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 animalsIn 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 companyThe 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 acquisitionIt’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 deathIt’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 accessA 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 groupsVia Singapore-based subsidiaries of Alibaba, Baidu and Tencent. (FT $)8 A daughter tested an AI “death bot” of her fatherThe technology provided both comfort and unease. (New Yorker $)9 An astronomer says the hunt for alien life needs more statisticsHe wants to replace speculation with mathematical frameworks. (Quanta)10 Pokémon Go players turned Times Square into a giant battlefieldMore 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 B.F. SKINNER FOUNDATION 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.

EXECUTIVE SUMMARY 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.” 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 chipsAlibaba, 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 tracksIt 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 sunDeflecting 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 emotionsIt 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 slowsThey’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 personFrom 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 kidsIt 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 SECDespite 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 spaceCubesats 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 recordThe 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.

EXECUTIVE SUMMARY 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 5.6The 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 modelsAlibaba, 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 planThey 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 momentIt 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 sayIt 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 eraIt’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 CursorThe 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 textBut researchers are very divided over its potential impact. (Nature $)9 Scientists have detected a mystery chemical on Pluto and TitanIt appears to absorb light in a way we don’t currently understand. (Wired $)10 A Waymo robotaxi reportedly called the cops on drinking teensOfficers 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 MICHAEL BYERS 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.