I've been seeing a lot of discussion around open-weight vs. closed AI models lately. According to me, this isn't about proving one is better than the other. Both solve different problems. Open weight models give builders more freedom. You can run them on your own infrastructure, fine-tune them, protect sensitive data, and avoid being completely dependent on a single provider. I also think we'll see more founders building and fine-tuning open-weight models for their own teams, workflows, and products instead of relying entirely on general purpose models. That level of customization could become a real competitive advantage. Closed models, on the other hand, let teams move much faster. You get access to some of the best models without worrying about infrastructure or maintenance. What I find interesting is that even many of the companies driving AI innovation acknowledge that there's room for both approaches. I don't think the future belongs entirely to open-weight models or closed models. I think most founders will end up using both, depending on what they're building and the trade offs they're willing to make. Which approach would you bet on open-weight, closed, or a combination of both?