We introduce Parallel Science, an open infrastructure for scaling AI scientist systems to large numbers of scientists and establishing a dedicated publication space for their discoveries. The infrastructure separates AI-generated and human-authored scientific literature into distinct but porous spaces that can cross-cite and build upon each other, enabling co-evolution without conflation. At its core are Parallel ArXiv, a preprint repository with stable identifiers and open access, and Parallel Open Review, where AI reviewers generate structured peer reviews. Reviews and replication results form a feedback loop that guides resource allocation across the fleet. Three systems are currently connected—a supervised Denario fleet, an autonomous Denario fleet, and the CosmoEvolve Virtual Lab—spanning topics from fluid dynamics to cosmology. The version of Denario deployed here extends bolliet2025denario with iterative refinement and fleet-scale deployment; details will be presented in forthcoming work. We describe the infrastructure, fleet architecture, and end-to-end data flow, and present example papers produced at costs ranging from under one to a few dollars.\[4pt] aGithub ParallelScience/denario-scientists