The Jevons Paradox Meets Chip Iteration: Revisiting AI Compute Investment
Gartner estimates that by 2030, AI data centers will need approximately $8.5 trillion in revenue to cover their investments, but actual AI-related revenue inflows will reach at most only $3.6 trillion, leaving more than $3.5 trillion in investment exposed to asset impairment risk. Based on Ornn leasing data, a16z points out that the actual rental rates for A100, H100, B200, and H200 are currently 9%, 54%, 111%, and 143% above expected levels, respectively. The author argues that demand growth and impairment of the first wave of infrastructure may occur simultaneously, and that the silicon roadmap determines the true pace of chip depreciation.
Gartner estimates that by 2030, AI data centers will need approximately $8.5 trillion in revenue to cover their investments, but actual AI-related revenue inflows will reach at most only $3.6 trillion, leaving more than $3.5 trillion in investment exposed to asset impairment risk. Based on Ornn leasing data, a16z points out that the actual rental rates for A100, H100, B200, and H200 are currently 9%, 54%, 111%, and 143% above expected levels, respectively. The author argues that demand growth an
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