The astronaut training tourists to fly in the world’s first commercial space station

For decades, space stations have been largely staffed by professional astronauts and operated by a handful of nations. But that’s about to change in the coming years, as companies including Axiom Space and Sierra Space launch commercial space stations that will host tourists and provide research facilities for nations and other firms. 

The first of those stations could be Haven-1, which the California-based company Vast aims to launch in May 2026. If all goes to plan, its earliest paying visitors will arrive about a month later. Drew Feustel, a former NASA astronaut, will help train them and get them up to speed ahead of their historic trip. Feustel has spent 226 days in space on three trips to the International Space Station (ISS) and the Hubble Space Telescope. 

Feustel is now lead astronaut for Vast, which he advised on the new station’s interior design. He also created a months-long program to prepare customers to live and work there. Crew members (up to four at a time) will arrive at Haven-1 via a SpaceX Dragon spacecraft, which will dock to the station and remain attached throughout each 10-day stay. (Vast hasn’t publicly said who will fly on its first missions or announced the cost of a ticket, though competing firms have charged tens of millions of dollars for similar trips.)

In this artist’s rendering, the Haven-1 space station is shown in orbit docked with the SpaceX Dragon spacecraft.
VAST

Haven-1 is intended as a temporary facility, to be followed by a bigger, permanent station called Haven-2. Vast will begin launching Haven-2’s modules in 2028 and says it will be able to support a crew by 2030. That’s about when NASA will start decommissioning the ISS, which has operated for almost 30 years. Instead of replacing it, NASA and its partners intend to carry out research aboard commercial stations like those built by Vast, Axiom, and Sierra. 

I recently caught up with Feustel in Lisbon at the tech conference Web Summit, where he was speaking about his role at Vast and the company’s ambitions. 

Responses have been edited and condensed. 

What are you hoping this new wave of commercial space stations will enable people to do?

Ideally, we’re creating access. The paradigm that we’ve seen for 25 years is primarily US-backed missions to the International Space Station, and [NASA] operating that station in coordination with other nations. But [it’s] still limited to 16 or 17 primary partners in the ISS program. 

Following NASA’s intentions, we are planning to become a service provider to not only the US government, but other sovereign nations around the world, to allow greater access to a low-Earth-orbit platform. We can be a service provider to other organizations and nations that are planning to build a human spaceflight program.

Today, you’re Vast’s lead astronaut after you were initially brought on to advise the company on the design of Haven-1 and Haven-2. What are some of the things that you’ve weighed in on? 

Some of the things where I can see tangible evidence of my work is, for example, in the sleep cores and sleep system—trying to define a more comfortable way for astronauts to sleep. We’ve come up with an air bladder system that provides distributed forces on the body that kind of emulate, or I believe will emulate, the gravity field that we feel in bed when we lie down, having that pressure of gravity on you. 

Oh, like a weighted blanket? 

Kind of like a weighted blanket, but you’re up against the wall, so you have to create, like, an inflatable bladder that will push you against the wall. That’s one of the very tangible, obvious things. But I work with the company on anything from crew displays and interfaces and how notifications and system information come through to how big a window should be. 

How big should a window be? I feel like the bigger the betterbut what are the factors that go into that, from an astronaut’s perspective? 

The bigger the better. And the other thing to think about is—what do you do with the window? Take pictures. The ability to take photos out a window is important—the quality of the window, which direction it points. You know, it’s not great if it’s just pointing up in space all the time and you never see the Earth. 

A person looks out the window of Haven-1 at the Earth.

VAST

You’re also in charge of the astronaut training program at Vast. Tell me what that program looks like, because in some cases you’ll have private citizens who are paying for their trip that have no experience whatsoever.

A typical training flow for two weeks on our space station is extended out to about an 11-month period with gaps in between each of the training weeks. And so if you were to press that down together, it probably represents about three to four months of day-to-day training. 

I would say half of it’s devoted to learning how to fly on the SpaceX Dragon, because that’s our transportation, and the greatest risk for anybody flying is on launch and landing. We want people to understand how to operate in that spacecraft, and that component is designed by SpaceX. They have their own training plans. 

What we do is kind of piggyback on those weeks. If a crew shows up in California to train at SpaceX, we’ll grab them that same week and say, “Come down to our facility. We will train you to operate inside our spacecraft.” Much of that is focused on emergency response. We want the crew to be able to keep themselves safe. In case anything happens on the vehicle that requires them to depart, to get back in the SpaceX Dragon and leave, we want to make sure that they understand all of the steps required. 

Another part is day-to-day living, like—how do you eat? How do you sleep, how do you use the bathroom? Those are really important things. How do you download the pictures after you take them? How do you access your science payloads that are in our payload racks that provide data and telemetry for the research you’re doing? 

We want to practice every one of those things multiple times, including just taking care of yourself, before you go to space so that when you get there, you’ve built a lot of that into your muscle memory, and you can just do the things you need to do instead of every day being like a really steep learning curve.

VAST

Strawberries and other perishable foods are freeze-dried by the Vast Food Systems team to prepare them for missions.

Making coffee in a zero-gravity environment calls for specialized devices.
VAST

Do you have a facility where you’ll take people through some of these motions? Or a virtual simulation of some kind? 

We have built a training mock-up, an identical vehicle to what people will live in in space. But it’s not in a zero-gravity environment. The only way to get any similar training is to fly on what we call a zero-g airplane, which does parabolas in space—it climbs up and then falls toward the Earth. Its nickname is the vomit comet. 

But otherwise, there’s really no way to train for microgravity. You just have to watch videos and talk about it a lot, and try to prepare people mentally for what that’s going to be like. You can also train underwater, but that’s more related to spacewalking, and it’s much more advanced. 

How do you expect people will spend their time in the station? 

If history is any indication, they will be quite busy and probably oversubscribed. Their time will be spent basically caring for themselves, and trying to execute their experiments, and looking out the window. Those are the three big categories of what you’re going to do in space. And public relation activities like outreach back to Earth, to schools or hospitals or corporations. 

This new era means that many more everyday people—though mostly wealthy ones at the beginning, because of ticket prices—will have this interesting view of Earth. How do you think the average person will react to that? 

A good analogy is to say, how are people reacting to sub-orbital flights? Blue Origin and Virgin Galactic offer suborbital flights, [which are] basically three or four minutes of floating and looking down at the Earth from an altitude that’s about a third or a fifth of the altitude that actual orbital and career astronauts achieve when they circle the planet. 

Shown here is Vast’s Haven-1 station as it completes testing in the Mojave Desert in 2025.
VAST

If you look at the reaction of those individuals and what they perceive, it’s amazing, right? It’s like awe and wonder. It’s the same way that astronauts react and talk when we see Earth—and say if more humans could see Earth from space, we’d probably be a little bit better about being humans on Earth. 

That’s the hope, is that we create that access and more people can understand what it means to live on this planet. It’s essentially a spacecraft—it’s got its own environmental control system that keeps us alive, and that’s a big deal. 

Some people have expressed ambitions for this kind of station to enable humans to become a multiplanetary species. Do you share that ambition for our species? If so, why? 

Yeah, I do. I just believe that humans need to have the ability to live off of the planet. I mean, we’re capable of it, and we’re creating that access now. So why wouldn’t we explore space and go further and farther and learn to live in other areas?

Not to say that we should deplete everything here and deplete everything there. But maybe we take some of the burden off of the place that we call home. I think there’s a lot of reasons to live and work in space and off our own planet. 

There’s not really a backup plan for no Earth. We know that there are risks from the space around us—dinosaurs fell prey to space hazards. We should be aware of those and work harder to extend our capabilities and create some backup plans. 

CES showed me why Chinese tech companies feel so optimistic

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

I decided to go to CES kind of at the last minute. Over the holiday break, contacts from China kept messaging me about their travel plans. After the umpteenth “See you in Vegas?” I caved. As a China tech writer based in the US, I have one week a year when my entire beat seems to come to me—no 20-hour flights required.

CES, the Consumer Electronics Show, is the world’s biggest tech show, where companies launch new gadgets and announce new developments, and it happens every January. This year, it attracted over 148,000 attendees and over 4,100 exhibitors. It sprawls across the Las Vegas Convention Center, the city’s biggest exhibition space, and spills over into adjacent hotels. 

China has long had a presence at CES, but this year it showed up in a big way. Chinese exhibitors accounted for nearly a quarter of all companies at the show, and in pockets like AI hardware and robotics, China’s presence felt especially dominant. On the floor, I saw tons of Chinese industry attendees roaming around, plus a notable number of Chinese VCs. Multiple experienced CES attendees told me this is the first post-covid CES where China was present in a way you couldn’t miss. Last year might have been trending that way too, but a lot of Chinese attendees reportedly ran into visa denials. Now AI has become the universal excuse, and reason, to make the trip.

As expected, AI was the biggest theme this year, seen on every booth wall. It’s both the biggest thing everyone is talking about and a deeply confusing marketing gimmick. “We added AI” is slapped onto everything from the reasonable (PCs, phones, TVs, security systems) to the deranged (slippers, hair dryers, bed frames). 

Consumer AI gadgets still feel early and of very uneven quality. The most common categories are educational devices and emotional support toys—which, as I’ve written about recently, are all the rage in China. There are some memorable ones: Luka AI makes a robotic panda that scuttles around and keeps a watchful eye on your baby. Fuzozo, a fluffy keychain-size AI robot, is basically a digital pet in physical form. It comes with a built-in personality and reacts to how you treat it. The companies selling these just hope you won’t think too hard about the privacy implications.

Ian Goh, an investor at 01.VC, told me China’s manufacturing advantage gives it a unique edge in AI consumer electronics, because a lot of Western companies feel they simply cannot fight and win in the arena of hardware. 

Another area where Chinese companies seem to be at the head of the pack is household electronics. The products they make are becoming impressively sophisticated. Home robots, 360 cams, security systems, drones, lawn-mowing machines, pool heat pumps … Did you know two Chinese brands basically dominate the market for home cleaning robots in the US and are eating the lunch of Dyson and Shark? Did you know almost all the suburban yard tech you can buy in the West comes from Shenzhen, even though that whole backyard-obsessed lifestyle barely exists in China? This stuff is so sleek that you wouldn’t clock it as Chinese unless you went looking. The old “cheap and repetitive” stereotype doesn’t explain what I saw. I walked away from CES feeling that I needed a major home appliance upgrade.

Of course, appliances are a safe, mature market. On the more experiential front, humanoid robots were a giant magnet for crowds, and Chinese companies put on a great show. Every robot seemed to be dancing, in styles from Michael Jackson to K-pop to lion dancing, some even doing back flips. Hangzhou-based Unitree even set up a boxing ring where people could “challenge” its robots. The robot fighters were about half the size of an adult human and the matches often ended in a robot knockout, but that’s not really the point. What Unitree was actually showing off was its robots’ stability and balance: they got shoved, stumbled across the ring, and stayed upright, recovering mid-motion. Beyond flexing dynamic movements like these there were also impressive showcases of dexterity: Robots could be seen folding paper pinwheels, doing laundry, playing piano, and even making latte art.

Attendees take photos of the UniTree autonomous robot which is posing with its boxing gloves and headgear

CAL SPORT MEDIA VIA AP IMAGES

However, most of these robots, even the good ones, are one-trick ponies. They’re optimized for a specific task on the show floor. I tried to make one fold a T-shirt after I’d flipped the garment around, and it got confused very quickly. 

Still, they’re getting a lot of hype as an  important next frontier because they could help drag AI out of text boxes and into the physical world. As LLMs mature, vision-language models feel like the logical next step. But then you run into the big problem: There’s far less physical-world data than text data to train AI on. Humanoid robots become both applications and roaming data-collection terminals. China is uniquely positioned here because of supply chains, manufacturing depth, and spillover from adjacent industries (EVs, batteries, motors, sensors), and it’s already developing a humanoid training industry, as Rest of World reported recently. 

Most Chinese companies believe that if you can manufacture at scale, you can innovate, and they’re not wrong. A lot of the confidence in China’s nascent humanoid robot industry and beyond is less about a single breakthrough and more about “We can iterate faster than the West.”

Chinese companies are not just selling gadgets, though—they’re working on every layer of the tech stack. Not just on end products but frameworks, tooling, IoT enablement, spatial data. Open-source culture feels deeply embedded; engineers from Hangzhou tell me there are AI hackathons every week in the city, where China’s new “little Silicon Valley” is located.

Indeed, the headline innovations at CES 2026 were not on devices but in cloud: platforms, ecosystems, enterprise deployments, and “hybrid AI” (cloud + on-device) applications. Lenovo threw the buzziest main-stage events this year, and yes, there were PCs—but the core story was its cross-device AI agent system, Qira, and a partnership pitch with Nvidia aimed at AI cloud providers. Nvidia’s CEO, Jensen Huang, launched Vera Rubin, a new data-center platform, claiming it would  dramatically lower costs for training and running AI. AMD’s CEO, Lisa Su, introduced Helios, another data-center system built to run huge AI workloads. These solutions point to the ballooning AI computing workload at data centers, and the real race of making cloud services cheap and powerful enough to keep up.

As I spoke with China-related attendees, the overall mood I felt was a cautious optimism. At a house party I went to, VCs and founders from China were mingling effortlessly with Bay Area transplants. Everyone is building something. Almost no one wants to just make money from Chinese consumers anymore. The new default is: Build in China, sell to the world, and treat the US market like the proving ground.

A new CRISPR startup is betting regulators will ease up on gene-editing

Here at MIT Technology Review we’ve been writing about the gene-editing technology CRISPR since 2013, calling it the biggest biotech breakthrough of the century. Yet so far, there’s been only one gene-editing drug approved. It’s been used commercially on only about 40 patients, all with sickle-cell disease.

It’s becoming clear that the impact of CRISPR isn’t as big as we all hoped. In fact, there’s a pall of discouragement over the entire field—with some journalists saying the gene-editing revolution has “lost its mojo.”

So what will it take for CRISPR to help more people? A new startup says the answer could be an “umbrella approach” to testing and commercializing treatments. Aurora Therapeutics, which has $16 million from Menlo Ventures and counts CRISPR co-inventor Jennifer Doudna as an advisor, essentially hopes to win approval for gene-editing drugs that can be slightly adjusted, or personalized, without requiring costly new trials or approvals for every new version.

The need to change regulations around gene-editing treatments was endorsed in November by the head of the US Food and Drug Administration, Martin Makary, who said the agency would open a “new” regulatory pathway for “bespoke, personalized therapies” that can’t easily be tested in conventional ways. 

Aurora’s first target, the rare inherited disease phenylketonuria, also known as PKU, is a case in point. People with PKU lack a working version of an enzyme needed to use up the amino acid phenylalanine, a component of pretty much all meat and protein. If the amino acid builds up, it causes brain damage. So patients usually go on an onerous “diet for life” of special formula drinks and vegetables.

In theory, gene editing can fix PKU. In mice, scientists have already restored the gene for the enzyme by rewriting DNA in liver cells, which both make the enzyme and are some of the easiest to reach with a gene-editing drug. The problem is that in human patients, many different mutations can affect the critical gene. According to Cory Harding, a researcher at Oregon Health Sciences University, scientists know about 1,600 different DNA mutations that cause PKU.

There’s no way anyone will develop 1,600 different gene-editing drugs. Instead, Aurora’s goal is to eventually win approval for a single gene editor that, with minor adjustments, could be used to correct several of the most common mutations, including one that’s responsible for about 10% of the estimated 20,000 PKU cases in the US.

“We can’t have a separate clinical trial for each mutation,” says Edward Kaye, the CEO of Aurora. “The way the FDA approves gene editing has to change, and I think they’ve been very understanding that is the case.”

A gene editor is a special protein that can zero in on a specific location in the genome and change it. To prepare one, Aurora will put genetic code for the editor into a nanoparticle along with a targeting molecule. In total, it will involve about 5,000 gene letters. But only 20 of them need to change in order to redirect the treatment to repair a different mutation.

“Over 99% of the drug stays the same,” says Johnny Hu, a partner at Menlo Ventures, which put up the funding for the startup.

The new company came together after Hu met over pizza with Fyodor Urnov, an outspoken gene-editing scientist at the University of California, Berkeley, who is Aurora’s cofounder and sits on its board.

In 2022, Urnov had written a New York Times editorial bemoaning the “chasm” between what editing technology can do and the “legal, financial, and organizational” realities preventing researchers from curing people.

“I went to Fyodor and said, ‘Hey, we’re getting all these great results in the clinic with CRISPR, but why hasn’t it scaled?” says Hu. Part of the reason is that most gene-editing companies are chasing the same few conditions, such as sickle-cell, where (as luck would have it) a single edit works for all patients. But that leaves around 400 million people who have 7,000 other inherited conditions without much hope to get their DNA fixed, Urnov estimated in his editorial.

Then, last May, came the dramatic demonstration of the first fully “personalized” gene-editing treatment. A team in Philadelphia, assisted by Urnov and others, succeeded in correcting the DNA of a baby, named KJ Muldoon, who had an entirely unique mutation that caused a metabolic disease. Though it didn’t target PKU, the project showed that gene editing could theoretically fix some inherited diseases “on demand.” 

It also underscored a big problem. Treating a single child required a large team and cost millions in time, effort, and materials—all to create a drug that would never be used again. 

That’s exactly the sort of situation the new “umbrella” trials are supposed to address. Kiran Musunuru, who co-led the team at the University of Pennsylvania, says he’s been in discussions with the FDA to open a study of bespoke gene editors this year focusing on diseases of the type Baby KJ had, called urea cycle disorders. Each time a new patient appears, he says, they’ll try to quickly put together a variant of their gene-editing drug that’s tuned to fix that child’s particular genetic problem.

Musunuru, who isn’t involved with Aurora, does not think the company’s plans for PKU count as fully personalized editors. “These corporate PKU efforts have nothing whatsoever to do with Baby KJ,” he says. He says his center continues to focus on mutations “so ultra-rare that we don’t see any scenario where a for-profit gene-editing company would find that indication to be commercially viable.”

Instead, what’s occurring in PKU, says Musunuru, is that researchers have realized they can assemble “a bunch” of the most frequent mutations “into a large enough group of patients to make a platform PKU therapy commercially viable.” 

While that would still leave out many patients with extra-rare gene errors, Musunuru says any gene-editing treatment at all would still be “a big improvement over the status quo, which  is zero genetic therapies for PKU.”

What new legal challenges mean for the future of US offshore wind

For offshore wind power in the US, the new year is bringing new legal battles.

On December 22, the Trump administration announced it would pause the leases of five wind farms currently under construction off the US East Coast. Developers were ordered to stop work immediately.

The cited reason? National security, specifically concerns that turbines can cause radar interference. But that’s a known issue, and developers have worked with the government to deal with it for years.

Companies have been quick to file lawsuits, and the court battles could begin as soon as this week. Here’s what the latest kerfuffle might mean for the struggling offshore wind industry in the US.

This pause affects $25 billion in investment in five wind farms: Vineyard Wind 1 off Massachusetts, Revolution Wind off Rhode Island, Sunrise Wind and Empire Wind off New York, and Coastal Virginia Offshore Wind off Virginia. Together, those projects had been expected to create 10,000 jobs and power more than 2.5 million homes and businesses.

In a statement announcing the move, the Department of the Interior said that “recently completed classified reports” revealed national security risks, and that the pause would give the government time to work through concerns with developers. The statement specifically says that turbines can create radar interference (more on the technical details here in a moment).

Three of the companies involved have already filed lawsuits, and they’re seeking preliminary injunctions that would allow construction to continue. Orsted and Equinor (the developers for Revolution Wind and Empire Wind, respectively) told the New York Times that their projects went through lengthy federal reviews, which did address concerns about national security.

This is just the latest salvo from the Trump administration against offshore wind. On Trump’s first day in office, he signed an executive order stopping all new lease approvals for offshore wind farms. (That order was struck down by a judge in December.)

The administration previously ordered Revolution Wind to stop work last year, also citing national security concerns. A federal judge lifted the stop-work order weeks later, after the developer showed that the financial stakes were high, and that government agencies had previously found no national security issues with the project.

There are real challenges that wind farms introduce for radar systems, which are used in everything from air traffic control to weather forecasting to national defense operations. A wind turbine’s spinning can create complex signatures on radar, resulting in so-called clutter.

Previous government reports, including one 2024 report from the Department of Energy and a 2025 report from the Government Accountability Office (an independent government watchdog), have pointed out this issue in the past.

“To date, no mitigation technology has been able to fully restore the technical performance of impacted radars,” as the DOE report puts it. However, there are techniques that can help, including software that acts to remove the signatures of wind turbines. (Think of this as similar to how noise-canceling headphones work, but more complicated, as one expert told TechCrunch.)

But the most widespread and helpful tactic, according to the DOE report, is collaboration between developers and the government. By working together to site and design wind farms strategically, the groups can ensure that the projects don’t interfere with government or military operations. The 2025 GAO report found that government officials, researchers, and offshore wind companies were collaborating effectively, and any concerns could be raised and addressed in the permitting process.

This and other challenges threaten an industry that could be a major boon for the grid. On the East Coast where these projects are located, and in New England specifically, winter can bring tight supplies of fossil fuels and spiking prices because of high demand. It just so happens that offshore winds blow strongest in the winter, so new projects, including the five wrapped up in this fight, could be a major help during the grid’s greatest time of need.

One 2025 study found that if 3.5 gigawatts’ worth of offshore wind had been operational during the 2024-2025 winter, it would have lowered energy prices by 11%. (That’s the combined capacity of Revolution Wind and Vineyard Wind, two of the paused projects, plus two future projects in the pipeline.) Ratepayers would have saved $400 million.

Before Donald Trump was elected, the energy consultancy BloombergNEF projected that the US would build 39 gigawatts of offshore wind by 2035. Today, that expectation has dropped to just 6 gigawatts. These legal battles could push it lower still.

What’s hardest to wrap my head around is that some of the projects being challenged are nearly finished. The developers of Revolution Wind have installed all the foundations and 58 of 65 turbines, and they say the project is over 87% complete. Empire Wind is over 60% done and is slated to deliver electricity to the grid next year.

To hit the pause button so close to the finish line is chilling, not just for current projects but for future offshore wind efforts in the US. Even if these legal battles clear up and more developers can technically enter the queue, why would they want to? Billions of dollars are at stake, and if there’s one word to describe the current state of the offshore wind industry in the US, it’s “unpredictable.”

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

America’s new dietary guidelines ignore decades of scientific research

The new year has barely begun, but the first days of 2026 have brought big news for health. On Monday, the US’s federal health agency upended its recommendations for routine childhood vaccinations—a move that health associations worry puts children at unnecessary risk of preventable disease.

There was more news from the federal government on Wednesday, when health secretary Robert F. Kennedy Jr. and his colleagues at the Departments of Health and Human Services and Agriculture unveiled new dietary guidelines for Americans. And they are causing a bit of a stir.

That’s partly because they recommend products like red meat, butter, and beef tallow—foods that have been linked to cardiovascular disease, and that nutrition experts have been recommending people limit in their diets.

These guidelines are a big deal—they influence food assistance programs and school lunches, for example. So this week let’s look at the good, the bad, and the ugly advice being dished up to Americans by their government.

The government dietary guidelines have been around since the 1980s. They are updated every five years, in a process that typically involves a team of nutrition scientists who have combed over scientific research for years. That team will first publish its findings in a scientific report, and, around a year later, the finalized Dietary Guidelines for Americans are published.

The last guidelines covered the period 2020 to 2025, and new guidelines were expected in the summer of 2025. Work had already been underway for years; the scientific report intended to inform them was published back in 2024. But the publication of the guidelines was delayed by last year’s government shutdown, Kennedy said last year. They were finally published yesterday.

Nutrition experts had been waiting with bated breath. Nutrition science has evolved slightly over the last five years, and some were expecting to see new recommendations. Research now suggests, for example, that there is no “safe” level of alcohol consumption.

We are also beginning to learn more about health risks associated with some ultraprocessed foods (although we still don’t have a good understanding of what they might be, or what even counts as “ultraprocessed”.) And some scientists were expecting to see the new guidelines factor in environmental sustainability, says Gabby Headrick, the associate director of food and nutrition policy at George Washington University’s Institute for Food Safety & Nutrition Security in Washington DC.

They didn’t.

Many of the recommendations are sensible. The guidelines recommend a diet rich in whole foods, particularly fresh fruits and vegetables. They recommend avoiding highly processed foods and added sugars. They also highlight the importance of dietary protein, whole grains, and “healthy” fats.

But not all of them are, says Headrick. The guidelines open with a “new pyramid” of foods. This inverted triangle is topped with “protein, dairy, and healthy fats” on one side and “vegetables and fruits” on the other.

USDA

There are a few problems with this image. For starters, its shape—nutrition scientists have long moved on from the food pyramids of the 1990s, says Headrick. They’re confusing and make it difficult for people to understand what the contents of their plate should look like. That’s why scientists now use an image of a plate to depict a healthy diet.

“We’ve been using MyPlate to describe the dietary guidelines in a very consumer-friendly, nutrition-education-friendly way for over the last decade now,” says Headrick. (The UK’s National Health Service takes a similar approach.)

And then there’s the content of that food pyramid. It puts a significant focus on meat and whole-fat dairy produce. The top left image—the one most viewers will probably see first—is of a steak. Smack in the middle of the pyramid is a stick of butter. That’s new. And it’s not a good thing.

While both red meat and whole-fat dairy can certainly form part of a healthy diet, nutrition scientists have long been recommending that most people try to limit their consumption of these foods. Both can be high in saturated fat, which can increase the risk of cardiovascular disease—the leading cause of death in the US. In 2015, on the basis of limited evidence, the World Health Organization classified red meat as “probably carcinogenic to humans.” 

Also concerning is the document’s definition of “healthy fats,” which includes butter and beef tallow (a MAHA favorite). Neither food is generally considered to be as healthy as olive oil, for example. While olive oil contains around two grams of saturated fat per tablespoon, a tablespoon of beef tallow has around six grams of saturated fat, and the same amount of butter contains around seven grams of saturated fat, says Headrick.

“I think these are pretty harmful dietary recommendations to be making when we have established that those specific foods likely do not have health-promoting benefits,” she adds.

Red meat is not exactly a sustainable food, and neither are dairy products. And the advice on alcohol is relatively vague, recommending that people “consume less alcohol for better overall health” (which might leave you wondering: Less than what?).

There are other questionable recommendations in the guidelines. Americans are advised to include more protein in their diets—at levels between 1.2 and 1.6 grams daily per kilo of body weight, 50% to 100% more than recommended in previous guidelines. There’s a risk that increasing protein consumption to such levels could raise a person’s intake of both calories and saturated fats to unhealthy levels, says José Ordovás, a senior nutrition scientist at Tufts University. “I would err on the low side,” he says.

Some nutrition scientists are questioning why these changes have been made. It’s not as though the new recommendations were in the 2024 scientific report. And the evidence on red meat and saturated fat hasn’t changed, says Headrick.

In reporting this piece, I contacted many contributors to the previous guidelines, and some who had led research for 2024’s scientific report. None of them agreed to comment on the new guidelines on the record. Some seemed disgruntled. One merely told me that the process by which the new guidelines had been created was “opaque.”

“These people invested a lot of their time, and they did a thorough job [over] a couple of years, identifying [relevant scientific studies],” says Ordovás. “I’m not surprised that when they see that [their] work was ignored and replaced with something [put together] quickly, that they feel a little bit disappointed,” he says.

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.

The man who made India digital isn’t done yet

Nandan Nilekani can’t stop trying to push India into the future. He started nearly 30 years ago, masterminding an ongoing experiment in technological state capacity that started with Aadhaar—the world’s largest digital identity system. Aadhaar means “foundation” in Hindi, and on that bedrock Nilekani and people working with him went on to build a sprawling collection of free, interoperating online tools that add up to nothing less than a digital infrastructure for society. They cover government services, digital payments, banking, credit, and health care, offering convenience and access that would be eye-popping in wealthy countries a tenth of India’s size. In India those systems are called, collectively, “digital public infrastructure,” or DPI.

At 70 years old, Nilekani should be retired. But he has a few more ideas. India’s electrical grid is creaky and prone to failure; Nilekani wants to add a layer of digital communication to stabilize it. And then there’s his idea to expand the financial functions in DPI to the rest of the world, creating a global digital backbone for commerce that he calls the “finternet.”

“It sounds like some crazy stuff,” Nilekani says. “But I think these are all big ideas, which over the next five years will have demonstrable, material impact.” As a last act in public life, why not Aadhaarize the world?

India’s digital backbone

Today, a farmer in a village in India, hours from the nearest bank, can collect welfare payments or transfer money by simply pressing a thumb to a fingerprint scanner at the local store. Digitally authenticated copies of driver’s licenses, birth certificates, and educational records can be accessed and shared via a digital wallet that sits on your smartphone.

In big cities, where cash is less and less common (just trying to break a bill can be a major headache), mobile payments are ubiquitous, whether you’re buying a TV from a high-street retailer or a coconut from a roadside cart. There are no fees, and any payment app or bank account can send money to any other. The country’s chaotic patchwork of public and private hospitals have begun digitizing all their medical records and uploading them to a nationwide platform. On the Open Network for Digital Commerce (ONDC), people can do online shopping searches on whatever app they want, and the results show sellers from an array of other platforms, too. The idea is to liberate small merchants and consumers from the walled gardens of online shopping giants like Amazon and the domestic giant Flipkart. 

In the most populous nation on Earth—with 1.4 billion people—a large portion of the bureaucracy anyone encounters in daily life happens seamlessly and in the cloud.

At the heart of all these tools is Aadhaar. The system gives every Indian a 12-digit number that, in combination with either a fingerprint scan or an SMS code, allows access to government services, SIM cards, basic bank accounts, digital signature services, and social welfare payments. The Indian government says that since its inception in 2009, Aadhaar has saved 3.48 trillion rupees ($39.2 billion) by boosting efficiency, bypassing corrupt officials, and cutting other types of fraud. The system is controversial and imperfect—a database with 1.4 billion people in it comes with inherent security and privacy concerns. Still, in the most populous nation on Earth, a big portion of the bureaucracy anyone might encounter in daily life just happens in the cloud.

Nilekani was behind much of that innovation, marshaling an army of civil servants, tech companies, and volunteers. Now he sees it in action every day. “It reinforces that what you have done is not some abstract stuff, but real stuff for real people,” he says.

By his own admission, Nilekani is entering the twilight of his career. But it’s not over yet. He’s now “chief mentor” for the India Energy Stack (IES), a government initiative to connect the fragmented data held by companies responsible for generating, transmitting, and distributing power. India’s grids are unstable and disparate, but Nilekani hopes an Aadhaar-like move will help. IES aims to give unique digital identities not only to power plants and energy storage facilities but even to rooftop solar panels and electric vehicles. All the data attached to those things—device characteristics, energy rating certifications, usage information—will be in a common, machine-readable format and shared on the same open protocols.

Ideally, that’ll give grid operators a real-time view of energy supply and demand. And if it works, it might also make it simpler and cheaper for anyone to connect to the grid—even everyday folks selling excess power from their rooftop solar rigs, says RS Sharma, the chair of the project and Nilekani’s deputy while building Aadhaar.

Nilekani’s other side hustle is even more ambitious. His idea for a global “finternet” combines Aadhaarization with blockchains—creating digital representations called tokens for not only financial instruments like stocks or bonds but also real-world assets like houses or jewelry. Anyone from a bank to an asset manager or even a company could create and manage these tokens, but Nilekani’s team especially hopes the idea will help poor people trade their assets, or use them as loan collateral—expanding financial services to those who otherwise couldn’t access them. 

It sounds almost wild-eyed. Yet the finternet project has 30 partners across four continents. Nilekani says it’ll launch next year.

A call to service

Nilekani was born in Bengaluru, in 1955. His family was middle class and, Nilekani says, “seized with societal issues and challenges.” His upbringing was also steeped in the kind of socialism espoused by the newish nation’s first prime minister, Jawaharlal Nehru.

After studying electrical engineering at the Indian Institute of Technology, in 1981 Nilekani helped found Infosys, an information technology company that pioneered outsourcing and helped turned India into the world’s IT back office. In 1999, he was part of a government-appointed task force trying to upgrade the infrastructure and services in Bengaluru, then emerging as India’s tech capital. But Nilekani was at the time leery of being viewed as just another techno-optimist. “I didn’t want to be seen as naive enough to believe that tech could solve everything,” he says.

Nilekani holds a device to one eye
Nilekani demonstrates the biometric technology at the heart of Aadhaar, the system he spearheaded that provides a unique digital identity number to all Indians.
PALLAVA BAGLA/CORBIS/GETTY IMAGES

Seeing the scope of the problem changed his mind—sclerotic bureaucracy, endemic corruption, and financial exclusion were intractable without technological solutions. In 2008 Nilekani published a book, Imagining India: The Idea of a Renewed Nation. It was a manifesto for an India that could leapfrog into a networked future.

And it got him a job. At the time more than half the births in the country were not recorded, and up to 400 million Indians had no official identity document. Manmohan Singh, the prime minister, asked Nilekani to put into action an ill-defined plan to create a national identity card.

Nilekani’s team made a still-controversial decision to rely on biometrics. A system based on people’s fingerprints and retina scans meant nobody could sign up twice, and nobody had to carry paperwork. In terms of execution, it was like trying to achieve industrialization but skip a steam era. Deployment required a monumental data collection effort, as well as new infrastructure that could compare each new enrollment against hundreds of millions of existing records in seconds. At its peak, the Unique Identification Authority of India (UIDAI), the agency responsible for administering Aadhaar, was registering more than a million new users a day. That happened with a technical team of just about 50 developers, and in the end cost slightly less than half a billion dollars.

Buoyed by their success, Nilekani and his allies started casting around for other problems they could solve using the same digitize-the-real-world playbook. “We built more and more layers of capability,” Nilekani says, “and then this became a wider-ranging idea. More grandiose.”

While other countries were building digital backbones with full state control (as in China) or in public-private partnerships that favored profit-seeking corporate approaches (as in the US), Nilekani thought India needed something else. He wanted critical technologies in areas like identity, payments, and data sharing to be open and interoperable, not monopolized by either the state or private industry. So the tools that make up DPI use open standards and open APIs, meaning that anyone can plug into the system. No single company or institution controls access—no walled gardens.

A contested legacy

Of course, another way to look at putting financial and government services and records into giant databases is that it’s a massive risk to personal liberty. Aadhaar, in particular, has faced criticism from privacy advocates concerned about the potential for surveillance. Several high-profile data breaches of Aadhaar records held by government entities have shaken confidence in the system, most recently in 2023, when security researchers found hackers selling the records of more than 800 million Indians on the dark web.

Technically, this shouldn’t matter—an Aadhaar number ought to be useless without biometric or SMS-based authentication. It’s “a myth that this random number is a very powerful number,” says Sharma, the onetime co-lead of UIDAI. “I don’t have any example where somebody’s Aadhaar disclosure would have harmed somebody.” 

One problem is that in everyday use, Aadhaar users often bypass the biometric authentication system. To ensure that people use a genuine address at registration, Aadhaar administrators give people their numbers on an official-looking document. Indians co-opted this paperwork as a proof of identity on its own. And since the document—Indians even call it an “Aadhaar card”—doesn’t have an expiration date, it’s possible for people to get multiple valid cards with different details by changing their address or date of birth. That’s quite a loophole. In 2018 an NGO report found that 67% of people using Aadhaar to open a bank account relied on this verification document rather than digital authentication. That report was the last time anyone published data on the problem, so nobody knows how bad it is today. “Everybody’s living on anecdotes,” says Kiran Jonnalagadda, an anti-Aadhaar activist.

In other cases, flaws in Aadhaar’s biometric technology have caused people to be denied essential government services. The government downplays these risks, but again, it’s impossible to tell how serious the problem is because the UIDAI won’t disclose numbers. “There needs to be a much more honest acknowledgment, documentation, and then an examination of how those exclusions can be mitigated,” says Apar Gupta, director of the Internet Freedom Foundation.

Beyond the potential for fraud, it’s also true that the free and interoperable tools haven’t reached all the people who might find them useful, especially among India’s rural and poorer populations. Nilekani’s hopes for openness haven’t fully come to pass. Big e-commerce companies still dominate, and retail sales on ONDC have been dropping steadily since 2024, when financial incentives to participate began to taper off. The digital payments and government documentation services have hundreds of millions of users, numbers most global technology companies would love to see—but in a country as large as India, that leaves a lot of people out.

Going global

The usually calm Nilekani bristles at that criticism; he has heard it before. Detractors overlook the dysfunction that preceded these efforts, he says, and he remains convinced that technology was the only way forward. “How do you move a country of 1.4 billion people?” he asks. “There’s no other way you can fix it.”

The proof is self-evident, he says. Indians have opened more than 500 million basic bank accounts using Aadhaar; before it came into use, millions of those people had been completely unbanked. Earlier this year, India’s Unified Payments Interface overtook Visa as the world’s largest real-time payments system. “There is no way Aadhaar could have worked but for the fact that people needed this thing,” Nilekani says. “There’s no way payments would have worked without people needing it. So the voice of the people—they’re voting with their feet.”

A street vendor in Kolkata displays a QR code that lets him get paid via India’s Unified Payments Interface, part of the digital public infrastructure Nilekani helped build. The Reserve Bank of India says more than 657 million people used the system in the financial year 2024–2025.
DEBAJYOTI CHAKRABORTY/NURPHOTO/GETTY IMAGES

That need might be present in countries beyond India. “Many countries don’t have a proper birth registration system. Many countries don’t have a payment system. Many countries don’t have a way for data to be leveraged,” Nilekani says. “So this is a very powerful idea.” It seems to be spreading. Foreign governments regularly send delegations to Bengaluru to study India’s DPI tools. The World Bank and the United Nations have tried to introduce the concept to other developing countries equally eager to bring their economies into the digital age. The Gates Foundation has established projects to promote digital infrastructure, and Nilekani has set up and funded a network of think tanks, research institutes, and other NGOs aimed at, as he says, “propagating the gospel.”

Still, he admits he might not live to see DPI go global. “There are two races,” Nilekani says. “My personal race against time and India’s race against time.” He worries that the economic potential of its vast young population—the so-called demographic dividend—could turn into a demographic disaster. Despite rapid growth, gains have been uneven. Youth unemployment remains stubbornly high—a particularly volatile problem in a large and economically turbulent country. 

“Maybe I’m a junkie,” he says. “Why the hell am I doing all this? I think I need it. I think I need to keep curious and alive and looking at the future.” But that’s the thing about building the future: It never quite arrives.

Edd Gent is a journalist based in Bengaluru, India.

LLMs contain a LOT of parameters. But what’s a parameter?

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.

I am writing this because one of my editors woke up in the middle of the night and scribbled on a bedside notepad: “What is a parameter?” Unlike a lot of thoughts that hit at 4 a.m., it’s a really good question—one that goes right to the heart of how large language models work. And I’m not just saying that because he’s my boss. (Hi, Boss!)

A large language model’s parameters are often said to be the dials and levers that control how it behaves. Think of a planet-size pinball machine that sends its balls pinging from one end to the other via billions of paddles and bumpers set just so. Tweak those settings and the balls will behave in a different way.  

OpenAI’s GPT-3, released in 2020, had 175 billion parameters. Google DeepMind’s latest LLM, Gemini 3, may have at least a trillion—some think it’s probably more like 7 trillion—but the company isn’t saying. (With competition now fierce, AI firms no longer share information about how their models are built.)

But the basics of what parameters are and how they make LLMs do the remarkable things that they do are the same across different models. Ever wondered what makes an LLM really tick—what’s behind the colorful pinball-machine metaphors? Let’s dive in.  

What is a parameter?

Think back to middle school algebra, like 2a + b. Those letters are parameters: Assign them values and you get a result. In math or coding, parameters are used to set limits or determine output. The parameters inside LLMs work in a similar way, just on a mind-boggling scale. 

How are they assigned their values?

Short answer: an algorithm. When a model is trained, each parameter is set to a random value. The training process then involves an iterative series of calculations (known as training steps) that update those values. In the early stages of training, a model will make errors. The training algorithm looks at each error and goes back through the model, tweaking the value of each of the model’s many parameters so that next time that error is smaller. This happens over and over again until the model behaves in the way its makers want it to. At that point, training stops and the values of the model’s parameters are fixed.

Sounds straightforward …

In theory! In practice, because LLMs are trained on so much data and contain so many parameters, training them requires a huge number of steps and an eye-watering amount of computation. During training, the 175 billion parameters inside a medium-size LLM like GPT-3 will each get updated tens of thousands of times. In total, that adds up to quadrillions (a number with 15 zeros) of individual calculations. That’s why training an LLM takes so much energy. We’re talking about thousands of specialized high-speed computers running nonstop for months.

Oof. What are all these parameters for, exactly?

There are three different types of parameters inside an LLM that get their values assigned through training: embeddings, weights, and biases. Let’s take each of those in turn.

Okay! So, what are embeddings?

An embedding is the mathematical representation of a word (or part of a word, known as a token) in an LLM’s vocabulary. An LLM’s vocabulary, which might contain up to a few hundred thousand unique tokens, is set by its designers before training starts. But there’s no meaning attached to those words. That comes during training.  

When a model is trained, each word in its vocabulary is assigned a numerical value that captures the meaning of that word in relation to all the other words, based on how the word appears in countless examples across the model’s training data.

Each word gets replaced by a kind of code?

Yeah. But there’s a bit more to it. The numerical value—the embedding—that represents each word is in fact a list of numbers, with each number in the list representing a different facet of meaning that the model has extracted from its training data. The length of this list of numbers is another thing that LLM designers can specify before an LLM is trained. A common size is 4,096.

Every word inside an LLM is represented by a list of 4,096 numbers?  

Yup, that’s an embedding. And each of those numbers is tweaked during training. An LLM with embeddings that are 4,096 numbers long is said to have 4,096 dimensions.

Why 4,096?

It might look like a strange number. But LLMs (like anything that runs on a computer chip) work best with powers of two—2, 4, 8, 16, 32, 64, and so on. LLM engineers have found that 4,096 is a power of two that hits a sweet spot between capability and efficiency. Models with fewer dimensions are less capable; models with more dimensions are too expensive or slow to train and run. 

Using more numbers allows the LLM to capture very fine-grained information about how a word is used in many different contexts, what subtle connotations it might have, how it relates to other words, and so on.

Back in February, OpenAI released GPT-4.5, the firm’s largest LLM yet (some estimates have put its parameter count at more than 10 trillion). Nick Ryder, a research scientist at OpenAI who worked on the model, told me at the time that bigger models can work with extra information, like emotional cues, such as when a speaker’s words signal hostility: “All of these subtle patterns that come through a human conversation—those are the bits that these larger and larger models will pick up on.”

The upshot is that all the words inside an LLM get encoded into a high-dimensional space. Picture thousands of words floating in the air around you. Words that are closer together have similar meanings. For example, “table” and “chair” will be closer to each other than they are to “astronaut,” which is close to “moon” and “Musk.” Way off in the distance you can see “prestidigitation.” It’s a little like that, but instead of being related to each other across three dimensions, the words inside an LLM are related across 4,096 dimensions.

Yikes.

It’s dizzying stuff. In effect, an LLM compresses the entire internet into a single monumental mathematical structure that encodes an unfathomable amount of interconnected information. It’s both why LLMs can do astonishing things and why they’re impossible to fully understand.    

Okay. So that’s embeddings. What about weights?

A weight is a parameter that represents the strength of a connection between different parts of a model—and one of the most common types of dial for tuning a model’s behavior. Weights are used when an LLM processes text.

When an LLM reads a sentence (or a book chapter), it first looks up the embeddings for all the words and then passes those embeddings through a series of neural networks, known as transformers, that are designed to process sequences of data (like text) all at once. Every word in the sentence gets processed in relation to every other word.

This is where weights come in. An embedding represents the meaning of a word without context. When a word appears in a specific sentence, transformers use weights to process the meaning of that word in that new context. (In practice, this involves multiplying each embedding by the weights for all other words.)

And biases?

Biases are another type of dial that complement the effects of the weights. Weights set the thresholds at which different parts of a model fire (and thus pass data on to the next part). Biases are used to adjust those thresholds so that an embedding can trigger activity even when its value is low. (Biases are values that are added to an embedding rather than multiplied with it.) 

By shifting the thresholds at which parts of a model fire, biases allow the model to pick up information that might otherwise be missed. Imagine you’re trying to hear what somebody is saying in a noisy room. Weights would amplify the loudest voices the most; biases are like a knob on a listening device that pushes quieter voices up in the mix. 

Here’s the TL;DR: Weights and biases are two different ways that an LLM extracts as much information as it can out of the text it is given. And both types of parameters are adjusted over and over again during training to make sure they do this. 

Okay. What about neurons? Are they a type of parameter too? 

No, neurons are more a way to organize all this math—containers for the weights and biases, strung together by a web of pathways between them. It’s all very loosely inspired by biological neurons inside animal brains, with signals from one neuron triggering new signals from the next and so on. 

Each neuron in a model holds a single bias and weights for every one of the model’s dimensions. In other words, if a model has 4,096 dimensions—and therefore its embeddings are lists of 4,096 numbers—then each of the neurons in that model will hold one bias and 4,096 weights. 

Neurons are arranged in layers. In most LLMs, each neuron in one layer is connected to every neuron in the layer above. A 175-billion-parameter model like GPT-3 might have around 100 layers with a few tens of thousands of neurons in each layer. And each neuron is running tens of thousands of computations at a time. 

Dizzy again. That’s a lot of math.

That’s a lot of math.

And how does all of that fit together? How does an LLM take a bunch of words and decide what words to give back?

When an LLM processes a piece of text, the numerical representation of that text—the embedding—gets passed through multiple layers of the model. In each layer, the value of the embedding (that list of 4,096 numbers) gets updated many times by a series of computations involving the model’s weights and biases (attached to the neurons) until it gets to the final layer.

The idea is that all the meaning and nuance and context of that input text is captured by the final value of the embedding after it has gone through a mind-boggling series of computations. That value is then used to calculate the next word that the LLM should spit out. 

It won’t be a surprise that this is more complicated than it sounds: The model in fact calculates, for every word in its vocabulary, how likely that word is to come next and ranks the results. It then picks the top word. (Kind of. See below …) 

That word is appended to the previous block of text, and the whole process repeats until the LLM calculates that the most likely next word to spit out is one that signals the end of its output. 

That’s it?  

Sure. Well …

Go on.

LLM designers can also specify a handful of other parameters, known as hyperparameters. The main ones are called temperature, top-p, and top-k.

You’re making this up.

Temperature is a parameter that acts as a kind of creativity dial. It influences the model’s choice of what word comes next. I just said that the model ranks the words in its vocabulary and picks the top one. But the temperature parameter can be used to push the model to choose the most probable next word, making its output more factual and relevant, or a less probable word, making the output more surprising and less robotic. 

Top-p and top-k are two more dials that control the model’s choice of next words. They are settings that force the model to pick a word at random from a pool of most probable words instead of the top word. These parameters affect how the model comes across—quirky and creative versus trustworthy and dull.   

One last question! There has been a lot of buzz about small models that can outperform big models. How does a small model do more with fewer parameters?

That’s one of the hottest questions in AI right now. There are a lot of different ways it can happen. Researchers have found that the amount of training data makes a huge difference. First you need to make sure the model sees enough data: An LLM trained on too little text won’t make the most of all its parameters, and a smaller model trained on the same amount of data could outperform it. 

Another trick researchers have hit on is overtraining. Showing models far more data than previously thought necessary seems to make them perform better. The result is that a small model trained on a lot of data can outperform a larger model trained on less data. Take Meta’s Llama LLMs. The 70-billion-parameter Llama 2 was trained on around 2 trillion words of text; the 8-billion-parameter Llama 3 was trained on around 15 trillion words of text. The far smaller Llama 3 is the better model. 

A third technique, known as distillation, uses a larger model to train a smaller one. The smaller model is trained not only on the raw training data but also on the outputs of the larger model’s internal computations. The idea is that the hard-won lessons encoded in the parameters of the larger model trickle down into the parameters of the smaller model, giving it a boost. 

In fact, the days of single monolithic models may be over. Even the largest models on the market, like OpenAI’s GPT-5 and Google DeepMind’s Gemini 3, can be thought of as several small models in a trench coat. Using a technique called “mixture of experts,” large models can turn on just the parts of themselves (the “experts”) that are required to process a specific piece of text. This combines the abilities of a large model with the speed and lower power consumption of a small one.

But that’s not the end of it. Researchers are still figuring out ways to get the most out of a model’s parameters. As the gains from straight-up scaling tail off, jacking up the number of parameters no longer seems to make the difference it once did. It’s not so much how many you have, but what you do with them.

Can I see one?

You want to see a parameter? Knock yourself out: Here’s an embedding.

hello

What’s next for AI in 2026

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

In an industry in constant flux, sticking your neck out to predict what’s coming next may seem reckless. (AI bubble? What AI bubble?) But for the last few years we’ve done just that—and we’re doing it again. 

How did we do last time? We picked five hot AI trends to look out for in 2025, including what we called generative virtual playgrounds, a.k.a world models (check: From Google DeepMind’s Genie 3 to World Labs’s Marble, tech that can generate realistic virtual environments on the fly keeps getting better and better); so-called reasoning models (check: Need we say more? Reasoning models have fast become the new paradigm for best-in-class problem solving); a boom in AI for science (check: OpenAI is now following Google DeepMind by setting up a dedicated team to focus on just that); AI companies that are cozier with national security (check: OpenAI reversed position on the use of its technology for warfare to sign a deal with the defense-tech startup Anduril to help it take down battlefield drones); and legitimate competition for Nvidia (check, kind of: China is going all in on developing advanced AI chips, but Nvidia’s dominance still looks unassailable—for now at least). 

So what’s coming in 2026? Here are our big bets for the next 12 months. 

More Silicon Valley products will be built on Chinese LLMs

The last year shaped up as a big one for Chinese open-source models. In January, DeepSeek released R1, its open-source reasoning model, and shocked the world with what a relatively small firm in China could do with limited resources. By the end of the year, “DeepSeek moment” had become a phrase frequently tossed around by AI entrepreneurs, observers, and builders—an aspirational benchmark of sorts. 

It was the first time many people realized they could get a taste of top-tier AI performance without going through OpenAI, Anthropic, or Google.

Open-weight models like R1 allow anyone to download a model and run it on their own hardware. They are also more customizable, letting teams tweak models through techniques like distillation and pruning. This stands in stark contrast to the “closed” models released by major American firms, where core capabilities remain proprietary and access is often expensive.

As a result, Chinese models have become an easy choice. Reports by CNBC and Bloomberg suggest that startups in the US have increasingly recognized and embraced what they can offer.

One popular group of models is Qwen, created by Alibaba, the company behind China’s largest e-commerce platform, Taobao. Qwen2.5-1.5B-Instruct alone has 8.85 million downloads, making it one of the most widely used pretrained LLMs. The Qwen family spans a wide range of model sizes alongside specialized versions tuned for math, coding, vision, and instruction-following, a breadth that has helped it become an open-source powerhouse.

Other Chinese AI firms that were previously unsure about committing to open source are following DeepSeek’s playbook. Standouts include Zhipu’s GLM and Moonshot’s Kimi. The competition has also pushed American firms to open up, at least in part. In August, OpenAI released its first open-source model. In November, the Allen Institute for AI, a Seattle-based nonprofit, released its latest open-source model, Olmo 3. 

Even amid growing US-China antagonism, Chinese AI firms’ near-unanimous embrace of open source has earned them goodwill in the global AI community and a long-term trust advantage. In 2026, expect more Silicon Valley apps to quietly ship on top of Chinese open models, and look for the lag between Chinese releases and the Western frontier to keep shrinking—from months to weeks, and sometimes less.

Caiwei Chen

The US will face another year of regulatory tug-of-war

T​​he battle over regulating artificial intelligence is heading for a showdown. On December 11, President Donald Trump signed an executive order aiming to neuter state AI laws, a move meant to handcuff states from keeping the growing industry in check. In 2026, expect more political warfare. The White House and states will spar over who gets to govern the booming technology, while AI companies wage a fierce lobbying campaign to crush regulations, armed with the narrative that a patchwork of state laws will smother innovation and hobble the US in the AI arms race against China.

Under Trump’s executive order, states may fear being sued or starved federal funding if they clash with his vision for light-touch regulation. Big Democratic states like California—which just enacted the nation’s first frontier AI law requiring companies to publish safety testing for their AI models—will take the fight to court, arguing that only Congress can override state laws. But states that can’t afford to lose federal funding, or fear getting in Trump’s crosshairs, might fold. Still, expect to see more state lawmaking on hot-button issues, especially where Trump’s order gives states a green light to legislate. With chatbots accused of triggering teen suicides and data centers sucking up more and more energy, states will face mounting public pressure to push for guardrails. 

In place of state laws, Trump promises to work with Congress to establish a federal AI law. Don’t count on it. Congress failed to pass a moratorium on state legislation twice in 2025, and we aren’t holding out hope that it will deliver its own bill this year. 

AI companies like OpenAI and Meta will continue to deploy powerful super-PACs to support political candidates who back their agenda and target those who stand in their way. On the other side, super-PACs supporting AI regulation will build their own war chests to counter. Watch them duke it out at next year’s midterm elections.

The further AI advances, the more people will fight to steer its course, and 2026 will be another year of regulatory tug-of-war—with no end in sight.

Michelle Kim

Chatbots will change the way we shop

Imagine a world in which you have a personal shopper at your disposal 24-7—an expert who can instantly recommend a gift for even the trickiest-to-buy-for friend or relative, or trawl the web to draw up a list of the best bookcases available within your tight budget. Better yet, they can analyze a kitchen appliance’s strengths and weaknesses, compare it with its seemingly identical competition, and find you the best deal. Then once you’re happy with their suggestion, they’ll take care of the purchasing and delivery details too.

But this ultra-knowledgeable shopper isn’t a clued-up human at all—it’s a chatbot. This is no distant prediction, either. Salesforce recently said it anticipates that AI will drive $263 billion in online purchases this holiday season. That’s some 21% of all orders. And experts are betting on AI-enhanced shopping becoming even bigger business within the next few years. By 2030, between $3 trillion and $5 trillion annually will be made from agentic commerce, according to research from the consulting firm McKinsey. 

Unsurprisingly, AI companies are already heavily invested in making purchasing through their platforms as frictionless as possible. Google’s Gemini app can now tap into the company’s powerful Shopping Graph data set of products and sellers, and can even use its agentic technology to call stores on your behalf. Meanwhile, back in November, OpenAI announced a ChatGPT shopping feature capable of rapidly compiling buyer’s guides, and the company has struck deals with Walmart, Target, and Etsy to allow shoppers to buy products directly within chatbot interactions. 

Expect plenty more of these kinds of deals to be struck within the next year as consumer time spent chatting with AI keeps on rising, and web traffic from search engines and social media continues to plummet. 

Rhiannon Williams

An LLM will make an important new discovery

I’m going to hedge here, right out of the gate. It’s no secret that large language models spit out a lot of nonsense. Unless it’s with monkeys-and-typewriters luck, LLMs won’t discover anything by themselves. But LLMs do still have the potential to extend the bounds of human knowledge.

We got a glimpse of how this could work in May, when Google DeepMind revealed AlphaEvolve, a system that used the firm’s Gemini LLM to come up with new algorithms for solving unsolved problems. The breakthrough was to combine Gemini with an evolutionary algorithm that checked its suggestions, picked the best ones, and fed them back into the LLM to make them even better.

Google DeepMind used AlphaEvolve to come up with more efficient ways to manage power consumption by data centers and Google’s TPU chips. Those discoveries are significant but not game-changing. Yet. Researchers at Google DeepMind are now pushing their approach to see how far it will go.

And others have been quick to follow their lead. A week after AlphaEvolve came out, Asankhaya Sharma, an AI engineer in Singapore, shared OpenEvolve, an open-source version of Google DeepMind’s tool. In September, the Japanese firm Sakana AI released a version of the software called SinkaEvolve. And in November, a team of US and Chinese researchers revealed AlphaResearch, which they claim improves on one of AlphaEvolve’s already better-than-human math solutions.

There are alternative approaches too. For example, researchers at the University of Colorado Denver are trying to make LLMs more inventive by tweaking the way so-called reasoning models work. They have drawn on what cognitive scientists know about creative thinking in humans to push reasoning models toward solutions that are more outside the box than their typical safe-bet suggestions.

Hundreds of companies are spending billions of dollars looking for ways to get AI to crack unsolved math problems, speed up computers, and come up with new drugs and materials. Now that AlphaEvolve has shown what’s possible with LLMs, expect activity on this front to ramp up fast.    

Will Douglas Heaven

Legal fights heat up

For a while, lawsuits against AI companies were pretty predictable: Rights holders like authors or musicians would sue companies that trained AI models on their work, and the courts generally found in favor of the tech giants. AI’s upcoming legal battles will be far messier.

The fights center on thorny, unresolved questions: Can AI companies be held liable for what their chatbots encourage people to do, as when they help teens plan suicides? If a chatbot spreads patently false information about you, can its creator be sued for defamation? If companies lose these cases, will insurers shun AI companies as clients?

In 2026, we’ll start to see the answers to these questions, in part because some notable cases will go to trial (the family of a teen who died by suicide will bring OpenAI to court in November).

At the same time, the legal landscape will be further complicated by President Trump’s executive order from December—see Michelle’s item above for more details on the brewing regulatory storm.

No matter what, we’ll see a dizzying array of lawsuits in all directions (not to mention some judges even turning to AI amid the deluge).

James O’Donnell

3 things Will Douglas Heaven is into right now

The most amazing drummer on the internet

My daughter introduced me to El Estepario Siberiano’s YouTube channel a few months back, and I have been obsessed ever since. The Spanish drummer (real name: Jorge Garrido) posts videos of himself playing supercharged cover versions of popular tracks, hitting his drums with such jaw-dropping speed and technique that he makes other pro drummers shake their heads in disbelief. The dozens of reaction videos posted by other musicians are a joy in themselves. 

Jorge Garrido playing drums

EL ESTEPARIO SIBERIANO VIA YOUTUBE

Garrido is up-front about the countless hours that it took to get this good. He says he sat behind his kit almost all day, every day for years. At a time when machines appear to do it all, there’s a kind of defiance in that level of human effort. It’s why my favorites are Garrido’s covers of electronic music, where he out-drums the drum machine. Check out his version of Skrillex and Missy Elliot’s “Ra Ta Ta” and tell me it doesn’t put happiness in your heart.

Finding signs of life in the uncanny valley

Watching Sora ­videos of Michael Jackson stealing a box of chicken nuggets or Sam Altman biting into the pink meat of a flame-grilled Pikachu has given me flashbacks to an Ed Atkins exhibition at Tate Britain I saw a few months ago. Atkins is one of the most influential and unsettling British artists of his generation. He is best known for hyper-detailed CG animations of himself (pore-perfect skin, janky movement) that play with the virtual representation of human emotions. 

Still from ED ATKINS PIANOWORK 2 2023
COURTESY: THE ARTIST, CABINET GALLERY, LONDON, DÉPENDANCE, BRUSSELS, GLADSTONE GALLERY

In The Worm we see a CGI Atkins make a long-distance call to his mother during a covid lockdown. The audio is from a recording of an actual conversation. Are we watching Atkins cry or his avatar? Our attention flickers between two realities. “When an actor breaks character during a scene, it’s known as corpsing,” Atkins has said. “I want everything I make to corpse.” Next to Atkins’s work, generative videos look like cardboard cutouts: lifelike but not alive.

A dark and dirty book about a talking dingo

What’s it like to be a pet? Australian author Laura Jean McKay’s debut novel, The Animals in That Country, will make you wish you’d never asked. A flu-like pandemic leaves people with the ability to hear what animals are saying. If that sounds too Dr. Dolittle for your tastes, rest assured: These animals are weird and nasty. A lot of the time they don’t even make any sense. 

cover of book

SCRIBE

With everybody now talking to their computers, McKay’s book resets the anthropomorphic trap we’ve all fallen into. It’s a brilliant evocation of what a nonhuman mind might containand a meditation on the hard limits of communication.

Job titles of the future: Head-transplant surgeon

The Italian neurosurgeon Sergio Canavero has been preparing for a surgery that might never happen. His idea? Swap a sick person’s head—or perhaps just the brain—onto a younger, healthier body.

Canavero caused a stir in 2017 when he announced that a team he advised in China had exchanged heads between two corpses. But he never convinced skeptics that his technique could succeed—or to believe his claim that a procedure on a live person was imminent. The Chicago Tribune labeled him the “P.T. Barnum of transplantation.”

Canavero withdrew from the spotlight. But the idea of head transplants isn’t going away. Instead, he says, the concept has recently been getting a fresh look from life-extension enthusiasts and stealth Silicon Valley startups.

Career path

It’s been rocky. After he began publishing his surgical ideas a decade ago, Canavero says, he got his “pink slip” from the Molinette Hospital in Turin, where he’d spent 22 years on staff. “I’m an out-of-the-establishment guy. So that has made things harder, I have to say,” he says.  

Why he persists

No other solution to aging is on the horizon. “It’s become absolutely clear over the past years that the idea of some incredible tech to rejuvenate elderly people—­happening in some secret lab, like Google—is really going nowhere,” he says. “You have to go for the whole shebang.”

The whole shebang?

He means getting a new body, not just one new organ. Canavero has an easy mastery of English idioms and an unexpected Southern twang. He says that’s due to a fascination with American comics as a child. “For me, learning the language of my heroes was paramount,” he says. “So I can shoot the breeze.” 

Cloned bodies

Canavero is now an independent investigator and has advised entrepreneurs who want to create brainless human clones as a source of DNA-matched organs that wouldn’t get rejected by a recipient’s immune system. “I can tell you there are guys from top universities involved,” he says.

What’s next

Combining the necessary technologies, like reliably precise surgical robots and artificial wombs to grow the clones, is going to be complex and very, very expensive. Canavero lacks the funds to take his plans further, but he believes “the money is out there” for a commercial moonshot project: “What I say to the billionaires is ‘Come together.’ You will all have your own share, plus make yourselves immortal.”