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Dylan Patel

What Dylan Patel thinks about Anthropic

@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.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

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Only Dylan Patel on Anthropic. See everyone on Anthropic

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 readingThe model companies' margins will rise this year, because being capacity-constrained forces them to destroy their own demand.

    Their wordsUm I think at least this year we're going to see margins for the model vendors go up a lot, right? Because they're so capacity constrained, they have to demand destroy demand, right? there is there's no way they can continue anthropic can continue at the current pace without destroying demand.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 3rd of 28 in this recording

  3. Our readingAnthropic saw the coming shortage of chips before Google did, and bought Google's own TPUs out from under it.

    Google

    Their wordsBecause Enthropic saw it before Google. And then Google had Nano Banano and Gemini 3 which caused their user metrics to skyrocket and leadership at Google was like oh and then they started making the statement of we have to double compute every is it 6 months or I don't remember the exact number that they said.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 5th of 28 in this recording

  5. 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