OpenAI Fails to Meet Internal User Growth and Revenue Targets
OpenAI has recently failed to meet its internally set targets for new users and revenue.
ChatGPT did not achieve the goal of 1 billion weekly active users by the end of 2025, with actual figures around 900 million; in the first half of 2026, it repeatedly failed to meet monthly revenue targets, while its market share in coding and enterprise was eroded by Anthropic.
In market mechanisms, massive capital for AI infrastructure continues to flow into data center contracts, while slowing revenue growth has put pressure on OpenAI's cash flow. Compute suppliers benefit in the short term from locked-in orders, and investors and competitors like Anthropic gain from market share competition, putting OpenAI's high valuation under pressure for reassessment.
Source: Public Information
ABAB AI Insight
OpenAI previously set aggressive growth KPIs, including reaching 1 billion weekly active users for ChatGPT by the end of 2025. CFO Sarah Friar has repeatedly warned internally about the risks of matching compute spending with revenue. This multiple misses continue the slowdown trend into 2026 amid direct competition with Anthropic in the enterprise sector.
On the capital front, OpenAI has committed to approximately $600 billion in compute contracts and completed $122 billion in financing, with resources highly concentrated in data centers and model training. However, slowing user growth and delayed enterprise adoption have led to revenue falling short of expectations. Strategically, OpenAI is accelerating the monetization of enterprise paid products and APIs, while attempting to regain market share in coding through vertical proxy products like Codex.
Similar cases include Uber, which achieved an IPO after multiple early misses on growth targets through continuous financing and model adjustments, and WeWork, which collapsed after high cash-burning expansion failed to meet revenue expectations. Currently, OpenAI is at a critical transition stage from explosive user growth to sustainable profitability verification, with its valuation still maintaining a high level of $850 billion.
Essentially, this is a matter of capital concentration: the AI compute heavy asset model requires exponential revenue matching. The mechanism is that under the high fixed costs of training and inference combined with intensified competition, the slowdown in user and paid conversion growth leads to tight cash flow, with pricing power shifting from aggressively expanding labs to competitors and compute suppliers that can quickly achieve profitability, while amplifying the risk of valuation reassessment before an IPO.