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.”
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
How to measure the returns on R&D spending
Given the draconian cuts to US federal funding for science, it’s worth asking some hard-nosed money questions: How much should we be spending on R&D? How much value do we get out of such investments, anyway?
To answer that, in several recent papers, economists have approached this issue in clever new ways. And, though they ask slightly different questions, their conclusions share a bottom line: R&D is, in fact, one of the better long-term investments that the government can make. Read the full story.
—David Rotman
This article is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.
If you’re interested in reading more about America’s economic situation, check out:
+ Sweeping tariffs could threaten the US manufacturing rebound—and they could stunt its ability to make tomorrow’s breakthroughs. Read the full story.
+ The surprising barrier that keeps us from building the housing we need. Read the full story.
+ How to fine-tune AI for prosperity.
+ People are worried that AI will take everyone’s jobs. We’ve been here before.
MIT Technology Review Narrated: How AI can help supercharge creativity
Forget one-click creativity. Artists and musicians are finding new ways to make art using AI, by injecting friction, challenge, and serendipity into the process.
This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 TikTok’s buyers may include Oracle, Silver Lake and Andreessen Horowitz
They would control around 80% of the business, with Chinese shareholders holding the rest. (WSJ $)
+ We still have plenty of unanswered questions about the deal. (Bloomberg $)
+ It was brokered in Madrid. (The Guardian)
2 OpenAI is working on a version of ChatGPT for teenagers
And it’ll use age-prediction tech to bar them from the standard version. (Axios)
+ The move comes as the US Senate is hearing evidence about chatbot harms. (404 Media)
+ The looming crackdown on AI companionship. (MIT Technology Review)
3 China has banned tech firms from buying Nvidia’s chips
In an effort to boost its own companies. (FT $)
+ Alibaba and ByteDance have been instructed to terminate orders. (Bloomberg $)
4 Anthropic refuses to let US law enforcement use its models
Much to the White House’s chagrin. (Semafor)
5 Tesla’s doors may trap passengers inside its cars
Vehicle safety regulators are investigating after people reported being forced to break windows to retrieve children. (NYT $)
6 How AI companies train their models to do white-collar jobs
After hitting a wall, they’re throwing money at the problem. (The Information $)
+ New training ‘environments’ are a hot AI topic right now. (TechCrunch)
+ How AI is shaking up corporate hierarchies. (WSJ $)
7 Inside Damascus’ bid to become a tech hub
The city’s tech industry has been embraced by its new government. (Rest of World)
8 A supply shipment to the ISS has been delayed
NASA is blaming engine trouble. (Ars Technica)
+ The great commercial takeover of low Earth orbit. (MIT Technology Review)
9 Our darkest nights are getting lighter
Artificial light is ruining our chances of seeing starry skies. (IEEE Spectrum)
+ Bright LEDs could spell the end of dark skies. (MIT Technology Review)
10 You can now book a safari through Uber 

Expedition into Nairobi National Park, anyone? (Bloomberg $)
Quote of the day
“What began as a homework helper gradually turned itself into a confidant and then a suicide coach.”
—Matthew Raine, whose 16-year old son Adam died by suicide after repeatedly sharing his intentions with ChatGPT, gives evidence to a Senate Judiciary subcommittee investigating chatbot dangers, the Washington Post reports.
One more thing

AI is coming for music, too
While large language models that generate text have exploded in the last three years, a different type of AI, based on what are called diffusion models, is having an unprecedented impact on creative domains.
By transforming random noise into coherent patterns, diffusion models can generate new images, videos, or speech, guided by text prompts or other input data. The best ones can create outputs indistinguishable from the work of people
Now these models are marching into a creative field that is arguably more vulnerable to disruption than any other: music. Read the full story.
—James O’Donnell
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ Food, in all shapes and forms, is bigger than ever. So why aren’t we watching cooking shows any more?
+ Kate Bush’s Hounds of Love turns 40 this year, but still sounds as fresh as ever.
+ Here’s how to maximize your chances of booking a bargain flight.
+ Robert Redford, you were one of a kind.
MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.
Given the draconian cuts to US federal funding for science, including the administration’s proposal to reduce the 2026 budgets of the National Institutes of Health by 40% and the National Science Foundation by 57%, it’s worth asking some hard-nosed money questions: How much should we be spending on R&D? How much value do we get out of such investments, anyway? To answer that, it’s important to look at both successful returns and investments that went nowhere.
Sure, it’s easy to argue for the importance of spending on science by pointing out that many of today’s most useful technologies had their origins in government-funded R&D. The internet, CRISPR, GPS—the list goes on and on. All true. But this argument ignores all the technologies that received millions in government funding and haven’t gone anywhere—at least not yet. We still don’t have DNA computers or molecular electronics. Never mind the favorite examples cited by contrarian politicians of seemingly silly or frivolous science projects (think shrimp on treadmills).
While cherry-picking success stories help illustrate the glories of innovation and the role of science in creating technologies that have changed our lives, it provides little guidance for how much we should spend in the future—and where the money should go.
A far more useful approach to quantifying the value of R&D is to look at its return on investment (ROI). A favorite metric for stock pickers and PowerPoint-wielding venture capitalists, ROI weighs benefits versus costs. If applied broadly to the nation’s R&D funding, the same kind of thinking could help account for both the big wins and all the money spent on research that never got out of the lab.
The problem is that it’s notoriously difficult to calculate returns for science funding—the payoffs can take years to appear and often take a circuitous route, so the eventual rewards are distant from the original funding. (Who could have predicted Uber as an outcome of GPS? For that matter, who could have predicted that the invention of ultra-precise atomic clocks in the late 1940s and 1950s would eventually make GPS possible?) And forget trying to track the costs of countless failures or apparent dead ends.
But in several recent papers, economists have approached the problem in clever new ways, and though they ask slightly different questions, their conclusions share a bottom line: R&D is, in fact, one of the better long-term investments that the government can make.
This story is part of MIT Technology Review’s “America Undone” series, examining how the foundations of US success in science and innovation are currently under threat. You can read the rest here.
That might not seem very surprising. We’ve long thought that innovation and scientific advances are key to our prosperity. But the new studies provide much-needed details, supplying systematic and rigorous evidence for the impact that R&D funding, including public investment in basic science, has on overall economic growth.
And the magnitude of the benefits is surprising.
Bang for your buck
In “A Calculation of the Social Returns to Innovation,” Benjamin Jones, an economist at Northwestern University, and Lawrence Summers, a Harvard economist and former US Treasury secretary, calculate the effects of the nation’s total R&D spending on gross domestic product and our overall standard of living. They’re taking on the big picture, and it’s ambitious because there are so many variables. But they are able to come up with a convincing range of estimates for the returns, all of them impressive.
On the conservative end of their estimates, says Jones, investing $1 in R&D yields about $5 in returns—defined in this case as additional GDP per person (basically, how much richer we become). Change some of the assumptions—for example, by attempting to account for the value of better medicines and improved health care, which aren’t fully captured in GDP—and you get even larger payoffs.
While the $5 return is at the low end of their estimates, it’s still “a remarkably good investment,” Jones says. “There aren’t many where you put in $1 and get $5 back.”
That’s the return for the nation’s overall R&D funding. But what do we get for government-funded R&D in particular? Andrew Fieldhouse, an economist at Texas A&M, and Karel Mertens at the Federal Reserve Bank of Dallas looked specifically at how changes in public R&D spending affect the total factor productivity (TFP) of businesses. A favorite metric of economists, TFP is driven by new technologies and innovative business know-how—not by adding more workers or machines—and is the main driver of the nation’s prosperity over the long term.
The economists tracked changes in R&D spending at five major US science funding agencies over many decades to see how the shifts eventually affected private-sector productivity. They found that the government was getting a huge bang for its nondefense R&D buck.
The benefits begin kicking in after around five to 10 years and often have a long-lasting impact on the economy. Nondefense public R&D funding has been responsible for 20% to 25% of all private-sector productivity growth in the country since World War II, according to the economists. It’s an astonishing number, given that the government invests relatively little in nondefense R&D. For example, its spending on infrastructure, another contributor to productivity growth, has been far greater over those years.
The large impact of public R&D investments also provides insight into one of America’s most troubling economic mysteries: the slowdown in productivity growth that began in the 1970s, which has roiled the country’s politics as many people face stunted living standards and limited financial prospects. Their research, says Fieldhouse, suggests that as much as a quarter of that slowdown was caused by a decline in public R&D funding that happened roughly over the same time.
After reaching a high of 1.86% of GDP in 1964, federal R&D spending began dropping. Starting in the early 1970s, TFP growth also began to decline, from above 2% a year in the late 1960s to somewhere around 1% since the 1970s (with the exception of a rise during the late 1990s), roughly tracking the spending declines with a lag of a few years.
If in fact the productivity slowdown was at least partially caused by a drop in public R&D spending, it’s evidence that we would be far richer today if we had kept up a higher level of science investment. And it also flags the dangers of today’s proposed cuts. “Based on our research,” says Fieldhouse, “I think it’s unambiguously clear that if you actually slash the budget of the NIH by 40%, if you slash the NSF budget by 50%, there’s going to be a deceleration in US productivity growth over the next seven to 10 years that will be measurable.”
Out of whack
Though the Trump administration’s proposed 2026 budget would slash science budgets to an unusual degree, public funding of R&D has actually been in slow decline for decades. Federal funding of science is at its lowest rate in the last 70 years, accounting for only around 0.6% of GDP.
Even as public funding has dropped, business R&D investments have steadily risen. Today businesses spend far more than the government; in 2023, companies invested about $700 billion in R&D while the US government spent $172 billion, according to data from the NSF’s statistical agency. You might think, Good—let companies do research. It’s more efficient. It’s more focused. Keep the government out of it.
But there is a big problem with that argument. Publicly funded research, it turns out, tends to lead to relatively more productivity growth over time because it skews more toward fundamental science than the applied work typically done by companies.
In a new working paper called “Public R&D Spillovers and Productivity Growth,” Arnaud Dyèvre, an assistant professor at of economics at HEC Paris, documents the broad and often large impacts of so-called knowledge spillovers—the benefits that flow to others from work done by the original research group. Dyèvre found that the spillovers of public-funded R&D have three times more impact on productivity growth across businesses and industries than those from private R&D funding.
The findings are preliminary, and Dyèvre is still updating the research—much of which he did as a postdoc at MIT—but he says it does suggest that the US “is underinvesting in fundamental R&D,” which is heavily funded by the government. “I wouldn’t be able to tell you exactly which percentage of R&D in the US needs to be funded by the government or what percent needs to be funded by the private sector. We need both,” he says. But, he adds, “the empirical evidence” suggests that “we’re out of balance.”
The big question
Getting the balance of funding for fundamental science and applied research right is just one of the big questions that remain around R&D funding. In mid-July, Open Philanthropy and the Alfred P. Sloan Foundation, both nonprofit organizations, jointly announced that they planned to fund a five-year “pop-up journal” that would attempt to answer many of the questions still swirling around how to define and optimize the ROI of research funding.
“There is a lot of evidence consistent with a really high return to R&D, which suggests we should do more of it,” says Matt Clancy, a senior program officer at Open Philanthropy. “But when you ask me how much more, I don’t have a good answer. And when you ask me what types of R&D should get more funding, we don’t have a good answer.”
Pondering such questions should keep innovation economists busy for the next several years. But there is another mystifying piece of the puzzle, says Northwestern’s Jones. If the returns on R&D investments are so high—the kind that most venture capitalists or investors would gladly take—why isn’t the government spending more?
“I think it’s unambiguously clear that if you actually slash the budget of the NIH by 40%, if you slash the NSF budget by 50%, there’s going to be a deceleration in US productivity growth over the next seven to 10 years that will be measurable.”
Jones, who served as a senior economic advisor in the Obama administration, says discussions over R&D budgets in Washington are often “a war of anecdotes.” Science advocates cite the great breakthroughs that resulted from earlier government funding, while budget hawks point to seemingly ludicrous projects or spectacular failures. Both have plenty of ammunition. “People go back and forth,” says Jones, “and it doesn’t really lead to anywhere.”
The policy gridlock is rooted in in the very nature of fundamental research. Today’s science will lead to great advances. And there will be countless failures; a lot of money will be wasted on fruitless experiments. The problem, of course, is that when you’re deciding to fund new projects, it’s impossible to predict which the outcome will be, even in the case of odd, seemingly silly science. Guessing just what research will or will not lead to the next great breakthrough is a fool’s errand.
Take the cuts in the administration’s proposed fiscal 2026 budget for the NSF, a leading funder of basic science. The administration’s summary begins with the assertion that its NSF budget “is prioritizing investments that complement private-sector R&D and offer strong potential to drive economic growth and strengthen U.S. technological leadership.” So far, so good. It cites the government’s commitment to AI and quantum information science. But dig deeper and you will see the contradictions in the numbers.
Not only is NSF’s overall budget cut by 57%, but funding for physical sciences like chemistry and materials research—fields critical to advancing AI and quantum computers—has also been blown apart. Funding for the NSF’s mathematical and physical sciences program was reduced by 67%. The directorate for computer and information science and engineering fared little better; its research funding was cut by 66%.
There is a great deal of hope among many in the science community that Congress, when it passes the actual 2026 budget, will at least partially reverse these cuts. We’ll see. But even if it does, why attack R&D funding in the first place? It’s impossible to answer that without plunging into the messy depths of today’s chaotic politics. And it is equally hard to know whether the recent evidence gathered by academic economists on the strong returns to R&D investments will matter when it comes to partisan policymaking.
But at least those defending the value of public funding now have a far more productive way to make their argument, rather than simply touting past breakthroughs. Even for fiscal hawks and those pronouncing concerns about budget deficits, the recent work provides a compelling and simple conclusion: More public funding for basic science is a sound investment that makes us more prosperous.
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