“The Gap Map comes out of cumulative work to identify the points of highest leverage in the open source AI stack: where to build something new, where to invest in capability, where to open up the tools. By creating an up-to-date visualization of the ecosystem where we can all see both the progress and the gaps in the space, we hope to rally the community around a collective roadmap. Today, thanks to a handful of amazing designers and data scientists (and a lot of startup hustle), we’re sharing the v0.1 of the Open Source AI Gap Map. The Gap Map surveys over 24,626 projects to show what technical components exist now, their state of maturity, and where builders are needed to fill critical gaps in the open source AI stack. 

Over time, the Gap Map should answer:

• What projects exist across the various layers of the stack?
• What layers are overinvested in, or underfunded?
• Where are open source options lagging because of adoption, maturity, or capability?
• And, most importantly: What building blocks are missing for creating completely open source AI products?
• From Map to Roadmap All over the world, entrepreneurs, funders, governments, and designers are frustrated with proprietary AI, and are clamoring for an alternative. Some are looking to save money for their startups, others call it AI resilience, and many want sovereignty in the AI stack. We believe the solution lies in an open and public-interest AI stack. The Gap Map is a first of a series of tools Current AI will invest in to help make this complex environment legible. If we see the gaps, we can prioritize them, and then we can collectively direct energy and funding to closing them. That’s how we believe we get to an open AI alternative.
• Methodology To create this first version of the Gap Map, we used both a discovery step to identify projects within the ecosystem (from leading open source AI experts at the Columbia Convening, MOF, and Hugging Face), and a more rigorous scoring and enrichment step to grade each product. We identified and evaluated over 24,626 projects from foundation models through inference backends, assessing projects across openness (how open is it?), capability (how good is it?), and adoption (how used is it?). The taxonomy we use to categorize products descends directly from the 2024 Columbia Convening on Openness in AI. The Gap Map v0.1 details 421 products in depth: 266 software tools and libraries, 85 models, 50 datasets, and 20 hardware projects, produced by 228 organizations. These products are organized into 14 categories across 3 layers of the stack (model components, product / UX, and infrastructure). The remaining 24,400 artifacts constitute the uncategorized long tail of the open source AI ecosystem, and will carry no score until they are researched and cited. Note that within the Gap Map, we intentionally don’t compare closed versus open AI…”