State of Open Source AI

* Report: The state of open source AI. Mozilla, 2026.

URL = https://stateofopensource.ai/

Context

"In New Zealand's far north, a Māori broadcaster trains speech models for te reo — a language too small for any market — under a license that keeps the data with its people. PwC, one of the largest accounting firms in the world, fine-tuned an open model on the language of finance and runs it today for hundreds of clients, on its own hardware, with no per-token meter running. Researchers in Lausanne built an open medical model with the Red Cross, tuned to its humanitarian guidelines, and are preparing clinical trials at home and in Tanzania. In East Africa, farmers diagnose cassava disease with a model that runs on the phone itself, offline, in fields the cloud has never reached. In Switzerland, a public consortium trained a national model on public supercomputers and released all of it: weights, data, training code. None of them asked permission, and none of them could have rented this. They own it — that is the whole idea."

Description

Excerpts:

"Open weights are no longer a compromise. They are where the work happens: a majority of production tokens now route through them, and the five highest-volume models on OpenRouter are all open. Closed models still lead at the frontier, on reasoning and multimodality, but the frontier is not what most workloads need. Commodity inputs do not hold pricing power. Value moves up, to the agentic harness.

Open models lead in adoption: 79% of developers adding AI functionality use them, against 71% for closed, and the two are largely complementary, with half of developers using both. But production is where teams stall: only 51% of open-model teams reach production versus 63% for closed. The gap is operational tooling and trust, not model capability.

Open-weight AI is a commercial market at multi-hundred-billion-dollar scale, built by funded companies and run in production by global enterprises. Databricks crossed a $5.4B run-rate; Mistral scaled 20× to ~$400M ARR in twelve months; DeepSeek reached ~$220M ARR and recently raised $7.4B at a valuation over $50B. Five revenue models are proven at scale: hosted inference, enterprise platforms, on-prem licensing, fine-tuning services, and harness tooling."