In March 2026, I attended the Precision Medicine World Conference in Santa Clara. As is expected at all conferences these days, there were several sessions focusing on how AI is solving biology problems. In some ways, this is nothing new. Let’s face it, before AI, there was always some “silver bullet” technology that was hailed as the one to further a field, but this time, something felt different. First, the silver bullet tools of the past have often been field-specific—think CRISPR in genome editing or CAR T in immunooncology—while the AI allure seems universal across research areas. Secondly, AI being a tech-powered tool has given a whole new meaning to biotech, where the biology component seems to become irrelevant to a certain extent, meaning that it can all just be reduced to “data.”
As life scientists, we have been trained to think of biology as complex, and the intricate pathways it entails as an unravelable mesh that can only be resolved by pulling on one thread at a time. So, when a tech leader simplified it to a finite number of pathways that can be thrown in AI models, I was probably not the only one who winced instinctively. Yet, it is also a refreshing take: Someone unburdened by the baggage of biology’s complexities might be able to look beyond historical constraints, shoot for the impossible, and possibly even achieve it. Doesn’t most progress usually happen when someone dares to break the mold? And yet I felt an emotional tug thinking about losing the intricacies that make biology so delicate and beautiful.
While it will take active unlearning and keeping an open mind—luckily, we are trained to do so as scientists—to embrace the world of AI-driven biology, the power of the tools is undeniable. Venture capitalist Vinod Khosla shared his vision for the future of biology in one of the sessions where he described how AI could make N-of-1 medicine an affordable reality in the near future. Interestingly, within a month, an Australian tech entrepreneur, Paul Conynham, took the internet by storm when he used ChatGPT and AlphaFold to create a cancer vaccine for his dying dog, Rosie. Keeping debates regarding whether this approach alone helped Rosie’s recovery aside, what excites me the most about this story is the potential for democratizing medicine unlike what we have ever seen before. If a person with no biology background could secure enough information through AI to get scientists to test his idea at a reasonable cost—approximately 3,000 Australian dollars—bespoke therapies certainly don’t seem far off.
The words “revolution” and “transformation” are often overused in describing scientific tools, but there is certainly a major upheaval happening in healthcare and biotech. An anecdote that resonated with me was when Greg Brockman, president and cofounder of OpenAI, described why it would be hard to explain the concept of Uber to someone in the 1950s. It took convergence of several technologies—internet, GPS, and smartphones—that did not exist back then for this idea to emerge and work. Similarly, it is hard to predict where AI and emerging technologies will take us in the next few years. We can only buckle up and keep an open mind, because it may be well beyond what we can imagine today.
