Open vs Closed Source AI: Best Development Approach

1.0. Open-Source AI: Driving Transparent and Collaborative Innovation

Many of the world’s greatest technological advancements—like the Linux operating system or Apache Web Server—originated in the open-source community. In the realm of AI development, open-source frameworks promote transparency, collaboration, and democratized innovation.

Open-source AI models, such as those hosted on Hugging Face, foster rapid experimentation by enabling developers, researchers, and enterprises to build on shared codebases. This accelerates progress in areas like natural language processing (NLP), computer vision, and machine learning model development.

2.0. Closed-Source AI: Prioritizing Security, Control, and Commercial Viability

Closed-source AI models are typically developed by private companies that keep their source code and data proprietary. This approach is often chosen to maintain:

• Data security and privacy
• Commercial advantage and monetization
• Tighter control over ethical and regulatory risks

In strategic sectors like national defense, healthcare, and finance, closed AI systems are essential for safeguarding sensitive information and mitigating AI misuse. The ability to limit access helps address growing concerns around AI safety, model integrity, and intellectual property (IP) protection.

3.0. Why a Balanced AI Development Approach is the Future

Where critics of open-source AI often question its validity over issues relating to intellectual property protection and commercial viability, these challenges can easily be met with good regulatory frameworks. Clear guidance on the development and deployment of responsible AI will help balance fostering innovation against its perils.

The EU's proposed AI Act is an epitome of such a regulatory effort that wishes to set harmonized rules of the game for the development and use of AI systems, including open-source models. The EU's proposal also imposes stricter requirements for AI applications that are considered the most risky and lays down a regulatory framework with a risk-based approach for a well-functioning, balanced, and trusted AI system.

Given these considerations, I propose a mixed approach to AI development for the United States. That would encourage taking great benefit from innovation and transparency of the open-source AI, using great frameworks towards security and ethical concerns.

For example, we can encourage sharing AI models or researches in such a way that they would be open for community contribution and scrutiny, while keeping certain parts secure.

That sets up the need for industry leaders to learn how to be transparent and work with researchers and communities by contributing to open-source AI initiatives. Developers and researchers must contribute to and participate in the open-source project in accordance with existing guidelines and norms in the development of responsible AI.

Thus, the debate of whether AI should be open or closed source is in no way binary but a spectrum of possibilities. We need a balanced way, where the cherry-picked values between the strengths of the two models would exist in a way commensurate with the requirements of innovation, national security, and ethical standards in the development of AI.

4.0. Final Thoughts: Open vs Closed Source AI—Not Either-Or, but Both-And

Instead of treating open-source and closed-source AI as opposites, stakeholders should see them as complementary forces. AI developers, policymakers, and industry leaders must work together to:

• Contribute to ethical open-source AI projects
• Enforce accountability through AI auditability and transparency
• Implement AI risk management frameworks
• Encourage responsible innovation without compromising security or ethics

In short, the path forward in AI development strategy lies in adopting a hybrid approach—leveraging the agility of open-source AI and the safeguards of closed systems.