Billion-Dollar IPOs at Anthropic & OpenAI: Why That Shouldn't Change Your AI Decision

Two of the world's most valuable private companies are currently preparing to go public in parallel. Anthropic confidentially filed a draft prospectus (S-1) with the US Securities and Exchange Commission on June 1, 2026, and a public filing was expected for late August 2026 – but at the time of writing this article, there was no confirmed source that this public filing had actually taken place yet. OpenAI followed suit confidentially as early as spring 2026 – exact dates vary across sources between May 22 and June 8, 2026 – originally aiming for an IPO in September 2026. The valuations under discussion are staggering: for Anthropic, press reports from the New York Times mention figures of up to $2 trillion; for OpenAI, figures range from $852 billion to over $1 trillion.

If you're a managing director or IT decision-maker at a German Mittelstand company currently building an AI strategy or planning your agent infrastructure, a legitimate question arises: Does this change anything about your decision on which provider to work with? Should you now rather wait until the situation on the capital markets has become clearer? The short answer: No – and the facts from the past few weeks offer surprisingly good arguments for that. Looking closer at the details of these IPO preparations, you'll even find evidence that the real decision isn't "which provider wins the valuation race," but rather "how do I build my AI infrastructure so that this question becomes irrelevant to me."

Two AI giants on the road to going public – but at different speeds

Anthropic's pre-IPO numbers are impressive. Annualized revenue run rate stood at more than $65 billion at the end of July 2026 – a more than sevenfold increase compared to the end of 2025. Quarterly revenue rose, according to an investor update, to $11.5 billion, compared to $787 million in the corresponding quarter of the previous year, 2025. In the Series H funding round of May 2026, the company additionally raised $65 billion at a valuation of $965 billion. It's worth taking a close look here, because the two figures look identical at first glance but mean something completely different: the $65 billion from the Series H round is the capital investors made available to Anthropic. The $65 billion revenue run rate is the projection of what Anthropic is actually generating from its customers right now. That both figures happen to be similarly large is purely a coincidence of timing – but the difference matters for how you interpret the situation.

On secondary markets – meaning trading of private shares outside an official stock exchange listing – Anthropic shares were already trading in mid-July 2026 at valuations of $1.05 to $1.15 trillion, well above the official Series H valuation from May 2026. Whether the speculated up to $2 trillion will actually be reached at an IPO is pure speculation from a press article and not a confirmed figure from the S-1 filings themselves.

OpenAI is also large in terms of revenue and user numbers, but clearly smaller than Anthropic in terms of growth pace based on the most recently reported figures: around $2 billion in revenue per month, 50 million paying consumer users, 9 million business users. Some sources cite an annualized run rate of around $40 billion, which could not be confirmed by a second independent source and doesn't exactly match the monthly figure – caution is warranted here in interpreting this. Also noteworthy is a detail that gets lost in many headlines: CNBC reported on August 21, 2026, that Anthropic's IPO filings will explicitly list growing societal pushback against AI companies as a risk factor. Even the biggest names in the industry are themselves expecting headwinds – a sign that "big" and "unassailable" are two very different things.

OpenAI's possible delay – why SpaceX plays a role

Particularly revealing is a development that has surprised many observers: according to a New York Times report, OpenAI is now considering postponing its own IPO to 2027. The wording matters here – this is a consideration, not a decided fact, and the situation can change again with further news. Two factors are cited as reasons: general market volatility and the bumpy share price performance of SpaceX after its IPO.

SpaceX went public on June 12, 2026, raising around $75 billion and being valued at $1.77 trillion – the largest IPO in history, far ahead of the previous record holder Saudi Aramco, which raised around $29 billion in 2019. At one point, SpaceX's market capitalization even reached around $2.65 trillion. But afterward, the share price became volatile and fell significantly from that high. Sam Altman reportedly wants to stick to the $1 trillion target valuation for OpenAI rather than accept a valuation discount for an earlier IPO.

Even a record-breaking IPO with $75 billion in proceeds is no guarantee of stable prices afterward. If market volatility can make even the largest and best-funded technology companies in the world reconsider their own timelines, that should also temper your expectations about the planning certainty of any single AI provider.

For you as a Mittelstand business owner, this means above all one thing: even companies with the best future prospects in the tech industry cannot reliably predict their own timelines. Anyone who ties their AI strategy to a specific milestone of a single provider – such as "we'll wait for the IPO, then things will be clearer" – is building on an assumption that can turn out to be wrong within a matter of weeks.

Will prices rise after the IPO? Counter-evidence from practice

An obvious concern for many companies is: once an AI provider is publicly listed and has to deliver quarterly results, prices for API access and subscriptions will presumably rise. The data from recent weeks, however, paints a different picture. On July 30, 2026, OpenAI cut prices significantly: GPT-5.6 Luna became 80 percent cheaper and was made the new default free model for ChatGPT, while GPT-5.6 Terra was reduced by 20 percent to $2 per million input tokens and $12 per million output tokens. xAI reacted promptly and matched with Grok 4.6 at comparable prices.

This is remarkable, because it disproves the naive assumption that "an IPO automatically leads to higher prices for customers" – at least for the current moment. The trend we're observing right now looks more like a price war between the major providers than a coordinated price increase. For your business decision, this means: the fear of committing to a provider "too early," before it's publicly listed and raises prices, is currently not backed up empirically. More important than the question of timing is the question of architecture – because prices can move in either direction, and no one can seriously predict how price competition will develop once both companies are actually publicly listed and have to answer to shareholders on a regular basis.

This is precisely the core argument of this article: instead of trying to predict the next price or valuation moves of individual providers, it's worth your while as a Mittelstand business to build infrastructure that is largely independent of such movements.

MCP as a vendor-neutral standard – the real lever

Here's a development that gets far too little attention in the public discussion around billion-dollar valuations, but is decisive for your practical AI strategy: Anthropic handed over the Model Context Protocol (MCP), which it developed, to the newly founded "Agentic AI Foundation" under the umbrella of the Linux Foundation in December 2025. MCP is therefore no longer a proprietary Anthropic tool, but an open, vendor-neutrally governed standard that lets AI agents access external tools, data sources, and systems – regardless of which language model runs in the background.

The adoption of this standard is now considerable, even though the following figures come from industry estimates and not from official press releases by Anthropic or the Linux Foundation: according to one survey, around 78 percent of enterprise AI teams were already using MCP-based agents in production by July 2026, and 28 percent of Fortune 500 companies operate their own MCP servers. Monthly SDK downloads stood at around 97 million in March 2026 – a 970-fold increase within just 18 months.

What does that mean for you in concrete terms? MCP works like a kind of USB-C port for AI agents: an agent connected via MCP to your internal systems, your CRM, your accounting, or your communication channels can, ideally, be switched from one language model to another with manageable effort – whether from Claude to GPT, from GPT to Gemini, or to a European provider. The actual value creation no longer lies solely in the model, but in the integration layer above it. This exact architectural decision – building agents and workflows so that the underlying model remains interchangeable – is the practical lever that lets you become independent of the valuation swings and pricing decisions of individual providers.

What studies show about vendor lock-in in the Mittelstand

Concern about being too tightly bound to a single AI provider isn't just a theoretical consideration – it's also backed up by study data. An IONOS study titled "German Mittelstand wants AI but mistrusts overseas providers" found that 36 percent of German companies surveyed would prefer an AI provider from Germany or Europe. Especially against the backdrop of the current IPO uncertainty at the two major US providers – the possible delay at OpenAI, the volatility in Anthropic's valuations – this mistrust is understandable and provides an additional argument for not relying one-sidedly on a single large US provider.

A study by the IBM Institute for Business Value together with Oxford Economics, which surveyed around 1,000 executives from 14 countries and 17 industries between February and April 2026, arrives at a similar finding: many companies in the EMEA region have little visibility into their own AI dependencies. Vendor lock-in and data residency requirements remain central, often underestimated risks.

An even more concrete picture emerges from a survey – US-focused, not specific to the Mittelstand – of 100 US enterprise CIOs from 2026: 37 percent of the companies surveyed already run five or more AI models in production side by side, up from just 29 percent the previous year – a clear trend toward multi-model strategies. 81 percent of the executives surveyed are at least somewhat concerned about dependence on a single AI provider, and 47 percent even expect that losing their main provider would meaningfully disrupt a core function of their business. Although these figures come from the US market and don't translate one-to-one to the German Mittelstand, they show a clear trend: more and more companies recognize dependence on a single provider as a strategic risk and are responding with deliberate diversification.

What this concretely means for your AI decision

Let's summarize the facts: two of the world's most valuable AI companies are preparing billion-dollar IPOs, with valuations shifting by hundreds of billions of dollars within a matter of weeks. At the same time – contrary to some fears – the actual prices for the models being used are falling. At the same time, even a company with record revenues like OpenAI is possibly pushing back its own timeline by an entire year because the market is volatile. And at the same time, MCP is creating a technical infrastructure that allows you to build your agents and workflows independently of the underlying model.

The obvious conclusion isn't to wait until the situation on the capital markets has "settled" – it won't, for the foreseeable future, since IPOs of this scale inevitably come with fluctuations, as the SpaceX example shows. The right conclusion is to build your AI infrastructure from the outset so that the question "which provider wins the valuation race" becomes practically irrelevant for your business. Concretely, that means:

First: rely on open, vendor-neutral standards like MCP for agents and automations, rather than on proprietary solutions deeply embedded in a single ecosystem. Second: keep track of how many of your business-critical processes actually depend on a single AI provider – the studies cited show that this overview is exactly what's missing for many companies. Third: actively use the current market phase of falling prices and growing competition, instead of hesitating out of uncertainty about future valuations and pricing developments. And fourth: regularly, but without haste, check whether switching providers or adding further models makes sense for your specific use case – not because an IPO is imminent, but because a robust architecture allows for flexibility anyway.

In the end, what matters for your company isn't whether Anthropic is valued at $2 trillion or OpenAI at $1 trillion. What matters is whether your customer inquiries are handled reliably, whether your internal processes run smoothly, and whether you can react quickly and without major effort if something fundamentally changes at a provider – whether in price, terms of use, or strategic direction after an IPO. This kind of robustness can be built today, regardless of when and at what price Anthropic or OpenAI actually go public.