Harvey, Abridge, Ramp, Rogo, and other AI startups are embracing open-weight models or training their own models to reduce expensive reliance on frontier labs
The $15.6 billion legal startup Harvey built its business around training AI models like OpenAI's GPT-4 to do specialized work for lawyers.
Context & Ripple Effects
Harvey began as a legal-AI company built around generative models, raising a $21 million Series A backed by Sequoia and the OpenAI Startup Fund in 2023. Its August launch of Harvey Tenet, an in-house legal model, made the shift from using general-purpose models to controlling more of the model stack concrete.
The move comes as investors scrutinize whether open-weight model developers can turn technical interest into durable revenue, as detailed in coverage of the US open-weight model ecosystem. Harvey, Abridge, Ramp, and Rogo give that ecosystem an important customer-side rationale: lowering the cost and dependency associated with frontier-model APIs.
First-order effects
- Harvey, Abridge, Ramp, and Rogo can reduce their exposure to frontier-lab model costs by running open-weight models or training models tailored to their own workflows.
- Harvey's Tenet strategy gives its legal product a proprietary model layer rather than relying solely on GPT-4-class external models.
Second-order effects
- Frontier labs face pressure to justify premium API pricing and differentiated capabilities when specialized software companies can substitute internally trained or open-weight alternatives for parts of their workloads.
- Open-weight model providers gain a clearer route to adoption through vertical software companies that need lower-cost, controllable inference rather than a general-purpose model endpoint.
Third-order effects
- If vertical AI vendors keep moving model development in-house, competitive advantage shifts from access to a frontier model toward proprietary training data, workflow integration, and the ability to operate models economically.
- The open-weight ecosystem's commercial test becomes less about selling models directly and more about enabling software companies to own strategic parts of their AI stack.
The trend: Vertical AI companies are bringing model capability closer to the application layer to reduce frontier-lab dependence and protect unit economics.