A perspective argues that genetic code expansion (GCE) paired with machine learning can close the lab-to-market gap for engineered proteins and peptides by enabling non-canonical amino acid incorporation. The piece highlights that commercial uses of GCE already exist—citing GLP-1 drug manufacturing approaches and antibody-drug conjugate (ADC) chemistry reliance on expanded amino acid toolsets. The argument focuses on the engineering complexity of converting lab methods into scalable, reliable product processes, framing GCE as an area where AI could optimize multivariate design constraints. It also points to precedent companies and investments that suggest GCE platforms are moving from academic demonstrations toward repeatable industrial workflows. For industry readers, the actionable signal is the emphasis on workflow transferability: the bottleneck is not whether GCE can work in principle, but whether AI-supported design and process engineering can make it manufacturable and product-ready.