Zack Li

some recent thoughts (not that recent lol)

A lot of robotics teams are still building a nuke with their hands today. From day one at Dyna, we felt pretty strongly that infra couldn’t be something you “add later.” It has to grow with the research itself. AI research is largely about iteration speed: how fast you can turn an idea into an experiment, get a result, understand it, and decide what to try next. If every 10x increase in data or experiments also creates 10x more manual work, that loop eventually breaks. When we entered robotics, we were surprised by how much of the research process was still being carried by hand. Even in conversations with some very strong robotics teams, we saw the same pattern: people naturally focus on what’s directly in front of them, while the infrastructure needed to make the next hundred iterations easier keeps getting pushed out. Our team came from pretty diverse backgrounds, and we quickly realized it's an incorrect path to go. Today we shared some of the concrete infrastructure behind training on 1M+ hours of data, but that’s only one visible slice of the system we’ve already built. The broader infrastructure spans the full research loop and is designed to make research as automated, repeatable, and scalable as possible. In some ways, it’s broader than what people currently mean by #AutoResearch. For us, it comes down to three things: - Scale: As the corpus grows, the same system should keep handling the full pipeline—ingestion, processing, labeling, curation, training, and evaluation—without being reinvented at every new scale. - Speed: Modeling, data analysis, training, and evaluation should live in one tight loop, so each result can quickly shape the next hypothesis. - Attention: Researchers should be able to define a goal, launch the workflow, and step away. Scheduling, visibility, recovery, and provenance should be handled by the system. When all of this works together, infra stops feeling like something around the research. It just becomes part of how research gets done. We’ll share more of the full system in the future!