[Morgan Stanley: Open-weight models reduce AI costs, but 'Jevons Paradox' may drive sustained growth in computing power demand] Morgan Stanley pointed out in its latest report, *'Open-weight Models and Three Future Scenarios,'* that open-weight models do not necessarily weaken AI computing power demand. On the contrary, lower usage costs may accelerate AI adoption, forming a classic 'Jevons Paradox': as the cost of single inference decreases, businesses will apply AI to more tasks, ultimately driving up the total demand for tokens, computing power, electricity, and infrastructure. The report emphasizes that open weights do not equate to being completely free—companies still need to bear costs for GPUs, cloud services, operations, and security, with actual economic viability depending on the application scenario. Morgan Stanley believes that regardless of changes in model openness, companies like NVIDIA are likely to benefit.