Open vs Closed: Why Foundation Models Are Diverging

The AI stack is splitting. Closed systems minimize surface area—single sign-on for text, vision, and speech with predictable SLAs—but at the cost of extensibility. Open, modular stacks ask more work upfront but pay back in control, unit economics, and IP safety. Most teams don't need to train from scratch; they need a place to customize: retrieval, tool hooks, and secure prompts. If your data changes weekly, choose composable pieces; if your value is UX and routing, a managed bundle. The mature posture is hybrid: closed where latency/scale matter; open where differentiation lives.