In 1980, Richard Stallman argued that hiding software code was like hiding knowledge itself. His fight for free, open source software reshaped the modern world, and now, AI is following the same path. The most advanced AI systems are locked behind closed doors, leaving governments, companies, and nonprofits with limited access to the tools that could drive the next wave of innovation.

You don’t have to take our word for it. The same principles that powered the rise of open source, transparency, collaboration, and shared knowledge, are exactly what’s needed to ensure AI benefits everyone. This article shows why free, open source AI is essential for progress, and what happens when it’s not.

The AI Access Gap: Why Closed Systems Are a Barrier to Progress

The most advanced AI systems are closed completely, and the trend is accelerating. This creates a knowledge monopoly that stifles innovation and widens the gap between those who can access cutting-edge tools and those who cannot. Just as proprietary software in the 1980s limited the spread of knowledge, closed AI systems today prevent collaboration and slow down progress. When companies and governments rely on black-box models, they lose control over how AI is developed and deployed. The result is a world where AI benefits a few at the expense of the many. Openness is not just a principle, it’s a practical necessity for progress.

The Legacy of Open Source: Lessons from the Past

How open source accelerated software innovation

Open source didn’t just democratize software, it accelerated innovation. The same principles that led to the creation of GCC and GNU/Linux are now missing in AI. When code is shared, it’s not just more accessible, it’s more adaptable. Engineers can build on existing work, fix bugs, and push the envelope faster than in closed environments. This is how entire ecosystems form, and how companies like Red Hat and SUSE built billion-dollar businesses on open source foundations.

The role of transparency in security and trust

Transparency isn’t just good for innovation, it’s essential for security. Closed AI systems hide flaws, making it harder to detect vulnerabilities. Open source, on the other hand, allows the entire community to find and fix issues. As Stallman argued, hiding software is hiding knowledge. The open source model proved that transparency builds trust, and that applies just as much to AI today.

The importance of shared knowledge for future generations

Knowledge shared today is knowledge that shapes tomorrow’s engineers. When systems are closed, the next generation learns from outdated textbooks, not real-world tools. Open source gave the world a living textbook. AI needs the same. Without shared knowledge, we risk creating a future where only a few understand the systems that shape the world.

The Modern AI Dilemma: Closed vs. Open

Why the most advanced AI models are closed

The most advanced AI systems are locked behind closed doors. Companies see these models as strategic assets, and in many cases, they are proprietary. This creates a situation where only a few organizations have access to the best tools, while others are left with outdated or limited alternatives. The result is a monopoly on knowledge and innovation that stifles progress.

The risks of a closed AI ecosystem

A closed AI ecosystem limits transparency and accountability. Without access to the inner workings of AI models, companies and governments cannot fully understand how decisions are made, leading to potential biases and errors. This lack of openness also makes it harder to improve or adapt AI systems to specific needs, slowing down the pace of innovation.

The few open source AI initiatives making an impact

Despite the dominance of closed systems, a few open source AI initiatives are making an impact. Projects like TensorFlow and PyTorch have shown that open source can drive innovation in AI. These platforms provide access to cutting-edge tools and foster collaboration among developers worldwide. However, they are still far from matching the capabilities of the most advanced closed models.

What Governments, Companies, and Nonprofits Can Do Now

Invest in open source AI research and development

Support open source AI projects by funding R&D initiatives that prioritize transparency and collaboration. Governments can allocate grants to universities and startups working on open AI models, while companies can contribute resources to open source foundations. This ensures that innovation isn’t locked behind corporate walls. As Richard Stallman once said, transparency lets a worldwide community find and fix problems, this principle applies to AI as much as it did to software in the 1980s.

Support open source AI education and training

Encourage the development of open source AI curricula and training programs. Universities and nonprofits should offer courses using open tools and frameworks, ensuring that the next generation of engineers and data scientists can learn from and contribute to open projects. This builds a talent pipeline that is not dependent on proprietary systems or expensive licensing fees.

Advocate for open AI standards and policies

Push for regulatory frameworks that promote open AI standards and interoperability. Governments and industry groups should work together to create policies that require transparency in AI development and deployment. This reduces the risk of monopolies and ensures that AI systems are developed with public interest in mind. Nonprofits can lead advocacy efforts, using their influence to shape ethical and inclusive AI practices.

The ROI of Open Source AI: What It Looks Like

Cost savings from shared innovation

Open source AI eliminates redundant development. When organizations contribute to and use open models, they avoid reinventing the wheel. This reduces R&D costs and accelerates time-to-market. Red Hat and SUSE built billion-dollar businesses by leveraging shared code, similar benefits can emerge in AI when companies collaborate rather than compete in isolation.

Long-term competitive advantage

Investing in open source AI builds a foundation for future innovation. Companies that participate in open ecosystems gain access to continuous improvements and avoid being locked into proprietary systems. This creates a sustainable edge, as open models evolve with input from a global community of developers and users.

Improved public trust and collaboration

Transparency in AI development fosters trust. When governments and organizations use open models, they demonstrate accountability and openness. This can lead to stronger partnerships with stakeholders, including customers, regulators, and researchers. As Richard Stallman once said, transparency lets a worldwide community find and fix problems, this principle applies to AI as much as it did to software in the 1980s.

A Call to Action: Building an Open Future for AI

The role of collaboration in AI progress

Collaboration is the foundation of open source success. Just as the MIT AI Lab and the GNU/Linux community propelled software innovation, AI progress depends on shared knowledge. When companies, governments, and nonprofits work together, they avoid duplication, accelerate problem-solving, and ensure that AI benefits society as a whole.

What the next decade holds for open source AI

The next decade will define whether AI remains a closed, exclusive tool or becomes a shared resource. The same forces that made open source software the backbone of modern technology will shape AI’s trajectory. We’re already seeing signs of this shift, with more organizations recognizing the value of open models and shared development.

How to get started supporting open source AI

Support open source AI by investing in projects that prioritize transparency and collaboration. Contribute code, fund research, or advocate for open standards. The principles that Richard Stallman championed in the 1980s, transparency, shared knowledge, and community-driven development, are more relevant than ever. The future of AI isn’t just technical, it’s a choice we make today.

Source: siegelendowment.org