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Open source is already a significant part of organizational AI: The Linux Foundation’s 2025 report says 89% of organizations use some form of open source in their AI stack, and 63% of companies use an open model. That adoption matters because openness can give teams more ways to inspect, adapt, and deploy AI—but an open model is not automatically a fully open AI system, and neither openness nor adoption guarantees lower costs, better results, or safer use.
Why does open source matter for AI?
AI is becoming part of products, services, and software-development workflows. Open source matters in that setting because it can make components available for use, inspection, adaptation, and sharing. Teams may gain more control over how a system is configured or deployed, while developers and organizations can collaborate on shared tools rather than each building every component independently.
Adoption figures show that open source is already involved in organizational AI use. The Linux Foundation Research’s 2025 report says 89% of organizations use some form of open source in their AI stack and 63% of companies use an open model. Those figures describe the populations and terms used in that report; they should not be read as universal estimates for every organization.
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What counts as open source AI?
People use “open source AI” in different ways, so it helps to distinguish an open model from an AI system that meets a formal definition of openness. The Open Source Initiative’s Open Source AI Definition 1.0, adopted October 27, 2024, centers on the freedoms to use, study, modify, and share an AI system.
For meaningful study and modification, the definition calls for information about training data, the complete code used to train and run the system, and the model parameters. Model parameters—often called weights—are important, but releasing weights alone does not necessarily meet the definition. Access to weights may let a user run or adapt a model, while leaving important parts of how it was built or operates unavailable.
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When evaluating a claim of openness, check what is actually published: the license and permissions, data information, training and inference code, and parameters. “Open weights” is a useful description of a particular release, not proof that the entire system is open source under the OSI definition.
Are open source AI models cheaper or better?
They can be a good fit, but there is no universal winner. The Linux Foundation’s 2025 report characterizes open source AI as cost-effective compared with proprietary solutions and associates it with productivity and collaborative innovation. It describes workforce effects as nuanced and more complementary than purely job-replacing. These are the report’s assessments, not guarantees for every model, task, or organization. The report page also states that the work was commissioned by Meta, context worth considering when weighing its conclusions.
Cost depends on the intended workload and the full operating setup, not just whether access to a model is free. Teams may need to account for infrastructure, engineering time, maintenance, security review, and support. Likewise, a model’s performance on one task does not establish that it will perform well on another. The sources cited here do not provide a head-to-head benchmark across named models.
Compare options against the work you need done:
- Permissions: Read the license terms for use, modification, redistribution, and any limits relevant to your deployment.
- What is available: Determine whether you have only weights or also data information and the code needed to train and run the system.
- Control: Check whether you can inspect, customize, and deploy the system in the way your product or service requires.
- Cost: Estimate expenses for the actual workload, including deployment and ongoing operation.
- Task performance: Evaluate the candidate on representative tasks and requirements rather than relying on a general reputation.
- Privacy and security: Establish what data the system processes, where it runs, and who is responsible for safeguards.
- Maintenance and support: Identify who will address defects, vulnerabilities, updates, and operational problems over time.
Can companies safely use open source AI?
Open source does not by itself make an AI system safe, private, compliant, or suitable for a regulated setting. It can offer opportunities to inspect or adapt components, but organizations remain responsible for reviewing the system, its dependencies, its data handling, and how it will be used. A model’s openness is not a substitute for security controls, privacy analysis, legal review, or ongoing maintenance.
Governance becomes especially important as AI systems gain the ability to take actions through tools and services. A Linux Foundation stakeholder discussion in February 2026 identified trust and identity, security and privacy, regulated-industry use, and open source in agentic AI as concerns. Its recommendations included clearer accountability and legal frameworks, standardized vocabulary, modernized security scaffolding, and support for open source communities. These priorities point to work organizations need to do; they do not amount to a blanket finding that agentic systems are safe for a particular use.
For organizational stewardship, The Linux Foundation Research’s 2025 report on open source program offices (OSPOs) describes their remit expanding into AI oversight, risk management, and supply-chain security. It also reports persistent strategy gaps and limited executive buy-in. Adoption therefore needs an owner: someone must define acceptable uses, review risks and dependencies, and ensure that systems continue to be maintained.
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How should an organization decide whether to use an open AI system?
- Define the job and constraints. Specify the task, quality requirements, data sensitivity, deployment environment, and any regulatory or contractual obligations.
- Verify what “open” means for the candidate. Review the license, weights, code, and available information about training data. Do not infer full openness from a model-weight release.
- Test the actual workload. Compare results on representative examples and assess failure modes, not just headline capabilities.
- Review the operating burden. Assign responsibility for security, privacy, updates, incident response, and ongoing support; include those needs in the cost assessment.
- Set governance before deployment. Establish accountability and approval processes suited to the system’s level of access and potential impact, especially when it can take actions.
Open source can expand choice and control as AI becomes embedded in more software, but those benefits depend on what is released and how the system is operated. Make the decision against the specific task, permissions, risks, and maintenance capacity—not the label alone.
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