Valuation: $1 Trillion. What the AI IPOs Mean for Your Investment Security

Two companies that just a few years ago were seen as ambitious research labs are currently preparing for the largest technology IPOs in history. In May 2026, OpenAI confidentially filed a draft S-1 with the US Securities and Exchange Commission (SEC), targeting a valuation of up to $1 trillion. Anthropic followed shortly after - also with a confidential IPO filing, just days after a $65 billion funding round at a $965 billion valuation. For headlines, these are dream numbers. For you as a mid-sized business that has built core processes on Claude or GPT, they are above all one thing: a signal to take a closer look now.

That's because an IPO doesn't just change a company's capital structure. It also changes the pressure under which pricing decisions are made, investor expectations for growth and margin - and, indirectly, the terms under which you'll still be accessing tomorrow the very API you've built your processes on today. This article breaks down what's verifiable in the numbers, what remains speculation - and what specifically follows for your investment security.

For German mid-sized companies, this development is not a distant Silicon Valley spectacle but an immediate planning question. Anyone running customer service agents, document processing, or software development workflows on Claude or GPT today is typically doing so under a single contract with a single provider. It is precisely this contract - its pricing clauses, its notice periods, its capacity commitments - that becomes the decisive lever the moment the provider's business foundation shifts through an IPO. It's worth knowing the facts now, rather than reacting only once the first price increase lands in your inbox.

Two valuations, one race: OpenAI and Anthropic ahead of going public

The facts, according to consistent reports from CNBC, Fortune, and Reuters: OpenAI confidentially filed a draft S-1 in May 2026 and is targeting an IPO for the fourth quarter of 2026 - possibly as early as September. The target valuation circulating in press reports ranges between $852 billion and $1 trillion. Importantly, since the filing is confidential and the final offering price is only set at the actual listing, "up to $1 trillion" is an upper target figure cited in several sources - not a fixed number.

Anthropic has also confidentially filed IPO documents, shortly after closing a Series H round of $65 billion in May 2026, led among others by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia. The resulting post-money valuation: $965 billion. According to Bloomberg Intelligence, Anthropic briefly exceeded OpenAI's valuation by more than $100 billion - a historic moment in which the younger, smaller company briefly overtook the presumed market leader.

For you, the exact figure after the decimal point is secondary. What matters is the order of magnitude: two providers, on whose infrastructure a significant share of the German AI-agent landscape is built, are moving toward a capital-markets event that will shape their entire business conduct - pricing, capacity allocation, pace of investment - over the coming 12 to 24 months.

An IPO is structurally more than just an inflow of capital. Once a company is publicly listed, it becomes subject to regular, audited reporting obligations to shareholders - quarter after quarter. Growth, margin, and cost discipline thus shift from internal targets to publicly observed metrics, against which the share price - and with it management compensation - is measured. Historically, comparable technology IPOs show a recurring pattern: in the first years after listing, the question "How do we monetize the existing user base more efficiently?" tends to move noticeably to the forefront. For customers of OpenAI and Anthropic, that doesn't necessarily mean immediate price jumps, but it does mean a structural tailwind toward tighter terms once the initial market-share phase gives way to a monetization phase.

Who is growing faster? Comparing revenue figures

To put the valuations into perspective, it helps to look at the actual revenue growth both companies can show - with the caveat that both firms are currently private and not subject to regular, audited quarterly reporting obligations. The figures circulating come from market analyses and press reports, not audited financial statements.

Anthropic's annualized revenue (run rate) has literally exploded within a few months: from $9 billion at the end of 2025, to $14 billion in February 2026, $19 billion in March, $30 billion in April, and up to $47 billion in May 2026. A key driver of this growth is Claude Code, which according to industry reports now holds around 54 percent market share in the AI coding segment. OpenAI, by contrast, had an annualized revenue of around $25 billion in February 2026, up from $20 billion at the end of 2025. Anthropic has thus overtaken OpenAI in run-rate revenue for the first time - a remarkable role reversal within just a few months.

Anthropic is planning to reach its first profitable quarter in company history in the second quarter of 2026, with a target revenue of $10.9 billion - according to press reports, with an operating profit of around $559 million. Importantly, Anthropic explicitly does not expect sustained profitability in subsequent quarters, according to its own investor communications, because it continues to invest massively in infrastructure. A claim circulating in some summaries that Anthropic plans "sustained profitability from 2029" could not be verified in this specific form in the sources researched and should be treated with corresponding caution.

OpenAI is significantly further behind on profitability: its operating margin was around minus 122 percent in the first quarter of 2026. Forecasts from futuresearch.ai and ValueAddVC estimate cash burn of around $27 billion for 2026 and around $63 billion for 2027 - according to these analyses, OpenAI would not become cash-flow positive before 2030 at the earliest.

Critical voices such as industry observer "Where's Your Ed At" and a Forbes article also point out that Anthropic's profitability presentation may be partly influenced by the gross booking of cloud reseller revenue via AWS, Google, and Microsoft - an effect that can make top-line figures appear larger compared to competitors that book on a net basis than the underlying operating substance would suggest.

For you as a decision-maker, this means: both sets of figures are impressive, but neither is an independently audited annual report. Sound investment decisions should be calculated cautiously on this basis, not euphorically.

Why capital needs and cash burn affect your pricing

The connection between capital needs and the prices you'll pay tomorrow for a million tokens is not an abstract thought experiment. A company with an operating margin of minus 122 percent and a projected multi-year, multi-billion-dollar cash burn comes under the watchful eye of investors after an IPO who eventually want to see a path to profitability. That path runs through two levers: higher prices or smaller discounts at constant costs - or radically more efficient infrastructure. Both paths directly affect the terms under which you sign contracts today.

Specifically, this means: the currently attractive introductory prices that providers like Anthropic use to compete for market share - for example for Claude Sonnet 5 - are typically time-limited market-entry or growth offers. You should factor into your budget planning that these terms will be adjusted after the IPO or once the targeted market position is reached. Token price increases in 2025 are already documented as a precedent in industry reports - so a repeated pattern is not unlikely, even if the exact timing naturally cannot be predicted.

Run the numbers concretely: a mid-sized company that spends a six-figure amount monthly on token costs feels a 50 percent per-token price increase - as has already occurred with previous model transitions - immediately in its IT or automation budget line. If such a jump only becomes visible after the contract is signed, the only options are often higher costs or a costly migration to another model carried out under time pressure. Anyone who factors in this possibility from the outset - for example through a contractually anchored price cap or through technical switching capability - negotiates from a significantly stronger position.

The Colossus example: when a provider buys up half a data center's capacity

A deal that makes the scale of current investment tangible shows just how serious both providers are about expanding their infrastructure: Anthropic has contractually secured the entire computing capacity of the Colossus 1 data center run by SpaceX/xAI in Memphis, Tennessee - over 300 megawatts of power and more than 220,000 Nvidia GPUs of the H100, H200, and GB200 series. According to reports, Anthropic is paying around $1.25 billion per month for this, with a contract term through May 2029 and a total volume of around $45 billion over three years, including the future Colossus II facility.

This scale shows two things. First: Anthropic is betting an enormous financial stake on years of exponential growth in demand for its models. Second: should that growth fail to materialize or slow down, considerable pressure arises to monetize the committed capacity - for example by prioritizing large customers with volume contracts over small and mid-sized customers, or through price adjustments that refinance these fixed costs. For you as a mid-sized company without a multi-million-volume framework agreement, this is relevant: in the event of capacity constraints, large customers tend to have the longer lever.

What studies say about vendor dependency

Concern about one-sided dependency is no longer merely theoretical - it's measurable in surveys. A 2026 enterprise survey finds that 81 percent of business leaders are concerned about their dependency on their AI provider. 47 percent say a core business function would come to a standstill if their primary AI provider failed. And only 6 percent believe they could switch to another provider without interruption. The primary source and methodology of this survey could not be independently verified - it is cited in secondary sources such as Swfte and Tensormesh analyses and should be read with this caveat in mind. However, the order of magnitude matches what many mid-sized IT leaders report in conversations.

A methodically better-documented finding adds to this: a Forrester survey of 500 CIOs from 2026 names "AI cost uncertainty" as the biggest adoption barrier - for 70 percent of respondents. This aligns with the observation that introductory prices are time-limited and that capital-market pressure after an IPO tends to lead to tighter pricing discipline.

As a drastic but genuinely documented cautionary example of failure risk, several industry articles cite the collapse of Builder.ai - a Microsoft-backed AI company that was at one point valued at $1.3 billion and whose customers were abruptly cut off from critical systems and data when it suddenly collapsed. The example shows: even well-funded, prominently backed AI providers can fail or discontinue services. A multi-billion or even trillion-dollar valuation is no guarantee against operational breaks for the individual customer.

The mid-market strategy: how to protect yourself

From all of this, a clear course of action emerges that has nothing to do with alarmism and everything to do with sound risk management - the same principle you would apply to any other strategic supplier.

First: don't tie core processes exclusively to a single API. If customer service, document processing, or software development depend entirely on a single model provider, you automatically take on that provider's business risk as your own. An internal adapter or abstraction layer that lets you switch between Claude, GPT, and, where applicable, open-source models significantly reduces this risk - and makes you independent of capacity decisions made by others on your behalf.

Second: monitor contracts and prices actively, not reactively. Deliberately build a buffer into your budget for the possibility that introductory prices rise after the IPO. This is not speculation but the logical consequence of an operating margin of minus 122 percent at OpenAI, and of a business model at Anthropic that, by its own account, remains dependent on heavy infrastructure investment even after its first profitable quarter.

Third: plan an exit before you need one. Data-export routines, contractual notice periods, documented alternative models - all of this costs little to implement but prevents exactly the scenario that Builder.ai's customers experienced. An exit plan is not a vote of no confidence in your current provider, but simply good corporate governance.

Fourth: don't mistake a single positive quarterly report for long-term price stability. Even Anthropic itself communicates to investors that its targeted profitable second quarter of 2026 will not necessarily be followed by further consistently profitable quarters. A company that shows a profit once has not thereby guaranteed a stable, permanently favorable pricing structure for its customers.

The upcoming IPOs of OpenAI and Anthropic are a significant event for the entire industry - they bring capital, visibility, and presumably further pace of innovation. For you as a mid-sized company, they are above all a good occasion to critically examine your own AI architecture: how many of your core processes depend on a single provider? How quickly could you switch in a worst-case scenario? And is your contract prepared for price changes after an IPO? Whoever answers these questions today won't have to answer them in a crisis.