# Is Open Source the Future of AI? A Data-Driven Approach
## 1 Introduction  
Artificial intelligence, particularly large language models (LLMs), is an important topic in the computer industry at present. Despite the numerous fears and dogmas surrounding AI, it is certain that it has become integral to our life. This paper focuses on a specific area within AI development: open-source models. While open-source software is well-defined, the concept of open-source AI remains ambiguous and subject to interpretation. The term has been applied to various models with different levels of accessibility, ranging from those with publicly available code and weights to models that impose limitations on use and distribution through restrictive licenses. There is considerable debate on whether this type of technology should be universally accessible. Our aim was to investigate whether the open-source community is actively contributing to the field, regardless of differing philosophical convictions. Due to their substantial computational requirements, running LLMs on personal computers was previously impractical. However, the development of increasingly compact versions with impressive capabilities is leading to a significant transformation. It is now feasible to run your own model, provided it is sufficiently small,on a home computer's graphics processing unit (GPU), even if the GPU is several years old [1]. In the field of LLMs, the open-source community relies heavily on the willingness of large corporations to release their models in the open-source domain. This transition is understandably hindered by challenges from competitive markets, as well as the high barrier to entry, given that developing such models requires a significant investment in both time and money [1,2]. Recent advancements in open-source models have significantly reduced the performance gap between them and proprietary models. [PERSON] et al. [3] demonstrated that open-source models achieve comparable results to closed models on many benchmarks and project that they may reach or exceed proprietary performance in the near future. Similarly, Epoch AI [4] estimates that open-source models currently lag behind proprietary models by approximately 15 months. However, they anticipate that this disparity will continue to diminish over time. The distinction between open and proprietary models has significant implications for AI governance. Models with publicly available weights foster broader research collaboration and enable modifications tailored to specific tasks. In contrast, proprietary models remain restricted to large corporations, which can leverage private datasets and advanced infrastructure to drive innovation. While open-source contributions enable iterative improvements, concerns persist regarding their potential misuse and security risks. Proprietary models, on the other hand, offer controlled environments but lack the transparency needed for independent audits and bias mitigation. To clarify these distinctions, it is useful to categorize AI models based on accessibility and use. Fully open-source models provide unrestricted access to code, weights, and documentation, allowing for modification and redistribution. In practice, however, no major AI company has released models that include all training data alongside model weights. Another group consists of models with publicly available weights, which allow for fine-tuning and adaptation but do not disclose training processes or datasets. Mistral and Llama models fall into this category, offering weight access while retaining proprietary datasets. In contrast, proprietary models remain fully closed, typically accessible only through APIs or enterprise licenses. GPT-4, Claude, and Gemini exemplify this category, where users can interact with the system but lack control over its implementation. Proponents of closed source argue that unrestricted availability of OSMs could pose significant risks if misused, enabling the creation of tools that are potentially harmful to society.