Microsoft and OpenAI end their exclusive and revenue-sharing deal

Original Article ↗ Hacker News Discussion ↗

Deal structure changes

  • Microsoft will stop sharing revenue from its AI products with OpenAI; commenters infer the old rev-share was mainly compensation for exclusivity.
  • OpenAI will still pay a revenue share to Microsoft until 2030, now capped; exact percentages and cap size are undisclosed.
  • Exclusivity largely ends: Microsoft remains “primary cloud provider” and gets models “first on Azure,” but OpenAI can sell and deploy on other clouds.
  • Microsoft keeps long‑term IP rights and a large equity stake (often cited as ~27%), plus OpenAI has contracted to buy an additional $250B of Azure services over ~a decade.
  • Many details (model pricing, whether Microsoft now “gets models for free,” precise exclusivity windows) are called unclear.

Who benefits? Perspectives

  • One view: this is a very strong deal for Microsoft—no rev‑share out, continued rev‑share in, big equity, and guaranteed Azure spend.
  • Opposing view: OpenAI “had to get out,” was compute‑constrained and frustrated with Azure quality, and needed freedom to work with AWS/GCP and others.
  • Some see it as a mutual damage‑control compromise after rising tensions and possible antitrust posturing on both sides.

Cloud and model ecosystem impact

  • Expected that OpenAI models will soon appear on AWS Bedrock (confirmed by public statements referenced in the thread) and potentially GCP.
  • This could make Google Cloud the only provider that could in theory offer all three major lab families (OpenAI, Anthropic, Gemini), though Google may keep Gemini exclusive.
  • Several argue that hyperscalers are becoming “infrastructure suppliers” to increasingly powerful model companies rather than the other way around.

Financial engineering and economics

  • Commenters highlight a “circular economy”: OpenAI commits massive Azure spend, Microsoft’s stake in OpenAI is enormous on paper, yet OpenAI is still burning large sums on training and infrastructure.
  • Debate over whether inference itself is profit‑making vs. the overall business being heavily loss‑making.
  • Some see the numbers as partly marketing theater designed to signal inevitability and scale.

AGI rhetoric and skepticism

  • Many are turned off by repeated AGI talk in the press materials and prior agreements.
  • The partnership previously used a financial definition of AGI (e.g., AI systems generating ~$100B profit); this is widely mocked as non‑scientific.
  • Long subthreads debate whether current LLMs are already a form of AGI, whether “AGI” is a moving goalpost, and whether the term has become mostly marketing.

Broader AI/LLM sentiment

  • Split between enthusiasm for rapid capability gains and strong skepticism about hype, economic sustainability, and genuine “intelligence.”
  • Several note that, regardless of AGI, open‑source and cheaper models (e.g., DeepSeek, Qwen, Chinese labs) are now “good enough” for many tasks, eroding moat narratives.