AI used to create viruses not found in nature for first time

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In findings published in the journal Science, researchers at Stanford University and the Broad Institute of MIT and Harvard said they used a naturally occurring phage – a type of virus that infects bacteria – as a template to generate thousands of genomes with AI. The researchers chemically synthesised nearly 300 of the genomes and tested them in laboratory conditions, resulting in 16 “viable” viruses, according to the findings published on Thursday. In tests, a mixture of the synthetic viruses proved more effective at killing E coli bacteria than the naturally occurring viruses, according to the findings. “Our approach expands what synthetic genomics can achieve alongside methods such as directed evolution and rational engineering, lays out a path for generating adaptive and resilient phage therapies against rapidly evolving pathogens, and establishes a foundation for the generative design of larger, more complex genomes,” the researchers wrote in Science. Brian Hie and Samuel King, two of the paper’s authors, did not immediately respond to requests for comment. Isaac Bogoch, an infectious disease specialist at the University of Toronto and Toronto General Hospital who was not involved in the study, said the research raised both hopeful and concerning implications. “AI-designed viruses could have some potential benefits, such as the creation of targeted bacteriophages that could possibly help us tackle antibiotic-resistant infections in new ways,” Bogoch told Al Jazeera. “But that same ability to design whole, functional viruses could easily become a serious biosecurity risk if applied to harmful pathogens, so strong guardrails, screening, and oversight need to grow alongside the technology,” Bogoch said. Fatemeh Vafaee, a professor at the UNSW School of Biotech & Biomolecular Science in Sydney, said that while the study itself posed no tangible risk to humans as phages can only infect bacteria, the techniques involved could conceivably be used for harm. “So it’s less ‘should we worry about this virus’ and more ‘AI can now do this at all,’ which is why researchers are already calling for stronger biosecurity oversight, as a forward-looking precaution rather than a response to any actual danger here,” Vafaee told Al Jazeera. The milestone comes as rapid advances in the capabilities of AI stoke concern about the possibility of harm by malicious human actors or systems going rogue. The United Kingdom’s government-run AI watchdog this week disclosed that Anthropic and OpenAI’s frontier AI models engaged in “autonomous” and “unsanctioned” malicious activity targeting real people and organisations during a recent routine safety evaluation. The AI Security Institute said the rogue activity included an incident in which Anthropic’s Claude Mythos 5 created fake online identities in a bid to insert malicious code into an open-source project on a developer platform. The watchdog’s findings followed announcements by OpenAI and Anthropic last month that their top-end models had engaged in hacking sprees against several organisations without human prompting. After championing light-touch regulation early in his second term, US President Donald Trump has taken an increasingly hands-on approach to AI regulation. In June, Trump signed an executive order to establish a voluntary framework for evaluating frontier AI models ahead of their release. The Trump administration has not publicly released its evaluation criteria or methods, drawing criticism from tech industry observers. Tom Ellis, an expert in synthetic genome engineering at Imperial College London, said that while the Stanford researchers’ findings were “impressive”, but also showed how far AI remains from being able to create more complex genomes for cells and larger viruses. “This phage genome is literally the smallest, easy genome to design and make – with phages known to be very tolerant of mutations and quick to evolve to make use of them,” Ellis told Al Jazeera. “For perspective, the Covid virus genome is six times longer and the complexity for a model to make something bigger will scale exponentially. So something six times longer will be likely be around 100 times harder to do.” While study had implications for biosecurity, Ellis said, the manipulation of naturally occurring viruses was a much more serious and pressing threat than AI-created pathogens. “It would be ludicrous to use AI to design a pathogen, when there are so many available in nature already,” he said. 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