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Open collaboration is helping AI progress by making more models, code, datasets, and infrastructure available for others to inspect, adapt, and improve. Stanford HAI counted 149 foundation models released in 2023; 65.7% were classified as open-source, up from 44.4% in 2022 and 33.3% in 2021. The same 2024 report counted about 1.8 million AI-related GitHub projects and 12.2 million stars in 2023.
Why collaboration matters to AI progress
Building an AI system involves more than training a model. Researchers and developers need implementations, data, evaluation methods, deployment tools, and documentation. When some of those components are shared, other teams can test them, adapt them to new tasks, find defects, and publish improvements rather than starting from scratch.
That work happens across organizations as well as within individual projects. Stanford HAI reported that 21 notable models in 2023 resulted from industry-academia collaboration. Academic research can contribute methods and analysis; industry partners can bring engineering capacity and deployment experience. The combination can help research move toward systems people can run and evaluate.
Shared repositories also make contributions more visible: people can inspect project activity, report problems, propose changes, and build on released work. But a repository’s existence alone does not prove that a system is reproducible, safe, or open in every meaningful sense.
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What the growth figures do—and do not—show
The Stanford HAI figures point to growth in both open-model releases and community activity, but they are dated measurements, not live counts. “Open-source” is the report’s classification of model releases; it should not be read as proof that every model’s training data, process, and evaluation materials were released. GitHub project and star totals indicate activity and interest, not the quality or maintenance of every repository.
Open-source collaboration is also part of organizational AI infrastructure. In a 2024 Linux Foundation Research survey of 316 professionals, 84% reported moderate to high generative-AI adoption, respondents characterized 41% of infrastructure as open source, and 82% agreed that open-source AI is critical for a positive AI future. Those results describe that survey’s respondents; they are not universal market shares or adoption rates.
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Different projects contribute at different layers
“Open-source AI” is an ecosystem, not one kind of project. Some efforts distribute models or datasets; others help teams serve models, move them between runtimes, or coordinate work on openly licensed models. Linux Foundation reporting described Hugging Face Hub as a central collaboration platform and reported more than 1.5 million models and 350,000 datasets on the Hub in 2025. These are reported platform totals, not a guarantee that every item has the same license or documentation.
| Project or platform | Role in the ecosystem | What it helps people do |
|---|---|---|
| Hugging Face Hub | Model and dataset sharing hub | Publish and discover shared models and datasets, supporting reuse and community feedback. |
| vLLM | Inference and serving | Run and serve models efficiently. |
| ONNX | Model interoperability | Move models between tools and runtimes. |
| Open Model Initiative | Openly licensed model collaboration | Coordinate a community effort focused on openly licensed AI models. |
| Linux Foundation AI & Data | Project hosting and neutral coordination | Provide a foundation for collaborative AI and data projects. |
These examples address different needs, so they are not direct substitutes. A team looking for a model to adapt has a different task from one choosing an inference server or a way to exchange models between software environments.
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How to tell whether an AI project is genuinely open enough
The label “open” can refer to only part of a system. A project might share source code but not model weights, or publish weights without training data or enough documentation to reproduce results. Before relying on a project, check the artifacts and rights that matter for your intended use.
- Openness: Identify what is actually released: code, model weights, data, training information, and evaluation materials.
- License: Read the terms for each component. Confirm that use, modification, and redistribution are allowed for your particular purpose; do not assume one project-wide license covers every artifact.
- Reproducibility: Look for documentation, dependencies, checkpoints, and instructions for accessing data. Consider whether they are sufficient to repeat the reported results.
- Governance: Find out who reviews contributions, sets priorities, resolves disagreements, and controls releases.
- Capability and efficiency: Evaluate performance for the task you actually need, along with the computing resources required to deploy it.
- Safety and accountability: Check whether limitations, risk evaluations, and processes for handling incidents are documented.
- Community health: Look for ongoing maintenance, meaningful review of proposed changes, and participation beyond a single contributor or organization.
These checks apply to adoption as well as contribution. A permissive license cannot compensate for missing evaluation evidence, and a popular repository is not automatically suitable for a high-stakes use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to contribute to open AI projects
Contribution does not have to mean training a large model. Projects need code, documentation, testing, issue triage, dataset curation, evaluation, and clear reports of failures. Choose work that matches your skills and the project’s needs.
- Choose a layer and a concrete need. Decide whether you want to work on shared models or datasets, inference, interoperability, or project documentation. Read the project’s current contribution guidance and open issues before proposing work.
- Check the terms and expectations. Review the licenses and contribution rules for the relevant repository or dataset. Confirm that you have the rights to submit any code, data, or other materials you contribute.
- Start with a reproducible, bounded change. Examples include improving an explanation, adding a test, documenting a failure case, or fixing a contained bug. State the environment and steps needed to reproduce technical problems.
- Submit work for review and respond to feedback. Follow the project’s review process, explain what changed, and revise or clarify the contribution when maintainers request it.
- Keep safety and limitations visible. For model- or data-related work, document relevant evaluation results, known gaps, and restrictions rather than presenting a partial improvement as a complete assurance.
The most useful contribution is often one that makes another person’s work easier to verify, reproduce, or maintain.
What open-source AI still cannot guarantee
Sharing components can broaden access and scrutiny, but it does not automatically make a system fully transparent or responsible. Data may be unavailable, licenses may limit particular uses, documentation may be incomplete, and evaluation may not cover the risks of a specific deployment. Governance and maintenance also vary from project to project.
Open collaboration is therefore best understood as a way to expand participation and make parts of AI systems more inspectable—not as a blanket certification of quality, safety, or unrestricted use. The Stanford and Linux Foundation measurements capture growth and reported adoption at particular points in time; they do not establish which project will lead in the future.
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