The evolving concept of open source in AI
Open source has been a core of software innovation, driving collaboration and rapid technological advancement. It’s what built the internet. But AI is rewriting the rules of the game. Traditionally, open source meant freely sharing code, building up a community of developers who could use, modify, and improve upon it. The Open Source Initiative (OSI) has long been the gatekeeper, defining and enforcing what qualifies as “open source.”
Enter AI, and suddenly, the old definitions seem outdated. Meta’s Llama, for instance, is labeled “open source,” but it comes with restrictions—entities with more than 700 million monthly users are barred from using it. Yet most developers don’t seem to mind. Why? Because the model works, and it works well. For the vast majority, these restrictions are irrelevant. This shift highlights a critical evolution: today’s developers prioritize functionality and efficiency over ideological purity. They want tools that solve problems, not abstract labels.
The tension here isn’t trivial either. It reflects the growing gap between open-source ideals and the realities of AI, where proprietary data and models often dominate. Companies are now blending openness with strategic limits to stay competitive. And, like it or not, this new definition of open source is gaining ground.
Developers prioritize capability over strict open-source adherence
To be clear, open source is still important, but it’s no longer the top priority for developers. Think about Linux or Apache, as they were game-changers because they gave developers the tools they needed to build the internet. But today, developers are looking for what works best. Rowan Trollope, CEO of Redis, put it succinctly: “What developers care about is capability.”
This is especially true in AI, where speed, scalability, and ease of integration are king. Tools like AWS became dominant because they offered reliable, easy-to-use cloud services—not because they were open source. The same applies to Meta’s Llama. Developers embrace it because it accelerates their work, not because it checks every box on the OSI’s open-source list.
“The bottom line is that developers want tools that give them a competitive edge. They care less about the philosophical debates surrounding open source and more about creating powerful, efficient applications. In the race to innovate, pragmatism is winning out over ideology.”
OSI’s struggle to define open source for AI
The Open Source Initiative has been the industry’s north star for decades, making sure open source stays true to its principles. But AI is throwing a wrench in the works. Recently, the OSI introduced its Open Source AI Definition 1.0 to clarify what “open source” means in the AI era. It fell short of expectations though. For example, the definition didn’t require training datasets to be open, a glaring omission for many in the community.
This gap has created an opening for companies like Meta and OpenAI to redefine what “open” means. They use the term liberally, often to signal accessibility and innovation, even when their practices don’t align with traditional standards. The OSI, meanwhile, is struggling to keep up. Its rules, built for an era of packaged software, don’t fully address the complexities of AI, where massive datasets, proprietary algorithms, and cloud-based models dominate.
This matters because it’s about more than just definitions; it’s about control. If the OSI can’t adapt, it risks becoming irrelevant in a field it once helped shape. Meanwhile, companies are setting their own terms, pushing the boundaries of what “open source” means in ways that are both exciting and unsettling. For businesses, this creates both opportunities and challenges as they navigate an evolving market where the old rules no longer apply.
Meta’s pragmatic approach to open-source AI
Meta’s open-source strategy is both deliberate and practical, balancing accessibility with competitive advantage. The company has earned a reputation for its major contributions to open-source projects like React, GraphQL, and PyTorch—tools that have become industry standards for web development, data management, and machine learning. These projects demonstrate Meta’s commitment to advancing open innovation, and Llama continues that tradition, albeit with a few strategic restrictions.
Limiting Llama’s use to entities with fewer than 700 million monthly active users, Meta protects its market position while keeping the tool accessible to 99.99% of developers. Meta recognizes that by providing powerful tools to a vast majority of developers, it fosters widespread adoption and maintains its influence in shaping the open-source landscape.
Meta’s selective openness highlights the realities of modern AI competition. The company’s strategy shows that open source isn’t an all-or-nothing proposition, but rather about finding the right balance to drive both innovation and business success. It makes sure innovation continues without sacrificing competitive edge.
The marketing appeal of “Open” in AI
The word “open” carries weight in the tech world. It suggests transparency, accessibility, and trust. Companies like Meta and OpenAI understand this, leveraging the term to build credibility and attract developers, even when their practices don’t fully align with traditional open-source principles.
Take OpenAI as an example. Despite its name, much of its software runs on closed systems, with proprietary models hidden behind cloud-based infrastructures. Yet the name alone conveys a sense of openness and innovation. Similarly, Meta’s branding of Llama as “open source” appeals to developers who value accessibility, even though its license includes certain restrictions.
For developers, the term “open” has evolved. It no longer strictly means free access to source code. Instead, it often signals tools with user-friendly interfaces, accessible APIs, or cost-effective cloud services. These factors drive adoption, making the tools feel “open” enough, even if they don’t meet OSI’s criteria.
“This flexible use of “open” reflects the shifting priorities of both developers and companies. It’s about meeting modern expectations for accessibility and performance, which matter more than rigid adherence to old definitions.”
Key takeaways for leaders
- Functionality over ideology: Developers prioritize tools that deliver value and efficiency, even if they don’t meet traditional open-source standards. Leaders should focus on providing accessible and high-performing solutions rather than adhering rigidly to outdated definitions.
- Outdated frameworks: The OSI’s traditional definition of open source struggles to keep pace with the complexities of AI, such as proprietary data and restricted models. Companies should actively shape modern open-source norms to align with practical industry needs.
- Balancing access and control: Meta’s approach to Llama demonstrates how strategic restrictions can protect competitive advantages while maintaining widespread developer adoption. Decision-makers should evaluate how selective openness can drive both innovation and market leadership.
- Open as a marketing tool: Companies like Meta and OpenAI leverage “open” to signal accessibility, even when imposing limitations. Executives should consider how flexible interpretations of openness can improve brand perception and attract developer communities.
