Reports dated October 9, 2026, said OpenAI was negotiating to raise at least $30 billion. Its revenue annualized from the end of September was about $50 billion, and the company is said to be targeting at least $70 billion by year-end, driven by the expansion of its enterprise business.
Making Sense of the Difference Between $50 Billion and $70 Billion
The first point to keep in mind is that annualized revenue is not the same as revenue actually earned over a year. Annualization is a measure of business scale based on the assumption that the current monthly revenue continues for 12 months. The figure of about $50 billion is not revenue confirmed in annual financial results.
There is another caveat to the roughly $70 billion figure initially reported. It is said to be an adjusted figure intended to make comparisons with rival Anthropic easier by aligning how revenue is recognized. It does not mean that actual annualized revenue at the end of September was about $70 billion.
The difference arises in sales made through cloud companies and similar partners. Anthropic records the full amount paid by customers as revenue and treats the cloud provider’s share as an expense. OpenAI, by contrast, records only its own share as revenue in some partnership transactions. Both approaches are said to comply with U.S. accounting standards, with the treatment depending on which party is responsible for customer relations and service delivery in a given transaction.
In other words, simply placing revenue figures side by side does not provide an apples-to-apples comparison of business size. The target of reaching at least $70 billion by year-end should also be considered separately from historical figures adjusted for comparison.
Enterprise Business Drives Growth
Reports said total annualized revenue grew 77% in the third quarter, while enterprise revenue rose 107%. The materials provided do not specify the comparison period, so the figures cannot be described definitively as year-over-year growth, but they show enterprise revenue growing faster than the overall figure.
Figures that OpenAI reportedly presented at a developer event also point to substantial usage: ChatGPT has more than 1.2 billion weekly users; ChatGPT Work and the coding assistant Codex have more than 35 million weekly users combined; and 2.5 million companies use the company’s products.
However, user numbers and revenue are not the same metric. Separating broad adoption, including free use, from the fees businesses pay for work-related use brings the focus of this growth into view. Reports say expanded business with companies that integrate AI into their work and pay for it on an ongoing basis is driving revenue higher.
Caution Around the $30 Billion Fundraising and an IPO
In the new fundraising talks, OpenAI is said to be targeting a $1.4 trillion valuation before the funds are raised. The fundraising target of at least $30 billion and the valuation of the company as a whole are separate figures.
In March 2026, reports said OpenAI raised up to $122 billion at a post-money valuation of $852 billion. The latest target is higher, but pre-money and post-money valuations are based on different terms and cannot be compared directly on the same basis. The materials provided also do not identify the investors in the new talks or their final terms.
Meanwhile, the initial public offering is said to have been pushed back to the following year. CEO Sam Altman reportedly cited safety risks, but accounts also say discussions about the delay were underway before the recent cybersecurity issue. It cannot be concluded that safety was the sole reason for the delay.
Investment Returns Cannot Be Measured by Revenue Growth Alone
After annualized revenue of about $50 billion was reported, technology stocks came under selling pressure, including semiconductor stocks, which fell by several percent. However, stock prices move in response to multiple factors, so it is not possible to conclude that this report alone caused the declines.
Assessing the growth of AI companies requires looking at revenue growth alongside the spending needed to generate that revenue. Increased usage can create more revenue opportunities, but training AI models and generating responses require computing infrastructure. For companies facing enormous data center-related expenses, higher revenue does not automatically mean higher profits.
From a business customer’s perspective, the reason to keep paying is whether the company can verify that the service saves time or improves results. The materials show growth in enterprise revenue, but do not provide specific figures for productivity gains among customers or the profitability of OpenAI’s business after accounting for spending on its computing infrastructure.
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