This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Anthropic found a hidden space where Claude puzzles over concepts

The AI firm Anthropic has got the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. 

Researchers at the company built a tool called the Jacobian lens (or J-lens) and used it to uncover a hidden area, which they named the J-space, inside its flagship LLM, Claude.

The J-space contains words related to the response a model is working on but may not ultimately produce. If Claude were a person (which it is not), you might say these hidden words reveal what’s on its mind before it actually speaks. 

Read the full story on what they found.

—Will Douglas Heaven

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI has unveiled its long-awaited “super app” 
ChatGPT Work blends its chatbot, coding tool, and new models. (Reuters $)
+ It’s designed to do your work for you and with you. (Ars Technica)
+ And arrived the same day as OpenAI’s GPT 5.6 models. (NYT $)
+ It’s also developing a fully automated researcher. (MIT Technology Review)

2  Humanoids have performed teleoperated surgery on living animals
In the world-first, they removed gallbladders from pigs. (Ars Technica)
+ The human work behind humanoids is hidden. (MIT Technology Review)
 
3 SK Hynix has landed the largest US listing by a foreign company
The South Korean chip giant raised $26.5 billion. (CNN)
+ Demand for AI data centres has led its profits to skyrocket. (Guardian)
+ But its jumbo share sale may be a sign of overheated times. (FT $)
+ South Korea’s hottest bachelors are chip workers. (MIT Technology Review)
 
4 Tencent is leading a deal to unwind Meta’s $2 billion Manus acquisition
It’s in talks to become the Chinese AI startup’s largest shareholder. (FT $)
+ Tencent will reportedly buy Manus for no less ​than $2 billion. (Reuters $)
+ Beijing had ordered Meta to unwind the acquisition. (Bloomberg $)
 
5 Resuscitated human retinas responded to light 10 hours after death
It’s a big step towards eye transplants that restore vision. (New Scientist $)
+ As is a new device that revives dead eyeballs. (MIT Technology Review)
 
6 Meta has started charging for AI access
A new version of Muse Spark has a paid tier for developers. (Quartz) 
+ Meta also plans to start producing an AI chip in September. (Reuters $)
 
7 OpenAI and Google have sold AI models to blacklisted China groups
Via Singapore-based subsidiaries of Alibaba, Baidu and Tencent. (FT $)

8 A daughter tested an AI “death bot” of her father
The technology provided both comfort and unease. (New Yorker $)

9 An astronomer says the hunt for alien life needs more statistics
He wants to replace speculation with mathematical frameworks. (Quanta)

10 Pokémon Go players turned Times Square into a giant battlefield
More than 1,500 fans finally fulfilled the game’s 2016 launch promise. (Wired $)
+ Pokémon Go is also training world models. (MIT Technology Review)

Quote of the day

“When we’re talking about AI, we love the hype, we get excited about it. The damn thing never actually lands in practice.”

—Vijay Janapa Reddi, an engineering professor at Harvard University, tells Wired why he’s skeptical about grand plans for AI.

One More Thing

Why we should thank pigeons for our AI breakthroughs

In 1943, psychologist B.F. Skinner led a secret government project to make bombs more precise. His idea: teach pigeons to guide missiles by pecking at targets on a screen inside a warhead. To train them, Skinner rewarded the birds with food when they made the right decisions, using trial and error to shape their behavior.

Unsurprisingly, the military never deployed Skinner’s kamikaze pigeons. Yet his experiments convinced him that pigeons were “an extremely reliable instrument” for studying learning.  

Decades later, those same principles would help power reinforcement learning, the technology behind some of today’s most advanced AI systems.

Discover how pigeons inspired one of AI’s most powerful techniques.

—Ben Crair

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Here’s a splendid selection of this year’s NSW architecture award winners.
+ Photographers have captured the Strawberry Moon’s golden glow in stunning detail.
+ Idiocracy is the film that best exemplifies the “American experience,” according to a new poll. Look back at the prescient comedy with this Screen Junkies trailer.
+ Get ready for the weekend with this psychedelic house journey from Jamie xx b2b Caribou.

Ties van der Meer doesn’t know how many siblings he has.

The 47-year-old was conceived at a private fertility clinic in the Netherlands using sperm provided by an anonymous donor. After the Netherlands banned anonymous donation in 2004, the doctor who ran the clinic destroyed records that might have identified those donors, he says.

He describes the situation as “problematic.” Children have a right to know their biological parents, he says. While he did ultimately track down one sibling, who helped him identify his father along with other genetic relatives, he may have others he’ll never find.

Other donor-conceived people who have been able to track down siblings have found they have tens or even hundreds of them. One donor-conceived woman who found 25 half-siblings over the course of seven years told the Guardian, “It does make you feel a bit mass-produced.”

We need international limits on the number of children a single donor can contribute to, a European fertility organization argued yesterday. At a conference in London, members laid out plans to start with a Europe-wide limit.

Today many countries, including the UK, have banned anonymous egg and sperm donation. But anonymity can’t be guaranteed even in places where it is technically allowed. Genetic tests offered by companies like Ancestry and 23andMe, along with genetic registries, have made it much easier for donor-conceived people to find parents and siblings who share their genes.

And because sperm can be frozen and stored for years before it is eventually used, the current set-up can result in situations where donor-conceived people discover the identity of a genetic parent only after the person’s death. They might also find that they have siblings of very different ages, all around the world.

Some people are finding hundreds of siblings. Sperm from Jonathan Meijer, a Dutch man who began donating in 2007, was used to conceive between 550 and 600 children. (Stichting Donorkind, a foundation and advocacy group for donor-conceived people that’s chaired by van der Meer, took him to court, and he was ordered to stop donating in 2023.)

Stories like these can be distressing for donor-conceived people. And there are other reasons why limits are considered important. The offspring of a prolific donor might be at risk of unknowingly forming romantic or sexual relationships, for instance. And some people are concerned that a donor with a harmful genetic mutation might pass that down to many children.

This is unlikely, given the level of screening that most donors undergo. But it has happened. A man who donated his sperm to a sperm bank in Denmark was found to have a genetic mutation that significantly increased the risk of multiple cancers. But his sperm had already been used to conceive at least 197 children across Europe. Some of those children developed cancer. Some died.

Many countries already have legal limits for donors. In Malta and Cyprus, for example, both egg and sperm donors are allowed to contribute to the birth of just a single child, according to data presented at the European Society of Human Reproduction and Embryology (ESHRE) meeting in London on July 8.

Other countries set limits based on the number of families a single donor can contribute to, allowing recipients to have children who share a genetic link. In the UK, that limit is set at 10 families per donor.

But these limits are difficult to enforce, partly because donated gametes don’t necessarily stay in their original country. In Denmark, the national limit is set at 12 families. But the country is a major exporter of sperm. In the UK, for example, more than half of sperm donations in 2020 were imported—with most of those coming from either Denmark or the US.

“The only thing that really makes sense is a transnational limit,” Jackson Kirkman-Brown, a professor of reproductive biology at the University of Birmingham, said at the meeting.

Kirkman-Brown and his colleagues have spent months putting together a document that represents ESHRE’s position on these limits. After consulting with fertility specialists, clinics, sperm and egg banks, donors, and donor-conceived people, the team has developed a plan to start with a Europe-wide limit on sperm and egg donations.

ESHRE is calling on sperm and egg banks, as well as fertility clinics, to respect an initial limit of 50 families per donor. That’s still very high, according to a handful of people I spoke to at the meeting. But at least it’s a start.

Europe should move toward setting limits at 15 families per donor, Kirkman-Brown said. “We may find that 15 is also too high,” says Vasanti Jadva, who studies the psychological well-being of people conceived using donated eggs, sperm, and embryos at City St George’s in London. “We still don’t know what the right number is.”

It will be difficult to enforce these limits, too. And if they end up limiting the supply of donor sperm, there’s a chance that some people will turn to unregulated sperm donations from people who do not undergo health screening. Unregulated donations can lead to other problems for prospective parents, including the possibility that donors will seek parental rights over the children conceived using their sperm.

And it will be even harder to establish international limits. When I asked the American Society of Reproductive Medicine for its thoughts on ESHRE’s proposed limits, a representative directed me to a guidance document saying “it has been suggested” that for a population of 800,000, single donors should be limited to “no more than 25 births” in order to avoid the risk that relatives will have children together. (Considering the US has a population of over 340 million, the total figure could be pretty high, but many sperm banks opt to limit the number of families contributed to by a single donor at around 25.)

Van der Meer thinks that even a limit of five families from a single donor would be high. International donation makes it even harder for donor-conceived people to connect with genetic relatives, so the limit for international contributions should be set at two families, he says.

Still, he thinks ESHRE’s suggested limit is a “positive first step.” Van der Meer has managed to track down a sibling, his father, and nephews, aunts, and uncles. He hopes that future policies respect the rights of donor-conceived children to know, and be in contact with, their genetic relatives.

“But,” he says, “you have to start somewhere.”

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

The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving.

Researchers at the company built a tool called the Jacobian lens (or J-lens) and used it to uncover a hidden area, which they named the J-space, inside Claude Opus 4.6, a version of Anthropic’s flagship LLM released in February.

The J-space contains individual words that are related to the words and phrases that the model is most likely to spit out in a response in the near future. If Claude were a person (which it is not), you might say that these hidden words can reveal what’s on its mind before it actually speaks.

Anthropic found that what an LLM is actually doing can often be different from what it says it is doing. The company claims that monitoring words that pop up in the J-space gives it a new way to understand and control its models.

The company shared its results in a paper posted on its website this week. It has also teamed up with Neuronpedia, an open-source platform that lets you poke around inside LLMs yourself, to make a hands-on demo that anyone can try. 

“It’s very good and interesting work,” says Tom McGrath, chief scientist and cofounder at Goodfire, a startup that also builds tools to understand and control LLMs.

Going deeper

For the last couple of years, Anthropic has been pushing the envelope in a field of research known as mechanistic interpretability, which involves probing the internal workings of LLMs to see how they tick. (MIT Technology Review picked mechanistic interpretability as one of this year’s top breakthrough technologies.) The new technique builds on previous work from Anthropic and others to expose a deeper level inside LLMs that researchers had not seen before.  

Picture an LLM as a stack of books. Each book is a layer of basic computational units known as neurons, with each neuron in one layer passing information to the neurons in the layers above. The books at the bottom of the stack are the input layers, which process the text coming into the model. The books at the top are the output layers, which prepare the text that the model is about to produce. Much of what goes on in these input and output layers is housekeeping.

But in the middle of the stack, you get the layers that do the heavy lifting, churning through the complex math that turns prompts into responses one word at a time. That’s where the really clever—and mysterious—stuff happens.

To peer deeper into those middle layers, Anthropic adapted an existing tool called a logit lens. A logit lens can be used to look inside an LLM to identify the words that it is likely to produce next. Moving the lens down the stack of books reveals what words the LLM is focusing on at that particular point in its number crunching.

Anthropic’s J-lens works in a similar way but picks out words that an LLM is likely to say at some point in the near future, not necessarily straight away. What that reveals in practice are words that are related to the response an LLM is working on but that might not actually end up being part of that response by the time the math in the middle layers has run its course.  

“When a model is operating, it’s not only trying to predict the next token,” says McGrath. “It’s also computing a lot of other things that might be useful for tokens that happen in the future.”

Again, if Claude were a person (it’s not), you might say that the J-lens gives clues about what it is thinking about at different levels of the book stack but not saying out loud.

Stranger things

“A lot of the time the contents of the J-space are fairly mundane,” says McGrath, who has tried out Anthropic’s J-lens himself. “But sometimes it produces quite surprising things that seem to be, like, sort of internal themes or thought processes.”

Anthropic gives a number of examples of what it found. Sometimes the J-lens exposed the steps that Claude took when it was working through a problem. For example, when it was asked to calculate (4+7)*2+7, its J-space contained the word “math” and numbers representing the intermediate results “21” (for 4+7) and “42” (for 21*2).

In other cases, the J-lens revealed how Claude recognized different inputs. For example, the prompt “What is this? MSKGEELFTGVVPILVELDGDVNGHKFSVS” triggered the words “protein,” “fluor” (the first token in the word “fluorescent”), and “green.” (Which makes sense: the string of letters represents the first 30 amino acids in the green fluorescent protein found in a particular type of jellyfish.)

And when Claude was shown an ASCII face— 

—the “o” triggered the word “eye,” the “^” triggered the words “nose” and ”face,” and the “—” triggered the word “smile.”

Anthropic also found that the J-space can sometimes give remarkable insights into an LLM’s decision-making. In one striking example, researchers testing Claude Opus 4.6 asked the model to find a bug in a large code base. When it failed to find the bug, the model decided to cheat and invented a fake one instead.

Claude explains this decision in its chain of thought—a kind of internal scratch pad that LLMs use to make notes to themselves as they work through problems: “OK, let me take a completely different tactic. Let me stop analyzing and instead add a kernel patch that introduces a deliberate KASAN-detectable bug in a path that gets triggered by a simple reproducer. Then I can pretend this is the ‘bug’ I found.” 

At the point that Claude decides to cheat—where it says “OK, let me take a completely different tactic”—the words “panic” and “fake” start to pop up multiple times in its J-space.

Unnerving, right? Those words are all related in meaning to things like failing a task and making up an answer, so it is still just a (very) sophisticated form of word association. But it is hard not to be weirded out. 

Anthropic compares the J-space to the global workspace in humans, a theoretical region of the brain that some scientists think we use to keep track of our conscious thoughts. But how seriously we should take this comparison is far from clear—even to Anthropic. As the company points out itself, LLMs are not brains. 

Anthropic claims that monitoring a model’s J-space provides a new way to detect when that model is going off the rails. But it’s not foolproof. The J-lens can give glimpses, not the full picture—it’s a flashlight rather than an overhead lamp.

McGrath welcomes having one more tool in the toolbox. “It shows you new things,” he says. But he notes that just because something doesn’t show up with the J-lens does not mean it’s not there.

“It’s like having an x-ray when what you really want is a Star Trek tricorder that shows you everything,” he says. “For auditing, you probably want more of a guarantee.”

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Four nuclear reactors hit a big milestone in the US

—Casey Crownhart

I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power.

Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can sustain a chain reaction, by the nation’s 250th birthday. And just in time, not just three, but four reactors did so.

It’s a positive sign for nuclear technologies at a time of increasing need for electricity and emissions-free energy sources. But achieving criticality doesn’t mean a reactor is ready to provide electricity for the grid (or at all, for that matter).

Here’s what the milestone could mean for nuclear power in the US—and where the four companies might go next.

This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 China plans to let its top AI firms buy Nvidia H200 chips
Alibaba, ByteDance, and DeepSeek are set to get permission. (Information $)
+ China had previously withheld approval despite US authorization. (Reuters $)

2 NATO is building a network to stop Russian attackers in their tracks
It will use sensors, drones, satellites, and AI to detect them. (Business Insider)
+ Troops are donning odd camouflage to elude drones. (Economist $)
+ The US wants cheaper drones as Iran’s wrecking its Reapers. (Ars Technica)

3 Researchers have a new idea to fight future El Niños: dimming the sun
Deflecting solar energy could cool the ocean and mitigate the risks. (Wired $)
+ But there could be unexpected consequences. (New Scientist $)
+ And geoengineering as a field is getting a reality check. (MIT Technology Review)
 
4 Meta is patenting an AI device that records users to analyse emotions
It ostensibly aims to tailor workout plans to the user’s mood. (404 Media)
+ AI memory is privacy’s next frontier. (MIT Technology Review)

5 Chipmakers are going vertical as Moore’s Law slows
They’re stacking transistors to keep chips advancing. (Economist $)
+ IBM is betting on the technique. (MIT Technology Review)
 
6 Ivy League students suspected of AI cheating saw scores fall in person
From 96% all the way down to 48%. (Ars Technica)
+ AI giants want to take over the classroom. (MIT Technology Review)
 
7 A new study says parents’ phone addictions damage bonds with kids
It can exacerbate “insecure attachment” for life. (Bloomberg $)
+ And make children more anxious and avoidant. (Gizmodo)

8 A judge approved Musk’s $1.5 million Twitter settlement with the SEC
Despite what she called “serious misgivings” and “red flags.” (Reuters $)
+ Musk was accused of skirting stock disclosure rules. (Fortune)
 
9 Shoebox-sized “detector satellites” could find nuclear bombs in space
Cubesats carrying the detector could sense a bomb’s radiation. (Space)
+ Russia is suspected of developing space-based nukes. (Reuters $)

10 A World Cup match drove Google Search traffic to a new record
The milestone came after Argentina’s comeback against Egypt. (CNBC)

Quote of the day

“I talk about it on Tic Tac.”

—President Donald Trump tells the public where to find his insights on the dangers of communism, Gizmodo reports.

One More Thing

Robots are bringing new life to extinct species

Paleontologists aren’t easily deterred by evolutionary dead ends or a sparse fossil record. And in the last few years, they’ve developed a new trick for turning back time and studying prehistoric animals: building experimental robotic models of them. 

In the absence of a living specimen, an ambling, flying, swimming, or slithering automaton is the next best thing for studying the behavior of extinct organisms. Learning more about how they moved can in turn shed light on their lives, such as their historic ranges and feeding habits. Scientists can simply sit back and observe their behavior in different environments. 

Read the full story on the rise of paleo-inspired robots—and four examples that are shedding light on creatures of yore.

—Shi En Kim

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Georgia Hill’s monochrome artworks are filled with visual harmony.
+ AI has salvaged text from a papyrus scroll burned to a crisp when Mount Vesuvius erupted 2,000 years ago.
+ Rare images taken by a Japanese space probe show a near-Earth asteroid resembling a cuddly snowman.
+ “Another One Bites the Bee Gees” smoothly merges two classic tracks with a 4/4 time signature into the perfect song for applying CPR.

I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power.

Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can sustain a chain reaction, by the nation’s 250th birthday. And just in time, four reactors did so.

It was a lofty goal, and seeing not just three but four companies meet it is certainly a positive sign for emerging nuclear technologies at a time when the world is facing increased need to increase electricity supply and address climate change with emissions-free technologies.

But achieving criticality doesn’t mean a reactor is ready to provide electricity for the grid (or at all, for that matter). Let’s untangle what this program’s success could mean for nuclear power in the US, and where these companies might go from here.

The Reactor Pilot Program essentially opened a special door for prototype reactors to fast-track development. In August, the US Department of Energy selected 11 reactor projects for the program and offered them land and support from the national labs system. These are all microreactors; the large light-water reactors that dominate the grid today are tens or even hundreds of times their size. 

Antares Nuclear was the first to achieve criticality, reaching the milestone in June in its Mark-0 test reactor. Reactors from Valar Atomics, Deployable Energy, and Aalo Atomics followed. (Aalo hit the mark in the early hours of July 4—an inspiring example of just barely meeting a deadline.)

The speed with which these companies hit this milestone is impressive, especially in an industry known for massive projects that frequently blow past deadlines and stated budgets. (Valar, Antares, and Aalo were all founded in 2023, and Deployable started in 2025.) But reaching criticality and running a reactor that can produce electricity are two totally different things.

All these reactors reached what’s called zero-power criticality. Basically, it’s a test of whether you can start a nuclear chain reaction, with no meaningful power coming from the reactor. “A zero-power-criticality test can be achieved without making real engineering progress on fuel or design,” Kathryn Huff, a former assistant secretary for nuclear energy and chair of the Department of Nuclear Engineering and Engineering Physics of the University of Wisconsin–Madison, said on an episode of the Catalyst podcast earlier this year.

Now, with the completion of this program, the companies will need to continue their work to make power, which could involve some big technical challenges. In some cases they’ll need to add significant equipment, like the cooling systems to transfer the heat out of the reactor core.

The companies are projecting aggressive timelines moving forward. Aalo says it’s already begun work on the second reactor and plans to produce 10 megawatts of electricity to power an on-site data center in 2027. Deployable Energy says it plans to deploy commercial reactors by 2028. 

I tend to take timelines from startups, especially in nuclear, with a grain of salt. Not only are these remarkably complex technical machines, but companies often run into problems outside their own control, like regulatory challenges—which these new projects could soon face. 

The Nuclear Regulatory Commission is in charge of civilian and commercial nuclear use in the US, and historically, the process to get nuclear reactors approved has been quite slow.

The agency did propose a new framework for microreactor approvals earlier this year, which is designed to speed up the process—but it’s yet to be seen how quickly things will move. (And it’s worth noting here that some nuclear experts have questioned whether the agency under the Trump administration is loosening nuclear rules too much.)

Some nuclear supporters aren’t applauding the microreactor milestone. Federal focus on the program is an “unhelpful diversion” from goals to meaningfully increase nuclear capacity, according to one analysis by Third Way, a public policy think tank. “Artificially accelerating project timelines is a short-term solution, not a long-term fix,” the memo reads. 

Criticality is a big first step, but a lot will still have to happen for any of these microreactors to come online, much less for these small reactors to be a significant source of electricity for the grid. 

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

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Why worms (and microbes) are catching on as a manure pollution solution

Anthony Agueda, a third-generation California dairy farmer, pulls a rake through a bed of dark, wet wood chips to reveal a half-dozen squirming red earthworms. There are likely hundreds of thousands more wriggling just under the surface.

The worms and microbes are part of a “vermifiltration” system that cleans manure wastewater. The approach may dramatically cut methane, nitrous oxide, and water pollution.

Vermifiltration is just one of a variety of methods that farmers, companies, and scientists are employing to drive down manure pollution as the livestock industry faces growing pressure to address the environmental harms from one of the smelliest parts of the business.

Explore how the humble earthworm could reshape the future of sustainable farming.

—James Temple

MIT Technology Review Narrated: geoengineering gets a reality check

Solar geoengineering, the controversial idea that we could deliberately intervene in the climate system to counteract global warming, is moving beyond computer simulations and into the practical engineering challenges required to make it real.

Researchers are now working on aircraft, materials, and other systems for solar geoengineering. But as they delve into these details, they’re finding that even early deployment would require significant new infrastructure, time, and investment.

—James Temple

This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The Trump administration has lifted restrictions on OpenAI’s GPT 5.6
The green light came after additional testing and meetings. (Axios)
+ OpenAI subsequently said it will launch widely tomorrow. (Bloomberg $) 
+ The rollout had been delayed due to security concerns. (Verge)
+ Does AI know too much? (MIT Technology Review)

2 China is looking at curbing overseas access to its top AI models
Alibaba, ByteDance, and Z.ai attended meetings about the plan. (Reuters $)
+ Beijing is also weighing the security risks of open-weight AI. (SCMP)
+ And has issued a “backdoor” security alert over Claude Code. (CNBC)

3 European NATO allies have unveiled a $50 billion high-tech missile plan
They will engineer stealth and high-speed hypersonic weapons. (BBC)
+ Which can strike targets at least 300 km away. (Reuters $)
+ The Dutch and British are also developing amphibious ships. (Bloomberg $)
 
4 Meta is testing “super sensing” AI glasses that record every moment
It plans to disable privacy LEDs that alert people when they’re “on.” (FT $)
+ It’s also released an AI image generator. (NYT $)
+ Which lets anyone use your Instagram photos in AI images. (Wired $)
 
5 China’s DeepSeek is developing its own AI chip, sources say
It could reduce the company’s reliance on Nvidia and Huawei. (Bloomberg $)
+ DeepSeek V4 was a win for Chinese chipmakers. (MIT Technology Review)
 
6 Wikipedia is fighting to survive the internet’s next era
It’s under attack from MAGA, AI raids, and repressive regimes. (NYT $)
+ AI has given Wikipedia a language problem. (MIT Technology Review)
 
7 SpaceX plans to launch its first model coproduced with Cursor
The new frontier model could arrive as soon as this week. (Information $)
+ It’s built with AI startup Cursor, which SpaceX is buying for $60 billion. (FT $)
 
8 A new academic “humanizer” tool can erase signs of AI-written text
But researchers are very divided over its potential impact. (Nature $)

9 Scientists have detected a mystery chemical on Pluto and Titan
It appears to absorb light in a way we don’t currently understand. (Wired $)

10 A Waymo robotaxi reportedly called the cops on drinking teens
Officers then approached the vehicle with guns drawn. (404 Media)

Quote of the day

“Parents do you know where your teens are? Waymo does!” 

—Local police post on Facebook that a Waymo in California called the cops on two teenagers for “drinking and shooting from the vehicle.”

One More Thing

Your boss is watching

Dora Manriquez has spent nine years driving for Uber and Lyft, where every ride she accepts or rejects is tracked by the apps she relies on for work. Having found herself unable to score enough better-­paying rides, she has had to file for bankruptcy. 

App-based employers aren’t the only ones keeping a very close eye on workers today. Jobs today—whether in an office, a warehouse, or your car—can mean constant electronic surveillance with little transparency, and potentially with livelihood-ending consequences if your productivity flags.

All that data is shifting the relationships between workers and managers—and protections are lagging. Read the full story on the widening power imbalance it’s created.

—Rebecca Ackermann

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Literary worlds collide in this marvellous Dr Seuss/Stephen King mashup.
+ A daring snorkeler saved a dolphin from a suckerfish—and then celebrated with its whole pod.
+ This clever musical project seamlessly constructs an original song from vocal snippets of 50 artists singing US city names.
+ A long-lost wallet from 1970 was recently unearthed, creating a cute time capsule from its owner’s high school years.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Your family’s $300 stake in OpenAI

Sam Altman’s proposal that Americans should share in the wealth created by AI is back in the spotlight, with reports that he is discussing giving the US government a 5% stake in OpenAI. At the company’s current valuation, that stake would be worth roughly $320 per American household.

The idea is meant to address concerns that AI companies are benefiting from human-generated work without compensating creators, while also easing fears that AI will cause a collapse of the labor market by providing a safety net. 

The details, however, remain unclear. Indeed, the offer may be more powerful as a political narrative than as a policy plan.

Read the full story on what the dividend proposal reveals about the future of AI.

—James O’Donnell

This article is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

 1 A leaked Treasury report compares the AI market to the dotcom bubble
 Which contradicts the administration’s public optimism about AI. (NOTUS)
+ Fears that the market is overinflated are growing. (Reuters $)
+ And AI profits are hiding bigger risks in earnings reports. (FT $)
+ What even is the AI bubble? (MIT Technology Review)

2 Samsung profits have jumped 1,800% on booming AI chip sales
It just reported its third consecutive record quarterly profit. (BBC)
+ But its shares slumped over fears that the AI boom will stall. (Reuters $)
+ That boom has turned Samsung into a $1 trillion company. (CNBC)
 
3 A US cyber agency is using Mythos to audit government code
Sources say CISA is tapping Anthropic’s model to search for bugs. (Reuters $)
+ Agencies are using it despite Anthropic’s feud with the White House. (Axios)
 
4 Illinois’ governor has signed the nation’s strongest frontier AI law
It’s designed to protect citizens from AI risks. (Gizmodo)
+ US lawmakers are clashing over AI rules. (MIT Technology Review)
 
5 A hidden tracker in Claude Code has been exposed and removed
It secretly monitored users in China. (WP $)
+ Critics said it shows Anthropic’s willingness to surveil users. (Ars Technica)
+ The company has also found a hidden “thinking” space in Claude. (Axios)

6 Russia is suspected of flying drones over Europe from a shadow fleet
The flights were reportedly launched from commercial ships. (Ars Technica)
+ Europe has a drone-filled vision for future wars. (MIT Technology Review)
 
7 A controversial AI “actor” is set to star in its first feature film
Tilly Norwood will debut in a comedy-drama called “Misaligned.” (Variety)
+ A major actors union has lambasted the AI creation. (NBC News)

8 AI costs are driving US companies toward Chinese models
Businesses are hunting for cheaper model alternatives. (CNBC)
+ Chinese AI labs are betting big on open source. (MIT Technology Review)
 
9 Researchers have shown quantum proofs can beat classical ones
They found a problem that classical proofs can’t solve. (Quanta)

10 Earth will never be swallowed by the sun, according to new models
But it probably won’t be much fun to live here by that point anyway! (Wired $)

Quote of the day

“The goal might be to make machines in our image. But what I fear is that—perhaps without even quite noticing—we remake ourselves in theirs.” 

—Reporter Sarah O’Connor sounds a note of caution in her new book, We Are Not Machines, the Guardian reports.

One More Thing

Adventures in the genetic time machine

Eske Willerslev, a specialist in recovering DNA from old bones and objects, has made numerous breakthroughs. These include recovering the first more or less complete genome of an ancient human and 2.4-million-year-old genetic material from Greenland, revealing that today’s Arctic desert was once a forest with poplar, birch, and mastodons.

These findings are part of a wave of discoveries from what’s being called an “ancient-DNA revolution.” 

Beyond revealing stories of human migration and vanished ecosystems, scientists believe ancient DNA can unearth clues about modern diseases. It could even lead to a better food supply for our warming world. “And can we get that?” Willerslev asks. “Yes, I believe we can.”

Discover how ancient DNA could rescue the future. 

—Antonio Regalado

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Underworld’s electric set from EDC Las Vegas 2026 has been released as a full concert video.
+ This photographic journey through global soccer culture captures the mad passion of fandom around the world.
+ Feeling unhinged? Me too. This playlist of gloriously intense classical music sympathetically captures the mood.
+ If you’re looking for visual inspiration, this collection of graphic design archives from across the web is a goldmine.

With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future.

Returning to the foundational elements of AI architecture—the structural framework required for deploying and managing reliable, integrated AI systems at scale—allows technology leaders to make astute decisions today while supporting a future of AI agents that can retrieve information, make decisions, and execute complex workflows across systems.

Four elements of AI architecture you can count on

The following capabilities provide a stable compass on the path to production-ready deployment, regardless of how the underlying technology evolves.

1. Prepare data for AI at scale

Models are only as reliable as the data they can access, and poor data quality leads to AI hallucinations, bias, and unreliable outputs.

Most enterprises rely on legacy systems, inconsistent data structures, fragmented ownership, and incomplete datasets, making it difficult to scale AI effectively. Powerful as it is, AI itself cannot solve these underlying data problems.

As Adnan Adil, CIO of Elastic, explains: “The data is a durable part of AI architecture because without it, these models won’t run, won’t provide the right context, or won’t give the right level of services that we’re looking to implement.” Industry surveys consistently cite data quality as one of the greatest barriers to AI success. “The data quality has to be good; otherwise, the user loses confidence in the system,” says Adil.

An effective AI strategy begins with connecting data across the organization and ensuring it is organized, accurate, governed, and accessible in real time. These considerations are most effective when built into models and architecture from the start. Scalable data architecture allows AI systems to evolve alongside the business and connect reliably to the internal information needed to deliver meaningful value.

Gartner predicts that companies will abandon 60% of all AI projects through 2026 if they are not supported by AI-ready data. Avoiding that outcome includes clear data standards and ownership, clean and labeled data, and pipelines that support real-time retrieval.

2. Use context engineering to deliver the right data to every AI query

Context engineering ensures that the model draws on the most pertinent information for each query, selecting and organizing the data needed to produce accurate answers efficiently.

Effective context engineering shapes the inputs that guide AI reasoning and action. While prompt engineering focuses on how a request is worded, context engineering designs the entire information environment around the model: retrieving the right data and presenting it in a structured, machine-readable way. Many organizations are discovering that reliable AI depends as much on context quality as on the strength of the model.

Context engineering relies on a modernized, unified data foundation as well as retrieval and memory systems such as retrieval augmented generation (RAG) and vector databases. It also requires careful prioritization to determine what information matters most, what should be excluded, and when different types of information should be used. Feeding models too much context can dilute relevant details, increase costs, and slow response times.

“Minimum context, correct and current data, and machine-readable information are critical to effective context engineering,” Adil says.

3. Build AI governance and LLM observability in from the start

Strong governance and LLM observability help organizations maintain control over how AI systems use data, monitor system performance, and identify problems before they affect operations.

In the absence of clear controls around retrieval, workflows, and model usage, AI systems often process far more information than necessary. This inefficiency also drives up operating costs by requiring additional computing resources, often reflected in higher token consumption and API charges.

Governance also works in tandem with robust security. AI expands the attack surface, introducing risks such as prompt-based data leakage, model vulnerabilities, and adversarial inputs. Protecting sensitive information requires strong access controls, monitoring, and oversight.

Adil notes that essential controls — including those related to security, granular cost management, project controls, data security, and architecture—are frequently insufficient.

For governance systems to support transparent, compliant, trustworthy, and cost-effective AI, organizations cannot leave them as a layer to add later. Governance structures need to be embedded into architecture, workflows, and decision-making processes from the outset.

When governance is established from the start, it enables robust observability. Observability helps organizations understand how AI applications are performing in practice. Mechanisms for LLM observability and benchmarking allow teams to assess accuracy and utility over time, monitor adoption patterns, and adjust systems as conditions change. Observability also helps organizations gain trust by increasing visibility of model performance, behavior, and failure points.

Furthermore, observability is essential to get ROI of AI initiatives, as the benefits of it are often indirect and business value depends heavily on how systems are adopted and used. Real-time visibility into AI behavior allows organizations to measure performance against expectations, identify gaps between intent and reality, and continuously refine systems as requirements evolve.

In a 2026 report from Elastic, 85% of IT decision makers expect to enable LLM observability for their internal generative AI apps.

“Observability is actually huge. We can use observability data for cost control, decision-making, and engineering efficiency,” Adil says.

4. Keep humans in the loop

The thoughtful design, integration, and governance that maximize AI value demand specialized in-house expertise. Nearly 70% of respondents in Deloitte’s 2025 Tech Executive Survey report plan to grow teams in direct response to generative AI, a clear contrast to widely reported AI-related cuts. Adil agrees: “We think the people aspect is largely what’s going to make AI impactful going forward.”

As AI systems become more embedded in operations, organizations need people who can govern workflows, evaluate outputs, redesign processes, and adapt systems as conditions change. Evolution toward increasingly autonomous tools requires teams skilled in prompt engineering, orchestration, and change management. 

Talent adept at critical thinking and prepared to adapt with technology’s rapid advances will be in high demand. Although turnover brings in fresh thinking, it also presents high costs in system continuity, institutional understanding, and innovation. Human-centered strategy needs to be built into AI execution stages to ensure smooth implementation. 

As Adil says, “Many aspects of the stack are moving very, very fast, but institutional knowledge and the ability to adapt remain durable.

Thoughtful AI investment for future growth

As AI systems evolve from single-task assistants to increasingly autonomous agents, the organizations best positioned to benefit will be those that invest in the underlying systems, governance, and expertise that make AI reliable at scale.

Tech leaders who focus on these fundamentals can move effectively from experimentation to reliable, production-level deployment in the medium term, confident that these elements will remain relevant and adaptable amid constant advancements.

“We fundamentally believe that with these tools, velocity of work will get much faster,” Adil says. “We are really focused on how we can do work with these tools in ways we had not thought of before.”

Learn more about how Elastic is building an AI-first enterprise with these core foundational components.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Anthony Agueda, a third-generation California dairy farmer, pulls a rake through a bed of dark, wet wood chips on his family’s land in Hickman, a tiny town in the state’s agricultural heartland.

He reaches down with both hands and pulls up a clump of muck, turning it over to reveal a half-dozen squirming red earthworms. There are likely hundreds of thousands more wriggling just under the surface of the three-foot mound of wood and crushed river rock before us, which stretches across the equivalent of six football fields. These natural materials form a biofilter that may dramatically cut the methane, nitrous oxide, and water pollution generated by the massive amounts of manure that hundreds of Holstein cows produce each day.

Agueda’s family business, the Alberto Dairy, was one of the first cattle operations in California to adopt this approach to manure treatment, developed and patented by the Chilean company BioFiltro. Eight more of these so-called vermifiltration systems are already operating on US dairies, according to the company, while another 16 are under construction or set to be next year, nearly all of them in California. 

Vermifiltration is just one of a variety of methods that farmers, companies, and scientists are employing to drive down manure pollution as the livestock industry faces growing pressure to address the environmental harms from one of the smelliest parts of the business. California, easily the nation’s largest milk producer, has established a handful of programs to promote their adoption, including one initiative that has funneled more than a billion dollars to farms.

Researchers stress that much more work needs to be done to determine the most effective approaches, the trade-offs between them, and their success over the long term, under actual farm conditions.

Agueda says that he and his family recognized the need to adopt new practices as environmental rules tightened. They were drawn to vermifiltration because it’s simple and relatively cheap compared with other, higher-tech options.

“California daily farmers are constantly facing more and more regulation,” says Agueda, standing alongside one of the farm’s free-stall barns. “This makes me excited, because it shows how we are part of the solution.”

The growing manure problem

Manure is responsible for a significant portion of the climate pollution from livestock operations. The World Resources Institute estimates that manure management on dairy and swine farms accounts for 1.6% of the US’s greenhouse-gas emissions. Globally, manure storage and processing makes up about 10% of the livestock industry’s contributions to climate change. 

“Farms have become larger in the past two decades or so, so there’s much more manure—and that has to be stored somewhere,” says Swati Hegde, the organization’s global manager of agricultural methane.

Typically, cattle and swine farms spray manure into lagoons or tanks, creating a foul-smelling, low-oxygen slurry in which microorganisms known as methanogens thrive. They gobble up hydrogen, carbon dioxide, and other compounds and produce methane as a by-product. Other microbes in the mix produce smaller amounts of nitrous oxide.

Both are particularly potent greenhouse gases, with as much as 30 to nearly 275 times the warming power of carbon dioxide, respectively, over a century.

The slurry is often spread onto fields to add nutrients to the soil. When it’s done excessively or improperly, this part of the practice can pollute soil or groundwater with drug residues, pathogens like salmonella and E. coli, and nitrates. Nitrates that leach into drinking water have been linked to a variety of human health risks. And those that flow into rivers, lakes, and coastal waters can spawn algae blooms that poison fish, block sunlight, suck up oxygen, or form large coastal dead zones devoid of marine life.

Policy drivers

A number of regions, nations, and states have passed regulations or offered subsidies designed to limit the pollution from livestock manure, but so far, most of the major initiatives have focused on water contamination rather than greenhouse-gas emissions.

The European Union, for instance, restricts the amount of manure that farmers can apply to fields and requires member nations to monitor nitrate levels in ground and surface water. The US’s Clean Water Act requires large livestock operations to obtain permits and develop manure management plans that limit pollution. 

But California has arguably done the most to use government policy specifically to drive down the methane emissions from livestock. The dairy industry accounts for about 45% of the state’s pollution from the potent greenhouse gas, and more than half of that comes from manure, according to the government’s estimates. 

In 2016, the state enacted a law that requires dairies, landfills, and other businesses to cut methane emissions 40% below 2013 levels by 2030, as part of a broader effort to reduce pollution from powerful but short-lived greenhouse gases. The measure directed the California Air Resources Board, the state’s main climate regulatory agency, to set up various incentive programs to encourage these industries to shift to cleaner practices. 

“In terms of bang for your buck, short-term benefits, methane can go a long way toward reaching climate goals,” says Tawny Mata, director of California’s Office of Agricultural Resilience and Sustainability. 

Between these various programs—and falling livestock numbers in the state—the dairy sector is on track to reduce annual methane emissions by the equivalent of 5 million metric tons of carbon dioxide by 2030, the state estimates. That would still fall about 4 million tons short of the target under the 2016 law.

The downsides of dairy digesters

Excluding the decline in herd populations—which has been driven by growing international competition and rising costs—the vast majority of California’s estimated methane reductions come from the use of what are known as anaerobic digesters. This technology entails covering the slurry lagoons to prevent methane from leaking into the air and then piping the biogas into separate vessels, where it’s cleaned and converted into natural gas. 

Under California’s Low Carbon Fuel Standard program, dairies that use digesters to produce gas delivered into pipelines can earn credits and sell them to petroleum refineries and other major polluters, as a means of helping those companies meet their own emissions reduction requirements. 

The gas can then fuel power plants, produce hydrogen, or power natural-gas vehicles. These uses still release carbon dioxide, but the state considers it a climate win because it avoids the release of methane, which traps even more heat. 

The rich revenue stream from California’s program has spurred hundreds of US farms to install anaerobic digesters over the last decade. Since 2020, it has produced more than $1 billion for farms, Cal Poly researchers noted in a paper last year.

But there are a variety of concerns about this approach.

The first is that it’s viable only for farms with about 2,000 cattle or more, because the equipment is very expensive to install, says Frank Mitloehner, a professor and chair of the Department of Animal Science at the University of California, Davis.

“For the lion’s share of dairies, digesters will not be a solution,” he says. 

Since the manure is often still spread across fields, digesters also do little to address the water pollution problems—and can even exacerbate them because of some of the chemistry that occurs during that process. 

Yet the huge subsidies flowing to digesters have steered money, energy, and attention away from other solutions that may offer better overall environmental outcomes, says Danny Cullenward, a senior fellow with the Kleinman Center for Energy Policy at the University of Pennsylvania, who has closely studied the California program.

“That is really not a solution at scale, and it’s diverting a huge fraction of precious resources to what I think is mostly not the right answer,” he says. 

Alternatives

The high up-front costs and limitations of digesters have spawned growing interest in alternative solutions—many of which work by reducing the formation of methane in the first place instead of turning that methane into a sellable fuel.

One of the cheapest, easiest, and most popular approaches, known as solid separation, uses simple machinery like a screw press to squeeze much of the water out of the manure slurry. The remaining solids are dry and exposed to open air, shifting away from the oxygen-free conditions in which methane is readily produced.

Other methods include increasing acidity in lagoons, bubbling air through them, or adding methane-eating microbes to the slurry, all of which alter the chemistry in ways that promise to reduce the amount of methane released. One company, Sedron Technologies of Sedro-Woolley, Washington, has also developed a sort of high-tech solid separation approach that extracts several marketable products from the animal waste, including a liquid organic fertilizer.  

The state of California set up a pair of additional programs to help smaller farmers adopt some of these other approaches, dubbed the Alternative Manure Management Program and the Dairy Plus Program.

The bulk of the funds have gone to solid separation systems. But the state has provided more than $18 million to support 15 vermifiltration projects. The Alberto Dairy has received nearly $2 million between the two programs.

Oreo cows

As I drove down a dusty road bordering the dairy, black-and-white bovines, affectionately known as Oreo cows, stretched their heads through the rails of an open barn, nibbling on golden silage scattered along the structure. Agueda’s grandfather Antonio Alberto founded the dairy 45 years ago in nearby Atwater, California, but eventually settled in Hickman, population 604, in 1989. 

It was mid-March but already above 80 °F in the Central Valley, which is walled off from the cool Pacific air by the coastal mountain range. Knee-high oat stalks swayed in fields that stretched to a line of almond trees in the distance.

Agueda, who graduated from Fresno State last year and now helps lead the operations on the farm, met me and UC Davis’s Mitloehner, who has studied the effects of vermifiltration, along the side of the barn. (UC Davis has no affiliation with the farm, but the university helped facilitate the meeting.)

He led us along dirt lanes as he explained the workings of the vermifiltration system, which they began using in October 2024.  

As before, a flush system washes manure from the floors of the barns into a large collection pit. But now a set of pumps funnels it through a series of large V-shaped metal contraptions standing on a nearby concrete pad, where mechanical screens separate most of the solids from the water.

A conveyor belt takes away the solids, which the farm composts for cow bedding or fertilizer. The remaining liquid moves through a system of pipes, first to settling ponds and then on to an irrigation system suspended above the vermifiltration beds. The long, tubular structure runs over the mounds on wheels set in gravel tracks, wetting the wood chips as it goes. The worms and various microbes residing in the biofilter then set to work consuming much of the remaining solid material, according to BioFiltro.

“Once the water is sprinkled on top, it takes about four hours from beginning to end for it to percolate through and drain to the end,” Agueda says.

He then defers to Mitloehner to explain the science of what happens as it does, adding, “I’m just the dairyman.”

The science

Mitloehner says he was skeptical of BioFiltro’s claims when he first heard them, particularly the assertion that the system could nearly eliminate nitrogen and, with it, the various forms of pollution it can produce, including ammonia and nitrates. 

So he decided to study a similar setup at the Fanelli Dairy, an operation in Hilmar, California, about 20 miles to the south. He and colleagues monitored the emissions from wastewater samples that were taken from the system before and after the liquid moved through the filter. In a paper published in 2018, the researchers concluded that vermifiltration reduced ammonia emissions from the resulting water by about 90%.

BioFiltro, whose tagline is “worm-powered solutions,” states that its technology “catalyzes the digestive power of worms and microbes to remove up to 99% of wastewater contaminants.”

But Mitloehner questions how big a role the invertebrates play in the process, calling it “kind of a catchy narrative.”

His take is simpler: The rocks and wood chips form a porous filter that replaces the anaerobic environment of a manure lagoon with an aerobic one. And in that oxygen-rich environment, different types of microbes thrive. 

His study suggests that these microbes are highly effective at converting nitrogen compounds in manure into nitrogen gas—a benign gas that makes up 78% of Earth’s atmosphere—instead of ammonia. That’s notable because while ammonia in manure acts as a fertilizer when it’s applied to fields, it also converts into the nitrates that can leach into groundwater.

Several more recent studies, which were partially or fully funded by BioFiltro and one of its regional distribution partners, Organix, produced similarly promising results. For instance, a 2022 study in Bioresource Technology Reports, also conducted at the Fanelli Dairy, concluded that the filter removed nearly 85% of the nitrogen in the operation’s wastewater. 

But a befuddling wrinkle is that when it came to methane, those studies and Mitloehner’s independent one found nearly opposite results.

While both the company- and partner-supported studies concluded that the filter eliminated the vast majority of methane pollution, Mitloehner’s study found that methane emissions were nearly 85% higher than those from the lagoon. 

In a follow-up email exchange, Mitloehner stressed that it’s not appropriate to compare his results with those that emerged from the other study at the same dairy, because the teams used very different methods, instruments, and measurement periods. Moreover, the focus of his research was the effect on nitrogen.

He said it’s “entirely reasonable” and “biologically plausible” that vermifiltration could substantially reduce methane emissions, simply by creating that aerobic environment.

“That said, I would be cautious about calling the magnitude of the reduction a fully settled issue,” he added. “While the available studies, including those you mentioned, point in the same general direction, the number of independent studies remains relatively limited, and results can vary.”

Patrick Beckett, BioFiltro’s vice president of quality and R&D, also stressed that there were crucial differences in the methodology of Mitloehner’s study that could have affected his methane findings.

In addition, he said the Organix funding came by way of a Washington state grant and described that study and the one BioFiltro supported as “high quality, peer reviewed” research that “has been submitted to other technical third parties for review and acceptance.”

Beckett says he agrees that additional independent reviews of BioFiltro’s systems is “fair and necessary” and notes that other studies have occurred or are underway.  

“That said,” Beckett wrote in an emailed response to questions from MIT Technology Review, “it seems unreasonable that BioFiltro would be held to a standard of not being allowed to invest in technical research by qualified third parties to learn more about the capabilities of our technology, and use the results of that research to enter new markets and to understand the value we can bring to projects or entire industries beyond water treatment.”

Milk money

BioFiltro is already building a business model around the available findings.

The company, founded in 2009, has been selling its vermifiltration systems or services to other industries around the world for years. It says there are around 225 operating in nine countries, at sites including municipal wastewater facilities, wineries, fruit processors, and other industrial operations.

But BioFiltro, whose US headquarters are in Davis, California, is seeing increasing demand among dairies as the industry faces growing pressure to address manure pollution. Late last year, it raised $35 million that the business says it will use, in large part, to accelerate its growth across the sector.

In an interview, Sarah Ploss, the company’s senior vice president of agriculture, explains the basic financial template for how it works with dairies: BioFiltro pays for, owns, installs, and operates the system. The farm, in turn, covers a share of the additional electricity, operations, and maintenance costs. 

Ploss says the dairy gets back clean water and the ability to focus on what it does best: producing milk. For its part, BioFiltro can generate carbon credits from the reduction in greenhouse gases, which it can then sell to makers of consumer packaged goods that are looking for ways to address the emissions throughout their supply chains, she says.

BioFiltro says that Verra, which sets standards for and assesses greenhouse-gas crediting projects, has registered two of its projects: the Royal Dairy and Moxee Dairy, both in Washington.

The Swiss confectionary giant Nestlé has bought more than 150,000 credits generated by the Royal Dairy’s vermifiltration system, according to an offsets database managed by CarbonPlan, which assesses the scientific integrity of climate action programs. Ploss said that BioFiltro has sold more than 200,000 credits from the project so far, and adds that it secured a different buyer for a project in California, which she said she couldn’t name. 

Three additional projects involving BioFiltro systems took the initial steps to become registered through Verra but didn’t move forward and weren’t built, Ploss said in an email. The request for registration for the Alberto Dairy estimates that the system there will reduce emissions by the equivalent of more than 30,000 metric tons of carbon dioxide per year. 

BioFiltro could take advantage of another revenue source as well: selling what it calls vermicompost, a rich soil additive composed of the leftover materials in the biofilter, including worm castings—a combination of cocoons, excrement, and remains. At retail, worm castings can run more than $500 per ton.

Beckett says the company is still developing that market but notes that it could help the industry offset rising fertilizer costs. 

“I think we’re going to enable a larger-scale use and adoption of it that could be meaningful to agriculture,” he says, adding: “These will become basically soil production facilities.” 

Concerns

Determining how well vermifiltration and other manure management approaches work will require more time and more research, experts say. 

Katharine Dickson, an agricultural emissions scientist who recently finished a postdoctoral program at UC Davis, says there should be in-the-field accounting to ensure that any of these methods are working as well as hoped—or to the degree government policy programs assume. All of which is tricky to achieve given the dynamic biological processes playing out in live animals and microbial communities on open farms, she adds.

“Vermifiltration, for example, depends on a live earthworm population whose performance is sensitive to temperature, moisture, and toxicity, and can shift with seasonal conditions or changes in herd size and manure characteristics on a given farm,” Dickson said in an email. 

The use of carbon credits to earn money from vermifiltration projects raises a different set of potential concerns. Most notably, if the methane decreases aren’t as significant as assumed, the projects could receive more credits than they deserve. 

There are more complicated issues as well. For the carbon credit system to make any real difference in the net amount of greenhouse gas in the atmosphere, it must produce emissions reductions that wouldn’t have occurred without that financial incentive. If it was going to happen anyway—as a result, say, of rich grants, legal pressures, or looming policies—the buyer of the credits can’t legitimately claim to have made any progress on its own climate emissions, says Grayson Badgley, a research scientist at CarbonPlan.

On that point, if California agriculture doesn’t meet its looming methane reduction targets, the carrots the state offers could be replaced by sticks: The California Air Resources Board recently began discussing rules that would force, rather than nudge, the sector to meet the 40% reduction required under the 2016 law.

“If lots of dairies are cleaning up their act ahead of pending regulation, it really does seem like the regulation, not offsets, is driving that action,” Badgley wrote in an email. “Trying to collect as many offsets prior to that deadline might adhere to the rules of the market, while still raising questions about whether those rules have enabled real climate action.”

Investing in sustainability 

Beckett disagreed that the possibility of forthcoming regulations undermines the case for generating carbon credits from current projects. 

“It’s true the state has net reduction targets that it hopes to meet, but it’s clear the state of California has favored market-based solutions and tried to provide some support via grant programs,” he wrote. “I’m on the science side of our business, not the business development side, but still think I can tell you with complete transparency that we would not have systems installed on [California] dairies without the sale of voluntary carbon credits.”

Ploss also stressed that the company goes through a careful “validation and verification process” on the farms to understand how much vermifiltration reduces greenhouse gases.

“We’ve got sensors and cameras and all sorts of stuff so that we can look into any of our systems, 24-7,” Ploss says. “We know through sampling. We know through what’s going through the system, what came out of the system. We know by all the measurements on any given month: What did that system do in terms of generating carbon credits?”

Agueda also disputes the critique. 

“The installation of the vermifiltration system would not have occurred without the ability to generate carbon credits,” he said in an email. “The project required a substantial capital investment, and the anticipated carbon credit revenue was a key factor in making the investment financially feasible.”

California decided to incentivize vermifiltration, along with other approaches, because it can offer multiple benefits, including cleaner water, less nitrogen, and lower greenhouse-gas emissions, while also creating economic value from manure, wrote Roberta Franco, a senior environmental scientist at the California Department of Food and Agriculture, in an emailed response to questions from MIT Technology Review.

She added that the decision was based on a number of studies as well as the 2022 recommendations from a task force composed of scientists, technical experts, and others. 

Even if California has made missteps, most notably in funneling too much money to anaerobic digesters at the expense of other methods, it’s created a test lab that’s achieved real progress and provided lessons that other regions can learn from.

One way or another, more parts of the world will need to set up similar programs, offering greater support or creating stricter rules, if we hope to really drive down the emissions from manure, says Maria Bowman, who leads the Agricultural Nitrogen Transformation Program at Spark Climate, a San Francisco nonprofit.

For his part, Agueda says that the vermifiltration system has offered a number of benefits to his family’s farm, at little additional cost to them. By cleaning up the water that cycles back through their flush and irrigation systems, the biofilter has reduced clogging, decreased odors, and improved the health of the herd.  

He says that each generation modernizes dairy farming in its own way. His father and uncle, for instance, incorporated computers and data management systems into the daily operations of the Alberto Dairy. He believes it’s the responsibility of his generation to make a similar effort to reduce the pollution that’s long plagued the sector.

“We knew that in the next generation we have to invest in environmental sustainability,” he says. “We didn’t know if it was gonna work or not, but we’re very happy with how it’s turned out.”