AI biomanufacturing uses machine learning to redesign the Design-Build-Test-Learn loop that produces industrial microbial strains. Instead of relying on expert intuition and years of trial and error, teams now plan metabolic pathways with genome-scale models, engineer enzymes computationally, build strain libraries with robots, and let screening data retrain the design step. The result compresses strain development from three to five years down to months, and shifts the competitive bottleneck from algorithm design to proprietary data and automation throughput. Why strain engineering is still the bottleneck Every biomanufacturing plant runs on a strain. A good strain makes the factory profitable; a weak one leaves the fermenters idle. Pilot-scale failure rates stay…