Morgan Stanley: Open-Weight Models May Boost AI Compute Demand via Jevons Paradox

Morgan Stanley argues open-weight models lower inference costs, potentially triggering a Jevons Paradox that increases total compute, energy, and infrastructure demand despite per-unit savings.

Woofun AI reports that Morgan Stanley’s latest analysis suggests open-weight AI models may not reduce overall compute demand. The firm highlights a potential "Jevons Paradox", where decreased inference costs accelerate adoption across more tasks, thereby increasing aggregate requirements for tokens, processing power, electricity, and infrastructure. While open weights do not eliminate expenses such as GPU hardware, cloud services, and security, the report maintains that companies like Nvidia are positioned to benefit regardless of model openness levels.