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Jeff Dean

What Jeff Dean thinks about energy

@jeff-dean · 22 positions · On ppll ↗

Chief Scientist at Google DeepMind and Google Research, at Google since 1999, where he co-created MapReduce, Bigtable, TensorFlow and the TPU.

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Only Jeff Dean on energy. See everyone on energy

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

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  1. Our readingInference, not training, is now the constraint, and specialized low-energy hardware will beat general-purpose GPUs and TPUs on latency.

    semiconductors

    Their wordsYeah, I mean it's a little different, but I think uh you're going to see more and more uh uh high performance and um low energy uh inference hardware systems because I think everyone is now realizing that inference is the key to making you know these agent-based systems be available to more and more people and that latency is really important and that specialization of the hardware is a really key way you can make uh things that are more energy efficient and lower latency than more general purpose uh computational devices like say GPUs or TPUs

    ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 2nd of 22 in this recording

  3. Our readingThe numbers an AI engineer should know by heart are memory bandwidths and the energy cost of a single operation.

    Their wordsYeah, I mean I think if you looked at what is important in AI systems these days, you would want to know things like the bandwidth between you know your main memory system on your accelerator to the onchip memory to the um you know the multiplier unit or whatever. You want to know how much energy does it take to do a single multiplier operation.

    ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 6th of 22 in this recording

  5. Our readingBatching exists only because moving data costs about a thousand times more energy than computing on it.

    Their wordsUm because if you didn't have that thousandx difference then you know you wouldn't have to do batching but you have to do batching of you know many examples or maybe many tokens at once in order to amortize that data movement

    ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 7th of 22 in this recording