A team from Stanford University and the Arc Institute had an AI model design complete viral genomes from scratch, then built 16 functional viruses in the lab that don't exist in nature. The work had previously only been available as a preprint. It has now been peer-reviewed and published in the journal Science：http://www.science.org/doi/10.1126/science.aec2657.
The New York Times reports：https://www.nytimes.com/2026/08/06/science/ai-viruses-bacteria-arc.html new details, particularly about the hit rate. The model, called Evo, proposed 700,000 possible genomes. The team pursued only the most promising candidates, had 285 sequences chemically synthesized as DNA, and inserted them into bacteria. Sixteen of those produced viruses capable of replicating. The preprint had mentioned 302 synthesized genomes.
The training process is also coming into sharper focus. Evo first learned from roughly nine trillion nucleotides drawn from millions of animals, plants, microbes, and viruses, picking up patterns that run through the entire tree of life. Only then did a second, specialized training round follow, using the 11 genes of the phage Phi X-174 and about 15,000 of its closest relatives. For doctoral student and co-author Samuel King, it was the logical move. "It just felt like the obvious next step," he said.
The resulting viruses weren't just weak copies, either. They proved as robust as natural ones, and some replicated even faster than Phi X-174. "They're not just sickly versions of stuff that already exists," says Oliver Crook, a protein chemist at the University of Oxford who wasn't involved in the study. Patrick Cai, a synthetic biologist at the University of Manchester, calls the work an "important milestone."
Crook tempers expectations, though. The AI didn't invent anything fundamentally new. The viruses are very similar to natural species：https://www.biorxiv.org/content/10.64898/2026.06.12.731871v1 and rely on the same biology. Whether Evo would be equally successful with other virus groups remains an open question. If it is, the results could yield useful tools for medicine and biotech. "A lot of our science rests on viruses as technology," Crook says.
A gap in biosafety regulation is now more visible than ever. The U.S. National Institutes of Health released a policy on high-risk life sciences research：https://www.nih.gov/about-nih/nih-director/statements/announcement-release-us-government-policy-stopping-high-risk-life-sciences-research in late July. It bans experiments that make pathogens more dangerous. But purely computational work, meaning designing viral DNA on a computer, isn't covered "unless it involves an entity of concern," the agency said.
The problem is obvious. With smallpox, that classification is clear-cut. With a virus that came out of an AI model, it isn't. "What is the risk of what I've never seen before?" asks Moritz Hanke of the Johns Hopkins Center for Health Security. He sees a wide gap between the pace of research and the guardrails around it. "There's just a huge disconnect." His misuse scenario: "You could say, 'Hey, genomic language model, make me an influenza genome that is modified to be more transmissible or to be more lethal.'"
The team took precautions on its own. During training, Evo received no data on viruses that infect humans, nor on related pathogens from animals, plants, or fungi. That means the model can't generate those genomes in the first place. "We just wanted to be extra careful," says Brian Hie, a computational biologist at Stanford and co-author of the study. Hanke calls that "quite commendable," especially because no official rules required it. "Because they don't get any guidance from anywhere on what they should be doing," he says.
Original article from September 21, 2025:
A research team in California has used artificial intelligence to design working viruses that kill bacteria, in what they describe as the "first generative design of complete genomes." The project marks an early step toward AI-designed life forms, according to a report in MIT Technology Review.
The work was carried out by scientists at Stanford University and the nonprofit Arc Institute. In a preprint paper：https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1, they describe how an AI system proposed new genetic codes for viruses. The team then chemically printed 302 of these designs as DNA strands and exposed them to E. coli bacteria. Sixteen of the AI-generated viruses successfully replicated and destroyed their bacterial hosts.
"That was pretty striking, just actually seeing this AI-generated sphere," said Brian Hie, who runs the Arc Institute lab where the viruses were created.
At the center of the project is an AI called Evo：https://the-decoder.com/evo-2-an-ai-model-that-understands-the-language-of-life/, which functions like a large language model but is trained on biology instead of text. Instead of learning from books and articles, Evo was trained on about two million bacteriophage genomes. For this study, the researchers tasked it with proposing variants of phiX174, a simple bacteriophage containing only 11 genes and about 5,000 DNA letters.
Jef Boeke, a biologist at NYU Langone Health, described the project as an "impressive first step" toward AI-designed life, even though viruses themselves are not technically alive. He said the AI's performance was "surprisingly good" and its designs "unexpected," with changes to gene orders and arrangements that human scientists hadn't considered.
Not everyone is convinced. J. Craig Venter, who helped pioneer synthetic DNA, called the method "just a faster version of trial-and-error experiments." His lab once created synthetic cells through a similar process, but with much slower, manual searches through scientific literature.
The technology could have major applications. Doctors have long experimented with phage therapy as a treatment for multidrug-resistant bacterial infections. Viruses are also a key tool in gene therapy, where they deliver new genes into human cells. AI-designed viruses could make both approaches more effective.
But the risks are equally clear. The team deliberately avoided training Evo on human pathogens. Even so, Venter raised "grave concerns" about what could happen if the same approach were used on dangerous viruses like smallpox or anthrax. "One area where I urge extreme caution is any viral enhancement research, especially when it's random so you don't know what you are getting," he said.
Scaling the method to living cells is also far more complex. A bacterium like E. coli has about 1,000 times more DNA than phiX174. "The complexity would rocket from staggering to way, way more than the number of subatomic particles in the universe," Boeke warned.
Despite this, Jason Kelly, CEO of Ginkgo Bioworks, argues that pursuing AI-designed cells should be a national priority. He imagines automated labs that could continuously test AI-generated genome designs, feeding results back into the model. "This would be a nation-scale scientific milestone, as cells are the building blocks of all life," he said. "The US should make sure we get to it first."
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