Open-Weight Models vs. Open Stack: Understanding the Difference in AI Transparency

Open-weight AI models, such as Llama, Mistral, and Gemma, allow users to download and run model weights on their own infrastructure, but they typically share little about how the model was built. An 'open stack' goes further by making the full development pipeline more transparent, including training data, code, recipes, evaluation methods, and deployment tools. Openness in AI is not a fixed standard but rather a spectrum, with some projects releasing model weights and architecture while keeping training and evaluation details private. Projects like OLMo by Ai2 and BLOOM by BigScience represent more comprehensive open approaches, sharing not just the final model but the entire process behind it. Understanding this distinction helps developers and researchers assess how much insight they truly have into the AI tools they use.