Open Source AI Gap Map Current AI is "a global partnership building a public option for AI", founded as a non-profit at the AI Action Summit in Paris in February 2025 and backed by serious capital ($400m already committed). They launched their Gap Map a couple of days ago - an attempt at indexing the current state of open source 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. ✦ AI generated
Beyond the 421 deeply-indexed products, tens of thousands of additional artifacts remain unscored until the project researches and cites them.
Simon Willison: 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.
Willison points out that because the Gap Map's data lives on GitHub, it can be explored interactively via Datasette Lite, including a CSV of 16,185 tracked GitHub repos.
Simon Willison: Since the files are on GitHub you can use Datasette Lite to explore some of them - here are 16,185 GitHub repos the project is tracking as a CSV file loaded into Datasette Lite.
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Current AI describes itself as a global partnership building a public option for AI; it was founded as a non-profit at the Paris AI Action Summit in February 2025 and has already secured $400m in funding.
Simon Willison: Current AI is "a global partnership building a public option for AI", founded as a non-profit at the AI Action Summit in Paris in February 2025 and backed by serious capital ($400m already committed).
The first version of the Gap Map details 421 open source AI products across software, models, datasets, and hardware, organized into 14 categories spanning three layers of the AI stack.
Simon Willison: 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).
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Willison finds the openly-licensed raw dataset behind the Gap Map—1,184 YAML files plus supporting notebooks and schemas—more compelling than the map visualization itself.
Simon Willison: The map itself is interesting to explore, but I'm more excited about the underlying data - released under an MIT license in the currentai-org/os-ai-map GitHub account: 1,184 YAML files plus the notebooks, schemas and other scripts used to help gather them.