Generative AI’s Impact on Computing Power: Lessons from a Bullish Model of Global Demand

By Azeem Azhar, François Candelon, Riccarda Joas, Nathan Warren and David Zuluaga Martínez

Introduction

There has been much anxiety in the last couple of years over the future availability of affordable computing power, the hardware that underpins the modern economy’s digital infrastructure. Such worries are understandable for anyone tracking the rapid advancements in generative artificial intelligence (GenAI). The time it takes to double compute demand to train such models is now faster than Moore’s Law—and poised to accelerate further, as the tech giants continue to bet on scale as the driver of progress in artificial intelligence (AI). The joint Microsoft and OpenAI plan for a $100 billion supercomputer is one recent indication of this trend. Spending on computing power by businesses has climbed alongside the growing compute intensity of AI: 2023 was the year when Google for the first time spent more on computing than people.

While demand for AI workloads continues to rise, the supply…