For decades, DNA synthesis has been the limiting reagent in synthetic biology — reliable for short sequences, but increasingly error-prone and costly as designs scale past 10kb. That ceiling is now cracking. New enzymatic synthesis platforms, error-correction chemistries, and assembly pipelines are extending what’s possible, opening the door to rapid construction of full pathways, microbial genomes, and even mammalian chromosomes. This session will explore how innovators are breaking past the 10kb barrier, what technical and economic breakthroughs are needed next, and how longer, cheaper, and faster synthesis could fundamentally change how we design biology at scale.

AI-enabled, self-driving labs are still emerging, but their foundations are already transforming how teams design, run, and interpret experiments. This session offers a practical guide for scientists and R&D leaders who want to understand what can be done today — from tightening design–test–learn loops to reducing manual error and capturing early benefits of autonomous experimentation. Rather than presenting an unrealized future, speakers will focus on practical, real-world steps that give organizations a competitive edge as SDL capabilities evolve and mature. Speakers will explore what’s working, what’s not, and how autonomous lab systems are reshaping protein engineering, pathway optimization, and therapeutic design.

Biology is entering an era where AI agents don’t just analyze data — they co-design, plan, and execute experiments. Multi-agent systems like CRISPR-GPT demonstrate how AI can act as a true lab co-pilot: decomposing complex genome editing projects into stepwise workflows, selecting tools, troubleshooting, and even drafting protocols that allow junior researchers to perform sophisticated edits on their first attempt . Beyond CRISPR, new systems like BioMARS integrate reasoning agents with robotics, while biotech companies are testing “AI lab assistants” that monitor and adjust experiments in real time. This session explores how multi-agent copilots are making biology more reproducible, democratizing complex workflows, and pushing the boundaries of lab autonomy. The central question: when AI can plan, troubleshoot, and validate experiments end-to-end, how should scientists and institutions govern this new power?

Instrumentation remains one of the greatest bottlenecks in bioinnovation. For decades, meaningful progress required billion-dollar facilities and industrial-scale reactors. Today, that paradigm is shifting. Emerging tools — from smart shake flasks and modular bioreactors to microfluidic systems, desktop DNA printers, and next-generation sequencing devices — are flipping the economics of scale.

"Reading, writing, and editing DNA were just the prelude. The new frontier is composition—designing entire genomes like symphonies, guided by AI models trained on millions of sequences. In this new paradigm, biology becomes a writable medium where genes, circuits, and chromosomes are arranged with intention rather than discovered by chance. From AI-optimized CRISPR systems and compact editors like TIGR to CRISPR SWAPnDROP and Bridge Recombinases capable of megabase-scale rewrites in human cells, a new toolkit is emerging that treats the genome not as a fragile molecule but as an editable architecture. These molecular instruments bypass the cell’s own repair machinery, offering a level of precision and predictability that brings genome design closer to true composition—a craft as deliberate and creative as writing code or composing music.

What happens when AI systems stop being tools and begin acting like collaborators in scientific thought? Multi-agent architectures such as SciAgents and Google’s “AI Co-Scientist” are pioneering hypothesis generation by dividing scientific reasoning into specialized sub-agents: literature retrievers, causal mappers, and graph-based reasoners. Unlike single models, these teams of agents mimic the structure of scientific collaboration itself — brainstorming, critiquing, and refining ideas. In synthetic biology, such systems could propose new gene circuits, uncover hidden regulatory logic, or suggest underexplored protein folds. This session asks: how far should we trust AI-generated hypotheses, and how do we validate them responsibly? With machine-driven insight now on the horizon, the very architecture of discovery may shift — from lone researchers and teams of humans to networks of humans and machines co-creating the future of biology.