Bloomberg reports that an increasing number of startups are building custom systems using open source weight models to cut costs and reduce reliance on OpenAI and Anthropic.

Legal tech company Harvey, valued at $15.6 billion, previously focused on specialized legal tasks using closed-source models like GPT-4. After a March update to its agents, customer usage surged, and insiders reported that gross margins fell from about 50% at the beginning of the year to approximately -50% by June. The company subsequently joined the ranks of those using open source weights, including models from Chinese labs, to create its own systems. In August, it released its proprietary model based on the Moonshot Kimi K3 training, with gross margins turning positive as usage adjusted. Harvey declined to comment on specific financials. The company claims token usage increased about 20 times that year, with two leading labs charging enterprises based on usage beyond subscriptions.

Similar actions have spread. Abridge is developing clinical models based on Nvidia's open source weights; Decagon claims about 80% of customer queries are handled by its proprietary models; Ramp raised $750 million in June, with the co-CEO stating that self-training made no sense a year ago but now does. Sequoia and General Catalyst are funding this chain. Anthropic reminded investors in an internal meeting that Harvey's most challenging tasks still require its flagship model.

Closed-source pricing based on tokens directly translates agent usage into cost curves; open source weights allow fine-tuning with proprietary data and deployment in proprietary systems, converting variable bills into fixed investments in computing power and engineering. Chinese open source weights have become an option due to low prices and availability for download, while in the U.S., companies like Arcee, Reflection, and Poolside are providing domestic open source alternatives.

Buyers are application-layer companies whose usage bills have eroded gross margins and must regain control over inference; sellers are labs expecting revenue solely from closed-source APIs. The event-driven aspect is that companies at the level of Harvey are reporting negative margins and model shifts in the same article. Beneficiaries include open source weights, inference clouds, and fine-tuning toolchains; those under pressure are cutting-edge closed-source interfaces that charge per usage.

The market trades on gross margins, not on who ranks first in models.

Source: Public Information