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Dylan Patel

What Dylan Patel thinks about scaling laws

@dylan-patel · 50 positions · On ppll ↗

Founder and chief executive of SemiAnalysis, the research firm that tracks the semiconductor and AI data-centre supply chain.

Dylan Patel did not write this page.

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Only Dylan Patel on scaling laws. See everyone on scaling laws

3 dated positions, 2026, in their own words. Our reading of what Dylan Patel has said — not written or endorsed by them.

3 positions so far — this page is not yet offered to search engines.

  1. Our readingAn H100 is worth more today than it was when it was new, because the models it runs got better faster than its replacements arrived.

    Their wordsif improvement stopped you know here the value of an H100 is now predicated on the value that GPD 5.4 four can get out of it instead of the value that GP4 can get out of it and the margins and all that stuff that these labs are doing and they're in a competitive environment so their margins can't go to infinity. Um so you sort of have this like dynamic that is quite interesting in that an H100 is worth more today than it was 3 years ago.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 1st of 28 in this recording

  3. Our readingNobody wants a slow model, which is why labs will not trade inference speed for cheaper memory even though they easily could.

    Their wordsThey could release claw slow mode and have an increase in tokens per dollar by a significant amount. Um they could probably like reduce the price of Opus 46 by you know 4x 5x and reduce the speed by another by maybe just like 2x like the curve on inference throughput versus speed is there already just on hm um and yet they don't um because no one actually wants to use a slow model

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 16th of 28 in this recording

  5. Our readingA lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from.

    Their wordswell the model the compute efficiency gains you get from research are so large you actually want most of your compute to go to research not to development because you know all these researchers are generating new ideas trying them out testing them and continuing to march along this and push the prao optimal curve of scaling laws further and further and further

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 25th of 28 in this recording