On February 27, OpenAI closed its $110 billion funding round — the largest private raise in history. In our latest publication, we ask the question most enterprise risk frameworks have not yet modeled: what would happen if the world’s most critical AI infrastructure were to fail?
The raise extends OpenAI’s runway, but it does not change the structural argument.
In this interactive, we map the four failure scenarios risk managers need to stress-test:
- A funding gap as $150B meets a burn rate projected to hit $74 billion in operating losses in 2028 alone
- A competition collapse as Google’s structural cost advantage — roughly 3x cheaper per token — compounds with scale
- A governance crisis in a company whose board has already been tested to failure under oath
- A margin inversion that no funding round can resolve
We also quantify the blast radius: 92% of Fortune 500 companies run production workflows on OpenAI infrastructure. Over 6,800 SaaS platforms depend on the API. The contagion chain — from investors to Microsoft to AI-native startups to healthcare and financial services — runs from hours to months.
Alongside the scenario analysis, we include interactive tools: a Monte Carlo runway simulator, a chain-reaction VaR stress tester, a real-time contagion ring map, and a prediction markets dashboard tracking what traders with money on the line are actually pricing.
These dashboards prototype how a RiskSmart AI Concentration Risk module works in practice — because the question is not whether OpenAI is too big to fail. It’s whether your risk desk has modeled what happens if it does.
To see what’s possible, click here.
