The attorney for Nikko D’Ambrosio, who tried and failed to sue women for posting about him in an “Are We Dating the Same Guy” Facebook group, has apparently been using AI to file non-existent citations, according to a judge.
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The guy who sued 27 women, one man, and several platforms after users in a Facebook group called him “clingy” and “psycho” had his case against Meta dismissed after a judge suggested that his attorney filed AI-generated errors and non-existent citations.
In Nikko D’Ambrosio’s complaint, he claimed Facebook profited off of disparaging posts about him in a Chicago-based Are We Dating the Same Guy (AWDTSG) group. Judge David Hamilton wrote: “The brief included no citation to any legislative findings, let alone any including the statute’s targets as the brief asserted... These mistakes and fictitious quotations bear the hallmarks of the misuse of generative artificial intelligence.”
The detail was spotted by attorney Rob Freund on X:
The Chicago man who brought a defamation case over FB comments in "Are We Dating the Same Guy?" group appealed the dismissal of his case.
He loses again, and this time the court calls out his lawyers' AI misuse, noting some irony around it.
According to D’Ambrosio’s complaint, a woman posted in the group that she’d blocked his number. “Very clingy [and] very fast,” she wrote in the Facebook group. “Flaunted money very awkwardly and kept talking about how I don’t want to see his bad side.“ She blocked his number and he texted her from another one, she wrote. His response, included as an exhibit in the case—which he didn’t dispute until very late in the trial—was as follows, with redactions by the court: “Speak for yourself you ugly vial [sic] fake whore. Your ego matches that fake f****** face where you can’t even smile in pictures because your teeth are so f*****. The truth hurts b**** and my message will stay with you forever c***.”
D’Ambrosio’s initial attempts at suing the moderators of the groups, specific women who posted in the group about allegedly being harassed by him, and GoFundMe and Meta floundered under multiple revised complaints and finally, a dismissal in May 2025. He and his attorneys appealed two months later.
In 2024, in the middle of these case proceedings, including a failed class-action lawsuit that attempted to bring together men who felt wronged by Are We Dating the Same Guy groups, D’Ambrosio was sentenced to a year in prison for tax fraud. D’Ambrosio’s attorney at the time insinuated to the jury his client was too dumb to do his own taxes and therefore was innocent: “I don’t mean this to disparage Nikko in any way, but as you can see from his educational records, he is not the most sophisticated human being,” attorney Christopher Grohman said. “Somebody with his skill set is not doing his own taxes, and nor should he be, frankly. You go to a professional. And the professional he relied upon was his cousin.”
Are We Dating the Same Guy groups allow members to crowdsource “red flags or tea” about men they’re dating.
D’Ambrosio didn’t argue his “reportedly obnoxious behavior on dates and after a breakup” as listed in the AWDTSG group, the judge wrote, until it came time for oral arguments to appeal a dismissal of the case, “meaning any potential claim based on that statement was doomed as well.”
Judge Hamilton lists many reasons why D’Ambrosio doesn’t have a case strong enough to maintain that Meta violated any right-to-publicity laws or profited off his likeness through the AWDTSG group. Among them: his attorney Aaron Walner’s “sloppy” use of AI.
“We see such sloppy work in briefs fairly often, and almost always let it pass without comment as we try to focus on the merits of appeals,” Hamilton wrote. “But the next sentence in attorney Walner’s opening brief for D’Ambrosio said. Not only did Walner cite cases that didn’t support his argument, the only place judges could find one of the citations was in a decision that supported the opposite of the point he was apparently trying to make.
Aaron Walner is an attorney at Marc Trent’s law firm. Trent’s website, as the judge points out, brags extensively about Trent’s use of AI. In a blog post titled "How Marc Trent Uses AI to Deliver Cutting-Edge Legal Solutions," he lists “AI-Powered Case Management” and “Smarter Legal Strategies" as ways he practices law using LLMs: “Gone are the days of sifting through mountains of paperwork. Our AI tools automate document review, flagging key information and identifying relevant case law in seconds,” the site says.
The court demanded Walner, Trent and D’Ambrosio answer for their AI-generated filings or face sanctions. Lawyers getting caught and sanctioned for using AI and wasting the court’s time and clients’ resources happens so often now it barely makes the news anymore. This phenomenon started in the last year, and has since exploded into a legal-world epidemic, with judges’ patiences wearing thin and more people choosing to represent themselves in court, with the “help” of an LLM like ChatGPT. Lawyers, meanwhile, blame everything from family emergencies to technical difficulties when they get caught, and often throw their own paralegals under the bus.
Trent Law Firm did not respond to a request for comment.
“We don’t just use AI for the sake of it. Every tool and strategy is aimed at one thing: winning your case,” Trent’s site says. In D’Ambrosio’s case, it helped lose it.
Only a couple vendors could likely fulfill what the FBI is after, namely Flock and Motorola.
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The FBI wants to buy access to automated license plate readers (ALPRs) nationwide, which would likely allow the agency to track the movements of vehicles—and by extension people—across the country without a warrant, according to FBI procurement records reviewed by 404 Media.
The documents show that ALPRs continue to be a sought-after tool for law enforcement, not just for local police and individual communities, but federal agencies too. The news also comes as protests and pushback against ALPRs have spread around the country.
💡Do you work at Flock or Motorola? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
“The FBI has a crucial need for accessible LPRs to provide a diverse and reliable range of collections across the United States. This data should be available across major highways and in an array of locations for maximum usefulness to law enforcement,” a statement of work, which describes what data the FBI is seeking access to, reads.
ALPR cameras generally work by constantly scanning the color, brand, model, and license plate of vehicles that drive by. This creates a timestamped record of where a particular vehicle was at a specific time that law enforcement can then query, effectively letting them see exactly where someone drove across time. The technology has existed for decades, but has become more pervasive in recent years.
The FBI says it is looking for a vendor that will let it log into a Software-as-a-Service system and then query the collected ALPR data with license plate information, a description of the vehicle, a time or date, and geolocation information.
The FBI says it is looking for ALPR coverage in the following areas: Eastern 48 (East of the Mississippi River); Western 48 (West of the Mississippi River); Hawaii; Puerto Rico; Alaska; and outlying areas such as Guam, the U.S. Virgin Islands, or Tribal Territories. In effect, the FBI is looking for ALPR data nationwide and even beyond. An attached price template indicates the FBI is willing to pay $6 million for each of those broad areas, bringing the total to $36 million.
Images: Screenshots from the documents.
The FBI says it intends to award the contract to a single vendor, but if any such vendor is unable to fulfill all of the requirements, the agency may award the contract to up to two vendors. The contract is specifically for the FBI’s Directorate of Intelligence, which oversees the agency’s intelligence mission. The FBI is not only a law enforcement agency, but also part of the Intelligence Community.
There are a limited number of companies that might be able to provide the sort of data the FBI is seeking. One of those is Flock, whose ALPR cameras are stationed in communities all across the country. According to data 404 Media and researchers have obtained through public records requests, Flock has at least 80,000 cameras connected to its national lookup tool. At one point, Homeland Security Investigations (HSI), a section of Immigration and Customs Enforcement (ICE); the Secret Service, and the Navy’s criminal investigation arm all had access to Flock’s nationwide network as part of a pilot program. As 404 Media revealed, local police have performed lookups in the Flock nationwide database on behalf of ICE.
The other is Motorola Solutions, which acquired Vigilant Solutions. Motorola has a massive database of ALPR information built with cameras installed in police officer’s roaming vehicles. An arm of the company that sells to private industry has essentially outsourced image collection to repo men. In 2019, I reported on that part of the business, called Digital Recognition Network (DRN), and was shown how powerful its tracking capabilities can be. 404 Media reported last year that ICE recently invited staff to demos of a Motorola app that let officers scan a license plate and add it to a database of billions of records that show where else that vehicle has been spotted.
Josh Thomas, chief communications officer at Flock, told 404 Media in an email “We’re not going to speculate on prospective deals. But it’s worth noting that we already work with several federal agencies, all of whom are subject to the same obligations, constraints, and transparency mechanisms that apply to every other Flock customer. We also rebuilt our product from the ground up, starting last year, to ensure all local customers could trust that they can use Flock in full compliance with local and state laws. A big part of that is our Audit Assistance tool.”
Neither Motorola nor the FBI responded to a request for comment.
Only a couple vendors could likely fulfill what the FBI is after, namely Flock and Motorola.
Show full content
The FBI wants to buy access to automated license plate readers (ALPRs) nationwide, which would likely allow the agency to track the movements of vehicles—and by extension people—across the country without a warrant, according to FBI procurement records reviewed by 404 Media.
The documents show that ALPRs continue to be a sought-after tool for law enforcement, not just for local police and individual communities, but federal agencies too. The news also comes as protests and pushback against ALPRs have spread around the country.
💡Do you work at Flock or Motorola? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
“The FBI has a crucial need for accessible LPRs to provide a diverse and reliable range of collections across the United States. This data should be available across major highways and in an array of locations for maximum usefulness to law enforcement,” a statement of work, which describes what data the FBI is seeking access to, reads.
ALPR cameras generally work by constantly scanning the color, brand, model, and license plate of vehicles that drive by. This creates a timestamped record of where a particular vehicle was at a specific time that law enforcement can then query, effectively letting them see exactly where someone drove across time. The technology has existed for decades, but has become more pervasive in recent years.
The FBI says it is looking for a vendor that will let it log into a Software-as-a-Service system and then query the collected ALPR data with license plate information, a description of the vehicle, a time or date, and geolocation information.
The FBI says it is looking for ALPR coverage in the following areas: Eastern 48 (East of the Mississippi River); Western 48 (West of the Mississippi River); Hawaii; Puerto Rico; Alaska; and outlying areas such as Guam, the U.S. Virgin Islands, or Tribal Territories. In effect, the FBI is looking for ALPR data nationwide and even beyond. An attached price template indicates the FBI is willing to pay $6 million for each of those broad areas, bringing the total to $36 million.
Images: Screenshots from the documents.
The FBI says it intends to award the contract to a single vendor, but if any such vendor is unable to fulfill all of the requirements, the agency may award the contract to up to two vendors. The contract is specifically for the FBI’s Directorate of Intelligence, which oversees the agency’s intelligence mission. The FBI is not only a law enforcement agency, but also part of the Intelligence Community.
There are a limited number of companies that might be able to provide the sort of data the FBI is seeking. One of those is Flock, whose ALPR cameras are stationed in communities all across the country. According to data 404 Media and researchers have obtained through public records requests, Flock has at least 80,000 cameras connected to its national lookup tool. At one point, Homeland Security Investigations (HSI), a section of Immigration and Customs Enforcement (ICE); the Secret Service, and the Navy’s criminal investigation arm all had access to Flock’s nationwide network as part of a pilot program. As 404 Media revealed, local police have performed lookups in the Flock nationwide database on behalf of ICE.
The other is Motorola Solutions, which acquired Vigilant Solutions. Motorola has a massive database of ALPR information built with cameras installed in police officer’s roaming vehicles. An arm of the company that sells to private industry has essentially outsourced image collection to repo men. In 2019, I reported on that part of the business, called Digital Recognition Network (DRN), and was shown how powerful its tracking capabilities can be. 404 Media reported last year that ICE recently invited staff to demos of a Motorola app that let officers scan a license plate and add it to a database of billions of records that show where else that vehicle has been spotted.
Josh Thomas, chief communications officer at Flock, told 404 Media in an email “We’re not going to speculate on prospective deals. But it’s worth noting that we already work with several federal agencies, all of whom are subject to the same obligations, constraints, and transparency mechanisms that apply to every other Flock customer. We also rebuilt our product from the ground up, starting last year, to ensure all local customers could trust that they can use Flock in full compliance with local and state laws. A big part of that is our Audit Assistance tool.”
Neither Motorola nor the FBI responded to a request for comment.
Only a couple vendors could likely fulfill what the FBI is after, namely Flock and Motorola.
Show full content
The FBI wants to buy access to automated license plate readers (ALPRs) nationwide, which would likely allow the agency to track the movements of vehicles—and by extension people—across the country without a warrant, according to FBI procurement records reviewed by 404 Media.
The documents show that ALPRs continue to be a sought-after tool for law enforcement, not just for local police and individual communities, but federal agencies too. The news also comes as protests and pushback against ALPRs have spread around the country.
💡Do you work at Flock or Motorola? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
“The FBI has a crucial need for accessible LPRs to provide a diverse and reliable range of collections across the United States. This data should be available across major highways and in an array of locations for maximum usefulness to law enforcement,” a statement of work, which describes what data the FBI is seeking access to, reads.
ALPR cameras generally work by constantly scanning the color, brand, model, and license plate of vehicles that drive by. This creates a timestamped record of where a particular vehicle was at a specific time that law enforcement can then query, effectively letting them see exactly where someone drove across time. The technology has existed for decades, but has become more pervasive in recent years.
The FBI says it is looking for a vendor that will let it log into a Software-as-a-Service system and then query the collected ALPR data with license plate information, a description of the vehicle, a time or date, and geolocation information.
The FBI says it is looking for ALPR coverage in the following areas: Eastern 48 (East of the Mississippi River); Western 48 (West of the Mississippi River); Hawaii; Puerto Rico; Alaska; and outlying areas such as Guam, the U.S. Virgin Islands, or Tribal Territories. In effect, the FBI is looking for ALPR data nationwide and even beyond. An attached price template indicates the FBI is willing to pay $6 million for each of those broad areas, bringing the total to $36 million.
Images: Screenshots from the documents.
The FBI says it intends to award the contract to a single vendor, but if any such vendor is unable to fulfill all of the requirements, the agency may award the contract to up to two vendors. The contract is specifically for the FBI’s Directorate of Intelligence, which oversees the agency’s intelligence mission. The FBI is not only a law enforcement agency, but also part of the Intelligence Community.
There are a limited number of companies that might be able to provide the sort of data the FBI is seeking. One of those is Flock, whose ALPR cameras are stationed in communities all across the country. According to data 404 Media and researchers have obtained through public records requests, Flock has at least 80,000 cameras connected to its national lookup tool. At one point, Homeland Security Investigations (HSI), a section of Immigration and Customs Enforcement (ICE); the Secret Service, and the Navy’s criminal investigation arm all had access to Flock’s nationwide network as part of a pilot program. As 404 Media revealed, local police have performed lookups in the Flock nationwide database on behalf of ICE.
The other is Motorola Solutions, which acquired Vigilant Solutions. Motorola has a massive database of ALPR information built with cameras installed in police officer’s roaming vehicles. An arm of the company that sells to private industry has essentially outsourced image collection to repo men. In 2019, I reported on that part of the business, called Digital Recognition Network (DRN), and was shown how powerful its tracking capabilities can be. 404 Media reported last year that ICE recently invited staff to demos of a Motorola app that let officers scan a license plate and add it to a database of billions of records that show where else that vehicle has been spotted.
Josh Thomas, chief communications officer at Flock, told 404 Media in an email “We’re not going to speculate on prospective deals. But it’s worth noting that we already work with several federal agencies, all of whom are subject to the same obligations, constraints, and transparency mechanisms that apply to every other Flock customer. We also rebuilt our product from the ground up, starting last year, to ensure all local customers could trust that they can use Flock in full compliance with local and state laws. A big part of that is our Audit Assistance tool.”
Neither Motorola nor the FBI responded to a request for comment.
Only a couple vendors could likely fulfill what the FBI is after, namely Flock and Motorola.
Show full content
The FBI wants to buy access to automated license plate readers (ALPRs) nationwide, which would likely allow the agency to track the movements of vehicles—and by extension people—across the country without a warrant, according to FBI procurement records reviewed by 404 Media.
The documents show that ALPRs continue to be a sought-after tool for law enforcement, not just for local police and individual communities, but federal agencies too. The news also comes as protests and pushback against ALPRs have spread around the country.
💡Do you work at Flock or Motorola? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
“The FBI has a crucial need for accessible LPRs to provide a diverse and reliable range of collections across the United States. This data should be available across major highways and in an array of locations for maximum usefulness to law enforcement,” a statement of work, which describes what data the FBI is seeking access to, reads.
ALPR cameras generally work by constantly scanning the color, brand, model, and license plate of vehicles that drive by. This creates a timestamped record of where a particular vehicle was at a specific time that law enforcement can then query, effectively letting them see exactly where someone drove across time. The technology has existed for decades, but has become more pervasive in recent years.
The FBI says it is looking for a vendor that will let it log into a Software-as-a-Service system and then query the collected ALPR data with license plate information, a description of the vehicle, a time or date, and geolocation information.
The FBI says it is looking for ALPR coverage in the following areas: Eastern 48 (East of the Mississippi River); Western 48 (West of the Mississippi River); Hawaii; Puerto Rico; Alaska; and outlying areas such as Guam, the U.S. Virgin Islands, or Tribal Territories. In effect, the FBI is looking for ALPR data nationwide and even beyond. An attached price template indicates the FBI is willing to pay $6 million for each of those broad areas, bringing the total to $36 million.
Images: Screenshots from the documents.
The FBI says it intends to award the contract to a single vendor, but if any such vendor is unable to fulfill all of the requirements, the agency may award the contract to up to two vendors. The contract is specifically for the FBI’s Directorate of Intelligence, which oversees the agency’s intelligence mission. The FBI is not only a law enforcement agency, but also part of the Intelligence Community.
There are a limited number of companies that might be able to provide the sort of data the FBI is seeking. One of those is Flock, whose ALPR cameras are stationed in communities all across the country. According to data 404 Media and researchers have obtained through public records requests, Flock has at least 80,000 cameras connected to its national lookup tool. At one point, Homeland Security Investigations (HSI), a section of Immigration and Customs Enforcement (ICE); the Secret Service, and the Navy’s criminal investigation arm all had access to Flock’s nationwide network as part of a pilot program. As 404 Media revealed, local police have performed lookups in the Flock nationwide database on behalf of ICE.
The other is Motorola Solutions, which acquired Vigilant Solutions. Motorola has a massive database of ALPR information built with cameras installed in police officer’s roaming vehicles. An arm of the company that sells to private industry has essentially outsourced image collection to repo men. In 2019, I reported on that part of the business, called Digital Recognition Network (DRN), and was shown how powerful its tracking capabilities can be. 404 Media reported last year that ICE recently invited staff to demos of a Motorola app that let officers scan a license plate and add it to a database of billions of records that show where else that vehicle has been spotted.
Josh Thomas, chief communications officer at Flock, told 404 Media in an email “We’re not going to speculate on prospective deals. But it’s worth noting that we already work with several federal agencies, all of whom are subject to the same obligations, constraints, and transparency mechanisms that apply to every other Flock customer. We also rebuilt our product from the ground up, starting last year, to ensure all local customers could trust that they can use Flock in full compliance with local and state laws. A big part of that is our Audit Assistance tool.”
Neither Motorola nor the FBI responded to a request for comment.
Britt Paris's new book 'Radical Infrastructure: Imagining the Internet from the Ground Up' tells the story of the physical internet, and how it can benefit people, not corporations.
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As you scroll around the web, how often to you think about the physical infrastructure—the miles of cables, acres of land—that makes up the internet? This is where real power lies, and there are ways to imagine it differently, as serving the people who use these utilities instead of big tech execs.
This week, I’m delighted to be joined by Britt Paris. Britt is a critical informatics scholar and Associate Professor of Library and Information Science at Rutgers University’s School of Communication & Information. Her work focuses on Internet infrastructure, artificial intelligence-generated information objects, digital labor, civic data, and social epistemology. She’s also a fellow with AI Now. Her book Radical Infrastructure: Imagining the Internet from the Ground Up just came out in February.
Britt tells us about how her great-great-great uncle started a telecommunications cooperative in rural Missouri before the city even had connection, how examples like NEMR show us an alternative to monopolies that provide internet access and let people decide how they want their internet to work for them, and what’s giving her hope as she helps bargain for educators’ rights at Rutgers.
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.
Britt Paris's new book 'Radical Infrastructure: Imagining the Internet from the Ground Up' tells the story of the physical internet, and how it can benefit people, not corporations.
Show full content
As you scroll around the web, how often to you think about the physical infrastructure—the miles of cables, acres of land—that makes up the internet? This is where real power lies, and there are ways to imagine it differently, as serving the people who use these utilities instead of big tech execs.
This week, I’m delighted to be joined by Britt Paris. Britt is a critical informatics scholar and Associate Professor of Library and Information Science at Rutgers University’s School of Communication & Information. Her work focuses on Internet infrastructure, artificial intelligence-generated information objects, digital labor, civic data, and social epistemology. She’s also a fellow with AI Now. Her book Radical Infrastructure: Imagining the Internet from the Ground Up just came out in February.
Britt tells us about how her great-great-great uncle started a telecommunications cooperative in rural Missouri before the city even had connection, how examples like NEMR show us an alternative to monopolies that provide internet access and let people decide how they want their internet to work for them, and what’s giving her hope as she helps bargain for educators’ rights at Rutgers.
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.
Britt Paris's new book 'Radical Infrastructure: Imagining the Internet from the Ground Up' tells the story of the physical internet, and how it can benefit people, not corporations.
Show full content
As you scroll around the web, how often to you think about the physical infrastructure—the miles of cables, acres of land—that makes up the internet? This is where real power lies, and there are ways to imagine it differently, as serving the people who use these utilities instead of big tech execs.
This week, I’m delighted to be joined by Britt Paris. Britt is a critical informatics scholar and Associate Professor of Library and Information Science at Rutgers University’s School of Communication & Information. Her work focuses on Internet infrastructure, artificial intelligence-generated information objects, digital labor, civic data, and social epistemology. She’s also a fellow with AI Now. Her book Radical Infrastructure: Imagining the Internet from the Ground Up just came out in February.
Britt tells us about how her great-great-great uncle started a telecommunications cooperative in rural Missouri before the city even had connection, how examples like NEMR show us an alternative to monopolies that provide internet access and let people decide how they want their internet to work for them, and what’s giving her hope as she helps bargain for educators’ rights at Rutgers.
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.
Britt Paris's new book 'Radical Infrastructure: Imagining the Internet from the Ground Up' tells the story of the physical internet, and how it can benefit people, not corporations.
Show full content
As you scroll around the web, how often to you think about the physical infrastructure—the miles of cables, acres of land—that makes up the internet? This is where real power lies, and there are ways to imagine it differently, as serving the people who use these utilities instead of big tech execs.
This week, I’m delighted to be joined by Britt Paris. Britt is a critical informatics scholar and Associate Professor of Library and Information Science at Rutgers University’s School of Communication & Information. Her work focuses on Internet infrastructure, artificial intelligence-generated information objects, digital labor, civic data, and social epistemology. She’s also a fellow with AI Now. Her book Radical Infrastructure: Imagining the Internet from the Ground Up just came out in February.
Britt tells us about how her great-great-great uncle started a telecommunications cooperative in rural Missouri before the city even had connection, how examples like NEMR show us an alternative to monopolies that provide internet access and let people decide how they want their internet to work for them, and what’s giving her hope as she helps bargain for educators’ rights at Rutgers.
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.
“With your permission, your child’s lead teacher may wear a small teacher-worn camera that captures the teacher's approximate first-person perspective, and/or we may place a fixed video camera in the classroom,” a document given to parents and later shared with 404 Media reads.
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University of Washington researchers planned to have preschool teachers wear cameras that would record everything they saw from a first-person perspective, including the children they were teaching, then use that footage to develop AI models. Crucially, the program was presented as opt-out, rather than opt-in, meaning that parents had to take steps to prevent recordings of their children being processed by AI.
“With your permission, your child’s lead teacher may wear a small teacher-worn camera that captures the teacher's approximate first-person perspective, and/or we may place a fixed video camera in the classroom,” a document given to parents and later shared with 404 Media reads. “These videos simply capture the normal interactions between teachers and children during regular classroom activities. Recordings occur during morning program hours up to 150 minutes, up to 4 visits in one month. Your child will not be asked to do anything new or different. Their daily routine will stay exactly the same.”
💡Do you know anything else about how researchers are using AI? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
“With your permission, your child’s lead teacher may wear a small teacher-worn camera that captures the teacher's approximate first-person perspective, and/or we may place a fixed video camera in the classroom,” a document given to parents and later shared with 404 Media reads.
Show full content
University of Washington researchers planned to have preschool teachers wear cameras that would record everything they saw from a first-person perspective, including the children they were teaching, then use that footage to develop AI models. Crucially, the program was presented as opt-out, rather than opt-in, meaning that parents had to take steps to prevent recordings of their children being processed by AI.
“With your permission, your child’s lead teacher may wear a small teacher-worn camera that captures the teacher's approximate first-person perspective, and/or we may place a fixed video camera in the classroom,” a document given to parents and later shared with 404 Media reads. “These videos simply capture the normal interactions between teachers and children during regular classroom activities. Recordings occur during morning program hours up to 150 minutes, up to 4 visits in one month. Your child will not be asked to do anything new or different. Their daily routine will stay exactly the same.”
404 Media has repeatedly covered how AI is permeating through education. That includes students using AI themselves, and even the creation of entire AI-powered schools. Now, the University of Washington research shows how AI data collection is pushing into early childhood education too.
💡Do you know anything else about how researchers are using AI? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
Or, it would have, if parents didn’t revolt. After a backlash, the University of Washington told 404 Media it has now shelved the planned research.
“The goal of this study is to better understand children’s everyday learning experiences and to develop Al tools that can help assess classroom interaction quality,” the document says. The research was being led by Dr. Gail Joseph and the Cultivate Learning team at the University of Washington, it says. Joseph’s work focuses on early childhood education.
The document says that this collected footage would have been used to “develop and evaluate AI models for assessing classroom interaction quality.” That includes human reviewers watching and annotating the videos, with that data then improving AI models. “AI tools will also analyze the same recordings to generate codes and justifications,” the document reads. The document doesn’t name any specific AI providers, but says, “Video data may be processed using cloud-based AI services.”
Photos of the notice given to parents. Image: 404 Media.
Only the research teams would have used the annotated videos to train “secure, private AI models.”
Teachers were to be given a “written observation summary,” it adds. The researchers say the footage and audio may have been used in academic papers or for conferences, but the researchers planned to blur faces and edit out names “whenever possible.”
Finally, the collected footage and data may be shared with others “to support future early childhood education research,” the document says.
A parent who received the document said they were “taken aback” after reading it. “I am troubled by the idea of using my child's likeness in unknown AI tools and how this could be abused,” she added. “I was particularly concerned about families’ ability to give informed consent. As a native English speaker, the vague language in the handout left me with a slew of questions. Many families in our school are migrants and non-native English speakers, but forms were not provided in any of their native languages.” 404 Media granted the parent anonymity to avoid repercussions.
404 Media sent sections of the document to multiple experts in education and AI. “The excerpt doesn’t provide important information, and those omissions concern me (assuming they’re not provided in another part of the letter I haven’t seen). Who may the data may be shared with? How long will it be maintained? Who is funding the research? Those are questions that I would want answers to, and the answers could exist,” Faith Boninger, co-director of the National Education Policy Center, told 404 Media. “A big question that doesn’t have an answer relates to the language that describes the purposes for which the videos may be used. The wording ‘not limited to’ implies that there could be any number of future uses to which the data may be put that haven’t even been thought of yet.”
“I am always hopeful we will continue to find ways to improve support for teachers and students. While I don’t know the details of this specific study, from what you shared, I am glad to see research that includes humans in the loop and clear disclosure of data collection and use,” Jake Baskin, executive director of the Computer Science Teachers Association, told 404 Media. “That said, anytime we bring cameras and AI into the classroom, protecting student and teacher data must be the highest priority. Rigorous research with transparent publication of results is how we will learn what actually helps educators in the classroom.”
The document presents participation in the research as “completely voluntary.” But it is not an opt-in model. Instead, parents have to opt-out if they don’t want their children to be recorded by a teacher-worn camera and have that footage processed by AI. “You may decline or withdraw your child from the research at any time. Your decision will not affect your child’s enrollment or standing in the program,” it says.
That raised questions around how that would practically work. If one parent opted their child out, would only they be omitted from any footage? Jackson Holtz, assistant director of University of Washington News, told 404 Media in an email that if a parent did opt-out, that entire class would be removed from the research. “The consent process was designed so that not only could families opt out, if even a single family decided to opt out, their entire classroom would be excluded,” he wrote. The parent said, “Only through questioning teachers and school administrators did we learn the researchers would put stickers on children who opted out, but no further information was provided on whether they would still be filmed.”
“Our initial outreach was intended to help us better understand how families would feel about a project that uses artificial intelligence to support teachers,” Holtz continued. Holtz said after that feedback, the University of Washington has stopped the research. “Given the early responses from parents, we have terminated the study and are no longer seeking participation at any site. (It is not unusual to terminate a study in the early stages as we receive feedback from community partners.) All programs are in the process of being notified that this particular study is now terminated,” he wrote.
After 404 Media contacted the university for comment, the section of its website describing the study was taken offline.
“With your permission, your child’s lead teacher may wear a small teacher-worn camera that captures the teacher's approximate first-person perspective, and/or we may place a fixed video camera in the classroom,” a document given to parents and later shared with 404 Media reads.
Show full content
University of Washington researchers planned to have preschool teachers wear cameras that would record everything they saw from a first-person perspective, including the children they were teaching, then use that footage to develop AI models. Crucially, the program was presented as opt-out, rather than opt-in, meaning that parents had to take steps to prevent recordings of their children being processed by AI.
“With your permission, your child’s lead teacher may wear a small teacher-worn camera that captures the teacher's approximate first-person perspective, and/or we may place a fixed video camera in the classroom,” a document given to parents and later shared with 404 Media reads. “These videos simply capture the normal interactions between teachers and children during regular classroom activities. Recordings occur during morning program hours up to 150 minutes, up to 4 visits in one month. Your child will not be asked to do anything new or different. Their daily routine will stay exactly the same.”
404 Media has repeatedly covered how AI is permeating through education. That includes students using AI themselves, and even the creation of entire AI-powered schools. Now, the University of Washington research shows how AI data collection is pushing into early childhood education too.
💡Do you know anything else about how researchers are using AI? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
Or, it would have, if parents didn’t revolt. After a backlash, the University of Washington told 404 Media it has now shelved the planned research.
“The goal of this study is to better understand children’s everyday learning experiences and to develop Al tools that can help assess classroom interaction quality,” the document says. The research was being led by Dr. Gail Joseph and the Cultivate Learning team at the University of Washington, it says. Joseph’s work focuses on early childhood education.
The document says that this collected footage would have been used to “develop and evaluate AI models for assessing classroom interaction quality.” That includes human reviewers watching and annotating the videos, with that data then improving AI models. “AI tools will also analyze the same recordings to generate codes and justifications,” the document reads. The document doesn’t name any specific AI providers, but says, “Video data may be processed using cloud-based AI services.”
Photos of the notice given to parents. Image: 404 Media.
Only the research teams would have used the annotated videos to train “secure, private AI models.”
Teachers were to be given a “written observation summary,” it adds. The researchers say the footage and audio may have been used in academic papers or for conferences, but the researchers planned to blur faces and edit out names “whenever possible.”
Finally, the collected footage and data may be shared with others “to support future early childhood education research,” the document says.
A parent who received the document said they were “taken aback” after reading it. “I am troubled by the idea of using my child's likeness in unknown AI tools and how this could be abused,” she added. “I was particularly concerned about families’ ability to give informed consent. As a native English speaker, the vague language in the handout left me with a slew of questions. Many families in our school are migrants and non-native English speakers, but forms were not provided in any of their native languages.” 404 Media granted the parent anonymity to avoid repercussions.
404 Media sent sections of the document to multiple experts in education and AI. “The excerpt doesn’t provide important information, and those omissions concern me (assuming they’re not provided in another part of the letter I haven’t seen). Who may the data may be shared with? How long will it be maintained? Who is funding the research? Those are questions that I would want answers to, and the answers could exist,” Faith Boninger, co-director of the National Education Policy Center, told 404 Media. “A big question that doesn’t have an answer relates to the language that describes the purposes for which the videos may be used. The wording ‘not limited to’ implies that there could be any number of future uses to which the data may be put that haven’t even been thought of yet.”
“I am always hopeful we will continue to find ways to improve support for teachers and students. While I don’t know the details of this specific study, from what you shared, I am glad to see research that includes humans in the loop and clear disclosure of data collection and use,” Jake Baskin, executive director of the Computer Science Teachers Association, told 404 Media. “That said, anytime we bring cameras and AI into the classroom, protecting student and teacher data must be the highest priority. Rigorous research with transparent publication of results is how we will learn what actually helps educators in the classroom.”
The document presents participation in the research as “completely voluntary.” But it is not an opt-in model. Instead, parents have to opt-out if they don’t want their children to be recorded by a teacher-worn camera and have that footage processed by AI. “You may decline or withdraw your child from the research at any time. Your decision will not affect your child’s enrollment or standing in the program,” it says.
That raised questions around how that would practically work. If one parent opted their child out, would only they be omitted from any footage? Jackson Holtz, assistant director of University of Washington News, told 404 Media in an email that if a parent did opt-out, that entire class would be removed from the research. “The consent process was designed so that not only could families opt out, if even a single family decided to opt out, their entire classroom would be excluded,” he wrote. The parent said, “Only through questioning teachers and school administrators did we learn the researchers would put stickers on children who opted out, but no further information was provided on whether they would still be filmed.”
“Our initial outreach was intended to help us better understand how families would feel about a project that uses artificial intelligence to support teachers,” Holtz continued. Holtz said after that feedback, the University of Washington has stopped the research. “Given the early responses from parents, we have terminated the study and are no longer seeking participation at any site. (It is not unusual to terminate a study in the early stages as we receive feedback from community partners.) All programs are in the process of being notified that this particular study is now terminated,” he wrote.
After 404 Media contacted the university for comment, the section of its website describing the study was taken offline.
“With your permission, your child’s lead teacher may wear a small teacher-worn camera that captures the teacher's approximate first-person perspective, and/or we may place a fixed video camera in the classroom,” a document given to parents and later shared with 404 Media reads.
Show full content
University of Washington researchers planned to have preschool teachers wear cameras that would record everything they saw from a first-person perspective, including the children they were teaching, then use that footage to develop AI models. Crucially, the program was presented as opt-out, rather than opt-in, meaning that parents had to take steps to prevent recordings of their children being processed by AI.
“With your permission, your child’s lead teacher may wear a small teacher-worn camera that captures the teacher's approximate first-person perspective, and/or we may place a fixed video camera in the classroom,” a document given to parents and later shared with 404 Media reads. “These videos simply capture the normal interactions between teachers and children during regular classroom activities. Recordings occur during morning program hours up to 150 minutes, up to 4 visits in one month. Your child will not be asked to do anything new or different. Their daily routine will stay exactly the same.”
404 Media has repeatedly covered how AI is permeating through education. That includes students using AI themselves, and even the creation of entire AI-powered schools. Now, the University of Washington research shows how AI data collection is pushing into early childhood education too.
💡Do you know anything else about how researchers are using AI? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
Or, it would have, if parents didn’t revolt. After a backlash, the University of Washington told 404 Media it has now shelved the planned research.
“The goal of this study is to better understand children’s everyday learning experiences and to develop Al tools that can help assess classroom interaction quality,” the document says. The research was being led by Dr. Gail Joseph and the Cultivate Learning team at the University of Washington, it says. Joseph’s work focuses on early childhood education.
The document says that this collected footage would have been used to “develop and evaluate AI models for assessing classroom interaction quality.” That includes human reviewers watching and annotating the videos, with that data then improving AI models. “AI tools will also analyze the same recordings to generate codes and justifications,” the document reads. The document doesn’t name any specific AI providers, but says, “Video data may be processed using cloud-based AI services.”
Photos of the notice given to parents. Image: 404 Media.
Only the research teams would have used the annotated videos to train “secure, private AI models.”
Teachers were to be given a “written observation summary,” it adds. The researchers say the footage and audio may have been used in academic papers or for conferences, but the researchers planned to blur faces and edit out names “whenever possible.”
Finally, the collected footage and data may be shared with others “to support future early childhood education research,” the document says.
A parent who received the document said they were “taken aback” after reading it. “I am troubled by the idea of using my child's likeness in unknown AI tools and how this could be abused,” she added. “I was particularly concerned about families’ ability to give informed consent. As a native English speaker, the vague language in the handout left me with a slew of questions. Many families in our school are migrants and non-native English speakers, but forms were not provided in any of their native languages.” 404 Media granted the parent anonymity to avoid repercussions.
404 Media sent sections of the document to multiple experts in education and AI. “The excerpt doesn’t provide important information, and those omissions concern me (assuming they’re not provided in another part of the letter I haven’t seen). Who may the data may be shared with? How long will it be maintained? Who is funding the research? Those are questions that I would want answers to, and the answers could exist,” Faith Boninger, co-director of the National Education Policy Center, told 404 Media. “A big question that doesn’t have an answer relates to the language that describes the purposes for which the videos may be used. The wording ‘not limited to’ implies that there could be any number of future uses to which the data may be put that haven’t even been thought of yet.”
“I am always hopeful we will continue to find ways to improve support for teachers and students. While I don’t know the details of this specific study, from what you shared, I am glad to see research that includes humans in the loop and clear disclosure of data collection and use,” Jake Baskin, executive director of the Computer Science Teachers Association, told 404 Media. “That said, anytime we bring cameras and AI into the classroom, protecting student and teacher data must be the highest priority. Rigorous research with transparent publication of results is how we will learn what actually helps educators in the classroom.”
The document presents participation in the research as “completely voluntary.” But it is not an opt-in model. Instead, parents have to opt-out if they don’t want their children to be recorded by a teacher-worn camera and have that footage processed by AI. “You may decline or withdraw your child from the research at any time. Your decision will not affect your child’s enrollment or standing in the program,” it says.
That raised questions around how that would practically work. If one parent opted their child out, would only they be omitted from any footage? Jackson Holtz, assistant director of University of Washington News, told 404 Media in an email that if a parent did opt-out, that entire class would be removed from the research. “The consent process was designed so that not only could families opt out, if even a single family decided to opt out, their entire classroom would be excluded,” he wrote. The parent said, “Only through questioning teachers and school administrators did we learn the researchers would put stickers on children who opted out, but no further information was provided on whether they would still be filmed.”
“Our initial outreach was intended to help us better understand how families would feel about a project that uses artificial intelligence to support teachers,” Holtz continued. Holtz said after that feedback, the University of Washington has stopped the research. “Given the early responses from parents, we have terminated the study and are no longer seeking participation at any site. (It is not unusual to terminate a study in the early stages as we receive feedback from community partners.) All programs are in the process of being notified that this particular study is now terminated,” he wrote.
After 404 Media contacted the university for comment, the section of its website describing the study was taken offline.
A type of crystal lattice called a clathrate structure has been found for the first time in the fallout of a nuclear detonation.
Show full content
Welcome back to the Abstract! Here are the studies this week that were long in the tooth, trapped in the lattice, unearthed in Thailand, and entombed in post-apocalyptic waters.
First, scientists discover that even Neanderthals had to go to the dentist. Then: a nuke-born crystal, a 60,000-pound herbivore, and life after the death of most species on the planet.
Neanderthals performed dental interventions at least 59,000 years ago, pushing the timeline of dentistry back by tens of thousands of years, according to a study about a molar from Chagyrskaya Cave in southwestern Siberia.
Early humans used rudimentary dental tools, like toothpicks, for well over a million years. But scientists have now identified evidence that Neanderthals used drills to treat cavities at the Siberian site, performing an Ice Age version of a root canal. Previously, the oldest tooth that showed signs of a dental checkupt belonged to “Villabruna,” a prehistoric human male who lived in Italy 14,000 years ago.
The remnants of the Neanderthal tooth adds to a growing body of research that has overturned the stereotype of Neanderthals as cognitively inferior to Homo sapiens and hints at “cognitive convergence” between the two species, according to the study.
The Chagyrskaya Cave molar. Image: Zubova et al., 2026, PLOS One, CC-BY 4.0
The Chagyrskaya Cave tooth shows “evidence of two distinct types of manipulations requiring different tools, in addition to the drilling/rotating technique, necessitating complex finger movements,” said researchers led by Alisa Zubova from the Peter the Great Museum of Anthropology and Ethnography (Kunstkamera).
The study suggests that Neanderthals at this site “possessed the cognitive capacity to intuit the source of pain, comprehend the feasibility of its elimination, and deliberately select the most efficacious dental intervention,” the team added. “The technical proficiency required for this procedure…reflects a capacity for causal reasoning, anticipatory planning, and volitional endurance, contradicting earlier assumptions regarding Neanderthal behavioral limitations.”
It's not clear if this Neanderthal patient got a complimentary toothpick at the end of the visit, but at the very least, they received some temporary relief from a bad toothache.
Scientists have discovered a weird new type of crystal in the ashes of the Trinity test, the first detonation of a nuclear bomb, which took place in the early morning of July 16, 1945, in New Mexico.
Trinity’s “gadget” unleashed a powerful fireball that vaporized its test tower and transformed the desert sand into a glassy residue called trinitite. For decades, researchers have found novel and bizarre compounds in the fallout. A new study now reports the first known instance of a clathrate structure—a crystal lattice that can trap “guest” molecules inside its cagelike scaffolding—in red trinitite.
The sample of red trinitite that contained the clathrate. Image: Bindi, Luca et al.
“The discovery of this phase represents the first crystallographically confirmed identification of a clathrate structure among the solid-state products of a nuclear explosion,” said researchers led by Luca Bindi of the University of Florence.
“This work underscores how rare, high-energy events—such as nuclear detonations, lightning strikes, and hypervelocity impacts—serve as natural laboratories for producing unexpected crystalline matter,” the team added.
In addition to being one of the most pivotal split-seconds in history, the Trinity test spun sand into exotic materials that are still generating discoveries more than 80 years later.
Meet the largest dinosaur ever found in Southeast Asia: Nagatitan chaiyaphumensis, a hulking sauropod that lived more than 100 million years ago in what is now Thailand.
Weighing in at an estimated 60,000 pounds and measuring nearly 90 feet from head to tail-tip, this massive herbivore belonged to the titanosaur family, the largest animals ever to walk on land.
“We estimate a body mass of 25–28 tonnes for Nagatitan, and suggest it was part of a broader middle Cretaceous body size increase in Asian titanosauriforms, facilitated by rising temperatures and expanded suitable habitat,” said researchers co-led by Thitiwoot Sethapanichsakul of University College London and Sasa-On Khansubha of Sirindhorn Museum in Thailand.
“The discovery of Nagatitan expands the known diversity of Southeast Asian sauropods and improves our understanding of titanosauriform biogeography within the region,” the team added.
While it’s mind-boggling to imagine a 90-foot-long, 25-tonne animal casually ambling around, Nagatitan is only mid-sized for a titanosaur. The biggest behemoths in this family may have exceeded 120 feet in length and boasted 130,000 pounds of fully plant-powered body mass.
With that said, the all-time heavyweight champion of the animal kingdom is our own contemporary, the blue whale, which tips the scales at an astonishing 400,000 pounds. Have you ever felt so puny in your life?
The last titanosaurs were wiped out by the asteroid that brought the age of dinosaurs to a sudden and brutal end 66 million years ago, killing off about two-thirds of all species on Earth. But though the space rock eradicated the land giants, some animals managed to pull through, including a large fish that lived within 2,000 years of the impact.
Scientists led by Jacob Wilson of the Denver Museum of Nature & Science described the anatomy of a gar and weighed in on its possible taxonomy, building on the initial 2022 study that first reported the specimen. Measuring about five feet in length, this gar inhabited a post-apocalyptic world that is preserved within the Fort Union Formation of North Dakota.
The specimen “is notable both for its size (more than 1 meter) and its precise stratigraphic placement 18 centimeters above the Cretaceous/Paleogene (K/Pg) boundary clay,” the team said. “Our conclusions support the inference that gars were prominent members of freshwater ecosystems and, in turn, freshwater ecosystems were capable of supporting large-bodied predators within ∼2000 years after the K/Pg extinction.”
This gar hatched into an eerily empty ecosphere, mere centuries after a planetary nightmare, yet it still grew into a fisherman’s dream catch. It’s a testament to the resilience of life on Earth, which could not be fully stomped out even by a direct cosmic punch to the face.
A type of crystal lattice called a clathrate structure has been found for the first time in the fallout of a nuclear detonation.
Show full content
Welcome back to the Abstract! Here are the studies this week that were long in the tooth, trapped in the lattice, unearthed in Thailand, and entombed in post-apocalyptic waters.
First, scientists discover that even Neanderthals had to go to the dentist. Then: a nuke-born crystal, a 60,000-pound herbivore, and life after the death of most species on the planet.
Neanderthals performed dental interventions at least 59,000 years ago, pushing the timeline of dentistry back by tens of thousands of years, according to a study about a molar from Chagyrskaya Cave in southwestern Siberia.
Early humans used rudimentary dental tools, like toothpicks, for well over a million years. But scientists have now identified evidence that Neanderthals used drills to treat cavities at the Siberian site, performing an Ice Age version of a root canal. Previously, the oldest tooth that showed signs of a dental checkupt belonged to “Villabruna,” a prehistoric human male who lived in Italy 14,000 years ago.
The remnants of the Neanderthal tooth adds to a growing body of research that has overturned the stereotype of Neanderthals as cognitively inferior to Homo sapiens and hints at “cognitive convergence” between the two species, according to the study.
The Chagyrskaya Cave molar. Image: Zubova et al., 2026, PLOS One, CC-BY 4.0
The Chagyrskaya Cave tooth shows “evidence of two distinct types of manipulations requiring different tools, in addition to the drilling/rotating technique, necessitating complex finger movements,” said researchers led by Alisa Zubova from the Peter the Great Museum of Anthropology and Ethnography (Kunstkamera).
The study suggests that Neanderthals at this site “possessed the cognitive capacity to intuit the source of pain, comprehend the feasibility of its elimination, and deliberately select the most efficacious dental intervention,” the team added. “The technical proficiency required for this procedure…reflects a capacity for causal reasoning, anticipatory planning, and volitional endurance, contradicting earlier assumptions regarding Neanderthal behavioral limitations.”
It's not clear if this Neanderthal patient got a complimentary toothpick at the end of the visit, but at the very least, they received some temporary relief from a bad toothache.
Scientists have discovered a weird new type of crystal in the ashes of the Trinity test, the first detonation of a nuclear bomb, which took place in the early morning of July 16, 1945, in New Mexico.
Trinity’s “gadget” unleashed a powerful fireball that vaporized its test tower and transformed the desert sand into a glassy residue called trinitite. For decades, researchers have found novel and bizarre compounds in the fallout. A new study now reports the first known instance of a clathrate structure—a crystal lattice that can trap “guest” molecules inside its cagelike scaffolding—in red trinitite.
The sample of red trinitite that contained the clathrate. Image: Bindi, Luca et al.
“The discovery of this phase represents the first crystallographically confirmed identification of a clathrate structure among the solid-state products of a nuclear explosion,” said researchers led by Luca Bindi of the University of Florence.
“This work underscores how rare, high-energy events—such as nuclear detonations, lightning strikes, and hypervelocity impacts—serve as natural laboratories for producing unexpected crystalline matter,” the team added.
In addition to being one of the most pivotal split-seconds in history, the Trinity test spun sand into exotic materials that are still generating discoveries more than 80 years later.
Meet the largest dinosaur ever found in Southeast Asia: Nagatitan chaiyaphumensis, a hulking sauropod that lived more than 100 million years ago in what is now Thailand.
Weighing in at an estimated 60,000 pounds and measuring nearly 90 feet from head to tail-tip, this massive herbivore belonged to the titanosaur family, the largest animals ever to walk on land.
“We estimate a body mass of 25–28 tonnes for Nagatitan, and suggest it was part of a broader middle Cretaceous body size increase in Asian titanosauriforms, facilitated by rising temperatures and expanded suitable habitat,” said researchers co-led by Thitiwoot Sethapanichsakul of University College London and Sasa-On Khansubha of Sirindhorn Museum in Thailand.
“The discovery of Nagatitan expands the known diversity of Southeast Asian sauropods and improves our understanding of titanosauriform biogeography within the region,” the team added.
While it’s mind-boggling to imagine a 90-foot-long, 25-tonne animal casually ambling around, Nagatitan is only mid-sized for a titanosaur. The biggest behemoths in this family may have exceeded 120 feet in length and boasted 130,000 pounds of fully plant-powered body mass.
With that said, the all-time heavyweight champion of the animal kingdom is our own contemporary, the blue whale, which tips the scales at an astonishing 400,000 pounds. Have you ever felt so puny in your life?
The last titanosaurs were wiped out by the asteroid that brought the age of dinosaurs to a sudden and brutal end 66 million years ago, killing off about two-thirds of all species on Earth. But though the space rock eradicated the land giants, some animals managed to pull through, including a large fish that lived within 2,000 years of the impact.
Scientists led by Jacob Wilson of the Denver Museum of Nature & Science described the anatomy of a gar and weighed in on its possible taxonomy, building on the initial 2022 study that first reported the specimen. Measuring about five feet in length, this gar inhabited a post-apocalyptic world that is preserved within the Fort Union Formation of North Dakota.
The specimen “is notable both for its size (more than 1 meter) and its precise stratigraphic placement 18 centimeters above the Cretaceous/Paleogene (K/Pg) boundary clay,” the team said. “Our conclusions support the inference that gars were prominent members of freshwater ecosystems and, in turn, freshwater ecosystems were capable of supporting large-bodied predators within ∼2000 years after the K/Pg extinction.”
This gar hatched into an eerily empty ecosphere, mere centuries after a planetary nightmare, yet it still grew into a fisherman’s dream catch. It’s a testament to the resilience of life on Earth, which could not be fully stomped out even by a direct cosmic punch to the face.
A type of crystal lattice called a clathrate structure has been found for the first time in the fallout of a nuclear detonation.
Show full content
Welcome back to the Abstract! Here are the studies this week that were long in the tooth, trapped in the lattice, unearthed in Thailand, and entombed in post-apocalyptic waters.
First, scientists discover that even Neanderthals had to go to the dentist. Then: a nuke-born crystal, a 60,000-pound herbivore, and life after the death of most species on the planet.
Neanderthals performed dental interventions at least 59,000 years ago, pushing the timeline of dentistry back by tens of thousands of years, according to a study about a molar from Chagyrskaya Cave in southwestern Siberia.
Early humans used rudimentary dental tools, like toothpicks, for well over a million years. But scientists have now identified evidence that Neanderthals used drills to treat cavities at the Siberian site, performing an Ice Age version of a root canal. Previously, the oldest tooth that showed signs of a dental checkupt belonged to “Villabruna,” a prehistoric human male who lived in Italy 14,000 years ago.
The remnants of the Neanderthal tooth adds to a growing body of research that has overturned the stereotype of Neanderthals as cognitively inferior to Homo sapiens and hints at “cognitive convergence” between the two species, according to the study.
The Chagyrskaya Cave molar. Image: Zubova et al., 2026, PLOS One, CC-BY 4.0
The Chagyrskaya Cave tooth shows “evidence of two distinct types of manipulations requiring different tools, in addition to the drilling/rotating technique, necessitating complex finger movements,” said researchers led by Alisa Zubova from the Peter the Great Museum of Anthropology and Ethnography (Kunstkamera).
The study suggests that Neanderthals at this site “possessed the cognitive capacity to intuit the source of pain, comprehend the feasibility of its elimination, and deliberately select the most efficacious dental intervention,” the team added. “The technical proficiency required for this procedure…reflects a capacity for causal reasoning, anticipatory planning, and volitional endurance, contradicting earlier assumptions regarding Neanderthal behavioral limitations.”
It's not clear if this Neanderthal patient got a complimentary toothpick at the end of the visit, but at the very least, they received some temporary relief from a bad toothache.
Scientists have discovered a weird new type of crystal in the ashes of the Trinity test, the first detonation of a nuclear bomb, which took place in the early morning of July 16, 1945, in New Mexico.
Trinity’s “gadget” unleashed a powerful fireball that vaporized its test tower and transformed the desert sand into a glassy residue called trinitite. For decades, researchers have found novel and bizarre compounds in the fallout. A new study now reports the first known instance of a clathrate structure—a crystal lattice that can trap “guest” molecules inside its cagelike scaffolding—in red trinitite.
The sample of red trinitite that contained the clathrate. Image: Bindi, Luca et al.
“The discovery of this phase represents the first crystallographically confirmed identification of a clathrate structure among the solid-state products of a nuclear explosion,” said researchers led by Luca Bindi of the University of Florence.
“This work underscores how rare, high-energy events—such as nuclear detonations, lightning strikes, and hypervelocity impacts—serve as natural laboratories for producing unexpected crystalline matter,” the team added.
In addition to being one of the most pivotal split-seconds in history, the Trinity test spun sand into exotic materials that are still generating discoveries more than 80 years later.
Meet the largest dinosaur ever found in Southeast Asia: Nagatitan chaiyaphumensis, a hulking sauropod that lived more than 100 million years ago in what is now Thailand.
Weighing in at an estimated 60,000 pounds and measuring nearly 90 feet from head to tail-tip, this massive herbivore belonged to the titanosaur family, the largest animals ever to walk on land.
“We estimate a body mass of 25–28 tonnes for Nagatitan, and suggest it was part of a broader middle Cretaceous body size increase in Asian titanosauriforms, facilitated by rising temperatures and expanded suitable habitat,” said researchers co-led by Thitiwoot Sethapanichsakul of University College London and Sasa-On Khansubha of Sirindhorn Museum in Thailand.
“The discovery of Nagatitan expands the known diversity of Southeast Asian sauropods and improves our understanding of titanosauriform biogeography within the region,” the team added.
While it’s mind-boggling to imagine a 90-foot-long, 25-tonne animal casually ambling around, Nagatitan is only mid-sized for a titanosaur. The biggest behemoths in this family may have exceeded 120 feet in length and boasted 130,000 pounds of fully plant-powered body mass.
With that said, the all-time heavyweight champion of the animal kingdom is our own contemporary, the blue whale, which tips the scales at an astonishing 400,000 pounds. Have you ever felt so puny in your life?
The last titanosaurs were wiped out by the asteroid that brought the age of dinosaurs to a sudden and brutal end 66 million years ago, killing off about two-thirds of all species on Earth. But though the space rock eradicated the land giants, some animals managed to pull through, including a large fish that lived within 2,000 years of the impact.
Scientists led by Jacob Wilson of the Denver Museum of Nature & Science described the anatomy of a gar and weighed in on its possible taxonomy, building on the initial 2022 study that first reported the specimen. Measuring about five feet in length, this gar inhabited a post-apocalyptic world that is preserved within the Fort Union Formation of North Dakota.
The specimen “is notable both for its size (more than 1 meter) and its precise stratigraphic placement 18 centimeters above the Cretaceous/Paleogene (K/Pg) boundary clay,” the team said. “Our conclusions support the inference that gars were prominent members of freshwater ecosystems and, in turn, freshwater ecosystems were capable of supporting large-bodied predators within ∼2000 years after the K/Pg extinction.”
This gar hatched into an eerily empty ecosphere, mere centuries after a planetary nightmare, yet it still grew into a fisherman’s dream catch. It’s a testament to the resilience of life on Earth, which could not be fully stomped out even by a direct cosmic punch to the face.
A type of crystal lattice called a clathrate structure has been found for the first time in the fallout of a nuclear detonation.
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Welcome back to the Abstract! Here are the studies this week that were long in the tooth, trapped in the lattice, unearthed in Thailand, and entombed in post-apocalyptic waters.
First, scientists discover that even Neanderthals had to go to the dentist. Then: a nuke-born crystal, a 60,000-pound herbivore, and life after the death of most species on the planet.
Neanderthals performed dental interventions at least 59,000 years ago, pushing the timeline of dentistry back by tens of thousands of years, according to a study about a molar from Chagyrskaya Cave in southwestern Siberia.
Early humans used rudimentary dental tools, like toothpicks, for well over a million years. But scientists have now identified evidence that Neanderthals used drills to treat cavities at the Siberian site, performing an Ice Age version of a root canal. Previously, the oldest tooth that showed signs of a dental checkupt belonged to “Villabruna,” a prehistoric human male who lived in Italy 14,000 years ago.
The remnants of the Neanderthal tooth adds to a growing body of research that has overturned the stereotype of Neanderthals as cognitively inferior to Homo sapiens and hints at “cognitive convergence” between the two species, according to the study.
The Chagyrskaya Cave molar. Image: Zubova et al., 2026, PLOS One, CC-BY 4.0
The Chagyrskaya Cave tooth shows “evidence of two distinct types of manipulations requiring different tools, in addition to the drilling/rotating technique, necessitating complex finger movements,” said researchers led by Alisa Zubova from the Peter the Great Museum of Anthropology and Ethnography (Kunstkamera).
The study suggests that Neanderthals at this site “possessed the cognitive capacity to intuit the source of pain, comprehend the feasibility of its elimination, and deliberately select the most efficacious dental intervention,” the team added. “The technical proficiency required for this procedure…reflects a capacity for causal reasoning, anticipatory planning, and volitional endurance, contradicting earlier assumptions regarding Neanderthal behavioral limitations.”
It's not clear if this Neanderthal patient got a complimentary toothpick at the end of the visit, but at the very least, they received some temporary relief from a bad toothache.
Scientists have discovered a weird new type of crystal in the ashes of the Trinity test, the first detonation of a nuclear bomb, which took place in the early morning of July 16, 1945, in New Mexico.
Trinity’s “gadget” unleashed a powerful fireball that vaporized its test tower and transformed the desert sand into a glassy residue called trinitite. For decades, researchers have found novel and bizarre compounds in the fallout. A new study now reports the first known instance of a clathrate structure—a crystal lattice that can trap “guest” molecules inside its cagelike scaffolding—in red trinitite.
The sample of red trinitite that contained the clathrate. Image: Bindi, Luca et al.
“The discovery of this phase represents the first crystallographically confirmed identification of a clathrate structure among the solid-state products of a nuclear explosion,” said researchers led by Luca Bindi of the University of Florence.
“This work underscores how rare, high-energy events—such as nuclear detonations, lightning strikes, and hypervelocity impacts—serve as natural laboratories for producing unexpected crystalline matter,” the team added.
In addition to being one of the most pivotal split-seconds in history, the Trinity test spun sand into exotic materials that are still generating discoveries more than 80 years later.
Meet the largest dinosaur ever found in Southeast Asia: Nagatitan chaiyaphumensis, a hulking sauropod that lived more than 100 million years ago in what is now Thailand.
Weighing in at an estimated 60,000 pounds and measuring nearly 90 feet from head to tail-tip, this massive herbivore belonged to the titanosaur family, the largest animals ever to walk on land.
“We estimate a body mass of 25–28 tonnes for Nagatitan, and suggest it was part of a broader middle Cretaceous body size increase in Asian titanosauriforms, facilitated by rising temperatures and expanded suitable habitat,” said researchers co-led by Thitiwoot Sethapanichsakul of University College London and Sasa-On Khansubha of Sirindhorn Museum in Thailand.
“The discovery of Nagatitan expands the known diversity of Southeast Asian sauropods and improves our understanding of titanosauriform biogeography within the region,” the team added.
While it’s mind-boggling to imagine a 90-foot-long, 25-tonne animal casually ambling around, Nagatitan is only mid-sized for a titanosaur. The biggest behemoths in this family may have exceeded 120 feet in length and boasted 130,000 pounds of fully plant-powered body mass.
With that said, the all-time heavyweight champion of the animal kingdom is our own contemporary, the blue whale, which tips the scales at an astonishing 400,000 pounds. Have you ever felt so puny in your life?
The last titanosaurs were wiped out by the asteroid that brought the age of dinosaurs to a sudden and brutal end 66 million years ago, killing off about two-thirds of all species on Earth. But though the space rock eradicated the land giants, some animals managed to pull through, including a large fish that lived within 2,000 years of the impact.
Scientists led by Jacob Wilson of the Denver Museum of Nature & Science described the anatomy of a gar and weighed in on its possible taxonomy, building on the initial 2022 study that first reported the specimen. Measuring about five feet in length, this gar inhabited a post-apocalyptic world that is preserved within the Fort Union Formation of North Dakota.
The specimen “is notable both for its size (more than 1 meter) and its precise stratigraphic placement 18 centimeters above the Cretaceous/Paleogene (K/Pg) boundary clay,” the team said. “Our conclusions support the inference that gars were prominent members of freshwater ecosystems and, in turn, freshwater ecosystems were capable of supporting large-bodied predators within ∼2000 years after the K/Pg extinction.”
This gar hatched into an eerily empty ecosphere, mere centuries after a planetary nightmare, yet it still grew into a fisherman’s dream catch. It’s a testament to the resilience of life on Earth, which could not be fully stomped out even by a direct cosmic punch to the face.
The former Crown Prince of Iran is meeting with Iranian diaspora tech and business leaders on Saturday to discuss the future of the country. Attendees include the CEO of Uber.
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A who’s who of the Iranian diaspora will meet at Uber HQ on Saturday to discuss tech and the future of Iran, according to an email about the event viewed by 404 Media. The guest list includes venture capitalists, angel investors, tech CEOs, and the son of Iran’s former leader who was deposed almost 50 years ago.
On Friday afternoon, people representing the group of Iranian business leaders cold-emailed invitations for the event to journalists. “This Saturday, a private conference on the future of Iran will take place at Uber Headquarters in San Francisco, bringing together leaders in technology, finance, and geopolitics for an off-the-record discussion on Iran’s future and regional developments,” the email said. “Featured speakers include Reza Pahlavi, Dara Khosrowshahni, Shervin Pishevar, and Hamid Moghadam. The event waitlist has already surpassed 2,000 applicants.”
Khosrowshahi is the CEO of Uber; Moghadam is the CEO of San Francisco based investment trust Prologis; Pishevar is the former CEO of HyperLoop and an angel investor who put money into Uber, Airbnb, Slack, and Robinhood; and Pahlvani is the former Crown Prince of Iran, the son of the Shah deposed during Islamic Revolution in 1979. Also in attendance will be a SpaceX engineer, a Tesla engineer, and the senior global commodity manager at Nvidia, according to the invite.
It’s unclear what, exactly, these elite members of the Iranian diaspora will discuss on Saturday morning. The schedule calls for a 9:30 reception followed by 30 minutes for “strategic rebuild,” 30 minutes for “future tech,” and 30 minutes for “internet” followed by “open dialogue.”
The meeting is called the “Tech X Future of Iran” and the flyer with the guests and schedule included a pre-Islamic Republic version of the Iranian flag. Pahlavi is a complicated and controversial figure who has lived most of his life outside of Iran. He has said, repeatedly, that if he returned to lead he would only do so as a bridge to democratic rule.
“Millions of Iranians inside Iran and outside of Iran are calling my name,” he told 60 Minutes earlier this year. “They recognize in me the person uniquely placed to play a role of transitional leadership. Not running for office, because that's not what I'm doing, but to be a bridge to that destiny.”
But for Pahlavi to enter Iran or any of these tech moguls to see their ambitions fulfilled, a lot has to happen. Iran would have to lose the war and the Islamic Republic and its military would need to fall. Neither seem like a possibility at the moment.
The war isn’t over and it’s unclear when it will be. Iran is in control of the Strait of Hormuz and has been hitting US allies and military bases in the region. Reports from U.S. intelligence agencies indicate that Tehran still has 70 percent of its missile launchers and pre-war missile inventory meaning it can fight the US for months. It also still has all its nuclear material and recovering it without a peace deal would be a deadly and costly operation.
A representative for “Tech X Future of Iran” did not return 404 Media’s request for comment.
The former Crown Prince of Iran is meeting with Iranian diaspora tech and business leaders on Saturday to discuss the future of the country. Attendees include the CEO of Uber.
Show full content
A who’s who of the Iranian diaspora will meet at Uber HQ on Saturday to discuss tech and the future of Iran, according to an email about the event viewed by 404 Media. The guest list includes venture capitalists, angel investors, tech CEOs, and the son of Iran’s former leader who was deposed almost 50 years ago.
On Friday afternoon, people representing the group of Iranian business leaders cold-emailed invitations for the event to journalists. “This Saturday, a private conference on the future of Iran will take place at Uber Headquarters in San Francisco, bringing together leaders in technology, finance, and geopolitics for an off-the-record discussion on Iran’s future and regional developments,” the email said. “Featured speakers include Reza Pahlavi, Dara Khosrowshahni, Shervin Pishevar, and Hamid Moghadam. The event waitlist has already surpassed 2,000 applicants.”
Khosrowshahi is the CEO of Uber; Moghadam is the CEO of San Francisco based investment trust Prologis; Pishevar is the former CEO of HyperLoop and an angel investor who put money into Uber, Airbnb, Slack, and Robinhood; and Pahlvani is the former Crown Prince of Iran, the son of the Shah deposed during Islamic Revolution in 1979. Also in attendance will be a SpaceX engineer, a Tesla engineer, and the senior global commodity manager at Nvidia, according to the invite.
It’s unclear what, exactly, these elite members of the Iranian diaspora will discuss on Saturday morning. The schedule calls for a 9:30 reception followed by 30 minutes for “strategic rebuild,” 30 minutes for “future tech,” and 30 minutes for “internet” followed by “open dialogue.”
The meeting is called the “Tech X Future of Iran” and the flyer with the guests and schedule included a pre-Islamic Republic version of the Iranian flag. Pahlavi is a complicated and controversial figure who has lived most of his life outside of Iran. He has said, repeatedly, that if he returned to lead he would only do so as a bridge to democratic rule.
“Millions of Iranians inside Iran and outside of Iran are calling my name,” he told 60 Minutes earlier this year. “They recognize in me the person uniquely placed to play a role of transitional leadership. Not running for office, because that's not what I'm doing, but to be a bridge to that destiny.”
But for Pahlavi to enter Iran or any of these tech moguls to see their ambitions fulfilled, a lot has to happen. Iran would have to lose the war and the Islamic Republic and its military would need to fall. Neither seem like a possibility at the moment.
The war isn’t over and it’s unclear when it will be. Iran is in control of the Strait of Hormuz and has been hitting US allies and military bases in the region. Reports from U.S. intelligence agencies indicate that Tehran still has 70 percent of its missile launchers and pre-war missile inventory meaning it can fight the US for months. It also still has all its nuclear material and recovering it without a peace deal would be a deadly and costly operation.
A representative for “Tech X Future of Iran” did not return 404 Media’s request for comment.
The former Crown Prince of Iran is meeting with Iranian diaspora tech and business leaders on Saturday to discuss the future of the country. Attendees include the CEO of Uber.
Show full content
A who’s who of the Iranian diaspora will meet at Uber HQ on Saturday to discuss tech and the future of Iran, according to an email about the event viewed by 404 Media. The guest list includes venture capitalists, angel investors, tech CEOs, and the son of Iran’s former leader who was deposed almost 50 years ago.
On Friday afternoon, people representing the group of Iranian business leaders cold-emailed invitations for the event to journalists. “This Saturday, a private conference on the future of Iran will take place at Uber Headquarters in San Francisco, bringing together leaders in technology, finance, and geopolitics for an off-the-record discussion on Iran’s future and regional developments,” the email said. “Featured speakers include Reza Pahlavi, Dara Khosrowshahni, Shervin Pishevar, and Hamid Moghadam. The event waitlist has already surpassed 2,000 applicants.”
Khosrowshahi is the CEO of Uber; Moghadam is the CEO of San Francisco based investment trust Prologis; Pishevar is the former CEO of HyperLoop and an angel investor who put money into Uber, Airbnb, Slack, and Robinhood; and Pahlvani is the former Crown Prince of Iran, the son of the Shah deposed during Islamic Revolution in 1979. Also in attendance will be a SpaceX engineer, a Tesla engineer, and the senior global commodity manager at Nvidia, according to the invite.
It’s unclear what, exactly, these elite members of the Iranian diaspora will discuss on Saturday morning. The schedule calls for a 9:30 reception followed by 30 minutes for “strategic rebuild,” 30 minutes for “future tech,” and 30 minutes for “internet” followed by “open dialogue.”
The meeting is called the “Tech X Future of Iran” and the flyer with the guests and schedule included a pre-Islamic Republic version of the Iranian flag. Pahlavi is a complicated and controversial figure who has lived most of his life outside of Iran. He has said, repeatedly, that if he returned to lead he would only do so as a bridge to democratic rule.
“Millions of Iranians inside Iran and outside of Iran are calling my name,” he told 60 Minutes earlier this year. “They recognize in me the person uniquely placed to play a role of transitional leadership. Not running for office, because that's not what I'm doing, but to be a bridge to that destiny.”
But for Pahlavi to enter Iran or any of these tech moguls to see their ambitions fulfilled, a lot has to happen. Iran would have to lose the war and the Islamic Republic and its military would need to fall. Neither seem like a possibility at the moment.
The war isn’t over and it’s unclear when it will be. Iran is in control of the Strait of Hormuz and has been hitting US allies and military bases in the region. Reports from U.S. intelligence agencies indicate that Tehran still has 70 percent of its missile launchers and pre-war missile inventory meaning it can fight the US for months. It also still has all its nuclear material and recovering it without a peace deal would be a deadly and costly operation.
A representative for “Tech X Future of Iran” did not return 404 Media’s request for comment.
The former Crown Prince of Iran is meeting with Iranian diaspora tech and business leaders on Saturday to discuss the future of the country. Attendees include the CEO of Uber.
Show full content
A who’s who of the Iranian diaspora will meet at Uber HQ on Saturday to discuss tech and the future of Iran, according to an email about the event viewed by 404 Media. The guest list includes venture capitalists, angel investors, tech CEOs, and the son of Iran’s former leader who was deposed almost 50 years ago.
On Friday afternoon, people representing the group of Iranian business leaders cold-emailed invitations for the event to journalists. “This Saturday, a private conference on the future of Iran will take place at Uber Headquarters in San Francisco, bringing together leaders in technology, finance, and geopolitics for an off-the-record discussion on Iran’s future and regional developments,” the email said. “Featured speakers include Reza Pahlavi, Dara Khosrowshahni, Shervin Pishevar, and Hamid Moghadam. The event waitlist has already surpassed 2,000 applicants.”
Khosrowshahi is the CEO of Uber; Moghadam is the CEO of San Francisco based investment trust Prologis; Pishevar is the former CEO of HyperLoop and an angel investor who put money into Uber, Airbnb, Slack, and Robinhood; and Pahlvani is the former Crown Prince of Iran, the son of the Shah deposed during Islamic Revolution in 1979. Also in attendance will be a SpaceX engineer, a Tesla engineer, and the senior global commodity manager at Nvidia, according to the invite.
It’s unclear what, exactly, these elite members of the Iranian diaspora will discuss on Saturday morning. The schedule calls for a 9:30 reception followed by 30 minutes for “strategic rebuild,” 30 minutes for “future tech,” and 30 minutes for “internet” followed by “open dialogue.”
The meeting is called the “Tech X Future of Iran” and the flyer with the guests and schedule included a pre-Islamic Republic version of the Iranian flag. Pahlavi is a complicated and controversial figure who has lived most of his life outside of Iran. He has said, repeatedly, that if he returned to lead he would only do so as a bridge to democratic rule.
“Millions of Iranians inside Iran and outside of Iran are calling my name,” he told 60 Minutes earlier this year. “They recognize in me the person uniquely placed to play a role of transitional leadership. Not running for office, because that's not what I'm doing, but to be a bridge to that destiny.”
But for Pahlavi to enter Iran or any of these tech moguls to see their ambitions fulfilled, a lot has to happen. Iran would have to lose the war and the Islamic Republic and its military would need to fall. Neither seem like a possibility at the moment.
The war isn’t over and it’s unclear when it will be. Iran is in control of the Strait of Hormuz and has been hitting US allies and military bases in the region. Reports from U.S. intelligence agencies indicate that Tehran still has 70 percent of its missile launchers and pre-war missile inventory meaning it can fight the US for months. It also still has all its nuclear material and recovering it without a peace deal would be a deadly and costly operation.
A representative for “Tech X Future of Iran” did not return 404 Media’s request for comment.
The change comes as arXiv and others struggle to manage an influx of AI-generated materials masquerading as rigorous science.
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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.
The change comes as arXiv and others struggle to manage an influx of AI-generated materials masquerading as rigorous science.
Show full content
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.
The change comes as arXiv and others struggle to manage an influx of AI-generated materials masquerading as rigorous science.
Show full content
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.
The change comes as arXiv and others struggle to manage an influx of AI-generated materials masquerading as rigorous science.
Show full content
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.
This week, we discuss developers' AI woes, how the magic happens, and the Beach Boys.
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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.
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.
This week, we discuss developers' AI woes, how the magic happens, and the Beach Boys.
Show full content
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.
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.
This week, we discuss developers' AI woes, how the magic happens, and the Beach Boys.
Show full content
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.
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.
This week, we discuss developers' AI woes, how the magic happens, and the Beach Boys.
Show full content
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.
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.
Mayo Clinic's "Ambient Listening" has been around for a couple of years, but clearly not all patients know their interactions with nurses are being passively recorded and processed by AI.
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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.
💡Do you know anything else about AI use in healthcare? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
Mayo Clinic's "Ambient Listening" has been around for a couple of years, but clearly not all patients know their interactions with nurses are being passively recorded and processed by AI.
Show full content
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.
💡Do you know anything else about AI use in healthcare? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
Mayo Clinic's "Ambient Listening" has been around for a couple of years, but clearly not all patients know their interactions with nurses are being passively recorded and processed by AI.
Show full content
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.
💡Do you know anything else about AI use in healthcare? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
Mayo Clinic's "Ambient Listening" has been around for a couple of years, but clearly not all patients know their interactions with nurses are being passively recorded and processed by AI.
Show full content
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.
💡Do you know anything else about AI use in healthcare? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
The dismantling of the United States Agency for International Development (USAID) is associated with measurable increases in Africa, especially in areas most dependent on the agency’s support.
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The sudden shuttering of the United States Agency for International Development (USAID) by DOGE in 2025 is associated with a rise in violent conflicts across Africa, according to a study published on Thursday in Science.
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:
“The probability of protests and riots was 10% greater, the number of conflict events increased by 10.6%, battle counts increased by 6.9%, and battle-related fatalities increased by 9.3%. Event-study analysis confirmed no preexisting differences in conflict trends between high- and low-exposure regions before the shutdown. Effects are of similar size, with a 12.3% relative increase in the number of conflict events.“
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.”
🌘Subscribe to 404 Media to get The Abstract, our newsletter about the most exciting and mind-boggling science news and studies of the week.
The dismantling of the United States Agency for International Development (USAID) is associated with measurable increases in Africa, especially in areas most dependent on the agency’s support.
Show full content
🌘Subscribe to 404 Media to get The Abstract, our newsletter about the most exciting and mind-boggling science news and studies of the week.
The sudden shuttering of the United States Agency for International Development (USAID) by DOGE in 2025 is associated with a rise in violent conflicts across Africa, according to a study published on Thursday in Science.
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:
“The probability of protests and riots was 10% greater, the number of conflict events increased by 10.6%, battle counts increased by 6.9%, and battle-related fatalities increased by 9.3%. Event-study analysis confirmed no preexisting differences in conflict trends between high- and low-exposure regions before the shutdown. Effects are of similar size, with a 12.3% relative increase in the number of conflict events.“
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.”
🌘Subscribe to 404 Media to get The Abstract, our newsletter about the most exciting and mind-boggling science news and studies of the week.
The dismantling of the United States Agency for International Development (USAID) is associated with measurable increases in Africa, especially in areas most dependent on the agency’s support.
Show full content
🌘Subscribe to 404 Media to get The Abstract, our newsletter about the most exciting and mind-boggling science news and studies of the week.
The sudden shuttering of the United States Agency for International Development (USAID) by DOGE in 2025 is associated with a rise in violent conflicts across Africa, according to a study published on Thursday in Science.
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:
“The probability of protests and riots was 10% greater, the number of conflict events increased by 10.6%, battle counts increased by 6.9%, and battle-related fatalities increased by 9.3%. Event-study analysis confirmed no preexisting differences in conflict trends between high- and low-exposure regions before the shutdown. Effects are of similar size, with a 12.3% relative increase in the number of conflict events.“
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.”
🌘Subscribe to 404 Media to get The Abstract, our newsletter about the most exciting and mind-boggling science news and studies of the week.
The dismantling of the United States Agency for International Development (USAID) is associated with measurable increases in Africa, especially in areas most dependent on the agency’s support.
Show full content
🌘Subscribe to 404 Media to get The Abstract, our newsletter about the most exciting and mind-boggling science news and studies of the week.
The sudden shuttering of the United States Agency for International Development (USAID) by DOGE in 2025 is associated with a rise in violent conflicts across Africa, according to a study published on Thursday in Science.
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:
“The probability of protests and riots was 10% greater, the number of conflict events increased by 10.6%, battle counts increased by 6.9%, and battle-related fatalities increased by 9.3%. Event-study analysis confirmed no preexisting differences in conflict trends between high- and low-exposure regions before the shutdown. Effects are of similar size, with a 12.3% relative increase in the number of conflict events.“
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.”
🌘Subscribe to 404 Media to get The Abstract, our newsletter about the most exciting and mind-boggling science news and studies of the week.
"I hoarded a large database of something valuable, just not what you expect… 150k stools images."
Show full content
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:
• 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.
"I hoarded a large database of something valuable, just not what you expect… 150k stools images."
Show full content
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:
• 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.
"I hoarded a large database of something valuable, just not what you expect… 150k stools images."
Show full content
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:
• 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.
"I hoarded a large database of something valuable, just not what you expect… 150k stools images."
Show full content
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:
• 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.
Jeff Bezos learns being good at YouTube is not so easy.
Show full content
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.
Jeff Bezos learns being good at YouTube is not so easy.
Show full content
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.
Jeff Bezos learns being good at YouTube is not so easy.
Show full content
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.
Jeff Bezos learns being good at YouTube is not so easy.
Show full content
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.
Spools of cable are critical for internet infrastructure and jam-proof drones but skyrocketing costs are making it hard to field them.
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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.
Spools of cable are critical for internet infrastructure and jam-proof drones but skyrocketing costs are making it hard to field them.
Show full content
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.
Spools of cable are critical for internet infrastructure and jam-proof drones but skyrocketing costs are making it hard to field them.
Show full content
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.
Spools of cable are critical for internet infrastructure and jam-proof drones but skyrocketing costs are making it hard to field them.
Show full content
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 got Haotian AI, the Chinese-language deepfake software powering scams. We also talk about a man finding $1 million of Yu-Gi-Oh cards, and how the AI hard drive shortage is impacting internet archiving.
Show full content
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.
We got Haotian AI, the Chinese-language deepfake software powering scams. We also talk about a man finding $1 million of Yu-Gi-Oh cards, and how the AI hard drive shortage is impacting internet archiving.
Show full content
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.
We got Haotian AI, the Chinese-language deepfake software powering scams. We also talk about a man finding $1 million of Yu-Gi-Oh cards, and how the AI hard drive shortage is impacting internet archiving.
Show full content
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.
We got Haotian AI, the Chinese-language deepfake software powering scams. We also talk about a man finding $1 million of Yu-Gi-Oh cards, and how the AI hard drive shortage is impacting internet archiving.
Show full content
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.
Tech company executives are confident that AI will completely transform the economy and point to the changes they see in-house to prove that this change is coming fast. At Meta, Google, Microsoft, and others, leadership says that AI generates a growing share of the overall code, which makes it cheaper and faster to produce. The implication is that if this AI is good enough that tech companies are using it internally to improve efficiency and reduce headcount, it’s only a matter of time until every other industry is similarly transformed.
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.
💡Are you a developer at Google, Microsoft, or another tech being pressured to use AI? I would love to hear from you. Using a non-work device, you can message me securely on Signal at (609) 678-3204. Otherwise, send me an email at emanuel@404media.co.
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).
Tech company executives are confident that AI will completely transform the economy and point to the changes they see in-house to prove that this change is coming fast. At Meta, Google, Microsoft, and others, leadership says that AI generates a growing share of the overall code, which makes it cheaper and faster to produce. The implication is that if this AI is good enough that tech companies are using it internally to improve efficiency and reduce headcount, it’s only a matter of time until every other industry is similarly transformed.
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.
💡Are you a developer at Google, Microsoft, or another tech being pressured to use AI? I would love to hear from you. Using a non-work device, you can message me securely on Signal at (609) 678-3204. Otherwise, send me an email at emanuel@404media.co.
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).
Tech company executives are confident that AI will completely transform the economy and point to the changes they see in-house to prove that this change is coming fast. At Meta, Google, Microsoft, and others, leadership says that AI generates a growing share of the overall code, which makes it cheaper and faster to produce. The implication is that if this AI is good enough that tech companies are using it internally to improve efficiency and reduce headcount, it’s only a matter of time until every other industry is similarly transformed.
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.
💡Are you a developer at Google, Microsoft, or another tech being pressured to use AI? I would love to hear from you. Using a non-work device, you can message me securely on Signal at (609) 678-3204. Otherwise, send me an email at emanuel@404media.co.
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).
Tech company executives are confident that AI will completely transform the economy and point to the changes they see in-house to prove that this change is coming fast. At Meta, Google, Microsoft, and others, leadership says that AI generates a growing share of the overall code, which makes it cheaper and faster to produce. The implication is that if this AI is good enough that tech companies are using it internally to improve efficiency and reduce headcount, it’s only a matter of time until every other industry is similarly transformed.
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.
💡Are you a developer at Google, Microsoft, or another tech being pressured to use AI? I would love to hear from you. Using a non-work device, you can message me securely on Signal at (609) 678-3204. Otherwise, send me an email at emanuel@404media.co.
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 comments made by a senior ICE official at a trade show highlight how Palantir is increasing the speed at which ICE operates. Most people detained by ICE have no criminal conviction.
Show full content
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.
💡Do you work for Palantir or ICE? Did you used to? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
The comments made by a senior ICE official at a trade show highlight how Palantir is increasing the speed at which ICE operates. Most people detained by ICE have no criminal conviction.
Show full content
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.
💡Do you work for Palantir or ICE? Did you used to? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
The comments made by a senior ICE official at a trade show highlight how Palantir is increasing the speed at which ICE operates. Most people detained by ICE have no criminal conviction.
Show full content
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.
💡Do you work for Palantir or ICE? Did you used to? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
The comments made by a senior ICE official at a trade show highlight how Palantir is increasing the speed at which ICE operates. Most people detained by ICE have no criminal conviction.
Show full content
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.
💡Do you work for Palantir or ICE? Did you used to? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.
The logic behind Polymarket, Kalshi and sports betting apps can be traced back to the inner workings of the slot machine.
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How did we get to a point where it’s legal for anyone to bet on anything? Be it the results of a baseball game or a land war in Europe, if you have access to a credit card and a computer you can try to predict the outcome of anything that’s happening in the world and win a little bit of money if you’re right. If we know that gambling can lead to high rates gambling addiction and financial ruin, why does it seem like our culture has suddenly embraced it?
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.
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.
The logic behind Polymarket, Kalshi and sports betting apps can be traced back to the inner workings of the slot machine.
Show full content
How did we get to a point where it’s legal for anyone to bet on anything? Be it the results of a baseball game or a land war in Europe, if you have access to a credit card and a computer you can try to predict the outcome of anything that’s happening in the world and win a little bit of money if you’re right. If we know that gambling can lead to high rates gambling addiction and financial ruin, why does it seem like our culture has suddenly embraced it?
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.
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.
The logic behind Polymarket, Kalshi and sports betting apps can be traced back to the inner workings of the slot machine.
Show full content
How did we get to a point where it’s legal for anyone to bet on anything? Be it the results of a baseball game or a land war in Europe, if you have access to a credit card and a computer you can try to predict the outcome of anything that’s happening in the world and win a little bit of money if you’re right. If we know that gambling can lead to high rates gambling addiction and financial ruin, why does it seem like our culture has suddenly embraced it?
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.
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.
The logic behind Polymarket, Kalshi and sports betting apps can be traced back to the inner workings of the slot machine.
Show full content
How did we get to a point where it’s legal for anyone to bet on anything? Be it the results of a baseball game or a land war in Europe, if you have access to a credit card and a computer you can try to predict the outcome of anything that’s happening in the world and win a little bit of money if you’re right. If we know that gambling can lead to high rates gambling addiction and financial ruin, why does it seem like our culture has suddenly embraced it?
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.
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.
AI writing is impossible to avoid, is making everything sound the same, and is driving us crazy.
Show full content
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.”
AI writing is impossible to avoid, is making everything sound the same, and is driving us crazy.
Show full content
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.”
AI writing is impossible to avoid, is making everything sound the same, and is driving us crazy.
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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.”
AI writing is impossible to avoid, is making everything sound the same, and is driving us crazy.
Show full content
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.”
A commencement speaker at the University of Central Florida was booed, with graduating humanities students yelling out, "AI SUCKS!"
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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!”
A commencement speaker at the University of Central Florida was booed, with graduating humanities students yelling out, "AI SUCKS!"
Show full content
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!”
A commencement speaker at the University of Central Florida was booed, with graduating humanities students yelling out, "AI SUCKS!"
Show full content
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!”
A commencement speaker at the University of Central Florida was booed, with graduating humanities students yelling out, "AI SUCKS!"
Show full content
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!”
