Andrea Magnorsky presented on Byte-Sized Architecture at Cloud Native Summit 2025, as a format for building shared understanding through small, recurrent workshops. Ahilan Ponnusamy and Andreas Grabner discussed the Technology Operating Model for AI adoption. Both approaches drew on the Open Practice Library for human-centred collaboration and driving architectural evolution.

By Rafiq Gemmail

Background: Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. Objective: We examined users’ interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users’ clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. Methods: We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. Results: Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. Conclusions: Findings highlight the chatbot’s effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot’s scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

Artificial intelligence can draw cat pictures and write emails. Now the same technology can compose a working genome.

A research team in California says it used AI to propose new genetic codes for viruses—and managed to get several of these viruses to replicate and kill bacteria.

The scientists, based at Stanford University and the nonprofit Arc Institute, both in Palo Alto, say the germs with AI-written DNA represent the “the first generative design of complete genomes.”

The work, described in a preprint paper, has the potential to create new treatments and accelerate research into artificially engineered cells. It is also an “impressive first step” toward AI-designed life forms, says Jef Boeke, a biologist at NYU Langone Health, who was provided an advance copy of the paper by MIT Technology Review.  

Boeke says the AI’s performance was surprisingly good and that its ideas were unexpected. “They saw viruses with new genes, with truncated genes, and even different gene orders and arrangements,” he says.

This is not yet AI-designed life, however. That’s because viruses are not alive. They’re more like renegade bits of genetic code with relatively puny, simple genomes. 

In the new work, researchers at the Arc Institute sought to develop variants of a bacteriophage—a virus that infects bacteria—called phiX174, which has only 11 genes and about 5,000 DNA letters.

To do so, they used two versions of an AI called Evo, which works on the same principles as large language models like ChatGPT. Instead of feeding them textbooks and blog posts to learn from, the scientists trained the models on the genomes of about 2 million other bacteriophage viruses.

But would the genomes proposed by the AI make any sense? To find out, the California researchers chemically printed 302 of the genome designs as DNA strands and then mixed those with E. coli bacteria.

That led to a profound “AI is here” moment when, one night, the scientists saw plaques of dead bacteria in their petri dishes. They later took microscope pictures of the tiny viral particles, which look like fuzzy dots.

“That was pretty striking, just actually seeing, like, this AI-generated sphere,” says Brian Hie, who leads the lab at the Arc Institute where the work was carried out.

Overall, 16 of the 302 designs ended up working—that is, the computer-designed phage started to replicate, eventually bursting through the bacteria and killing them.

J. Craig Venter, who created some of the first organisms with lab-made DNA nearly two decades ago, says the AI methods look to him like “just a faster version of trial-and-error experiments.”

For instance, when a team he led managed to create a bacterium with a lab-printed genome in 2008, it was after a long hit-or-miss process of testing out different genes. “We did the manual AI version—combing through the literature, taking what was known,” he says. 

But speed is exactly why people are betting AI will transform biology. The new methods already claimed a Nobel Prize in 2024 for predicting protein shapes. And investors are staking billions that AI can find new drugs. This week a Boston company, Lila, raised $235 million to build automated labs run by artificial intelligence.

Computer-designed viruses could also find commercial uses. For instance, doctors have sometimes tried “phage therapy” to treat patients with serious bacterial infections. Similar tests are underway to cure cabbage of black rot, also caused by bacteria.

“There is definitely a lot of potential for this technology,” says Samuel King, the student who spearheaded the project in Hei’s lab. He notes that most gene therapy uses viruses to shuttle genes into patients’ bodies, and AI might develop more effective ones.

The Stanford researchers say they purposely haven’t taught their AI about viruses that can infect people. But this type of technology does create the risk that other scientists—out of curiosity, good intentions, or malice—could turn the methods on human pathogens, exploring new dimensions of lethality.

“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,” says Venter. “If someone did this with smallpox or anthrax, I would have grave concerns.”

Whether an AI can generate a bona fide genome for a larger organism remains an open question. For instance, E. coli has about a thousand times more DNA code than phiX174 does. “The complexity would rocket from staggering to … way way more than the number of subatomic particles in the universe,” says Boeke.

Also, there’s still no easy way to test AI designs for larger genomes. While some viruses can “boot up” from just a DNA strand, that’s not the case with a bacterium, a mammoth, or a human. Scientists would instead have to gradually change an existing cell with genetic engineering—a still laborious process.

Despite that, Jason Kelly, the CEO of Ginkgo Bioworks, a cell-engineering company in Boston, says exactly such an effort is needed. He believes it could be carried out in “automated” laboratories where genomes get proposed and tested and the results are fed back to AI for further improvement.

 “This would be a nation-scale scientific milestone, as cells are the building blocks of all life,” says Kelly. “The US should make sure we get to it first.”

Nature, Published online: 17 September 2025; doi:10.1038/s41586-025-09505-x

People cheat more when they delegate tasks to artificial intelligence, and large language models are more likely than humans to comply with unethical instructions—a risk that can be minimized by introducing prohibitive, task-specific guardrails.

Meta launches Hyperscape into beta, allowing users to turn real world spaces into digital spaces for VR.

Nature, Published online: 17 September 2025; doi:10.1038/d41586-025-02993-x

A modified large language model called Delphi-2M analyses a person’s medical records and lifestyle to provide risk estimates for more than 1,000 diseases.

Nature, Published online: 17 September 2025; doi:10.1038/d41586-025-02931-x

The Science Foundation seeks donations to fund ten $10,000 ‘what if’ seed grants.