The article argues that AI buyers give away proprietary knowledge when they feed data into models, creating an asymmetry where providers learn from user inputs while customers gain little insight in return. It calls for enterprises to build private learning environments, control their evaluation metrics and data traces, and decouple orchestration from specific models to protect and compound their unique intelligence.

• Feeding proprietary data into AI models lets vendors learn from your patterns while you get no equivalent insight back into theirs
• Vendors lock down rights to ingest usage logs, corrections, and evaluations, compounding their advantage over time at the enterprise's expense
• The fix is a private AI environment inside company infrastructure that owns evals, memory traces, feedback loops, and adapted weights
• Four pillars—control, capability, choice, and cost efficiency—let firms improve their own systems instead of fueling the vendor's global model