Why Do AI Systems Still Need Central Processors?

Rene Haas says central processors remain the heart of every computing problem, including artificial intelligence. After ChatGPT took off, attention locked onto accelerators, yet no computing problem has been invented that can skip the microprocessor.

As more work moved from training toward inference, training stayed important for recursive learning and reinforcement learning, while inference uses the tokens and the information. Accelerators act like a token factory. Something still has to orchestrate, arbitrate, and decide where the tokens go, then carry them to users. Central processors do that job. Until another mechanism is invented, demand for processors stays large right beside the accelerators that generate the tokens. A computer still follows a von Neumann layout: a central processor, an accelerator such as floating point or a GPU, and memory. The layout has not changed. The same split holds for a data center, an automobile, a robot, a phone, and wearables.