“Compute is scarce because the amount of intelligence you can pack sort of per watt, let's say, keeps growing. So these models get smarter faster than our ability to stand up compute. And when you're in the intelligence market, that's kind of like the market of artificial labor.”

Prof G Markets · Money & Markets · November 2026

Context

Laskin was explaining why inference margins stay healthy even as model prices fall. Compute is the bottleneck, he says, because intelligence per watt keeps rising faster than data centers can be built.

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Transcript

Prof G Markets Around 20:13 into the episode

Misha Laskin

…source software is so ubiquitous and robust, because it's had hundreds of thousands of developer eyeballs on it looking at various sorts of vulnerabilities. And while they might not patch all of them, open source software is definitely tends to be much more hardened and robust than closed software.

Ed Elson

…open models, is how is that business model actually going to work? Because my understanding is that to build a model and to train it, that's the most expensive part of the whole thing. And that's the part that is technically being given away. So, how will the business model work over the long term?

Misha Laskin

…in terms of margin compression on the model side is a healthy thing because intelligence should be viewed like as foundational and fundamental as electricity water. It's a commodity. Now, the inference margins, even on open models, are actually quite high. And the reason for that is because compute is scarce. Now, the reason is compute is scarce, which is GPUs effectively, but CPUs as well. Compute is scarce because the amount of intelligence you can pack sort of per watt, let's say, keeps growing. So these models get smarter faster than our ability to stand up compute. And when you're in the intelligence market, that's kind of like the market of artificial labor. And it's a very kind of positive sum market in the sense that how much artificial labor would I throw at really difficult problems like drug discovery or cyber defense, right? You'd probably throw as much as possible at it in order to get the best cyber defensive systems, in order to discover new drugs, right? And what I think will continue happening is that compute for the foreseeable future, I…

Ed Elson

On the inference, though, because and so I think the utility point is really interesting. And I think Sam Altman has said that he thinks that there's a world where that might happen with open AI. It doesn't seem to be the direction that they're actually headed in terms of what they're pursuing as a…

Misha Laskin

Perhaps in some future. I think right now the market is kind of too early to tell, right? Because I guess what I'm describing is kind of the desirable end state for AI is that it's a utility and right, like internet, right? The internet is effectively a utility, right? It's a commodity that is…

Ed Elson

So the Chinese open models are so interesting because DeepSeek comes along and I mean, we all remember what happened in the market. Suddenly everyone panics and you see all of the AI, US AI stocks going down. But it seems like what happened, what is happening in China, which is why you have emerged…

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

More from Prof G Markets

“Reinforcement learning as a problem was solved actually faster than we anticipated. We thought it would take years. It took less than a year, both within external labs, but even within our own experiments.” — Misha Laskin, Prof G Markets

“When something is not the core strategy of the company, you tend to not pay as much attention to it. And you tend to open source, you know, maybe like a lesser version of the thing that you're actually kind of monetizing. And that's kind of what we've seen from open source launches from Google… similar when OpenAI launched GPT-OSS.” — Misha Laskin, Prof G Markets