Olmo, Molmo, and Open Multimodal AI Infrastructure to Accelerate Science
Date:
Modern AI systems are often cast as products: a model, a chatbot, an API. But when we think about using AI for science, that framing is too narrow. What scientific communities need is open AI infrastructure: data, codebases, pre- and post-training recipes, documentation, evaluations, and access to model flows across stages of development, not just a final released set of model weights. In this talk, I will use Ai2’s Olmo project portfolio as a case study in what it means to build that infrastructure in the open. Drawing on recent results from our team, including work that exposes and studies multiple stages of model construction rather than only final models, I will argue that openness at the level of infrastructure is not only a scientific virtue but a practical necessity. If researchers are going to build AI systems for their own communities, and if universities, nonprofits, and governments are going to harness AI to serve the public interest, they must be able to invest in, contribute to, and use open infrastructure. Our goal is not to reproduce commercial AI, but to create a healthier open ecosystem to accelerate scientific discovery.