The GPU Myth: State of AI Compute 2026 | Stephen Balaban
episodePreviously titled “The Neocloud Boom: State of AI Compute 2026 | Stephen Balaban” — renamed by the publisher on Aug 2, 2026
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Why is GPU compute not becoming a commodity?
It's pretty clear that we have an amazing system that can take in Money and output software. The people who are the naysayer that you're gonna throw these GPUs out in five years are completely wrong. They're completely wrong and they've been wrong the entire time. We continue to be generally underbuilding. Most people that are sort of in leadership positions at NeoClouds or within the market have been recognizing this insatiable amount of demand for large language. Models to do everything from being an assistant to code generation, we continue to see no end to the scaling laws.
Hi, I'm Matt Turk from Firstmark. Welcome to the Mad Podcast. My guest today is Steven Balaban, co-founder and CTO of Lambda, one of the top neo clouds powering the AI boom. This episode goes deep on the physical layer that everything else in AI runs on. We get into why GPU compute was never actually a commodity, how you finance billions of dollars of data centers and chips, why a 2023 H100 can be more expensive to list today than. when it was bought and what it actually takes to stand up a gigawatt scale AI factory. We also cover Lambda's wild origin story, from a facial recognition startup to a baseball cap with a camera in it to a near billion dollar cloud business today. Please enjoy this amazing and very educational conversation with Steven.
There was a moment in time in Silicon Valley a few years ago. If you had asked most people, they would have said that neo clouds were going to be uh a commodity, uh, in particular because GPU compute was going to get commoditized. Uh and if you fast forward to today, it seems to be exactly the opposite. Both Lambda, uh, but several of your competitors seem to be uh absolutely ripping. So what is it that uh naysayer
The big thing is that cloud compute is not a commodity service. It is a very complicated eile vertically integrated type of service that spans everything from land land entitlement Construction. HPC high performance computing design. software virtualization cloud services on top. And there's a reason why the biggest companies in the world, these multi trillion dollar market cap businesses, whether it's Amazon, Microsoft, Google, Oracle, are all in the cloud computing business is because it's a great business. And so I think that's like probably the fundamental thing that was misunderstood is that, oh, this is somehow a little bit different than a normal cloud service. Um, but really what it was was it's a cloud service that's designed for the age of AI.
But there is some element of uh competitization, right? The the price of rental of a GPU is going down. But uh what you're saying is that to some extent it doesn't matter because it's only one layer of the cake.
Yeah. So when you look at, for example, I I think it's like actually worth doing is to try to like kind of dig into some of the methodology on, for example, an index like There's there's the there's the index that's on Bloomberg for eight one hundred rental prices. And What we're actually seeing in the market is that, first of all, there's two different rates. There's a public cloud on demand rate and then there's a long term rental rate. And I think that some of these in in indices don't properly take that into account because what we're actually seeing Is a very consistent if not increasing long term rental rate. And very consistent and increasing on-demand rental rates. And so what happens is if if if the index mix, for example, if the methodology and the index biases towards long-term contracts being a a bigger part of the volume,
that will look like a decline in the index when the reality is it's just a decline in the mix that the index is method you know, the index is covering.
Fascinating. So I'm curious about your thoughts as a key leading player in the neo cloud ecosystem, about how you see the market evolve. How much of uh the competitive advantage that uh you guys are building and other players are building is based on technology versus a financing uh race?
There's a few different layers on it, which is there's a lot of differentiation and work that's being put into, for example, the cloud software orchestration layer, which allows us to, for example, take a very large scale GPU cluster and partition it up for our customers.
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Chapters
8 chapters
1
Why is GPU compute not becoming a commodity?
0:00–9:09
2
What does the H100 price index reveal about GPU valuation?
9:09–18:40
3
How does NVIDIA’s technology and financing create a moat for AI compute?
18:40–28:11
4
Are we overbuilding or underbuilding AI compute infrastructure?
28:11–37:06
5
What are the main physical bottlenecks for building gigawatt‑scale AI factories?
37:06–45:42
6
How does Lambda’s financing stack enable large‑scale data‑center deployment?
45:42–54:46
7
What is the origin story behind Lambda and its evolution to a near‑billion‑dollar cloud business?
54:46–1:02:56
8
What are the future visions of neural software, one‑GPU‑per‑person, and AI’s role in software?
1:02:56–1:14:27
Speakers
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