Dwarkesh Patel
Hosts the Dwarkesh Podcast, a long-form interview show on AI progress, timelines, and the people building frontier models, along with economists and historians.
“As these companies go public and are accountable to investors — say they want to go from 60% of compute on training to 70% — their investors are going to say: if you're generating $100 billion per gigawatt, you're basically saying no to $200 billion of revenue in order to increase your training compute. What the fuck? You're already spending so much on training, why are you spending even more?”
Dwarkesh Podcast · AI Research & Frontier Labs · August 2026
Pressing Dylan Patel on whether public-market pressure eventually forces labs to move compute from training to inference, where it earns money now. Patel's own view is the reverse — that labs will allocate less and less compute to inference over time as recursive self-improvement draws near.
Transcript
Right. Yeah, maybe there's like some slowdown in AI progress or the deployment of AI that means that the revenue per gigawatt can be lower. But that's the only way I could see it being only 100 per megawatt by the end of next year.
…of safety and regulation, but also, you know, the competitive advantage. And then that six-month difference, if progress accelerates, is actually a bigger differential. So that's the thing that would cap revenue per megawatt gains to a much lower growth than we've seen in the first half this year.
Here's something I'm very interested in. As these companies go public and they're accountable to investors, and let's say end of next year, they have, I don't know, close to 20 gigawatts. So like 10% of the compute, two gigawatts. Let's say they want to go from 60% compute to training to 70% compute to training. And their investors are like, well, if you're able to generate $100 billion per gigawatt, you're basically saying no to like $200 billion of revenue in order to increase your training compute. And so investors are like, what the fuck? You're already spending so much on training. Why are you spending even more on training? As a public company, do you think, yeah, what do you think would happen if they're just like, no, we will keep increasing the share of compute we spent on training to offset the increase in revenue that each gigawatt of compute is giving us
yeah so so this is sort of what i i personally believe that the labs are going to allocate less and less compute to inference over time which i think is very non-consensus right everyone's sort of the standard belief of most people is oh most compute will go to inference um most of it will go to…
while each increment of compute is getting more and more profit generating if they had dedicated to inference
right and and so the whole point is well okay if i'm gener if i'm selling tokens is anthrop is open ai releasing ultra fast mode for just external or are they doing it internally too and it turns out no actually i'm going to allocate it to internal and external because my internal you know value…
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