- Published on
Open Source AI Gap Map
- Authors
- Name
- DeepHorus
Current AI, a non-profit global partnership, has released the Open Source AI Gap Map, an ambitious effort to index the state of open source AI. The map, launched on July 1, 2026, provides a comprehensive snapshot of the ecosystem, highlighting both its breadth and fragmentation. This tool aims to identify gaps and opportunities for collaboration in the rapidly evolving field.
Founded at the AI Action Summit in Paris in February 2025, Current AI is dedicated to building a "public option" for artificial intelligence. The organization has secured $400 million in committed funding, reflecting the strategic importance of open source AI. The Gap Map is a key initiative to map the landscape and guide future investments.
Version 0.1 of the Gap Map details 421 products in depth, including 266 software tools and libraries, 85 models, 50 datasets, and 20 hardware projects. These products come from 228 organizations and are organized into 14 categories across three layers of the AI stack: model components, product/user experience, and infrastructure. Additionally, the map lists 24,400 uncategorized artifacts in the "long tail" of open source AI, which will remain unscored until further research is conducted.
The map reveals a thriving but uneven ecosystem, with some categories densely populated while others remain sparse. This granular data can help developers, policymakers, and investors identify where resources are most needed. The underlying data is released under an MIT license on GitHub, enabling widespread use and analysis.
The open source community has welcomed the initiative for its transparency and potential to foster collaboration. Researchers can explore the 1,184 YAML files and accompanying notebooks, schemas, and scripts used to gather the data. The project also tracks 16,185 GitHub repos, providing a dynamic view of the landscape that can be queried using tools like Datasette Lite.
Current AI plans to update the map regularly, with community contributions encouraged to fill in gaps. As the open source AI movement grows, the Gap Map could become a critical tool for aligning efforts and avoiding duplication. Future versions may include scoring for the long-tail artifacts and deeper analysis of dependencies between projects.
