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This article is about silicon. But the other shortage that matters is meat-compute. AI is trained on human intelligence. The hyper-scalers are squeezing every last drop of automatically verified reward, and that may get us very far. A compiler passes or fails in milliseconds for free, forever. But.. "good design taste" has no compiler. Of course, taste isn't unverifiable. But it's is expensively verifiable. Noisy, slow, and orders of magnitude lower throughput. People with deep domain knowledge often can't articulate well _why_ one design works and the other doesn't. So, judgment arrives as a verdict, and not a crisp rationale. I guess we'll see if sample efficiency outpaces the cost of human judgement. In the mean time, leverage will sit with whoever holds this tacit knowledge (incumbents). I.e., hospital systems, law firms, chip designers, studios, SaaS that are dominating their niche.. and not with the labs training on it. To me, this is why valuations of companies like Palantir could potentially make sense. |
Before you even get the code for a compiler to do its thing, you need an AI to ingest and emit thousands of tokens autoregressively. That's what makes RLVR so expensive.
Likewise - I don't think your "design taste" argument holds? Areas where "taste" exists are far easier for AI to tackle than areas where no data exists. It's easier to teach an AI how to make an image that looks good than to teach an AI how to de-solder a BGA chip. "Tacit knowledge" yes, "taste" - not really?