Kenneth Arrow’s original Information Paradox points out that sellers can’t prove the value of their knowledge without giving it away. In the AI era, buyers face a flipped version: to get useful results from an AI model, companies must feed it their own proprietary data—trade secrets, workflow details, performance metrics. The more you tailor the model, the more of your internal know-how you hand over. Meanwhile, the vendor harvests that “exhaust,” learns your patterns, and gains insights you’ll never see. That imbalance deepens over time. Public data can legally train base models, but providers then lock down the rights around how customers can use the model outputs. They keep the power to ingest usage logs, corrections and evaluations. Enterprises end up fueling the vendor’s competitive edge while getting little transparency or recourse. The article argues for a hard trust boundary: a private AI environment inside each firm’s infrastructure. Companies should hold their evals, memory traces, feedback loops and adapted weights. They need the ability to fine-tune or train models on their own turf. Four pillars make it work: control (own your data and evaluation metrics), capability (run training workflows privately), choice (swap or replace underlying models without losing ground) and cost efficiency (mix context, models and tasks without bloated vendor fees). Pulling these together creates a self-reinforcing learning loop. You use model outputs to improve your own version, not the vendor’s global system. That way, firms retain the unique intelligence they bring to the table instead of fueling someone else’s advantage—the core of the Reverse Information Paradox.