The Design–Build–Test–Learn (DBTL) cycle remains the core engine of biological engineering, yet its iteration speed still lags far behind software development. As AI systems begin to design, plan, and execute experiments, a new paradigm is emerging: DBTL as an autonomous, continuously optimizing system. Next-generation platforms combine AI-assisted rational design, high-throughput construction and perturbation, real-time data acquisition, and active learning to close the loop at unprecedented scale. Agent-powered lab-in-a-loop workflows, lab-on-a-chip systems, and advances at the silicon-to-carbon interface are enabling tighter integration between computation and biology, from semiconductor-enabled sensing to real-time feedback and decision-making. This session explores how autonomous DBTL stacks could unlock software-like iteration velocity in biology, redefine experimentation, and reshape the future of programmable discovery.

For decades, meaningful progress in biotechnology depended on access to million to billion-dollar facilities, specialized infrastructure, and industrial-scale equipment. Today, that paradigm is rapidly shifting. A new generation of tools, from smart shake flasks and modular bioreactors to microfluidic platforms, desktop DNA printers, and compact sequencing devices; is compressing the scale of biological experimentation while expanding who can participate. These technologies are transforming the economics of innovation, enabling startups, academic labs, and distributed research teams to design, build, and test biological systems without massive capital investment. As instrumentation becomes smaller, smarter, and increasingly automated, biology is moving from centralized mega-facilities toward a more distributed model of experimentation. This session explores how advances in lab automation, miniaturized bioreactors, and accessible bioinstrumentation are lowering the barriers to experimentation — and what this shift means for the speed, diversity, and geography of the next wave of bioinnovation.