#91: Why You Should Not Want the AI Labs to IPO
Three words: public market scrutiny
For those of you who are deep in Claude Code after the Relentlessly Curious How to Vibe (Claude) Code series, you’ll want to pay close attention to this one. For those of you who are public market investors, this one matters too.
Last week, Anthropic announced a change to how they bill Claude usage via X, which goes into effect on June 15th. Big shoutout to @arckollect on X who broke down this technical announcement that candidly goes over my head. I’ll do my best to further summarize (I read through this post five times).
Basically, there’s two ways people use Claude today: hands-on use and agentic use via API. Hands-on use implies that you are typing prompts into Claude, while programmatic use means you’ve built a process (i.e. agentic workflow) that leverages Claude to get work done via API. Not much will change if you are a hands-on user assuming you are not heavily using the API. But if you are heavily relying on agentic systems, your Claude usage now comes out of a separate bucket with a fixed cap. After you hit your cap, it’s up to you to pay a variable rate based on usage. Or you’re out of luck for the rest of the month.
Historically, many heavy users benefited from generous pooled usage limits. If you’re tinkering around with Claude Code, you’re probably not affected by this change. But if you are building a vertical-layer AI company or the first one-person billion-dollar company, you’re relying on AI as an operating system. Thus, your programmatic use may get a lot more expensive.
This subtle change in pricing is just the start of AI labs starting to think more about their own unit economics. It’s a safe bet to assume that AI will cost more for the user in the coming months and years as AI labs need to demonstrate a long-term viable business without the constant injections of massive capital. With public markets ambitions, these private market goliaths will need show they can tidy up their financial statements. The issue isn’t whether Anthropic and OpenAI are valuable businesses. They are. It’s whether public markets will tolerate the economics required to build these companies.
I’m curious about how this impacts the unit economics of vertical AI companies. You know, the start-ups that are built on top of Claude, OpenAI, and Gemini models. I’d imagine that few AI start-ups are profitable (please correct me if my assumption is wrong). For those that are already deeply unprofitable at the unit economics level, they will really struggle to get to profitability if token costs continue to go up. I’m afraid that entire AI businesses may be getting built on temporarily mispriced intelligence.
But these start-ups have other options in the near term. They can switch from Claude models to OpenAI’s Codex. The new agentic coding solution from OpenAI received a major upgrade thanks to the release of GPT-5.5, and OpenAI is letting developers run wild with token usage (much less restrictive compared to Anthropic). Additionally, OpenAI recently formed a joint venture with several major private equity firms called DeployCo for the purpose of growing adoption of OpenAI models at major enterprises. Despite major brand hits over the past few months, OpenAI is turning on the jets on B2B and creating serious competition for Anthropic while Anthropic takes criticism from power users.
Note: Although I rely almost exclusively on Claude models for work, I have tested out Codex and am pleased with its efficient token usage and higher-level reasoning for complex requests. Though switching my entire AI setup is time-consuming, Anthropic would need to significantly increase prices and further reduce token usage for me to fully switch to Codex.
Here’s the thing though: it was only a matter of time until Anthropic curtailed token usage. They are burning through billions of dollars in compute each month and it’s public information that they may look to IPO by the end of the year. And when they file for an IPO, the world will get to see their S-1 offering (think of this as a prospectus for potential investors), showing what is really going on under the hood from a financial perspective.
Even with significant reductions in token usage offered on fixed rate plans, I have to imagine that unit economics on compute are a disaster. Making up numbers here, imagine a situation where users are paying $1 for $10 worth of tokens. That type of pricing arbitrage doesn’t just close overnight. It would take years and require threading the needle while staving off competition that can be subsidized with many hundreds of billions of dollars of capital. I know it doesn’t seem like it now, but eventually investor appetite will dry up. It remains to be seen how much people are willing to pay for AI if prices go up. Are they willing to pay double? Probably. But are they willing to pay ten times? You’ll need a super clear business case to do so.
Last week on Prof G Markets, guest Ed Zitron argued that Anthropic co-founder Dario Amodei’s repeated statement that Anthropic is profitable on inference (the process of providing responses to users) is misleading. Zitron makes the point that being profitable on inference is a helpful indicator as it takes many billions of dollars to train the model in the first place. A major sign of long-term business viability would be if Anthropic were profitable on both inference and training.
But maybe this time is different. Maybe unit economics doesn’t matter because AI is a winner-take-all market and massive scale is paramount to winning. Take Uber for example, who operated at a loss for well over a decade, including several years as a public company. AI is about narrative and it’s the hottest narrative I’ve seen in my career.
Narratives can stretch economics for a long time, but not indefinitely. And when the S-1 comes out, is when Wall Street gets a peek at how bad the unit economics are and what accounting assumptions are being used to support unit economics figures. I’m not suggesting anything fraudulent is going on, but my guess is that there are aggressive interpretations around profitability metrics.
The thing is, Uber eventually raised prices and is a highly cash-flow generative, profitable company now. The best-case scenario is that Anthropic or OpenAI can change their entire pricing model to variable, and people stay on their current plan.
Because the US stock market is so heavily concentrated in technology (Magnificent Seven make up roughly 35% of the S&P 500 value) and Big Tech plans to spend nearly $700 billion on AI capital expenditures this year, anything that adversely impacts the growth of the AI labs is likely to cause a cascading effect on the rest of the market. As Scott Galloway says, “the market is a giant bet on AI”.
When Anthropic and OpenAI go public (likely later this year), they will be at the whims of public market forces and investor scrutiny. They can’t hide their numbers anymore behind hundreds of billions of dollars of private market money. If the narrative around AI labs’ ability to continue growing at a record-setting clip and ability to inch towards profitability breaks, then the rest of the market will take a major dip. For those of you who invest in only broad-based index funds like the SPY (S&P 500 tracker), you’re exposed big time.
Accessing public markets brings enormous amounts of capital to the AI labs, however I believe it increases their risk profile. They’ll be subject to quarterly earnings requirements, and Wall Street will hold them to revenue, user, and profitability targets. If they stumble, the rest of the market will fall on its face given how dependent the market is on AI capex generating meaningful returns. And that is likely to affect valuations far and wide.
I wish I could invest in Anthropic and OpenAI. They are businesses that I believe will change humanity for the better. But sheesh, my investment portfolio is perfectly okay with them remaining private businesses for a little while longer.
Check out the How to Vibe (Claude) Code series!