The two hosts had been discussing chip companies using their balance sheets to finance customers, which they called "balance sheet as a service". Goldberg, a longtime semiconductor analyst, said a company that misses a product cycle while carrying debt payments may not be able to fund the next one.
So, so AMD cannot compete when it comes to balance sheet. Yeah. Even Broadcom is going to struggle to compete here against NVIDIA. But Broadcom does have the advantage that it could call on deep pools of private equity capital to step
in. Right. Okay. All of that to say, this is one of the most interesting competitive dynamics that has entered the equation: balance sheet as a service. It's at some point might be worth like, I don't know, an whole episode on this and the strategic implications and beneficiaries, but it is one of the newest dynamics that I think is extremely fascinating. So more on that. But I think what we've established is it's not going away. Perhaps more players may enter the equation or Broadcom will do more. But it is now a key part of, I think, how you analyze who's got longevity and the bar of competition and how it's changed. So
I think if we did an episode on data center finance, we would have to title it Running Into the Street Screaming with My Hair on Fire in Sheer Abject Terror. Because, right? Because I've been covering SEMIs for a long time, as have you. And like, if you, if you told me five years ago, the center, the central competitive dynamic in semiconductors was use of balance sheet and taking on debt, I would have believed you. I would have, because like for years, like semiconductor companies didn't take on debt for good reason. It's cyclical and debt is very, very constrained. And it's a high fixed cost business. And it's a high fixed cost business, right? And if you miss a cycle in semis, if you miss a product and you miss the cycle, that's really bad. If you miss a cycle and you can't afford RD because you have debt payments and so you can't invest in the next product cycle, that's that's terminal, right? And so this industry in general has been very, very cautious about taking on debt. And obviously today's semiconductor companies are very different than they were 10 years ago. They have a lot of cash and a lot of balance sheet, but still causes this old analyst to get a little nervous.
Yes. Noted. Understand. Okay. Well, as I said, I do think this is a worthy topic to dig into more. All right. Let's talk tokenomics.
Tokenomics. So one of the things that has frustrated me for a long time, and longtime listeners will know this, is I get really frustrated by the way that when we talk about AI, even like seasoned industry professionals talk about AI and different features of software and silicon, everybody gets a little bit hand-wavy, right? Remember earlier in the year, the hot thing was reasoning models. Everyone's talking, oh, reasoning models, this is going to change everything. Did it like, and I, or even just like tokens, everybody's talking about tokens all the time, but like, what is a token? Like, what is it worth? And it just, it frustrates me that it's also imprecise and sort of qualitative conversations. And it occurs to me that if you look at all the data that's now available out there, you can construct an analysis that lets you piece it all together and actually quantify a lot of these things, right? Because you can look at token prices and you can look at token prices for reasoning models, for example, and realize that like a year ago, the labs were charging a premium for reasoning models and that that premium has gone away. It's not a differentiated feature. It's sort of bundled into the frontier models. It's just a feature. It wasn't really worth much. As opposed to, if you look at input tokens versus output tokens, they have very, very different pricing structures because the nature of compute that underlies it is very different, very different cost structures. And you can sort of piece together the ways that those that plays out into the end customers with the AI labs economics. And then you can work backwards and you figure out, oh, this is why things like NVIDIA buying Grock for $20 billion make a certain kind of sense. And I think there is a way now to quantify, quantify the economics of AI. It's possible now. And it is something that I think everyone's going to be doing in a few months. It's an important form of analysis to really just be able to talk about these things in like hard dollar terms and not just say, oh, I'm going to have this. I'm going to have this feature in my model and it's going to make everything faster and better. No, it's going to reduce your cost of decode by 20%. And that means you can price accordingly. Right.
Well, and I think that highlights the OpenAI's dev day this week where they announced, I think the brand's ultra fast for Astra, the higher tier, which is, I think, 250 tokens per second, which then very quickly was like, is this Cerebris? Because I'm not sure because they showed a much higher token model. Yes, it was on Sol, but and then it came out afterwards that it is a low-batch optimized version of NVIDIA GPUs that are actually driving that. But two things. One, impressive that they could optimize NVIDIA GPUs to get a higher tier without Grok or any specialized premium accelerator. I get that it's not 750 tokens per second, but it's still more than worth. And that OpenAI can and probably will charge more for that. So I would add to your tokenomics point that there's the base level that I agree with you with, and then the kind of greenfield model opportunity or for what you would consider premium tokens or premium pricing and how big of a market that could be, which OpenAI is trying to justify, right, with a higher tier.