AI Just Built Unnatural Viruses in a Lab. But Don't Panic.
Scientists used a freely downloadable model to design viruses that killed their targets. This is actually great news for antibiotic development. The biosecurity picture isn't as happy.
A.I. Disclosure: I use LLM technology to help with research, fact-checking, document summaries, editing, and rewrites. I’m trying to use it responsibly, but I’m learning as I go. You can read my full ethics disclosure here.
Last year, in a Stanford lab, researchers looked into their petri dishes and saw an unnatural massacre: clear spots spreading across a lawn of E. coli, dead bacteria scattered across a petri dish, killed by viruses no cell had ever carried. The genomes of these viruses had been written by software, and AI had essentially generated the whole scene. “That was pretty striking, just actually seeing, like, this AI-generated sphere,” Brian Hie, who runs the lab where the work was done, told MIT Tech Review.
His team, led by graduate student Samuel King, fine-tuned an open AI model called Evo, fed it the DNA sequences of a well-studied phage, and generated 302 complete genome designs. They printed them, mixed them with bacteria, and 16 came alive; some killed faster than the natural virus. The work, first published last year, is now peer-reviewed in Science. I covered it on AC360 last night.
Don’t panic: the phages are harmless to people. They infect only bacteria, and the model’s makers deliberately left human-infecting viruses out of its training data. But don’t just relax, either: The scientists’ fine-tuned AI models sit on an open site, right now, for anyone to use.
Let’s weigh the good and the bad. First the good.
AI is making all sorts of incredible breakthroughs possible in foundational and applied science, from reading entombed Roman scrolls to identifying alien worlds humans could conceivably walk and even breathe on.
In this case, the research could help with an important and frightening problem: the dead end we’ve reached in antibiotic development. There are bacterial infections that our existing range of medicines more or less cannot fight, and scientists anticipate the problem is getting worse, mostly because the money to be made developing new antibiotics isn’t enough to get large pharmaceutical companies on the case. As one survey of the crisis put it in the journal Microbial Biotechnology, “the development of new antibiotics, frequently described as the drying of the pipeline, is less limited by lack of scientific insight than by lack of the mobilization of the monetary resources needed to bring these discoveries to the market despite recent financial push and pull efforts of the public sector.”
If AI can take the intense and expensive trial-and-error process of building the means to deliver bacteria-killing viruses that can’t hurt humans and collapse it into a series of software runs, hey, that’s the kind of thing the AI companies have been boasting their technology is for.
Also, pulling this off wasn’t a matter of a couple of chatbot prompts. It required task-specific fine-tuning, custom prediction tools, DNA synthesis, and a novel lab protocol to screen roughly 300 designs down to 16 that worked. As Jennifer Doudna told Bloomberg in June 2026, biology is complex and innovation “is still really in the domain of human beings right now.” It takes real expertise and a real lab to pull this off.
The bad is a question of access and ability.
