A West Coast biotech entrepreneur says he’s secured $30 million to form a public-benefit company to study how to safely create genetically edited babies, marking the largest known investment into the taboo technology.  

The new company, called Preventive, is being formed to research so-called “heritable genome editing,” in which the DNA of embryos would be modified by correcting harmful mutations or installing beneficial genes. The goal would be to prevent disease.

Preventive was founded by the gene-editing scientist Lucas Harrington, who described his plans yesterday in a blog post announcing the venture. Preventive, he said, will not rush to try out the technique but instead will dedicate itself “to rigorously researching whether heritable genome editing can be done safely and responsibly.”

Creating genetically edited humans remains controversial, and the first scientist to do it, in China, was imprisoned for three years. The procedure remains illegal in many countries, including the US, and doubts surround its usefulness as a form of medicine.

Still, as gene-editing technology races forward, the temptation to shape the future of the species may prove irresistible, particularly to entrepreneurs keen to put their stamp on the human condition. In theory, even small genetic tweaks could create people who never get heart disease or Alzheimer’s, and who would pass those traits on to their own offspring.

According to Harrington, if the technique proves safe, it “could become one of the most important health technologies of our time.” He has estimated that editing an embryo would cost only about $5,000 and believes regulations could change in the future. 

Preventive is the third US startup this year to say it is pursuing technology to produce gene-edited babies. The first, Bootstrap Bio, based in California, is reportedly seeking seed funding and has an interest in enhancing intelligence. Another, Manhattan Genomics, is also in the formation stage but has not announced funding yet.

As of now, none of these companies have significant staff or facilities, and they largely lack any credibility among mainstream gene-editing scientists. Reached by email, Fyodor Urnov, an expert in gene editing at the University of California, Berkeley, where Harrington studied, said he believes such ventures should not move forward.

Urnov has been a pointed critic of the concept of heritable genome editing, calling it dangerous, misguided, and a distraction from the real benefits of gene editing to treat adults and children. 

In his email, Urnov said the launch of still another venture into the area made him want to “howl with pain.”  

Harrinton’s venture was incorporated in Delaware in May 2025,under the name Preventive Medicine PBC. As a public-benefit corporation, it is organized to put its public mission above profits. “If our research shows [heritable genome editing] cannot be done safely, that conclusion is equally valuable to the scientific community and society,” Harrington wrote in his post.

Harrington is a cofounder of Mammoth Biosciences, a gene-editing company pursuing drugs for adults, and remains a board member there.

In recent months, Preventive has sought endorsements from leading figures in genome editing, but according to its post, it had secured only one—from Paula Amato, a fertility doctor at Oregon Health Sciences University, who said she had agreed to act as an advisor to the company.

Amato is a member of a US team that has researched embryo editing in the country since 2017, and she has promoted the technology as a way to increase IVF success. That could be the case if editing could correct abnormal embryos, making more available for use in trying to create a pregnancy.

It remains unclear where Preventive’s funding is coming from. Harrington said the $30 million was gathered from “private funders who share our commitment to pursuing this research responsibly.” But he declined to identify those investors other than SciFounders, a venture firm he runs with his personal and business partner Matt Krisiloff, the CEO of the biotech company Conception, which aims to create human eggs from stem cells.

That’s yet another technology that could change reproduction, if it works. Krisiloff is listed as a member of Preventive’s founding team.

The idea of edited babies has received growing attention from figures in the cryptocurrency business. These include Brian Armstrong, the billionaire founder of Coinbase, who has held a series of off-the-record dinners to discuss the technology (which Harrington attended). Armstrong previously argued that the “time is right” for a startup venture in the area.

Will Harborne, a crypto entrepreneur and partner at LongGame Ventures, says he’s “thrilled” to see Preventive launch. If the technology proves safe, he argues, “widespread adoption is inevitable,” calling its use a “societal obligation.”

Harborne’s fund has invested in Herasight, a company that uses genetic tests to rank IVF embryos for future IQ and other traits. That’s another hotly debated technology, but one that has already reached the market, since such testing isn’t strictly regulated. Some have begun to use the term “human enhancement companies” to refer to such ventures.

What’s still lacking is evidence that leading gene-editing specialists support these ventures. Preventive was unsuccessful in establishing a collaboration with at least one key research group, and Urnov says he had harsh words for Manhattan Genomics when that company reached out to him about working together. “I encourage you to stop,” he wrote back. “You will cause zero good and formidable harm.”

Harrington thinks Preventive could change such attitudes, if it shows that it is serious about doing responsible research. “Most scientists I speak with either accept embryo editing as inevitable or are enthusiastic about the potential but hesitate to voice these opinions publicly,” he told MIT Technology Review earlier this year. “Part of being more public about this is to encourage others in the field to discuss this instead of ignoring it.”

A team at Microsoft says it used artificial intelligence to discover a “zero day” vulnerability in the biosecurity systems used to prevent the misuse of DNA.

These screening systems are designed to stop people from purchasing genetic sequences that could be used to create deadly toxins or pathogens. But now researchers led by Microsoft’s chief scientist, Eric Horvitz, say they have figured out how to bypass the protections in a way previously unknown to defenders. 

The team described its work today in the journal Science.

Horvitz and his team focused on generative AI algorithms that propose new protein shapes. These types of programs are already fueling the hunt for new drugs at well-funded startups like Generate Biomedicines and Isomorphic Labs, a spinout of Google. 

The problem is that such systems are potentially “dual use.” They can use their training sets to generate both beneficial molecules and harmful ones.

Microsoft says it began a “red-teaming” test of AI’s dual-use potential in 2023 in order to determine whether “adversarial AI protein design” could help bioterrorists manufacture harmful proteins. 

The safeguard that Microsoft attacked is what’s known as biosecurity screening software. To manufacture a protein, researchers typically need to order a corresponding DNA sequence from a commercial vendor, which they can then install in a cell. Those vendors use screening software to compare incoming orders with known toxins or pathogens. A close match will set off an alert.

To design its attack, Microsoft used several generative protein models (including its own, called EvoDiff) to redesign toxins—changing their structure in a way that let them slip past screening software but was predicted to keep their deadly function intact.

The researchers say the exercise was entirely digital and they never produced any toxic proteins. That was to avoid any perception that the company was developing bioweapons. 

Before publishing the results, Microsoft says, it alerted the US government and software makers, who’ve already patched their systems, although some AI-designed molecules can still escape detection. 

“The patch is incomplete, and the state of the art is changing. But this isn’t a one-and-done thing. It’s the start of even more testing,” says Adam Clore, director of technology R&D at Integrated DNA Technologies, a large manufacturer of DNA, who is a coauthor on the Microsoft report. “We’re in something of an arms race.”

To make sure nobody misuses the research, the researchers say, they’re not disclosing some of their code and didn’t reveal what toxic proteins they asked the AI to redesign. However, some dangerous proteins are well known, like ricin—a poison found in castor beans—and the infectious prions that are the cause of mad-cow disease.

“This finding, combined with rapid advances in AI-enabled biological modeling, demonstrates the clear and urgent need for enhanced nucleic acid synthesis screening procedures coupled with a reliable enforcement and verification mechanism,” says Dean Ball, a fellow at the Foundation for American Innovation, a think tank in San Francisco.

Ball notes that the US government already considers screening of DNA orders a key line of security. Last May, in an executive order on biological research safety, President Trump called for an overall revamp of that system, although so far the White House hasn’t released new recommendations.

Others doubt that commercial DNA synthesis is the best point of defense against bad actors. Michael Cohen, an AI-safety researcher at the University of California, Berkeley, believes there will always be ways to disguise sequences and that Microsoft could have made its test harder.

“The challenge appears weak, and their patched tools fail a lot,” says Cohen. “There seems to be an unwillingness to admit that sometime soon, we’re going to have to retreat from this supposed choke point, so we should start looking around for ground that we can actually hold.” 

Cohen says biosecurity should probably be built into the AI systems themselves—either directly or via controls over what information they give. 

But Clore says monitoring gene synthesis is still a practical approach to detecting biothreats, since the manufacture of DNA in the US is dominated by a few companies that work closely with the government. By contrast, the technology used to build and train AI models is more widespread. “You can’t put that genie back in the bottle,” says Clore. “If you have the resources to try to trick us into making a DNA sequence, you can probably train a large language model.”

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.”

When scientists first sequenced the human genome in 2003, they revealed the full set of DNA instructions that make a person. But we still didn’t know what all those 3 billion genetic letters actually do. 

Now Google’s DeepMind division says it’s made a leap in trying to understand the code with AlphaGenome, an AI model that predicts what effects small changes in DNA will have on an array of molecular processes, such as whether a gene’s activity will go up or down. It’s just the sort of question biologists regularly assess in lab experiments.

“We have, for the first time, created a single model that unifies many different challenges that come with understanding the genome,” says Pushmeet Kohli, a vice president for research at DeepMind.

Five years ago, the Google AI division released AlphaFold, a technology for predicting the 3D shape of proteins. That work was honored with a Nobel Prize last year and spawned a drug-discovery spinout, Isomorphic Labs, and a boom of companies that hope AI will be able to propose new drugs.

AlphaGenome is an attempt to further smooth biologists’ work by answering basic questions about how changing DNA letters alters gene activity and, eventually, how genetic mutations affect our health. 

“We have these 3 billion letters of DNA that make up a human genome, but every person is slightly different, and we don’t fully understand what those differences do,” says Caleb Lareau, a computational biologist at Memorial Sloan Kettering Cancer Center who has had early access to AlphaGenome. “This is the most powerful tool to date to model that.”

Google says AlphaGenome will be free for noncommercial users and plans to release full details of the model in the future. According to Kohli, the company is exploring ways to “enable use of this model by commercial entities” such as biotech companies. 

Lareau says AlphaGenome will allow certain types of experiments now done in the lab to be carried out virtually, on a computer. For instance, studies of people who’ve donated their DNA for research often turn up thousands of genetic differences, each slightly raising or lowering the chance a person gets a disease such as Alzheimer’s.

Lareau says DeepMind’s software could be used to quickly make predictions about how each of those variants works at a molecular level, something that would otherwise require time-consuming lab experiments. “You’ll get this list of gene variants, but then I want to understand which of those are actually doing something, and where can I intervene,” he says. “This system pushes us closer to a good first guess about what any variant will be doing when we observe it in a human.”

Don’t expect AlphaGenome to predict very much about individual people, however. It offers clues to nitty-gritty molecular details of gene activity, not 23andMe-type revelations of a person’s traits or ancestry. 

“We haven’t designed or validated AlphaGenome for personal genome prediction, a known challenge for AI models,” Google said in a statement.

Underlying the AI system is the so-called transformer architecture invented at Google that also powers large language models like GPT-4. This one was trained on troves of experimental data produced by public scientific projects.

Lareau says the system will not broadly change how his lab works day to day but could permit new types of research. For instance, sometimes doctors encounter patients with ultra-rare cancers, bristling with unfamiliar mutations. AlphaGenome could suggest which of those mutations are really causing the root problem, possibly pointing to a treatment.

“A hallmark of cancer is that specific mutations in DNA make the wrong genes express in the wrong context,” says Julien Gagneur, a professor of computational medicine at the Technical University of Munich. “This type of tool is instrumental in narrowing down which ones mess up proper gene expression.” 

The same approach could apply to patients with rare genetic disease, many of whom never learn the source of their condition, even if their DNA has been decoded. “We can obtain their genomes, but we are clueless as to which genetic alterations cause the disease,” says Gagneur. He thinks AlphaGenome could give medical scientists a new way to diagnose such cases. 

Eventually, some researchers aspire to use AI to design entire genomes from the ground up and create new life forms. Others think the models will be used to create a fully virtual laboratory for drug studies. “My dream would be to simulate a virtual cell,” Demis Hassabis, CEO of Google DeepMind, said this year. 

Kohli calls AlphaGenome a “milestone” on the road to that kind of system. “AlphaGenome may not model the whole cell in its entirety … but it’s starting to sort of shed light on the broader semantics of DNA,” he says.

When you think of AI’s contributions to science, you probably think of AlphaFold, the Google DeepMind protein-folding program that earned its creator a Nobel Prize last year.

Now OpenAI says it’s getting into the science game too—with a model for engineering proteins.

The company says it has developed a language model that dreams up proteins capable of turning regular cells into stem cells—and that it has handily beat humans at the task.

The work represents OpenAI’s first model focused on biological data and its first public claim that its models can deliver unexpected scientific results. As such, it is a step toward determining whether or not AI can make true discoveries, which some argue is a major test on the pathway to “artificial general intelligence.”

Last week, OpenAI CEO Sam Altman said he was “confident” his company knows how to build an AGI, adding that “superintelligent tools could massively accelerate scientific discovery and innovation well beyond what we are capable of doing on our own.” 

The protein engineering project started a year ago when Retro Biosciences, a longevity research company based in San Francisco, approached OpenAI about working together.

That link-up did not happen by chance. Sam Altman, the CEO of OpenAI, personally funded Retro with $180 million, as MIT Technology Review first reported in 2023.

Retro has the goal of extending the normal human lifespan by 10 years. For that, it studies what are called Yamanaka factors. Those are a set of proteins that, when added to a human skin cell, will cause it to morph into a young-seeming stem cell, a type that can produce any other tissue in the body. 

It’s a phenomenon that researchers at Retro, and at richly funded companies like Altos Labs, see as the possible starting point for rejuvenating animals, building human organs, or providing supplies of replacement cells.

But such cell “reprogramming” is not very efficient. It takes several weeks, and less than 1% of cells treated in a lab dish will complete the rejuvenation journey.

OpenAI’s new model, called GPT-4b micro, was trained to suggest ways to re-engineer the protein factors to increase their function. According to OpenAI, researchers used the model’s suggestions to change two of the Yamanaka factors to be more than 50 times as effective—at least according to some preliminary measures. 

“Just across the board, the proteins seem better than what the scientists were able to produce by themselves,” says John Hallman, an OpenAI researcher.

Hallman and OpenAI’s Aaron Jaech, as well as Rico Meinl from Retro, were the model’s lead developers.

Outside scientists won’t be able to tell if the results are real until they’re published, something the companies say they are planning. Nor is the model available for wider use—it’s still a bespoke demonstration, not an official product launch.

“This project is meant to show that we’re serious about contributing to science,” says Jaech. “But whether those capabilities will come out to the world as a separate model or whether they’ll be rolled into our mainline reasoning models—that’s still to be determined.”

The model does not work the same way as Google’s AlphaFold, which predicts what shape proteins will take. Since the Yamanaka factors are unusually floppy and unstructured proteins, OpenAI said, they called for a different approach, which its large language models were suited to.

The model was trained on examples of protein sequences from many species, as well as information on which proteins tend to interact with one another. While that’s a lot of data, it’s just a fraction of what OpenAI’s flagship chatbots were trained on, making GPT-4b an example of a “small language model” that works with a focused data set.

Once Retro scientists were given the model, they tried to steer it to suggest possible redesigns of the Yamanaka proteins. The prompting tactic used is similar to the “few-shot” method, in which a user queries a chatbot by providing a series of examples with answers, followed by an example for the bot to respond to.

Although genetic engineers have ways to direct evolution of molecules in the lab, they can usually test only so many possibilities. And even a protein of typical length can be changed in nearly infinite ways (since they’re built from hundreds of amino acids, and each acid comes in 20 possible varieties).

OpenAI’s model, however, often spits out suggestions in which a third of the amino acids in the proteins were changed.

“We threw this model into the lab immediately and we got real-world results,” says Retro’s CEO, Joe Betts-Lacroix. He says the model’s ideas were unusually good, leading to improvements over the original Yamanaka factors in a substantial fraction of cases.

Vadim Gladyshev, a Harvard University aging researcher who consults with Retro, says better ways of making stem cells are needed. “For us, it would be extremely useful. [Skin cells] are easy to reprogram, but other cells are not,” he says. “And to do it in a new species—it’s often extremely different, and you don’t get anything.” 

How exactly the GPT-4b arrives at its guesses is still not clear—as is often the case with AI models. “It’s like when AlphaGo crushed the best human at Go, but it took a long time to find out why,” says Betts-Lacroix. “We are still figuring out what it does, and we think the way we apply this is only scratching the surface.”

OpenAI says no money changed hands in the collaboration. But because the work could benefit Retro—whose biggest investor is Altman—the announcement may add to questions swirling around the OpenAI CEO’s side projects.

Last year, the Wall Street Journal said Altman’s wide-ranging investments in private tech startups amount to an “opaque investment empire” that is “creating a mounting list of potential conflicts,” since some of these companies also do business with OpenAI.

In Retro’s case, simply being associated with Altman, OpenAI, and the race toward AGI could boost its profile and increase its ability to hire staff and raise funds. Betts-Lacroix did not answer questions about whether the early-stage company is currently in fundraising mode. 

OpenAI says Altman was not directly involved in the work and that it never makes decisions based on Altman’s other investments.