2025-12-24 19:00:00
Climate news hasn’t been great in 2025. Global greenhouse-gas emissions hit record highs (again). This year is set to be either the second or third warmest on record. Climate-fueled disasters like wildfires in California and flooding in Indonesia and Pakistan devastated communities and caused billions in damage.
In addition to these worrying indicators of our continued contributions to climate change and their obvious effects, the world’s largest economy has made a sharp U-turn on climate policy this year. The US under the Trump administration withdrew from the Paris Agreement, cut funds for climate research, and scrapped billions of dollars in funding for climate tech projects.
We’re in a severe situation with climate change. But for those looking for bright spots, there was some good news in 2025. Here are a few of the positive stories our climate reporters noticed this year.
One of the most notable and encouraging signs of progress this year occurred in China. The world’s second-biggest economy and biggest climate polluter has managed to keep carbon dioxide emissions flat for the last year and a half, according to an analysis in Carbon Brief.
That’s happened before, but only when the nation’s economy was retracting, including in the midst of the covid-19 pandemic. But emissions are now falling even as China’s economy is on track to grow about 5% this year, and electricity demands continue to rise.
So what’s changed? China has now installed so much solar and wind, and put so many EVs on the road, that its economy can continue to expand without increasing the amount of carbon dioxide it’s pumping into the atmosphere, decoupling the traditional link between emissions and growth.
Specifically, China added an astounding 240 gigawatts of solar power capacity and 61 gigawatts of wind power in the first nine months of the year, the Carbon Brief analysis noted. That’s nearly as much solar power as the US has installed in total, in just the first three quarters of this year.
It’s too early to say China’s emissions have peaked, but the country has said it will officially reach that benchmark before 2030.
To be clear, China still isn’t moving fast enough to keep the world on track for meeting relatively safe temperature targets. (Indeed, very few countries are.) But it’s now both producing most of the world’s clean energy technologies and curbing its emissions growth, providing a model for cleaning up industrial economies without sacrificing economic prosperity—and setting the stage for faster climate progress in the coming years.
It’s hard to articulate just how quickly batteries for grid storage are coming online. These massive arrays of cells can soak up electricity when sources like solar are available and prices are low, and then discharge power back to the grid when it’s needed most.
Back in 2015, the battery storage industry had installed only a fraction of a gigawatt of battery storage capacity across the US. That year, it set a seemingly bold target of adding 35 gigawatts by 2035. The sector passed that goal a decade early this year and then hit 40 gigawatts a couple of months later.
Costs are still falling, which could help maintain the momentum for the technology’s deployment. This year, battery prices for EVs and stationary storage fell yet again, reaching a record low, according to data from BloombergNEF. Battery packs specifically used for grid storage saw prices fall even faster than the average; they cost 45% less than last year.
We’re starting to see what happens on grids with lots of battery capacity, too: in California and Texas, batteries are already helping meet demand in the evenings, reducing the need to run natural-gas plants. The result: a cleaner, more stable grid.
The AI boom is complicated for our energy system, as we covered at length this year. Electricity demand is ticking up: the amount of power utilities supplied to US data centers jumped 22% this year and will more than double by 2030.
But at least one positive shift is coming out of AI’s influence on energy: It’s driving renewed interest and investment in next-generation energy technologies.
In the near term, much of the energy needed for data centers, including those that power AI, will likely come from fossil fuels, especially new natural-gas power plants. But tech giants like Google, Microsoft, and Meta all have goals on the books to reduce their greenhouse-gas emissions, so they’re looking for alternatives.
Meta signed a deal with XGS Energy in June to purchase up to 150 megawatts of electricity from a geothermal plant. In October, Google signed an agreement that will help reopen Duane Arnold Energy Center in Iowa, a previously shuttered nuclear power plant.
Geothermal and nuclear could be key pieces of the grid of the future, as they can provide constant power in a way that wind and solar don’t. There’s a long way to go for many of the new versions of the tech, but more money and interest from big, powerful players can’t hurt.
Perhaps the strongest evidence of collective climate progress so far: We’ve already avoided the gravest dangers that scientists feared just a decade ago.
The world is on track for about 2.6 °C of warming over preindustrial conditions by 2100, according to Climate Action Tracker, an independent scientific effort to track the policy progress that nations have made toward their goals under the Paris climate agreement.
That’s a lot warmer than we want the planet to ever get. But it’s also a whole degree better than the 3.6 °C path that we were on a decade ago, just before nearly 200 countries signed the Paris deal.
That progress occurred because more and more nations passed emissions mandates, funded subsidies, and invested in research and development—and private industry got busy cranking out vast amounts of solar panels, wind turbines, batteries, and EVs.
The bad news is that progress has stalled. Climate Action Tracker notes that its warming projections have remained stubbornly fixed for the last four years, as nations have largely failed to take the additional action needed to bend that curve closer to the 2 °C goal set out in the international agreement.
But having shaved off a degree of danger is still demonstrable proof that we can pull together in the face of a global threat and address a very, very hard problem. And it means we’ve done the difficult work of laying down the technical foundation for a society that can largely run without spewing ever more greenhouse gas into the atmosphere.
Hopefully, as cleantech continues to improve and climate change steadily worsens, the world will find the collective will to pick up the pace again soon.
2025-12-24 19:00:00
In April 2025, Ronald Deibert left all electronic devices at home in Toronto and boarded a plane. When he landed in Illinois, he took a taxi to a mall and headed directly to the Apple Store to purchase a new laptop and iPhone. He’d wanted to keep the risk of having his personal devices confiscated to a minimum, because he knew his work made him a prime target for surveillance. “I’m traveling under the assumption that I am being watched, right down to exactly where I am at any moment,” Deibert says.
Deibert directs the Citizen Lab, a research center he founded in 2001 to serve as “counterintelligence for civil society.” Housed at the University of Toronto, the lab operates independently of governments or corporate interests, relying instead on research grants and private philanthropy for financial support. It’s one of the few institutions that investigate cyberthreats exclusively in the public interest, and in doing so, it has exposed some of the most egregious digital abuses of the past two decades.
For many years, Deibert and his colleagues have held up the US as the standard for liberal democracy. But that’s changing, he says: “The pillars of democracy are under assault in the United States. For many decades, in spite of its flaws, it has upheld norms about what constitutional democracy looks like or should aspire to. [That] is now at risk.”
Even as some of his fellow Canadians avoided US travel after Donald Trump’s second election, Deibert relished the opportunity to visit. Alongside his meetings with human rights defenders, he also documented active surveillance at Columbia University during the height of its student protests. Deibert snapped photos of drones above campus and noted the exceptionally strict security protocols. “It was unorthodox to go to the United States,” he says. “But I really gravitate toward problems in the world.”
Deibert, 61, grew up in East Vancouver, British Columbia, a gritty area with a boisterous countercultural presence. In the ’70s, Vancouver brimmed with draft dodgers and hippies, but Deibert points to American investigative journalism—exposing the COINTELPRO surveillance program, the Pentagon Papers, Watergate—as the seed of his respect for antiestablishment sentiment. He didn’t imagine that this fascination would translate into a career, however.
“My horizons were pretty low because I came from a working-class family, and there weren’t many people in my family—in fact, none—who went on to university,” he says.
Deibert eventually entered a graduate program in international relations at the University of British Columbia. His doctoral research brought him to a field of inquiry that would soon explode: the geopolitical implications of the nascent internet.
“In my field, there were a handful of people beginning to talk about the internet, but it was very shallow, and that frustrated me,” he says. “And meanwhile, computer science was very technical, but not political—[politics] was almost like a dirty word.”
Deibert continued to explore these topics at the University of Toronto when he was appointed to a tenure-track professorship, but it wasn’t until after he founded the Citizen Lab in 2001 that his work rose to global prominence.
What put the lab on the map, Deibert says, was its 2009 report “Tracking GhostNet,” which uncovered a digital espionage network in China that had breached offices of foreign embassies and diplomats in more than 100 countries, including the office of the Dalai Lama. The report and its follow-up in 2010 were among the first to publicly expose cybersurveillance in real time. In the years since, the lab has published over 180 such analyses, garnering praise from human rights advocates ranging from Margaret Atwood to Edward Snowden.
The lab has rigorously investigated authoritarian regimes around the world (Deibert says both Russia and China have his name on a “list” barring his entry). The group was the first to uncover the use of commercial spyware to surveil people close to the Saudi dissident and Washington Post journalist Jamal Khashoggi prior to his assassination, and its research has directly informed G7 and UN resolutions on digital repression and led to sanctions on spyware vendors. Even so, in 2025 US Immigration and Customs Enforcement reactivated a $2 million contract with the spyware vendor Paragon. The contract, which the Biden administration had previously placed under a stop-work order, resembles steps taken by governments in Europe and Israel that have also deployed domestic spyware to address security concerns.
“It saves lives, quite literally,” Cindy Cohn, executive director of the Electronic Frontier Foundation, says of the lab’s work. “The Citizen Lab [researchers] were the first to really focus on technical attacks on human rights activists and democracy activists all around the world. And they’re still the best at it.”
When recruiting new Citizen Lab employees (or “Labbers,” as they refer to one another), Deibert forgoes stuffy, pencil-pushing academics in favor of brilliant, colorful personalities, many of whom personally experienced repression from some of the same regimes the lab now investigates.
Noura Aljizawi, a researcher on digital repression who survived torture at the hands of the al-Assad regime in Syria, researches the distinct threat that digital technologies pose to women and queer people, particularly when deployed against exiled nationals. She helped create Security Planner, a tool that gives personalized, expert-reviewed guidance to people looking to improve their digital hygiene, for which the University of Toronto awarded her an Excellence Through Innovation Award.
Work for the lab is not without risk. Citizen Lab fellow Elies Campo, for example, was followed and photographed after the lab published a 2022 report that exposed the digital surveillance of dozens of Catalonian citizens and members of parliament, including four Catalonian presidents who were targeted during or after their terms.
Still, the lab’s reputation and mission make recruitment fairly easy, Deibert says. “This good work attracts a certain type of person,” he says. “But they’re usually also drawn to the sleuthing. It’s detective work, and that can be highly intoxicating—even addictive.”
Deibert frequently deflects the spotlight to his fellow Labbers. He rarely discusses the group’s accomplishments without referencing two senior researchers, Bill Marczak and John Scott-Railton, alongside other staffers. And on the occasion that someone decides to leave the Citizen Lab to pursue another position, this appreciation remains.
“We have a saying: Once a Labber, always a Labber,” Deibert says.
While in the US, Deibert taught a seminar on the Citizen Lab’s work to Northwestern University undergraduates and delivered talks on digital authoritarianism at the Columbia University Graduate School of Journalism. Universities in the US had been subjected to funding cuts and heightened scrutiny from the Trump administration, and Deibert wanted to be “in the mix” at such institutions to respond to what he sees as encroaching authoritarian practices by the US government.
Since Deibert’s return to Canada, the lab has continued its work unearthing digital threats to civil society worldwide, but now Deibert must also contend with the US—a country that was once his benchmark for democracy but has become another subject of his scrutiny. “I do not believe that an institution like the Citizen Lab could exist right now in the United States,” he says. “The type of research that we pioneered is under threat like never before.”
He is particularly alarmed by the increasing pressures facing federal oversight bodies and academic institutions in the US. In September, for example, the Trump administration defunded the Council of the Inspectors General on Integrity and Efficiency, a government organization dedicated to preventing waste, fraud, and abuse within federal agencies, citing partisanship concerns. The White House has also threatened to freeze federal funding to universities that do not comply with administration directives related to gender, DEI, and campus speech. These sorts of actions, Deibert says, undermine the independence of watchdogs and research groups like the Citizen Lab.
Cohn, the director of the EFF, says the lab’s location in Canada allows it to avoid many of these attacks on institutions that provide accountability. “Having the Citizen Lab based in Toronto and able to continue to do its work largely free of the things we’re seeing in the US,” she says, “could end up being tremendously important if we’re going to return to a place of the rule of law and protection of human rights and liberties.”
Finian Hazen is a journalism and political science student at Northwestern University.
2025-12-24 00:00:00
At first glance, it looks like the start of a human pregnancy: A ball-shaped embryo presses gently into the receptive lining of the uterus and then grips tight, burrowing in as the first tendrils of a future placenta appear.
This is implantation—the moment that pregnancy officially begins.
Only none of it is happening inside a body. These images were captured in a Beijing laboratory, inside a microfluidic chip, as scientists watched the scene unfold.
In three papers published this week by Cell Press, scientists are reporting what they call the most accurate efforts yet to mimic the first moments of pregnancy in the lab. They’ve taken human embryos from IVF centers and let these merge with “organoids” made of endometrial cells, which form the lining of the uterus.
The reports—two from China and a third involving a collaboration among researchers in the United Kingdom, Spain, and the US—show how scientists are using engineered tissues to better understand early pregnancy and potentially improve IVF outcomes.
“You have an embryo and the endometrial organoid together,” says Jun Wu, a biologist at the University of Texas Southwestern Medical Center, in Dallas, who contributed to both Chinese reports. “That’s the overarching message of all three papers.”
According to the papers, these 3D combinations are the most complete re-creations yet of the first days of pregnancy and should be useful for studying why IVF treatments often fail.
In each case, the experiments were stopped when the embryos were two weeks old, if not sooner. That is due to legal and ethical rules that typically restrict scientists from going any further than 14 days.
In your basic IVF procedure, an egg is fertilized in the lab and allowed to develop into a spherical embryo called a blastocyst—a process that takes a few days. That blastocyst then gets put into a patient’s uterus in the hope it will establish itself there and ultimately become a baby.
But that’s a common failure point. Many patients will learn that their IVF procedure didn’t work because an embryo never attached.
In the new reports, it’s that initial bond between mother and embryo that is being reproduced in the lab. “IVF means in vitro fertilization, but now this is the stage of in vitro implantation,” says Matteo Molè, a biologist at Stanford University whose results with collaborators in Europe are among those published today. “Considering that implantation is a barrier [to pregnancy], we have the potential to increase the success rate if we can model it in the laboratory.”
Normally implantation is entirely hidden from view because it occurs in someone’s uterus, says Hongmei Wang, a developmental biologist at the Beijing Institute for Stem Cell and Regenerative Medicine, who co-led the effort there. Wang often studies monkeys because she can interrupt their pregnancies to collect the tissues she needs to see. “We’ve always hoped to understand human embryo implantation, but we have lacked a way to do so,” she says. “It’s all happening in the uterus.”
In the Beijing study, researchers tested about 50 donated IVF embryos, but they also ran a thousand more experiments using so-called blastoids. The latter are mimics of early-stage human embryos manufactured from stem cells. Blastoids are easy to make in large numbers and, since they aren’t true embryos, don’t have as many ethical rules on their use.
“The question was, if we have these blastoids, what can we use them for?” says Leqian Yu, the senior author of the report from the Beijing Institute. “The obvious next step was implantation. So how do you do that?”
For the Beijing team, the answer was to build a soft silicone chamber with tiny channels to add nutrients and a space to grow the uterine organoid. After that, blastoids—or real embryos—could be introduced through a window in the device, so the “pregnancy” could start.
“The key question we want to try to answer is what is the first cross-talk between embryo and mother,” says Yu. “I think this is maybe the first time we can see the entire process.”
This isn’t the first time researchers have tried using organoids for this kind of research. At least two startup companies have raised funds to commercialize similar systems—in some cases presenting the organoids as a tool to predict IVF success. In addition to Dawn Bio, a startup based in Vienna, there is Simbryo Technologies, in Houston, which last month said it would begin offering “personalized” predictions for IVF patients using blastoids and endometrial organoids.
To do that test, doctors will take a biopsy of a patient’s uterine lining and grow organoids from it. After that, blastoids will be added to the organoids to gauge whether a woman is likely to be able to support a pregnancy or not. If the blastoids don’t start to implant, it could mean the patient’s uterus isn’t receptive and is the reason IVF isn’t working.
The Beijing team thinks the pregnancy organoids could also be used to identify drugs that might help those patients. In their paper, they describe how they made organoids out of tissue taken from women who’ve had repeated IVF failures. Then they tested 1,119 approved drugs on those samples to see if anything improved.
Several seemed to have helpful effects. One chemical, avobenzone, an ingredient in some types of sunblock, increased the chance that a blastoid would start implanting from just 5% of the time to around 25% of the time. Yu says his center hopes to eventually start a clinical trial if they can find the right drug to try.
The Beijing group is working on ways to improve the organoid system so that it’s even more realistic. Right now, it lacks important cell types, including immune cells and a blood supply. Yu says a next step he’s working on is to add blood vessels and tiny pumps to his chip device, so that he can give the organoids a kind of rudimentary circulation.
This means that in the near future, blastoids or embryos could likely be grown longer, raising questions about how far scientists will be able to take pregnancy in the lab. “I think this technology does raise the possibility of growing things longer,” says Wu, who says some view the research as an initial step toward creating babies entirely outside the body.
However, Wu says incubating a human to term in the laboratory remains impossible, for the time being. “This technology is certainly related to ectogenesis, or development outside the body,” he says. “But I don’t think it’s anywhere near an artificial womb. That’s still science fiction.”
2025-12-23 18:00:00
Lately, everywhere I scroll, I keep seeing the same fish-eyed CCTV view: a grainy wide shot from the corner of a living room, a driveway at night, an empty grocery store. Then something impossible happens. JD Vance shows up at the doorstep in a crazy outfit. A car folds into itself like paper and drives away. A cat comes in and starts hanging out with capybaras and bears, as if in some weird modern fairy tale.
This fake-surveillance look has become one of the signature flavors of what people now call AI slop. For those of us who spend time online watching short videos, slop feels inescapable: a flood of repetitive, often nonsensical AI-generated clips that washes across TikTok, Instagram, and beyond. For that, you can thank new tools like OpenAI’s Sora (which exploded in popularity after launching in app form in September), Google’s Veo series, and AI models built by Runway. Now anyone can make videos, with just a few taps on a screen.
@absolutemem
If I were to locate the moment slop broke through into popular consciousness, I’d pick the video of rabbits bouncing on a trampoline that went viral this summer. For many savvy internet users, myself included, it was the first time we were fooled by an AI video, and it ended up spawning a wave of almost identical riffs, with people making videos of all kinds of animals and objects bouncing on the same trampoline.
My first reaction was that, broadly speaking, all of this sucked. That’s become a familiar refrain, in think pieces and at dinner parties. Everything online is slop now—the internet “enshittified,” with AI taking much of the blame. Initially, I largely agreed, quickly scrolling past every AI video in a futile attempt to send a message to my algorithm. But then friends started sharing AI clips in group chats that were compellingly weird, or funny. Some even had a grain of brilliance buried in the nonsense. I had to admit I didn’t fully understand what I was rejecting—what I found so objectionable.
To try to get to the bottom of how I felt (and why), I recently spoke to the people making the videos, a company creating bespoke tools for creators, and experts who study how new media becomes culture. What I found convinced me that maybe generative AI will not end up ruining everything. Maybe we have been too quick to dismiss AI slop. Maybe there’s a case for looking beyond the surface and seeing a new kind of creativity—one we’re watching take shape in real time, with many of us actually playing a part.
“AI slop” can and does refer to text, audio, or images. But what’s really broken through this year is the flood of quick AI-generated video clips on social platforms, each produced by a short written prompt fed into an AI model. Under the hood, these models are trained on enormous data sets so they can predict what every subsequent frame should look or sound like. It’s much like the process by which text models produce answers in a chat, but slower and far more power-hungry.
Early text-to-video systems, released around 2022 to 2023, could manage only a few seconds of blurry motion; objects warped in and out of existence, characters teleported around, and the giveaway that it was AI was usually a mangled hand or a melting face. In the past two years, newer models like Sora2, Veo 3.1, and Runway’s latest Gen-4.5 have dramatically improved, creating realistic, seamless, and increasingly true-to-prompt videos that can last up to a minute. Some of these models even generate sound and video together, including ambient noise and rough dialogue.
These text-to-video models have often been pitched by AI companies as the future of cinema—tools for filmmakers, studios, and professional storytellers. The demos have leaned into widescreen shots and dramatic camera moves. OpenAI pitched Sora as a “world simulator” while courting Hollywood filmmakers with what it boasted were movie-quality shorts. Google introduced Veo 3 last year as a step toward storyboards and longer scenes, edging directly into film workflows.
All this hinged on the idea that people wanted to make AI-generated videos that looked real. But the reality of how they’re being used is more modest, weirder—and arguably much more interesting. What has turned out to be the home turf for AI video is the six-inch screen in our hands.
Anyone can and does use these tools; a report by Adobe released in October shows that 86% of creators are using generative AI. But so are average social media users—people who aren’t “creators” so much as just people with phones.
That’s how you end up with clips showing things like Indian prime minister Narendra Modi dancing with Gandhi, a crystal that melts into butter the moment a knife touches it, or Game of Thrones reimagined as Henan opera—videos that are hypnotic, occasionally funny, and often deeply stupid. And while micro-trends didn’t start with AI—TikTok and Reels already ran on fast-moving formats—it feels as if AI poured fuel on that fire. Perhaps because the barrier to copying an idea becomes so low, a viral video like the bunnies on trampoline can easily and quickly spawn endless variations on the same concept. You don’t need a costume or a filming location anymore; you just tweak the prompt, hit Generate, and share.
Big tech companies have also jumped on the idea of AI videos as a new social medium. The Sora app allows users to insert AI versions of themselves and other users into scenes. Meta’s Vibes app wants to turn your entire feed into nonstop AI clips.
Of course, the same frictionless setup that allows for harmless, delightful creations also makes it easy to generate much darker slop. Sora has already been used to create so many racist deepfakes of Martin Luther King Jr. that the King estate pushed the company to block new MLK videos entirely. TikTok and X are seeing Sora-watermarked clips of women and girls being strangled circulating in bulk, posted by accounts seemingly dedicated to this one theme. And then there’s “nazislop,” the nickname for AI videos that repackage fascist aesthetics and memes into glossy, algorithm-ready content aimed at teens’ For You pages.
But the prevalence of bad actors hasn’t stopped short AI videos from flourishing as a form. New apps, Discord servers for AI creators, and tutorial channels keep multiplying. And increasingly, the energy in the community seems to be shifting away from trying to create stuff that “passes as real” toward embracing AI’s inherent weirdness. Every day, I stumble across creators who are stretching what “AI slop” is supposed to look like. I decided to talk to some of them.
Like those fake surveillance videos, many popular viral AI videos rely on a surreal, otherworldly quality. As Wenhui Lim, an architecture designer turned full-time AI artist, tells me, “There is definitely a competition of ‘How weird we can push this?’ among AI video creators.”
It’s the kind of thing AI video tools seem to handle with ease: pushing physics past what a normal body can do or a normal camera can capture. This makes AI a surprisingly natural fit for satire, comedy skits, parody, and experimental video art—especially examples involving absurdism or even horror. Several popular AI creators that I spoke with eagerly tap into this capability.
Drake Garibay, a 39-year-old software developer from Redlands, California, was inspired by body-horror AI clips circulating on social media in early 2025. He started playing with ComfyUI, a generative media tool, and ended up spending hours each week making his own strange creations. His favorite subject is morbid human-animal hybrids. “I fell right into it,” he says. “I’ve always been pretty artistic, [but] when I saw what AI video tools can do, I was blown away.”
Since the start of this year, Garibay has been posting his experiments online. One that went viral on TikTok, captioned “Cooking up some fresh AI slop,” shows a group of people pouring gooey dough into a pot. The mixture suddenly sprouts a human face, which then emerges from the boiling pot with a head and body. It has racked up more than 8.3 million views.
@digitalpersons
AI video technology is evolving so quickly that even for creative professionals, there is a lot to experiment with. Daryl Anselmo, a creative director turned digital artist, has been experimenting with the technology since its early days, posting an AI-generated video every day since 2021. He tells me that uses a wide range of tools, including Kling, Luma, and Midjourney, and is constantly iterating. To him, testing the boundaries of these AI tools is sometimes itself the reward. “I would like to think there are impossible things that you could not do before that are still yet to be discovered. That is exciting to me,” he says.
Anselmo has collected his daily creations over the past four years into an art project, titled AI Slop, that has been exhibited in multiple galleries, including the Grand Palais Immersif in Paris. There’s obvious attention to mood and composition. Some clips feel like something closer to an art-house vignette than a throwaway meme. Over time, Anselmo’s project has taken a darker turn as his subjects shift from landscapes and interior design toward more of the body horror that drew Garibay in.
His breakout piece, feel the agi, shows a hyperrealistic bot peeling open its own skull. Another video he shared recently features a midnight diner populated by anthropomorphized Tater Tots, titled Tot and Bothered; with its vintage palette and slow, mystical soundtrack, the piece feels like a late-night fever dream.
One further benefit of these AI systems is that they make it easier for creators to build recurring spaces and casts of characters that function like informal franchises. Lim, for instance, is the creator of a popular AI video account called Niceaunties, inspired by the “auntie culture” in Singapore, where she’s from.
“The word ‘aunties’ often has a slightly negative connotation in Singaporean culture. They are portrayed as old-fashioned, naggy, and lacking boundaries. But they are also so resourceful, funny, and at ease with themselves,” she says. “I want to create a world where it’s different for them.”
Her cheeky, playful videos show elderly Asian women merging with fruits, other objects, and architecture, or just living their best lives in a fantasy world. A viral video called Auntlantis, which has racked up 13.5 million views on Instagram, imagines silver-haired aunties as industrial mermaids working in an underwater trash-processing plant.
There’s also Granny Spills, an AI video account that features a glamorous, sassy old lady spitting hot takes and life advice to a street interviewer. It gained 1.8 million Instagram followers within three months of launch, posting new videos almost every day. Although the granny’s face looks slightly different in every video, the pink color scheme and her outfit stay mostly consistent. Creators Eric Suerez and Adam Vaserstein tell me that their entire workflow is powered by AI, from writing the script to constructing the scenes. Their role, as a result, becomes close to creative directing.
@grannyspills
These projects often spin off merch, miniseries, and branded universes. The creators of Granny Spills, for example, have expanded their network, creating a Black granny as well as an Asian granny to cater to different audiences. The grannies now appear in crossover videos, as if they share the same fictional universe, pushing traffic between channels.
In the same vein, it’s now more possible than ever to participate in an online trend. Consider “Italian brainrot,” which went viral earlier this year. Beloved by Gen Z and Gen Alpha, these videos feature human–animal–object hybrids with pseudo-Italian names like “Bombardiro Crocodilo” and “Tralalero Tralala.” According to Know Your Meme, the craze began with a few viral TikTok sounds in fake Italian. Soon, a lot of people were participating in what felt like a massive collaborative hallucination, inventing characters, backstories, and worldviews for an ever-expanding absurdist universe.
@patapimai
“Italian brainrot was great when it first hit,” says Denim Mazuki, a software developer and content creator who has been following the trend. “It was the collective lore-building that made it wonderful. Everyone added a piece. The characters were not owned by a studio or a single creator—they were made by the chronically online users.”
This trend and others are further enabled by specialized and sophisticated new tools—like OpenArt, a platform designed not just for video generation but for video storytelling, which gives users frame-to-frame control over a developing narrative.
Making a video on OpenArt is straightforward: Users start with a few AI-generated character images and a line of text as simple as “cat dancing in a park.” The platform then spins out a scene breakdown that users can tweak act by act, and they can run it through multiple mainstream models and compare the results to see which look best.
OpenArt cofounders Coco Mao and Chloe Fang tell me they sponsored tutorial videos and created quick-start templates to capitalize specifically on the trend of regular people wanting to get in on Italian brainrot. They say more than 80% of their users have no artistic background.
The current use of the word “slop” online traces back to the early 2010s on 4chan, a forum known for its insular and often toxic in-jokes. As the term has spread, its meaning has evolved; it’s now a kind of derogatory slur for anything that feels like low-quality mass production aimed at an unsuspecting public, says Adam Aleksic, an internet linguist. People now slap it onto everything from salad bowls to meaningless work reports.
But even with that broadened usage, AI remains the first association: “slop” has become a convenient shorthand for dismissing almost any AI-generated output, regardless of its actual quality. The Cambridge Dictionary’s new sense of “slop” will almost certainly cement this perception, describing it as “content on the internet that is of very low quality, especially when it is created by AI.”
Perhaps unsurprisingly, the word has become a charged label among AI creators.
Anselmo embraces it semi-ironically, hence the title of his yearslong art project. “I see this series as an experimental sketchbook,” he says. “I am working with the slop, pushing the models, breaking them, and developing a new visual language. I have no shame that I am deep into AI.” Anselmo says that he does not concern himself with whether his work is “art.”
Garibay, the creator of the viral video where a human face emerged from a pot of physical slop, uses the label playfully. “The AI slop art is really just a lot of weird glitchy stuff that happens, and there’s not really a lot of depth usually behind it, besides the shock value,” he says. “But you will find out really fast that there is a heck of a lot more involved, if you want a higher-end result.”
That’s largely in line with what Suerez and Vaserstein, the creators of Granny Spills, tell me. They actually hate it when their work is called slop, given the way the term is often used to dismiss AI-generated content out of hand. It feels disrespectful of their creative input, they say. Even though they do not write the scripts or paint the frames, they say they are making legitimate artistic choices.
Indeed, for most of the creators I spoke to, making AI content is rarely a one-click process. They tell me that it takes skill, trial and error, and a strong sense of taste to consistently get the visuals they want. Lim says a single one-minute video can take hours, sometimes even days, to make. Anselmo, for his part, takes pride in actively pushing the model rather than passively accepting its output. “There’s just so many things that you can do with it that go well beyond ‘Oh, way to go, you typed in a prompt,’” he says. Ultimately, slop evokes a lot of feelings. Aleksic puts it well: “There’s a feeling of guilt on the user end for enjoying something that you know to be lowbrow. There’s a feeling of anger toward the creator for making something that is not up to your content expectations, and all the meantime, there’s a pervasive algorithmic anxiety hanging over us. We know that the algorithm and the platforms are to blame for the distribution of this slop.”
And that anxiety long predates generative AI. We’ve been living for years with the low-grade dread of being nudged, of having our taste engineered and our attention herded, so it’s not surprising that the anger latches onto the newest, most visible culprit. Sometimes it is misplaced, sure, but I also get the urge to assert human agency against a new force that seems to push all of us away from what we know and toward something we didn’t exactly choose.
But the negative association has real harm for the earlier adopters. Every AI video creator I spoke to described receiving hateful messages and comments simply for using these tools at all. These messages accuse AI creators of taking opportunities away from artists already struggling to make a living, and some dismiss their work as “grifting” and “garbage.” The backlash, of course, did not come out of nowhere. A Brookings study of one major freelance marketplace found that after new generative-AI tools launched in 2022, freelancers in AI-exposed occupations saw about 2% decline in contracts and a 5% drop in earnings.
“The phrase ‘AI slop’ implies, like, a certain ease of creation that really bothers a lot of people—understandably, because [making AI-generated videos] doesn’t incorporate the artistic labor that we typically associate with contemporary art,” says Mindy Seu, a researcher, artist, and associate professor in digital arts at UCLA.
At the root of the conflict here is that the use of AI in art is still nascent; there are few best practices and almost no guardrails. And there’s a kind of shame involved—one I recognize when I find myself lingering on bad AI content.
Historically, new technology has always carried a whiff of stigma when it first appears, especially in creative fields where it seems to encroach on a previously manual craft. Seu says that digital art, internet art, and new media have been slow to gain recognition from cultural institutions, which remain key arbiters of what counts as “serious” or “relevant” art.
For many artists, AI now sits in that same lineage: “Every big advance in technology yields the question ‘What is the role of the artist?’” she says. This is true even if creators are not seeing it as a replacement for authorship but simply as another way to create.
Mao, the OpenArt founder, believes that learning how to use generative video tools will be crucial for future content creators, much as learning Photoshop was almost synonymous with graphic design for a generation. “It is a skill to be learned and mastered,” she says.
There is a generous reading of the phenomenon so many people call AI slop, which is that it is a kind of democratization. A rare skill shifts away from craftsmanship to something closer to creative direction: being able to describe what you want with enough linguistic precision, and to anchor it in references the model is likely to understand. You have to know how to ask, and what to point to. In that sense, discernment and critique sit closer to the center of the process than ever before.
It’s not just about creative direction, though, but about the human intention behind the creation. “It’s very easy to copy the style,” Lim says. “It’s very easy to make, like, old Asian women doing different things, but they [imitators] don’t understand why I’m doing it … Even when people try to imitate that, they don’t have that consistency.”
“It’s the idea behind AI creation that makes it interesting to look at,” says Zach Lieberman, a professor at the MIT Media Lab who leads a research group called Future Sketches, where members explore code-enabled images. Lieberman, who has been posting daily sketches generated by code for years, tells me that mathematical logic is not the enemy of beauty. He echoes Mao in saying that a younger generation will inevitably see AI as just another tool in the toolbox. Still, he feels uneasy: By relying so heavily on black-box AI models, artists lose some of the direct control over output that they’ve traditionally enjoyed.
For many people, AI slop is simply everything they already resent about the internet, turned up: ugly, noisy, and crowding out human work. It’s only possible because it’s been trained to take all creative work and make it fodder, stripped of origin, aura, or credit, and blended into something engineered to be mathematically average—arguably perfectly mediocre, by design. Charles Pulliam-Moore, a writer for The Verge, calls this the “formulaic derivativeness” that already defines so much internet culture: unimaginative, unoriginal, and uninteresting.
But I love internet culture, and I have for a long time. Even at its worst, it’s bad in an interesting way: It offers a corner for every kind of obsession and invites you to add your own. Years of being chronically online have taught me that the real logic of slop consumption isn’t mastery but a kind of submission. As a user, I have almost no leverage over platforms or algorithms; I can’t really change how they work. Submission, though, doesn’t mean giving up. It’s more like recognizing that the tide is stronger than you and choosing to let it carry you. Good scrolling isn’t about control anyway. It’s closer to surfing, and sometimes you wash up somewhere ridiculous, but not entirely alone.
Mass-produced click-bait content has always been around. What’s new is that we can now watch it being generated in real time, on a scale that would have been unimaginable before. And the way we respond to it in turn shapes new content (see the trampoline-bouncing bunnies) and more culture and so on. Perhaps AI slop is born of submission to algorithmic logic. It’s unserious, surreal, and spectacular in ways that mirror our relationship to the internet itself. It is so banal—so aggressively, inhumanly mediocre—that it loops back around and becomes compelling.
To “love AI slop” is to admit the internet is broken, that the infrastructure of culture is opportunistic and extractive. But even in that wreckage, people still find ways to play, laugh, and make meaning.
Earlier this fall, months after I was briefly fooled by the bunny video, I was scrolling on Rednote and landed on videos by Mu Tianran, a Chinese creator who acts out weird skits that mimic AI slop. In one widely circulated clip, he plays a street interviewer asking other actors, “Do you know you are AI generated?”—parodying an earlier wave of AI-generated street interviews. The actors’ responses seem so AI, but of course they’re not: Eyes are fixed just off-camera, their laughter a beat too slow, their movements slightly wrong.
Watching this, it was hard to believe that AI was about to snuff out human creativity. If anything, it has handed people a new style to inhabit and mock, another texture to play with. Maybe it’s all fine. Maybe the urge to imitate, remix, and joke is still stubbornly human, and AI cannot possibly take it away.
2025-12-23 18:00:00
Demis Hassabis, CEO of Google DeepMind, summed it up in three words: “This is embarrassing.”
Hassabis was replying on X to an overexcited post by Sébastien Bubeck, a research scientist at the rival firm OpenAI, announcing that two mathematicians had used OpenAI’s latest large language model, GPT-5, to find solutions to 10 unsolved problems in mathematics. “Science acceleration via AI has officially begun,” Bubeck crowed.
Put your math hats on for a minute, and let’s take a look at what this beef from mid-October was about. It’s a perfect example of what’s wrong with AI right now.
Bubeck was excited that GPT-5 seemed to have somehow solved a number of puzzles known as Erdős problems.
Paul Erdős, one of the most prolific mathematicians of the 20thcentury, left behind hundreds of puzzles when he died. To help keep track of which ones have been solved, Thomas Bloom, a mathematician at the University of Manchester, UK, set up erdosproblems.com, which lists more than 1,100 problems and notes that around 430 of them come with solutions.
When Bubeck celebrated GPT-5’s breakthrough, Bloom was quick to call him out. “This is a dramatic misrepresentation,” he wrote on X. Bloom explained that a problem isn’t necessarily unsolved if this website does not list a solution. That simply means Bloom wasn’t aware of one. There are millions of mathematics papers out there, and nobody has read all of them. But GPT-5 probably has.
It turned out that instead of coming up with new solutions to 10 unsolved problems, GPT-5 had scoured the internet for 10 existing solutions that Bloom hadn’t seen before. Oops!
There are two takeaways here. One is that breathless claims about big breakthroughs shouldn’t be made via social media: Less knee jerk and more gut check.
The second is that GPT-5’s ability to find references to previous work that Bloom wasn’t aware of is also amazing. The hype overshadowed something that should have been pretty cool in itself.
Mathematicians are very interested in using LLMs to trawl through vast numbers of existing results, François Charton, a research scientist who studies the application of LLMs to mathematics at the AI startup Axiom Math, told me when I talked to him about this Erdős gotcha.
But literature search is dull compared with genuine discovery, especially to AI’s fervent boosters on social media. Bubeck’s blunder isn’t the only example.
In August, a pair of mathematicians showed that no LLM at the time was able to solve a math puzzle known as Yu Tsumura’s 554th Problem. Two months later, social media erupted with evidence that GPT-5 now could. “Lee Sedol moment is coming for many,” one observer commented, referring to the Go master who lost to DeepMind’s AI AlphaGo in 2016.
But Charton pointed out that solving Yu Tsumura’s 554th Problem isn’t a big deal to mathematicians. “It’s a question you would give an undergrad,” he said. “There is this tendency to overdo everything.”
Meanwhile, more sober assessments of what LLMs may or may not be good at are coming in. At the same time that mathematicians were fighting on the internet about GPT-5, two new studies came out that looked in depth at the use of LLMs in medicine and law (two fields that model makers have claimed their tech excels at).
Researchers found that LLMs could make certain medical diagnoses, but they were flawed at recommending treatments. When it comes to law, researchers found that LLMs often give inconsistent and incorrect advice. “Evidence thus far spectacularly fails to meet the burden of proof,” the authors concluded.
But that’s not the kind of message that goes down well on X. “You’ve got that excitement because everybody is communicating like crazy—nobody wants to be left behind,” Charton said. X is where a lot of AI news drops first, it’s where new results are trumpeted, and it’s where key players like Sam Altman, Yann LeCun, and Gary Marcus slug it out in public. It’s hard to keep up—and harder to look away.
Bubeck’s post was only embarrassing because his mistake was caught. Not all errors are. Unless something changes researchers, investors, and non-specific boosters will keep teeing each other up. “Some of them are scientists, many are not, but they are all nerds,” Charton told me. “Huge claims work very well on these networks.”
*****
There’s a coda! I wrote everything you’ve just read above for the Algorithm column in the January/February 2026 issue of MIT Technology Review magazine (out very soon). Two days after that went to press, Axiom told me its own math model, AxiomProver, had solved two open Erdős problems (#124 and #481, for the math fans in the room). That’s impressive stuff for a small startup founded just a few months ago. Yup—AI moves fast!
But that’s not all. Five days later the company announced that AxiomProver had solved nine out of 12 problems in this year’s Putnam competition, a college-level math challenge that some people consider harder than the better-known International Math Olympiad (which LLMs from both Google DeepMind and OpenAI aced a few months back).
The Putnam result was lauded on X by big names in the field, including Jeff Dean, chief scientist at Google DeepMind, and Thomas Wolf, cofounder at the AI firm Hugging Face. Once again familiar debates played out in the replies. A few researchers pointed out that while the International Math Olympiad demands more creative problem-solving, the Putnam competition tests math knowledge—which makes it notoriously hard for undergrads, but easier, in theory, for LLMs that have ingested the internet.
How should we judge Axiom’s achievements? Not on social media, at least. And the eye-catching competition wins are just a starting point. Determining just how good LLMs are at math will require a deeper dive into exactly what these models are doing when they solve hard (read: hard for humans) math problems.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
2025-12-22 18:00:00
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.
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.
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.
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.
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.
“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.”
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.
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.)
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.
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.”
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.
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.
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.”
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,” a reference to 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.
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.
