In 2016, Noam Chomsky, the father of modern linguistics, published the book Who Rules the World? referring to the United States’ dominance in global affairs. Today, policymakers—such as U.S. President Donald J. Trump argue that whoever wins the artificial intelligence (AI) race will rule the world, driven by a relentless, borderless competition for technological supremacy. One strategy gaining traction is open-source AI. But is it advisable? The short answer, I believe, is no.

Closed-source and open-source represent the two main paradigms in software, and AI software is no exception. While closed-source refers to proprietary software with restricted use, open-source software typically involves making the underlying source code publicly available, allowing unrestricted use, including the ability to modify the code and develop new applications.

AI is impacting virtually every industry, and AI startups have proliferated nonstop in recent years. OpenAI secured a multi-billion-dollar investment from Microsoft, while Anthropic has attracted significant investments from Amazon and Google. These companies are currently leading the AI race with closed-source models, a strategy aimed at maintaining proprietary control and addressing safety concerns.

But open-source models have consistently driven innovation and competition in software. Linux, one of the most successful open-source operating systems ever, is pivotal in the computer industry. Google Android, which is used in approximately 70 percent of smartphones worldwide, Amazon Web Services, Microsoft Azure, and all of the world’s top 500 supercomputers run on Linux. The success story of open-source software naturally fuels enthusiasm for open-source AI software. And behind the scenes, companies such as Meta are emerging by developing open-source AI initiatives to promote the democratization and growth of AI through a joint effort.

Mark Zuckerberg, in promoting an open-source model for AI, recalled the story of Linux’s open-source operating system. Linux became “the industry standard foundation for both cloud computing and the operating systems that run most mobile devices—and we all benefit from superior products because of it.”

But the story of Linux is quite different from Meta’s “open-source” AI project, Llama. First and foremost, no universally accepted definition of open-source AI exists. Second, Linux had no “Big Tech” corporation behind it. Its success was made possible by the free software movement, led by American activist and programmer Richard Stallman, who created the GNU General Public License (GPL) to ensure software freedom. The GPL allowed for the free distribution and collaborative development of essential software, most notably the Linux open source operating system, developed by Finnish programmer Linus Torvalds. Linux has become the foundation for numerous open-source operating systems, developed by a global community that has fostered a culture of openness, decentralization, and user control. Llama is not distributed under a GPL.

Under the Llama 4 licensing agreement, entities with more than 700 million monthly active users in the preceding calendar month must obtain a license from Meta, “which Meta may grant to you in its sole discretion” before using the model. Moreover, algorithms powering large AI models rely on vast amounts of data to function effectively. Meta, however, does not make its training data publicly available.

Thus, can we really call it open source?

Most importantly, AI presents fundamentally different and more complex challenges than traditional software, with the primary concern being safety. Traditional algorithms are predictable; we know the inputs and outputs. Consider the Euclidean algorithm, which provides an efficient way for computing the greatest common divisor of two integers. Conversely, AI algorithms are typically unpredictable because they leverage a large amount of data to build models, which are becoming increasingly sophisticated.

Deep learning algorithms, which underlie large language models such as ChatGPT and other well-known AI applications, rely on increasingly complex structures that make AI outputs virtually impossible to interpret or explain. Large language models are performing increasingly well, but would you trust something that you cannot fully interpret and understand? Open-source AI, rather than offering a solution, may be amplifying the problem. Although it is often seen as a tool to promote democratization and technological progress, open source in AI increasingly resembles a Ferrari engine with no brakes.

Like cars, computers and software are powerful technologies—but as with any technology, AI can harm if misused or deployed without a proper understanding of the risks. Currently, we do not know what AI can and cannot do. Competition is important, and open-source software has been a key driver of technological progress, providing the foundation for widely used technologies such as Android smartphones and web infrastructure. It has been, and continues to be, a key paradigm for competition, especially in a digital framework.

Is AI different because we do not know how to stop this technology if required? Free speech, free society, and free software are all appealing concepts, but let us do better than that. In the 18th century, French philosopher Baron de Montesquieu argued that “Liberty is the right to do everything the law permits.” Rather than promoting openness and competition at any cost to rule the world, liberty in AI seems to require a calibrated legal framework that balances innovation and safety.