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

Two filings landing back to back can feel like a distant story — Silicon Valley news, stock-market news, easy to scroll past. But buried in those documents are hints about how much your monthly AI bill is about to change. That's the paradox at the heart of this: disclosure paperwork written to persuade investors turns out to be the most direct source of information for the people who actually use these tools.

What AI Companies Put in Their IPO Filings

In the US, a company preparing to go public files what's called an S-1 with the SEC. It's the precursor to the 10-K annual report that public companies are required to file every year afterward, and it lays out — in legally binding language — where the company makes money, where it loses money, and what risks threaten the business. The filing exists to protect investors, but it doubles as a window that lets the people who actually use the company's product see what's really going on inside it.

The largest share of OpenAI's revenue comes from consumer subscription feesChatGPT Plus, Team, Enterprise​ and enterprise API sales. Anthropic likewise draws most of its revenue from Claude API usage and long-term enterprise contracts. Both companies pour billions of dollars a year into running GPU clusters and researching and developing models — OpenAI's estimated annual revenue in 2024 was around $3.4 billion, but its infrastructure and research costs ran well past that. Both companies have posted negative operating income to date.

While these companies were private, nobody outside got to see these numbers. How much OpenAI earned and lost each year, which cost line moved the needle most — that was visible only to internal investors. Once the IPO filing lands, that changes. A document prepared for investors becomes public worldwide, and once the company is trading, quarterly shareholder reports turn the pressure to be profitable into hard numbers, visible every three months.

That pressure flows straight into pricing. As a private company, OpenAI competed by steadily cutting its API prices — from $0.06 per 1,000 tokens in GPT-4's early days down to roughly $0.002 in later models. It was a strategy for winning users and claiming market share early. But once a company has to report profitability to shareholders every quarter, the case for keeping that strategy alive gets weaker by the year.

The Case That Going Public Could Actually Help Users — and Its Limits

Before going further, it's worth looking honestly at the counterargument.

One argument holds that going public could actually push AI pricing down. Companies competing in public markets need to hold onto more customers, the reasoning goes, which gives them an incentive to keep prices below their rivals'. Google Cloud and Amazon Web Services, both publicly traded giants, have in fact cut their AI inference pricing steadily for years. And as long as free or open-source alternatives exist — Google's Gemini, Meta's Llama — there's a case that pricing power has real limits.

Transparency has its upside too. Before the IPO, OpenAI's actual profit and loss was invisible to outsiders, so the reasoning behind its pricing was opaque. After going public, financial statements come out every quarter, and the justification for any price hike gets exposed to the market. A pricing move built on thin justification, or one that looks excessive, can draw public criticism — and it's hard to rule out that this transparency offers users some real protection.

But this optimistic case rests on an important precondition: for competition to actually work, there needs to be a real alternative you can switch to without heavy cost or friction. Right now, the commercial APIs that genuinely compete with ChatGPT and Claude are limited to Gemini, Grok, and Meta AI, and each runs its own ecosystem, API spec, and billing model, which makes switching anything but instant. For anyone whose workflow is already deeply wired into a specific platform's API, the argument that competitive pressure will keep prices in check may simply not hold in practice.

Netflix offers a useful reference point. After going public in 2011, Netflix held its subscription price steady for a while — but once growth slowed and shareholders started pushing harder, it raised prices three times between 2022 and 2024. Rates went up even though plenty of streaming competitors already existed. There's no guarantee, at this point, that AI platforms will choose a different path.

What the Filings Tell Tool Users

So what should solo operators and small teams actually be watching for here?

Start by putting a number on your platform dependence. Fewer people than you'd expect have actually broken down their monthly AI spend by platform. If you're running an automation pipeline wired solely to the OpenAI API, now is the time to check whether you have a fallback for the day its per-call price jumps 30%. Running open-source models (the Llama and Mistral families) directly through a tool like Ollama, or spreading calls across several platforms depending on the task, are both already practical options.

Reading the fine print in your contract matters just as much. Platforms publish standard API price lists, but enterprise agreements and long-term commitments can carry separate price-protection clauses. Check in advance how much notice existing customers get before a price change and what the early-termination terms look like — that's what gives you room to react.

There's one more habit worth building: reading disclosure documents like S-1s and 10-Ks. They look dense with legal jargon, but even just learning to read the "Risk Factors" section pays off. When AI companies list things like "possible service disruption from GPU supply shortages," "rising operating costs from regulatory change," or "risk of model quality decline if key personnel leave," a tool user reads that same list as a rundown of the risks baked into their own dependence on that tool. That's exactly why practical guides to reading US corporate disclosures have been getting more attention lately in business and economics circles.

I'd argue this is basic business literacy for anyone who uses these tools. Once you understand the financial condition of the service you're paying for every month, and how much pressure it's under from shareholders, you can get a read on which direction it's headed before it gets there.

The moment OpenAI and Anthropic become companies that answer to shareholders for profit, a growing share of that profit is likely to come out of your subscription fee. Two AI platforms filed to go public a week apart — read that moment as a signal that the era of paying next to nothing for these tools is ending, and you can start preparing for the shift before it arrives.