This week we talk about Evo 2, bacteriophages, and antibiotics.

We also discuss AI models, medical innovations, and the Red Army.

Recommended Book: The Design of Everyday Things by Donald A. Norman

Transcript

A bacteriophage, sometimes just called a phage, is a type of virus that only infects bacteria. “Phage” means to devour, and that’s what bacteriophages do—they infect and replicate within bacteria that they target, injecting their own genome into that target’s cytoplasm, which are all the materials contained within the bacteria’s cell membrane.

Phages are super-abundant, by some measures more abundant than every living organism, including bacteria, on earth, combined. And they’re interesting in that they range from incredibly simple to quite complex, and have at times been used as alternatives to antibiotics, because they attack and feed on bacteria.

The use of phages to counter bacterial infections was all but abandoned in the mid-20th century when antibiotics were discovered and commercialized, their production industrialized and the substances themselves proving a lot easier to mass-produce, and a lot more predictable in their utility than phages. Phages were kinda sorta almost understood, but we didn’t really get what they were doing or why, so their application often felt more like folk remedies than real-deal science, despite the actual science underlying the practice.

Also, phages were primarily used as antibiotic treatments by the Red Army, the Soviet Union’s military. So throughout the West, which was rapidly scaling its production of antibiotic treatments, the use of bacteriophages was associated with Stalinist communism, and so the Red-scare, the demonization of anything associated with the Soviet Union, was partially responsible for the shelving of this approach and this realm of research, at least for a while.

Much of that existing research was also done in the Soviet Union, and the published documents were thus published in Russian or Georgian languages. And because much of the rest of the scientific publishing world was reorienting around English at this time, that meant these published works were often either ignored or unintelligible to the rest of the scientific community.

As with much of our microbiota, the invisibly small viruses, bacteria, archaea, and so on that make up the human microbiome, we have a general sense of how bacteriophages interact with some of what makes us, us, but only a general sense. We know that healthy individuals tend to contain a host of bacteriophages that people who have chronic conditions, like Crohn’s disease or ulcerative colitis are less likely to have, for instance, and there’s a chance that this lack is associated with those conditions—though each person’s body composition is unique, and this facet of biology is still relatively obscure; we really don’t know for certain what does what, because of how complex these interactions are.

What I’d like to talk about today is a recent development in the world of bacteriophages, and why the researchers behind it are both celebrating their accomplishment, and warning about potential dangers associated with the same.

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Back in 2025, a nonprofit called the Arc Institute, which has a stated goal of accelerating scientific progress and understanding the root causes of complex diseases, announced the release of a new language model, a new AI system, called Evo 2.

The Evo family of foundation models—a foundation model being a type of AI model that’s been trained on a huge corpus of data, but which is applicable for all sorts of purposes, including serving as the foundation of large-language models like ChatGPT or Claude—this family of foundation models is open-source and trained on raw genetic sequences, something like nine trillion nucleotides-worth of such sequences, making it distinct from other models in this space that have been trained on descriptions of biological systems, using human language.

The initial version of Evo was released in early 2024, and the newest version, Evo 2, which is an upgraded version of the Evo 2 model that is more efficient, so it can be run on less powerful hardware, was released in February of 2026.

So while many of the AI systems that non-biologists interact with on a regular basis have been trained on human language-based libraries, showing relationships and interactions between the words we use to communicate, these models have been trained on the fundamental building blocks of life; the nucleotides, Adenine, Thymine, Cytosine, and Guanine, ATCG of DNA, if you remember that from biology class, that are strung together into 64 different possible three-letter combinations. Chains of these nucleotides instruct cells to build proteins out of amino acids, and from that baseline, we get life.

We also get non-living things like viruses, which have no cells, metabolism, or independent reproduction, and phages are viruses.

And while other AI models have been shown to be great at designing proteins, before 2025 there was little evidence that such systems could design viable genomes: the combination of genetic information that makes up a complete, fully functional organism.

That’s what Arc decided to tackle with this Evo AI model. And back in 2025, Arc announced that it had successfully validated the first viable genome designs, created using generative AI.

These designs were for 16 bacteriophages, which were modeled on a virus that infects E. coli bacteria, and some of them worked just as well or better at infecting E. coli when compared to the actual, real-world phage they were modeled on. They were produced in the real world, a bacteria coaxed into producing them, and then they went on to successfully gobble up the E. coli test subjects they were meant to gobble up, demonstrating that they worked in practice, not just theory.

And a new paper published in early August of 2026 by the Arc Institute and Stanford University expounds upon this research, showing the results of an attempt to create entirely new viruses, not just altered existing viruses.

Rather than mutating that E. coli gobbling phage, as with the last experiment, tweaking an existing virus, this time they tasked Evo 2 with modeling how that E. coli attacking and eating process works, and then told it to come up with entirely new viruses that operate on the same premise, but which are structured differently; new viruses that eat the same thing in a similar way, but which are distinct from the original model.

Ultimately, it gave them 16 viable viruses of very different sizes and structure, all of which were created in a lab and successfully ate the targeted E. coli strain, as intended.

This is being seen as a pretty big deal, because while creating viruses in a lab is very modern technology, and mutating those viruses shows a lot of potential for manipulating what we already know works and then tweaking virus behaviors to, perhaps, help us create new medical treatments, the ability to generate, from scratch, entirely new viruses that hold together, with genomes that don’t just fall apart when they come into contact with the real world, and which can still do things, like attack bacteria—that opens a lot of new doors, potentially giving us the ability to say, okay, this bacteria is no longer responding to antibacterial drugs that we have available, so let’s make a virus that will kill the bacteria instead, and let’s make one that won’t harm the human that’s housing that bacteria.

We might also be able to create phages that eat other things, or which in some other way help the human body, or other biological entities, fight off chronic conditions, or recover or rebalance; there’s a lot of potential here, because this suggests AI systems trained on the right materials, on the building blocks of life, could generate all sorts of viable biological systems that we can then actually create. It’s a huge step forward, compared to systems that are also impressive, but which mostly help us understand the biological world better—like Alphafold, which solved the protein folding problem.

Those involved with this research have also been been flagging potential dangers with this development, though, including the potential for creating new viruses and other biological systems that could trigger unpredictable outcomes in other biological systems. There are a lot of potential hazards with this sort of research, and they’ve been very careful up till this point, sticking with test subjects that only target E. coli, but not everyone will necessarily be so careful, which might mean accidents, or it could mean people with less than benevolent intentions using these techniques to develop highly infectious viruses or other such pathogens; starting from smallpox to produce even more contagious and deadly ailments, for instance.

The optimistic view of this research is that it could contribute to the surge in new discoveries and technologies that we’re seeing around the world right now, that are resulting in new medical approaches and in some cases entirely new medical fields, which could help us do all sorts of things, including big-sky ambitions like curing cancer and doing away with chronic illnesses entirely.

Like most major scientific developments, though, these are also big developments for those who might want to do harm, and it also creates new opportunities for very serious, dangerous, deadly accidents, which means we’ll probably have to develop and implement more stringent safety protocols and regulatory efforts if we want to enjoy the full benefits of these innovations, without suffering significant new downsides, in the process.

Show Notes

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