The Disappearing DOGE Depositions

18 Mar 2026 · 47 min · 12 chapters

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In short

The 404 Media Podcast Episode Summary: The Disappearing DOGE Depositions

Podcast Overview

  • Title: The 404 Media Podcast
  • Hosts: Joseph, Sam, Emanuel, Jason
  • Description: A journalist-owned digital media company discussing how technology shapes our world through investigative journalism and unique storytelling.

Episode Details

  • Episode Title: The Disappearing DOGE Depositions
  • Episode Description: Focuses on the DOGE depositions, their subsequent removal from YouTube, and the ongoing struggles of AI data labelers.

Table of Contents

  1. [Introduction](#introduction)
  2. [DOGE Deposition Overview](#doge-deposition-overview)
  3. [Joseph's Insights](#josephs-insights)
  4. [Judge's Removal Order](#judges-removal-order)
  5. [Public Reaction](#public-reaction)
  6. [AI Data Labelers' Fight](#ai-data-labelers-fight)
  7. [Subscribers-Only Section](#subscribers-only-section)
  8. [Key Takeaways](#key-takeaways)
  9. [Conclusion](#conclusion)

Introduction

  • The episode begins with a brief introduction, noting the need for support for 404 Media through subscriptions.
  • Joseph, Emanuel, and Jason discuss recent stories, emphasizing the importance of audience engagement and feedback.

DOGE Deposition Overview

Joseph's Insights

  • Joseph watched six hours of depositions from DOGE members, particularly Justin Fox and Nate Kavanagh.
  • The depositions relate to their role in influencing government grants through a campaign.
  • Organizations involved in the lawsuit include the Modern Language Association, the American Council of Learned Societies, and the American Historical Association.

Judge's Removal Order

  • Following the depositions' virality, a judge ordered their removal from YouTube due to potential reputational harm and alleged threats against Fox.
  • The ruling raised concerns about First Amendment rights and public interest in the case.

Public Reaction

  • The removal of the videos led to significant backlash, showcasing the absurdity of protecting public officials from scrutiny due to public backlash.
  • Despite the order, the videos were quickly archived and preserved across various platforms, illustrating the Streisand Effect.

AI Data Labelers' Fight

  • Jason discusses the plight of AI data labelers, particularly in Kenya.
  • He highlights the formation of the Data Labelers Association, emphasizing the need for better working conditions and rights.
  • Data labelers perform tedious tasks, often underpaid and without mental health support, facing significant challenges in the tech industry.

Subscribers-Only Section

  • Jason shares insights on the misconceptions surrounding AI job losses and the real impacts of AI on employment, drawing from specific research findings.

Key Takeaways

  • Transparency: The DOGE depositions underline the importance of transparency and accountability in government actions.
  • Public Discourse: The public's reaction to the removal of the deposition videos emphasizes the critical nature of open discussion regarding public figures.
  • Labor Rights: The struggles of AI data labelers highlight the need for labor rights in tech, pushing for collective action and legal support.
  • AI's Real Impact: The discussion about AI job loss research suggests the need for more nuanced understanding of the impact of AI in various sectors.

Conclusion

  • The episode encapsulates significant discussions around government accountability and labor rights in the tech industry, urging listeners to reflect on the broader implications of technology in society.

Written by AI. May contain mistakes. Listen to the episode to check what was said.

Chapters

Tap a time to open that second in VO

Introduction to Doge Depositions

3:21 to 6:13

Discussion on the depositions of Doge members and their implications.

“Yeah, I think we actually have a flurry of stories that happened mostly over the weekend for reasons which we'll get into.”

Key Moments from the Depositions

6:13 to 12:39

Highlighting the evasive responses and controversial statements made during depositions.

“Yeah, it's mostly the clips that listeners or readers may have already seen.”

Videos Taken Down and Reactions

12:39 to 14:01

Discussion on the aftermath of the depositions, including video removals and public reactions.

“The reason I watched six hours was because I was on the plane a lot.”

Government's Viral Video Controversy

14:01 to 16:30

Discuss the government's response to viral videos and First Amendment implications.

“Do that article, as you say, on, I believe, Friday night.”

Judicial Intervention and Public Interest

16:31 to 18:46

Exploration of the judge's decision to remove videos and its implications.

“I mean, I think that occasionally judges will seal things if someone's safety is at risk, like sometimes.”

The Rise of Backups in the Digital Age

18:47 to 23:22

How removed videos were quickly backed up and the effectiveness of torrents.

“and then that immediately leads us to another headline here.”

AI's Impact on Kenyan Data Labelers

28:27 to 36:35

Discussion on the challenges faced by data labelers in Kenya and their fight for better rights.

“The headline is AI is African Intelligence.”

The Data Labelers Association's Fight

36:35 to 38:14

Explaining the formation and goals of the Data Labelers Association in Kenya.

“And so he and some colleagues formed the Data Labelers Association, which is...”

Challenges in Fighting for Worker Rights

38:14 to 40:03

Exploration of the obstacles faced by data labelers in advocating for their rights amidst corporate power.

“as possible to say, I support collective action.”

Content Moderation and AI Job Overlap

40:03 to 42:01

Discussion on the intersection of content moderation and AI jobs, including ethical concerns.

“You mentioned the sort of similarity to content moderation.”
Show all 12 chapters

The Impact of AI on Writing Styles

42:01 to 44:10

Explore how AI's training on diverse language styles influences perceptions of authenticity in writing.

“A lot of them do content moderation for chat GPT.”

Labor and Fairness in AI Development

44:11 to 44:58

Discuss the labor dynamics behind AI development and the associated inequalities faced by workers.

“I thought that was a really powerful quote from Michael where he was like, and everyone knows this, they should know this, but it's like, AI is not magic.”
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Transcript

Automatic transcript. May contain errors.

0:03Hello, and welcome to the 404 Media podcast, where we bring you unparalleled access to hidden worlds both online and IRL. 404 Media is a German-standard company and needs your support. To subscribe, go to 404media.co. As well as bonus content every single week, subscribers also get access to additional episodes where we respond to their best comments. Getting access to that content at 404media.co. I'm your host, Joseph. And with me are two of the other 404 Media co-founders. The first being Emmanuel Mayberg. Hello. And then Jason Kebler. Hello, what's up? All right. I'll give a quick shout out to Jason's interview podcast episode that just went up.

0:53People should definitely check that out if they haven't already. Jason, do you just want to give us the very brief overview of what it's about? Because it's pretty crazy what this guy did and why he did it. Yeah, yeah. It's a YouTube filmmaker who mapped the only unmapped city in America on Google Maps. As in, there's like one place called North Oaks, Minnesota that's not on Street View. And it's not on Street View for very specific and weird reasons that we get into in the podcast. and basically he took a drone and flew it around town and encountered some issues. But I found it to be super interesting.

1:36I had no idea that this existed. I just actually... Honestly, he emailed me because he is making an episode about Flock. And so he interviewed me for that and I was checking out his channel and I was like, oh, this is nuts. I had no idea that this was happening. So we did a bit of a trade Z's where I'm going on his YouTube channel and he's on ours. But yeah, please check it out.

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3:20All right, Emmanuel, do you want to take us through this first story? Yeah, I think we actually have a flurry of stories that happened mostly over the weekend for reasons which we'll get into. But the first one of them is from Joe. And the headline is, I watched six hours of Doge Bro testimony. Here's what they had to say for themselves. Joe, what are these videos? Where did you find them? So these are videos of depositions from two members of Doge, Justin Fox, I believe, and Nate Kavanagh. And they were part of a sort of Doge operation, campaign, mission, however you want to describe it, to cut a ton of government grants in NEH.

4:16And they went about and they did this and they were very instrumental in cutting hundreds of millions of dollars worth of grants. And because of that, they are being sued. And I just want to get the organizations correct. They're being sued by the Modern Language Association, the American Council of Learned Societies and the American Historical Association. They are suing NEH and a number of other parties, including these two Doge members. So these video depositions were recorded as part of that and then also just put on YouTube, which I feel is somewhat rare. You know, like we have the, was it the Bill Clinton, Epstein one recently?

5:01I have never really seen depositions before. Maybe that's why I was so captivated. And I watched six hours of Fox's depositions specifically. But very interesting or horrifying videos. And I spent a lot of time going through them, as the headline suggests. I've seen some depositions. I'm sure Jason has as well. Bill Gates had a famous one about the Monopoly trial, I think it was. And of course, Epstein has been in the news a lot. I've seen some of that. Uh, also, I just want to, it's like, it feels like ancient history, but not that long ago, we were writing all about these young doge guys trying to make cuts to the government.

5:47And this is going to be very interesting for many reasons, but it's the first time we really hear from them directly a lot for six hours, as you say, and not in a stage settings. I think Trump had some public meetings with them that were filmed, but this is in-depth questioning about what they were doing. So what are some of the moments that you pulled out of there in your six hours of viewing? Yeah, it's mostly the clips that listeners or readers may have already seen. because the way I saw it was that somebody on Blue Sky posted a link to the YouTube channel of one of these organizations that uploaded the videos.

6:31And then I saw a quick little clip where Fox was unable or unwilling to define DEI. He was asked repeatedly, how do you understand DEI? The reason being is that that was the reason or the justification given for severing a ton of these grants, right? And he kept pointing back to the executive order, which said, you know, we're trying to get DEI out of the government or whatever. But even when the lawyer, the attorney pushed him to be like, yes, but what's your understanding of DEI? He wouldn't do it. I don't want to say he couldn't do it. It's more unable or unwilling is sort of the term I'm using, but he's incredibly evasive, obviously.

7:15I'm sure we'll play a little bit so people can hear it. How do you interpret DEI?

7:28There was the EO explicitly laid out. The details, I don't remember it off the top of my head. I'm asking for your understanding of it. Yeah, my understanding was exactly what was written in the EO. I don't remember what was in the EO. So right now, do you have an understanding of what DEI is? Yeah. Okay, so what's your understanding as you sit here today in this deposition? Well, it was exactly what was written in the EO. And so anytime that we would look at a grant through the lens of complying with an executive order, we would just refer back to the EO and assess if this grant had relation to it.

8:00Okay, but I guess stepping back from your methodology strictly in terminating the grants, do you have an understanding as you sit here today of what the EO means? Yeah. Okay, so what's your understanding of what it means? Well, it is exactly what was written in the EO. Okay, so why is the documentary about Holocaust survivors DEI? Faction. It's the gender-based story that's inherently discriminatory to focus on this specific group. It's inherently discriminatory to focus on what specific group? the gender-based. So females

8:45during the Holocaust. And then the other parts are... I mean, he gets into very specific examples. There was one about a documentary, a grant for a documentary about black civil rights. And Fox says something to the effect of, well, we cut this grant because it wasn't for the benefit of humankind, which is obviously an absolutely insane thing to say he does walk that back and um they actually read back to him live in the deposition they read what he just said he's like well that's not what i meant blah blah but he did say it um and then there was another example of cutting funding for a holocaust documentary called my underground mother so it was really even though it was six hours of footage and of depositions it was kind of the same examples over and over and over and over again as you might imagine because he just wasn't answering the questions and I know that people may think well what's the value of it you know he's being evasive but as you said this is sort of the first time or one of the first times we've seen or heard them speak for themselves.

9:57And even though they're obviously being coached, there is a DOJ lawyer right next to these people that I don't know if you'd say representing technically, but definitely assisting them in this deposition. So of course, the answers are going to be coached. They are going to be massaged. But even then, I still think this is incredibly illuminating. But those are the examples that jumped out. beyond that it was just the repeating those over and over again essentially but very interesting stuff i think whoever the lawyer is who was asking him questions actually did a pretty good job because to this definition of dei it seems to me like the entire point of that line of questioning was to show that it's a ridiculous category.

10:46And what Fox repeatedly says is that his definition of DEI is whatever the executive order from Trump states. And that's clearly coaching, as you said, because I think that kind of makes it legal for him because he's like, I was just following the executive order and I'm following that to the letter and that's it. But then when he's asked to define DEI, I think it essentially, it's like, this is me interpreting a little bit, but it's like any activity that highlights any particular group. And then obviously it becomes ridiculous because it's like, if there's any initiative for women, if there's any documentary that focuses on women, it's like a certain minority.

11:28It's like, that is the EI and it's impossible to do anything. and he acknowledged that when they used ChatGPT they were searching for black, homosexual, LGBTQ +, but they didn't search for white or Caucasian and he does acknowledge that and he actually says we well could have done that yeah, but you didn't so that's the difference there but yeah, you're right the line of questioning was just so persistent I mean, again, it's over six hours and doing it in different ways where it just showed how sort of ridiculous their position was. Yeah, and our previous coverage showed that it was ridiculous in practice because you would just have these total blanket activities or actions where it essentially looked like they did word searches on studies and if it included the name of any minority or DEI or gender, it just got removed.

12:27And you can look back at all that coverage if you want to see how silly this was in practice. Okay, so you published a story. I think it was Friday, right? Yeah, I believe so. Because for context, I watched... The reason I watched six hours was because I was on the plane a lot. So I had a lot of time to watch these and at the gym. And then when I got back on the Friday, yeah. Yeah, so perfect flight activity. You land, you publish the post on Friday. And then I think because the... I think, well, actually, we also published a video that... Several, yeah. Several videos that kind of highlighted some of the things that we're talking about.

13:03And all this stuff went extremely viral, both your posts and the videos. And I think it's when a lot of people first learned that this was even happening and seeing what this guy was saying. And then that led us to a couple... It got so much attention that some stuff happened because of it. And so this leads us to your next headline, which is... Doge deposition videos taken down after judge order and widespread mockery. So what happened? Yeah, you're right in that we were one of the first outlets to clip the depositions and then posted those. And then it was very funny seeing the right wing. It was mostly right wing.

13:45I think there was other political leanings as well. But basically, X.com grifter accounts lifting our clip. It's like, that's the 404 Media font. That's 100%. There's nothing to beef about. It was just funny and just funny to see how that ecosystem works. But we clip those. They go viral. Do that article, as you say, on, I believe, Friday night. So very soon after we publish, the government then issues a filing into this lawsuit. And they say or ask the judge, we need you to intervene and get the other party to stop the spread of these videos to get them taken down because these may cause um harassment and reputational harm that is the argument from the government that seeing those people saying things with their own words is going to cause reputational damage um i'll leave it to others to decide well maybe that's a consequence of saying things um then further on in the filing they do get a bit more concrete or rather just before that it does actually cite our article specifically the i watched for six hours and our videos and whatever there was a huffington post video as well the government then later on in the filing does say fox specifically he was the focus of much of the videos he has allegedly faced death threats because this stuff went massively viral um i'm not necessarily doubting that i'm sure that people made those threats i would say that we haven't seen what those threats are or how concrete they were but that is the government's argument in this in this lawsuit so they do that filing um the judge i believe looks like they're going to agree and there's then an emergency filing from you know the other parties these language associations and organizations bringing the case and they're like well look actually there's a massive first amendment issue here these videos should be public um they were never under a protective order so you shouldn't order their removal the judge disagreed and late on friday night ordered that they be they be removed and then sure enough you go to the organization's youtube channel and the hours and hours of video spread across maybe not dozens but at least a dozen videos they've gone completely they've been wiped so a pretty wild series of events to go from something that's massively massively viral like it's not just us covering it it's basically everyone to a judge saying you must remove those from YouTube.

16:32Can we discuss how crazy this is? I mean, I think that occasionally judges will seal things if someone's safety is at risk, like sometimes. But I feel like the bar for that is usually pretty high. At least I'm not a lawyer, but that's like, as I understand it, that's pretty high. It's like we see court records all the time that have incredibly sensitive information on there, like details about people getting harassed, like addresses, phone numbers. Sometimes these things are redacted, but quite often they're not. And those are not filed under seal for the most part. And here we have a situation where these government employees in a highly publicized case of great public interest dealing with millions and millions, billions of dollars, I guess, probably of government grants that have led to people dying all over the world and in the United States because of the types of things that they did.

17:35And we are protecting them because people are being mean to them online. It's very wild. Maybe I missed if you said it, but also public servants. It's like, this is about stuff that they did for the government. It's not as if it's a private company or it's like a family matter or this is taxpayer funded activities. Yeah, and I don't know if it's one of those things where it's just like the judge ordered it taken down while they deliberate whether to put it back up as sort of an emergency measure, but regardless, this is very... I think it's crazy. I mean, I think that most people do considering how viral it went and all that sort of thing, but it is not normal, I guess.

18:27This is not supposed to happen, I don't think. Yeah. Incredibly unusual, I would say. The only information in there that is damaging to them is the fact that people really don't like what they did. It's like they're not talking about their home address or anything like that. Okay, so that happens. and then that immediately leads us to another headline here. The removed Doge deposition videos have already been backed up across the internet. Very predictable. So where do these videos live now? Yeah, so this was on Saturday, the day after the judge ordered the removal from YouTube and then seemingly the organizations went along with that because obviously it's a legal order.

19:25And then Jason was actually keeping an eye on the Data Hoarders subreddit, which is a very fascinating place. And there's always very interesting people doing very interesting things there. And I think when Jason flagged it, it was more people discussing, backing it up. And maybe that was Friday night. I can't quite remember. But come Saturday, somebody sent me a link to the Internet Archive. And on Saturday, someone had uploaded all of the videos. And I went through them and double-checked, like, yes, there's the Fox one. Yes, there's the Kavanaugh one. It was actually two depositions from another two NEH officials as well who were a bit more senior and sort of managing what these Doge people were doing as well.

20:11So their depositions were up there as well. so internet archive obviously a very useful resource for preserving this sort of thing the last thing i'll say just before i throw it to jason to talk about the torrent as well i think crucially the judge's order it wasn't like an order against youtube it wasn't an order against uh like a platform it was an order against the specific organizations in this lawsuit to be like you have to take steps to claw back these videos and obviously the most obvious way they would do that is they would remove it from their own youtube channel but i don't think they have really any power to remove you know our our instagram posts or really the internet archive stuff and i don't even know or i don't really expect them to be expected to then go do that as well like i don't really i'm not familiar with this judge or sort of their understanding of the internet but that it's just not how it works stuff is stuff is going to be out there and people very quickly archived it um while i was writing this jason you were editing it and you pointed to the to the torrent right yeah and so um i had seen not just that people were talking about backing it up.

21:36But there was someone on there who was like, I have the files and I'm going to torrent. I'm going to make a torrent. But I don't know how. So he was trying to learn how to do it. But yeah, it's the Streisand effect, which I think is real. I think there's been some studies that the Streisand effect is actually overstated to some degree that there's an initial spike in interest in people finding something when the government deletes it or when a company deletes it, but that in the long term, it becomes harder to find. But I think that in this case, because it's torrented, now it's censorship proof.

22:19It's decentralized. It cannot be deleted. I mean, I think it's still up on the internet archive and I hope that it stays up on the internet archive, but that's still a centralized place. Whereas this torrent, now there's tons of different seeders and it's like, torrents are undefeated in that way. It will live forever somewhere. I don't know. I downloaded copies of it. I'm not deleting them. We haven't uploaded them, but it's like they're useful to have if we report on it in the future, that sort of thing. So yeah, it's good to have multiple backups, I guess. yeah I mean on Saturday I was thinking before obviously it was clear they were already on the internet archive I was thinking like if we get these videos do we upload them to our site or something and you know kind of like what we did with some of the Epstein documents when Jason bought them off Pacer and then we just uploaded them so other people could access them I was thinking do we do the same here but we didn't need to frankly because other people had already archived them alright Right.

23:22Thank you for asking me those questions, Emmanuel. We'll leave that there. When we come back after the break, we're going to talk about Jason's story about African intelligence. A very, very interesting trip. I'll say that. We'll be right back after this.

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28:27All right, and we are back. Jason, this is one you wrote. The headline is AI is African Intelligence. The workers who train AI are fighting back. So you mentioned on the podcast recently that you took a trip to Kenya. I think at the time you were talking more about the conference, which is sort of the reason you went. You were giving a talk and that sort of thing. But as is detailed in this piece, you also did a fair bit of reporting while you were there as well, talking to various people and going to different events. What events related to data labeling did you go while you were in the area?

29:08Yeah, so I was aware of this guy named Michael Jeffrey Asia, who wrote a report for the Data Labelers Association, which is his organization and a few other people's organizations about his time as a data labeler. He was on the podcast as an interview episode a few weeks back. But I felt like there was more to the story than just his story, because it's a whole organization of thousands of people who are the very low-paid labor behind AI training. And that's very broadly defined. It's like a lot of them have worked for Sama, which is this company that has worked with Meta actually continues to work with Meta.

29:56They said that they've stopped, but they're doing their smart glasses data labeling stuff, which came out of, I believe, like a Swedish newspaper, Czech newspaper. It's in the article, I'm sorry. But there was a really good article about data labelers in Kenya who were looking at all of this highly sensitive video footage from Meta smart glasses. So data labeling is a very broad category of jobs that I would argue is quite related to content moderation. And so content moderators are people who look at violent content, really highly contentious political content, sexual content for different social media companies, and determine whether or not it violates the rules of a given platform.

30:45And as we've reported over the last few years, social media platforms have largely stopped giving a shit. And so as social media companies have taken a step back from content moderation, the jobs there have become a bit more scarce. And it's a similar type of job. Data labeling is a similar type of job. And it can include everything from looking at a bunch of pictures and saying what is happening in the pictures to drawing squares over the faces of people in different either footage or images to help train facial recognition systems to describing what's happening in porn, for example. So that's something that this guy, Michael Jeffrey Asia was doing.

31:35And his job was like, eight hours a day, he watched porn for some platform. He didn't know which platform it was because the way that it works is like you work through a subcontractor. And he was categorizing what was going on in any given scene so that the platform could categorize it for search. And then also, I don't know, sometimes you can jump to different parts of a video that's like, oh, now they're doing this. Now they're doing that. Blah, blah, blah. So he was doing that. And then after that, he had a second shift with a different job where he was an AI chatbot, like an AI sexbot, essentially.

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32:23So he was training AI companionship bots that were telling users they were talking to AI, but he was the one who was actually chatting. And he was given... Is he even training in that? Because it's almost like... I mean, yes, with the MetaSmart glasses one, they look at images and they're training it that way. With the porn one, it's like, what do you see? You categorize it as a position or whatever, you're training the data. I mean, I'm sure training is going on this one but it almost just sounds like he's just it's all just smoke and mirrors he's just being quote-unquote the ai essentially yeah i mean that that's that's like very interesting question and it's like we've seen different models like we've seen companies that just straight up lie and say that they're doing ai because ai sounds high tech and whatever but then you look under the hood and it's a bunch of human beings in kenya or india or pakistan who are just pretending to be AI.

33:25But then I think that the business models of a lot of these companies are to start with that human labor and then slowly automate it over time and eventually turn it over to the AI. And so it's hard to say because we actually don't know which platform he was working for. We know the name of the subcontractor he was working for, but we don't know. There's so many different AI companionship bots out there. And because of the way that this industry works, it's like, he basically just gets, he sits down at his computer or at a terminal and a window pops up and he's like, just given instructions to like sex with these people.

34:07Like that's, that's essentially what he was doing. And what he was saying was that he was given a persona. And so that persona could be, you're a straight man talking to a woman. You're a woman talking to a man. You are a lesbian, a teenage lesbian. He was like, I had to do all this stuff and take on these personas. And I had to switch between the personas all the time. And basically, his whole thing is that this ruined my life in many ways. It's like, I was paid very little. It was quite traumatizing because I felt pulled between all these different personas. I felt like I had to do things and say things that I didn't want to say because...

34:46What's going on him? he's like, I'm a Kenyan man and I'm being asked to be like a college student in the United States. Like, it was weird for him. And he was also looking at porn all day before that. And he was like, I basically became desensitized. I had trouble having sex with my wife. I had PTSD. I had insomnia because I was working like a zillion hours a day staring at a computer. and he did all of this because his son had lymphatic cancer and he needed a job. And it's like, this is... These are one of the biggest sectors of tech jobs in Kenya. And so I talked to a lot of people in Kenya just because I was at this conference.

35:28A lot of them worked in tech. A lot of them worked in journalism. I talked to various Uber drivers. I talked to servers and bartenders. And all of them knew what data labeling was, first of all. And many of them had done it themselves. It's kind of like door dashing here or Uber driving. It's largely gig work that you can just pick up and do on the side. And then some people make entire jobs out of it where they're just doing it all day, every day. And so basically... It's fully ingrained. Maybe the culture is the wrong word, but data labeling. In the economy, for sure. It's a big part of the economy.

36:03And it's like, you leave the Nairobi airport and you get a cab and you immediately drive past the headquarters of Sama, which is the biggest company that does the subcontracting. And it's huge. It's a huge campus on the side of the highway. But anyways, I know that was a long windup. But basically, after over a year of doing this, Michael was like, fuck this. This is terrible. I hate this. We need to fight for better rights. And so he and some colleagues formed the Data Labelers Association, which is... I mean, it's not formally like a labor union, as in they haven't been recognized by the companies and doing collective bargaining and things like that.

36:49But right now, they're growing power to basically push back against these companies and push for better working conditions. And so the event that they had... How are they doing that, exactly? The pushback? Well, right now, they're just signing people up. As in, they're just like, are you a data label or do you feel like you're mistreated? Here is what's happening, more or less. It's like, it doesn't have to be this way. Because people who do data labeling in the United States, there's not that many of them because they have to be paid minimum wage and things like that. But it's like, they're paid better wages.

37:23They have benefits. A lot of them have mental health support. A lot of people in other countries have better labor protections there. And so right now they're doing a lot of educating of people about how they are being taken advantage of and how this is an extractive industry. And then they also have worked with a lot of lawyers in Kenya because Kenya has laws that should prevent some of this stuff from happening. So there's a lawsuit against SEMA right now. There's a lawsuit against OpenAI. There's a lawsuit against Meta about how these people are treated, about the fact that a lot of them don't have mental health support, about the fact that a lot of them are very poorly paid and don't have benefits and all this sort of thing.

38:06And so I think it's a bit of a two-pronged approach where they're trying to get as many data labelers as possible to say, I support collective action. I want to make this a better job for people and myself. And then there's the legal aspect of it where... I spoke to one of the lawyers that is suing Meta. And she told me, we have laws that should protect against this. It's just a matter of getting them enforced. And some of these lawsuits have been winding their way through Kenyan court for years at this point. And it's just a matter of getting an injunction or getting a result where these companies will be required to treat the workers better.

38:54I think one of the scary things, and this is not to discourage them at all because what they're doing is great, is that a lot of these big companies will go to the Kenyan government and say, if you regulate us, we're just going to leave the country. We're going to go work in another country. And so that's the kind of thing that they're holding over the entire country at this point. It's not a matter of like, oh, the workers are pushing back. It's a matter of like, the government has largely, as I understand it, according to Mercy Mutemi, who's the lawyer I spoke to, like the government's largely kind of looked the other way because Kenya sees this, The Kenyan government sees this as a chance to work with American big tech and to gain access to these jobs.

39:47Because these jobs do exist, even though they suck. They are jobs. And so they're like, oh, we don't want to lose these jobs. And if we regulate these companies, Mark Zuckerberg is just going to go to Uganda or something instead. Or Southeast Asia or something like that. Right. Right. Two things I would just mention. You mentioned the sort of similarity to content moderation. And then we have these AI jobs. There's almost one in between, which is the translation jobs. I remember back at Motherboard, we got a leak talking about how workers were listening to Skype calls to aid in Microsoft's in improving the translation engine behind that.

40:32and you know is translation ai i don't know maybe people use chat gpt for translation all that sort of thing but that almost seems like a bridge between the content moderation stuff and the ai stuff and then i would also just say that you know there are some projects which there's sort of a spectrum right there's the i mean there's the really sensitive stuff like the porn stuff and then the metaglasses especially i probably put like listening to smart assistant audio in there as well then you have you know maybe training images for flock license plate reader cameras like we reported recently they're using overseas workers to train those algorithms you then also have some stuff which like straight up involves the military there was this recent article in the Bureau of Investigative Journalism in London.

41:25And the headline, I mean, I'll just read it and you'll get the picture. Gig workers in Africa have been helping the US military. They had no idea. And that was about Appen, which is this other huge consulting contracting firm. So yeah, we had waves of coverage of the content moderation stuff. And we did a lot of that. And then other technology websites did it. And now there's the wave of the AI coverage as well. Yeah, so there's a few things. One, you're absolutely right. A lot of it is translation. A lot of it is actually content moderation for AI chatbots. A lot of them do content moderation for chat GPT.

42:10There was a Time Magazine article about some SEMA workers maybe a year and a half ago, two years ago, where they were like, judging how... They were grading ChatGPT on the responses that it was giving to people. And then also they were looking at if someone was trying to make a bomb on ChatGPT or something, they were testing the effectiveness of the guardrails, more or less. And so that really bridges the gap as well. And then there's an article that I mentioned in the interview that I did with Michael. It was on a sub stack written by a Kenyan guy. And it was like, I don't write like ChatGPT.

43:00ChatGPT writes like me. and it's very interesting because a lot of Kenyans, at least according to this article, Michael said the same, when they post on LinkedIn or when they email people, they are getting told that they're using ChatGPT to write their things. And a lot of them say that they are not using ChatGPT. What's happening is that the people who are training ChatGPT how to write, like how they are tweaking the outputs and just like doing that it's like it's kenyan english and english is one of the two main languages or two uh official languages of kenya swahili is the other but it's like basically like we have now trained this robot to write like us and now when we write in the way that we were taught to write in school we're getting accused of using ai and we're not using ai and like that's leading to bad outcomes for us because some of them are like i'm a i'm a writer and now I'm being accused of using AI just because this robot writes like me.

44:04And I thought that that was super interesting. And then yeah, the title of the article is like, AI is African intelligence. I thought that was a really powerful quote from Michael where he was like, and everyone knows this, they should know this, but it's like, AI is not magic. It's like, there is just like zillions of human hours that go into not just, of course, all of the training data that comes in, all the stuff that's sucked into these tools, but then also managing the outputs of it and tweaking the outputs of it and making sure that it all works. It's like the people who are doing that, not all of them are African, but a lot of them are.

44:43And so they're like, this is our labor. We're getting paid$200 a month to do this and OpenAI is worth a trillion dollars or whatever. They feel like that's not fair. And I think it's very hard to argue with that. Yeah, it makes complete sense. I'm sure we'll keep an eye on that. But for now, if you're listening to the free version of the podcast, I'll now play us out. If you are a paying 404 Media subscriber, we're going to talk a little bit about jobs and AI. And, you know, maybe there's some inaccuracies about what is being reported or some stuff is being missed out. You can subscribe and gain access to that content at 404media.co.

45:26As a reminder, 404 Media is journalist-founded and supported by subscribers. If you do wish to subscribe to 404 Media and directly support our work, please go to 404media.co. You'll get unlimited access to our articles and an ad-free version of this podcast. You'll also get to listen to the subscribers-only section, where we talk about a bonus story each week. This podcast is made in partnership with Kaleidoscope and Alyssa Midcalf. Another way to support us is by leaving a five-star rating and review for the podcast. That stuff really helps us out. Here is some of a very long one from All My Art.

46:03Important, relevant reporting. I'm basically the opposite of a tech enthusiast, but this is one of my favorite podcasts. The reporters make it really clear why tech stories matter and how tech and tech billionaires are impacting our lives. Thank you so, so much. This has been Forrefour Media. We'll see you again next week.

From the publisher

This week we start with Joseph’s series of articles about the DOGE depositions. He watched hours and hours of them, then a judge ordered them removed from YouTube. But, they’ve already been archived all over the web. After the break, Jason tells us about the AI data labelers who are fighting back. In the subscribers-only section, Jason breaks down what’s wrong with all the AI job loss research at the moment.

0:00 - Intro
0:51 - Google Street View's Unmappable City
3:40 - I Watched 6 Hours of DOGE Bro Testimony. Here's What They Had to Say For Themselves
13:24 - DOGE Deposition Videos Taken Down After Judge Order and Widespread Mockery
18:58 - The Removed DOGE Deposition Videos Have Already Been Backed Up Across the Internet
28:32 - 'AI Is African Intelligence': The Workers Who Train AI Are Fighting Back

SUB'S STORY - AI Job Loss Research Ignores How AI Is Utterly Destroying the Internet

YouTube Version: https://youtu.be/xtMniLj_yzQ
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