Over the course of May, the Open Source Initiative featured the stories of maintainers of Open Source projects around the world. A common thread in many of their stories is the struggle to find time to work on their projects while working a full-time job, or get funding to work on their projects full-time. This underlines one of the core challenges Open Source communities face today: sustainability.

The OSI has been working to find solutions to sustainability challenges for some time. Through our outreach and education efforts, we set up exchanges between lawmakers and developers that have spurred changes in laws like the EU’s Cyber Resilience Act. In the long term, these changes will reduce burdens for Open Source developers and create an incentive for large users of Open Source software to fund its development. However, in the short-term, solutions are needed at a time when Open Source is more important than ever.

This year, as the EU prepares its upcoming budget, OSI joined Open Forum Europe in responding to the Public Consultation on the budget by calling for the creation of an EU Sovereign Tech fund, modeled on the German Sovereign Tech fund, which has already funded development, maintenance, and infrastructure for vital projects (like cURL, coreutils, Drupal, FFmpeg, Gnome, OpenStreetMap, OpenSSH, PHP, Wireguard, and many more). The new proposal would see the creation of a €53M fund taken from the EU budget to fund developers all over the world who work on projects recognised as being in the Public Interest.

It would also offer significant benefits to the EU, attracting more Open Source developers, increasing cybersecurity at a time where public authorities and businesses face heightened threats, and fostering the development of vital software the EU needs to assert its technological sovereignty at a time of heightened geopolitical tensions. Additionally, the benefits of supporting Open Source would be felt in the wider economy, with a European Commission study showing Open Source produces €4 of economic benefit for every €1 invested.

Initial discussions on the EU’s upcoming budget will begin in the coming weeks, on the basis of the feedback to the public consultation, with negotiations scheduled to begin in Q4 2025. OSI will continue its education and outreach work, giving lawmakers the tools they need to understand Open Source, its benefits, and why addressing issues around the sustainability of Open Source is so important.

The OFAI is a university-led, collaborative initiative that aims to bend the arc toward human-centered, responsible, transparent, and ethical AI. Led by Carnegie Mellon University, the OFAI includes university representatives from across the country as well as nonprofit voices and individual fellows from industry and government. As the policy working group lead for OFAI, we collaborated with the leaders of the other working groups (research, technical prototypes, community engagement, and talent for service) and OFAI’s Executive Director, Sayeed Choudhury, to develop recommendations for an AI R&D Strategy. Our comments highlight how openness can accelerate discovery and advance AI so that everyone can benefit from its use. 

We make five recommendations:

Expand access to open datasets for AI training and evaluation

The administration should invest in the development of free and open datasets for AI to ensure big and small players have access to high-quality data, making the systems they create more accurate and useful. 

Expand access to AI R&D resources and develop AI literacy

Universities need access to computing power and Open Source software as well as training and openly licensed educational materials so that they can continue developing AI tools for faculty and students. The administration should invest in these resources, specifically for universities and nonprofits, to ensure coordination across them and sustainability of their efforts. 

Invest in the development of Open Source AI

The administration should invest in the development and deployment of Open Source AI in alignment with OSI’s evolving definition, focusing on systems that can provide real-world solutions to public challenges such as those in healthcare, education, agriculture, and research. 

Conduct research into mechanisms for openness

The benefits of openness are vast but the research community still lacks consensus on how to evaluate AI systems. The administration should support research into methodologies for evaluating AI systems and the level of openness needed to do so while coordinating these efforts with the National Institute of Standards and Technology (NIST).

Track research into downstream impacts of openness policies

Regulatory frameworks impacting Open Source AI are being considered in many jurisdictions around the world. Understanding the implications of such proposals is critical. OFAI members are conducting research into the economic impacts of these various openness regulations. The administration should engage with the OFAI to learn more about the research in this space. 

As Fiscal Year 2026 funding discussions begin in the U.S. Congress, AI R&D is sure to come up, especially in the context of federal science agency budgets. Decisions regarding funding for AI R&D will impact developers of all stripes as well as downstream users and AI researchers. OSI will continue to track this area and share updates with the community. 

May 29, 2025

Dear Mr. D’Souza:

The Open Forum for Artificial Intelligence (OFAI) is a university-led, collaborative initiative that aims to bend the arc toward human-centered, responsible, transparent, and ethical AI. The OFAI seeks to bring an academic and nonprofit perspective to critical conversations, working alongside industry and government to foster innovation such that everyone benefits from the use of AI. OFAI is led by Carnegie Mellon University (CMU) and includes the open source program offices from George Washington University, Georgia Institute of Technology, University of Texas at Austin, and North Carolina A&T State University. OFAI also has voices from the nonprofit sector including the Open Source Initiative (OSI), Creative Commons, Conscience, and the Atlantic Council as well as individual fellows from industry and government. The OFAI is pleased to provide recommendations to the Office of Science & Technology Policy (OSTP) and the National Science Foundation that highlight how openness can be leveraged in the country’s AI R&D strategy to accelerate discovery and advance AI. 

Openness Unlocks AI Innovation 

Openness is a bedrock principle in research and development that has made the U.S. science and technology apparatus the envy of the world. It is a key characteristic with application to many parts of the innovation ecosystem—from open science and open education to open data and open source software. Openness is the mechanism by which researchers, developers, entrepreneurs, and investors can share and collaborate on emerging technology and new knowledge, just as they did with the advent of the Internet and the Web. The complex nature of AI systems and the speed at which they are being applied in our everyday lives makes openness even more important today. 

Openness in AI enhances both non-commercial and commercial applications by allowing the brightest minds to contribute to and evaluate systems so that the best ones can be developed and deployed. It breaks down silos and accelerates discovery in ways closed approaches cannot. For these reasons, we believe openness should be foundational in the administration’s AI R&D strategy. We offer recommendations in key areas at the intersection of AI R&D and openness. 

Expand Access to Open Datasets for AI Training and Evaluation

Open Corpus of Knowledge for AI Training 

Openly sharing data is a means by which researchers can show their work and build trust in their conclusions. The benefits of open data in the research context can also be applied to AI systems and the data they are trained on. While the legal aspects of using copyrighted content to train AI systems is playing out in U.S. courts, there remains a need to expand the corpus of knowledge available for AI training and evaluation to increase discoverability of such knowledge. The benefits of expanding access to datasets are multifold. Creating more “open” datasets ensures innovators of all stripes—from startups and university research teams to large tech firms—can evaluate and fine-tune new models and systems. It also builds trust and allows for better evaluations of the systems themselves, enabling researchers to root out low-quality training data. And finally, it provides clarity to developers who wish to use the data but are unsure if they are legally allowed to do so. 

For example, rich open datasets in pharmaceutical R&D have given way to groundbreaking tools like AlphaFold where experts can predict protein structures and the Human Genome Project which generated the first sequence of the human genome. These accomplishments would not have been possible without access to large, high-quality, open data. Yet, researchers predict that current efforts by private companies to create closed data for use in AlphaFold2 will not generate the breadth of data needed to truly power AI. Tools like AlphaFold2 will be less successful and feats like the Human Genome Project out of reach without more open and standardized datasets. 

There are many efforts underway to develop open datasets and connect existing ones so that information for AI training and evaluation is easier to find and use. However, these efforts may not be of immediate interest to commercial entities because the content by its nature will be open to everyone. That is precisely why government investment in such initiatives is so important—it provides the entire AI community with openly available resources to accelerate innovation. This will ensure big and small players have access to high-quality data, making the systems they create more accurate and useful. 

Federal investment in and coordination of these efforts would be beneficial to extend their impact and address the growing need for AI training datasets. We highlight a few initiatives below and welcome the opportunity to work with the administration on strategic investments in this area. 

• The Public Interest Corpus seeks to create high-quality AI training data from memory organizations (e.g., libraries, archives, museums) and their partners (e.g., publishers). 
• The Institutional Data Initiative is a team of data scientists and community builders working to make knowledge collections at universities, libraries, and government agencies available as open datasets that can be used to train AI models. 
• The U.S. Repository Network aims to create a more interoperable network of open repositories (government and non-government) in the U.S. so that the information in such repositories can be reused by others.

Along with the creation of more open data, governance and community standards are critical to facilitate access to complex data sources. Civil society groups have suggested that such a governance structure should be viewed as the “Data Commons.” That is, a governance structure that is flexible enough for varying use cases. It is crucial for the U.S. to be engaged in such global and domestic governance discussions. 

Expand Access to AI R&D Resources and Develop AI Literacy 

Universities across the U.S. are rapidly adopting AI tools and platforms for faculty, students, and researchers to work with models and learn valuable AI literacy skills. There are also a growing number of schools developing their own AI platforms. The Dietrich Analysis & Research Education (DARE) platform, built as an Open Source project at CMU, promotes human-centered AI by allowing students, faculty, and staff to leverage multiple large language models, transform data and experiment through a locally controlled LLM gateway. This enables faculty to augment research capabilities and develop curriculum in a platform that adapts to their pedagogical needs rather than adapting their pedagogy to fit available tools. DARE puts AI in the loop with humans in control, promoting human agency and interaction transparency while empowering students to use AI responsibly. Sage, a tool at the University of Texas at Austin, is an AI teaching and learning guide that draws on the LLM Claude and established principles of learning experience design and responsible AI adoption. With Sage, faculty at UT Austin can design tutoring sessions for students on any topic. 

While initiatives like those at CMU and UT Austin are growing across the country, the experts and technologists that build them need resources beyond just open data. They need access to computing power and Open Source software as well as training and openly licensed educational materials. Computing power in particular is often out of reach for tool builders at universities or nonprofit research centers due to prohibitive costs. Further, they need a sustainable funding environment for these resources such that they can take advantage of the latest model developments and immediately deploy them. 

The government’s AI R&D strategy should include investment in these resources to ultimately build a public infrastructure for AI as well as sustained funding for a coordinated network of universities building cutting-edge AI tools. The federal program supporting the network of universities should be flexible enough to enable experts at those universities to adapt to and leverage new information about AI instead of waiting months or years for another funding cycle. 

Invest in the Development of Open Source AI 

Open Source software is another crucial component of any AI system. And while an AI system is much broader than the software code, the ubiquitous nature of Open Source software provides a clear example of what can happen when technology is shared without restriction and creators are given the freedom to innovate. The Open Source Definition, maintained by the Open Source Initiative (OSI), removes barriers to learning, using, sharing, and improving software systems. Today, Open Source software accounts for more than 97 percent of applications we use and 90 percent of companies report using open source software in some way. Recognizing the need to apply Open Source principles to AI, OSI co-developed the Open Source AI Definition (OSAID), releasing version 1.0 in October 2024. In 2025, OSI is leading a community effort to evaluate the definition and identify models that meet it. This work will inform future iterations of the definition and best practices for developing truly Open Source AI. 

The Open Source AI Definition

An Open Source AI is an AI system made available under terms and in a way that grants the freedoms to:

• Use the system for any purpose and without having to ask for permission.
• Study how the system works and inspect its components.
• Modify the system for any purpose, including to change its output.
• Share the system for others to use with or without modifications, for any purpose.

The preferred form of making modifications to a machine-learning system must include all the elements below:

• Data Information: Preferably the original data, or if it is not legally possible, sufficiently detailed information about the data used to train the system so that a skilled person can build a substantially equivalent system. Data Information shall be made available under OSI-approved terms.
• Code: The complete source code used to train and run the system. The code shall represent the full specification of how the data was processed and filtered, and how the training was done. Code shall be made available under OSI-approved licenses.
• Parameters: The model parameters, such as weights or other configuration settings. Parameters shall be made available under OSI-approved terms.

The administration’s AI R&D strategy should include an investment in the development and deployment of Open Source AI in alignment with OSI’s evolving definition. Investments in Open Source AI should focus on systems that can provide real-world solutions to public challenges such as those in healthcare, education, agriculture, and research itself. For example, truly Open Source AI models like the Allen Institute’s (Ai2) OLMo provide researchers with a large language model that can be just as powerful as many of the proprietary ones but with access to the components and without the price tag. Ai2’s most recent release, OLMo 2 32B, outperforms GPT3.5-Turbo and GPT-4o mini on a variety of benchmarks. Especially at a time when new knowledge is being learned everyday about how to improve AI systems, researchers and developers need access that grants them the freedom to use, study, modify, and share the system and its components. 

Further, countries around the world are trying desperately to catch up to America’s lead in AI by developing innovative models that share some of their components openly. China’s DeepSeek is one such example that has demonstrated just how powerful an open model can be. The U.S. needs more Open Source AI systems to provide researchers, developers, entrepreneurs, and investors options for using and building on models that have greater transparency and lower barriers to adoption.   

Conduct Research into Mechanisms for Openness

The benefits of openness are vast but the research community still lacks consensus on how to evaluate AI systems. A recent survey of academics and corporate researchers conducted by the Association for the Advancement of Artificial Intelligence (AAAI) found that a lack of suitable evaluation methodologies and the black-box nature of AI systems were the biggest challenges to evaluating them. The administration should conduct research, through intramural and extramural grants, into methodologies for evaluating AI systems and the level of openness needed to do so. It should also consult AI researchers and drive consensus around such methods through the National Institute of Standards and Technology (NIST) and Federal science agencies. 

Research and consultations in this area should address the following:

• How should AI systems be evaluated for risk—from personal safety to national security in presence of access barriers? 
• What level of openness is needed to evaluate systems for such risks?
• What methodologies exist for providing that transparency?
  • For example, OSI’s Open Source AI Definition (OSAID) is an existing framework that ensures key information about AI systems are shared openly.

Track Research into Downstream Impacts of Openness Policies

Regulatory frameworks impacting Open Source AI are being implemented or are under consideration in many jurisdictions around the world. Understanding the implications of such proposals is critical. Members of the OFAI are conducting research into the economic impacts of various openness regulations. Their research aims to address the following:

• When do openness regulations enhance model access, transparency, and innovation, and when might they hinder these goals?
• How should openness regulation be designed to encourage greater competition and investment in AI development? 
• How should the concept of “openness” in AI be defined for regulatory purposes?

We urge the administration to review this research and engage with members of the OFAI to understand its implications. We would welcome the opportunity to meet with you and discuss this research further. 

We thank the Office of Science & Technology Policy and the National Science Foundation for the opportunity to contribute ideas to the administration’s 2025 National AI R&D Strategic Plan. We look forward to working together to accelerate AI-driven innovation. 

Sincerely, 

Contact

On October 14, 2025, Microsoft will officially end support for Windows 10. That means hundreds of millions of otherwise functional computers will suddenly be pushed toward obsolescence—not because they’re broken, but because they no longer fit within a closed and arbitrary upgrade cycle. Devices that lack the latest hardware, or simply fail to meet opaque requirements for Windows 11, will be cast aside.

But it doesn’t have to be this way.

A better way forward with Open Source

The reality is simple: your computer doesn’t have to become e-waste. You don’t have to buy new hardware. You don’t need to accept intrusive software or give up your digital autonomy.

You can install Linux. You can use Open Source software. You can take control.

Open Source software gives you the freedom to keep using the technology you already own. It allows you to run secure, fast, modern systems on machines new and old. And unlike proprietary alternatives, Linux distributions and other Open Source platforms put the user in charge—from privacy settings to software updates.

We believe the “End of 10” campaign is a powerful reminder of what Open Source is all about: sustainability, user empowerment, and choice.

5 reasons to upgrade your old computer to Linux

1. No new hardware, no licensing costs
   A new laptop costs a lot of money, but several Linux operating systems are available for free. Software updates are also free, forever. You can of course show your support with donations!
2. Enhanced privacy
   Windows comes with lots of ads and spyware. This slows down your computer, lets companies spy on you, and increases your energy bills.
3. Good for the planet
   Production of a computer accounts for 75+% of carbon emissions over its lifecycle. Keeping a functioning device longer is a hugely effective way to reduce emissions. With a Linux operating system you can use your device longer.
4. Community & professional support
   There are local repair cafes and independent, professional services and computer shops available for providing you help. You can find support in online forums, too.
5. Better user control
   Linux grants you the four freedoms of software. You are free to use, study, share, and improve the program, for as long as you wish. You are in control of your device.

Join us

If you’re staring down the end of Windows 10, know that you have options. Visit endof10.org to learn more about events where you can get in-person help migrating to Linux, find ongoing local support, and keep your hardware—and your freedom—working for you.

Do you know of a place or event in your area offering Linux support that is not listed on the website? Ask them to contact the campaign in order to extend the global Linux support network as widely as possible!

Open Source isn’t just about code. It’s about community, sustainability, and autonomy. The “End of 10” campaign is a chance to show the world how powerful those values can be.

We invite everyone—users, developers, educators, and advocates—to be part of this movement. Because the end of Windows 10 isn’t the end of the road. It’s a new beginning.

• Encourage the development and adoption of Open Source AI
• Establish a common definition of Open Source AI
• Promote a data commons approach for AI development 

The think tank used a variety of tools to analyze the public comments and extract specific recommendations. The recommendations are organized into 20 topics including Export Controls, Infrastructure, Security, Open Source, and Standards & Regulations. The Open Source category has 156 recommendations from 123 organizations/businesses. The database is a helpful resource that anyone can use to explore submissions. 

Other recommendations of note highlighted by IFP include:

• Increase government use of Open Source AI (Mozilla)
• Signal government support for Open Source AI (a16z)
• Allocate public computing resources for Open Source AI (Hugging Face)
• Preserve and promote open model development (Center for Democracy & Technology)
• Promote open innovation as a strategic advantage (Stanford Institute for Human-Centered AI)
• Support transparency in AI procurement, development and use (Electronic Frontier Foundation)

OSI thanks IFP for recognizing Open Source as an important theme in the AI Action Plan recommendations and looks forward to working with the administration and other organizations on these issues.

Whether you’re a developer, technologist, or organizational leader, CODE validates the skills enterprises need to harness Open Source effectively—not just technically, but responsibly and sustainably.

“25 years ago, Open Source was in a different place, and the needs of developers have changed. This certification helps both individual developers and the organizations that rely on the Open Source Definition to assure that Open Source continues providing critical value to the innovation engine upon which the global economy and human progress depend.” — Stefano Maffulli, Executive Director, Open Source Initiative

Why OSI supports CODE

As stewards of the Open Source Definition, OSI has long championed software freedom as a foundation for innovation. But Open Source software is more than a licensing model—it’s a shared responsibility. Developers are not only contributors; they’re gatekeepers of security, compliance, and sustainability.

The CODE certification bridges a crucial gap in the enterprise Open Source landscape. It trains certified professionals to apply Open Source practices in ways that align with enterprise risk management, strategic goals, and the values embedded in the Open Source community.

Domains and competencies

The CODE certification exam evaluates knowledge across key areas that are essential for navigating Open Source in the enterprise:

Fundamentals of Open Source Software Development

• Issues & Pull Requests
• Code Reviews
• Principles of Open Source Software
• Open Source Communities
• Release Management and Semantic Versioning

Open Source Licensing and Usage Guidelines

• Intellectual Property, Software Licensing, & Open Source Legalities
• Open Source & Copyleft License Compliance
• Risk Assessments
• Export Control Regulations & Compliance
• Contributor License Agreements (CLA) & Developer Certificate of Origins (DCO)

Consuming Open Source Software

• Codebase Risk
• Software Maintenance Plans
• The Software Supply Chain
• Code Dependencies
• Open Source Software Approval Process
• Software Bill of Materials (SBOM)

Contributing to Open Source

• Contribution Strategy
• Code and Documentation Best Practices
• Copyright Ownership and Intellectual Property
• Contribution Risks
• Project Types: Business, Personal, and Open Source
• Contribution Approval Processes

Open Source Management Operations

• Contributing to Upstream Projects
• Developer Support
• Open Source Management Roles and Escalation Paths
  

Acknowledgments to exam developers and beta testers

CODE certification would not have been possible without the dedication and expertise of CODE exam development team. These contributors helped define the core competencies, shape the exam outline, create exam content, and help establish the passing score, ensuring the exam reflects enterprise-level open source responsibilities:

• AmyJune Hineline
• Ana Jiménez Santamaría
• Angel Ramirez
• Alin Jerpelea
• Ariel Jolo
• Brittany Istenes
• Eduard Itrich
• Georg Kunz
• Ivan Cvitkovic
• Nico Rikken
• Nick Vidal
• Oluchi Nwenyi
• SKi Sankhe
• Thierry Carrez
• Tracy Hinds
• Victoria Poromon

In addition to these exam developers, over 30 subject matter experts (SMEs) contributed to other key areas of the certification, including validating exam questions and participating as beta testers. These professionals came from more than 30 different organizations worldwide across industries.

I deeply value the collective, vendor-neutral approach taken throughout the development of the CODE certification. It brings together real-world expertise from across the ecosystem, making CODE a strong, community-driven alternative to traditional single-vendor certification paths — Ana Jiménez Santamaría, Project Manager, TODO Group (Linux Foundation)

Get started today

When a developer earns the CODE credential, they signal more than technical expertise—they demonstrate a commitment to a collaboration mindset in the Open Source ecosystem. For enterprises, CODE helps reduce friction, avoid compliance pitfalls, and create a culture where Open Source can thrive and deliver massive value to everyone involved, from customers to partners.

The Open Source Initiative is proud to support the work of the TODO Group and the Linux Foundation in launching CODE as a valuable next step for professionals and organizations committed to software freedom, long-term sustainability, and collaboration at scale. Whether you’re building software or building teams, CODE gives you—and your stakeholders—the confidence to do it right.

Learn more and register here: Certified Open Source Developer for Enterprise (CODE)

Use redemption code APRIL25CODE before April 30, 2025 for a special launch discount.

The Open Source Initiative (OSI) is joining forces with fellow content partners Open Knowledge Foundation (OKF) and the Digital Public Goods Alliance (DPGA) to support this vital international event that brings together leaders from academia, industry, government, and civil society to explore the societal, economic, and political impact of open technologies in today’s complex world.

“The OFA Symposium is a unique event that brings together voices across academia, industry, and civil society. We’re honored to support it as a Content Partner, helping to foster the cross-sector dialogue essential to unlocking the full potential of openness for the public good.” – Nick Vidal, OSI Community Manager

“The partnership with these three esteemed organizations reflects our commitment to bringing diverse perspectives and expertise to the symposium. Their contributions will ensure rigorous, forward-thinking discussions that can inform policy and practice in the Open Source and open technology space.” – Astor Nummelin Carlberg, OpenForum Europe Executive Director

Why this event matters

The theme for this year—“Open Technology Impact in Uncertain Times”—could not be more timely. In an age marked by rapid technological change, geopolitical turbulence, and growing demands for digital sovereignty and transparency, open technologies offer a powerful alternative: one grounded in collaboration, shared knowledge, and public value.

Yet, despite their significance, Open Source software, standards, and data remain underappreciated—and under-researched. The OFA Symposium 2025 addresses this gap head-on, building a stronger evidence base for why openness matters and how it shapes markets, policy, innovation, and governance.

Key tracks

The event features four key tracks that reflect today’s most pressing challenges:

Track 1: Economic Impact of Open

• Economic Impact of Open Source
• Open Technologies and Innovation
• Open Technology and Entrepreneurship
• Open Source and Supply Chains
• Industrial Adoption of Open Source
• Digital Industrial Policy

Track 2: Open Technologies and Geopolitics

• Political and Social Impacts
• Open Technology and Digital Sovereignty
• Politics of Digital Public Infrastructure
• Global Governance of Open Source
• Governments and Open Technology
• Copyright and Trade Disputes

Track 3: Sustainability and Security

• Maintaining Open Source
• Open Source Cybersecurity
• Sustaining Open Technologies as Digital Commons
• Open Technologies as Infrastructure
• Open Hardware and Chips

Track 4: Open Source and AI

• Open Source AI Definition
• Open Data and Open Weights
• Data Diversity for AI Training
• Value/Economics of Open Source and AI
• Global and Local Governance of AI

Get involved

The Call for Proposals is open until June 1, 2025, and welcomes contributions across research, ongoing studies, and topical presentations. Whether you’re an academic, policy expert, developer, or open tech advocate, this is a unique opportunity to share your insights and collaborate with an international community dedicated to advancing openness. Submit your proposal or learn more at symposium.openforumeurope.org

Timeline:

• 31 March 2025: Release of the Call for Proposals
• 1 June 2025: Closing deadline of the Call for Proposals
• June 2025: Review of proposals by the Programme Committee
• By 25 June 2025: Program finalization and speaker notification
• By End of June: Program announcement
• 18-19 November 2025: Symposium 

We hope to see many of you in Rio—helping to shape the future of open technologies in impactful, inclusive, and global ways. Stay tuned for more updates and follow the conversation on social media using #OFASymposium2025.

This year’s insights reveal a dynamic landscape: Open Source is thriving, but not without growing pains.

Open Source adoption surges despite challenges

The Open Source wave shows no signs of slowing down. A remarkable 96% of organizations reported either increasing or maintaining their use of Open Source software in the past year. Even more telling: 26% significantly increased their adoption.

The number one reason? Cost efficiency. For the second year in a row, “no license cost/overall cost reduction” topped the list of motivators. Organizations continue to see Open Source as a smart financial move, particularly as they invest more in cloud infrastructure, containers, data platforms, and programming languages. Large enterprises are also ramping up focus on analytics and data processing tools to stay competitive.

Confidence in Big Data management remains low

Open Source may power today’s most sophisticated data systems, but managing them effectively is another story. Nearly half (47%) of organizations dealing with Big Data platforms reported low confidence in their ability to manage those tools successfully.

What’s holding them back? The report points to skill gaps and staffing shortages, with over 75% citing lack of personnel and expertise as a top barrier. The rapid evolution and complexity of Open Source data technologies are making it hard for teams to keep up.

The risky reality of end-of-life software

Despite strong Open Source adoption, many organizations are still clinging to outdated tools. A surprising 26% are still using End-of-Life (EOL) CentOS, including 40% of large enterprises. Even more concerning, one in four of these large organizations have not yet decided on a migration plan.

The consequences? Significant. Enterprises relying on EOL software like CentOS are nearly three times more likely to fail compliance audits, highlighting the urgent need for action and governance in Open Source usage.

Security and compliance still top the pain points

Security and compliance remain persistent pain points across the board. From staying current with patches and updates to meeting increasingly stringent security requirements, organizations are feeling the pressure.

Maintaining EOL software is again a culprit here—outdated tools not only pose compliance risks but also leave systems vulnerable to exploits. It’s clear that Open Source strategy today must include a strong focus on lifecycle management and security hygiene.

Download the report and join our webinar

Want to go deeper? Download the full 2025 State of Open Source Report to explore the full data and insights. Even better—join us on April 30 for a live webinar with OSI’s Stefano Maffulli, Perforce’s Matthew Weier O’Phinney, and Eclipse Foundation’s Gaël Blondelle. We’ll break down the report’s findings and look ahead to what’s next for the Open Source ecosystem. Register here.

Freedom isn’t a sliding scale

The concept of freedom lies at the heart of Open Source. And freedom, by its very nature, is binary. You either have it, or you don’t.

Software that carries restrictions on usage, modification, redistribution, or access to its building blocks isn’t partially open. It’s not Open Source at all. Those conditions—ethical restrictions, commercial limitations, time-delayed releases—are forms of restriction, not openness.

The Definition is clear—for a reason

The Open Source Definition exists precisely to make this boundary clear. It doesn’t weigh licenses on a sliding scale of freedom—it simply asks: does the license grant the freedoms to use, study, modify, and share the code?

If it does, it’s Open Source. If is does not, it isn’t.

The same must apply to AI systems. If a model is released without detailing its training data, or under terms that limit how it can be used or shared, it doesn’t matter how “open” it feels—it hasn’t crossed the line into true freedom.

Projects differ—their freedom doesn’t

Open Source isn’t about uniformity. Projects can look wildly different while still being genuinely open. SQLite has no public roadmap and few contributors. Kubernetes is backed by a global foundation with a sophisticated governance model. Both are Open Source.

Some projects embed ethical missions (like the UN’s Sustainable Development Goals), others are commercially driven. These values are layered on top of freedom—not prerequisites for it. As a user or contributor, you are free to value what you want in a project, because Open Source grants you that freedom in the first place.

Learning from the “Lost Decade”

During the rise of cloud and mobile computing, Open Source didn’t fully address the consequences of large-scale commercial adoption. Massive platforms forked Open Source projects and repackaged them into proprietary services with little community contribution. This era—Open Source’s “lost decade”—offers a lesson for AI.

We must not make the same mistake again. If we blur the line between open and closed, we risk enabling centralized control behind a façade of openness.

Upholding Open Source principles

For AI to be truly Open Source, it must uphold the same principles that have defined Open Source software for over two decades. There is no “80% open” or “open enough.” The freedom to use, study, modify, and share isn’t negotiable. It’s either there, or it isn’t.

That clarity is what makes Open Source resilient, inclusive, and powerful.

What is the Code of Practice for, and why is it important?

The European Union (EU) Code of Practice for General Purpose AI is an upcoming set of voluntary rules designed to help developers prove they are complying with the AI Act. It is a temporary measure while the EU develops in-depth standards on artificial intelligence (AI), but it will inspire the future standards the EU makes.

In August of 2024 the Open Source Initiative (OSI) applied to take part in the drafting process. We were accepted and have been working since then to make sure these new rules were written with Open Source in mind. 

What was wrong with the Code of Practice?

Overall, the Code of Practice proposes sensible practices to reduce the risks AI could pose. AI systems that comply with the OSI’s Open Source AI Definition already follow most of these rules, but there were a couple of elements in the Code of Practice that would have been impossible for Open Source developers to implement.

In particular, previous drafts of the Code of Practice mandated acceptable use policies, and a prohibition of certain uses of the AI system. But having these restrictions conflicts with the freedom of use that Open Source guarantees, in particular, rule 6 of the Open Source Definition (No Discrimination Against Fields of Endeavor). This would mean developers would have to choose between complying with the Code of Practice or being Open Source.

This is bad both for developers, and for the EU: companies might not consider Open Source solutions if they aren’t sure they can comply with the law, which could delay or even halt the development and deployment of Open Source AI systems in Europe. This would be particularly unfortunate, as Open Source AI systems are by nature the most transparent and accessible. Additionally, the proposed restrictions can’t actually be enforced; restrictions could be removed by a downstream user, and acceptable use policies could simply be ignored.

How the OSI worked to fix it

In the first round of feedback, we raised our concerns about the incompatibility of the Code of Practice with Open Source principles. After we saw no improvement in the second draft, we teamed up with like-minded organizations, and wrote a letter to the chairs, highlighting the issue. On the 11th of March the third draft of the Code of Practice was released, which made acceptable use policies optional, and exempted Open Source AI from prohibiting certain downstream uses.

We welcome these changes as they will allow Open Source AI developers to adhere to the Code of Practice, removing a serious barrier to Open Source AI development in Europe!

What’s next?

The Open Source Initiative remains committed to ensuring Open Source is taken into account when new rules like these are being made. We’ll continue to follow the drafting process and ensure these changes aren’t reversed. We’ll also continue to fight the openwashing of some AI models such as Meta’s Llama, while working to educate and inform lawmakers about Open Source and its benefits.

If you like what we are doing, consider donating! Your support makes our work possible! 

The OSI thanks all of those who participated in the 2025 board polls by asking questions to the candidates, engaging in discussions, and casting a ballot. Three candidates have been excluded from the final tally: Two were ineligible as they did not sign the current  board agreement; one returned the signed agreement after the deadline passed. The Open Source Initiative is committed to upholding its mission while adapting to the evolving landscape of Open Source.

Next steps

We look forward to welcoming the new Directors at the April board meeting.

The board has directed the Board Development committee to run a retrospective by April 19, 2025 and bring to the board any recommendations for the future.

Complete elections results

OSI Affiliate directors polls 2025

Counting ballots using Scottish STV. There are 4 valid candidates competing for 2 seats. The number of ballots is 48 and there were 47 valid ballots and 1 empty ballot.

Winners are Carlo Piana and Ruth Suehle.

OSI Individual directors polls 2025

Counting ballots using Scottish STV. There are 5 valid candidates competing for 1 seat. The number of ballots is 159 and there were 148 valid ballots and 11 empty ballots.

Winner is McCoy Smith.

Update: April 21, 2025

From the Board of Directors of the OSI

Since OSI introduced individual member and affiliate votes for some board seats in 2013, we have been in a continuous process of improving those elections.  This year, like in other election cycles, we discovered limitations and corner cases in our procedures. We renew our commitment to continuous improvement and have revised our internal procedures and templates to address the specific issues.

As we do each year, the OSI held an internal retrospective with directors and staff to reflect on public feedback, team observations, and technical difficulties, and identify action items for improvement. This retro included feedback given by several members via social media and discuss.opensource.org. We thank the members who provided this feedback; it helps us to identify problems that we would not have seen otherwise. Here are our takeaways from that process which involve the board and our members.

Improvements in communication

This election, as well as the OSAID process, made it apparent that our supporters want better channels to provide structured feedback to the Board about organizational direction, decisions, and priorities. Also, that it’s important to our supporters that feedback be clearly received and acknowledged, and that they receive word of any related board decision when one is made. Our elections are a poor vehicle for this kind of communication, so we must provide others.

The Board is working on a plan to reshape discussions with our supporters in order to realize better two-way communication. You can expect to hear more about this plan in upcoming months, and we hope you will help us improve it through your feedback.

As a smaller but nevertheless critical part of communication, we’ve been told by several of our members that they don’t understand the voting system we’re using, and it hasn’t been explained. In upcoming months, we will publish one or more articles and link to more materials explaining Single Transferable Vote and OpaVote, why we use them, and how your ballot expresses your opinion.

Improvements in candidate selection

In prior elections, we identified the issue that some candidates were not prepared for the actual requirements and duties of being on the OSI Board. We added candidate information sessions to the schedule and shared board materials with candidates in advance of the election. This was successful in allowing candidates to determine, themselves, whether they were ready to run for election, and it will remain a part of the election timeline.

What this election exposed was the need for the organization to also assess whether candidates were fully eligible to run and prepared to be seated on the board before voting begins. This is something we will add to the election timeline next year. While we have not finished figuring out all of the requirements for that assessment, part of it will be asking candidates to sign a Candidate Agreement* at nomination time. We also have some ideas on ways for potential candidates to have more information even before submitting a nomination.

The Board also noticed that, however successful we were in recruiting candidates (15 in this election), our candidate pool is not as representative of all of the different constituencies in our Open Source stakeholder groups as we would like it to be. We have begun working on some long-term plans to improve candidate diversity and involvement.

Conclusions

We acknowledge that we must improve all communications, so that we do not take for granted the level of familiarity any supporter may have with OSI’s work or its annual elections. OSI will improve transparency by creating better supporter engagement and feedback channels, clarifying the election process and candidate expectations, and enhancing public communication to be more inclusive and accessible.

* The board agreement governs our conduct as directors expected of leaders at OSI above and beyond, and the OSI code of conducts (License Review CoC, Event CoC, Discourse TOC) govern our whole community. To our knowledge, all involved with this election, even those who did not sign our board agreement, agreed to be held to the OSI code of conduct(s).