dailyai.report

Nathan Lambert predicts the gap between open and closed models will shrink by mid-2026. He argues that synthetic data and refined distillation techniques will allow open-source weights to match proprietary performance. This trend reduces the moat for closed labs. Practitioners should expect high-tier capabilities to move toward local, customizable deployments.