The Rise of Intelligence Ownership: a task-trained open source model vs the frontier
Fermisense
Companies that fine-tune open-source models with reinforcement learning on proprietary task data significantly outperform those relying on frontier models, achieving better accuracy at a fraction of the cost. Top AI adopters saw revenue more than double between November 2022 and December 2025 compared to 15% growth for non-adopters, with specialized models reducing costs by up to 98% while exceeding frontier model performance. Organizations like Bridgewater, Harvey, and Intercom are converging on this playbook of owning proprietary task-trained models rather than depending solely on vendor APIs, fundamentally shifting how companies deploy AI at scale.
Why it matters
Companies that adopt AI-first strategies see variations in performance, with successful AI implementation hinging on redesigning workflows, incentivizing experimentation, and managing AI budgets through fine-tuning open-source models.