# Is Open Source the Future of AI? A Data-Driven Approach
## 5 Discussion  
The ongoing debate about the openness of AI models is critically important, as the implications of adopting either extreme--completely closed or fully open--are profound. When addressing decisions of such significance, it is crucial to consider as many factors as possible. This paper aims to contribute to the discussion by offering insights through a data-driven approach. While we strive to maintain objectivity in interpreting the results, some conclusions are clear and are thus emphasized. Our findings indicate that the open-source community is expanding rapidly, attracting talent from across the world. This influx of contributors plays a vital role in enhancing existing models and ensuring stable development in the future. Furthermore, the open-source community serves as a significant source of new ideas and approaches, which private enterprises can access freely. However, the openness of AI also introduces ethical challenges. Open-access models can be misused for harmful applications such as misinformation campaigns, cyberattacks, or deepfake generation. Additionally, bias in training data may be reinforced without strict oversight, potentially leading to unfair outcomes in sensitive applications. That said, open-source development allows for greater transparency, enabling researchers to detect and mitigate biases more effectively than in closed systems. Businesses that develop proprietary AI models often focus on safeguarding intellectual property to maintain a competitive edge. We argue that traditional business models for proprietary software may not fully align with the unique characteristics of AI. A key distinction is the inability of the general public to privately run these models. Consequently, the future of AI development may lean toward a software-as-a-service (SaaS) model, where enterprises generate revenue from usage while benefiting from innovations originating in the open-source community. Policymakers must navigate the dual challenge of preventing misuse while fostering innovation and competition. Unlike proprietary AI, which can enforce access controls, open-source models exist beyond the jurisdiction of any single entity, complicating regulatory efforts. Instead of imposing heavy restrictions that could stifle progress, a more balanced approach may involve community-driven safeguards and responsible disclosure practices to mitigate risks while keeping AI development open and accessible. The data highlight that open-source AI is rapidly expanding, reinforcing the need for structured AI governance to ensure that this growth remains beneficial and sustainable. However, outside of academia, the usage permissions of the models remain a factor in determining the practical accessibility of open models. While many models are released under permissive licenses such as Apache 2.0 and MIT, a significant number impose restrictions on commercial use, limiting their broader adoption. Furthermore, a large portion of models on Hugging Face lack clearly defined licensing terms, creating legal uncertainties for businesses. This variation suggests that licensing choices influence how AI models are shared and used, but their broader impact remains uncertain. Future work should explore whether different licensing approaches affect innovation, accessibility, and ethical concerns, as well as how they shape interactions between open-source contributors and industry stakeholders. Additionally, our analysis shows that most open models are derived from a few dominant base models, such as Mistral and Llama. This suggests that community efforts tend to concentrate around specific architectures, potentially limiting diversity in model development. Further research should investigate whether this trend results from technical advantages, community momentum, or other factors such as licensing conditions or corporate involvement. Beyond governance and licensing, benchmarking remains an important aspect of evaluating AI progress.