Frontier AI companies like OpenAI and Anthropic have a narrow competitive moat—maybe one or two models ahead of open-source alternatives—and face a difficult choice: either compete on cheaper models with shrinking margins, or somehow lock down access through regulation. Neither path is straightforward, and their real survival depends on whether they can achieve recursive self-improvement before competitors catch up or customers stop paying.

• Frontier labs' advantage is temporary: they're only 1-2 models ahead of the competition, and that gap closes as talent and compute spread globally. Their moat depends partly on owning user data (code traces, conversations) that trains the next generation, but there's plenty of that data available elsewhere.
• Enterprise customers are already hitting their AI spending limits and demanding ROI. CFOs are questioning whether $200B+ in annual AI token spending actually translates to measurable business value, leading companies to restrict expensive frontier models and shift to cheaper alternatives.
• The labs' only real escape route is recursive self-improvement (RSI)—automating the research process so each model generates its successor faster. If that works, they leap ahead 20 generations instead of 2. If it doesn't, they're back to competing on commoditized inference margins.