifespan of AI GPUs is uncertain, with companies like Google, Oracle, and Microsoft estimating a maximum lifespan of six years, but potentially shorter [2][4] - Investors are concerned about the depreciation period, as longer asset lifespans lead to smaller impacts on profits [2] Depreciation Challenges - AI GPUs are relatively new, with NVIDIA's first AI-specific processor launched around 2018, and the current AI boom starting in late 2022 [4] - NVIDIA's data center revenue surged from $15 billion to $115 billion in the fiscal year ending January 2023 [4] - There is no historical reference for the lifespan of GPUs, making it difficult for companies to estimate depreciation accurately [4][5] Market Reactions - CoreWeave has set a six-year depreciation cycle for GPUs, indicating a data-driven approach to asset valuation [4][5] - Despite high demand for NVIDIA's A100 and H100 chips, CoreWeave's stock fell 16% after earnings guidance was affected by third-party data center developer delays [5][6] - The stock of Oracle has also dropped 34% since reaching a historical high in September [6] Skepticism in the Market - Short-seller Michael Burry has expressed doubts about the longevity of AI chips, suggesting that companies may be overstating their lifespan and underestimating depreciation costs [6] - Burry believes that the actual lifespan of server equipment is around two to three years, which could inflate reported earnings [6] Technological Advancements - AI chips may depreciate within six years due to wear and tear or obsolescence from newer models [8] - NVIDIA's CEO has indicated that older chip models will lose significant value as new models are released [8] - Amazon has shortened the expected lifespan of some servers from six years to five years due to rapid technological advancements [8][9] Strategic Procurement - Microsoft is diversifying its AI chip procurement to avoid over-investment in any single generation of processors [9] - The rapid iteration of technology in the AI sector complicates depreciation estimates, requiring careful financial forecasting [9]