A Final Answer: Is AI Really a Bubble?

With Anthropic having hit a $44 billion run rate, up from “just” $9 billion three months ago, and on a trend line to a $100 billion run rate by the end of the year, they are putting their business on par with some of the most cash-generating business models of all time. OpenAI’s growth with Codex is just as impressive.

And while I have my counterarguments, one way or another, AI has found some sort of product-market fit, and people have finally put to rest the idea that AI is a bubble.

Well, wrong.

The economic picture in AI is much more complicated than it meets the eye; it’s bubbly in ways people in San Francisco, too smart for their own good, fail to identify.

We’re tackling all angles today. CapEx, power, memory, advanced packaging, financials, and all else there is to know about AI in an effort to truly understand the state of the industry, as always in words you will understand.

Let’s dive in.

The technology is real

The first mistake in analyzing the AI buildout is pretending the technology is still fake.

It was a reasonable argument in 2023, when the core product was a chatbot, gross margins were horrendous, and the most visible use cases were better search, homework help, marketing copy, and Studio Ghibli selfies.

But it is no longer a reasonable argument at all because in late 2025 and early 2026, something broke loose: AI models hit a capability threshold, especially coding agents and research/analysis agents, starting to cross from demo into production, at least in those particular areas.

The clearest evidence is revenue. SemiAnalysis estimates that Anthropic’s ARR has exploded from $9 billion to over $44 billion in just a few months, while its inference infrastructure gross margins rose from 38% to over 70%.

Whether that exact number is perfect or not, it’s impossible to ignore that AI Labs are no longer just burning money to prove capability, and they are beginning to offer actual business models in return.

SemiAnalysis also argues that the value per token has risen sharply while the cost of producing tokens has fallen, because new hardware and software optimizations are increasing throughput faster than system cost.

In layman’s terms, the performance per dollar is growing because performance (the amount of operations per second our hardware can output) is increasing much faster than the prices of new servers.

As you can see below, the prices users and companies are paying for every “ounce” of compute is dropping fast, and the rate of acceleration of this decrease is only becoming more apparent.

This is mainly due to memory allocations more than transistor node progress. In other words, as AI is mostly a memory-bottlenecked game, most progress these days is driven by advanced packaging and memory. Put simply, NVIDIA is no longer the most important company in AI; that throne goes to TSMC and the Big Memory 3 (Hynix, Samsung, and Micron), as beacons of progress.

As mentioned, OpenAI’s numbers tell…

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