Under the surface of massive investment and high-profile expansion, OpenAI is quietly achieving an impressive leap in commercial efficiency. According to multiple sources, as of October 2025, a key internal metric at OpenAI—the "Compute Profit Margin"—has risen to 70%, a significant increase from 52% at the end of 2024 and double the level in January 2024. Although this data has not been officially confirmed by OpenAI (the company's spokesperson responded with "we do not disclose this metric"), it sends a strong signal: this AI giant, once known for "burning money," is accelerating its transformation from a technological pioneer into a high-efficiency profit engine.

What is "Compute Profit Margin"?

This metric refers to the profit margin of AI service revenue after deducting the cost of model operation (including GPU computing power, electricity, maintenance, etc.). For example, if a user pays $100 to use GPT-5, and $30 is used to cover the costs of inference and training computing power, then the compute profit margin is 70%. This indicator directly reflects the health of the unit economic model of large model business and is a core measure of whether an AI company can operate sustainably.

The Three Driving Forces Behind the Efficiency Leap

1. Model inference optimization: New models such as GPT-5.1 and Sora adopt sparse activation, dynamic batching, and quantization compression technologies, significantly reducing the cost per token for inference;

2. Self-developed computing power layout taking effect: The Stargate supercomputing center and custom AI chips are gradually being put into use, reducing reliance on expensive commercial cloud services;

3. Increasing proportion of high-value users: Enterprise API customers and GPT Enterprise subscription users are growing rapidly, and ARPU (average revenue per user) continues to rise.

What Does 70% Mean?

Compared to traditional cloud computing business profit margins, which typically range between 30% and 50%, OpenAI’s compute profit margin exceeding 70% indicates that its large model service has achieved profitability comparable to or even surpassing mature SaaS products. Although the company remains in a net loss position due to strategic investments like the Sora launch and global data center construction (projected negative cash flow over $9 billion in 2025), the core AI service cash flow engine has already started strongly.

Industry Implications: The AI Competition Enters a New "Efficiency-Driven" Phase

OpenAI’s efficiency leap may push the entire industry to shift from "scale-centric" to "unit economic optimization":

- Competitors such as Anthropic and Google DeepMind are accelerating model distillation and edge deployment;

- Open-source model providers like Mistral and DeepSeek are lowering inference costs through MoE architectures;

- Cloud providers are launching "AI-specific instances" to compete for high-profit AI workloads.