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OpenAI’s TPU lesson makes me more cautious on $CBRS. Richard Ho, OpenAI’s hardware chief and a former TPU designer, says one lesson from Google was that “you have to make it programmable.” Models change too quickly to overfit the chip to one architecture. That caught my attention because TPU is already programmable. Groq statically schedules execution in its compiler, while Cerebras makes a different bet on wafer-scale compute and distributed local memory. The question is how much of the performance advantage survives when models change, including MoE routing and memory demands. Ho also wants hardware that can move across inference workloads without locking the fleet into fixed ratios of specialized machines. That matters when buyers are committing years of capex and power. If OpenAI and other large buyers increasingly favor that flexibility, I expect CBRS to remain a specialist over the next few years. The absolute market can still be large enough for Cerebras to build a very good business, but I’m more skeptical that its architecture becomes the mainstream choice. "As long as you’re willing to live with those limitations, they’re good. But if you’re able to make it programmable, then you have a lot of flexibility for adapting to change. And we had to adapt to change. TPU also adapted to different changes in the algorithm, even at that time with CNNs. Clearly now, in the world of LLMs and agents, the mix is changing pretty rapidly. So that was the pitfall we really wanted to avoid: to overdesign and overfit to one particular model. We looked at it in that context." ---[54] The Man Who Built OpenAI's First Chip TechTechPotato

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