OpenAI’s IPO: $1T Valuation, Targets 2026 Launch

As of October 30, sources familiar with the matter have revealed that OpenAI has initiated preparations for its initial public offering (IPO), with a potential valuation of up to $1 trillion. If the listing is successful, it will rank among the largest IPOs in global history.

It is reported that OpenAI is considering submitting an IPO application to securities regulators as early as the second half of 2026. In preliminary discussions, the company has proposed a minimum fundraising target of $60 billion, and the actual amount raised is likely to far exceed this figure.

However, insiders emphasized that the discussions are still in the early stages, and the specific fundraising scale and timeline will be adjusted based on business development and market conditions.

It is understood that Sarah Friar, OpenAI’s Chief Financial Officer, has informed some partners that the company aims to complete the IPO by 2027. Nevertheless, some advisors predict that this process may be advanced to the end of 2026.

In response to the IPO rumors, an OpenAI spokesperson stated: “An IPO is not a current priority for the company, so no specific timeline has been set. We are focused on building a sustainable business model and advancing our mission to ensure artificial general intelligence (AGI) benefits all of humanity.”

Analysts believe that as OpenAI completes its complex organizational restructuring and reduces its reliance on Microsoft, the company will accelerate its IPO plans. Insiders pointed out that listing on the public market will open up a more efficient financing channel for OpenAI, accelerate business expansion through equity acquisitions, and provide funding support for the trillion-dollar AI infrastructure development plan proposed by CEO Sam Altman.

Notably, although the market expects the company—currently valued at $500 billion—to achieve an annualized revenue of over $20 billion by the end of the year, insiders disclosed that its loss scale continues to expand.

Altman also admitted during a recent live broadcast: “Given our future capital needs, going public is indeed the most likely path we will take.”

Restructuring Concluded, IPO Countdown Begins

OpenAI has a distinctive development history. Founded as a non-profit organization in 2015, it adjusted its structure a few years later and established a for-profit subsidiary overseen by the non-profit entity. This unique structure is designed to ensure the safe development of AI technology and prevent it from being dominated by commercial interests.

On Tuesday (U.S. local time), OpenAI announced another major adjustment: the newly established OpenAI Foundation, as a non-profit entity, holds a 26% stake in the OpenAI Group and has been granted stock options to increase its shareholding once the company achieves specific milestones. This arrangement allows the non-profit organization to continuously benefit from the company’s commercial success.

If OpenAI successfully goes public, investors such as SoftBank, Thrive Capital, and Abu Dhabi’s sovereign wealth fund MGX will reap substantial returns. Microsoft, the largest institutional investor, has invested a total of $13 billion and currently holds approximately 27% of OpenAI’s shares—valued at around $135 billion based on the current valuation.

OpenAI’s launch of IPO preparations coincides with a period when AI concepts are highly sought after in global capital markets. Earlier this year, CoreWeave, an AI cloud service provider, went public with a valuation of $23 billion, and its stock price has since nearly tripled.

On Wednesday, NVIDIA became the first listed company with a market value exceeding $5 trillion, highlighting the core position of the AI industry in global capital markets.

Start with the business driver behind the metric

A finance ratio is useful only when teams understand what operational behavior moves it. Break revenue, margin or payback into user volume, conversion, pricing, usage and variable cost so a variance can be traced to a real product or sales change.

This makes forecasts easier to update because assumptions can change independently instead of rebuilding one large top-down model.

Use cohort evidence before extrapolating growth

AI products often change quickly, so recent cohorts may behave differently from early adopters. Compare retention, spending and support cost by signup period or customer segment before assuming historical averages will continue.

Connect financial metrics to operational decisions

The purpose of the model is not only reporting. It should help decide pricing, hiring, infrastructure commitments and channel spend while there is still time to change course.