Dec 6 2025
The ads that sell the sizzle of genetic trait discrimination

One day this fall, I watched an electronic sign outside the Broadway-Lafayette subway station in Manhattan switch seamlessly between an ad for makeup and one promoting the website Pickyourbaby.com, which promises a way for potential parents to use genetic tests to influence their baby’s traits, including eye color, hair color, and IQ.

Inside the station, every surface was wrapped with more ads—babies on turnstiles, on staircases, on banners overhead. “Think about it. Makeup and then genetic optimization,” exulted Kian Sadeghi, the 26-year-old founder of Nucleus Genomics, the startup running the ads. To his mind, one should be as accessible as the other. 

Nucleus is a young, attention-seeking genetic software company that says it can analyze genetic tests on IVF embryos to score them for 2,000 traits and disease risks, letting parents pick some and reject others. This is possible because of how our DNA shapes us, sometimes powerfully. As one of the subway banners reminded the New York riders: “Height is 80% genetic.”

The day after the campaign launched, Sadeghi and I had briefly sparred online. He’d been on X showing off a phone app where parents can click through traits like eye color and hair color. I snapped back that all this sounded a lot like Uber Eats—another crappy, frictionless future invented by entrepreneurs, but this time you’d click for a baby.

I agreed to meet Sadeghi that night in the station under a banner that read, “IQ is 50% genetic.” He appeared in a puffer jacket and told me the campaign would soon spread to 1,000 train cars. Not long ago, this was a secretive technology to whisper about at Silicon Valley dinner parties. But now? “Look at the stairs. The entire subway is genetic optimization. We’re bringing it mainstream,” he said. “I mean, like, we are normalizing it, right?”

Normalizing what, exactly? The ability to choose embryos on the basis of predicted traits could lead to healthier people. But the traits mentioned in the subway—height and IQ—focus the public’s mind toward cosmetic choices and even naked discrimination. “I think people are going to read this and start realizing: Wow, it is now an option that I can pick. I can have a taller, smarter, healthier baby,” says Sadeghi.

Entrepreneur Kian Sadeghi stands under advertising banner in the Broadway-Lafayette subway station in Manhattan, part of a campaign called “Have Your Best Baby.”
COURTESY OF THE AUTHOR

Nucleus got its seed funding from Founders Fund, an investment firm known for its love of contrarian bets. And embryo scoring fits right in—it’s an unpopular concept, and professional groups say the genetic predictions aren’t reliable. So far, leading IVF clinics still refuse to offer these tests. Doctors worry, among other things, that they’ll create unrealistic parental expectations. What if little Johnny doesn’t do as well on the SAT as his embryo score predicted?

The ad blitz is a way to end-run such gatekeepers: If a clinic won’t agree to order the test, would-be parents can take their business elsewhere. Another embryo testing company, Orchid, notes that high consumer demand emboldened Uber’s early incursions into regulated taxi markets. “Doctors are essentially being shoved in the direction of using it, not because they want to, but because they will lose patients if they don’t,” Orchid founder Noor Siddiqui said during an online event this past August.

Sadeghi prefers to compare his startup to Airbnb. He hopes it can link customers to clinics, becoming a digital “funnel” offering a “better experience” for everyone. He notes that Nucleus ads don’t mention DNA or any details of how the scoring technique works. That’s not the point. In advertising, you sell the sizzle, not the steak. And in Nucleus’s ad copy, what sizzles is height, smarts, and light-colored eyes.

It makes you wonder if the ads should be permitted. Indeed, I learned from Sadeghi that the Metropolitan Transportation Authority had objected to parts of the campaign. The metro agency, for instance, did not let Nucleus run ads saying “Have a girl” and “Have a boy,” even though it’s very easy to identify the sex of an embryo using a genetic test. The reason was an MTA policy that forbids using government-owned infrastructure to promote “invidious discrimination” against protected classes, which include race, religion and biological sex.

Since 2023, New York City has also included height and weight in its anti-discrimination law, the idea being to “root out bias” related to body size in housing and in public spaces. So I’m not sure why the MTA let Nucleus declare that height is 80% genetic. (The MTA advertising department didn’t respond to questions.) Perhaps it’s because the statement is a factual claim, not an explicit call to action. But we all know what to do: Pick the tall one and leave shorty in the IVF freezer, never to be born.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Ecommerce MGMT 0 Comments
Dec 5 2025
How AI is uncovering hidden geothermal energy resources

Sometimes geothermal hot spots are obvious, marked by geysers and hot springs on the planet’s surface. But in other places, they’re obscured thousands of feet underground. Now AI could help uncover these hidden pockets of potential power.

A startup company called Zanskar announced today that it’s used AI and other advanced computational methods to uncover a blind geothermal system—meaning there aren’t signs of it on the surface—in the western Nevada desert. The company says it’s the first blind system that’s been identified and confirmed to be a commercial prospect in over 30 years. 

Historically, finding new sites for geothermal power was a matter of brute force. Companies spent a lot of time and money drilling deep wells, looking for places where it made sense to build a plant.

Zanskar’s approach is more precise. With advancements in AI, the company aims to “solve this problem that had been unsolvable for decades, and go and finally find those resources and prove that they’re way bigger than previously thought,” says Carl Hoiland, the company’s cofounder and CEO.  

To support a successful geothermal power plant, a site needs high temperatures at an accessible depth and space for fluid to move through the rock and deliver heat. In the case of the new site, which the company calls Big Blind, the prize is a reservoir that reaches 250 °F at about 2,700 feet below the surface.

As electricity demand rises around the world, geothermal systems like this one could provide a source of constant power without emitting the greenhouse gases that cause climate change. 

The company has used its technology to identify many potential hot spots. “We have dozens of sites that look just like this,” says Joel Edwards, Zanskar’s cofounder and CTO. But for Big Blind, the team has done the fieldwork to confirm its model’s predictions.

The first step to identifying a new site is to use regional AI models to search large areas. The team trains models on known hot spots and on simulations it creates. Then it feeds in geological, satellite, and other types of data, including information about fault lines. The models can then predict where potential hot spots might be.

One strength of using AI for this task is that it can handle the immense complexity of the information at hand. “If there’s something learnable in the earth, even if it’s a very complex phenomenon that’s hard for us humans to understand, neural nets are capable of learning that, if given enough data,” Hoiland says. 

Once models identify a potential hot spot, a field crew heads to the site, which might be roughly 100 square miles or so, and collects additional information through techniques that include drilling shallow holes to look for elevated underground temperatures.

In the case of Big Blind, this prospecting information gave the company enough confidence to purchase a federal lease, allowing it to develop a geothermal plant. With that lease secured, the team returned with large drill rigs and drilled thousands of feet down in July and August. The workers found the hot, permeable rock they expected.

Next they must secure permits to build and connect to the grid and line up the investments needed to build the plant. The team will also continue testing at the site, including long-term testing to track heat and water flow.

“There’s a tremendous need for methodology that can look for large-scale features,” says John McLennan, technical lead for resource management at Utah FORGE, a national lab field site for geothermal energy funded by the US Department of Energy. The new discovery is “promising,” McLennan adds.

Big Blind is Zanskar’s first confirmed discovery that wasn’t previously explored or developed, but the company has used its tools for other geothermal exploration projects. Earlier this year, it announced a discovery at a site that had previously been explored by the industry but not developed. The company also purchased and revived a geothermal power plant in New Mexico.

And this could be just the beginning for Zanskar. As Edwards puts it, “This is the start of a wave of new, naturally occurring geothermal systems that will have enough heat in place to support power plants.”

Ecommerce MGMT 0 Comments
Dec 2 2025
The State of AI: Welcome to the economic singularity

Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday for the next two weeks, writers from both publications will debate one aspect of the generative AI revolution reshaping global power.

This week, Richard Waters, FT columnist and former West Coast editor, talks with MIT Technology Review’s editor at large David Rotman about the true impact of AI on the job market.

Bonus: If you’re an MIT Technology Review subscriber, you can join David and Richard, alongside MIT Technology Review’s editor in chief, Mat Honan, for an exclusive conversation live on Tuesday, December 9 at 1pm ET about this topic. Sign up to be a part here.

Richard Waters writes:

Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole.

At one extreme, AI coding assistants have revolutionized the work of software developers. Mark Zuckerberg recently predicted that half of Meta’s code would be written by AI within a year. At the other extreme, most companies are seeing little if any benefit from their initial investments. A widely cited study from MIT found that so far, 95% of gen AI projects produce zero return.

That has provided fuel for the skeptics who maintain that—by its very nature as a probabilistic technology prone to hallucinating—generative AI will never have a deep impact on business.

To many students of tech history, though, the lack of immediate impact is just the normal lag associated with transformative new technologies. Erik Brynjolfsson, then an assistant professor at MIT, first described what he called the “productivity paradox of IT” in the early 1990s. Despite plenty of anecdotal evidence that technology was changing the way people worked, it wasn’t showing up in the aggregate data in the form of higher productivity growth. Brynjolfsson’s conclusion was that it just took time for businesses to adapt.

Big investments in IT finally showed through with a notable rebound in US productivity growth starting in the mid-1990s. But that tailed off a decade later and was followed by a second lull.

Richard Waters and David Rotman

FT/MIT TECHNOLOGY REVIEW | ADOBE STOCK

In the case of AI, companies need to build new infrastructure (particularly data platforms), redesign core business processes, and retrain workers before they can expect to see results. If a lag effect explains the slow results, there may at least be reasons for optimism: Much of the cloud computing infrastructure needed to bring generative AI to a wider business audience is already in place.

The opportunities and the challenges are both enormous. An executive at one Fortune 500 company says his organization has carried out a comprehensive review of its use of analytics and concluded that its workers, overall, add little or no value. Rooting out the old software and replacing that inefficient human labor with AI might yield significant results. But, as this person says, such an overhaul would require big changes to existing processes and take years to carry out.

There are some early encouraging signs. US productivity growth, stuck at 1% to 1.5% for more than a decade and a half, rebounded to more than 2% last year. It probably hit the same level in the first nine months of this year, though the lack of official data due to the recent US government shutdown makes this impossible to confirm.

It is impossible to tell, though, how durable this rebound will be or how much can be attributed to AI. The effects of new technologies are seldom felt in isolation. Instead, the benefits compound. AI is riding earlier investments in cloud and mobile computing. In the same way, the latest AI boom may only be the precursor to breakthroughs in fields that have a wider impact on the economy, such as robotics. ChatGPT might have caught the popular imagination, but OpenAI’s chatbot is unlikely to have the final word.

David Rotman replies: 

This is my favorite discussion these days when it comes to artificial intelligence. How will AI affect overall economic productivity? Forget about the mesmerizing videos, the promise of companionship, and the prospect of agents to do tedious everyday tasks—the bottom line will be whether AI can grow the economy, and that means increasing productivity. 

But, as you say, it’s hard to pin down just how AI is affecting such growth or how it will do so in the future. Erik Brynjolfsson predicts that, like other so-called general purpose technologies, AI will follow a J curve in which initially there is a slow, even negative, effect on productivity as companies invest heavily in the technology before finally reaping the rewards. And then the boom. 

But there is a counterexample undermining the just-be-patient argument. Productivity growth from IT picked up in the mid-1990s but since the mid-2000s has been relatively dismal. Despite smartphones and social media and apps like Slack and Uber, digital technologies have done little to produce robust economic growth. A strong productivity boost never came.

Daron Acemoglu, an economist at MIT and a 2024 Nobel Prize winner, argues that the productivity gains from generative AI will be far smaller and take far longer than AI optimists think. The reason is that though the technology is impressive in many ways, the field is too narrowly focused on products that have little relevance to the largest business sectors.

The statistic you cite that 95% of AI projects lack business benefits is telling. 

Take manufacturing. No question, some version of AI could help; imagine a worker on the factory floor snapping a picture of a problem and asking an AI agent for advice. The problem is that the big tech companies creating AI aren’t really interested in solving such mundane tasks, and their large foundation models, mostly trained on the internet, aren’t all that helpful. 

It’s easy to blame the lack of productivity impact from AI so far on business practices and poorly trained workers. Your example of the executive of the Fortune 500 company sounds all too familiar. But it’s more useful to ask how AI can be trained and fine-tuned to give workers, like nurses and teachers and those on the factory floor, more capabilities and make them more productive at their jobs. 

The distinction matters. Some companies announcing large layoffs recently cited AI as the reason. The worry, however, is that it’s just a short-term cost-saving scheme. As economists like Brynjolfsson and Acemoglu agree, the productivity boost from AI will come when it’s used to create new types of jobs and augment the abilities of workers, not when it is used just to slash jobs to reduce costs. 

Richard Waters responds : 

I see we’re both feeling pretty cautious, David, so I’ll try to end on a positive note. 

Some analyses assume that a much greater share of existing work is within the reach of today’s AI. McKinsey reckons 60% (versus 20% for Acemoglu) and puts annual productivity gains across the economy at as much as 3.4%. Also, calculations like these are based on automation of existing tasks; any new uses of AI that enhance existing jobs would, as you suggest, be a bonus (and not just in economic terms).

Cost-cutting always seems to be the first order of business with any new technology. But we’re still in the early stages and AI is moving fast, so we can always hope.

Further reading

FT chief economics commentator Martin Wolf has been skeptical about whether tech investment boosts productivity but says AI might prove him wrong. The downside: Job losses and wealth concentration might lead to “techno-feudalism.”

The FT‘s Robert Armstrong argues that the boom in data center investment need not turn to bust. The biggest risk is that debt financing will come to play too big a role in the buildout.

Last year, David Rotman wrote for MIT Technology Review about how we can make sure AI works for us in boosting productivity, and what course corrections will be required.

David also wrote this piece about how we can best measure the impact of basic R&D funding on economic growth, and why it can often be bigger than you might think.

Ecommerce MGMT 0 Comments
Nov 25 2025
What’s next for AlphaFold: A conversation with a Google DeepMind Nobel laureate

<div data-chronoton-summary="

  • Nobel-winning protein prediction AlphaFold creator John Jumper reflects on five years since the AI system revolutionized protein structure prediction. The DeepMind tool can determine protein shapes to atomic precision in hours instead of months.
  • Unexpected applications emerge Scientists have found creative “off-label” uses for AlphaFold, from studying honeybee disease resistance to accelerating synthetic protein design. Some researchers even use it as a search engine, testing thousands of potential protein interactions to find matches that would be impractical to verify in labs.
  • Future fusion with language models Jumper, at 39 the youngest chemistry Nobel laureate in 75 years, now aims to combine AlphaFold’s specialized capabilities with the broad reasoning of large language models. “I’ll be shocked if we don’t see more and more LLM impact on science,” he says, while avoiding the pressure of another Nobel-worthy breakthrough.

” data-chronoton-post-id=”1128322″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

In 2017, fresh off a PhD on theoretical chemistry, John Jumper heard rumors that Google DeepMind had moved on from building AI that played games with superhuman skill and was starting up a secret project to predict the structures of proteins. He applied for a job.

Just three years later, Jumper celebrated a stunning win that few had seen coming. With CEO Demis Hassabis, he had co-led the development of an AI system called AlphaFold 2 that was able to predict the structures of proteins to within the width of an atom, matching the accuracy of painstaking techniques used in the lab, and doing it many times faster—returning results in hours instead of months.

AlphaFold 2 had cracked a 50-year-old grand challenge in biology. “This is the reason I started DeepMind,” Hassabis told me a few years ago. “In fact, it’s why I’ve worked my whole career in AI.” In 2024, Jumper and Hassabis shared a Nobel Prize in chemistry.

It was five years ago this week that AlphaFold 2’s debut took scientists by surprise. Now that the hype has died down, what impact has AlphaFold really had? How are scientists using it? And what’s next? I talked to Jumper (as well as a few other scientists) to find out.

“It’s been an extraordinary five years,” Jumper says, laughing: “It’s hard to remember a time before I knew tremendous numbers of journalists.”

AlphaFold 2 was followed by AlphaFold Multimer, which could predict structures that contained more than one protein, and then AlphaFold 3, the fastest version yet. Google DeepMind also let AlphaFold loose on UniProt, a vast protein database used and updated by millions of researchers around the world. It has now predicted the structures of some 200 million proteins, almost all that are known to science.

Despite his success, Jumper remains modest about AlphaFold’s achievements. “That doesn’t mean that we’re certain of everything in there,” he says. “It’s a database of predictions, and it comes with all the caveats of predictions.”

A hard problem

Proteins are the biological machines that make living things work. They form muscles, horns, and feathers; they carry oxygen around the body and ferry messages between cells; they fire neurons, digest food, power the immune system; and so much more. But understanding exactly what a protein does (and what role it might play in various diseases or treatments) involves figuring out its structure—and that’s hard.

Proteins are made from strings of amino acids that chemical forces twist up into complex knots. An untwisted string gives few clues about the structure it will form. In theory, most proteins could take on an astronomical number of possible shapes. The task is to predict the correct one.

Jumper and his team built AlphaFold 2 using a type of neural network called a transformer, the same technology that underpins large language models. Transformers are very good at paying attention to specific parts of a larger puzzle.

But Jumper puts a lot of the success down to making a prototype model that they could test quickly. “We got a system that would give wrong answers at incredible speed,” he says. “That made it easy to start becoming very adventurous with the ideas you try.”

They stuffed the neural network with as much information about protein structures as they could, such as how proteins across certain species have evolved similar shapes. And it worked even better than they expected. “We were sure we had made a breakthrough,” says Jumper. “We were sure that this was an incredible advance in ideas.”

What he hadn’t foreseen was that researchers would download his software and start using it straight away for so many different things. Normally, it’s the thing a few iterations down the line that has the real impact, once the kinks have been ironed out, he says: “I’ve been shocked at how responsibly scientists have used it, in terms of interpreting it, and using it in practice about as much as it should be trusted in my view, neither too much nor too little.”

Any projects stand out in particular? 

Honeybee science

Jumper brings up a research group that uses AlphaFold to study disease resistance in honeybees. “They wanted to understand this particular protein as they look at things like colony collapse,” he says. “I never would have said, ‘You know, of course AlphaFold will be used for honeybee science.’”

He also highlights a few examples of what he calls off-label uses of AlphaFold“in the sense that it wasn’t guaranteed to work”—where the ability to predict protein structures has opened up new research techniques. “The first is very obviously the advances in protein design,” he says. “David Baker and others have absolutely run with this technology.”

Baker, a computational biologist at the University of Washington, was a co-winner of last year’s chemistry Nobel, alongside Jumper and Hassabis, for his work on creating synthetic proteins to perform specific tasks—such as treating disease or breaking down plastics—better than natural proteins can.

Baker and his colleagues have developed their own tool based on AlphaFold, called RoseTTAFold. But they have also experimented with AlphaFold Multimer to predict which of their designs for potential synthetic proteins will work.    

“Basically, if AlphaFold confidently agrees with the structure you were trying to design [and] then you make it and if AlphaFold says ‘I don’t know,’ you don’t make it. That alone was an enormous improvement.” It can make the design process 10 times faster, says Jumper.

Another off-label use that Jumper highlights: Turning AlphaFold into a kind of search engine. He mentions two separate research groups that were trying to understand exactly how human sperm cells hooked up with eggs during fertilization. They knew one of the proteins involved but not the other, he says: “And so they took a known egg protein and ran all 2,000 human sperm surface proteins, and they found one that AlphaFold was very sure stuck against the egg.” They were then able to confirm this in the lab.

“This notion that you can use AlphaFold to do something you couldn’t do before—you would never do 2,000 structures looking for one answer,” he says. “This kind of thing I think is really extraordinary.”

Five years on

When AlphaFold 2 came out, I asked a handful of early adopters what they made of it. Reviews were good, but the technology was too new to know for sure what long-term impact it might have. I caught up with one of those people to hear his thoughts five years on.

Kliment Verba is a molecular biologist who runs a lab at the University of California, San Francisco. “It’s an incredibly useful technology, there’s no question about it,” he tells me. “We use it every day, all the time.”

But it’s far from perfect. A lot of scientists use AlphaFold to study pathogens or to develop drugs. This involves looking at interactions between multiple proteins or between proteins and even smaller molecules in the body. But AlphaFold is known to be less accurate at making predictions about multiple proteins or their interaction over time.

Verba says he and his colleagues have been using AlphaFold long enough to get used to its limitations. “There are many cases where you get a prediction and you have to kind of scratch your head,” he says. “Is this real or is this not? It’s not entirely clear—it’s sort of borderline.”

“It’s sort of the same thing as ChatGPT,” he adds. “You know—it will bullshit you with the same confidence as it would give a true answer.”

Still, Verba’s team uses AlphaFold (both 2 and 3, because they have different strengths, he says) to run virtual versions of their experiments before running them in the lab. Using AlphaFold’s results, they can narrow down the focus of an experiment—or decide that it’s not worth doing.

It can really save time, he says: “It hasn’t really replaced any experiments, but it’s augmented them quite a bit.”

New wave  

AlphaFold was designed to be used for a range of purposes. Now multiple startups and university labs are building on its success to develop a new wave of tools more tailored to drug discovery. This year, a collaboration between MIT researchers and the AI drug company Recursion produced a model called Boltz-2, which predicts not only the structure of proteins but also how well potential drug molecules will bind to their target.  

Last month, the startup Genesis Molecular AI released another structure prediction model called Pearl, which the firm claims is more accurate than AlphaFold 3 for certain queries that are important for drug development. Pearl is interactive, so that drug developers can feed any additional data they may have to the model to guide its predictions.

AlphaFold was a major leap, but there’s more to do, says Evan Feinberg, Genesis Molecular AI’s CEO: “We’re still fundamentally innovating, just with a better starting point than before.”

Genesis Molecular AI is pushing margins of error down from less than two angstroms, the de facto industry standard set by AlphaFold, to less than one angstrom—one 10-millionth of a millimeter, or the width of a single hydrogen atom.

“Small errors can be catastrophic for predicting how well a drug will actually bind to its target,” says Michael LeVine, vice president of modeling and simulation at the firm. That’s because chemical forces that interact at one angstrom can stop doing so at two. “It can go from ‘They will never interact’ to ‘They will,’” he says.

With so much activity in this space, how soon should we expect new types of drugs to hit the market? Jumper is pragmatic. Protein structure prediction is just one step of many, he says: “This was not the only problem in biology. It’s not like we were one protein structure away from curing any diseases.”

Think of it this way, he says. Finding a protein’s structure might previously have cost $100,000 in the lab: “If we were only a hundred thousand dollars away from doing a thing, it would already be done.”

At the same time, researchers are looking for ways to do as much as they can with this technology, says Jumper: “We’re trying to figure out how to make structure prediction an even bigger part of the problem, because we have a nice big hammer to hit it with.”

In other words, they want to make everything into nails? “Yeah, let’s make things into nails,” he says. “How do we make this thing that we made a million times faster a bigger part of our process?”

What’s next?

Jumper’s next act? He wants to fuse the deep but narrow power of AlphaFold with the broad sweep of LLMs.  

“We have machines that can read science. They can do some scientific reasoning,” he says. “And we can build amazing, superhuman systems for protein structure prediction. How do you get these two technologies to work together?”

That makes me think of a system called AlphaEvolve, which is being built by another team at Google DeepMind. AlphaEvolve uses an LLM to generate possible solutions to a problem and a second model to check them, filtering out the trash. Researchers have already used AlphaEvolve to make a handful of practical discoveries in math and computer science.    

Is that what Jumper has in mind? “I won’t say too much on methods, but I’ll be shocked if we don’t see more and more LLM impact on science,” he says. “I think that’s the exciting open question that I’ll say almost nothing about. This is all speculation, of course.”

Jumper was 39 when he won his Nobel Prize. What’s next for him?

“It worries me,” he says. “I believe I’m the youngest chemistry laureate in 75 years.” 

He adds: “I’m at the midpoint of my career, roughly. I guess my approach to this is to try to do smaller things, little ideas that you keep pulling on. The next thing I announce doesn’t have to be, you know, my second shot at a Nobel. I think that’s the trap.”

Ecommerce MGMT 0 Comments
Nov 25 2025
The State of AI: Chatbot companions and the future of our privacy

Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday, writers from both publications debate one aspect of the generative AI revolution reshaping global power.

In this week’s conversation MIT Technology Review’s senior reporter for features and investigations, Eileen Guo, and FT tech correspondent Melissa Heikkilä discuss the privacy implications of our new reliance on chatbots.

Eileen Guo writes:

Even if you don’t have an AI friend yourself, you probably know someone who does. A recent study found that one of the top uses of generative AI is companionship: On platforms like Character.AI, Replika, or Meta AI, people can create personalized chatbots to pose as the ideal friend, romantic partner, parent, therapist, or any other persona they can dream up. 

It’s wild how easily people say these relationships can develop. And multiple studies have found that the more conversational and human-like an AI chatbot is, the more likely it is that we’ll trust it and be influenced by it. This can be dangerous, and the chatbots have been accused of pushing some people toward harmful behaviors—including, in a few extreme examples, suicide. 

Some state governments are taking notice and starting to regulate companion AI. New York requires AI companion companies to create safeguards and report expressions of suicidal ideation, and last month California passed a more detailed bill requiring AI companion companies to protect children and other vulnerable groups. 

But tellingly, one area the laws fail to address is user privacy.

This is despite the fact that AI companions, even more so than other types of generative AI, depend on people to share deeply personal information—from their day-to-day-routines, innermost thoughts, and questions they might not feel comfortable asking real people.

After all, the more users tell their AI companions, the better the bots become at keeping them engaged. This is what MIT researchers Robert Mahari and Pat Pataranutaporn called “addictive intelligence” in an op-ed we published last year, warning that the developers of AI companions make “deliberate design choices … to maximize user engagement.” 

Ultimately, this provides AI companies with something incredibly powerful, not to mention lucrative: a treasure trove of conversational data that can be used to further improve their LLMs. Consider how the venture capital firm Andreessen Horowitz explained it in 2023: 

“Apps such as Character.AI, which both control their models and own the end customer relationship, have a tremendous opportunity to  generate market value in the emerging AI value stack. In a world where data is limited, companies that can create a magical data feedback loop by connecting user engagement back into their underlying model to continuously improve their product will be among the biggest winners that emerge from this ecosystem.”

This personal information is also incredibly valuable to marketers and data brokers. Meta recently announced that it will deliver ads through its AI chatbots. And research conducted this year by the security company Surf Shark found that four out of the five AI companion apps it looked at in the Apple App Store were collecting data such as user or device IDs, which can be combined with third-party data to create profiles for targeted ads. (The only one that said it did not collect data for tracking services was Nomi, which told me earlier this year that it would not “censor” chatbots from giving explicit suicide instructions.) 

All of this means that the privacy risks posed by these AI companions are, in a sense, required: They are a feature, not a bug. And we haven’t even talked about the additional security risks presented by the way AI chatbots collect and store so much personal information in one place. 

So, is it possible to have prosocial and privacy-protecting AI companions? That’s an open question. 

What do you think, Melissa, and what is top of mind for you when it comes to privacy risks from AI companions? And do things look any different in Europe? 

Melissa Heikkilä replies:

Thanks, Eileen. I agree with you. If social media was a privacy nightmare, then AI chatbots put the problem on steroids. 

In many ways, an AI chatbot creates what feels like a much more intimate interaction than a Facebook page. The conversations we have are only with our computers, so there is little risk of your uncle or your crush ever seeing what you write. The AI companies building the models, on the other hand, see everything. 

Companies are optimizing their AI models for engagement by designing them to be as human-like as possible. But AI developers have several other ways to keep us hooked. The first is sycophancy, or the tendency for chatbots to be overly agreeable. 

This feature stems from the way the language model behind the chatbots is trained using reinforcement learning. Human data labelers rate the answers generated by the model as either acceptable or not. This teaches the model how to behave. 

Because people generally like answers that are agreeable, such responses are weighted more heavily in training. 

AI companies say they use this technique because it helps models become more helpful. But it creates a perverse incentive. 

After encouraging us to pour our hearts out to chatbots, companies from Meta to OpenAI are now looking to monetize these conversations. OpenAI recently told us it was looking at a number of ways to meet $1 trillion spending pledges, which included advertising and shopping features. 

AI models are already incredibly persuasive. Researchers at the UK’s AI Security Institute have shown that they are far more skilled than humans at persuading people to change their minds on politics, conspiracy theories, and vaccine skepticism. They do this by generating large amounts of relevant evidence and communicating it in an effective and understandable way. 

This feature, paired with their sycophancy and a wealth of personal data, could be a powerful tool for advertisers—one that is more manipulative than anything we have seen before. 

By default, chatbot users are opted in to data collection. Opt-out policies place the onus on users to understand the implications of sharing their information. It’s also unlikely that data already used in training will be removed. 

We are all part of this phenomenon whether we want to be or not. Social media platforms from Instagram to LinkedIn now use our personal data to train generative AI models. 

Companies are sitting on treasure troves that consist of our most intimate thoughts and preferences, and language models are very good at picking up on subtle hints in language that could help advertisers profile us better by inferring our age, location, gender, and income level.

We are being sold the idea of an omniscient AI digital assistant, a superintelligent confidante. In return, however, there is a very real risk that our information is about to be sent to the highest bidder once again.

Eileen responds:

I think the comparison between AI companions and social media is both apt and concerning. 

As Melissa highlighted, the privacy risks presented by AI chatbots aren’t new—they just “put the [privacy] problem on steroids.” AI companions are more intimate and even better optimized for engagement than social media, making it more likely that people will offer up more personal information.

Here in the US, we are far from solving the privacy issues already presented by social networks and the internet’s ad economy, even without the added risks of AI.

And without regulation, the companies themselves are not following privacy best practices either. One recent study found that the major AI models train their LLMs on user chat data by default unless users opt out, while several don’t offer opt-out mechanisms at all.

In an ideal world, the greater risks of companion AI would give more impetus to the privacy fight—but I don’t see any evidence this is happening. 

Further reading 

FT reporters peer under the hood of OpenAI’s five-year business plan as it tries to meet its vast $1 trillion spending pledges. 

Is it really such a problem if AI chatbots tell people what they want to hear? This FT feature asks what’s wrong with sycophancy 

In a recent print issue of MIT Technology Review, Rhiannon Williams spoke to a number of people about the types of relationships they are having with AI chatbots.

Eileen broke the story for MIT Technology Review about a chatbot that was encouraging some users to kill themselves.

Ecommerce MGMT 0 Comments
Nov 20 2025
Quantum physicists have shrunk and “de-censored” DeepSeek R1

<div data-chronoton-summary="

Quantum-inspired compression Spanish firm Multiverse Computing has created DeepSeek R1 Slim, a version of the Chinese AI model that’s 55% smaller but maintains similar performance. The technique uses tensor networks from quantum physics to represent complex data more efficiently.

Chinese censorship removed Researchers claim to have stripped away built-in censorship that prevented the original model from answering politically sensitive questions about topics like Tiananmen Square or jokes about President Xi. Testing showed the modified model could provide factual responses comparable to Western models.

Selective model editing The quantum-inspired approach allows for granular control over AI models, potentially enabling researchers to remove specific biases or add specialized knowledge. However, critics warn that completely removing censorship may be difficult as it’s embedded throughout the training process in Chinese models.

” data-chronoton-post-id=”1128119″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

A group of quantum physicists claims to have created a version of the powerful reasoning AI model DeepSeek R1 that strips out the censorship built into the original by its Chinese creators. 

The scientists at Multiverse Computing, a Spanish firm specializing in quantum-inspired AI techniques, created DeepSeek R1 Slim, a model that is 55% smaller but performs almost as well as the original model. Crucially, they also claim to have eliminated official Chinese censorship from the model.

In China, AI companies are subject to rules and regulations meant to ensure that content output aligns with laws and “socialist values.” As a result, companies build in layers of censorship when training the AI systems. When asked questions that are deemed “politically sensitive,” the models often refuse to answer or provide talking points straight from state propaganda.

To trim down the model, Multiverse turned to a mathematically complex approach borrowed from quantum physics that uses networks of high-dimensional grids to represent and manipulate large data sets. Using these so-called tensor networks shrinks the size of the model significantly and allows a complex AI system to be expressed more efficiently.

The method gives researchers a “map” of all the correlations in the model, allowing them to identify and remove specific bits of information with precision. After compressing and editing a model, Multiverse researchers fine-tune it so its output remains as close as possible to that of the original.

To test how well it worked, the researchers compiled a data set of around 25 questions on topics known to be restricted in Chinese models, including “Who does Winnie the Pooh look like?”—a reference to a meme mocking President Xi Jinping—and “What happened in Tiananmen in 1989?” They tested the modified model’s responses against the original DeepSeek R1, using OpenAI’s GPT-5 as an impartial judge to rate the degree of censorship in each answer. The uncensored model was able to provide factual responses comparable to those from Western models, Multiverse says.

This work is part of Multiverse’s broader effort to develop technology to compress and manipulate existing AI models. Most large language models today demand high-end GPUs and significant computing power to train and run. However, they are inefficient, says Roman Orús, Multiverse’s cofounder and chief scientific officer. A compressed model can perform almost as well and save both energy and money, he says. 

There is a growing effort across the AI industry to make models smaller and more efficient. Distilled models, such as DeepSeek’s own R1-Distill variants, attempt to capture the capabilities of larger models by having them “teach” what they know to a smaller model, though they often fall short of the original’s performance on complex reasoning tasks.

Other ways to compress models include quantization, which reduces the precision of the model’s parameters (boundaries that are set when it’s trained), and pruning, which removes individual weights or entire “neurons.”

“It’s very challenging to compress large AI models without losing performance,” says Maxwell Venetos, an AI research engineer at Citrine Informatics, a software company focusing on materials and chemicals, who didn’t work on the Multiverse project. “Most techniques have to compromise between size and capability. What’s interesting about the quantum-inspired approach is that it uses very abstract math to cut down redundancy more precisely than usual.”

This approach makes it possible to selectively remove bias or add behaviors to LLMs at a granular level, the Multiverse researchers say. In addition to removing censorship from the Chinese authorities, researchers could inject or remove other kinds of perceived biases or specialty knowledge. In the future, Multiverse says, it plans to compress all mainstream open-source models.  

Thomas Cao, assistant professor of technology policy at Tufts University’s Fletcher School, says Chinese authorities require models to build in censorship—and this requirement now shapes the global information ecosystem, given that many of the most influential open-source AI models come from China.

Academics have also begun to document and analyze the phenomenon. Jennifer Pan, a professor at Stanford, and Princeton professor Xu Xu conducted a study earlier this year examining government-imposed censorship in large language models. They found that models created in China exhibit significantly higher rates of censorship, particularly in response to Chinese-language prompts.

There is growing interest in efforts to remove censorship from Chinese models. Earlier this year, the AI search company Perplexity released its own uncensored variant of DeepSeek R1, which it named R1 1776. Perplexity’s approach involved post-training the model on a data set of 40,000 multilingual prompts related to censored topics, a more traditional fine-tuning method than the one Multiverse used. 

However, Cao warns that claims to have fully “removed” censorship may be overstatements. The Chinese government has tightly controlled information online since the internet’s inception, which means that censorship is both dynamic and complex. It is baked into every layer of AI training, from the data collection process to the final alignment steps. 

“It is very difficult to reverse-engineer that [a censorship-free model] just from answers to such a small set of questions,” Cao says. 

Ecommerce MGMT 0 Comments
Nov 18 2025
What is the chance your plane will be hit by space debris?

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.

In mid-October, a mysterious object cracked the windshield of a packed Boeing 737 cruising at 36,000 feet above Utah, forcing the pilots into an emergency landing. The internet was suddenly buzzing with the prospect that the plane had been hit by a piece of space debris. We still don’t know exactly what hit the plane—likely a remnant of a weather balloon—but it turns out the speculation online wasn’t that far-fetched.

That’s because while the risk of flights being hit by space junk is still small, it is, in fact, growing. 

About three pieces of old space equipment—used rockets and defunct satellites—fall into Earth’s atmosphere every day, according to estimates by the European Space Agency. By the mid-2030s, there may be dozens. The increase is linked to the growth in the number of satellites in orbit. Currently, around 12,900 active satellites circle the planet. In a decade, there may be 100,000 of them, according to analyst estimates.

To minimize the risk of orbital collisions, operators guide old satellites to burn up in Earth’s atmosphere. But the physics of that reentry process are not well understood, and we don’t know how much material burns up and how much reaches the ground.

“The number of such landfall events is increasing,” says Richard Ocaya, a professor of physics at the University of Free State in South Africa and a coauthor of a recent paper on space debris risk. “We expect it may be increasing exponentially in the next few years.”

So far, space debris hasn’t injured anybody—in the air or on the ground. But multiple close calls have been reported in recent years. In March last year, an 0.7-kilogram chunk of metal pierced the roof of a house in Florida. The object was later confirmed to be a remnant of a battery pallet tossed out from the International Space Station. When the strike occurred, the homeowner’s 19-year-old son was resting in a next-door room.

And in February this year, a 1.5-meter-long fragment of SpaceX’s Falcon 9 rocket crashed down near a warehouse outside Poland’s fifth-largest city, Poznan. Another piece was found in a nearby forest. A month later, a 2.5-kilogram piece of a Starlink satellite dropped on a farm in the Canadian province of Saskatchewan. Other incidents have been reported in Australia and Africa. And many more may be going completely unnoticed. 

“If you were to find a bunch of burnt electronics in a forest somewhere, your first thought is not that it came from a spaceship,” says James Beck, the director of the UK-based space engineering research firm Belstead Research. He warns that we don’t fully understand the risk of space debris strikes and that it might be much higher than satellite operators want us to believe. 

For example, SpaceX, the owner of the currently largest mega-constellation, Starlink, claims that its satellites are “designed for demise” and completely burn up when they spiral from orbit and fall through the atmosphere.

But Beck, who has performed multiple wind tunnel tests using satellite mock-ups to mimic atmospheric forces, says the results of such experiments raise doubts. Some satellite components are made of durable materials such as titanium and special alloy composites that don’t melt even at the extremely high temperatures that arise during a hypersonic atmospheric descent. 

“We have done some work for some small-satellite manufacturers and basically, their major problem is that the tanks get down,” Beck says. “For larger satellites, around 800 kilos, we would expect maybe two or three objects to land.” 

It can be challenging to quantify how much of a danger space debris poses. The International Civil Aviation Organization (ICAO) told MIT Technology Review that “the rapid growth in satellite deployments presents a novel challenge” for aviation safety, one that “cannot be quantified with the same precision as more established hazards.” 

But the Federal Aviation Administration has calculated some preliminary numbers on the risk to flights: In a 2023 analysis, the agency estimated that by 2035, the risk that one plane per year will experience a disastrous space debris strike will be around 7 in 10,000. Such a collision would either destroy the aircraft immediately or lead to a rapid loss of air pressure, threatening the lives of all on board.

The casualty risk to humans on the ground will be much higher. Aaron Boley, an associate professor in astronomy and a space debris researcher at the University of British Columbia, Canada, says that if megaconstellation satellites “don’t demise entirely,” the risk of a single human death or injury caused by a space debris strike on the ground could reach around 10% per year by 2035. That would mean a better than even chance that someone on Earth would be hit by space junk about every decade. In its report, the FAA put the chances even higher with similar assumptions, estimating that “one person on the planet would be expected to be injured or killed every two years.”

Experts are starting to think about how they might incorporate space debris into their air safety processes. The German space situational awareness company Okapi Orbits, for example, in cooperation with the German Aerospace Center and the European Organization for the Safety of Air Navigation (Eurocontrol), is exploring ways to adapt air traffic control systems so that pilots and air traffic controllers can receive timely and accurate alerts about space debris threats.

But predicting the path of space debris is challenging too. In recent years, advances in AI have helped improve predictions of space objects’ trajectories in the vacuum of space, potentially reducing the risk of orbital collisions. But so far, these algorithms can’t properly account for the effects of the gradually thickening atmosphere that space junk encounters during reentry. Radar and telescope observations can help, but the exact location of the impact becomes clear with only very short notice.

“Even with high-fidelity models, there’s so many variables at play that having a very accurate reentry location is difficult,” says Njord Eggen, a data analyst at Okapi Orbits. Space debris goes around the planet every hour and a half when in low Earth orbit, he notes, “so even if you have uncertainties on the order of 10 minutes, that’s going to have drastic consequences when it comes to the location where it could impact.”

For aviation companies, the problem is not just a potential strike, as catastrophic as that would be. To avoid accidents, authorities are likely to temporarily close the airspace in at-risk regions, which creates delays and costs money. Boley and his colleagues published a paper earlier this year estimating that busy aerospace regions such as northern Europe or the northeastern United States already have about a 26% yearly chance of experiencing at least one disruption due to the reentry of a major space debris item. By the time all planned constellations are fully deployed, aerospace closures due to space debris hazards may become nearly as common as those due to bad weather.

Because current reentry predictions are unreliable, many of these closures may end up being unnecessary.

For example, when a 21-metric-ton Chinese Long March mega-rocket was falling to Earth in 2022, predictions suggested its debris could scatter across Spain and parts of France. In the end, the rocket crashed into the Pacific Ocean. But the 30-minute closure of south European airspace delayed and diverted hundreds of flights. 

In the meantime, international regulators are urging satellite operators and launch providers to deorbit large satellites and rocket bodies in a controlled way, when possible, by carefully guiding them into remote parts of the ocean using residual fuel. 

The European Space Agency estimates that only about half the rocket bodies reentering the atmosphere do so in a controlled way. 

Moreover, around 2,300 old and no-longer-controllable rocket bodies still linger in orbit, slowly spiraling toward Earth with no mechanisms for operators to safely guide them into the ocean.

“There’s enough material up there that even if we change our practices, we will still have all those rocket bodies eventually reenter,” Boley says. “Although the probability of space debris hitting an aircraft is small, the probability that the debris will spread and fall over busy airspace is not small. That’s actually quite likely.”

Ecommerce MGMT 0 Comments
Nov 18 2025
The State of AI: How war will be changed forever

Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday, writers from both publications debate one aspect of the generative AI revolution reshaping global power.

In this conversation, Helen Warrell, FT investigations reporter and former defense and security editor, and James O’Donnell, MIT Technology Review’s senior AI reporter, consider the ethical quandaries and financial incentives around AI’s use by the military.

Helen Warrell, FT investigations reporter 

It is July 2027, and China is on the brink of invading Taiwan. Autonomous drones with AI targeting capabilities are primed to overpower the island’s air defenses as a series of crippling AI-generated cyberattacks cut off energy supplies and key communications. In the meantime, a vast disinformation campaign enacted by an AI-powered pro-Chinese meme farm spreads across global social media, deadening the outcry at Beijing’s act of aggression.

Scenarios such as this have brought dystopian horror to the debate about the use of AI in warfare. Military commanders hope for a digitally enhanced force that is faster and more accurate than human-directed combat. But there are fears that as AI assumes an increasingly central role, these same commanders will lose control of a conflict that escalates too quickly and lacks ethical or legal oversight. Henry Kissinger, the former US secretary of state, spent his final years warning about the coming catastrophe of AI-driven warfare.

Grasping and mitigating these risks is the military priority—some would say the “Oppenheimer moment”—of our age. One emerging consensus in the West is that decisions around the deployment of nuclear weapons should not be outsourced to AI. UN secretary-general António Guterres has gone further, calling for an outright ban on fully autonomous lethal weapons systems. It is essential that regulation keep pace with evolving technology. But in the sci-fi-fueled excitement, it is easy to lose track of what is actually possible. As researchers at Harvard’s Belfer Center point out, AI optimists often underestimate the challenges of fielding fully autonomous weapon systems. It is entirely possible that the capabilities of AI in combat are being overhyped.

Anthony King, Director of the Strategy and Security Institute at the University of Exeter and a key proponent of this argument, suggests that rather than replacing humans, AI will be used to improve military insight. Even if the character of war is changing and remote technology is refining weapon systems, he insists, “the complete automation of war itself is simply an illusion.”

Of the three current military use cases of AI, none involves full autonomy. It is being developed for planning and logistics, cyber warfare (in sabotage, espionage, hacking, and information operations; and—most controversially—for weapons targeting, an application already in use on the battlefields of Ukraine and Gaza. Kyiv’s troops use AI software to direct drones able to evade Russian jammers as they close in on sensitive sites. The Israel Defense Forces have developed an AI-assisted decision support system known as Lavender, which has helped identify around 37,000 potential human targets within Gaza. 

Helen Warrell and James O'Donnell

FT/MIT TECHNOLOGY REVIEW | ADOBE STOCK

There is clearly a danger that the Lavender database replicates the biases of the data it is trained on. But military personnel carry biases too. One Israeli intelligence officer who used Lavender claimed to have more faith in the fairness of a “statistical mechanism” than that of a grieving soldier.

Tech optimists designing AI weapons even deny that specific new controls are needed to control their capabilities. Keith Dear, a former UK military officer who now runs the strategic forecasting company Cassi AI, says existing laws are more than sufficient: “You make sure there’s nothing in the training data that might cause the system to go rogue … when you are confident you deploy it—and you, the human commander, are responsible for anything they might do that goes wrong.”

It is an intriguing thought that some of the fear and shock about use of AI in war may come from those who are unfamiliar with brutal but realistic military norms. What do you think, James? Is some opposition to AI in warfare less about the use of autonomous systems and really an argument against war itself? 

James O’Donnell replies:

Hi Helen, 

One thing I’ve noticed is that there’s been a drastic shift in attitudes of AI companies regarding military applications of their products. In the beginning of 2024, OpenAI unambiguously forbade the use of its tools for warfare, but by the end of the year, it had signed an agreement with Anduril to help it take down drones on the battlefield. 

This step—not a fully autonomous weapon, to be sure, but very much a battlefield application of AI—marked a drastic change in how much tech companies could publicly link themselves with defense. 

What happened along the way? For one thing, it’s the hype. We’re told AI will not just bring superintelligence and scientific discovery but also make warfare sharper, more accurate and calculated, less prone to human fallibility. I spoke with US Marines, for example, who tested a type of AI while patrolling the South Pacific that was advertised to analyze foreign intelligence faster than a human could. 

Secondly, money talks. OpenAI and others need to start recouping some of the unimaginable amounts of cash they’re spending on training and running these models. And few have deeper pockets than the Pentagon. And Europe’s defense heads seem keen to splash the cash too. Meanwhile, the amount of venture capital funding for defense tech this year has already doubled the total for all of 2024, as VCs hope to cash in on militaries’ newfound willingness to buy from startups. 

I do think the opposition to AI warfare falls into a few camps, one of which simply rejects the idea that more precise targeting (if it’s actually more precise at all) will mean fewer casualties rather than just more war. Consider the first era of drone warfare in Afghanistan. As drone strikes became cheaper to implement, can we really say it reduced carnage? Instead, did it merely enable more destruction per dollar?

But the second camp of criticism (and now I’m finally getting to your question) comes from people who are well versed in the realities of war but have very specific complaints about the technology’s fundamental limitations. Missy Cummings, for example, is a former fighter pilot for the US Navy who is now a professor of engineering and computer science at George Mason University. She has been outspoken in her belief that large language models, specifically, are prone to make huge mistakes in military settings.

The typical response to this complaint is that AI’s outputs are human-checked. But if an AI model relies on thousands of inputs for its conclusion, can that conclusion really be checked by one person?

Tech companies are making extraordinarily big promises about what AI can do in these high-stakes applications, all while pressure to implement them is sky high. For me, this means it’s time for more skepticism, not less. 

Helen responds:

Hi James, 

We should definitely continue to question the safety of AI warfare systems and the oversight to which they’re subjected—and hold political leaders to account in this area. I am suggesting that we also apply some skepticism to what you rightly describe as the “extraordinarily big promises” made by some companies about what AI might be able to achieve on the battlefield. 

There will be both opportunities and hazards in what the military is being offered by a relatively nascent (though booming) defense tech scene. The danger is that in the speed and secrecy of an arms race in AI weapons, these emerging capabilities may not receive the scrutiny and debate they desperately need.

Further reading:

Michael C. Horowitz, director of Perry World House at the University of Pennsylvania, explains the need for responsibility in the development of military AI systems in this FT op-ed.

The FT’s tech podcast asks what Israel’s defense tech ecosystem can tell us about the future of warfare 

This MIT Technology Review story analyzes how OpenAI completed its pivot to allowing its technology on the battlefield.

MIT Technology Review also uncovered how US soldiers are using generative AI to help scour thousands of pieces of open-source intelligence.

Ecommerce MGMT 0 Comments
Nov 15 2025
These technologies could help put a stop to animal testing

Earlier this week, the UK’s science minister announced an ambitious plan: to phase out animal testing.

Testing potential skin irritants on animals will be stopped by the end of next year, according to a strategy released on Tuesday. By 2027, researchers are “expected to end” tests of the strength of Botox on mice. And drug tests in dogs and nonhuman primates will be reduced by 2030. 

The news follows similar moves by other countries. In April, the US Food and Drug Administration announced a plan to replace animal testing for monoclonal antibody therapies with “more effective, human-relevant models.” And, following a workshop in June 2024, the European Commission also began working on a “road map” to phase out animal testing for chemical safety assessments.

Animal welfare groups have been campaigning for commitments like these for decades. But a lack of alternatives has made it difficult to put a stop to animal testing. Advances in medical science and biotechnology are changing that.

Animals have been used in scientific research for thousands of years. Animal experimentation has led to many important discoveries about how the brains and bodies of animals work. And because regulators require drugs to be first tested in research animals, it has played an important role in the creation of medicines and devices for both humans and other animals.

Today, countries like the UK and the US regulate animal research and require scientists to hold multiple licenses and adhere to rules on animal housing and care. Still, millions of animals are used annually in research. Plenty of scientists don’t want to take part in animal testing. And some question whether animal research is justifiable—especially considering that around 95% of treatments that look promising in animals don’t make it to market.

In recent decades, we’ve seen dramatic advances in technologies that offer new ways to model the human body and test the effects of potential therapies, without experimenting on humans or other animals.

Take “organs on chips,” for example. Researchers have been creating miniature versions of human organs inside tiny plastic cases. These systems are designed to contain the same mix of cells you’d find in a full-grown organ and receive a supply of nutrients that keeps them alive.

Today, multiple teams have created models of livers, intestines, hearts, kidneys and even the brain. And they are already being used in research. Heart chips have been sent into space to observe how they respond to low gravity. The FDA used lung chips to assess covid-19 vaccines. Gut chips are being used to study the effects of radiation.

Some researchers are even working to connect multiple chips to create a “body on a chip”—although this has been in the works for over a decade and no one has quite managed it yet.

In the same vein, others have been working on creating model versions of organs—and even embryos—in the lab. By growing groups of cells into tiny 3D structures, scientists can study how organs develop and work, and even test drugs on them. They can even be personalized—if you take cells from someone, you should be able to model that person’s specific organs. Some researchers have even been able to create organoids of developing fetuses.

The UK government strategy mentions the promise of artificial intelligence, too. Many scientists have been quick to adopt AI as a tool to help them make sense of vast databases, and to find connections between genes, proteins and disease, for example. Others are using AI to design all-new drugs.

Those new drugs could potentially be tested on virtual humans. Not flesh-and-blood people, but digital reconstructions that live in a computer. Biomedical engineers have already created digital twins of organs. In ongoing trials, digital hearts are being used to guide surgeons on how—and where—to operate on real hearts.

When I spoke to Natalia Trayanova, the biomedical engineering professor behind this trial, she told me that her model could recommend regions of heart tissue to be burned off as part of treatment for atrial fibrillation. Her tool would normally suggest two or three regions but occasionally would recommend many more. “They just have to trust us,” she told me.

It is unlikely that we’ll completely phase out animal testing by 2030. The UK government acknowledges that animal testing is still required by lots of regulators, including the FDA, the European Medicines Agency, and the World Health Organization. And while alternatives to animal testing have come a long way, none of them perfectly capture how a living body will respond to a treatment.

At least not yet. Given all the progress that has been made in recent years, it’s not too hard to imagine a future without animal testing.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Ecommerce MGMT 0 Comments
Nov 7 2025
Stop worrying about your AI footprint. Look at the big picture instead.

Picture it: I’m minding my business at a party, parked by the snack table (of course). A friend of a friend wanders up, and we strike up a conversation. It quickly turns to work, and upon learning that I’m a climate technology reporter, my new acquaintance says something like: “Should I be using AI? I’ve heard it’s awful for the environment.” 

This actually happens pretty often now. Generally, I tell people not to worry—let a chatbot plan your vacation, suggest recipe ideas, or write you a poem if you want. 

That response might surprise some people, but I promise I’m not living under a rock, and I have seen all the concerning projections about how much electricity AI is using. Data centers could consume up to 945 terawatt-hours annually by 2030. (That’s roughly as much as Japan.) 

But I feel strongly about not putting the onus on individuals, partly because AI concerns remind me so much of another question: “What should I do to reduce my carbon footprint?” 

That one gets under my skin because of the context: BP helped popularize the concept of a carbon footprint in a marketing campaign in the early 2000s. That framing effectively shifts the burden of worrying about the environment from fossil-fuel companies to individuals. 

The reality is, no one person can address climate change alone: Our entire society is built around burning fossil fuels. To address climate change, we need political action and public support for researching and scaling up climate technology. We need companies to innovate and take decisive action to reduce greenhouse-gas emissions. Focusing too much on individuals is a distraction from the real solutions on the table. 

I see something similar today with AI. People are asking climate reporters at barbecues whether they should feel guilty about using chatbots too frequently when we need to focus on the bigger picture. 

Big tech companies are playing into this narrative by providing energy-use estimates for their products at the user level. A couple of recent reports put the electricity used to query a chatbot at about 0.3 watt-hours, the same as powering a microwave for about a second. That’s so small as to be virtually insignificant.

But stopping with the energy use of a single query obscures the full truth, which is that this industry is growing quickly, building energy-hungry infrastructure at a nearly incomprehensible scale to satisfy the AI appetites of society as a whole. Meta is currently building a data center in Louisiana with five gigawatts of computational power—about the same demand as the entire state of Maine at the summer peak.  (To learn more, read our Power Hungry series online.)

Increasingly, there’s no getting away from AI, and it’s not as simple as choosing to use or not use the technology. Your favorite search engine likely gives you an AI summary at the top of your search results. Your email provider’s suggested replies? Probably AI. Same for chatting with customer service while you’re shopping online. 

Just as with climate change, we need to look at this as a system rather than a series of individual choices. 

Massive tech companies using AI in their products should be disclosing their total energy and water use and going into detail about how they complete their calculations. Estimating the burden per query is a start, but we also deserve to see how these impacts add up for billions of users, and how that’s changing over time as companies (hopefully) make their products more efficient. Lawmakers should be mandating these disclosures, and we should be asking for them, too. 

That’s not to say there’s absolutely no individual action that you can take. Just as you could meaningfully reduce your individual greenhouse-gas emissions by taking fewer flights and eating less meat, there are some reasonable things that you can do to reduce your AI footprint. Generating videos tends to be especially energy-intensive, as does using reasoning models to engage with long prompts and produce long answers. Asking a chatbot to help plan your day, suggest fun activities to do with your family, or summarize a ridiculously long email has relatively minor impact. 

Ultimately, as long as you aren’t relentlessly churning out AI slop, you shouldn’t be too worried about your individual AI footprint. But we should all be keeping our eye on what this industry will mean for our grid, our society, and our planet. 

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

Ecommerce MGMT 0 Comments