By Neil C. Thompson & Svenja Spanuth

It is a triumph of technology and of economics that our computer chips are so universal - the staggering variety of calculations they can compute make countless applications possible. But, this was not always the case. Computers used to be specialized, doing only narrow sets of calculations. Their rise as a ‘general purpose technology (GPT)’ only happened because of ground-breaking technical advancements by computer scientists like Von Neumann and Turing, and virtuous economics common to general purpose technologies, where product improvement and market growth fuel each other in a mutually reinforcing cycle.

This paper argues that technological and economic forces are now pushing computing in the opposite direction, making computer processors less general-purpose and more specialized. This process has already begun, driven by the slow down in Moore’s Law and the algorithmic success of Deep Learning. This threatens to fragment computing into those applications that get to be in 'fast lane' because special customized chips are developed for them, while other applications get stuck in the 'slow lane' using general-purpose chips whose progress fades.

The rise of general purpose computer chips has been remarkable. So, too, could be its fall. This paper outlines the forces already starting to fragment this general purpose technology.