  The convergence of artificial intelligence (AI) and synthetic biology is
rapidly accelerating the pace of biological discovery and engineering. AI
techniques, such as large language models and biological design tools, are
enabling the automated design, build, test, and learning cycles for engineered
biological systems. This convergence promises to democratize synthetic biology
and unlock novel applications across domains from medicine to environmental
sustainability. However, it also poses significant risks around reliability,
dual use, and governance. The opacity of AI models, the deskilling of
workforces, and the outdated nature of current regulatory frameworks present
challenges in ensuring responsible development. Urgent attention is needed to
update governance structures, integrate human oversight into increasingly
automated workflows, and foster a culture of responsibility among the growing
community of bioengineers. Only by proactively addressing these issues can we
realize the transformative potential of AI-driven synthetic biology while
mitigating its risks.
