In October 2024, OpenAI raised $6.6 billion from investors at a valuation of $157 billion. Roughly twenty months later, the same company filed paperwork with the U.S. Securities and Exchange Commission (SEC) to go public. It did so exactly one week after its rival Anthropic submitted the very same kind of document.

Two filings landing side by side may feel remote from your own life. It's a Silicon Valley story, a stock-market story — easy enough to scroll past. And yet, buried inside these documents are hints about how the monthly fee you pay for AI tools is about to change. Here begins a paradox: disclosure paperwork written to persuade investors ends up handing the most direct information to the people who actually use the tools.

What an AI Company Writes Down in Its IPO Filing

The document a U.S. company files with the SEC ahead of going public is called an S-1. It is the precursor to the annual report (the 10-K) that the company will be legally required to file every year once it is public, and it describes — in legally binding language — where the company makes money, where it loses money, and what risks threaten the business. The system exists to protect investors, but it is also a window through which the people using a service can look in on the company's real condition.

The largest share of OpenAI's revenue comes from consumer subscription feesChatGPT Plus, Team, Enterprise​ and sales of its enterprise API. Anthropic, too, earns most of its revenue from Claude API income and long-term enterprise contracts. Both companies pour tens of billions of dollars a year into running GPU clusters and into model research and development; as of 2024, OpenAI's estimated annual revenue was around $3.4 billion, but its infrastructure and research costs far exceeded that. To date, both companies have posted negative operating income.

While these companies were private, no one could see these numbers. How much OpenAI earned and lost in a given year, which cost line was the single biggest variable across the entire business — all of it was disclosed only to internal investors. From the moment these IPO filings are submitted, that changes. The document prepared for investors becomes public to the entire world, and once a company is listed, quarterly shareholder reporting turns the pressure to be profitable into concrete numbers.

That pressure flows into pricing policy. As a private company, OpenAI competed by steadily cutting its API prices. The rate that started at $0.06 per 1,000 tokens when GPT-4 first launched fell to around $0.002 in later models. It was a strategy to attract users and capture the market. But once you become a company that has to report profitability to shareholders every quarter, the rationale for sustaining that strategy gradually weakens.

The Case That Going Public Actually Helps — and Where It Breaks Down

Here we should honestly examine the opposing view first.

There is an argument that going public could actually lower AI service prices. The logic runs like this: a company competing in public markets has to retain more customers, and to do that it has an incentive to keep its prices below those of its rivals. Indeed, Google Cloud and Amazon Web Services are publicly listed giants, and they have nonetheless kept cutting their AI inference prices for years. As long as free or open-source alternatives such as Google's Gemini and Meta's Llama exist, the room to raise prices excessively is limited — that is another view.

Transparency has its upside, too. Before listing, OpenAI's actual profits and losses were never disclosed to outsiders, so the basis for its pricing was opaque. After listing, financial statements are published every quarter, and the rationale for any price increase is exposed to the market. Pricing that rests on thin justification, or that runs excessive, can draw public criticism, and it is hard to rule out the possibility that this transparency protects users to some degree.

But this optimism rests on an important premise. For competition to actually function, alternatives you can switch to without heavy cost and friction must genuinely exist. The commercial APIs that meaningfully rival ChatGPT and Claude today amount to roughly Gemini, Grok, and Meta AI — and each comes with its own ecosystem, API specification, and billing scheme, so switching on a dime is not easy. For someone whose workflow is already deeply wired into one platform's API, the logic that competitive pressure protects prices may not actually hold.

Netflix offers one reference point. After going public in 2011, Netflix held its subscription price steady for a while, but as growth slowed and shareholder demands intensified, it raised prices three times between 2022 and 2024. Plenty of streaming rivals already existed, and prices went up anyway. There is no guarantee, at this point, that AI platforms will choose a different path.

The Clues a Disclosure Document Hands to Tool Users

So in this situation, what should a solo operator or a small team be watching for?

Grasping your platform dependence as a number is the starting point. Fewer people than you would think have actually worked out how their monthly AI spending breaks down by platform. If you are running an automation pipeline wired solely to the OpenAI API, it is better to check now whether you would have an alternative when that pipeline's unit cost rises 30%. Running open-source models (the Llama and Mistral families) yourself through a tool like Ollama, or spreading your calls across several platforms depending on the task, has already become a practical option.

Looking closely at your contract terms is just as essential. API rates come with a published price list, but Enterprise contracts and long-term commitments may include separate price-guarantee clauses. Checking your contract in advance — how much notice existing customers get when rates change, and what the early-termination terms are — gives you room to respond.

There is one more habit I would recommend: practice reading disclosure documents like the S-1 and the 10-K. They may look dense with legal language, but learning even just the 'Risk Factors' section pays off. The items AI companies list there — "possible service disruption due to GPU supply shortages," "rising operating costs from regulatory change," "the risk of degraded model quality if key personnel depart" — read, from the perspective of someone who uses the service, as a list of the latent risks in your own tool dependence. This is exactly why practical guides on how to analyze U.S. corporate disclosures have lately drawn attention in the business and economics space.

I see this as a business sense that everyone who uses tools needs. Once you understand the financial condition and the shareholder pressure of the service provider you pay every month, you can gauge in advance which direction that service is likely to move.

The moment OpenAI and Anthropic become companies that have to report profits to shareholders, part of that profit will increasingly come from your subscription fee. Anyone who reads this moment — two AI platforms filing to go public one week apart — as a signal that the era of using these tools almost for free is drawing to a close can prepare for the change ahead of time.