Synthetic silk production has undergone significant technological and commercial advances over the past 5 years, with fibers from most labs and companies now regularly matching the properties of natural silk by one metric or another. Yet the fundamental links between silk protein processing and performance remain largely unresolved and fiber optimization is commonly achieved through non-natural methods. In an effort to address this challenge, data that closes this loop of processing and performance is presented by spinning a native silk feedstock ex vivo into a near-native fiber using just two naturally occurring parameters; pH activation and extensional flow (i.e., spinning rate). This allows us to link previous experimental and modelling hypothesis surrounding silk's pH responsiveness directly to multiscale hierarchical structure development during spinning. Finally, fibers that match, and then exceed, natural silk's mechanical properties are spun and understood by rate of work input. This approach not only provides energetic insights into natural silk spinning and controlled protein denaturation, but is believed will help interpret and improve synthetic silk processing. Ultimately, it is hoped that these results will contribute towards novel bioinspired energy-efficient processing strategies that are driven by work input optimization and where excellent mechanical properties are self-emergent
