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The bet hiding inside the AI hardware boom
There is a quiet but very expensive bet being made across the AI compute layer right now, and I think it deserves more scrutiny than it is getting. The bet is that the best way to handle the growing demand for AI compute is to build silicon shaped around the architecture we have today. In practice, that means chips increasingly tuned for transformers. Etched is the clearest example, with hardware designed explicitly around transformer workloads. But the broader pattern shows up across the industry too: more memory bandwidth tuned for attention, more matrix throughput tuned for the operations LLMs actually use, and more interconnect tuned for the shapes of current models. ...