Custom silicon

Plain English. The chips a lab designs itself instead of buying off the shelf. For most of the boom that meant renting general-purpose GPUs from Nvidia; increasingly the largest players draw their own silicon, tuned to their own models and their own data centres. The umbrella covers in-house accelerators like Google's TPU and fixed-function inference ASICs.

Why it moves money. The target is Nvidia's gross margin. Inference is the entry point because it is the growing majority of compute and the workload where efficiency per dollar and per watt beat generality. The evidence has turned third-party: SemiAnalysis measured Google's TPUv7 at up to 50 per cent better performance per dollar than Nvidia's B200/B300, while OpenAI's Jalapeño reportedly beats Rubin on tokens per megawatt — vendor-reported, with SemiAnalysis observing but not running the suite. The 16-month Jalapeño design cycle matters as much as the scores: custom silicon is arriving on schedules that used to be impossible.

What to watch. Third-party benchmarks displacing vendor decks; deployments outside the designer's own fleet (the test of merchant viability); and whether custom-chip volume shifts TSMC allocation away from Nvidia.

From the signals. SemiAnalysis: TPUv7 up to 50% better per dollar than B200/B300. OpenAI's first ASIC reportedly beats Rubin on tokens per megawatt. A 16-month ASIC cycle, and kernels the kernel team didn't write.