What GPU pricing can tell us about how the AI bubble will pop (www.ft.com)

The piece argues that GPU pricing is a leading indicator for the health of the AI boom: surging prices (and tight supply) reflect speculative demand for training and inference compute, while a sustained drop would signal overcapacity, cooling investor enthusiasm and a re-rating of AI startups. Because modern deep learning is compute‑hungry, access to high-end accelerators — and the margins they enable — has become a choke point. When that choke point loosens (through new silicon, more fabs, better cloud provisioning, or a glut in the secondary market), the scarce‑resource narrative that justified sky‑high valuations will weaken quickly. Technically, several forces can deflate GPU-driven hype: rising availability of accelerators (NVIDIA H100/A100 equivalents and specialized chips), model compression and quantization that cut FLOPS needs, and optimized software stacks that make cheaper hardware viable. That combination reduces capital intensity for AI products, shifting competitive advantage from raw compute to algorithmic efficiency, data quality, and software. For the ML community this means fundraising and hiring cycles will follow hardware economics, commodity pricing will favor pragmatic, efficiency‑focused engineering, and a market shakeout could favor startups and incumbents that optimize cost per useful inference rather than those promising scale alone.