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The article argues that enterprises should post-train their own custom AI models for mission-critical, high-volume use cases to achieve differentiation, cost savings, and control over tradeoffs, rather than relying solely on general frontier models.
AI application layer companies such as Cursor, Decagon, Harvey, and Notion are shifting from using large model APIs to self-trained models. This trend aims to regain control of the data flywheel rather than merely saving costs.
OpenAI introduces improvements to its fine-tuning API with new features including epoch-based checkpoints, comparative playground for model evaluation, third-party integrations, and enhanced dashboard capabilities. The company also expands its custom models program to give developers more control and flexibility in building domain-specific AI solutions.
Harvey, a generative AI platform for legal professionals, partnered with OpenAI to create a custom-trained case law model that reduces hallucinations and improves reasoning for complex legal tasks like document drafting and contract analysis. The custom model, trained on 10 billion tokens of U.S. case law, achieved 97% lawyer preference over standard foundation models.
OpenAI announced the Assistants API, enabling developers to build agent-like experiences with persistent threads, Code Interpreter, Retrieval, and function calling capabilities. The company also launched a Custom Models program for organizations to train domain-specific GPT-4 models with exclusive access.