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MTS · 3 min · Oct 22, 2026

AI algorithms should be designed around their hardware

AS

Aidan Smith

Co-founder, Flapping Airplanes

  • Theo JaffeeHost · Co-founder & host, MTS

    One of the first members of a16z's new media team; earlier hosted the Emergent Ventures-backed Theo Jaffee Podcast.

  • Sophia DewHost · Host, MTS

About this segment

Smith argues that brain-inspired AI should copy useful properties, not neuron mechanics, because algorithms must fit their compute substrate. He contrasts the brain’s 150-millisecond signal travel with GPU microseconds.

From “AI Models Will Tap the Whole Economy in 2 Years | Aidan Smith”, Oct 22, 2026. Watch the full episode

“everything fits to the hardware that it's given everything is downstream of the primitives that you give it”
ASAidan Smithat 5:48 · MTS5:48

Takeaways

  1. A signal crosses the brain in roughly 150 milliseconds.
  2. GPU communication runs in microseconds, so brain circuits don’t map directly.
  3. New GPU primitives can make different algorithms practical.
Watch 3:11 from 2:57

In this episode

Full episode
  1. 6:01Whole-brain emulation has two difficult bottlenecks2:36
  2. 11:05More efficient AI could unlock demand across the economy2:34
  3. 20:22Small labs attract talent by offering meaningful ownership2:36

  4. 2:57AI algorithms should be designed around their hardware3:11

  5. 13:32A new AI lab can compete beyond verifiable code2:58Up next
  6. 17:22Models cover far less real-world data than their scale suggests2:20

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 3:02

    and you can't be on the nice stage like everyone else doing the same thing with the same methods with the same people, you can actually start from scratch. Yeah. I mean, two things I'm particularly curious about. I think that what you guys came out with is the primitives for your GPUs. You guys can actually have different types of algorithms be really efficient there. And specifically with your background at Neuralink, I mean, it kind of hints to what's coming, and I know you guys didn't particularly release a model quite yet, but when you do, does anything from your background at Neuralink kind of shape what this is going towards?

  2. 3:32

    So it was hard to bring my dead horse to the studio to beat. It's a little bit too heavy, but the Neural stuff is a good inspiration. Folks have been inspired by the brain forever in ML, and I think that justifiably, it's the only system we know that actually attains this track record and this performance in the real world. i think that we are much more inspired than than stuck to the brain and that like you look at it you say okay here are these properties it has can we replicate those properties versus like i am now looking at the hodgkin huxley I'm going to be trying to port that over to my GPU because, frankly, you have different substrate. The best algorithm will change based on substrate. That gets back to our thesis really about how new GPU primitives are really unlocking algorithms. The brain is actually

  3. 4:10

    this motivating thing where it's like, nature had these tools. These tools, which I might add, are actually really bad. If you go and you look at how slow neurons are, how much energy it takes to any communication at all, you're like, how did we end up becoming reasonably smart even a little bit? This is remarkable. The brain is so low wattage and it's so small. It's like 20 watts, right? Same as a light bulb or something?

  4. 4:26

    Yeah. I mean, even more than that, though. Like, one thing I like to see is how slow do you think it is to go from one side of the brain to the other side if you were just to send a message? Probably much slower than the speed of light. Well, that's a very good guess as far as nothing is going to be faster than the speed of light. But, like, how – can you guess in terms of, like, orders of magnitude?

  5. 4:43

    Or give me a number in milliseconds. In milliseconds? So, like, in terms of, like, speed of light, it would be, like – what is that? 299 million meters per second or something. So that would be, like, what, nine orders of magnitude, less than a second? Okay, I'm going to guess it takes, like, I'm going to guess it takes eight milliseconds to get from one side of the brain to the other. It's not a bad guess, but it's actually wrong by, like, two orders of magnitude.

  6. 5:11

    It takes 150 milliseconds. And when you think about that compared to a GPU, in a GPU you're talking about microseconds. It's not even close. And when you even, like, look under the hood and you scrape back the white matter, and you look at it and you're like in order to even get that latency your brain has to pull some unbelievable bullshit your brain has to do this stuff where it's like stacking up all these fibers and spending you know 20x more basically energy on these areas to make these information highways and it's get tapping out at like 150 milliseconds that's so slow that like you would not be able to watch this video if it was streamed at that frequency right it's like yeah and you really you look at this and you're like holy crap everything fits to the hardware that it's given everything is downstream of the primitives that you give it and so by unlocking gpu primitives

  7. 5:59

    we can make models that are much much smarter than today's and also operate in different ways like have different latency characteristics and it can incorporate knowledge in more interesting