In this text, I explain—clearly and step by step—how the relationship between OpenAI and Microsoft was built and why it’s now under strain. I break it down into four parts: (1) the origins and governance structure of OpenAI, (2) how it was funded and why Microsoft stepped in, (3) what the AGI clause actually says and why it’s hard to enforce, and (4) what all this means for products, customers, and the wider market if no agreement is reached.

• Born as a non-profit. OpenAI started as a research organization aimed at advancing artificial intelligence while prioritizing safety and broad benefit over profit.

• For-profit “capped” arm. To scale compute and talent, OpenAI later created a controlled for-profit entity under the non-profit’s oversight. The foundation owns the intellectual property (IP) and can block its commercialization if considered risky.

• Board control. The board of the non-profit controls how IP moves to the commercial arm. The design: protect humanity first, even if it limits short-term monetization.

Practical takeaway: the foundation decides what tech can leave the lab. The commercial entity sells and ships it but doesn’t own the IP outright.

• Microsoft contributed: staged capital injections (2019, 2021, 2023), massive Azure compute capacity, technical support, and enterprise distribution (integrations into Microsoft 365/Copilot).

• In exchange Microsoft received:

  • Preferred Azure usage as OpenAI’s cloud backbone.

  • Priority access to models and IP.

  • Economic participation through a revenue-share mechanism until a set cap is recovered.

• Deal logic: OpenAI got scale and speed; Microsoft locked in cutting-edge AI for its products and enterprise clients.

Practical takeaway: Microsoft fueled OpenAI’s rapid growth; OpenAI powered Microsoft’s AI roadmap.

• Plain language: if OpenAI achieves AGI, Microsoft’s exclusive access ends.

• Key issue: no single operational definition of AGI. Three main schools of thought exist:

  1. Cognitive / multitask: match or exceed an average human across diverse reasoning, language, vision, planning.

  2. Behavioral / autonomy: ability to self-learn, adapt, and chain complex tasks with minimal supervision while avoiding systemic hallucinations.

  3. Economic / macro: measurable global productivity gains or other economic impact as proof of AGI-level capability.

• Why it’s stuck: no shared, auditable yardstick. Either side can argue we’re there—or not—depending on their interest.

Practical takeaway: the clause exists, but the “thermometer” to measure AGI doesn’t.

• Evolution: OpenAI moved from pure research to massive consumer and enterprise products (GPT models, productivity tools, APIs) and high-value consulting for industries and government.

• Impact: higher demand for compute, capital, and talent; growing interdependence with Microsoft for cloud and go-to-market; and friction as OpenAI’s new tools start overlapping with Microsoft’s core productivity suite.

Practical takeaway: OpenAI now behaves like a scaled tech company, not just a research lab. That shift strains its original deal.

• Infrastructure: exclusive Azure use vs. need for multi-cloud flexibility.

• Product overlap: OpenAI tools now touch spreadsheets, presentations, and collaboration—Microsoft’s stronghold.

• Governance: no agreed way to certify AGI or trigger the clause.

• Financial stakes: declaring AGI could cut Microsoft off from exclusivity and revenue share; delaying AGI constrains OpenAI’s ability to expand freely.

Practical takeaway: cloud lock-in, product collision, and undefined AGI criteria make the deal fragile.

What’s at stake: more than technical nuance, this is a definitions-and-incentives problem with huge financial and market impact. Without a shared, testable AGI benchmark, the clause is unworkable and invites conflict.

• For Microsoft: risk of losing exclusivity and expected returns.

• For OpenAI: barriers to raising capital, diversifying cloud providers, and scaling as an independent platform.

Realistic paths forward (from most to least practical):

1. Technical renegotiation: agree on auditable AGI metrics (multimodal reasoning, autonomy, safety thresholds), clear review windows, and a defined financial cap.

2. Independent oversight: create an external expert panel to verify AGI milestones instead of leaving it to one party’s call.

3. Commercial architecture refresh: allow multi-cloud, set non-compete boundaries in productivity products, and keep joint go-to-market where valuable.

4. Partial separation: maintain tactical integrations but loosen other constraints so each can compete and partner freely.

My operational view: in any high-impact AI partnership, you need measurable definitions, neutral technical governance, and clean exit clauses. Without them, rapid technical progress can turn a smart deal into systemic risk for both sides and for every customer built on top of it.