ArXiv, the open-access repository of preprint academic research, will ban authors of papers for a year if they submit obviously AI-generated work. 

Late Thursday evening, Thomas Dietterich, chair of the computer science section of ArXiv, wrote on X: “If generative AI tools generate inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in scientific works, it is the responsibility of the author(s). We have recently clarified our penalties for this. If a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can't trust anything in the paper.”

Examples of incontrovertible evidence, he wrote, include “hallucinated references, meta-comments from the LLM (‘here is a 200 word summary; would you like me to make any changes?’; ‘the data in this table is illustrative, fill it in with the real numbers from your experiments’.”

“The penalty is a 1-year ban from arXiv followed by the requirement that subsequent arXiv submissions must first be accepted at a reputable peer-reviewed venue,” Dietterich wrote. 

Dietterich told me in an email on Friday morning that this is a one-strike rule—meaning authors caught just once including AI slop in submissions will be banned—but that decisions will be open to appeal. “I want to emphasize that we only apply this to cases of incontrovertible evidence,” he said. “I should also add that our internal process requires first a moderator to document the problem and then for the Section Chair to confirm before imposing the penalty.”

In November 2025, arXiv announced it would no longer accept computer science review articles and position papers because it was being “flooded” with AI slop. “Generative AI/large language models have added to this flood by making papers—especially papers not introducing new research results—fast and easy to write. While categories across arXiv have all seen a major increase in submissions, it’s particularly pronounced in arXiv’s CS category,” arXiv wrote in a press release about the change at the time. 

And in January, it announced first-time submitters would need an endorsement from an established author due to a rise in fraudulent submissions. 

AI-generated, fabricated citations are a huge problem in research. A recent study by Columbia University researchers examined 2.5 million biomedical papers across three years, and found that one in 277 papers published in the first seven weeks of 2026 contained fabricated references; In 2023, it was one in 2,828, and in 2025, one in 458. AI-generated citations and papers are already straining the peer-review process, and more and more papers are making it through the pipeline with those meta-comments and hallucinated data intact. 

ArXiv is managed by Cornell Tech, but this July, it will become an independent nonprofit corporation. Greg Morrisett, dean and vice provost of Cornell Tech, told Science.org that this change will help arXiv raise more money from a wider range of donors, which Morrisett said is needed to deal with the emergence of “AI slop.”

This is Behind the Blog, where we share our behind-the-scenes thoughts about how a few of our top stories of the week came together. This week, we discuss developers' AI woes, how the magic happens, and the Beach Boys.

JOSEPH: Earlier in the week we published ICE Agents Have List of 20 Million People on Their iPhones Thanks to Palantir. This took a little while because I spoke to four people who attended the conference. I spoke to one, I asked if they knew anyone else there. Got another name and phone number, and so on.

I included this line in the copy: “The officials’ comments may need to be taken with a pinch of salt, but still reflect ICE’s position that Palantir is allowing the agency to identify people to arrest and locations to raid faster.”

I think that was important to include because these are comments and figures coming from senior ICE officials, and one in particular, Matthew Elliston, assistant director of Law Enforcement Systems & Analysis at ICE. As we all know, DHS lies. 

Sometimes I think there is some value in hearing from an official or agency even if you know they might be full of it. That’s as long as the comments are caveated or pushed back against. For example, Elliston says that ICE’s facial recognition app Mobile Fortify has been used 200,000 times with 0 mismatches. We then included the fact that the app misidentified a woman, twice. So that is obviously not true.

But still, there is value in hearing from ICE about how much they seemingly value Palantir. When I approach ICE for comment about its use of Palantir, I usually get nothing. So this information is something.

One thing to add though: ICE claims to have this number of 20 million. Obviously, that’s a big number that most likely does not reflect the number of undocumented people (which presumably ICE wants to detain) in the country. Research puts the number closer to 14 million. So, is Elliston just exaggerating the size of the list, or actually believes there are 20 million undocumented people, or the list includes other people who may not be technically undocumented but still in ICE’s crosshairs? Expired visas, that sort of thing. That we don’t know.

Readers aren’t dumb (well, most of them). I think someone can read this and think, ah, this is ICE’s position, okay, it doesn’t mean for sure everything they say is accurate. So I’m fine reporting what was said, personally.

We also didn’t publish the second half of DHS’s statement because it just didn’t seem valuable at all to readers. It was the repeating numbers that may have been massaged or are just flat out wrong. But since this is BTB, here is what it said: “We are not going to disclose law enforcement sensitive intelligence and methods. Nearly 70% of the arrests ICE made were of criminal illegal aliens. We are continuing to go after the worst of the worst—including gang members, pedophiles, and rapists. Under Secretary Mullin, we are delivering on President Trump's and the American people's mandate to arrest and deport criminal illegal aliens and make America safe.”

EMANUEL: This week I published a story based on a number of interviews I did with software developers about how they are being pressured to use AI tools at work to produce code, how that is often more trouble than it’s worth, and how they all feel like it’s frying their brains. 

The story started when a developer reached out to me when he reached a breaking point. He spent months giving LLMs the benefit of the doubt, and he did find them useful for some things, but he realized that the thing he loved to do—coding—was suffering because of how he was outsourcing a lot of his coding brain to a piece of software. He described it as a moment of realization that the emperor might have no clothes. 

I don’t want to say too much at this point because I’m hopefully saving it all for an article, but I’ve never seen so many people reach out to me wanting to share stories about their job as I did after publishing this article. Literally dozens of people from every corner of the tech industry have reached out to complain about how they’re being pressured to use AI at work. If you’re one of those people and you’re reading this, if I haven’t gotten back to you yet, I will very soon. 

I think software developers are feeling acute pain with the implementation of AI because the changes are coming to them first, they’re coming fast, and they’re coming for a workforce that had it comparatively good in recent history. As someone who has worked in media pretty much his whole life and who has lived through various cycles of collapse and being told that I should “learn to code,” I definitely feel the urge to embrace schadenfreude. When I was making $19 a blog post in San Francisco, developers who were hired right out of college or coding boot camps were being shuttled to lavish campuses around the Bay Area and making hundreds of thousands of dollars a year. As media companies went through round after round of layoffs, journalists got little solidarity from people in the tech industry, and if anything, were often chided for choosing a dying, obsolete profession. 

No one is missing the irony that developers ended up helping to build the tools that are now aggressively displacing them regardless of whether the tools are up for the job, and I think it’s fair to point out, but I also think this crisis presents a huge opportunity for change if we’re willing to listen to these workers, and if they are willing to listen to each other. Try as they might to replace them, tech companies still need human workers, and those workers are increasingly critical of their employers now that they’re being threatened with layoffs rather than being courted with perks and benefits. 

At the very least, we should listen to these workers because employers everywhere are going to try the same strategies that are being forced on them now.

Again, if you’re a software developer, especially if you’re at Microsoft, Google, Amazon, Meta, or other tech giants heavily pivoting to AI development, please get in touch via my email (emanuel@404media.co) or Signal (@emanuel.404). 

JASON: I got more positive feedback and friendly emails on “Your AI Use Is Breaking My Brain” than I have on maybe any piece I’ve ever written. I haven’t had time to go through and respond but I very much appreciate it, and am glad that it resonated with people because when I was thinking about it, I was wondering if maybe I had simply broken my own brain by purposefully looking at so much AI slop. I was heartened and horrified to learn that a lot of you have had pretty much the same experience as me, so that’s good/bad I guess.

Like a lot of stories, this one kinda just popped into my brain while I was ostensibly not working. I mentioned this in the article, but basically I crashed out while I was walking my dog and listening to a podcast. Also like a lot of stories, it sorta began by me ranting and raving in Slack. I *really* wanted to call the article “Your AI Use Is Giving Me Psychosis,” but as Sam and others have reported this is not what what it’s called and I also don’t have psychosis, so, problematic on a few levels. But that was basically my working title for the article, which quickly became “breaking my brain,” because as much as I’d like to think so, I am not a mental health professional so it was better to go with something less medical. Here’s how that initial crashout looked; pretty much all of it actually did end up going into the article. This is how the magic happens folks. 

That article was already pretty first person-y, so I guess there’s not too much more behind the scenes to say here, but I will say that in the last few days I’ve seen other companion piece-type articles and audio that I think are worth checking out. Vox’s Today, Explained did an episode this week called “Is it a bad book or is it AI?,” which is about what happens when a book author is accused of using (and did use) AI. More background on this saga is here in this Slate article, and here in this other Slate article. For me, at least, it’s not so much whether something is AI generated or not, it’s whether it is bad and boring and whether any sort of effort went into it at all. If AI is just enabling you to write 10x or 100x as much, why are you forcing me to read it? I thought the Verge’s Liz Lopatto—a very good journalist and writer—made a good point on Bluesky when she shared my article: The point, at least to me, is that there’s a lot more writing now and much of it is very bad and that is annoying. I am concerned about misinformation and disinformation and slop, but I am most concerned by the number of things I open that are completely devoid of any personality or usefulness whatsoever, which are overly long, and which are just generally bad. 

I saw a post somewhere yesterday that I have now lost, but it was about how Silicon Valley bros are calling people who don’t use AI “brain only” writers, which I think is really funny. I have tried to have some level of nuance on this topic because I don’t really think AI is going away and I don’t think it’s useful to say AI is not ever useful (it is and can be). I think it’s more just that generally the internet is breaking and this is another way we’re beginning to see it. 

SAM: I usually listen to music while I’m making the newsletter on Fridays, but it has to be strictly lyric-less music for writing in general. I'm sure I’ve told you before about how my Spotify Wrapped every single year is entirely Tron soundtracks. But Tron is my lock-in-and-just-fucking-write-it music, while Fridays I like to branch out a teeny bit, since the newsletter is more creative/moving things around on a page work and less focused writing. For example, Nine Inch Nails released an album of remixes with Boys Noize a couple weeks ago, so I was listening to that on repeat for a while. That led me to finding playlists of instrumentals-only NIN playlists, which are great for focus. This is going to sound stupid to anyone who’s been a fan forever, but the Tron: Ares soundtrack was a gateway to greater NIN appreciation for me; Trent is doing Disney films now, granted, but the band could have phoned that one in and instead ended up creating a truly emotional and beautiful soundtrack (to me! to me!). Ares is better if you watch it as a badly done music video for NIN and stop caring if a plot exists. 

Anyway, this morning, I saw there’s a new release of Pet Sounds sessions. I know nothing about how music is made, have never had any kind of ear, and only briefly knew how to read sheet music or guitar tabs because of church or classes as a kid. That’s at least partially why I never got into the Beach Boys, I think — I understand they, and especially Pet Sounds, contributed something monumental to music as a craft, but I am never reaching for the Boys on purpose. I like them when they come on by accident in a playlist or in the grocery store. 

But I saw this release has a bunch of backing track takes, which means (mostly) no singing unless it’s background vocalizing, so I gave it a try, and am surprised to be into it. I never appreciated before how much is going on in a Beach Boys song. The lyrics and syrupy fun-fun vocals (sorry) always overpowered the rest of the music, for me. But hearing all the little elements isolated and fussed over is super interesting. Some of the takes are “highlights,” where someone (Brian Wilson? I assume? Sorry again) stops the take and says NO NO NO like THIS and they say “take 14,” or some double-digit number. It makes me think, god, they did this so many times just to get the exact timing of that riff right, or move one trombone closer or farther from the mic, or whatever it may be. In “You Still Believe In Me:” “Don’t forget to do that ‘baahhh bah bom, du-U-duit’” (in reference to a clown horn). In “Sloop John B:” “Hold the flutes... right there RIGHT THERE DON’T MOVE!” It seems like such an arduous process. And it also involves being extremely picky about details no one else would likely notice, and gently but firmly holding everyone else to a vision. But the end result was one of the most iconic albums of all time, and I assume that’s the only way you get there. We don't have an alternative reality where Wilson said "I think that second flute was too soft, but whatever, no one will notice," so who can say.

Again, these are just musings from a musical ignoramus, but if you want something to listen to today, maybe check it out.

Mayo Clinic, the massive U.S. hospital network, is using what it describes as “Ambient Listening” to record patient interactions with nurses, including in emergency rooms, then using AI to process that collected data. The recording is opt-out, rather than opt-in, and at least some patients are likely not aware the recording is happening.

The recording brings up questions of informed consent and whether the generated notes may be accurate enough. A study last month found that AI-powered scribe tools sometimes produce much less accurate notes than humans depending on the situation.

“Mayo Clinic would like to record interactions between you and your nurse to assist with electronic health record documentation,” a notice posted in a Mayo Clinic location reads. A person who said they were taking their elderly father to the emergency room shared a photo of the notice with 404 Media.

“About halfway through his visit in the [emergency department], I had to stretch my legs and just happened to notice this poster, which was not close to the bed or visitor chairs, and was the size of a regular piece of printer paper. My dad certainly didn't notice or read it,” the person told 404 Media. 404 Media granted them anonymity to talk about their parent’s medical care.

“I did not talk to any staff about it, because we were in the middle of an emergency, which is part of my whole issue with this being an opt-out thing in the emergency department. So many people might not notice this or even be healthy enough to read or notice it,” the person added.

The notice specifically says the recording device might capture data that falls under HIPAA, the U.S.’s health data protection law. “This device may capture protected health information that is protected by the Health Insurance Portability and Accountability Act and Mayo Clinic’s Notice of Privacy Practices. Please ask a staff member for additional information,” it reads.

Mayo Clinic has been using Ambient Listening for a couple years at this point, but not all patients may be aware the recording is ongoing. A July 2024 press release says Mayo Clinic is working with medical technology giant Epic and AI company Abridge on “a generative AI ambient documentation workflow for nurses.”

On its website Abridge pitches itself as an “Enterprise-grade AI for clinical conversations—trusted by the largest healthcare systems. Measurably improving outcomes for clinicians, nurses, and revenue cycle teams at scale.” In December 2024, Johns Hopkins Medicine reached an agreement to deploy the Abridge ambient AI platform across 6,700 clinicians, six hospitals, and 40 patient-care centers, according to a press release from Abridge.

Mayo Clinic finalized an “enterprise-wide agreement” with Abridge last year, according to another press release. That paired the technology with around 2,000 clinicians who serve more than 1 million patients annually, the release says.

Neither Mayo Clinic nor Abridge responded to a request for comment.

A recent study found that human note takers create much better notes than AI-powered scribe tools. In some specific cases, the AI performed especially poorly compared to a human: when there was background noise; when the clinician and patient were wearing masks; and to a lesser extent when the patient had an accent, according to the American Medical Journal.

Doctors around the country are increasingly using AI in various forms, such as dictating notes to be added to a patient’s file. On the consumer side, a recent study found that chatbots can give out wildly different and incorrect medical advice to users.

Days into Donald Trump’s second term, his administration began rapidly dismantling USAID, which had, up until that point, been the world’s largest national humanitarian donor. Elon Musk, who spearheaded the Department of Government Efficiency, announced that his team had fed the agency “into the woodchipper” in February 2025. Tracking models suggest the collapse of USAID may have already caused 762,000 preventable deaths, of which 500,000 are children, and the cuts could lead to more than nine million preventable deaths by 2030, according to a study published in February 2026.

Now, a team reports “the earliest evidence of the impact of cuts to USAID on the incidence of violent events” which suggests that “the radical cuts…led to an increase in conflict in the regions that received the most aid from the United States,” according to the new study. 

“What we find is that with the USAID shutdown, there was a rapid increase in the likelihood of violence, the severity of violence, and the lethality of violence across nearly one thousand subnational administrative units across Africa,” said Austin L. Wright, study co-author and associate professor and director of strategic initiatives at the Harris School of Public Policy at the University of Chicago, in a call with 404 Media.

In regions that received the most support from USAID, the cuts were associated with a 6.5 percent probability of any conflict event, compared to regions that received no aid. To get a sense of the devastating impact of that statistic, here’s what the study reports:

Between 2021 and 2024, USAID is estimated to have saved 91 million lives, about a third of which are children under 5 years old. The agency was created by John F. Kennedy in 1961 and, in the years preceding Trump’s shutdown of the agency, accounted for less than 1 percent of total U.S. federal spending. 

The impact of aid on communities is complex and context-dependent. Aid may reduce conflicts in cases where the opportunity costs of violence are mitigated by an influx of resources, known as the “opportunity cost effect.” But aid can also fuel conflicts over the handling and distribution of those resources, known as the “rapacity effect.” 

The collapse of USAID, which is unprecedented in its scale and speed, has produced the worst of both worlds, according to the new study.

“When those funds rapidly go away, it's a shock to the opportunity cost, and now it becomes more and more attractive to participate in what we might call the unproductive part of the economy, which is participating in violence, engaging in crime, and other activities,” Wright said. “But because the shutdown was so rapid, it didn't really have an opportunity to bind on the rapacity effect, because it's not as if the bridges, roads, or full-on infrastructure went away. The things that individuals or groups might fight over were still present.” 

“It’s a bit of a ticking time bomb, because you're both removing the conflict-reducing side of aid, while leaving behind the conflict-enhancing part of aid,” he added. 

To quantify the impact of the cuts on violence, Wright and his colleagues examined the Geocoded Official Development Assistance Dataset (GODAD), which monitors geolocated information regarding foreign aid disbursements, alongside the Armed Conflict Location and Event Data (ACLED), which tracks violent events. 

The overlapping datasets revealed macro-level patterns between aid distribution and violence in the wake of the cuts, including significant upticks of violence in areas that had previously received large amounts of aid, or where the population had less control over their government due to weaker executive constraints. 

Moreover, this increase in conflict has persisted over the course of months and may continue in areas that fall into “conflict traps” defined by self-perpetuating cycles of violence.

These impacts are catastrophic for people who had relied on USAID, as evidenced by the estimated death tolls, and the increased risk of violent conflicts and upheavals. They also present new vulnerabilities for the United States and its allies. Though USAID had an altruistic mission, the agency also served as a vector of soft power and an early-warning system for tracking public health risks, like pandemics. The loss of the agency has already caused national security issues for the U.S., such as the seizure of discarded USAID supplies by Iran-backed Houthi groups in Yemen.  

“Those insecurities don't stay where they're created; they travel,” Wright said. “That unfortunately means that the vulnerabilities that are being created at the moment will likely have long-run consequences of creating insecurity that directly impacts the safety of Americans.”

Moreover, Trump’s demolition of USAID prompted many allies in Europe to pull back on their own foreign aid, exacerbating the effects. Though other humanitarian organizations are struggling to mitigate the consequences, the loss of trust caused by the shutdown of USAID is likely permanent, with ominous long-term consequences.  

“Even if you reactivated USAID and pretended as if it never went away, you can't reverse these effects because you've already communicated your bad faith behavior,” Wright said. “There is nothing quite like the reputational bomb of simply shutting down an agency, and what that does to the reputation that the U.S. might have if it ever wanted to reinitiate its interventions.”

“From the soft power lens, and a global lens, the reputational effects, I think, are tremendous and will create a bunch of wedges and inefficiencies,” he concluded. “If one simply wanted to restart USAID, it's going to cost much more to rebuild than simply the same budget all over again.”

A few weeks ago, I came across a wild post on Reddit’s r/DHExchange, a subreddit for trading large datasets: “I hoarded a large database of something valuable, just not what’s [sic] you expect…150k stools images.” 

The post, made by a user called Ill_Car_7351, was advertising exactly what it sounds like: A database of poop images, collected from an AI poop analyzing app that he had launched several years ago. Basically, 25,000 people had been taking images of their poop and uploading them to his app. He’d been collecting, analyzing, and annotating these images and now wanted to sell access to them: “I’ve got 150k+ labeled and classified images of 💩 from roughly 25K different people. Jokes aside, I know there’s a lot of value in it (hard to obtain, useful for ML [machine learning] training, cancer studies etc) but not sure on how to move about it. Feels like I’m sitting on a pile of shi..ny coins but can’t find who wants them.” The poster added that “the images are extremely rare,” and that he was trying to figure out how much money he could sell them for.

The comments were from people who were mostly horrified: “When I was 5 the teacher taught me how to read. I now regret that happened,” one read. “What in the fuck,” another read. “How to delete someone else’s post,” a third said. 

I messaged the poster and told him I was interested in obtaining the database. Thus began my journey into the Internet of Shit and, by extension, the unpleasant world of the underground sale of highly sensitive, app-collected user data for AI training. 

The poop database comes from an app called PoopCheck, an app made by a company called Soft All Things that purports to use AI to analyze images of one’s stool in order to give you a “daily gut health score.” 

“Our AI analyzes your poop using the Bristol Stool Scale and advanced pattern recognition. Get insights on consistency, color, shape, and what they mean for your digestive health,” the app advertises. The Bristol Stool Scale classifies stools into one of seven types ranging from “separate hard lumps, like little pebbles” to “watery with no solid pieces.”

The app also features a “community,” of 151,317 “shared stools” at the time of this writing and a “leaderboard,” where people can share images of their poop for commentary from other users and earn points for participating. I found the posts in the community a bit hard to stomach, with titles “like play dough,” “Concerned,” and “Dealing with this on and off for the past 3 weeks.” Pictures are not automatically shared to the community; when you take a photo it asks if you want to share it.

“Popular” posts on the app include people speculating as to whether their fellow community members have parasites or colon cancer; in the comments section of a few posts I saw people recommending ivermectin to the original poster. 

Though users have the option to share their poops with other users, the app provides mixed messages about the fact that the data uploaded to the app will be analyzed, annotated, and packaged with other poops into a commercial database to be sold to AI companies. 

On the App Store page for PoopCheck, it says “The developer does not collect any data from this app.” The link to the privacy policy from within the App Store download page does not mention anything about selling or sharing the data and says “your health data is encrypted in transit and at rest. Photos are processed securely. We implement industry-standard security measures to protect your data.” 

The PoopCheck website’s About page states “Privacy First.” And “Health data is sensitive. That’s why privacy isn’t a feature, it’s our foundation. Your photos are encrypted. You can delete everything at any time. We built PoopCheck the way we’d want our own health apps built.” The FAQ also notes “your privacy is our priority.”

This is completely different from the “Service Agreement” and “Terms and Conditions” people agree to when they actually open the app and make an account. The Service Agreement states that “by uploading stool images or any health-related data to the App, you grant Soft All Things LLC a worldwide, irrevocable, perpetual, unconditional, royalty-free, fully-paid, transferable, sub licensable license to use, reproduce, modify, adapt, distribute, sell, license, and create derivative works from such content for any lawful purpose, including but not limited to research, commercial exploitation, product development, and third party licensing. You acknowledge that your images and data may be used to create, train, improve, and commercialize AI technologies and machine learning models, and that such models and any outputs derived from your data may be licensed or sold to third parties, including medical organizations, research institutions, and commercial partners.”

It adds that “your data may be irreversibly incorporated into AI models and aggregated datasets. Deletion of your account will remove your personal profile data but does not require the removal of anonymized, aggregated, or derivative data already processed or incorporated into AI models.” Under a section called “Sharing of Information,” it adds that the company reserves the right to share or sell the data “for any business purpose,” including “AI and Data Licensing.” 

On Reddit, I messaged Ill_Car_7351 and said “Hi - am interested in this database you posted about. Can you share any more info about what you're looking for / details about the app where it was collected? also any chance there's like, a sample of what the data looks like etc?” They responded quickly and said “Hey! The db was gathered by real users, we had 25k users over the last couple years, since we launched the app. It’s called PoopCheck btw if you wanna see it. Let’s maybe talk via email? I’ll be happy to share a sample of the data if that interests you.”

I sent an email to someone named “Marco” at Soft All Things, who identified himself as one of the founders of PoopCheck. I said I had reached out on Reddit and was interested in a sample of the data. I used my real email address and real name.

“We can surely send you a sampling of the dataset, would a Google Drive link containing an image folder and JSON data work? We can also figure out other ways if you prefer,” Marco said. “In terms of the actual dataset you need, what would be the size of it for your needs? And what would you be using it for? Just so we can make sure it’s actually a good fit for your use case.”

I told Marco that I wanted 10,000 pieces of data and said I would use it for AI training. I asked him for pricing and what type of data was included. 

Marco responded:

“You'll find a folder with images and JSON metadata covering the key fields we capture per entry. Let us know if you have any questions about it.

To give you a better idea of the dataset and pricing options: we currently have over 150,000 images validated by AI. Around 5,000 of these have also been manually reviewed by a member of our team, who verified the AI output and labeling, making this portion more valuable and priced accordingly. It's also worth noting that certain types on the Bristol Stool Scale are rarer than others, so availability may vary depending on your specific needs.

With that in mind, here there is an estimation of pricing options:

• 10,000 unreviewed images (AI-validated) — $3,000

• 5,000 fully human-reviewed & annotated (on top of AI validation) — $4,000

• 5,000 reviewed + 5,000 unreviewed — $5,000

It would be great to have a quick call to take this further as there are a few things about the dataset's structure and coverage that are easier to walk through live.” 

The sample dataset Marco sent me included 20 images of poop from four specific users (five poops each). Each image was tied to a series of user-reported data points as well as AI analyses of each image. AI-analyzed datapoints included the time the poop was taken, the Bristol Type of each poop, whether it was “healthy” or “unhealthy,” the “shape” and “consistency,” whether there was blood or mucus in the poop, and the quantity (“large,” “normal,” or “small”), and whether it was “floating” or not. Each of these data points also had a “confidence” score for how confident the AI was in its analysis. Each image also had user-reported information, which included the answers to a series of questions including “when did you have your last meal,” “any discomfort while pooping? (“Hard to pass;” “burning”; “sharp pain” etc); “How long did it take?” “Did it smell stronger than usual?” “Coffee or alcohol in the last 12 hours?” The data also included demographic information, which includes age ranges, sex, height, weight, and sensitivities such as “lactose intolerance” or “irritable bowel syndrome.”  Each image is tied to a specific user through a field called “externalIndividualID.” 

Soft All Things is not exactly quiet about the database that it has created. On the Poop Check website, it has a page called “For Business,” which advertises its database. It sells access to both the “Stool Analysis API,” which “turns a stool photo into a structured health report,” as well as the “Annotated Dataset,” of 140,000+ images to “train your own models.” It advertises this as the “largest consumer stool image dataset we know of.”

It maybe should not be terribly surprising that a free app in which you upload images of your poop to a random company would have a business model focused on packaging and selling that data. But this type of data collection—of our literal poop—highlights how almost anything we do on our phones can ultimately end up for sale. The fact that it is advertising this for sale at all indicates that there is an AI goldrush for any and all types of data, even our literal waste. 

Research has shown, over and over again, that de-identified “anonymous” data doesn’t necessarily remain anonymous when combined with other datasets. Toward the end of last year, the appliance giant Kohler endured a security shitshow when a researcher showed that its stool-analyzing smart toilet camera was not actually properly encrypting the images that it sent to Kohler. The concern there was that your poop data would be somehow accessed by bad actors. In the case of PoopCheck, anyone can simply buy access.  

After I told Marco I was writing an article about PoopCheck and its database, he stopped responding to me and did not answer any of my questions.

An eon ago, in the year 2012, an editor at my first job at U.S. News and World Report had the idea that we should have a YouTube channel. It wasn’t a pivot to video, exactly, but it would be a bet on an emerging platform where some creators were beginning to go viral with news content. The idea was to put the journalists in front of the camera and have them talk about their articles and the news of the day. It did not go well. 

I was nervous, unconfident, had a bad haircut, and, like everyone in Washington, D.C. then and now, was very unfashionable. I had no media training, had never been on TV or video of any sort. I did not have a smartphone. I was socially awkward and spoke in monotone. I blinked endlessly while I talked and fidgeted like crazy with my hands. I constantly said um, tripped over my words, and generally had no idea what I was doing. We made a series of videos with titles like “Head Injury Studies Continue to Cause Alarm in NFL,” “Are the Politics of Climate Change Shifting?,” and “Which Party Will Get the ‘Internet Vote’?” The videos were poorly edited, sounded weird, and got zero traction.  

I did not want to make these videos but it was a newsroom-wide initiative and so I did it anyway.  Thankfully and mercifully, almost no one watched any of these videos, because they were bad. Then and now, they are the opposite of what anyone watches on the internet. And yet, these videos were roughly about as good as a series of podcast videos being released by the Washington Post’s new and drastically worsened Opinion section, apparently at great expense to the outlet. They were also about as popular, with many of my videos garnering upwards of several dozen views.

On Sunday, the very good media newsletter Status reported that the Washington Post recently invested $80,000 on new audio and video gear for its new Make It Make Sense podcast, which features the Washington Post Editorial Board. It has also remodeled a studio in its office, which seems apparent in a very bad trailer for the show titled “A News Show You Can Trust, Finally,” but not in any of its previously recorded videos (some of which were released this week). All of this has happened at the behest of opinion editor Adam O’Neal and Washington Post owner Jeff Bezos as part of the section’s shift rightward to focus on billionaire- and free market-friendly content. 

The podcast is not going well. Watching a few minutes of several of the videos immediately gave me flashbacks to the videos I was in at U.S. News, and served as a stark reminder that the executives running these media companies have zero clue what they’re doing. The videos posted by the Washington Post so far feel extremely dated, as though they were made either with zero resources in 2012 or by someone who has never watched a YouTube video or listened to a podcast in their lives. Everyone is wearing the same business casual and looks like they have been suddenly airdropped from a Pret a Manger on K Street into a nondescript glass cube. The podcasts follow zero of the best practices of YouTube or podcasting; the only indication that anyone involved has been on YouTube ever in their life are the podcast’s thumbnails, which are bad and weird in a different way entirely but at least attempt YouTube’s signature clickbait style, albeit with a weird yellow wash and a serif font. Some of the videos start mid-sentence with no introduction or grabby hook whatsoever. One video begins: “The president of the United States is going to head to the Supreme Court to listen to some of the experts, uh, I think this might be the first time a sitting president is going to hear arguments at the Supreme Court…” the host trails off. Another host says “I think so. I think,” and stops speaking. “This is, uhh, we’ll confirm that. We’ll fact check that.” This is the first 19 seconds of the video. 

Recent episodes of the podcast feature tired and milquetoast, recycled right-wing takes one could pull out of a hat, such as “What the Media Got Wrong During Covid,” “Weed Isn’t As Harmless As You Think,” and what-to-do-with-racist-statues. Other takes include college is too easy, billionaires actually do pay enough taxes, people who hate AI are unhinged, and—in a moment of actually trying to capture the zeitgeist—Hasan Piker is bad. None of the videos are popular. Some of them have fewer than 30 views, while others have ticked up into the triple digits primarily based on hate watches from people clowning on the podcast in recent days. The new studio has not helped, though it does at least look better. A video posted yesterday has 160 views at the time of this writing.

On audio-only platforms, the podcast is faring no better. Googling “Make It Make Sense podcast” brings up many other podcasts called Make It Make Sense, but not Jeff Bezos’s new flagship show. I was able to find the podcast in the Apple Podcast app, where it has four ratings and 2.3 stars out of 5, and the most glowing review is “This is bad and the people making it should feel bad.” On Spotify, it has a 2.8 out of 5 rating. 

I do feel for the people who are in these videos. It is not easy to be on camera and it is not easy to make engaging YouTube content (growing our own YouTube channel has been a slog, and has been far more difficult than growing an audience on any other platform). Over time, with lots of practice and following many mean YouTube comments, I now feel slightly more comfortable being on camera than I did in the U.S. News days. And yet media executives keep trying to make people who are not good at presenting video do it anyway.

The best thing that can be said about this project is that at least we know Jeff Bezos is not buying views on YouTube, which is a common practice for vanity venture capitalist podcasts that no one wants to watch or listen to. So, why write about this at all? 

Well, the show is the type of thing that we have seen time and time again from big media companies, and specifically, their airheaded executives who think that they have any idea how to make content that resonates with anyone at all. As Status pointed out, the Washington Post had a large and highly competent video team that made very good and successful video content. It laid the vast majority of them off, and this is what we’re left with. The Washington Post was known for having one of the most innovative, quirky, and successful TikTok channels, built in part by the journalist Dave Jorgenson. 

Jorgenson left the Post in July of last year to start his own channel and company. “Dear Jeff Bezos, if you’re reading this, you already know. I’m leaving the Washington Post and starting my own company,” Jorgenson said in a video announcing the channel. “My boss, and my boss’s boss are coming with me, so viewers can continue to expect the same high quality, fact-checked videos.” Jorgenson now has 328,000 subscribers on YouTube and 317,000 TikTok followers. The Washington Post’s TikTok now largely posts repurposed stock footage from news wires. We have seen similar at VICE (which just “relaunched” VICE News as Adobe sponcon), Deadspin, etc. 

Talented journalists—especially video journalists and podcasters—lose their jobs but the channels and feeds they created and built are zombified and repurposed for an executive’s passion project, staffed by people who have no idea what they’re doing. These projects inevitably also cost lots of money but with the added bonus that no one watches them.  The project inevitably fails and is ignored into the oblivion. It’s fine to just ignore these stupid projects but maybe also we should mention sometimes that this is all part of the systematic hollowing out of news institutions that once did very good work that people cared about.

Turns out anyone can make a podcast. That doesn’t mean anyone is going to listen.

Fiber-optic cable has become a staple of drone war. From Ukraine to the Sahel, combatants are fielding quadcopters piloted via kilometer-long lengths of cable that allows operators to control them across vast distances while insulating the drone from being knocked from the sky. This technique was once a cheap way for militaries to beat their opponents' electronic warfare, but demand for cable from data centers and war is raising the cost of every flight.

War is a cat and mouse game. One side deploys a devastating tactic and the other side figures out a way to defeat it. When small and cheap quadcopter drones began to dominate the skies, first by Islamic State and then in Russia’s war on Ukraine, fighters quickly learned it was easier to knock them out of the sky with electronic warfare than it was to shoot them down.

Then, in 2023, Russia began to deploy FPV drones controlled via lengths of fiber-optic cable. The cable sits spooled in a tube below the drone that unwinds as it flies. The fiber-optic cable provides a fast and clear connection between a drone and its operator and no signal is flying through the air which makes it immune to jamming.

Ukraine took heavy vehicle losses when Moscow began using fiber-optic drones but Kyiv quickly adopted the tactic and now wheat fields in the country are covered in discarded cable. Three years ago, this was a cheap and effective means of slipping past enemy defenses. In 2026 it’s not nearly as cost effective.

“Fiber-optics is still happening at the battlefield, although not as much as it used to be. It's extremely pricey now. We used to buy 50km spool for $300, now it's easily $2500. Just so you know,” Dimko Zhluktenko, a Ukrainian soldier, said in a post on X on May 10.

The price of fiber-optic cable has been steadily rising since about 2023 and has almost doubled in just the past few months. In January, Shanghai based fiber-optic company Sun Telecom declared there would be a “fiber famine” in 2026. Last year, a kilometer of its G.652D fiber cable cost $2.20. By December of 2025 the same length of cable cost $3. A month later, Sun Telecom had increased the price again to $4.1.

One of the big market shifts driving up the cost of fiber is an increased demand for data centers as companies rush to build out the compute infrastructure they believe they’ll need for AI. “Almost every phone call I get from my customers is trying to see, how do we get them more? I think next year the hyperscalers will be our biggest customers,” Wendell Weeks, the CEO of fiber-optic cable manufacturer Corning, told CNBC after his company signed a deal with Meta for $6 billion in cable.

In a January LinkedIn post, North Carolina telecom company Brightspeed warned of “fiber-supply shortages.” Two other American ISPs told trade publication Broadband Breakfast said they’d seen orders for fiber unexpectedly cancelled. “We have heard concerns in recent weeks of timeframes slipping, and concerns about the ability to obtain supplies at all, as circumstances change,” Mike Romano, the CEO of NTCA, a rural broadband tradegroup, told Broadband Breakfast.

Data center driven demand is only part of the story. Wars in Ukraine, Iran, and the Sahel region of Africa are hungry for fiber-optic cable and manufacturers can barely keep up. Combined, Russia and Ukraine consume 50-60 million kilometers of fiber-optic cable every year, according to Kyiv Post. Most of this comes from China because both countries lack the domestic manufacturing base to produce that much cable. The demand has caused the price of a kilometer of Chinese fiber-optic to go from $2.33 in 2025 to $5.83 in 2026.

The core component of fiber-optic cables is a long piece of flexible and manufactured glass or plastic called an optical fiber. The delicate strands are about the width of a human hair. Ukraine doesn’t manufacture optical fibers. Russia had one factory in the city of Saransk but Ukraine destroyed it with drones in the spring of 2025. Now both countries rely on China to keep drones in the air. Exports on fiber-optic cable to Russia spiked after Ukraine destroyed the factory, hitting a height of 717.5 million meters in November of 2025.

“Ukraine has recently expanded its use of Starlink communications for attack drones, which are impractical for Russia to jam. The cost of a Starlink antenna—which is expended in an attack—is now lower than the cost of the longest-range FPV fiber-optic spools,” Roy Gardiner, an OSINT analyst at Defense Tech for Ukraine told 404 Media. “The drive toward the development and deploying at least partial autonomous control for drones to defeat electronic warfare jamming will accelerate as fiber optic FPVs become less available.”

During war humans become great innovators. The game of cat and mouse continues and fighters are developing strategies to combat fiber-optic drones. In September of 2025, Russian and Ukrainian military bloggers began to report a new technique for countering the wire driven drones: a 150-meter-long fence made of spinning barbed wire. The theory is that the fiber-optic cable, dragged along the ground, will get caught in the fence and severed. 

Despite rising costs and the dangers posed by barbed wire, the drones keep flying. In March, Iran used fiber-optic controlled drones to strike American targets in the gulf, including the destruction of a Black Hawk helicopter parked in Iraq. The known fiber-optic FPV drones top out at about 50 kilometers of cable, a distance that will clear the Strait of Hormuz at its narrowest point.

We start this week with Joseph’s story about how we obtained Haotian AI, a sought-after piece of realtime video deepfake software that lets you turn into anyone else during Microsoft Teams, WhatsApp, or Zoom calls. After the break, Matthew tells us about some insane Yu-Gi-Oh trading card drama. In the subscribers-only section, Jason explains how the hard drive shortage is impacting those archiving the internet.

Listen to the weekly podcast on Apple Podcasts, Spotify, or YouTube. Become a paid subscriber for access to this episode's bonus content and to power our journalism. If you become a paid subscriber, check your inbox for an email from our podcast host Transistor for a link to the subscribers-only version! You can also add that subscribers feed to your podcast app of choice and never miss an episode that way. The email should also contain the subscribers-only unlisted YouTube link for the extended video version too. It will also be in the show notes in your podcast player.

• ⁠‘HELLO BOSS’: Inside the Chinese Realtime Deepfake Software Powering Scams Around the World⁠
• ⁠Man Finds $1 Million Worth of Yu-Gi-Oh Cards in a Dumpster⁠
• ⁠The AI Hard Drive Shortage Is Making It More Expensive and Harder to Archive the Internet⁠

Developers who are told to use AI whether they like it or not, however, tell a different story. On Reddit, Hacker News and other places where people in software development talk to each other, more and more people are becoming disillusioned with the promise of code generated by large language models. Developers talk not just about how the AI output is often flawed, but that using AI to get the job done is often a more time consuming, harder, and more frustrating experience because they have to go through the output and fix its mistakes. More concerning, developers who use AI at work report that they feel like they are de-skilling themselves and losing their ability to do their jobs as well as they used to. 

“We're being told to use [AI] agents for broad changes across our codebase. There's no way to evaluate whether that much code is well-written or secure—especially when hundreds of other programmers in the company are doing the same,” a UX designer at a midsized tech company told me. 404 Media granted all the developers we talked to for this story anonymity because they signed non-disclosure agreements or because they fear retribution from their employers. “We're building a rat's nest of tech debt that will be impossible to untangle when these models become prohibitively expensive (any minute now...).”

The actual quality of output doesn't matter as much as our willingness to participate.

Tech company executives love to brag about how much of the code at their company is AI-generated. In April, Google said that three quarters of new code at the company was generated by AI. Last year, Microsoft CEO Satya Nadella said up to 30 percent of the company’s code was generated by AI. Microsoft’s CTO Kevin Scott said he expects 95 percent of all code at the company to be AI-generated by 2030. Meta’s Mark Zuckerberg said last year he expects AI to write most of the code improving AI within 12-18 months. Anthropic says 90 percent of the code written by most if its team is AI generated. Tech companies have also been bragging about their “tokenmaxxing,” or how much money they’re spending on AI tools instead of human employees.

Predictably, the huge spike in productivity that these companies claim their own AI products have enabled hasn’t resulted in more or better products, shorter work weeks, or better consumer experiences. Mostly, AI implementation in tech companies has been used to justify multiple massive rounds of layoffs. To name just a few examples where tech companies said they reduced headcount because of AI use, more recently, Meta said it would cut 10 percent of its workforce (around 8,000 people), Microsoft said it would offer voluntary retirement to 7 percent of its American workforce (around 125,000 people). Snapchat said it would lay off 16 percent of its full-time staffers (about 1,000 people). 

The developers I talked to contradicted the narrative about AI’s utility in coding in many ways, but the most glaring issue with the narrative AI company executives are pitching is that the adoption of AI tools they see internally isn’t voluntary or organic. Developers say they are either explicitly ordered to use AI tools or heavily pressured to use them. 

“AI in some shape or form is all but explicitly mandated,” a software engineer at a FAANG company that brags publicly about its internal AI adoption told me. “Its usage is part of our performance review criteria and most (maybe all?) of us have been reorganized into AI focused ‘pods.’ We're absolutely flooded with AI tooling and it feels like the answer to every problem is ‘use AI first.’”

“We've been told performance evaluations are tied to AI adoption,” the UX designer told me. “This has led to most of my teammates using it performatively, even if most of us implicitly know that the output is flawed. The actual quality of output doesn't matter as much as our willingness to participate.”

Another software engineer at a financial technology company told me that he was never forced to use LLMs but that the companies where he worked changed in a way that encouraged their use. His previous employer didn’t demand developers use AI but it was encouraged and developers were given access to Cursor, one of the leading coding agents. 

“It started as a ‘who wants to try it’ and I volunteered. Later it was slowly, due to costs, that we stopped renewing our JetBrains IDE and forced everyone to move to Cursor (though the editor itself doesn't force you to use AI),” he said. JetBrains IDE is an integrated development environment used by software developers. “Adoption came mostly from inside the engineering team, with a single engineer manager trying to champion it and writing project based rules for Cursor to try to make the output better.”

All the developers I talked to were excited to try using LLMs at work at first, or were at least curious about them. Their feelings about the tools, based on their personal experience, are now overwhelmingly negative. 

“There were almost no productivity gains using IDE-based AI tools. AI-generated code ended up with more bugs because I am working on distributed web apps, highly complex multi-system things, so giving the LLM context is very difficult,” a software developer at a small web design firm told me. “Another developer on a contract working with me at the moment generates massive amounts of code, leaving me with 1000+ lines of pull requests to review and it takes massive amounts of time to do this. This leads to me feeling more tired and burned out than I've ever felt in my entire life. The cognitive overhead of switching between prompting, coding, checking the LLM's output is a massive energy drain. It has not been a productivity booster at all, it feels like a speedrun towards severe mental exhaustion.”

The developer in fintech I talked to also said that one major problem with LLMs is that it can generate more code than developers can properly vet or explain. “The sheer breadth of code makes it impossible to be critical enough and then you're either throwing it away or submitting it and feeling scared there might be really low quality stuff that if someone notices will make you embarrassed (and even more embarrassing to say: ‘oh i don't know what that is, the AI did that’),” he said. “Or worse, you ship it without someone noticing and that is really hit or miss.”

“I have gotten stuck on bug fixes where, when I run out of Anthropic tokens in Claude Code, I couldn't work anymore. The current system I am working on started to become a monstrosity of complexity where I didn't even know what most of it does anymore, and when I had to fix a bug, it took longer than I would have taken in the past to debug,” the software developer at a small web design firm told me.

The developers I talked to found AI useful for some tasks. Several developers said that it was good for experimentation, allowing them to quickly prototype an idea or to implement something in a domain they’re unfamiliar with. One developer said it was a good information interface. Specifically, he said, the AI helped him find where on the server a certain request is handled, summarize logs, or find documentation related to code changes. 

The problem all the developers I talked to agreed on is that the more they relied on AI to code, the more the skills they’ve honed for years deteriorated. This is by now a well studied phenomenon sometimes referred to as "cognitive debt” or "cognitive atrophy.” The idea is that people who use AI to automate certain parts of their job lose the ability to do those tasks well, therefore de-skilling themselves. 

“I had some issues where I forgot how to implement a Laravel API and it scared the shit out of me. I went to university for this, I've been a software engineer for many years now and it feels like I am back before I ever wrote a single line of code,” the software developer at a small web design firm told me.

“It's making me dumber for sure,” the fintech software developer told me. “It's like when we got cellphones and stopped remembering phone numbers, but it's grown to me mentally outsourcing ‘thinking’ in general. I feel my critical thinking and ability to sit and reason about a problem or a design has degraded because the all-knowing-dalai-llama is just a question away from giving me his take. And supposedly I tell myself ill just use it for inspiration but it ends up being my only thought. It gives you the illusion of productivity and expertise but at the end of the day you are more divorced from the output you submit than before.”

“When I was using it for code generation, I found myself having a lot of trouble building and maintaining a mental model of the code I was working with,” the software engineer at the FAANG told me. “Another aspect is that I joined late last year and [the company’s] codebase is massive. As a new hire, part of my job is to learn how to navigate the codebase and use the established conventions, but I think the AI push really hampered my ability to do that.”

The developers I talked to agreed that LLMs will stick around and play a role in programming in the future in some fashion, but worried about how the industry will adapt to executives’ current obsession with the technology, especially when it comes to fostering future generations of developers. 

“Older programmers will be fine if there are any jobs left in a few years, but I worry for people early in their careers,” the UX designer told me. “We are hiring junior programmers who rely on AI to complete the simplest tasks. They don't have the knowledge or experience to know when AI output is error-laden or inefficient.”

“I wish I had a crystal ball for this one, but my gut feeling is that this method of building software will be unsustainable either economically or in terms of tech debt,” the software engineer at the FAANG company said. “There's a pretty clear split on my team between people who love AI coding and those who just do it because it's what the company wants, and generally speaking I find that the people who are still [technically focused individual contributors] with their nose in code all the time are less likely to be big AI boosters. I think the tech and its outputs start to really break down the more you question them and those who are doing that day in and day out tend to have a worse opinion of the tech.”

“I think there will be a ‘reckoning’ or ‘awakening’ from the industry notion that now everyone can code and that vibe coding is viable for a real production app and software companies are dead,” the developer in fintech said. “I think we will grow to find the patterns and industry best practices that will balance the negatives of LLM development (hallucination, unstructured code) with better techniques to verify the output's correctness at scale, and the hype and techno optimism of AI will get to a saner middle ground.” 

Immigration and Customs Enforcement’s (ICE) use of Palantir systems now means agency officials effectively have a list of 20 million people readily accessible on their iPhones, increasing the speed at which ICE can find houses to raid and people to arrest, according to comments made by a senior ICE official last week during a border security conference.

While ICE and the Department of Homeland Security (DHS) generally won’t answer questions from journalists about how the agency is using Palantir’s technology, senior officials were much more talkative during the Border Security Expo which took place in Phoenix, Arizona, last week. 404 Media spoke to four people who attended the conference. Here companies looking to sell their technology to ICE or other agencies gathered for two days of speeches, Q&As, and product pitches.

The officials’ comments may need to be taken with a pinch of salt, but still reflect ICE’s position that Palantir is allowing the agency to identify people to arrest and locations to raid faster. Although the Trump administration has attempted to step back from its mass deportation rhetoric and city wide raids, especially in the wake of killing multiple people, ICE continues to violently and wrongfully detain people. Data from April showed that 70.8 percent, or 42,722, of people held in ICE detention have no criminal conviction. 

The four people who attended the Border Security Expo saw Matthew Elliston, assistant director of Law Enforcement Systems & Analysis at ICE, and other DHS officials speak.

At one point, Elliston made the comment about ICE agents having 20 million targets, or potential people to detain, on their iPhones. This list can lead ICE agents to an individual and a house; they can then see if another target might be next door. This target may be a lower priority, but ICE can now use that information to arrest more people.

At another point, Elliston said that Palantir’s technology has increased ICE’s rate of successfully locating a target from around 27 percent to just under 80 percent. 

Two of the attendees were Kenny Morris, a campaigns strategist within the American Friends Service Committee (AFSC) Action Center for Corporate Accountability, and Dov Baum, director of AFSC’s Action Center for Corporate Accountability. 404 Media is not naming the other two attendees to protect them from professional repercussions.

Investigative work that used to take hours now takes 10 to 15 minutes, Elliston said. Elliston added Palantir gives the agency access to between 30 and 40 datasets. 

Palantir generally doesn’t generate its own datasets; instead, its tools are broadly used to bring usually disparate datasets together and let them be queried as one.

In January, 404 Media revealed Palantir was working on a tool for ICE called ELITE, or Enhanced Leads Identification & Targeting for Enforcement. This tool populates a map with potential deportation targets, brings up a dossier on each person including their personal information, and provides a “confidence score” on that person’s current address. Those addresses came from various sources including the Department of Health and Human Service (HHS) and Thomson Reuters’ CLEAR product, according to an ELITE user guide 404 Media obtained.

Palantir has worked with DHS, and specifically Homeland Security Investigations (HSI), for years. This work was previously focused on the Investigative Case Management (ICM) system, which HSI used. In the second Trump administration, Palantir became a “more mature partner to ICE,” the company said in a leaked Palantir wiki obtained by 404 Media. 

Palantir’s closer work with ICE has triggered some protests around the country, including one in April outside Palantir’s offices in New York City.

Palantir did not respond to a request for comment for this article. The company previously wrote a blog post after 404 Media first revealed the existence of ELITE, writing, “The ELITE tool is used for prioritized enforcement to surface the likely addresses of specific individuals, such as those with final orders of removal or with high severity criminal charges.”

A DHS spokesperson told 404 Media in an email: “U.S. Immigration and Customs Enforcement is committed to achieving the nation’s mandate to clear the backlog of illegal aliens who pose a threat to the security of our communities. Like other law enforcement agencies, ICE employs various forms of technology while respecting civil liberties and privacy interests.”

Elliston also discussed Mobile Fortify, ICE and CBP’s facial recognition app. Elliston claimed the app has been used 200,000 times with a 0 percent mismatch rate. 404 Media reported in January that Mobile Fortify misidentified a woman, twice.

At one point, Elliston said that the agency has a lot of money and he’s open for business. If you want to show me something, send me a LinkedIn message, he said. After the session, one attendee said they watched as a huge line of people waited for their chance to speak to Elliston.

For years, anyone who has reported on our increasing addiction to technology has found their way to Natasha Natasha Dow Schüll’s book Addiction by Design: Machine Gambling in Las Vegas. The book is an ethnography of slot machines. It is based on many interviews with the people who make them and play them, a deep investigation of how they work, and how they fit into the larger context of casinos, Las Vegas, and gambling more broadly. 

Since it was published more than a decade ago, the logic of slot machines has extended far beyond Las Vegas. Every notification on our phone, trading platforms like Robinhood, the crypto craze, and now prediction markets, can be understood through the lens of slot machine design and Schüll work. That’s why I was incredibly happy she agreed to come on the podcast this week to discuss our current gambling-obsessed culture. 

404 Media is a journalist-founded company and needs your support. To subscribe, go to 404media.co. As well as bonus content every single week, subscribers get access to additional episodes where we respond to their best comments. Subscribers also get early access to our interview series. Gain access to that content at 404media.co.

Listen to the weekly podcast on Apple Podcasts, Spotify, or YouTube. 

Become a paid subscriber for early access to these interview episodes and to power our journalism. If you become a paid subscriber, check your inbox for an email from our podcast host Transistor for a link to the subscribers-only version! You can also add that subscribers feed to your podcast app of choice and never miss an episode that way. The email should also contain the subscribers-only unlisted YouTube link for the extended video version too. It will also be in the show notes in your podcast player.

A few years ago, while I was covering the rise of AI slop on Facebook, I asked my friends and family if they were getting AI spam fed into their timelines and if they could send me examples. A handful of them responded, sending me obviously AI-generated science fiction scenescapes, shrimp Jesus, and forlorn, starving children begging for sympathy. But a few of my friends sent me images that they thought were AI but were not. Their mental guard was up to the point where they were looking at human-made art and photos and thought it safer to dismiss them as AI rather than be fooled by it.

To browse the internet today, to consume any sort of content at all, is to be bombarded with AI of all sorts. People think things that are fake are real, things that are real are fake. Much has been written about “AI psychosis,” the nonspecific, nonscientific diagnosis given to people who have lost themselves to AI. Less has been said about the cognitive load of what other people’s AI use is doing to the rest of us, and the insidious nature of having to navigate an internet and a world where lazy AI has infiltrated everything. Our brains are now performing untold numbers of calculations per day: Is this AI? Do I care if it’s AI? Why does this sound or look or read so weird? Does this person just write like this? Is this a person at all? 

I see AI content where I’m conditioned to expect and ignore it: In Google’s “AI Overviews” that famously told us to eat glue pizza, in engagement-bait LinkedIn posts, and throughout our Facebook and Instagram feeds. But increasingly I have the feeling that it’s everywhere, coming from all directions, completely unavoidable. It’s not exactly that I have a revulsion to AI-assisted content or don’t want to get fooled by it. It’s that something is happening where my brain has become the AI police because everything feels incredibly uncanny. I will be going about my day reading, watching, or listening to something and, suddenly, I notice that something is wildly off. Quite simply, I feel like I’m going nuts. 

An example: Last week, in a desperate attempt to avoid yet another take on the White House Correspondents Dinner shooting, I was listening to an episode of Everyone’s Talkin’ Money, a podcast I’ve been listening to off-and-on for years about taxes (yikes). This podcast has been going on for years, has a human host named Shari Rash, and hundreds of episodes. Rash started reading the intro script: “The shift I want you to make today—and this is the shift that changes everything—is starting to see your tax return as information—not a bill, not a badge of shame, but information.” The script went on and on and on like this, with AI writing trope after AI writing trope. My brain shut down and stopped paying attention to the script and started wondering if Rash was using AI just for the intro script? What about for the research? Did she edit the script at all? I turned the podcast off. 

Later that day, I was scrolling the Orioles Hangout forums, a small community of diehards obsessed with the Baltimore Orioles that I have been lurking on for decades. Until recently, it had been one of the few places on the internet that I could safely assume was not full of AI. Except now, it is. The site’s administrator has started using AI to analyze player performance and to help him write some of his posts. To his credit, he explains how he’s using AI and prefaces these posts by noting they are AI-assisted analysis. Some of them are interesting. But now, most days I’m browsing the forums, I will see arguments between posters who have been there for years that seem overly generic or don’t really make sense. One recent post arguing about the timetable for an injured player’s return suggested a ludicrously long recovery. One poster pointed this out: “You said 10-18 months and I said it won’t take that long for a position player.” The poster responded: “You’re right I did. The 10-18 months was an AI generated answer … consider it a small cautionary tale about trusting AI and another on the benefits of seeking out actual medical research on questions like this.” Every day I now scroll the forum and see people noting that they plugged something into ChatGPT or Gemini and have copy pasted the answers for other people to see. In this 30-year-old community of human beings discussing sports, AI is unavoidable. 

It is, of course, not just me. Friends send me screenshots of texts they’ve gotten from people they’ve started dating, wondering if they’re using ChatGPT to flirt. I’ve gotten obviously AI-generated apologies or excuses from people trying to bail on a social engagement. I’ve been to weddings where the speeches felt—and were—partially AI-generated. 

A recent PEW poll showed that people believe it is important to be able to tell whether an image, video, or piece of writing was AI-generated, AI-assisted, or written by a human. And it showed that a majority of people do not believe that they are able to tell the difference between AI-generated works and human made works. Studies have repeatedly shown that humans judge AI-generated art and writing more harshly than human works, and a study published in the Journal of Experimental Psychology found that when people know or perceive a piece of writing to be AI-generated, it is “stubbornly difficult to mitigate” and “remarkably persistent, holding across the time period of our study; across different evaluation metrics, contexts, and different types of written content.” Put simply, it is not just me who hates AI writing or finds it annoying. Even if AI writing can be “fine,” it very often feels bland, weird, formulaic. The writer Eve Fairbanks wrote a thread the other day that I thought more or less nailed it: “The tell for AI isn’t rhythm, wording, or fact errors. It’s that problems with *all these elements* exist equally & at once.” 

“With AI writing, everything is off: the tone grates, individual word choices baffle, the structure lacks sense, key pieces of argument are missing…the key is that they all exist simultaneously to the same degree,” she added. “Superficially, AI text can read smoothly—'cleaner' than a human’s draft … but it’s almost impossible to make sensible. And it’s driving me crazy.” 

Last week, New York City Mayor Zohran Mamdani tweeted about swastikas being painted on synagogues in Queens: “This is not just vandalism—it is a deliberate act of antisemitic hatred meant to instill fear,” he wrote. Max Spero, the CEO of Pangram Labs, an AI detection firm, highlighted this passage and tweeted “Mamdani nooo ,” the implication being that this passage was written by AI, or at least seemed like it was. Spero’s tweet had more than 4 million views at the time I talked to him. (Disclosure: Pangram Labs previously advertised on 404 Media).

Spero’s company uses AI to detect AI writing, meaning it is not perfect. But as far as these tools go, Pangram is considered quite good, and has been widely used in research about AI content on the internet. Spero told me when I called him that immersing himself in the internet has his brain in AI-detection mode pretty much all the time. “I’m totally on guard, and I have been for a while,” he said. Spero said he first began to notice it on restaurant reviews on Yelp and Google Reviews a few years ago. “I started seeing them everywhere. There’s people who are Yelp Elite and all they do is post one or two AI-generated reviews a day. Fast forward to today, and I think we’ve seen the mainstream growth of AI everywhere, but I think some people can tell, and some people have no intuition for it.” 

I have always aspired to write like I talk. I don’t really concern myself so much with the craft of writing or turning a beautiful sentence, I usually try to just convey information in a straightforward, personable way. I want my articles to feel like slightly more polished, more researched versions of my text messages, like the things I would say on a podcast or at the bar to a friend. Often my writing process involves me thinking about sentences or ideas I want to convey while I’m walking my dog or in the shower or surfing, and I hope that when I actually sit down to write, the words flow from my brain through the keyboard in a way that pretty much makes sense. 

When I sat down to write this article, in which, to be clear, I did not use AI, I found myself writing the following sentence: “It’s not just in places we’re conditioned to see AI—Google AI overviews, LinkedIn influencer posts, and Facebook feeds—I’ve started seeing AI…” I stopped typing, freaked out, and deleted the sentence. Have I always written this way? I honestly don’t know. 

This negative parallelism—“it’s not just x, it’s y” is maybe the most infamous AI writing-ism there is. It is something that is regularly called out as being obviously AI, and is the formation in the sentence Mamdani wrote that Spero called out. But I didn’t use AI. Did I use that construction because I’ve been immersed on an internet full of generic AI writing on every platform all day everyday for years? Or did I just happen to think that was the best way to phrase it at the time? 

The idea that humans may be subconsciously mimicking or learning from the AI writing that they’re reading is not some isolated thought I had. It’s kind of the business model of any number of AI-for-education startups, and it’s an idea that has been raised in lots of articles about AI in schools. Last month, the New York Times quoted a teacher who said “They are using generative A.I. to write before they learn how to write.” Teachers I spoke to last year lamented that they are spending their very real human hours and considerable brain power trying to determine whether they are grading essays that are written by humans or robots, and know that they are often giving writing notes on papers that were likely written by AI. 

The thing is, human writers do sometimes write like AI, and this will probably become more common. “If you showed me the Mamdani tweet in a vacuum I’d be like, almost certainly it’s AI,” Spero said. “But with Mamdani I’m less sure because his history is almost everything else seems to be human written. With my own writing, I don’t want to sound like AI even a little bit. I have some concerns about, like, the students who have grown up with ChatGPT and their entire school career has been ChatGPT assisted so now they actually do write like this.” 

Fairbanks had the same thought, and she told me that the person she originally wrote her thread about claims that he actually didn’t use AI to write it. 

“It’s possible it was written by him!,” she told me in an email. “In which case it appears his writing was shaped by the AI voice. I feel self-conscious now that I’m picking up habits not directly from AI but from people who may have used AI, or that AI is somehow exposing, like a fluorescent light on our naked body in the doctor's office, the defects in my writing style insofar as they turn out to overlap with what everybody now believes is a totally shit style. I always used em dashes!”

“Somebody on my thread made the observation that somehow it’s more likely that we’ll all start to sound more like AI than that AI will sound more human to us,” she added. “That felt right to me, although I couldn’t technically say why. But I was listening to a New York Times podcast and noticed the presenter used the ‘it’s not x, it’s y’ formula. I really assume she didn’t generate the sentence with AI because she was speaking out loud, in conversation. But it now stood out as formula to me.”

I emailed Rash, the host of the podcast who originally made me think “this is an AI script,” and asked her if it was an AI script. She said “I use AI to help brainstorm, organize ideas, outline, and refine language. The line you referenced reflects a point I often make with clients and listeners … I review and edit all of my content and I am responsible for everything that goes out under my name.”

Earlier this year I read an article by the writer Marcus Olang called “I’m Kenyan. I don’t write like ChatGPT. ChatGPT writes like me.” Olang’s article highlighted a phenomenon he and other Kenyans have experienced, where they are constantly accused of using AI to write, and have lost out on opportunities because of it. Olang notes that the Kenyan education system tended to teach a formal, structured, rules-focused type of English that was largely a product of colonialism. 

“The bedrock of my writing style was not programmed in Silicon Valley. It was forged in the high-pressure crucible of the Kenya Certificate of Primary Education…The English we were taught was not the fluid, evolving language of modern-day London or California, filled with slang and convenient abbreviations. It was the Queen's English, the language of the colonial administrator, the missionary, the headmaster,” he wrote. “It was the language of the Bible, of Shakespeare, of the law. It was a tool of power, and we were taught to wield it with precision. Mastering its formal cadences, its slightly archaic vocabulary, its rigid grammatical structures, was not just about passing an exam. It was a signal. It was proof that you were educated, that you were civilised, that you were ready to take your place in the order of things.”

As we’ve noted before, many AI tools have been trained, tested, and moderated on thousands of hours of labor from low-paid workers around the world, including many Kenyans. So not only did Olang learn a type of English writing that tends to be generated by AI tools, a lot of the moderation and testing of those tools was judged by people who went through that same education system. “If humanity is now defined by the presence of casual errors, American-centric colloquialisms, and a certain informal, conversational rhythm, then where does that leave the rest of us?,” Olang wrote. 

Olang makes important points in his article, but one of the great things about writing and the internet in general is that there are all sorts of different dialects and styles and things that can work online. And so maybe what I have been noticing is a sameness, a homogenizing of large parts of the internet, including places I often felt were very human. This is objectively happening, researchers believe. A study published last month by researchers at Imperial College London, Stanford, and the Internet Archive called “The Impact of AI-Generated Text on the Internet,” found that roughly 35 percent of new websites are AI-generated. It confirmed the researchers’ hypotheses that “As AI content becomes more common on the internet, online writing feels increasingly sanitized and artificially cheerful,” and “as AI text becomes more common on the internet, the range of unique ideas and diverse viewpoints shrinks.”

Besides people copy pasting things from ChatGPT or other AI tools, AI writing “assistance” has been shoved directly into word processors like Google Docs, email clients like Gmail, and social media networks like LinkedIn. The process of “writing” is being automated and filtered through these tools. It is everywhere.

Last month, a Harvard MBA grad named Ben Horwitz launched Sinceerly, an “AI to undo your AI writing.” The Chrome extension has three modes: “Subtle,” “Human,” and “CEO,” which takes AI-generated text and gets rid of em dashes, adds typos, slang, acronyms, puts words all in lowercase, etc. Horwitz wrote on the website that he built Sinceerly because “I got sick of everyone in my inbox sounding like AI.” I used Sinceerly to email Horwitz and ask for an interview. When I called him and told him this, he said he didn’t notice, so, mission accomplished. 

“To be clear, this is mainly a satirical project meant to hold a mirror up to people who use AI as an alternative to thinking, but it is legit in that I built this tool and it does work,” Horwitz said. “But I do feel like everything is starting to sound the same and I’m experiencing the same thing as you—the homogeneity I find incredibly frustrating and boring, and it makes me less apt to use social media because everything sounds the same.”

He said that since he’s launched Sinceerely, he’s gotten emails from actual users who have used it to de-AIify their writing and who are frustrated that they are sometimes not getting responses. “Many people have DMed me and been like ‘Hey, can you help me make this email sound more human?,” he said. “Think about how much work all of this actually is. In theory you’re written something as a prompt into the AI and so you have actually written something. And then you’re copy pasting it into an email and using this tool on it. I hope it gets people to think about what they’re actually doing.” 

The irony is that in making his satirical project, Horwitz has actually replicated, albeit in a funnier way, an already existing type of AI tool called “humanizers,” which are designed to defeat AI detection software like Spero’s Pangram. Spero said he “thought Sincerely was a very funny project. It’s like a first impression, someone sees a typo and they give a sigh of relief that a real human is behind that, but we’ve actually been seeing this more and more. AI-generated marketing emails over the last year with intentional typos.”

Humanizers add typos, randomly replaces words, removes “AI tells,” and sometimes inserts random characters. Spero said Pangram has been collecting as much data as they can to try to detect “humanized” AI, but that “it’s pretty adversarial” and that there is likely to be an ongoing cat-and-mouse game between humanizer AI and AI detecting AI. 

“It’s kind of looking grim for the future of the internet,” he said.  

In my many, many hours of browsing AI slop on Facebook, I spent an absurd amount of time scrolling through the comments on AI-generated images. One exchange has stuck in my mind years later. It was an AI-generated image of a wood deck outside a house. In the comments, obviously real people were arguing back and forth as to whether the nonexistent deck would pass code inspection. I remember thinking something uncharitable and cancelable at the time, something that I think I wrote in a draft of one of my articles but that got edited out because it was mean. I remember thinking, basically, that Facebook had become a virtual nursing home for delusional and quite possibly stupid old people, a place where people argue back and forth about things that don’t exist, forever, until they die. 

I ended up calling this the “Zombie Internet,” which is something I considered to be worse than the “Dead Internet,” the popular but too simplistic idea that large portions of the internet are bots interacting with each other. I called it the Zombie Internet because the truth is that large parts of the internet are not just bots talking to bots or bots talking to people. It’s people talking to bots, people talking to people, people creating “AI agents” and then instructing them to interact with people. It’s people using AI talking to people who are not using AI, and it’s people using AI talking to other people who are using AI. It’s influencer hustlebros who are teaching each other how to make AI influencers and have spun up automated YouTube channels and blogs and social media accounts that are spamming the internet for the sole purpose of making money. It is whatever the fuck “Moltbook” is and whatever the fuck X and LinkedIn have become. It’s AI summaries of real books being sold as the book itself and inspirational Reddit posts and comment threads in which people give heartfelt advice to some account that’s actually being run by a marketing firm. It’s fake Yelp reviews for real restaurants and real Yelp reviews for fake restaurants using AI-generated food images being run out of ghost kitchens. It’s armies of AI-assisted clippers who used to steal people’s content to make money on social media but now get paid to do so. It’s the boring history YouTube videos I use to fall asleep that used to be quirky and weird but are now AI channels. It’s my email inbox, in which I used to occasionally get poorly-formatted, poorly written, extremely long emails from delusional people who were positive the CIA had imprisoned them in a virtual torture chamber using undisclosed secret technology but where I now get well-formatted, passably written, extremely long emails from delusional people who are positive they have proven AI sentience and have the AI transcripts to prove it. It's the New York Times having to issue corrections multiple times in the last few weeks because its writers have included AI-generated hallucinations in the newspaper. It’s the pitches I get that start “Hi Jason, I’m Hatoshi. I’m an AI agent. I run Clanker Records — An AI-operated label with AI artists,” and the pitches I get that are probably written by AI agents or someone who has automated the process but hasn’t bothered to tell me. 

What’s driving me crazy, then, is not the idea that AI exists or that people are using AI. It’s that I have a finite time on this earth that I mostly want to spend interacting with other human beings. I don’t want to be the person arguing with a robot, or wasting my time reading something that a real person couldn’t be bothered to write.  

Speaking to graduates of University of Central Florida’s College of Arts and Humanities and Nicholson School of Communication and Media on May 8, commencement speaker Gloria Caulfield, vice president of strategic alliances at Tavistock Group, told graduating humanities students that AI is the “next industrial revolution,” and was met with thousands of booing graduates.

“And let’s face it, change can be daunting. The rise of artificial intelligence is the next industrial revolution,” Caulfield said. At that point, murmurs rippled through the crowd. Caulfield paused, and the crowd erupted into boos. “Oh, what happened?” Caulfield said, turning around with her hands out. “Okay, I struck a chord. May I finish?” Someone in the crowd yelled, “AI SUCKS!”

Her speech begins around the hour and 15 minute mark in the UCF livestream. According to her bio on the Tavistock Group’s website, Caulfield “oversees the health and medical partnerships as well as business development for Tavistock’s visionary Lake Nona community.” Lake Nona is a planned community in Florida. Caulfield is “instrumental in managing corporate partnerships and identifying strategic intersections with stakeholders in the Lake Nona community,” her bio says.

Before the industrial revolution comment, Caulfield praised Jeff Bezos for his passion and use of Amazon as a “stepping stone” to his real dream: spaceflight. Rattled after the crowd’s reaction, she continued her speech: “Only a few years ago, AI was not a factor in our lives.” The crowd cheered. “Okay. We've got a bipolar topic here I see,” Caulfield said. “And now AI capabilities are in the palm of our hands.” The crowd booed again. “I love it, passion, let's go,” she said.

“AI is beginning to challenge all major sectors to find their highest and best use,” she continued. “Okay, I don't want any giggles when I say this. We have been through this before, these industrial revolutions. In my graduation era, we were faced with the launch of the internet.” 

She goes on to talk about how cellphones used to be the size of briefcases. “At that time we had no idea how any of these technologies would impact the world and our lives. [...] These were some of the same trepidations and concerns we are now facing. But ultimately it was a game changer for global economic development and the proliferation of new businesses that never existed like Apple and Google and Meta and so many others, and not to mention countless job opportunities. So being an optimist here, AI alongside human intelligence has the potential to help us solve some of humanity's greatest problems. Many of you in this graduating class will play a role in making this happen.” 

Caulfield is saying this to humanities and communications graduates, who are entering a workforce that AI has been gutting with increasing intensity for years. Not even the people and companies she valorizes in her speech believe that these graduates are headed for an easy time in the workforce: In April, Palantir CEO Alex Karp said AI will “destroy” humanities jobs, and last week, a report found that AI is blamed for one in four lost jobs, amounting to 21,490 AI-related cuts last month, or 26 percent of the 88,387 total, “marking the second straight month the technology has been the top driver of layoffs,” CBS reported. 

At the companies Caulfield referenced as existing because of advances in technology, CEOs blame AI for massive job cuts; Meta announced last month that it would cut 10 percent of its workforce later this month due to focusing more on AI, with more cuts to come. People who keep their jobs at these companies are often made miserable by the ways they’re forced to do AI busywork.

Within the humanities, the field these graduates have spent the last several years of their lives studying for careers in, AI is adding stress and dysfunction to library work and academia. A recent study by Microsoft ranked historians and interpreters and translators as the most likely professionals to have AI disrupt their work. Last year, Anthropic CEO Dario Amodei said he believed AI could wipe out half of all white collar entry-level jobs. This is not the crowd to tell they should embrace the “change” that AI brings.

UCF did not immediately respond to a request for comment. 

First, scientists have confirmed the identities of four sailors who died in a grisly Victorian voyage. Then: the sights and sounds of an Arctic seafloor, a glimpse into the guts of ice giants, and a wedding kiss of death.

As always, for more of my work, check out my book First Contact: The Story of Our Obsession with Aliens or subscribe to my personal newsletter the BeX Files.

Putting a face, and names, to lost Arctic sailors

Stenton, Douglas R. et al. “DNA identifications of three 1845 Franklin expedition sailors from HMS Erebus.” Journal of Archaeological Science: Reports.

Stenton, Douglas R. et al “‘Some very hard ground to heave’: DNA identification of Harry Peglar, Captain of the Foretop, HMS Terror.” Polar Record.

Scientists have identified four men who died in Sir John Franklin’s disastrous expedition of 1845, a British mission to chart a passage through the Arctic that ended in misery, starvation, and cannibalism, leaving no survivors.

“Since the late nineteenth century the coast of Erebus Bay on King William Island, Nunavut, has been a focal point for historical and archaeological investigations of the 1845 Franklin Northwest Passage expedition,” said researchers led by Douglas Stenton of the University of Waterloo. “Its significance comes from the nature and volume of materials derived from an extraordinary and ultimately tragic event: the fatal attempt by 105 surviving sailors to escape their icebound ships in the spring of 1848 by walking hundreds of kilometres south to the mainland of North America.”

Using DNA extracted from skeletal remains, a study has confirmed that the 180-year-old bones belong to the able seaman William Orren, the ship's boy David Young, the officers' steward John Bridgens, and captain of the foretop Harry Peglar. Orren, Young, and Bridgens served on HMS Erebus, the expedition’s flagship, and their remains ended up in Erebus Bay on Canada’s King William Island. 

The remains of Peglar, who served on the secondary vessel HMS Terror, were found nearly 80 miles away from the others and are reported in a separate study led by Stenton. Stenton’s team has previously identified the Erebus engineer John Gregory as well as the Captain of Erebus, James Fitzjames, whose remains were subject to cannibalism.

The researchers matched the DNA of these sailors to samples provided by living descendants or relatives to conclusively confirm their identities. In addition to solving a scientific mystery, this process literally puts a face to one man as the team included a reconstructed portrait of David Young, who was around 20 when he died.

The results also help to piece together key details of the nightmarish fates that befell these sailors as they endured starvation, exposure, disease, and despair. 

“For their descendants, the identifications of John Bridgens, David Young, and William Orren reveal that, like John Gregory, they had survived the first three years of the expedition,” the researchers said. “They also unveil the locations where their deaths occurred, and the fact that none of the men were alone when they died.”

Peglar did die alone, however, and he remains the only member of the Terror crew who has been identified. In the study about his farflung remains, the team concludes with a passage Peglar wrote a few days before the survivors abandoned their stuck vessels and embarked on the retreat that would ultimately kill them all. 

Peglar noted the need to procure new boots as “we have got some very hard ground to heave.”

In other news…

Scenes from an Arctic seafloor

Podolskiy, Evgeny A. et al. “Seafloor video-acoustic monitoring in a Greenlandic glacial fjord records hyperbenthos, backward-swimming fish, and narwhals.” PLOS One.

Though the Arctic has many deadly perils, this region is also home to some of the most amazing lifeforms found anywhere on the planet. Scientists have now captured rare footage and recordings of “a highly turbulent environment” on the seafloor of a glacial fjord in northwest Greenland, according to a study. 

Here, at depths of about 850 feet, the songs of narwhals reverberate along the seafloor, crustaceans called copepods move in sudden hops, and “marine snow” made of particulate matter falls in blizzardlike bursts. A snailfish was also caught on tape making a particularly memorable exit.

“One snailfish showed peculiar backward swimming, passively drifting backward with the current,” said researchers led by Evgeny A. Podolskiy of Hokkaido University. “It curled its tail and remained motionless for at least 16 seconds before disappearing from view.” 

You’ve heard of the Irish Goodbye and the Minnesota Goodbye, but I’m not sure anything can top the Greenlandic Glacial Fjord Snailfish Goodbye.

The flavorful fillings of ice giants

Ramirez, Vanesa et al. “Reassessing planetary composition: Evidence of rock-dominated envelopes in Uranus and Neptune.” Astronomy & Astrophysics. 

What’s inside Uranus? Or Neptune, for that matter? Nobody really knows, and we have to rely on models until someone can figure out how to get a direct look inside the guts of these ice giants. 

To that end, researchers ran simulations of the possible evolution and composition of the two planets’ interiors based in part on observations of their atmospheres. The results suggest that “the deep interiors of the two planets exhibit distinct compositions” with Neptune having “relatively rock-rich mantles…whereas Uranus is inferred to have more ice-rich mantles,” according to researchers led by Vanesa Ramirez of Leiden University.  

“Our results indicate fundamental differences in the internal architectures of Uranus and Neptune, challenging the traditional view of these planets as compositional twins,” the team added.

To put in confectionary terms, Neptune appears to be more of a rocky road, while Uranus may be a refreshing ice slushy. Either way, the study underscores how much there is left to learn about these solar system worlds.

Mob Wives, but it’s science

Catino, Maurizio et al. “Marrying for power: Gendered alliances in mafias.” PLOS One.

In a genuinely gangster new study, scientists took a whack at unraveling the marital power dynamics at work within the 'Ndrangheta mafia syndicate, an infamous crime ring built around familial ties. 

“Interfamily marriages have long been recognized as a strategic resource in mafia organizations,” said researchers led by Maurizio Catino of the University of Milano-Bicocca. “Drawing on judicial records documenting…906 marriages among 623 ’Ndrangheta clans, we analyze how matrimonial ties relate to power and cohesion within the organization.”

While nuptials between the most powerful clans are important for group cohesion, the team found that the marriages among less influential families were the real “load-bearing” relationships in the network. In part, this is because boss families tended to be “associated with redundant, overlapping unions” whereas there is more elasticity in the outer circles.

The study is packed with wild and often disturbing anecdotes—and some that seem directly lifted from a Scorsese flick. 

For instance, take the case of Emanuele Mancuso, whose aunt tried to dissuade him from cooperating with law enforcement with this pitch-perfect guilt trip: “How is your mother doing? She’s not well! She knows she no longer has a son, how do you think she feels?” 

It’s stressful enough to plan a wedding without the additional pressure of figuring out how you will fit into an international criminal syndicate. You can only hope the union will end in holy (not holey) matrimony. 

Thanks for reading! See you next week.

The University of Michigan has sent a legal threat over a yearlong pause that would prevent water hookup to a proposed nuclear weapons research and AI data center. Los Alamos National Laboratory and the University of Michigan are looking to build a $1.2 billion, 220,000 square foot data center in Ypsitlanti Township. On April 22, the Ypsilanti Community Utility Authority (YCUA) passed a 365-day moratorium on the delivery of water to hyperscale data centers in the area while it conducted environmental sustainability and long-term water use studies.

As first reported by MLive, the University hand delivered and emailed a legal threat to the YCUA on April 21, the day before it was to vote on the proposed water moratorium. According to a copy of the letter obtained by 404 Media, the university feels the moratorium is “unlawfully discriminatory” against data centers and it promised to pursue “all rights and claims for relief” if its demands weren’t met.

Luther Blackburn, YCUA’s executive director, told 404 Media that the organization had no comment on potential or pending litigation, but did confirm that he’d received a legal communication from the university.  “YCUA staff are working on a Request for Proposal to complete the investigations and studies outlined in the moratorium,” he said. “I believe YCUA has acted lawfully and in accordance with industry best practices by issuing the moratorium.”

The university disagreed. “The University objects to any such sector-specific moratorium which would be legally invalid because, among other defects, it would be unrelated to any documented utility or public health needs,” the letter said, according to a copy obtained by 404 Media. “As a threshold matter, a moratorium on utility service is permissible only when linked to legitimate utility considerations such as documented capacity constraints, public health issues, or genuine financing challenges.”

The University argued, citing various legal precedents, that the courts will not be on Ypsilanti’s  side and claimed that the area has plenty of water. “The record contains no evidence supporting any such YCUA capacity constraint,” the letter said. “To the contrary, YCUA’s leadership has publicly stated that serving the University’s proposed facility would not affect the authority’s ability to provide or treat water.”

The letter quoted Blackburn as saying he had confirmed in 2025 that the data center’s proposed use of 200,000 gallons a day were within YCUA’s 8-10 million gallon per day capacity. “In addition, YCUA leadership has stated that serving the University's project would likely help mitigate overall utility costs by improving efficiency and cost distribution,” the letter said.

Sean Knapp, the YCUA’s director of service operations, told Planet Detroit last year that the YCUA is operating below capacity at the moment. “Adding the data center as a customer would help mitigate overall costs by improving efficiency and cost distribution,” he said at the time.

After saying it was illegal for the Ypsilanti community to not give it water, the University claimed the moratorium discriminated against data centers. “Beyond the above legal deficiencies, the proposed moratorium is pretextual and unlawfully discriminatory because it singles out ‘data centers’ by label rather than by utility impact,” the letter said. “It is discriminatory to permit other users to connect and consume currently available capacity while the utility conducts undefined studies to determine whether there is sufficient capacity for the University’s proposed facility.”

The University then asked the YCUA not to pass a moratorium and promised to “pursue” the matter. “The University respectfully requests that YCUA refuse to issue any sector-specific moratorium, instead basing any service decisions on documented utility factors, applied evenhandedly through existing permitting and technical review processes,” the letter said. “If these legal requirements are not followed by YCUA, the University reserves the right to pursue all rights and claims for necessary relief.”

The University of Michigan did not return 404 Media’s request for comment.

Ypsilanti Township has been fighting the proposed datacenter for more than a year now. Data centers are wildly unpopular in the United States. They often cause noise pollution, affect water quality, and drive up utility bills for their neighbors. Local opposition to the Ypsilanti Township data center has been compounded by its connection to America’s nuclear weapons industry.