I have been diving into / reading up this area lately. A few quick notes. Worth specifying that these were hand selected candidates. Out of these human selecte

I have been diving into / reading up this area lately. A few quick notes. Worth specifying that these were hand selected candidates. Out of these human selected ones, 5% were successful. The Stanford researchers picked the most promising 302 AI designs and synthesised them in the lab. Of these, 16 proved effective at killing E. coli bacteria. "picked the most promising 302 AI designs" out of a n that isn't mentioned in this article. Based on my quick reading, it feels like the BBC left out a ton of context that makes this work feel more novel than it is. There's a ton of prior art here (including some from the same team) For example, older iterations of the same model family were used to generate novel genes and proteins. Including anti-CRISPR proteins that were tested against phages. That work is honestly more interesting. From, "Semantic design of functional de novo genes from a genomic language model," https://www.nature.com/articles/s41586-025-09749-7 https://www.nature.com/articles/s41586-025-09749-7 Anti-CRISPRs (Acrs) are proteins used by phages to neutralize bacterial CRISPR–Cas systems (Fig. 3a). Many Acrs represent striking examples of rapid protein evolution, appearing as novel innovations without detectable similarity to other protein families [..] [..] After filtering for size, complexity and structure (Extended Data Fig. 5b), we next used PaCRISPR, a machine learning model trained to identify potential Acr proteins, to evaluate our generated candidates [..] [..] we further identified five proteins (EvoAcr1–5) that demonstrated strong protection against SpCas9 cleavage in both liquid culture survival assays (Fig. 3f and Extended Data Fig. 5d) and phage infection experiments (Fig. 3g and Extended Data Fig. 5f), while maintaining normal host growth Of note, semantic design is not limited to Evo 1.5 and can leverage any language model trained on prokaryotic or phage genomes. Improvements in genomic language models, as well as a better understanding of prokaryotic gene synteny, should therefore directly translate to improvements in semantic design. Also the same project has been covered before. Prior pre-publication / release from 2025 called "AI-Designed Phages," the piece is really well written and I recommend reading it, https://www.asimov.press/p/ai-phages https://www.asimov.press/p/ai-phages edit - as flobosg notes one of them describes the model, "Genome modelling and design across all domains of life with Evo 2," March 2026, https://www.nature.com/articles/s41586-026-10176-5 https://www.nature.com/articles/s41586-026-10176-5 pre-print of the asimov press piece, "Generative design of novel bacteriophages with genome language models," September 2025, https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 All research comes from somewhere. There's an entire sub-field / world here that goes back decades at this point. Leon Chan refactored a bacteriophage by hand in 2005 https://link.springer.com/article/10.1038/msb4100025 https://link.springer.com/article/10.1038/msb4100025 The first synthetic organism was Venter's mycoplasma laboratorium / Synthia in 2010. The first organism to be synthetically assembled (not designed, but assembled ikea style) was a phage in 2003 also by Venter & co. In 2021 another team made entities that can "find and combine building blocks into self-copies," Kinematic self-replication in reconfigurable organisms https://pmc.ncbi.nlm.nih.gov/articles/PMC8670470/ https://pmc.ncbi.nlm.nih.gov/articles/PMC8670470/ From 2019 / 2020, algorithmically designed synthetic life / "xenobots", https://www.nytimes.com/2020/04/03/science/xenobots-robots-frogs-xenopus.html https://www.nytimes.com/2020/04/03/science/xenobots-robots-f... https://en.wikipedia.org/wiki/Xenobot https://en.wikipedia.org/wiki/Xenobot Or, "Team builds first living robots—that can reproduce" https://wyss.harvard.edu/news/team-builds-first-living-robots-that-can-reproduce/ https://wyss.harvard.edu/news/team-builds-first-living-robot... , 2021 Zurich in 2019, https://web.archive.org/web/20191008040536/https://www.eurekalert.org/pub_releases/2019-04/ez-fbg032819.php https://web.archive.org/web/20191008040536/https://www.eurek... And if you expand computing to include molecular processes for genes, then there's stuff like PACE that uses phages as a process, https://en.wikipedia.org/wiki/Phage-assisted_continuous_evolution https://en.wikipedia.org/wiki/Phage-assisted_continuous_evol... (final edit - thanks flobosg!)