THE COST OF making further progress in artificial intelligence is becoming as startling as a hallucination by ChatGPT. Demand for the graphics chips known as GPUs needed for large-scale AI training has driven prices of the crucial components through the roof. The race to compete in AI also means that data centers are now consuming worrying amounts of energy. The idea that a broader rethink of computing is needed may be gaining momentum as the industry runs into the difficulty of maintaining Moore’s law, the long-standing prediction that the density of components on chips continues shrinking. “Even if Moore's law wasn't slowing down, you still have a massive problem, because the model sizes that OpenAI and others have been releasing are growing way faster than chip capacity,” says Peter McMahon, a professor at Cornell University who works on novel ways of computing. In other words, we might well need to exploit new ways of computing to keep the AI hype train on track.