As artificial intelligence and biotechnology converge, experts warn that existing governance systems are struggling to keep pace with emerging risks — particularly in areas where AI can be used to accelerate biological research in ways that could be harmful. The article argues that while AI promises breakthroughs in areas such as drug discovery and personalized medicine, it also enables dual-use capabilities that could be misused intentionally or accidentally. These dual-use concerns arise when technologies designed for beneficial purposes can also be harnessed to create biological threats or bypass safety measures.
A central challenge in managing these risks is the illusion of control: policymakers often assume that regulations and technical safeguards will prevent misuse, but the rapid evolution of AI tools is outstripping the ability of institutions to monitor or regulate them effectively. Traditional oversight frameworks, which were built for slower, more transparent scientific processes, are ill-equipped to govern AI-driven developments that can occur with little visibility. This gap means potentially dangerous innovations could emerge without adequate scrutiny or preparation.
The article highlights the need for new governance models that do not rely solely on reactive regulation. Instead, proactive, multidisciplinary approaches are necessary to anticipate and mitigate risks before they manifest. This could include greater collaboration between AI developers, biologists, ethicists, and policymakers to create shared norms, early warning systems, and adaptive oversight mechanisms. Without such efforts, the authors argue, society may remain blind to critical vulnerabilities until after harm has occurred.
Ultimately, the piece calls for a reassessment of how emerging technologies are governed — moving away from outdated assumptions about predictability and control, and toward systems that embrace uncertainty, prioritize transparency, and engage diverse stakeholders. Only by acknowledging the limitations of current frameworks can governments and societies begin to build more resilient strategies to manage the complex interplay between AI and biology.
