On the floor of the New York Stock Exchange, AI’s foremost critic is flouting the dress code. This building is the Vatican of big business, and he’s there to appear on CNBC, where the suits deliver their sermons. But Ed Zitron has arrived at the broadcast desk in a charcoal gray T-shirt.
Right off the top, host Leslie Picker tests him. For years now, Zitron has been pointing out that while AI industry leaders OpenAI and Anthropic might have huge valuations—and might soon stage giant initial public offerings (IPOs)—they don’t actually make much money.
“They wouldn’t be the first with bad financial profiles to go public,” Picker says, alluding to past young tech companies, like Uber, believed to have world-changing potential.
“They’d be the first to be this bad, other than WeWork,” Zitron swiftly replies over the din of the trading floor, citing the super start-up of a decade ago that raised ungodly sums from venture capitalists before imploding after a failed attempt to go public. “And even then, this is so much worse than that. OpenAI burned $20.9 billion in 2025.”
In recent months, this 40-year-old former tech publicist has been popping up on Bloomberg, CNBC, MS NOW, and all over financial YouTube in his increasingly trademark Zelenskyycore. When he arrives, the locals generally seem amazed at his command of the numbers and the logic beneath the biggest economic story of our time.
There’s been a broad consensus for three years now that large language models (LLMs), like Claude and ChatGPT, represent a technological revolution that will transform nearly every aspect of our lives—and, naturally, make a lot of money for the companies involved. As a result, investors have poured money into virtually any firm associated with these technologies. In stock-speak, it’s called “the AI trade.” There are dissidents, like Zitron or Michael Burry of Big Short fame, but they are few and far between in financial and tech media.
At the top of the AI pyramid, there’s Nvidia, making GPUs (graphics processing units, a kind of advanced microchip) that are installed in giant data centers. Those facilities are built and serviced by the businesses one level down in the pyramid: big-tech “hyperscalers,” like Amazon, Meta, Microsoft, Alphabet, and Oracle, plus an array of “neoclouds,” such as CoreWeave and Nebius.
The data centers offer computing power (“compute”) that those firms rent to companies at the bottom of the pyramid—the ones closest to the consumer, like OpenAI and Anthropic. They use that compute to handle your prompt when you ask, “What color was George Washington’s white horse?”
That process of delivering your answer is called “inference,” and it’s measured in “tokens.” Each prompt uses a certain number of tokens. Complex requests—like coding—require a lot of tokens. The more tokens needed, the more money your prompt costs the company that runs your LLM. But most people using Claude or ChatGPT are paying a flat monthly fee, not a fee per token. For a company like Anthropic, that’s created a scenario allowing people to spend $200 for a month of Claude premium but use thousands of dollars worth of compute.