A Nature paper demonstrated that RFdiffusion, an AI protein‑design method, can generate de novo antibodies that bind user‑specified epitopes with atomic accuracy and whose binding poses were validated by cryo‑EM, marking a milestone in computational biologics design. The work, co‑authored by David Baker’s group and collaborators, showed designs for multiple therapeutic targets and confirmed structures experimentally. The advance short‑circuits labor‑intensive experimental screening and suggests a route to design antibodies with tailored specificity and improved developability earlier in discovery. In a companion commentary and interviews, Baker and others counseled cautious optimism: the models accelerate design but require rigorous validation and downstream optimization for manufacturability and immunogenicity. Biotech companies and venture investors aiming to commercialize AI‑driven biologics will treat the results as proof of concept that computational pipelines can create candidate‑grade molecules, but translational risk remains in moving designs through tox, CMC and clinical stages.