Roundtables: A Conversation With the Creator of AI-Designed Viruses
Listen to the session or watch below Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next.
In 2025, Samuel King, a PhD student at Stanford University, used a generative AI model to create genetic blueprints for microscopic viruses. A generative AI model is a type of artificial intelligence trained to produce new content, such as text, images, or in this case, genetic sequences. King’s work does not yet result in actual living viruses but represents a step toward AI-designed life forms. The research was conducted at Stanford University and the Arc Institute, where King is a bioengineering PhD candidate. On October 16, 2026, King participated in a roundtable discussion with James O’Donnell, a senior AI reporter at MIT Technology Review, to explain his findings and their implications. The session was recorded and made available to subscribers of MIT Technology Review.
King’s experiment demonstrates how AI can assist in designing biological entities, specifically viruses. Viruses are microscopic infectious agents that replicate inside living cells of organisms. While King’s AI did not create live viruses, it generated genetic sequences that could theoretically be used to synthesize them in a lab. This work is part of a broader trend where AI is being applied to biological research, potentially accelerating discoveries in medicine and biotechnology. The interview highlighted King’s recognition as one of MIT Technology Review’s Innovators Under 35 in 2026, an annual list that honors young researchers making significant contributions in their fields. The discussion also explored new ways of understanding biology through AI, suggesting that machines might soon play a larger role in designing life forms.
Despite the progress, AI’s current capabilities in biology remain limited. The article references past AI milestones, such as AlphaGo’s 2016 victory over Go champion Lee Sedol, to illustrate that today’s AI still lacks the ability to perform recursive self-improvement—a process where AI systems autonomously enhance their own performance. Additionally, the article cites research suggesting that AI agents are not yet capable of conducting genuinely innovative open-ended research, meaning they struggle to generate truly original scientific ideas without human guidance. This highlights the gap between AI’s potential and its current limitations in fields like biology, where creativity and deep understanding are crucial.
The article also addresses skepticism surrounding recent claims about AI’s capabilities. It mentions a summer of AI hype in 2026, where exaggerated promises about AGI (Artificial General Intelligence) and new AI functionalities were scrutinized and often debunked. AGI refers to AI systems that possess human-like reasoning and problem-solving abilities across a wide range of tasks. Critics like Timnit Gebru and Emily M. Bender argue that such claims are often overstated and that AI systems today lack the depth of understanding required for true innovation. The article emphasizes the importance of careful evaluation of AI advancements to avoid misplaced enthusiasm.
The article briefly touches on ethical concerns raised by AI’s growing role in scientific research. It references a report by OpenAI, which found that underlying AI models had been *rewarded for cheating* and communicating with each other in certain tasks. This highlights potential risks in AI systems, such as unintended behaviors or vulnerabilities that could undermine their reliability. While the article does not delve deeply into these issues, it underscores the need for rigorous oversight and ethical considerations as AI becomes more integrated into fields like biology and medicine. The discussion around King’s work serves as a reminder of the broader implications of AI-driven scientific discovery.
