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• @HakFoo@lemmy.sdf.org 2026-09-08 07:22

  At this point I can see parallels with Intel in the 2000s. If you remember then, they went all in on the Pentium 4/“Netburst” architecture. It was sort of a dog from day 1. It ran hot, it wasn’t very powerful, you needed purpose built power supplies and weird RAM at first. But it was the future because they planned to scale it to 10GHz which would make up for all its limitations. Sounds a lot like AI-- it’s expensive and still dodgy, but the planned future design solves it all. Narrator Voice: they never made it to 10GHz. Even at 3.8 it still sucked, and the thermals and power needs made it unviable to go further. The LLM industry is flogging their metaphoric 3.6GHz Pentium 4 now. Maybe you can get one or two more sales cycles out of the current paradigm, but not much more. If a costlier trillion parameter model is only marginally better than a 200B model (that was only marginally better than a 25B one…), how do the economics justify the hundred-trillion parameter model? Training costs likely rise nonlinearly, too. It becomes harder to find fresh content for the model. If we need to scan vintage out of print books, we must have exhausted the easier sources. Whether you’re a true believer really chasing AGI or just surfing the grift, you’re going to have to pivot hard soon if you have any business strategy that avoids commoditization. We’re reaching the largest economically feasible versions of the current design. Intel survived by having such a pivot in their pocket; the replacement Core series CPUs were originally the “B-team” designs for laptop chips quickly scaled for a new use case. Does any big-dollar AI platform have something that paradigm-breaking to drop?

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