Core Insights - The article discusses the significant financial implications of determining the depreciation period for GPUs as major tech companies plan to invest $1 trillion in AI data centers over the next five years [1] - The depreciation period directly affects financial performance, with longer periods allowing companies to spread costs over more years, thus reducing profit impact [1][4] - Concerns about AI spending are reflected in stock price declines for companies like CoreWeave and Oracle, indicating investor skepticism about over-investment in AI [1] Depreciation Challenges - Estimating GPU depreciation is complicated due to a lack of historical usage data, as the first AI processors from NVIDIA were launched around 2018, and the current AI boom began in late 2022 [4] - CoreWeave has adopted a six-year depreciation cycle for its infrastructure, while its CEO emphasizes a data-driven approach to assess GPU lifespan [5] - Market opinions vary, with some suggesting actual GPU lifespan may be as short as two to three years, leading to concerns about inflated earnings projections by major tech firms [5] Technological Pressure - The rapid pace of technological advancement is a key factor in GPU depreciation, with new models potentially rendering older ones obsolete within a short timeframe [6][7] - NVIDIA has shifted to an annual release cycle for new AI chips, increasing the risk of older models losing value quickly [7] - Amazon has reduced the estimated lifespan of some servers from six years to five due to accelerated technological development in AI and machine learning [7] Strategic Responses from Tech Giants - Microsoft is diversifying its AI chip procurement strategy to avoid over-investment in any single generation of processors, learning from NVIDIA's rapid product cycles [8] - Depreciation estimates in fast-evolving industries like technology require careful consideration of various factors, including technological obsolescence and historical lifespan data [8]