Inference Is Unlikely to Ever Be a Low Marginal Cost Operational Node, & the Other Reasons Why the Anthropic and OpenAI IPOs Ought to Fail

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Digital Gods, real costs: why a rational world would see the doom of the foundation‑model-builder IPO, because the AI labs are highly unlikely to ever get profits, let alone hyperprofits. Inference never becomes sufficiently cheap, AI-entity judgment stays bad, and durable quasi-rents flow to NVIDIA & company—not to the model‑makers…I have no idea whether OpenAI or Anthropic or both with launch an IPO this year, and I have no idea what the results of it will be.But it is clear to me that, if either one does, it ought to fail.That is clear to me in a way that it was not clear to me back in the day that the Google or the FaceBook or the Microsoft IPOs were unsound. I thought all three of those were very risky, yes. But, even though the valuations seemed very high to me, I did see a possible path to durable hyperprofitability for each.ShareShare DeLong's Grasping Reality: Economy in the 2000s & BeforeI do not see such a path for either Anthropic or Open AI. That has now crystalized for me. And it is reading Paolo Perrone that has done it, and that has led me to the conclusion in the title.From Paolo Perrone I get four things:(1) “Inference” is very unlikely to ever become a low marginal-cost node in the system:Paolo Perrone: Why is Inference Slow and Expensive? <https://theaiengineer.substack.com/p/why-is-inference-slow-and-expensive>: ‘Your inference bill…. Memory bandwidth…. KV cache reads…. GPU idle time…. The electricity bill for running all that idle silicon…. The industry’s largest AI lab spent $8.67 billion on inference in the first three quarters of 2025, nearly double…