AI-Written Virus Marks a New Step Towards Lab-Designed Life

A research team in California has demonstrated that artificial intelligence can design a fully synthetic virus from scratch, raising questions about how life itself may be engineered in the future.

What Has Been Developed?

The work centres on a new synthetic virus known as Evo-Φ2147, created using a generative AI model developed by researchers at Stanford University (in Silicon Valley, California) in collaboration with the Arc Institute and the University of California, Berkeley. The research was led by Brian Hie, who runs Stanford’s Laboratory of Evolutionary Design and works at the intersection of machine learning and biology.

Evo-Φ2147 was generated using Evo 2, an advanced version of Evo, a large language model for DNA. Instead of predicting words or images, Evo analyses genetic sequences and learns the underlying patterns that govern how DNA, RNA and proteins function together inside living organisms. Once trained, it can generate entirely new genetic sequences that have never existed in nature.

Asked The Evo2 Model To Make The Virus

As a proof of concept, the researchers asked Evo 2 to design new bacteriophages, viruses that infect bacteria rather than humans. The model then generated 285 complete viral genomes, all designed computationally rather than derived from natural viruses. Sixteen of those synthetic viruses were then tested in the lab and shown to successfully infect and kill Escherichia coli, a bacterium responsible for serious infections and a growing problem due to antibiotic resistance.

Evo-Φ2147 emerged as one of the most effective designs and, while it remains simple by biological standards, containing just 11 genes, it demonstrated that an AI-designed genome could function inside a living cell exactly as intended.

Why Evo-Φ2147 Matters Scientifically

What makes Evo-Φ2147 scientifically so significant is not the virus itself, but the method used to create it. For the first time, researchers have shown that an AI system can design a complete, functional genome at once, rather than tweaking or modifying existing biological sequences.

In the paper published in Science (.org), the authors describe Evo as “a genomic foundation model that enables prediction and generation tasks from the molecular to the genome scale.” Put simply, this means that Evo is AI that can understand DNA, predict what genetic changes will do, and design new genetic code, from tiny DNA parts right up to almost whole genomes (the complete set of genetic instructions inside an organism).

Trained

The model was trained on 2.7 million prokaryotic and phage genomes, representing around 300 billion DNA nucleotides. This scale allowed Evo to learn how tiny changes at the level of individual DNA bases can affect the fitness and behaviour of an entire organism.

The researchers emphasised that Evo operates at single-nucleotide resolution (at the level of individual DNA letters) and across very long sequences, up to 131,000 DNA bases at once. This matters because even the simplest microbes contain millions of base pairs, and previous AI tools struggled to capture long-range genetic interactions.

In the study, Evo was able to generate DNA sequences exceeding one million base pairs that showed realistic genome-like structure, including gene clusters and regulatory patterns seen in natural organisms. The researchers said that Evo “learns both the multimodality of the central dogma and the multiscale nature of evolution,” meaning it understands how DNA, RNA and proteins interact across molecular, cellular and organism-wide levels.

Evo-Φ2147 demonstrates that this understanding is not purely theoretical. It translated into a working biological system that could infect bacteria and replicate within them.

Is It Really “Life”?

Describing Evo-Φ2147 as life is a little controversial. For example, it is true to say that it does behave like a virus, which sits in a grey area between living and non-living systems. It contains genetic information, interacts with a host, and replicates using cellular machinery. However, it cannot reproduce independently and lacks the complexity associated with autonomous life.

The researchers themselves are being quite cautious, perhaps because Evo-Φ2147 does not meet most biological definitions of life, and its genome is vastly simpler than even the smallest free-living organisms. To give it some context, the smallest known bacterial genomes contain around 580,000 DNA base pairs, while a human genome contains roughly 20,000 genes.

What makes this situation a bit different is that the genome was not discovered or evolved through natural selection, but was written intentionally by an AI system. British molecular biologist Adrian Woolfson described this as a turning point, arguing that evolution has historically been blind, while genome-scale AI introduces foresight and design into biology for the first time.

This is why some researchers view Evo-Φ2147 as an early step towards lab-grown life, even if it does not yet qualify as life in a strict sense.

How This Fits Into The World of Synthetic Biology

Synthetic biology has long aimed to redesign living systems, but progress has typically relied on modifying existing organisms, and Evo represents a move from editing life to generating it computationally.

Earlier advances such as CRISPR gene editing allowed scientists to cut and paste DNA with precision. Evo goes further by designing entire genetic systems at once. In the Science paper, the authors reported that Evo successfully generated novel CRISPR-Cas systems and transposable elements that were validated experimentally, marking “the first examples of protein-RNA and protein-DNA codesign with a language model.”

This essentially places Evo within a growing movement to treat biology as an information science. DNA becomes a form of code, evolution a dataset, and AI a design engine capable of exploring biological possibilities far faster than natural processes or traditional lab work.

The researchers have explicitly framed Evo as a foundation model, comparable to large language models in AI, designed to underpin many downstream applications rather than a single use case.

Ethical, Security and Governance Questions

The ability to design and generate complete genetic systems using AI also raises some legitimate concerns about misuse, because the same tools that can create beneficial biological systems could, in theory, be applied in harmful ways, an issue the Evo team addressed directly by excluding viruses that infect humans and other eukaryotes from the training data.

For example, in the published Science paper, the authors warned that genome-scale AI “simultaneously raises biosafety and ethical considerations” and called for “clear, comprehensive guidelines that delineate ethical practices for the field.”

They pointed to frameworks such as those developed by the Global Alliance for Genomics and Health as a starting point, stressing the need for transparency, international cooperation and shared responsibility.

Importantly, in documenting their discovery, the researchers seem to have avoided too much sensationalism. For example, Evo does not enable the creation of dangerous organisms overnight, and its outputs remain constrained by biological reality, laboratory validation, and existing safety controls. The risks are real, but incremental rather than immediate.

Its Value to Humanity (and Business)

The most immediate promise of this discovery lies in medicine and biotechnology. For example, AI-designed bacteriophages could offer new ways to fight antibiotic-resistant infections, a growing global health threat. During the COVID-19 pandemic, the researchers noted that similar tools could have dramatically reduced vaccine development timelines.

Beyond healthcare, genome-scale design could also influence agriculture, materials science, and environmental remediation. The Stanford team highlighted potential applications such as reprogramming microbes to improve photosynthesis, capture carbon, or break down microplastics.

For businesses, this could signal a future where biological design cycles become faster, more predictable, and more software-driven. Companies working in pharmaceuticals, bio-manufacturing, and sustainable materials are likely to be among the earliest beneficiaries, while regulators and insurers will face new questions about oversight and risk.

Challenges and Questions

Despite the technical breakthrough, some significant challenges remain, because Evo’s generated genomes still lack many features found in natural organisms, including full sets of essential genes and robust regulatory systems, leading the researchers to describe current genome-scale outputs as “blurry images” of life that capture high-level structure while missing fine-grained detail.

Critics also argue that calling such systems a step towards creating life risks overstating what has actually been achieved. It is worth noting here that Evo accelerates design, but it does not eliminate the complexity, uncertainty, and failure rates inherent in biology.

Other critics have pointed to possible governance gaps, particularly around who decides what kinds of genomes should or should not be designed. As Woolfson put it, society will need to decide “who is going to define the guard rails” as these tools become more capable.

What Evo-Φ2147 ultimately represents is not the arrival of artificial life but offers a clear signal that the boundary between computation and biology is rapidly dissolving, with consequences that science, industry, and society are only beginning to understand.

What Does This Mean For Your Business?

This research shows that AI is no longer just analysing biology but beginning to shape it, turning genome design into something closer to a computational process that is then tested in the lab. Evo-Φ2147 does not redefine life, but it does change how genetic systems can be created and refined, replacing slow trial-and-error approaches with AI-driven design followed by targeted validation.

The wider impact of this capability lies in what it could unlock, because faster genome design has the potential to accelerate medical research, support the development of new treatments, and shorten response times during future health crises, while also increasing the importance of clear ethical oversight and realistic safety governance. For UK businesses operating in life sciences, pharmaceuticals, and sustainable manufacturing, this development points towards shorter development cycles and a growing reliance on advanced computing and biological expertise working together.

Taken together, Evo-Φ2147 highlights how quickly the boundary between computation and biology is fading, placing responsibility for how these tools are used not just with researchers, but with regulators, businesses, and wider society that will ultimately shape where genome-scale AI is allowed to go next.