Leaked internal documents reported by TechCrunch expose the scale of payments between OpenAI and Microsoft and make one thing clear: operating large generative AI systems is extraordinarily expensive. These disclosures pushed phrases like OpenAI Microsoft payments and OpenAI financials leaked into the headlines and into investor conversations about AI infrastructure spending.

Introduction

The documents show OpenAI paid roughly $493.8 million to Microsoft in 2024 and about $865.8 million in the first three quarters of 2025. Even more striking, inferred inference costs were about $8.65 billion in the first nine months of 2025 compared with roughly $3.8 billion for all of 2024. Those figures are central to searches such as How much does OpenAI pay Microsoft and OpenAI computing costs 2025.

Background: Why the OpenAI and Microsoft Deal Matters

OpenAI and Microsoft have a layered commercial relationship that mixes capital investment, infrastructure provisioning, and revenue sharing. Microsoft provides cloud compute through Azure and integrates OpenAI models across products such as Bing and enterprise services. In return, OpenAI shares a portion of revenues and receives credits that offset some training expenses. This arrangement is often discussed with search terms like Microsoft revenue share from OpenAI and OpenAI revenue versus expenses.

Key Terms Explained

• Inference costs: expenses tied to running model predictions for users, for example generating text or images on demand. These costs scale with usage and compute intensity and are central to queries about AI inference costs.
• Training costs: large, one time expenses for training or fine tuning models on vast datasets and specialized hardware.
• Revenue share agreement: a contractual split of income where one party returns a percentage of sales or related revenues to another. This is what drove the reported OpenAI Microsoft payments figures.

What the Leaks Reveal

Main disclosures from the leaked documents include:

• Payments to Microsoft: about $493.8 million in 2024 and roughly $865.8 million in the first three quarters of 2025 under revenue share and infrastructure arrangements.
• Inference or operational costs: approximately $8.65 billion in the first nine months of 2025 versus roughly $3.8 billion for all of 2024, illustrating rapid growth in run time expenses as usage scales.
• Deal structure complexity: Microsoft appears to cover some model training costs via credits while Azure and Bing related revenues flow back to OpenAI in part, creating intricate cash flows that show up in searches like OpenAI financials leaked and OpenAI revenue share agreement details.
• Timing and valuation risk: the numbers raise questions about profitability and valuation dynamics ahead of any potential public offering.

Implications for Industry and Investors

Key takeaways for industry watchers, customers, and investors include:

• Margin pressure is real. Even with substantial revenue growth, inference costs in the billions create downward pressure on gross margins. If usage scales faster than pricing or efficiency gains, profitability will be difficult to achieve.
• Microsoft is a major beneficiary. The structure funnels cash and usage to Microsoft through direct payments and Azure consumption, underscoring the importance of cloud partnerships in stories about AI infrastructure spending.
• Valuation and IPO timing are affected. Leaked payments and cost figures force fresh scrutiny of OpenAI unit economics and long term sustainability.
• Operational levers will matter for profitability. Providers can pursue model efficiency, custom hardware or discounted capacity through negotiated cloud contracts, price differentiation for high cost operations, and enterprise contracts with long term commitments.

Practical Advice for Businesses

Businesses planning to adopt generative AI should model not only software fees but also the potential for high variable costs as usage grows. Search queries such as AI compute infrastructure expenses and OpenAI revenue versus expenses reflect the questions procurement and finance teams will ask when evaluating projects.

Conclusion

The leaked OpenAI and Microsoft documents make a blunt point: running state of the art AI at scale is costly and the financial arrangements that enable it are complex. For customers and competitors, expect AI pricing and feature roadmaps to be shaped as much by compute economics as by innovation. For investors and regulators, these numbers shift focus from growth alone to profitability mechanics and partner dependencies.

Observers should watch for provider responses through efficiency gains, new pricing models, or deeper cloud partnerships because those choices will determine whether large scale AI becomes a durable business or an expensive experiment. Common search queries in this area include How much does OpenAI pay Microsoft and AI inference costs 2025 which can help readers find more detailed coverage and analysis.