Dec 23 2025
This company is developing gene therapies for muscle growth, erectile dysfunction, and “radical longevity”

At some point next month, a handful of volunteers will be injected with two experimental gene therapies as part of an unusual clinical trial. The drugs are potential longevity therapies, says Ivan Morgunov, the CEO of Unlimited Bio, the company behind the trial. His long-term goal: to achieve radical human life extension.

The 12 to 15 volunteers—who will be covering their own travel and treatment costs—will receive a series of injections in the muscles of their arms and legs. One of the therapies is designed to increase the blood supply to those muscles. The other is designed to support muscle growth. The company hopes to see improvements in strength, endurance, and recovery. It also plans to eventually trial similar therapies in the scalp (for baldness) and penis (for erectile dysfunction).

But some experts are concerned that the trial involves giving multiple gene therapies to small numbers of healthy people. It will be impossible to draw firm conclusions from such a small study, and the trial certainly won’t reveal anything about longevity, says Holly Fernandez Lynch, a lawyer and medical ethicist at the University of Pennsylvania in Philadelphia.

Unlimited Bio’s muscle growth therapy is already accessible at clinics in Honduras and Mexico, says Morgunov—and the company is already getting some publicity. Khloe Kardashian tagged Unlimited Bio in a Facebook post about stem-cell treatments she and her sister Kim had received at the Eterna clinic in Mexico in August. And earlier this week, the biohacking influencer Dave Asprey posted an Instagram Reel of himself receiving one of the treatments in Mexico; it was shared with 1.3 million Instagram followers. In the video, Eterna’s CEO, Adeel Khan, says that the therapy can “help with vascular health systemically.” “I’m just upgrading my system for a little while to reduce my age and reduce my vascular risk,” Asprey said.

Genes for life

Gene therapies typically work by introducing new genetic code into the body’s cells. This code is then able to make proteins. Existing approved gene therapies have typically been developed for severe diseases in which the target proteins are either missing or mutated.

But several groups are exploring gene therapies for healthy people. One of these companies is Minicircle, which developed a gene therapy to increase production of follistatin, a protein found throughout the body that has many roles and is involved in muscle growth. The company says this treatment will increase muscle mass—and help people live longer. Minicircle is based in Próspera, a special economic zone in Honduras with its own bespoke regulatory system. Anyone can visit the local clinic and receive that therapy, for a reported price of $25,000. And many have, including the wealthy longevity influencer Bryan Johnson, who promoted the therapy in a Netflix documentary.

Unlimited Bio’s Morgunov, a Russian-Israeli computer scientist, was inspired by Minicircle’s story. He is also interested in longevity. Specifically, he’s committed to radical life extension and has said that he could be part of “the last generation throughout human history to die from old age.” He believes the biggest “bottleneck” slowing progress toward anti-aging or lifespan-extending therapies is drug regulation. So he, too, incorporated his own biotech company in Próspera.

“A company like ours couldn’t exist outside of Próspera,” says Unlimited Bio’s chief operating officer, Vladimir Leshko.

There, Morgunov and his colleagues are exploring two gene therapies. One of these is another follistatin therapy, which the team hopes will increase muscle mass. The other codes for a protein called vascular endothelial growth factor, or VEGF. This compound is known to encourage the growth of blood vessels. Morgunov and his colleagues hope the result will be increased muscle growth, enhanced muscle repair, and longer life. Neither treatment is designed to alter a recipient’s DNA, and therefore it won’t be inherited by future generations.

The combination of the two therapies could benefit healthy people and potentially help them live longer, says Leshko, a former electrical engineer and professional poker player who retrained in biomedical engineering. “We would say that it’s a preventive-slash-enhancing indication,” he says. “Potentially participants can experience faster recovery from exercise, more strength, and more endurance.”

Of the 12 to 15 volunteers who participate in the trial, half will receive only the VEGF therapy. The other half will receive both the VEGF and the follistatin therapies. The treatments will involve a series of injections throughout large muscles in the arms and legs, says Morgunov.

He is confident that the VEGF therapy is safe. It was approved in Russia over a decade ago to treat lower-limb ischemia—a condition that can cause pain, numbness, and painful ulcers in the legs and feet. Morgunov reckons that around 10,000 people in Russia have already had the drug, although he says he hasn’t “done deep fact-checking on that.”

Other researchers aren’t convinced.

Limited bio

VEGF is a powerful compound, says Seppo Ylä-Herttuala, a professor of molecular medicine at the University of Eastern Finland who has been studying VEGF and potential VEGF therapies for decades. He doesn’t know how many people have had VEGF gene therapy in Russia. But he does know that the safety of the therapy will depend on how much is administered and where. Previous attempts to inject the therapy into the heart, for example, have resulted in edema, a sometimes fatal buildup of fluid. Even if the therapy is injected elsewhere, VEGF can travel around the body, he says. If it gets to the eye, for example, it could cause blindness. Leshko counters that the VEGF should remain where it is injected, and any other circulation in the body, if it occurs, should be short-lived. 

And while the therapy has been approved in Russia, there’s a reason it hasn’t been approved elsewhere, says Ylä-Herttuala: The clinical trials were not as rigorous as they could have been. While “it probably works in some patients,” he says, the evidence to support the use of this therapy is weak. At any rate, he adds, VEGF will only support the growth of blood vessels—it won’t tackle aging.  “VEGF is not a longevity drug,” he says.

Leshko points to a 2021 study in mice, which suggested that a lack of VEGF activity might drive aging in the rodents. “We’re convinced it qualifies as a potential longevity drug,” he says.

There is even less data about follistatin. Minicircle, the company selling another follistatin gene therapy, has not published any rigorous clinical trial data. So far, much of the evidence for follistatin’s effects comes from research in rodents, says Ylä-Herttuala.

Clinical trials like this one should gather more information, both about the therapies and about the methods used to get those therapies into the body. Unlimited Bio’s VEGF therapy will be delivered via a circular piece of genetic code called a plasmid. Its follistatin therapy, on the other hand, will be delivered via an adeno-associated virus (AAV). Plasmid therapies are easier to make, and they have a shorter lifespan in the body—only a matter of days. They are generally considered to be safer than AAV therapies. AAV therapies, on the other hand, tend to stick around for months, says Ylä-Herttuala. And they can trigger potentially dangerous immune reactions.

It’s debatable whether healthy people should be exposed to these risks, says Fernandez Lynch. The technology “still has serious questions about its safety and effectiveness,” even for people with life-threatening diseases, she says. “If you are a healthy person, the risk of harm is more substantial because it’ll be more impactful on your life.”

But Leshko is adamant. “Over 120,000 humans die DAILY from age-related causes,” he wrote in an email. “Building ‘ethical’ barriers around ‘healthy’ human (in fact, aging human) trials is unethical.” Morgunov did not respond to a request for comment.

Some people want to take those risks anyway. In his video, the biohacker influencer Asprey—who has publicly stated that he’s “going to live to 180”—described VEGF as a “longevity compound,” and Eterna’s CEO Khan, who delivered the treatment, described it as “the ultimate upgrade.” Neither Asprey nor Khan clinic responded to requests for comment. 

Michael Gusmano, a professor of health policy at Lehigh University in Bethlehem, Pennsylvania, worries that this messaging might give trial participants unrealistic expectations about how they might benefit. There is “huge potential for therapeutic misconception when you have some kind of celebrity online influencer touting something about which there is relatively sparse scientific evidence,” he says. In reality, he adds, “the only thing you can guarantee is that [the volunteers] will be contributing to our knowledge of how this intervention works.”

“I would certainly not recommend that anyone I know enter into such a trial,” says Gusmano.

A penis project

The muscle study is only the first step. The Unlimited Bio team hopes to trial the VEGF therapy for baldness and erectile dysfunction, too. Leshko points to research in mice that links high VEGF levels to larger, denser hair follicles. He hopes to test a series of VEGF therapy injections into the scalps of volunteers. Morgunov, who is largely bald, has already started to self-experiment with the approach.

An erectile dysfunction trial may follow. “That one we think has great potential because injecting gene therapy into the penis sounds exciting,” says Leshko. A protocol for that trial has not yet been finalized, but he imagines it would involve “five to 10” injections.

Ylä-Herttuala isn’t optimistic about either approach. Hair growth is largely hormonal, he says. And injecting anything into a penis risks damaging it (although Leshko points out that a similar approach was taken by another company almost 20 years ago). Injecting a VEGF gene therapy into the penis would also risk edema there, Ylä-Herttuala adds.

And he points out that we already have some treatments for hair loss and erectile dysfunction. While they aren’t perfect, their existence does raise the bar for any potential future therapies—not only do they have to be safe and effective, but they must be safer or more effective than existing ones.

That doesn’t mean the trials will flop. No small trial can be definitive, but it could still provide some insight into how these drugs are working. It is possible that the therapies will increase muscle mass, at least, and that this could be beneficial to the healthy recipients, says Ylä-Herttuala. 

Before our call, he had taken a look at Unlimited Bio’s website, which carries the tagline “The Most Advanced Rejuvenation Solution.” “They promise a lot,” he said. “I hope it’s true.”

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Dec 23 2025
Welcome to Kenya’s Great Carbon Valley: a bold new gamble to fight climate change

The earth around Lake Naivasha, a shallow freshwater basin in south-central Kenya, does not seem to want to lie still. 

Ash from nearby Mount Longonot, which erupted as recently as the 1860s, remains in the ground. Obsidian caves and jagged stone towers preside over the steam that spurts out of fissures in the soil and wafts from pools of boiling-hot water—produced by magma that, in some areas, sits just a few miles below the surface. 

It’s a landscape born from violent geologic processes some 25 million years ago, when the Nubian and Somalian tectonic plates pulled apart. That rupture cut a depression in the earth some 4,000 miles long—from East Africa up through the Middle East—to create what’s now called the Great Rift Valley. 

This volatility imbues the land with vast potential, much of it untapped. The area, no more than a few hours’ drive from Nairobi, is home to five geothermal power stations, which harness the clouds of steam to generate about a quarter of Kenya’s electricity. But some energy from this process escapes into the atmosphere, while even more remains underground for lack of demand. 

That’s what brought Octavia Carbon here. 

In June, just north of the lake in the small but strategically located town of Gilgil, the startup began running a high-stakes test. It’s harnessing some of that excess energy to power four prototypes of a machine that promises to remove carbon dioxide from the air in a manner that the company says is efficient, affordable, and—crucially—scalable.

In the short term, the impact will be small—each device’s initial capacity is just 60 tons per year of CO2—but the immediate goal is simply to demonstrate that carbon removal here is possible. The longer-term vision is far more ambitious: to prove that direct air capture (DAC), as the process is known, can be a powerful tool to help the world keep temperatures from rising to ever more dangerous levels. 

“We believe we are doing what we can here in Kenya to address climate change and lead the charge for positioning Kenya as a climate vanguard,” Specioser Mutheu, Octavia’s communications lead, told me when I visited the country last year. 

The United Nations’ Intergovernmental Panel on Climate Change has stated that in order to keep the world from warming more than 1.5 °C over preindustrial levels (the threshold set out in the Paris Agreement), or even the more realistic but still difficult 2 °C, it will need to significantly reduce future fossil-fuel emissions—and also pull from the atmosphere billions of tons of carbon that have already been released. 

Some argue that DAC, which uses mechanical and chemical processes to suck carbon dioxide from the air and store it in a stable form (usually underground), is the best way to do that. It’s a technology with immense promise, offering the possibility that human ingenuity and innovation can get us out of the same mess that development caused in the first place. 

Last year, the world’s largest DAC plant, Mammoth, came online in Iceland, offering the eventual capacity to remove up to 36,000 tons of CO₂ per year—roughly equal to the emissions of 7,600 gas-powered cars. The idea is that DAC plants like this one will remove and permanently store carbon and create carbon credits that can be purchased by corporations, governments, and local industrial producers, which will collectively help keep the world from experiencing the most dangerous effects of climate change. 

large pipes run along the ground with the buildings of the Climeworks' Mammoth plant in the distance
Climeworks’ Mammoth carbon removal plant near Reykjavik, Iceland.
JOHN MOORE/GETTY IMAGES

Now, Octavia and a growing number of other companies, politicians, and investors from Africa, the US, and Europe are betting that Kenya’s unique environment holds the keys to reaching this lofty goal—which is why they’re pushing a sweeping vision to remake the Great Rift Valley into the “Great Carbon Valley.” And they hope to do so in a way that provides a genuine economic boost for Kenya, while respecting the rights of the Indigenous people who live on this land. If they can do so, the project could not just give a needed jolt to the DAC industry—it could also provide proof of concept for DAC across the Global South, which is particularly vulnerable to the ravages of climate change despite bearing very little responsibility for it. 

But DAC is also a controversial technology, unproven at scale and wildly expensive to operate. In May, an Icelandic news outlet published an investigation into Climeworks, which runs the Mammoth plant, finding that it didn’t even pull in enough carbon dioxide to offset its own emissions, let alone the emissions of other companies. 

Critics also argue that the electricity DAC requires can be put to better use cleaning up our transportation systems, heating our homes, and powering other industries that still rely largely on fossil fuels. What’s more, they say that relying on DAC can give polluters an excuse to delay the transition to renewables indefinitely. And further complicating this picture is shrinking demand from governments and corporations that would be DAC’s main buyers, which has left some experts questioning whether the industry will even survive. 

Carbon removal is a technology that seems always on the verge of kicking in but never does, says Fadhel Kaboub, a Tunisian economist and advocate for an equitable green transition. “You need billions of dollars of investment in it, and it’s not delivering, and it’s not going to deliver anytime soon. So why do we put the entire future of the planet in the hands of a few people and a technology that doesn’t deliver?” 

Layered on top of concerns about the viability and wisdom of DAC is a long history of distrust from the Maasai people who have called the Great Rift Valley home for generations but have been displaced in waves by energy companies coming in to tap the land’s geothermal reserves. And many of those remaining don’t even have access to the electricity generated by these plants. 

Maasai men walk along the road beside the Olkaria geothermal plant.
REDUX PICTURES

It’s an immensely complicated landscape to navigate. But if the project can indeed make it through, Benjamin Sovacool, an energy policy researcher and director of the Boston University Institute for Global Sustainability, sees immense potential for countries that have been historically marginalized from climate policy and green energy investment. Though he’s skeptical about DAC as a near-term climate solution, he says these nations could still see big benefits from what could be a multitrillion-dollar industry. 

“[Of] all the technologies we have available to fight climate change, the idea of reversing it by sucking CO2 out of the air and storing it is really attractive. It’s something even an ordinary person can just get,” Sovacool says. “If we’re able to do DAC at scale, it could be the next huge energy transition.” 

But first, of course, the Great Carbon Valley has to actually deliver.

Challenging the power dynamic

The “Great Carbon Valley” is both a broad vision for the region and a company founded to shepherd that vision into reality. 

Bilha Ndirangu, a 42-year-old MIT electrical engineering graduate who grew up in Nairobi, has long worried about the impacts of climate change on Kenya. But she doesn’t want the country to be a mere victim of rising temperatures, she tells me; she hopes to see it become a source of climate solutions. So in 2021, Ndirangu cofounded Jacob’s Ladder Africa, a nonprofit with the goal of preparing African workers for green industries. 

COURTESY OF BILHA NDIRANGU

She also began collaborating with the Kenyan entrepreneur James Irungu Mwangi, the CEO of Africa Climate Ventures, an investment firm focused on building and accelerating climate-smart businesses. He’d been working on an idea that spoke to their shared belief in the potential for the country’s vast geothermal capacity; the plan was to find buyers for Kenya’s extra geothermal energy in order to kick-start the development of even more renewable power. One energy-hungry, climate-positive industry stood out: direct air capture of carbon dioxide. 

The Great Rift Valley was the key to this vision. The thinking was that it could provide the cheap energy needed to power affordable DAC at scale while offering an ideal geology to effectively store carbon deep underground after it was extracted from the air. And with nearly 90% of the country’s grid already powered by renewable energy, DAC wouldn’t be siphoning power away from other industries that need it. Instead, attracting DAC to Kenya could provide the boost needed for energy providers to build out their infrastructure and expand the grid—ideally connecting the roughly 25% of people in the country who lack electricity and reducing scenarios in which power has to be rationed. 

“This push for renewable energy and the decarbonization of industries is providing us with a once-in-a-lifetime sort of opportunity,” Ndirangu tells me. 

So in 2023, the pair founded Great Carbon Valley, a project development company whose mission is attracting DAC companies to the area, along with other energy-intensive industries looking for renewable power. 

It has already brought on high-profile companies like the Belgian DAC startup Sirona Technologies, the French DAC company Yama, and Climeworks, the Swiss company that operates Mammoth and another DAC plant in Iceland (and was on MIT Technology Review’s 10 Breakthrough Technologies list in 2022, and the list of Climate Tech Companies to Watch in 2023). All are planning on launching pilot projects in Kenya in the coming years, with Climeworks announcing plans to complete its Kenyan DAC plant by 2028. GCV has also partnered with Cella, an American carbon-storage company that works with Octavia, and is facilitating permits for the Icelandic company Carbfix, which injects the carbon from Climeworks’ DAC facilities.

drone view of shipping container buildings next to a solar array
Cella and Sirona Technologies have a pilot program in the Great Rift Valley called Project Jacaranda.
SIRONA TECHNOLOGIES

“Climate change is disproportionately impacting this part of the world, but it’s also changing the rules of the game all over the world,” Cella CEO and cofounder Corey Pattison tells me, explaining the draw of Mwangi and Ndirangu’s concept. “This is also an opportunity to be entrepreneurial and creative in our thinking, because there are all of these assets that places like Kenya have.”

Not only can the country offer cheap and abundant renewable energy, but supporters of Kenyan DAC hope that the young and educated local workforce can supply the engineers and scientists needed to build out this infrastructure. In turn, the business could open opportunities to the country’s roughly 6 million un- or under-employed youths. 

“It’s not a one-off industry,” Ndirangu says, highlighting her faith in the idea that jobs will flow from green industrialization. Engineers will be needed to monitor the DAC facilities, and the additional demand for renewable power will create jobs in the energy sector, along with related services like water and hospitality. 

“You’re developing a whole range of infrastructure to make this industry possible,” she adds. “That infrastructure is not just good for the industry—it’s also just good for the country.”

The chance to solve a “real-world issue”

In June of last year, I walked up a dirt path to the HQ of Octavia Carbon, just off Nairobi’s Eastern Bypass Road, on the far outskirts of the city. 

The staffers I met on my tour exuded the kind of boundless optimism that’s common in early-stage startups. “People used to write academic articles about the fact that no human will ever be able to run a marathon in less than two hours,” Octavia CEO Martin Freimüller told me that day. The Kenyan marathon runner Eliud Kipchoge broke that barrier in a race in 2019. A mural of him features prominently on the wall, along with the athlete’s slogan, “No human is limited.” 

“It’s impossible, until Kenya does it,” Freimüller added. 

In June, Octavia started testing its technology in the field in a pilot project in Gilgil.
OCTAVIA CARBON

Although not an official partner of Ndirangu’s Great Carbon Valley venture, Octavia aligns with the larger vision, he told me. The company got its start in 2022, when Freimüller, an Austrian development consultant, met Duncan Kariuki, an engineering graduate from the University of Nairobi, in the OpenAir Collective, an online forum devoted to carbon removal. Kariuki introduced Freimüller to his classmates Fiona Mugambi and Mike Bwondera, and the four began working on a DAC prototype, first in lab space borrowed from the university and later in an apartment. It didn’t take long for neighbors to complain about the noise, and within six months, the operation had moved to its current warehouse. 

That same year, they announced their first prototype, affectionately called Thursday after the day it was unveiled at a Nairobi Climate Network event. Soon, Octavia was showing off its tech to high-profile visitors including King Charles III and President Joe Biden’s ambassador to Kenya, Meg Whitman. 

Three years later, the team has more than 40 engineers and has built its 12th DAC unit: a metal cylinder about the size of a large washing machine, containing a chemical filter using an amine, an organic compound derived from ammonia. (Octavia declined to provide further details about the arrangement of the filter inside the machine because the company is awaiting approval of a patent for the design.)

Octavia relies on an amine absorption method similar to the one used by other DAC plants around the world, but its project stands apart—having been tailored to suit the local climate and run on more than 80% thermal energy.
OCTAVIA CARBON

Hannah Wanjau, an engineer at the company, explained how it works: Fans draw air from the outside across the filter, causing carbon dioxide (which is acidic) to react with the basic amine and form a carbonate salt. When that mixture is heated inside a vacuum to 80 to 100 °C, the CO2 is released, now as a gas, and collected in a special chamber, while the amine can be reused for the next round of carbon capture. 

The amine absorption method has been used in other DAC plants around the world, including those operated by Climeworks, but Octavia’s project stands apart on several key fronts. Wanjau explained that its technology is tailored to suit the local climate; the company has adjusted the length of time for absorption and the temperature for CO2 release, making it a potential model for other countries in the tropics. 

And then there’s its energy source: The device operates on more than 80% thermal energy, which in the field will consist of the extra geothermal energy that the power plants don’t convert into electricity. This energy is typically released into the atmosphere, but it will be channeled instead to Octavia’s machines. What’s more, the device’s modular design can fit inside a shipping container, allowing the company to easily deploy dozens of these units once the demand is there, Mutheu told me. 

This technology is being tested in the field in Gilgil, where Mutheu told me the company is “continuing to capture and condition CO₂ as part of our ongoing operations and testing cycles.” (She declined to provide specific data or results at this stage.)

Once the CO2 is captured, it will be heated and pressurized. Then it will be pumped to a nearby storage facility operated by Cella, where the company will inject the gas into fissures underground. The region’s special geology again offers an advantage: Much of the rock found underground here is basalt, a volcanic mineral that contains high concentrations of calcium and magnesium ions. They react with carbon dioxide to form substances like calcite, dolomite, and magnesite, locking the carbon atoms away in the form of solid minerals. 

This process is more durable than other forms of carbon storage, making it potentially more attractive to buyers of carbon credits, says Pattison, the Cella CEO. Non-geologic carbon mitigation methods, such as cookstove replacement programs or nature-based solutions like tree planting, have recently been rocked by revelations of fraud or exaggeration. The money for Cella’s pilot, which will see the injection of 200 tons of CO2 this year, has come mainly from the Frontier advance market commitment, under which a group of companies including Stripe, Google, Shopify, Meta, and others has collectively pledged to spend $1 billion on carbon removal by 2030. 

The modular design of Octavia’s device can fit inside a shipping container, allowing the company to easily deploy dozens of these units once demand is there. 
OCTAVIA CARBON

These projects have already opened up possibilities for young Kenyans like Wanjau. She told me there were not a lot of opportunities for aspiring mechanical engineers like her to design and test their own devices; many of her classmates were working for construction or oil companies, or were unemployed. But almost immediately after graduation, Wanjau began working for Octavia. 

“I’m happy that I’m trying to solve a problem that’s a real-world issue,” she told me. “Not many people in Africa get a chance to do that.” 

An uphill climb

Despite the vast enthusiasm from partners and investors, the Great Carbon Valley faces multiple challenges before Ndirangu and Mwangi’s vision can be fully realized. 

Since its start, the venture has had to contend with “this perception that doing projects in Africa is risky,” says Ndirangu. Of the dozens of DAC facilities planned or in existence today, only a handful are in the Global South. Indeed, Octavia has described itself as the first DAC plant to be located there. “Even just selling Kenya as a destination for DAC was quite a challenge,” she says.

So Ndirangu played up Kenya’s experience developing geothermal resources, as well as local engineering talent and a lower cost of labor. GCV has also offered to work with the Kenyan government to help companies secure the proper permits to break ground as soon as possible. 

In pitching the Great Carbon Valley, Ndirangu has played up Kenya’s experience developing geothermal resources, as well as local engineering talent and a lower cost of labor.
ALAMY

Ndirangu says that she’s already seen “a real appetite” from power producers who want to build out more renewable-energy infrastructure, but at the same time they’re waiting for proof of demand. She envisions that once that power is in place, lots of other industries—from data centers to producers of green steel, green ammonia, and sustainable aviation fuels—will consider basing themselves in Kenya, attracting more than a dozen projects to the valley in the next few years.  

But recent events could dampen demand (which some experts already worried was insufficient). Global governments are retreating from climate action, particularly in the US. The Trump administration has dramatically slashed funding for development related to climate change and renewable energy. The Department of Energy appears poised to terminate a $50 million grant to a proposed Louisiana DAC plant that would have been partially operated by Climeworks, and in May, not long after that announcement, the company said it was cutting 22% of its staff. 

At the same time, many companies that would have likely been purchasers of carbon credits—and that a few years ago had voluntarily pledged to reduce or eliminate their carbon emissions—are quietly walking back their commitments. Over the long term, experts warn, there are limits to the amount of carbon removal that companies will ever voluntarily buy. They argue that governments will ultimately have to pay for it—or require polluters to do so. 

Further compounding all these challenges are costs. Critics say DAC investments are a waste of time and money compared with other forms of carbon drawdown. As of mid-December, carbon removal credits in the European Union’s Emissions Trading System, one of the world’s largest carbon markets, were priced at around $84 per ton. The average price per DAC credit, for comparison, is nearly $450. Natural processes like reforestation absorb millions of tons of carbon annually and are far cheaper (though programs to harness them for carbon credits are beset with their own controversies). Ultimately, DAC continues to operate on a small scale, removing only about 10,000 metric tons of CO2 each year.

Even if DAC suppliers do manage to push past these obstacles, there are still thorny questions coming from inside Kenya. Groups like Power Shift Africa, a Nairobi-based think tank that advocates for climate action on the continent, have derided carbon credits as “pollution permits” and blamed them for delaying the move toward electrification. 

“The ultimate goal of [carbon removal] is that you can say at the end, well, we can actually continue our emissions and just recapture them with this technology,” says Kaboub, the Tunisian economist, who has worked with Power Shift Africa. “So there’s no need to end fossil fuels, which is why you get a lot of support from oil countries and companies.”

Another problem he sees is not limited to DAC but extends to the way that Kenya and other African nations are pursuing their goal of green industrialization. While Kenyan President William Ruto has courted international financial investment to turn Kenya into a green energy hub, his administration’s policies have deepened the country’s external debt, which in 2024 was equal to around 30% of its GDP. Geothermal energy development in Kenya has often been financed by loans from international institutions or other governments. As its debt has risen, the country has enacted national austerity measures that have sparked deadly protests.

Kenya may indeed have advantages over other countries, and DAC costs will most likely go down eventually. But some experts, such as Boston University’s Sovacool, aren’t quite sold on the idea that the Great Carbon Valley—or any DAC venture—can significantly mitigate climate change. Sovacool’s research has found that at best, DAC will be ready to deploy on the necessary scale by midcentury, much too late to make it a viable climate solution. And that’s if it can overcome additional costs—such as the losses associated with corruption in the energy sector, which Sovacool and others have found is a widespread problem in Kenya. 

MIRIAM MARTINCIC

Nevertheless, others within the carbon removal industry remain more optimistic about DAC’s overall prospects and are particularly hopeful that Kenya can address some of the challenges the technology has encountered elsewhere. Cost is “not the most important thing,” says Erin Burns, executive director of Carbon180, a nonprofit that advocates for the removal and reuse of carbon dioxide. “There’s lots of things we pay for.” She notes that governments in Japan, Singapore, Canada, Australia, the European Union, and elsewhere are all looking at developing compliance markets for carbon, even though the US is stagnating on this front. 

The Great Carbon Valley, she believes, stands poised to benefit from these developments. “It’s big. It’s visionary,” Burns says. “You’ve got to have some ambition here. This isn’t something that is like deploying a technology that’s widely deployed already. And that comes with an enormous potential for huge opportunity, huge gains.”

Back to the land 

More than any external factor, the Great Carbon Valley’s future is perhaps most intimately intertwined with the restless earth on which it’s being built, and the community that has lived here for centuries. 

To the Maasai people, nomadic pastoralists who inhabit swathes of Eastern Africa, including Kenya, this land around Lake Naivasha is “ol-karia,” meaning “ochre,” after the bright red clay found in abundance.

South of the lake is Hell’s Gate National Park, a 26-square-mile nature reserve where the region’s five geothermal power complexes—with a sixth under construction—churn on top of the numerous steam vents. The first geothermal power plant here was brought into service in 1981 by KenGen, a majority-state-owned electricity company; it was named Olkaria. 

But for decades most of the Maasai haven’t had access to that electricity. And many of them have been forced off the land in a wave of evictions. In 2014, construction on a KenGen geothermal complex expelled more than 2,000 people and led to a number of legal complaints. At the same time, locals living near a different, privately owned geothermal complex 50 miles north of Naivasha have complained of noise and air pollution; in March, a Kenyan court revoked the operating license of one of the project’s three plants. 

Neither Octavia or Cella is powered by output from these two geothermal producers, but activists have warned that similar environmental and social harms could resurface if demand for new geothermal infrastructure grows in Kenya—demand that could be driven by DAC. 

Ndirangu says she believes some of the complaints about displacement are “exaggerated,” but she nonetheless acknowledges the need for stronger community engagement, as does Octavia. In the long term, Ndirangu says, she plans to provide job training to residents living near the affected areas and integrate them into the industry, although she also says those plans need to be realistic. “You don’t want to create the wrong expectation that you will hire everyone from the community,” she says.  

That’s part of the problem for Maasai activists like Agnes Koilel, a teacher living near the Olkaria geothermal field. Despite past promises of employment at the power plants, the jobs that are offered are lower-paying positions in cleaning or security. “Maasai people are not [as] employed as they think,” she says.  

The Maasai people have inhabited swathes of Eastern Africa, including Kenya, for centuries, though many still lack access to the power that’s now produced there.
ALAMY

DAC is a small industry, and it can’t do everything. But if it’s going to become as big as Ndirangu, Freimüller, and other proponents of the Great Carbon Valley hope it will be, creating jobs and driving Kenya’s green industrialization, communities like Koilel’s will be among those most directly affected—much as they are by climate change. 

When I asked Koilel what she thought about DAC development near her home, she told me she had never heard of the Great Carbon Valley idea, or of carbon removal in general. She wasn’t necessarily against geothermal power development on principle, or opposed to any of the industries that might push it to expand. She just wants to see some benefits, like a health center for her community. She wants to reverse the evictions that have pushed her neighbors off their land. And she wants electricity—the same kind that would power the fans and pumps of future DAC hubs. 

Power “is generated from these communities,” Koilel said. “But they themselves do not have that light.” 

Diana Kruzman is a freelance journalist covering environmental and human rights issues around the world. Her writing has appeared in New Lines Magazine, The Intercept, Inside Climate News, and other publications. She lives in New York City.

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Dec 19 2025
Can AI really help us discover new materials?

Judging from headlines and social media posts in recent years, one might reasonably assume that AI is going to fix the power grid, cure the world’s diseases, and finish my holiday shopping for me. But maybe there’s just a whole lot of hype floating around out there.

This week, we published a new package called Hype Correction. The collection of stories takes a look at how the world is starting to reckon with the reality of what AI can do, and what’s just fluff.

One of my favorite stories in that package comes from my colleague David Rotman, who took a hard look at AI for materials research. AI could transform the process of discovering new materials—innovation that could be especially useful in the world of climate tech, which needs new batteries, semiconductors, magnets, and more. 

But the field still needs to prove it can make materials that are actually novel and useful. Can AI really supercharge materials research? What could that look like?

For researchers hoping to find new ways to power the world (or cure disease or achieve any number of other big, important goals), a new material could change everything.

The problem is, inventing materials is difficult and slow. Just look at plastic—the first totally synthetic plastic was invented in 1907, but it took until roughly the 1950s for companies to produce the wide range we’re familiar with today. (And of course, though it is incredibly useful, plastic also causes no shortage of complications for society.)

In recent decades, materials science has fallen a bit flat—David has been covering this field for nearly 40 years, and as he puts it, there have been just a few major commercial breakthroughs in that time. (Lithium-ion batteries are one.)

Could AI change everything? The prospect is a tantalizing one, and companies are racing to test it out.

Lila Sciences, based in Cambridge, Massachusetts, is working on using AI models to uncover new materials. The company can not only train an AI model on all the latest scientific literature, but also plug it into an automated lab, so it can learn from experimental data. The goal is to speed up the iterative process of inventing and testing new materials and look at research in ways that humans might miss.

At an MIT Technology Review event earlier this year, I got to listen to David interview Rafael Gómez-Bombarelli, one of Lila’s cofounders. As he described what the company is working on, Gómez-Bombarelli acknowledged that AI materials discovery hasn’t yet seen a big breakthrough moment. Yet.

Gómez-Bombarelli described how models Lila has trained are providing insights that are “as deep [as] or deeper than our domain scientists would have.” In the future, AI could “think” in ways that depart from how human scientists approach a problem, he added: “There will be a need to translate scientific reasoning by AI to the way we think about the world.”

It’s exciting to see this sort of optimism in materials research, but there’s still a long and winding road before we can satisfyingly say that AI has transformed the field. One major difficulty is that it’s one thing to take suggestions from a model about new experimental methods or new potential structures. It’s quite another to actually make a material and show that it’s novel and useful.

You might remember that a couple of years ago, Google’s DeepMind announced it had used AI to predict the structures of “millions of new materials” and had made hundreds of them in the lab.

But as David notes in his story, after that announcement, some materials scientists pointed out that some of the supposedly novel materials were basically slightly different versions of known ones. Others couldn’t even physically exist in normal conditions (the simulations were done at ultra-low temperatures, where atoms don’t move around much).

It’s possible that AI could give materials discovery a much-needed jolt and usher in a new age that brings superconductors and batteries and magnets we’ve never seen before. But for now, I’m calling hype. 

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

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Dec 19 2025
The 8 worst technology flops of 2025

Welcome to our annual list of the worst, least successful, and simply dumbest technologies of the year.

This year, politics was a recurring theme. Donald Trump swept back into office and used his executive pen to reshape the fortunes of entire sectors, from renewables to cryptocurrency. The wrecking-ball act began even before his inauguration, when the president-elect marketed his own memecoin, $TRUMP, in a shameless act of merchandising that, of course, we honor on this year’s worst tech list.

We like to think there’s a lesson in every technological misadventure. But when technology becomes dependent on power, sometimes the takeaway is simpler: it would have been better to stay away.

That was a conclusion Elon Musk drew from his sojourn as instigator of DOGE, the insurgent cost-cutting initiative that took a chainsaw to federal agencies. The public protested. Teslas were set alight, and drivers of his hyped Cybertruck discovered that instead of a thumbs-up, they were getting the middle finger.

On reflection, Musk said he wouldn’t do it again. “Instead of doing DOGE, I would have, basically … worked on my companies,” he told an interviewer this month. “And they wouldn’t have been burning the cars.”

Regrets—2025 had a few. Here are some of the more notable ones.

NEO, the home robot

1X TECH

Imagine a metal butler that fills your dishwasher and opens the door. It’s a dream straight out of science fiction. And it’s going to remain there—at least for a while.

That was the hilarious, and deflating, takeaway from the first reviews of NEO, a 66-pound humanoid robot whose maker claims it will “handle any of your chores reliably” when it ships next year.

But as a reporter for the Wall Street Journal learned, NEO took two minutes to fold a sweater and couldn’t crack a walnut. Not only that, but the robot was teleoperated the entire time by a person wearing a VR visor.

Still interested? Neo is available on preorder for $20,000 from startup 1X.

More: I Tried the Robot That’s Coming to Live With You. It’s Still Part Human (WSJ), The World’s Stupidest Robot Maid (The Daily Show) Why the humanoid workforce is running late (MIT Technology Review), NEO The Home Robot | Order Today (1X Corp.)

Sycophantic AI

It’s been said that San Francisco is the kind of place where no one will tell you if you have a bad idea. And its biggest product in a decade—ChatGPT—often behaves exactly that way.

This year, OpenAI released an especially sycophantic update that told users their mundane queries were brilliantly incisive. This electronic yes-man routine isn’t an accident; it’s a product strategy. Plenty of people like the flattery.

But it’s disingenuous and dangerous, too. Chatbots have shown a willingness to indulge users’ delusions and worst impulses, up to and including suicide.

In April, OpenAI acknowledged the issue when the company dialed back a model update whose ultra-agreeable personality, it said, had the side effect of “validating doubts, fueling anger, urging impulsive actions, or reinforcing negative emotions.”

Don’t you dare agree the problem is solved. This month, when I fed ChatGPT one of my dumbest ideas, its response began: “I love this concept.”

More: What OpenAI Did When ChatGPT Users Lost Touch With Reality (New York Times), Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence (arXiv), Expanding on what we missed with sycophancy (OpenAI)

The company that cried “dire wolf”

Two dire wolves are seen at 3 months old.

COLOSSAL BIOSCIENCES

When you tell a lie, tell it big. Make it frolic and give it pointy ears. And make it white. Very white.

That’s what the Texas biotech concern Colossal Biosciences did when it unveiled three snow-white animals that it claimed were actual dire wolves, which went extinct more than 10 millennia ago.

To be sure, these genetically modified gray wolves were impressive feats of engineering. They’d been made white via a genetic mutation and even had some bits and bobs of DNA copied over from old dire wolf bones. But they “are not dire wolves,” according to canine specialists at the International Union for Conservation of Nature.

Colossal’s promotional blitz could hurt actual endangered species. Presenting de-extinction as “a ready-to-use conservation solution,” said the IUCN, “risks diverting attention from the more urgent need of ensuring functioning and healthy ecosystems.”

In a statement, Colossal said that sentiment analysis of online activity shows 98% agreement with its furry claims. “They’re dire wolves, end of story,” it says.  

More: Game of Clones: Colossal’s new wolves are cute, but are they dire? (MIT Technology Review), Conservation perspectives on gene editing in wild canids (IUCN),  A statement from Colossal’s Chief Science Officer, Dr. Beth Shapiro (Reddit)

mRNA political purge

RFK Jr composited with a vaccine vial that has a circle and slash icon over it

MITTR | GETTY IMAGES

Save the world, and this is the thanks you get?

During the covid-19 pandemic, the US bet big on mRNA vaccines—and the new technology delivered in record time. 

But now that America’s top health agencies are led by the antivax wackadoodle Robert F. Kennedy Jr., “mRNA” has become a political slur.

In August, Kennedy abruptly canceled hundreds of millions in contracts for next-generation vaccines. And shot maker Moderna—once America’s champion—has seen its stock slide by more than 90% since its Covid peak.

The purge targeting a key molecule of life (our bodies are full of mRNA) isn’t just bizarre. It could slow down other mRNA-based medicine, like cancer treatments and gene editing for rare diseases.

In August, a trade group fought back, saying: “Kennedy’s unscientific and misguided vilification of mRNA technology and cancellation of grants is the epitome of cutting off your nose to spite your face.”

More: HHS Winds Down mRNA Vaccine Development (US Department of Health and Human Services),  Cancelling mRNA studies is the highest irresponsibility (Nature), How Moderna, the company that helped save the world, unraveled (Stat News)

​​Greenlandic Wikipedia

WIKIPEDIA

Wikipedia has editions in 340 languages. But as of this year, there’s one less: Wikipedia in Greenlandic is no more.

Only around 60,000 people speak the Inuit language. And very few of them, it seems, ever cared much about the online encyclopedia. As a result, many of the entries were machine translations riddled with errors and nonsense.

Perhaps a website no one visits shouldn’t be a problem. But its existence created the risk of a linguistic “doom spiral” for the endangered language. That could happen if new AIs were trained on the corrupt Wikipedia articles.  

In September, administrators voted to close Greenlandic Wikipedia, citing possible “harm to the Greenlandic language.”

Read more:  Can AI Help Revitalize Indigenous Languages? (Smithsonian), How AI and Wikipedia have sent vulnerable languages into a doom spiral (MIT Technology Review), Closure of Greenlandic Wikipedia (Wikimedia)

Tesla Cybertruck

Tesla Cybertruck-rows of new cars in port

ADOBE STOCK

There’s a reason we’re late to the hate-fest around Elon Musk’s Cybertruck. That’s because 12 months ago, the polemical polygon was the #1 selling electric pickup in the US.

So maybe it would end up a hit.

Nope. Tesla is likely to sell only around 20,000 trucks this year, about half last year’s total. And a big part of the problem is that the entire EV pickup category is struggling. Just this month, Ford decided to scrap its own EV truck, the F-150 Lightning. 

With unsold inventory building, Musk has started selling Cybertrucks as fleet vehicles to his other enterprises, like SpaceX.

More: Elon’s Edsel: Tesla Cybertruck Is The Auto Industry’s Biggest Flop In Decades (Forbes), Why Tesla Cybertrucks Aren’t Selling (CNBC), Ford scraps fully-electric F-150 Lightning as mounting losses and falling demand hits EV plans (AP)

Presidential shitcoin

VIA GETTRUMPMEMES.COM

Donald Trump launched a digital currency called $TRUMP just days before his 2025 inauguration, accompanied by a logo showing his fist-pumping “Fight, fight, fight” pose.

This was a memecoin, or shitcoin, not real money. Memecoins are more like merchandise—collectibles designed to be bought and sold, usually for a loss. Indeed, they’ve been likened to a consensual scam in which a coin’s issuer can make a bundle while buyers take losses.

The White House says there’s nothing amiss. “The American public believe[s] it’s absurd for anyone to insinuate that this president is profiting off of the presidency,” said spokeswoman Karoline Leavitt in May.

More: Donald and Melania Trump’s Terrible, Tacky, Seemingly Legal Memecoin Adventure (Bloomberg), A crypto mogul who invested millions into Trump coins is getting a reprieve (CNN), How the Trump companies made $1 bn from crypto (Financial Times), Staff Statement on Meme Coins (SEC)

“Carbon-neutral” Apple Watch

Apple's Carbon Neutral logo with the product Apple Watch

APPLE

In 2023, Apple announced its “first-ever carbon-neutral product,” a watch with “zero” net emissions. It would get there using recycled materials and renewable energy, and by preserving forests or planting vast stretches of eucalyptus trees.

Critics say it’s greenwashing. This year, lawyers filed suit in California against Apple for deceptive advertising, and in Germany, a court ruled that the company can’t advertise products as carbon neutral because the “supposed storage of CO2 in commercial eucalyptus plantations” isn’t a sure thing.

Apple’s marketing team relented. Packaging for its newest watches doesn’t say “carbon neutral.” But Apple believes the legal nitpicking is counterproductive, arguing that it can only “discourage the kind of credible corporate climate action the world needs.”

More: Inside the controversial tree farms powering Apple’s carbon neutral goal (MIT Technology Review), Apple Watch not a ‘CO2-neutral product,’ German court finds (Reuters), Apple 2030: Our ambition to become carbon neutral (Apple)

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Dec 19 2025
Take our quiz on the year in health and biotechnology

In just a couple of weeks, we’ll be bidding farewell to 2025. And what a year it has been! Artificial intelligence is being incorporated into more aspects of our lives, weight-loss drugs have expanded in scope, and there have been some real “omg” biotech stories from the fields of gene therapy, IVF, neurotech, and more.   

As always, the team at MIT Technology Review has been putting together our 2026 list of breakthrough technologies. That will be published in the new year (watch this space). In the meantime, my colleague Antonio Regalado has compiled his traditional list of the year’s worst technologies.

I’m inviting you to put your own memory to the test. Just how closely have you been paying attention to the Checkup emails that have been landing in your inbox this year?!

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.

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Dec 19 2025
China figured out how to sell EVs. Now it has to bury their batteries.

In August 2025, Wang Lei decided it was finally time to say goodbye to his electric vehicle.

Wang, who is 39, had bought the car in 2016, when EVs still felt experimental in Beijing. It was a compact Chinese brand. The subsidies were good, and the salesman talked about “supporting domestic innovation.” At the time, only a few people around him were driving on batteries. He liked being early.

But now, the car’s range had started to shrink as the battery’s health declined. He could have replaced the battery, but the warranty had expired; the cost and trouble no longer felt worth it. He also wanted an upgrade, so selling became the obvious choice.

His vague plans turned into action after he started seeing ads on Douyin from local battery recyclers. He asked around at a few recycling places, and the highest offer came from a smaller shop on the outskirts of town. He added the contact on WeChat, and the next day someone drove over to pick up his car. He got paid 8,000 yuan. With the additional automobile scrappage subsidy offered by the Chinese government, Wang ultimately pocketed about 28,000 yuan.

Wang is part of a much larger trend. In the past decade, China has seen an EV boom, thanks in part to government support. Buying an electric car has gone from a novel decision to a routine one; by late 2025, nearly 60% of new cars sold were electric or plug-in hybrids.

But as the batteries in China’s first wave of EVs reach the end of their useful life, early owners are starting to retire their cars, and the country is now under pressure to figure out what to do with those aging components.

The issue is putting strain on China’s still-developing battery recycling industry and has given rise to a gray market that often cuts corners on safety and environmental standards. National regulators and commercial players are also stepping in, building out formal recycling networks and take-back programs, but so far these efforts have struggled to keep pace with the flood of batteries coming off the road.

Like the batteries in our phones and laptops, those in EVs today are mostly lithium-ion packs. Their capacity drops a little every year, making the car slower to charge, shorter in range, and more prone to safety issues. Three professionals who work in EV retail and battery recycling told MIT Technology Review that a battery is often considered to be ready to retire from a car after its capacity has degraded to under 80%. The research institution EVtank estimates that the year’s total volume of retired EV batteries in China will come in at 820,000 tons, with annual totals climbing toward 1 million tons by 2030. 

In China, this growing pile of aging batteries is starting to test a recycling ecosystem that is still far from fully built out but is rapidly growing. By the end of November 2025, China had close to 180,000 enterprises involved in battery recycling, and more than 30,000 of them had been registered since January 2025. Over 60% of the firms were founded within the past three years. This does not even include the unregulated gray market of small workshops.

Typically, one of two things happens when an EV’s battery is retired. One is called cascade utilization, in which usable battery packs are tested and repurposed for slower applications like energy storage or low-speed vehicles. The other is full recycling: Cells are dismantled and processed to recover metals such as lithium, nickel, cobalt, and manganese, which are then reused to manufacture new batteries. Both these processes, if done properly, take significant upfront investment that is often not available to small players. 

But smaller, illicit battery recycling centers can offer higher prices to consumers because they ignore costs that formal recyclers can’t avoid, like environmental protection, fire safety, wastewater treatment, compliance, and taxes, according to the three battery recycling professionals MIT Technology Review spoke to.

“They [workers] crack them open, rearrange the cells into new packs, and repackage them to sell,” says Gary Lin, a battery recycling worker who worked in several unlicensed shops from 2022 to 2024. Sometimes, the refurbished batteries are even sold as “new” to buyers, he says. When the batteries are too old or damaged, workers simply crush them and sell them by weight to rare-metal extractors. “It’s all done in a very brute-force way. The wastewater used to soak the batteries is often just dumped straight into the sewer,” he says. 

This poorly managed battery waste can release toxic substances, contaminate water and soil, and create risks of fire and explosion. That is why the Chinese government has been trying to steer batteries into certified facilities. Since 2018, China’s Ministry of Industry and Information Technology has issued five “white lists” of approved power-battery recyclers, now totaling 156 companies. Despite this, formal recycling rates remain low compared with the rapidly growing volume of waste batteries.

China is not only the world’s largest EV market; it has also become the main global manufacturing hub for EVs and the batteries that power them. In 2024, the country accounted for more than 70% of global electric-car production and more than half of global EV sales, and firms like CATL and BYD together control close to half of global EV battery output, according to a report by the International Energy Agency. These companies are stepping in to offer solutions to customers wishing to offload their old batteries. Through their dealers and 4S stores, many carmakers now offer take-back schemes or opportunities to trade in old batteries for discount when owners scrap a vehicle or buy a new one. 

BYD runs its own recycling operations that process thousands of end-of-life packs a year and has launched dedicated programs with specialist recyclers to recover materials from its batteries. Geely has built a “circular manufacturing” system that combines disassembly of scrapped vehicles, cascade use of power batteries, and high recovery rates for metals and other materials.

CATL, China’s biggest EV maker, has created one of the industry’s most developed recycling systems through its subsidiary Brunp, with more than 240 collection depots, an annual disposal capacity of about 270,000 tons of waste batteries, and metal recovery rates above 99% for nickel, cobalt, and manganese. 

“No one is better equipped to handle these batteries than the companies that make them,” says Alex Li, a battery engineer based in Shanghai. That’s because they already understand the chemistry, the supply chain, and the uses the recovered materials can be put to next. Carmakers and battery makers “need to create a closed loop eventually,” he says.

But not every consumer can receive that support from the maker of their EV, because many of those manufacturers have ceased to exist. In the past five years, over 400 smaller EV brands and startups have gone bankrupt as the price war made it hard to stay afloat, leaving only 100 active brands today. 

Analysts expect many more used batteries to hit the market in the coming years, as the first big wave of EVs bought under generous subsidies reach retirement age. Li says, “China is going to need to move much faster toward a comprehensive end-of-life system for EV batteries—one that can trace, reuse and recycle them at scale, instead of leaving so many to disappear into the gray market.”

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Dec 18 2025
This Nobel Prize–winning chemist dreams of making water from thin air

Omar Yaghi was a quiet child, diligent, unlikely to roughhouse with his nine siblings. So when he was old enough, his parents tasked him with one of the family’s most vital chores: fetching water. Like most homes in his Palestinian neighborhood in Amman, Jordan, the Yaghis’ had no electricity or running water. At least once every two weeks, the city switched on local taps for a few hours so residents could fill their tanks. Young Omar helped top up the family supply. Decades later, he says he can’t remember once showing up late. The fear of leaving his parents, seven brothers, and two sisters parched kept him punctual.

Yaghi proved so dependable that his father put him in charge of monitoring how much the cattle destined for the family butcher shop ate and drank. The best-­quality cuts came from well-fed, hydrated animals—a challenge given that they were raised in arid desert.

Specially designed materials called metal-organic frameworks can pull water from the air like a sponge—and then give it back.

But at 10 years old, Yaghi learned of a different occupation. Hoping to avoid a rambunctious crowd at recess, he found the library doors in his school unbolted and sneaked in. Thumbing through a chemistry textbook, he saw an image he didn’t understand: little balls connected by sticks in fascinating shapes. Molecules. The building blocks of everything.

“I didn’t know what they were, but it captivated my attention,” Yaghi says. “I kept trying to figure out what they might be.”

That’s how he discovered chemistry—or maybe how chemistry discovered him. After coming to the United States and, eventually, a postdoctoral program at Harvard University, Yaghi devoted his career to finding ways to make entirely new and fascinating shapes for those little sticks and balls. In October 2025, he was one of three scientists who won a Nobel Prize in chemistry for identifying metal-­organic frameworks, or MOFs—metal ions tethered to organic molecules that form repeating structural landscapes. Today that work is the basis for a new project that sounds like science fiction, or a miracle: conjuring water out of thin air.

When he first started working with MOFs, Yaghi thought they might be able to absorb climate-damaging carbon dioxide—or maybe hold hydrogen molecules, solving the thorny problem of storing that climate-friendly but hard-to-contain fuel. But then, in 2014, Yaghi’s team of researchers at UC Berkeley had an epiphany. The tiny pores in MOFs could be designed so the material would pull water molecules from the air around them, like a sponge—and then, with just a little heat, give back that water as if squeezed dry. Just one gram of a water-absorbing MOF has an internal surface area of roughly 7,000 square meters.

Yaghi wasn’t the first to try to pull potable water from the atmosphere. But his method could do it at lower levels of humidity than rivals—potentially shaking up a tiny, nascent industry that could be critical to humanity in the thirsty decades to come. Now the company he founded, called Atoco, is racing to demonstrate a pair of machines that Yaghi believes could produce clean, fresh, drinkable water virtually anywhere on Earth, without even hooking up to an energy supply.

That’s the goal Yaghi has been working toward for more than a decade now, with the rigid determination that he learned while doing chores in his father’s butcher shop.

“It was in that shop where I learned how to perfect things, how to have a work ethic,” he says. “I learned that a job is not done until it is well done. Don’t start a job unless you can finish it.”

Most of Earth is covered in water, but just 3% of it is fresh, with no salt—the kind of water all terrestrial living things need. Today, desalination plants that take the salt out of seawater provide the bulk of potable water in technologically advanced desert nations like Israel and the United Arab Emirates, but at a high cost. Desalination facilities either heat water to distill out the drinkable stuff or filter it with membranes the salt doesn’t pass through; both methods require a lot of energy and leave behind concentrated brine. Typically desal pumps send that brine back into the ocean, with devastating ecological effects.

hand holding a ball and stick model
Heiner Linke, chair of the Nobel Committee for Chemistry, uses a model to explain how metalorganic frameworks (MOFs) can trap smaller molecules inside. In October 2025, Yaghi and two other scientists won the Nobel Prize in chemistry for identifying MOFs.
JONATHAN NACKSTRAND/GETTY IMAGES

I was talking to Atoco executives about carbon dioxide capture earlier this year when they mentioned the possibility of harvesting water from the atmosphere. Of course my mind immediately jumped to Star Wars, and Luke Skywalker working on his family’s moisture farm, using “vaporators” to pull water from the atmosphere of the arid planet Tatooine. (Other sci-fi fans’ minds might go to Dune, and the water-gathering technology of the Fremen.) Could this possibly be real?

It turns out people have been doing it for millennia. Archaeological evidence of water harvesting from fog dates back as far as 5000 BCE. The ancient Greeks harvested dew, and 500 years ago so did the Inca, using mesh nets and buckets under trees.

Today, harvesting water from the air is a business already worth billions of dollars, say industry analysts—and it’s on track to be worth billions more in the next five years. In part that’s because typical sources of fresh water are in crisis. Less snowfall in mountains during hotter winters means less meltwater in the spring, which means less water downstream. Droughts regularly break records. Rising seas seep into underground aquifers, already drained by farming and sprawling cities. Aging septic tanks leach bacteria into water, and cancer-causing “forever chemicals” are creating what the US Government Accountability Office last year said “may be the biggest water problem since lead.” That doesn’t even get to the emerging catastrophe from microplastics.

So lots of places are turning to atmospheric water harvesting. Watergen, an Israel-based company working on the tech, initially planned on deploying in the arid, poorer parts of the world. Instead, buyers in Europe and the United States have approached the company as a way to ensure a clean supply of water. And one of Watergen’s biggest markets is the wealthy United Arab Emirates. “When you say ‘water crisis,’ it’s not just the lack of water—it’s access to good-quality water,” says Anna Chernyavsky, Watergen’s vice president of marketing.

In other words, the technology “has evolved from lab prototypes to robust, field-deployable systems,” says Guihua Yu, a mechanical engineer at the University of Texas at Austin. “There is still room to improve productivity and energy efficiency in the whole-system level, but so much progress has been steady and encouraging.”

MOFs are just the latest approach to the idea. The first generation of commercial tech depended on compressors and refrigerant chemicals—large-scale versions of the machine that keeps food cold and fresh in your kitchen. Both use electricity and a clot of pipes and exchangers to make cold by phase-shifting a chemical from gas to liquid and back; refrigerators try to limit condensation, and water generators basically try to enhance it.

That’s how Watergen’s tech works: using a compressor and a heat exchanger to wring water from air at humidity levels as low as 20%—Death Valley in the spring. “We’re talking about deserts,” Chernyavsky says. “Below 20%, you get nosebleeds.”

children in queue at a blue Watergen dispenser
A Watergen unit provides drinking water to students and staff at St. Joseph’s, a girls’ school in Freetown, Sierra Leone. “When you say ‘water crisis,’ it’s not just the lack of water— it’s access to good-quality water,” says Anna Chernyavsky, Watergen’s vice president of marketing.
COURTESY OF WATERGEN

That still might not be good enough. “Refrigeration works pretty well when you are above a certain relative humidity,” says Sameer Rao, a mechanical engineer at the University of Utah who researches atmospheric water harvesting. “As the environment dries out, you go to lower relative humidities, and it becomes harder and harder. In some cases, it’s impossible for refrigeration-based systems to really work.”

So a second wave of technology has found a market. Companies like Source Global use desiccants—substances that absorb moisture from the air, like the silica packets found in vitamin bottles—to pull in moisture and then release it when heated. In theory, the benefit of desiccant-­based tech is that it could absorb water at lower humidity levels, and it uses less energy on the front end since it isn’t running a condenser system. Source Global claims its off-grid, solar-powered system is deployed in dozens of countries.

But both technologies still require a lot of energy, either to run the heat exchangers or to generate sufficient heat to release water from the desiccants. MOFs, Yaghi hopes, do not. Now Atoco is trying to prove it. Instead of using heat exchangers to bring the air temperature to dew point or desiccants to attract water from the atmosphere, a system can rely on specially designed MOFs to attract water molecules. Atoco’s prototype version uses an MOF that looks like baby powder, stuck to a surface like glass. The pores in the MOF naturally draw in water molecules but remain open, making it theoretically easy to discharge the water with no more heat than what comes from direct sunlight. Atoco’s industrial-scale design uses electricity to speed up the process, but the company is working on a second design that can operate completely off grid, without any energy input.

Yaghi’s Atoco isn’t the only contender seeking to use MOFs for water harvesting. A competitor, AirJoule, has introduced MOF-based atmospheric water generators in Texas and the UAE and is working with researchers at Arizona State University, planning to deploy more units in the coming months. The company started out trying to build more efficient air-­conditioning for electric buses operating on hot, humid city streets. But then founder Matt Jore heard about US government efforts to harvest water from air—and pivoted. The startup’s stock price has been a bit of a roller-­coaster, but Jore says the sheer size of the market should keep him in business. Take Maricopa County, encompassing Phoenix and its environs—it uses 1.2 billion gallons of water from its shrinking aquifer every day, and another 874 million gallons from surface sources like rivers.

“So, a couple of billion gallons a day, right?” Jore tells me. “You know how much influx is in the atmosphere every day? Twenty-five billion gallons.”

My eyebrows go up. “Globally?”

“Just the greater Phoenix area gets influx of about 25 billion gallons of water in the air,” he says. “If you can tap into it, that’s your source. And it’s not going away. It’s all around the world. We view the atmosphere as the world’s free pipeline.”

Besides AirJoule’s head start on Atoco, the companies also differ on where they get their MOFs. AirJoule’s system relies on an off-the-shelf version the company buys from the chemical giant BASF; Atoco aims to use Yaghi’s skill with designing the novel material to create bespoke MOFs for different applications and locations.

“Given the fact that we have the inventor of the whole class of materials, and we leverage the stuff that comes out of his lab at Berkeley—everything else equal, we have a good starting point to engineer maybe the best materials in the world,” says Magnus Bach, Atoco’s VP of business development.

Yaghi envisions a two-pronged product line. Industrial-scale water generators that run on electricity would be capable of producing thousands of liters per day on one end, while units that run on passive systems could operate in remote locations without power, just harnessing energy from the sun and ambient temperatures. In theory, these units could someday replace desalination and even entire municipal water supplies. The next round of field tests is scheduled for early 2026, in the Mojave Desert—one of the hottest, driest places on Earth.

“That’s my dream,” Yaghi says. “To give people water independence, so they’re not reliant on another party for their lives.”

Both Yaghi and Watergen’s Chernyavsky say they’re looking at more decentralized versions that could operate outside municipal utility systems. Home appliances, similar to rooftop solar panels and batteries, could allow households to generate their own water off grid.

That could be tricky, though, without economies of scale to bring down prices. “You have to produce, you have to cool, you have to filter—all in one place,” Chernyavsky says. “So to make it small is very, very challenging.”

Difficult as that may be, Yaghi’s childhood gave him a particular appreciation for the freedom to go off grid, to liberate the basic necessity of water from the whims of systems that dictate when and how people can access it.

“That’s really my dream,” he says. “To give people independence, water independence, so that they’re not reliant on another party for their livelihood or lives.”

Toward the end of one of our conversations, I asked Yaghi what he would tell the younger version of himself if he could. “Jordan is one of the worst countries in terms of the impact of water stress,” he said. “I would say, ‘Continue to be diligent and observant. It doesn’t really matter what you’re pursuing, as long as you’re passionate.’”

I pressed him for something more specific: “What do you think he’d say when you described this technology to him?”

Yaghi smiled: “I think young Omar would think you’re putting him on, that this is all fictitious and you’re trying to take something from him.” This reality, in other words, would be beyond young Omar’s wildest dreams.

Alexander C. Kaufman is a reporter who has covered energy, climate change, pollution, business, and geopolitics for more than a decade.

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Dec 17 2025
Why it’s time to reset our expectations for AI

Can I ask you a question: How do you feel about AI right now? Are you still excited? When you hear that OpenAI or Google just dropped a new model, do you still get that buzz? Or has the shine come off it, maybe just a teeny bit? Come on, you can be honest with me.

Truly, I feel kind of stupid even asking the question, like a spoiled brat who has too many toys at Christmas. AI is mind-blowing. It’s one of the most important technologies to have emerged in decades (despite all its many many drawbacks and flaws and, well, issues).

At the same time I can’t help feeling a little bit: Is that it?

If you feel the same way, there’s good reason for it: The hype we have been sold for the past few years has been overwhelming. We were told that AI would solve climate change. That it would reach human-level intelligence. That it would mean we no longer had to work!

Instead we got AI slop, chatbot psychosis, and tools that urgently prompt you to write better email newsletters. Maybe we got what we deserved. Or maybe we need to reevaluate what AI is for.

That’s the reality at the heart of a new series of stories, published today, called Hype Correction. We accept that AI is still the hottest ticket in town, but it’s time to re-set our expectations.

As my colleague Will Douglas Heaven puts it in the package’s intro essay, “You can’t help but wonder: When the wow factor is gone, what’s left? How will we view this technology a year or five from now? Will we think it was worth the colossal costs, both financial and environmental?” 

Elsewhere in the package, James O’Donnell looks at Sam Altman, the ultimate AI hype man, through the medium of his own words. And Alex Heath explains the AI bubble, laying out for us what it all means and what we should look out for.

Michelle Kim analyzes one of the biggest claims in the AI hype cycle: that AI would completely eliminate the need for certain classes of jobs. If ChatGPT can pass the bar, surely that means it will replace lawyers? Well, not yet, and maybe not ever. 

Similarly, Edd Gent tackles the big question around AI coding. Is it as good as it sounds? Turns out the jury is still out. And elsewhere David Rotman looks at the real-world work that needs to be done before AI materials discovery has its breakthrough ChatGPT moment.

Meanwhile, Garrison Lovely spends time with some of the biggest names in the AI safety world and asks: Are the doomers still okay? I mean, now that people are feeling a bit less scared about their impending demise at the hands of superintelligent AI? And Margaret Mitchell reminds us that hype around generative AI can blind us to the AI breakthroughs we should really celebrate.

Let’s remember: AI was here before ChatGPT and it will be here after. This hype cycle has been wild, and we don’t know what its lasting impact will be. But AI isn’t going anywhere. We shouldn’t be so surprised that those dreams we were sold haven’t come true—yet.

The more likely story is that the real winners, the killer apps, are still to come. And a lot of money is being bet on that prospect. So yes: The hype could never sustain itself over the short term. Where we’re at now is maybe the start of a post-hype phase. In an ideal world, this hype correction will reset expectations. 

Let’s all catch our breath, shall we?

This story first appeared in The Algorithm, our weekly free newsletter all about AI. Sign up to read past editions here.

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Dec 16 2025
AI coding is now everywhere. But not everyone is convinced.

Depending who you ask, AI-powered coding is either giving software developers an unprecedented productivity boost or churning out masses of poorly designed code that saps their attention and sets software projects up for serious long term-maintenance problems.

The problem is right now, it’s not easy to know which is true.

As tech giants pour billions into large language models (LLMs), coding has been touted as the technology’s killer app. Both Microsoft CEO Satya Nadella and Google CEO Sundar Pichai have claimed that around a quarter of their companies’ code is now AI-generated. And in March, Anthropic’s CEO, Dario Amodei, predicted that within six months 90% of all code would be written by AI. It’s an appealing and obvious use case. Code is a form of language, we need lots of it, and it’s expensive to produce manually. It’s also easy to tell if it works—run a program and it’s immediately evident whether it’s functional.

This story is part of MIT Technology Review’s Hype Correction package, a series that resets expectations about what AI is, what it makes possible, and where we go next.

Executives enamored with the potential to break through human bottlenecks are pushing engineers to lean into an AI-powered future. But after speaking to more than 30 developers, technology executives, analysts, and researchers, MIT Technology Review found that the picture is not as straightforward as it might seem.  

For some developers on the front lines, initial enthusiasm is waning as they bump up against the technology’s limitations. And as a growing body of research suggests that the claimed productivity gains may be illusory, some are questioning whether the emperor is wearing any clothes.

The pace of progress is complicating the picture, though. A steady drumbeat of new model releases mean these tools’ capabilities and quirks are constantly evolving. And their utility often depends on the tasks they are applied to and the organizational structures built around them. All of this leaves developers navigating confusing gaps between expectation and reality. 

Is it the best of times or the worst of times (to channel Dickens) for AI coding? Maybe both.

A fast-moving field

It’s hard to avoid AI coding tools these days. There are a dizzying array of products available, both from model developers like Anthropic, OpenAI, and Google and from companies like Cursor and Windsurf, which wrap these models in polished code-editing software. And according to Stack Overflow’s 2025 Developer Survey, they’re being adopted rapidly, with 65% of developers now using them at least weekly.

AI coding tools first emerged around 2016 but were supercharged with the arrival of LLMs. Early versions functioned as little more than autocomplete for programmers, suggesting what to type next. Today they can analyze entire code bases, edit across files, fix bugs, and even generate documentation explaining how the code works. All this is guided through natural-language prompts via a chat interface.

“Agents”—autonomous LLM-powered coding tools that can take a high-level plan and build entire programs independently—represent the latest frontier in AI coding. This leap was enabled by the latest reasoning models, which can tackle complex problems step by step and, crucially, access external tools to complete tasks. “This is how the model is able to code, as opposed to just talk about coding,” says Boris Cherny, head of Claude Code, Anthropic’s coding agent.

These agents have made impressive progress on software engineering benchmarks—standardized tests that measure model performance. When OpenAI introduced the SWE-bench Verified benchmark in August 2024, offering a way to evaluate agents’ success at fixing real bugs in open-source repositories, the top model solved just 33% of issues. A year later, leading models consistently score above 70%. 

In February, Andrej Karpathy, a founding member of OpenAI and former director of AI at Tesla, coined the term “vibe coding”—meaning an approach where people describe software in natural language and let AI write, refine, and debug the code. Social media abounds with developers who have bought into this vision, claiming massive productivity boosts.

But while some developers and companies report such productivity gains, the hard evidence is more mixed. Early studies from GitHub, Google, and Microsoft—all vendors of AI tools—found developers completing tasks 20% to 55% faster. But a September report from the consultancy Bain & Company described real-world savings as “unremarkable.”

Data from the developer analytics firm GitClear shows that most engineers are producing roughly 10% more durable code—code that isn’t deleted or rewritten within weeks—since 2022, likely thanks to AI. But that gain has come with sharp declines in several measures of code quality. Stack Overflow’s survey also found trust and positive sentiment toward AI tools falling significantly for the first time. And most provocatively, a July study by the nonprofit research organization Model Evaluation & Threat Research (METR) showed that while experienced developers believed AI made them 20% faster, objective tests showed they were actually 19% slower.

Growing disillusionment

For Mike Judge, principal developer at the software consultancy Substantial, the METR study struck a nerve. He was an enthusiastic early adopter of AI tools, but over time he grew frustrated with their limitations and the modest boost they brought to his productivity. “I was complaining to people because I was like, ‘It’s helping me but I can’t figure out how to make it really help me a lot,’” he says. “I kept feeling like the AI was really dumb, but maybe I could trick it into being smart if I found the right magic incantation.”

When asked by a friend, Judge had estimated the tools were providing a roughly 25% speedup. So when he saw similar estimates attributed to developers in the METR study he decided to test his own. For six weeks, he guessed how long a task would take, flipped a coin to decide whether to use AI or code manually, and timed himself. To his surprise, AI slowed him down by an median of 21%—mirroring the METR results.

This got Judge crunching the numbers. If these tools were really speeding developers up, he reasoned, you should see a massive boom in new apps, website registrations, video games, and projects on GitHub. He spent hours and several hundred dollars analyzing all the publicly available data and found flat lines everywhere.

“Shouldn’t this be going up and to the right?” says Judge. “Where’s the hockey stick on any of these graphs? I thought everybody was so extraordinarily productive.” The obvious conclusion, he says, is that AI tools provide little productivity boost for most developers. 

Developers interviewed by MIT Technology Review generally agree on where AI tools excel: producing “boilerplate code” (reusable chunks of code repeated in multiple places with little modification), writing tests, fixing bugs, and explaining unfamiliar code to new developers. Several noted that AI helps overcome the “blank page problem” by offering an imperfect first stab to get a developer’s creative juices flowing. It can also let nontechnical colleagues quickly prototype software features, easing the load on already overworked engineers.

These tasks can be tedious, and developers are typically  glad to hand them off. But they represent only a small part of an experienced engineer’s workload. For the more complex problems where engineers really earn their bread, many developers told MIT Technology Review, the tools face significant hurdles.

Perhaps the biggest problem is that LLMs can hold only a limited amount of information in their “context window”—essentially their working memory. This means they struggle to parse large code bases and are prone to forgetting what they’re doing on longer tasks. “It gets really nearsighted—it’ll only look at the thing that’s right in front of it,” says Judge. “And if you tell it to do a dozen things, it’ll do 11 of them and just forget that last one.”

DEREK BRAHNEY

LLMs’ myopia can lead to headaches for human coders. While an LLM-generated response to a problem may work in isolation, software is made up of hundreds of interconnected modules. If these aren’t built with consideration for other parts of the software, it can quickly lead to a tangled, inconsistent code base that’s hard for humans to parse and, more important, to maintain.

Developers have traditionally addressed this by following conventions—loosely defined coding guidelines that differ widely between projects and teams. “AI has this overwhelming tendency to not understand what the existing conventions are within a repository,” says Bill Harding, the CEO of GitClear. “And so it is very likely to come up with its own slightly different version of how to solve a problem.”

The models also just get things wrong. Like all LLMs, coding models are prone to “hallucinating”—it’s an issue built into how they work. But because the code they output looks so polished, errors can be difficult to detect, says James Liu, director of software engineering at the advertising technology company Mediaocean. Put all these flaws together, and using these tools can feel a lot like pulling a lever on a one-armed bandit. “Some projects you get a 20x improvement in terms of speed or efficiency,” says Liu. “On other things, it just falls flat on its face, and you spend all this time trying to coax it into granting you the wish that you wanted and it’s just not going to.”

Judge suspects this is why engineers often overestimate productivity gains. “You remember the jackpots. You don’t remember sitting there plugging tokens into the slot machine for two hours,” he says.

And it can be particularly pernicious if the developer is unfamiliar with the task. Judge remembers getting AI to help set up a Microsoft cloud service called an Azure Functions, which he’d never used before. He thought it would take about two hours, but nine hours later he threw in the towel. “It kept leading me down these rabbit holes and I didn’t know enough about the topic to be able to tell it ‘Hey, this is nonsensical,’” he says.

The debt begins to mount up

Developers constantly make trade-offs between speed of development and the maintainability of their code—creating what’s known as “technical debt,” says Geoffrey G. Parker, professor of engineering innovation at Dartmouth College. Each shortcut adds complexity and makes the code base harder to manage, accruing “interest” that must eventually be repaid by restructuring the code. As this debt piles up, adding new features and maintaining the software becomes slower and more difficult.

Accumulating technical debt is inevitable in most projects, but AI tools make it much easier for time-pressured engineers to cut corners, says GitClear’s Harding. And GitClear’s data suggests this is happening at scale. Since 2020, the company has seen a significant rise in the amount of copy-pasted code—an indicator that developers are reusing more code snippets, most likely based on AI suggestions—and an even bigger decline in the amount of code moved from one place to another, which happens when developers clean up their code base.

And as models improve, the code they produce is becoming increasingly verbose and complex, says Tariq Shaukat, CEO of Sonar, which makes tools for checking code quality. This is driving down the number of obvious bugs and security vulnerabilities, he says, but at the cost of increasing the number of “code smells”—harder-to-pinpoint flaws that lead to maintenance problems and technical debt. 

Recent research by Sonar found that these make up more than 90% of the issues found in code generated by leading AI models. “Issues that are easy to spot are disappearing, and what’s left are much more complex issues that take a while to find,” says Shaukat. “That’s what worries us about this space at the moment. You’re almost being lulled into a false sense of security.”

If AI tools make it increasingly difficult to maintain code, that could have significant security implications, says Jessica Ji, a security researcher at Georgetown University. “The harder it is to update things and fix things, the more likely a code base or any given chunk of code is to become insecure over time,” says Ji.

There are also more specific security concerns, she says. Researchers have discovered a worrying class of hallucinations where models reference nonexistent software packages in their code. Attackers can exploit this by creating packages with those names that harbor vulnerabilities, which the model or developer may then unwittingly incorporate into software. 

LLMs are also vulnerable to “data-poisoning attacks,” where hackers seed the publicly available data sets models train on with data that alters the model’s behavior in undesirable ways, such as generating insecure code when triggered by specific phrases. In October, research by Anthropic found that as few as 250 malicious documents can introduce this kind of back door into an LLM regardless of its size.

The converted

Despite these issues, though, there’s probably no turning back. “Odds are that writing every line of code on a keyboard by hand—those days are quickly slipping behind us,” says Kyle Daigle, chief operating officer at the Microsoft-owned code-hosting platform GitHub, which produces a popular AI-powered tool called Copilot (not to be confused with the Microsoft product of the same name).

The Stack Overflow report found that despite growing distrust in the technology, usage has increased rapidly and consistently over the past three years. Erin Yepis, a senior analyst at Stack Overflow, says this suggests that engineers are taking advantage of the tools with a clear-eyed view of the risks. The report also found that frequent users tend to be more enthusiastic and more than half of developers are not using the latest coding agents, perhaps explaining why many remain underwhelmed by the technology.

Those latest tools can be a revelation. Trevor Dilley, CTO at the software development agency Twenty20 Ideas, says he had found some value in AI editors’ autocomplete functions, but when he tried anything more complex it would “fail catastrophically.” Then in March, while on vacation with his family, he set the newly released Claude Code to work on one of his hobby projects. It completed a four-hour task in two minutes, and the code was better than what he would have written.

“I was like, Whoa,” he says. “That, for me, was the moment, really. There’s no going back from here.” Dilley has since cofounded a startup called DevSwarm, which is creating software that can marshal multiple agents to work in parallel on a piece of software.

The challenge, says Armin Ronacher, a prominent open-source developer, is that the learning curve for these tools is shallow but long. Until March he’d remained unimpressed by AI tools, but after leaving his job at the software company Sentry in April to launch a startup, he started experimenting with agents. “I basically spent a lot of months doing nothing but this,” he says. “Now, 90% of the code that I write is AI-generated.”

Getting to that point involved extensive trial and error, to figure out which problems tend to trip the tools up and which they can handle efficiently. Today’s models can tackle most coding tasks with the right guardrails, says Ronacher, but these can be very task and project specific.

To get the most out of these tools, developers must surrender control over individual lines of code and focus on the overall software architecture, says Nico Westerdale, chief technology officer at the veterinary staffing company IndeVets. He recently built a data science platform 100,000 lines of code long almost exclusively by prompting models rather than writing the code himself.

Westerdale’s process starts with an extended conversation with the modelagent to develop a detailed plan for what to build and how. He then guides it through each step. It rarely gets things right on the first try and needs constant wrangling, but if you force it to stick to well-defined design patterns, the models can produce high-quality, easily maintainable code, says Westerdale. He reviews every line, and the code is as good as anything he’s ever produced, he says: “I’ve just found it absolutely revolutionary,. It’s also frustrating, difficult, a different way of thinking, and we’re only just getting used to it.”

But while individual developers are learning how to use these tools effectively, getting consistent results across a large engineering team is significantly harder. AI tools amplify both the good and bad aspects of your engineering culture, says Ryan J. Salva, senior director of product management at Google. With strong processes, clear coding patterns, and well-defined best practices, these tools can shine. 

DEREK BRAHNEY

But if your development process is disorganized, they’ll only magnify the problems. It’s also essential to codify that institutional knowledge so the models can draw on it effectively. “A lot of work needs to be done to help build up context and get the tribal knowledge out of our heads,” he says.

The cryptocurrency exchange Coinbase has been vocal about its adoption of AI tools. CEO Brian Armstrong made headlines in August when he revealed that the company had fired staff unwilling to adopt AI tools. But Coinbase’s head of platform, Rob Witoff, tells MIT Technology Review that while they’ve seen massive productivity gains in some areas, the impact has been patchy. For simpler tasks like restructuring the code base and writing tests, AI-powered workflows have achieved speedups of up to 90%. But gains are more modest for other tasks, and the disruption caused by overhauling existing processes often counteracts the increased coding speed, says Witoff.

One factor is that AI tools let junior developers produce far more code,. As in almost all engineering teams, this code has to be reviewed by others, normally more senior developers, to catch bugs and ensure it meets quality standards. But the sheer volume of code now being churned out i whichs quickly saturatinges the ability of midlevel staff to review changes. “This is the cycle we’re going through almost every month, where we automate a new thing lower down in the stack, which brings more pressure higher up in the stack,” he says. “Then we’re looking at applying automation to that higher-up piece.”

Developers also spend only 20% to 40% of their time coding, says Jue Wang, a partner at Bain, so even a significant speedup there often translates to more modest overall gains. Developers spend the rest of their time analyzing software problems and dealing with customer feedback, product strategy, and administrative tasks. To get significant efficiency boosts, companies may need to apply generative AI to all these other processes too, says Jue, and that is still in the works.

Rapid evolution

Programming with agents is a dramatic departure from previous working practices, though, so it’s not surprising companies are facing some teething issues. These are also very new products that are changing by the day. “Every couple months the model improves, and there’s a big step change in the model’s coding capabilities and you have to get recalibrated,” says Anthropic’s Cherny.

For example, in June Anthropic introduced a built-in planning mode to Claude; it has since been replicated by other providers. In October, the company also enabled Claude to ask users questions when it needs more context or faces multiple possible solutions, which Cherny says helps it avoid the tendency to simply assume which path is the best way forward.

Most significant, Anthropic has added features that make Claude better at managing its own context. When it nears the limits of its working memory, it summarizes key details and uses them to start a new context window, effectively giving it an “infinite” one, says Cherny. Claude can also invoke sub-agents to work on smaller tasks, so it no longer has to hold all aspects of the project in its own head. The company claims that its latest model, Claude 4.5 Sonnet, can now code autonomously for more than 30 hours without major performance degradation.

Novel approaches to software development could also sidestep coding agents’ other flaws. MIT professor Max Tegmark has introduced something he calls “vericoding,” which could allow agents to produce entirely bug-free code from a natural-language description. It builds on an approach known as “formal verification,” where developers create a mathematical model of their software that can prove incontrovertibly that it functions correctly. This approach is used in high-stakes areas like flight-control systems and cryptographic libraries, but it remains costly and time-consuming, limiting its broader use.

Rapid improvements in LLMs’ mathematical capabilities have opened up the tantalizing possibility of models that produce not only software but the mathematical proof that it’s bug free, says Tegmark. “You just give the specification, and the AI comes back with provably correct code,” he says. “You don’t have to touch the code. You don’t even have to ever look at the code.”

When tested on about 2,000 vericoding problems in Dafny—a language designed for formal verification—the best LLMs solved over 60%, according to non-peer-reviewed research by Tegmark’s group. This was achieved with off-the-shelf LLMs, and Tegmark expects that training specifically for vericoding could improve scores rapidly.

And counterintuitively, Tthe speed at which AI generates code could actuallylso ease maintainability concerns. Alex Worden, principal engineer at the business software giant Intuit, notes that maintenance is often difficult because engineers reuse components across projects, creating a tangle of dependencies where one change triggers cascading effects across the code base. Reusing code used to save developers time, but in a world where AI can produce hundreds of lines of code in seconds, that imperative has gone, says Worden.

Instead, he advocates for “disposable code,” where each component is generated independently by AI without regard for whether it follows design patterns or conventions. They are then connected via APIs—sets of rules that let components request information or services from each other. Each component’s inner workings are not dependent on other parts of the code base, making it possible to rip them out and replace them without wider impact, says Worden. 

“The industry is still concerned about humans maintaining AI-generated code,” he says. “I question how long humans will look at or care about code.”

A narrowing talent pipeline

For the foreseeable future, though, humans will still need to understand and maintain the code that underpins their projects. And one of the most pernicious side effects of AI tools may be a shrinking pool of people capable of doing so. 

Early evidence suggests that fears around the job-destroying effects of AI may be justified. A recent Stanford University study found that employment among software developers aged 22 to 25 fell nearly 20% between 2022 and 2025, coinciding with the rise of AI-powered coding tools.

Experienced developers could face difficulties too. Luciano Nooijen, an engineer at the video-game infrastructure developer Companion Group, used AI tools heavily in his day job, where they were provided for free. But when he began a side project without access to those tools, he found himself struggling with tasks that previously came naturally. “I was feeling so stupid because things that used to be instinct became manual, sometimes even cumbersome,” says Nooijen.

Just as athletes still perform basic drills, he thinks the only way to maintain an instinct for coding is to regularly practice the grunt work. That’s why he’s largely abandoned AI tools, though he admits that deeper motivations are also at play. 

Part of the reason Nooijen and other developers MIT Technology Review spoke to are pushing back against AI tools is a sense that they are hollowing out the parts of their jobs that they love. “I got into software engineering because I like working with computers. I like making machines do things that I want,” Nooijen says. “It’s just not fun sitting there with my work being done for me.”

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Dec 16 2025
A brief history of Sam Altman’s hype

Each time you’ve heard a borderline outlandish idea of what AI will be capable of, it often turns out that Sam Altman was, if not the first to articulate it, at least the most persuasive and influential voice behind it. 

For more than a decade he has been known in Silicon Valley as a world-class fundraiser and persuader. OpenAI’s early releases around 2020 set the stage for a mania around large language models, and the launch of ChatGPT in November 2022 granted Altman a world stage on which to present his new thesis: that these models mirror human intelligence and could swing the doors open to a healthier and wealthier techno-utopia.

This story is part of MIT Technology Review’s Hype Correction package, a series that resets expectations about what AI is, what it makes possible, and where we go next.

Throughout, Altman’s words have set the agenda. He has framed a prospective superintelligent AI as either humanistic or catastrophic, depending on what effect he was hoping to create, what he was raising money for, or which tech giant seemed like his most formidable competitor at the moment. 

Examining Altman’s statements over the years reveals just how much his outlook has powered today’s AI boom. Even among Silicon Valley’s many hypesters, he’s been especially willing to speak about open questions—whether large language models contain the ingredients of human thought, whether language can also produce intelligence—as if they were already answered. 

What he says about AI is rarely provable when he says it, but it persuades us of one thing: This road we’re on with AI can go somewhere either great or terrifying, and OpenAI will need epic sums to steer it toward the right destination. In this sense, he is the ultimate hype man.

To understand how his voice has shaped our understanding of what AI can do, we read almost everything he’s ever said about the technology (we requested an interview with Altman, but he was not made available). 

His own words trace how we arrived here.

In conclusion … 

Altman didn’t dupe the world. OpenAI has ushered in a genuine tech revolution, with increasingly impressive language models that have attracted millions of users. Even skeptics would concede that LLMs’ conversational ability is astonishing.

But Altman’s hype has always hinged less on today’s capabilities than on a philosophical tomorrow—an outlook that quite handily doubles as a case for more capital and friendlier regulation. Long before large language models existed, he was imagining an AI powerful enough to require wealth redistribution, just as he imagined humanity colonizing other planets. Again and again, promises of a destination—abundance, superintelligence, a healthier and wealthier world—have come first, and the evidence second. 

Even if LLMs eventually hit a wall, there’s little reason to think his faith in a techno-utopian future will falter. The vision was never really about the particulars of the current model anyway. 

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