Open vs Closed Ecosystems

One of the most consequential debates in AI right now is about openness. Should the weights and architectures of powerful AI models be publicly available, or should they be kept behind APIs and commercial licences? Open-weight models like Meta's Llama series, Mistral's offerings, and a growing number of alternatives let anyone download, modify, and deploy the technology. Proponents argue this democratises access, enables innovation, supports academic research, and reduces the concentration of power. Closed-model providers - OpenAI and Anthropic among them - argue that restricting access to the most capable systems is a safety measure, preventing misuse and allowing for more controlled deployment. The reality is more nuanced than either side suggests. "Open" doesn't mean "free of risk," and "closed" doesn't mean "safe." Open models can be fine-tuned for harmful purposes, but closed models can be accessed through jailbreaks and are subject to the commercial incentives of their providers. For your organisation, the open-vs-closed question has practical implications: open models offer more control and avoid vendor lock-in but require more technical capability to deploy and maintain. Closed models are easier to use but come with dependency on the provider's roadmap, pricing, and policies.