The MAD Podcast with Matt Turck
Jul 16, 2026 · 44 min · 11 segments
Is the race to scale AI computing the largest infrastructure build-out in human history? In this episode of The MAD Podcast, OpenAI's Head of Industrial Compute, Sachin Katti, pulls back the curtain…
Sachin KattiGuest
Matt TurckHost
Obviously, I cannot, uh, resist, uh, asking the inevitable question around, uh, the potential risk of, uh, overbuilding, um, given the lag between demand and usage and how long it takes to build a data center.
I think you mentioned, uh, somewhere that you were, uh, deliberately very paranoid about the problems, um, ahead in the next, uh, three years.
Very paranoid about the surprises ahead, um, which sounds like a very healthy a-approach.
Uh, so how do you, how do you think about, um, that? Uh, is there any way to mitigate that, or is just like ultimately, uh, a con- deep belief, uh, that this is the future and, you know, we should all just go, go, go?
We have deep conviction in scaling, right? And, uh, history has borne us out, right? So effectively, our revenue, for example, has tracked compute.
Uh, and if anything, we are seeing that scaling laws on research and training continue to hold, and potentially the pace at which we are doing research is accelerating, right? Because of AI itself.
And so one of the subtle implications of that is, is previously our researchers used to run experiments, and they needed compute to run experiments.
But the number of experiments they could run was limited by the number of human researchers they have, which is a scarce resource in the world, right? There's not a lot of people who can do AI research.
Now if AI itself can do AI research, the number of experiments we can run explodes, and therefore, the amount of compute you need for research also explodes.
So we don't see a world where we will have unused less compute for the foreseeable future, right?
When I was referring to surprises, my worry is more on the downside of we are not able to actually build all the compute we want.
Obviously, I cannot, uh, resist, uh, asking the inevitable question around, uh, the potential risk of, uh, overbuilding, um, given the lag between demand and usage and how long it takes to build a data center.
I think you mentioned, uh, somewhere that you were, uh, deliberately very paranoid about the problems, um, ahead in the next, uh, three years.
Very paranoid about the surprises ahead, um, which sounds like a very healthy a-approach.
Uh, so how do you, how do you think about, um, that? Uh, is there any way to mitigate that, or is just like ultimately, uh, a con- deep belief, uh, that this is the future and, you know, we should all just go, go, go?
We have deep conviction in scaling, right? And, uh, history has borne us out, right? So effectively, our revenue, for example, has tracked compute.
Uh, and if anything, we are seeing that scaling laws on research and training continue to hold, and potentially the pace at which we are doing research is accelerating, right? Because of AI itself.
And so one of the subtle implications of that is, is previously our researchers used to run experiments, and they needed compute to run experiments.
But the number of experiments they could run was limited by the number of human researchers they have, which is a scarce resource in the world, right? There's not a lot of people who can do AI research.
Now if AI itself can do AI research, the number of experiments we can run explodes, and therefore, the amount of compute you need for research also explodes.
So we don't see a world where we will have unused less compute for the foreseeable future, right?
When I was referring to surprises, my worry is more on the downside of we are not able to actually build all the compute we want.
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