Anthropic eyes the biggest IPO in history: what changes for companies using AI

Anthropic investors expect the company to go public in October 2026 at a valuation of US$ 2 trillion or more. If it happens, it will be the largest initial public offering in history — surpassing SpaceX's June debut at US$ 1.77 trillion, and more than doubling Anthropic's own last private valuation of US$ 965 billion.

Before going further, one caveat almost every headline left out: that number is what investors expect, not a company target. According to the Financial Times, senior Anthropic executives have not set a valuation goal even in private conversations.

From the outside it reads like financial-market news. But there is a concrete consequence for any company that has put AI inside a process: the price you pay today is the price of a company that does not yet need to turn a profit. An IPO changes that yardstick.

The numbers behind the subsidy

Look at OpenAI, the direct competitor, in the first quarter of 2026: US$ 5.7 billion in revenue and US$ 3.7 billion burned in the same period. For the full year, the company projects revenue around US$ 30 billion against a cash burn of US$ 25 to 27 billion.

Zoom out and it gets clearer: OpenAI tells investors it is targeting roughly US$ 600 billion in compute through 2030 — and that figure is already revised downward, after Sam Altman spoke of US$ 1.4 trillion in infrastructure commitments. Projected cumulative cash burn through 2030 reaches US$ 665 billion.

None of that is mature-market pricing. It is the price of buying a market, funded by investment rounds. And investment rounds tolerate years of losses; public shareholders tend to be less patient.

Anthropic's side

The trajectory is remarkable and worth recording: annualised revenue passed US$ 47 billion in May, and investors project somewhere between US$ 100 and 120 billion by year-end — more than ten times where it stood at the start of 2026. The company filed a draft registration with the SEC in June, moving ahead of OpenAI in the race to list, days after closing a US$ 65 billion Series H.

But the same reporting points to three pressures the enthusiasm hides:

• Price. Anthropic's top model costs more than 2.5 times OpenAI's flagship.
• Open competition. Chinese open-weight alternatives deliver comparable capability at a fraction of the cost — and already lead benchmarks in specific areas, such as cybersecurity.
• Regulation. Revenue growth slowed in June after the U.S. Department of Commerce imposed temporary export controls on the company's best models.

OpenAI, meanwhile, appears to be moving the other way: New York Times reporting indicates the listing is being pushed to 2027, with Altman treating any valuation below US$ 1 trillion as unacceptable.

What changes inside your company

A publicly traded company answers to quarterly results. In practice, for anyone consuming AI, that usually shows up in four places:

1. Usage pricing. Cost per token and enterprise subscriptions stop being a land-grab instrument and become a margin line.
2. The free tier. Free plans exist to build habit. After an IPO they are cost without revenue — and they tend to shrink.
3. Older models. Keeping old versions running costs money. The pressure is to retire them faster, and whoever built on a specific model redoes the work.
4. Usage limits. Quotas and rate limits get stricter, because every call carries a real compute cost.

None of this is a catastrophe. It is the normal cycle of any technology that went through a subsidised phase — it happened with cloud computing, with ride-hailing apps, with food delivery. The mistake is planning as if today's price were permanent.

How to prepare without giving up AI

• Know what you spend and model double. If doubling the cost breaks the process, that process is fragile — and you want to find out now, not in the repricing email.
• Do not tie a critical process to a single vendor. The right question is: if this vendor triples its price tomorrow, how long would it take me to switch?
• Favour an architecture that allows swapping. Isolating the AI call behind your own layer costs little while building and pays off later. Changing the engine should be a configuration change, not a rewrite.
• Treat open models as a real plan B. They are no longer the poor alternative: on specific tasks the open ones already compete with closed models at far lower cost. Worth testing before you need it.
• Document what the AI does in your process. If the logic only exists inside a prompt someone wrote and nobody recorded, you do not have a process — you have a dependency.

Whoever has built AI applied to the business with portability in mind will barely feel it. Whoever tied everything to one vendor because it was cheap will discover the real cost all at once.

The honest summary

A US$ 2 trillion IPO would be the largest debut in history — and it is still an investor expectation, neither a done deal nor a stated target. It may come in smaller, it may come later, it may not happen in October.

What is already fact: the two largest AI companies in the world are heading to the stock market, and both burn billions per quarter to sustain current prices. That combination has only one predictable ending, and it is not prices falling forever.

Facts drawn from Financial Times and New York Times reporting, market filings and the technology press of August 2026. Anthropic's valuation and revenue projections come from investors, not from the company.