The Journalist Who Tracked Epstein Island Visitors’ Phones

30 Mar 2026 · 44 min · 13 chapters

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In short

Dhruv Maratra (Bloomberg) discusses how technologists become investigative/computational journalists, and how surveillance-location tools can be measured and verified. He highlights his work on Epstein-related reporting, phone-location tracking (including IMSI-catcher/stingray detection), and other investigations using decompiled apps, radio/network data, and signal analysis.

Guests

Dhruv Maratra, data journalism and investigations at Bloomberg; previously at Wired and Gizmodo; earlier long-term work at Center for Investigative Reporting/Reveal. Background includes programming after college, building community GSM/mesh networks in Kenya and Nicaragua, and systems engineering with software-defined radios.

Key claims

Tech skills don’t require huge datasets; you can “put a computer on the story” via decompiling, scraping, and signal collection. AI is mainly useful for coding help/debugging, not writing full code. IMSI-catchers are often misunderstood; tools can confirm whether a fake cell tower was actually detected.

Notable examples

Standing Rock flight tracking via ADS-B; “Goodbye Big Five” VPN story; DNC Chicago stingray detection using an IMSI-catcher detector and Bluetooth MAC counting; analysis of an email inbox tied to Epstein dumps from the DOJ.

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

Dhruv's Background and Career Path

1:13 to 2:16

Explore Dhruv Maratra's journey from technologist to journalist.

“He does data journalism and investigations at Bloomberg.”

The Intersection of Tech and Journalism

2:17 to 3:15

Discuss the unique skill set required to combine technology with journalism.

“because he didn't start as a journalist.”

Building Community-Run GSM Networks

3:16 to 7:24

Delve into Dhruv's work on open source cell phone networks in underserved areas.

“So I started to learn how to program computers after college.”

First Encounters with Journalism

7:25 to 10:05

Learn about how Dhruv's technology work led him to journalism and his early experiences.

“And a lot of these places where I worked initially, everyone is self-taught.”

The Impact of Big Tech in Journalism

10:06 to 13:00

Discuss Dhruv's first paid journalism work and its implications on big tech.

“And as I was there, I like noticed, you know, there were all these surveillance airplanes that were kind of constantly hovering around low and then middle of the night or throughout the day, really.”

Tech Techniques in Storytelling

13:01 to 14:03

Examine the duality of technique versus question in journalistic storytelling.

“But at the time, this was really the first piece of work that put in front of you that, holy shit, these monopolies have real tangible control over our lives.”

Navigating the Intersection of Tech and Journalism

14:03 to 21:01

Learn how the guest's unique technical background impacts their approach to journalism.

“I'm just curious for you, is it one of those, both of those, neither of those?”

The Challenge of Long-Term Investigative Journalism

26:07 to 28:00

Explore the dynamics and challenges faced during longer investigative projects in journalism.

“when it came to the longer cadence at Reveal, Whereas you say you're just doing maybe one large thing a year or something like that.”

Exploring Surveillance Technology at the DNC

28:00 to 30:10

Learn about the experience of using technology to track surveillance devices at the DNC.

“There's a lot of people who think they're connected to them all the time or, you know, it's hard to know exactly when you're connected to a fake cell tower or if you even are in the first place.”

Understanding IMSI Catchers and Their Usage

30:10 to 34:10

Discover the implications of IMSI catchers in protests and law enforcement.

“Yeah, I'll just say on that, I'm not entirely sure when this episode will come out because we do record them in advance.”
Show all 13 chapters

The Role of AI in Modern Journalism

34:10 to 36:38

Examine how AI is influencing journalism and improving reporting efficiency.

“either basically to just transcribe non-sensitive audio, which I think every journalist does.”

Innovations in Data Journalism at Bloomberg

36:38 to 40:10

Learn about the innovative approaches to storytelling at Bloomberg.

“But I think it's interesting that you are using it to be more efficient at journalism.”

Rethinking Data Reporting in Journalism

40:10 to 42:06

Understand the value of technical skills in storytelling beyond big datasets.

“And of course I'm going to agree because I'm much better at handling smaller data sets because when it gets large, I'm like, oh my God, I can't do this.”
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Transcript

Automatic transcript. May contain errors.

0:00This episode is brought to you by Indeed. Stop waiting around for the perfect candidate. Instead, use Indeed Sponsored Jobs to find the right people with the right skills fast. It's a simple way to make sure your listing is the first candidate C. According to Indeed data, Sponsored Jobs have four times more applicants than non-sponsored jobs. So go build your dream team today with Indeed. Get a$75 sponsored job credit at Indeed.com slash podcast. Terms and conditions apply. Immediately after getting hired, they gutted the entire desk and fired everyone on it except for me. That was my first time in the newsroom.

0:33I didn't know how to write a story, record a story, or do anything. All I knew how to do was like tech stuff.

0:44Hello, and welcome to the 404 Media Podcast, where we bring you unparalleled access to hidden worlds, both online and IRL. 404 Media is a journalist-founding 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 the best comments. And they get early access to our interview series too, like this episode. Gain access to that content at 404media.co. Dhruv Maratra is my guest this week. He does data journalism and investigations at Bloomberg. Kind of a vague title. You'll learn more specifics in the interview itself.

1:32He was previously at Wired, where you're probably familiar with quite a bit of his work. There's IMSI captures the DNC using mobile phone location data to track people going to Epstein Island. We've also worked on stories together also about location data, actually and the apps being used or hijacked to get your location information um i've really admired drew's work uh from afar uh for years really and then of course i was very happy to get the chance to work with him and some collaborations at wired i'm really really happy to have this conversation as well we run through his career which i think is very interesting because he didn't start as a journalist.

2:26He started much more as a technologist who then moved into journalism. And I think that's a very difficult thing because as you hear me say, and as the conversation goes on, there are lots of people who are good at the tech, but it's exceptionally rare to find somebody who can do both the tech and the journalism part. And I don't just mean picking up the phone. Anybody can do that. I mean, understanding what makes a good, interesting, and potentially impactful story. In the show notes, there'll be links to some of Drew's previous work. As you'll hear, when the time of this recording, he was on paternity leave, so he hadn't actually done all that much in Bloomberg yet, but then he's going to be going back.

3:11He's done some stuff on Epstein emails that they managed to get hold of. but without any further ado i really hope you enjoy the conversation

3:28thank you for joining us i'm going to jump straight into questions because as i was just telling you i've already explained to listeners sort of who you are and your work and that sort of thing but if we can go sort of way back and i mean even before journalism really like i'm i'm really really curious because i don't know anything about this part of your history where does sort of the technological background start for you like even before journalism Yeah. So I started to learn how to program computers after college. I basically was sick of working at coffee shops and I saw it as a good opportunity to not work in service anymore and get a job doing programming work.

4:24So I kind of taught myself how to program and landed a job at this funny little startup that was building all sorts of weird types of technology. The first job I was working on for them was building a sex toy, actually. Wow, that's amazing. So that's so funny because, of course, Sam obviously covers internet-connected sex toys all the time. We covered them. Motherboard, the smart sex toys and how they could be hacked. that sort of thing that's so funny i didn't realize that at all where when what what year are we talking if you can remember to get an idea that was like 2012 maybe 2012 2013 and i truly did not know how what i was doing it was like a four-person company and i think they hired me because like i met them at a bar in new york city and they're like oh you seem cool like like let's see what you can do so that was the first thing they they had me work on and then from there i sort of just kept on doing like different odd jobs programming and eventually ended up working on open source cell phone networks.

5:36That was sort of my weird segue to journalism. I was doing like these community run GSM networks in Kenya and Nicaragua using software defined radios. And I was basically a systems engineer. And through some of that work, I was introduced to reporters and And generally, that launched my career working in newsrooms. That's really, really interesting. Yeah, there's definitely slowed down on that for a second. Because there is something I came across that you, I believe you worked on called OtherNet. And maybe we'll get to that in a second. Because I feel like that's separate to what you're talking about.

6:12When you say here, you were working on networks in Kenya and other places. What was that exactly? that's setting up mobile phone networks is setting up mesh networks like what was that exactly and was like volunteer based or yeah so it was largely grant funded um and these were community run gsm networks so they were you know cell phone networks that was built with low cost hardware and they were basically like the goal was to make communications in these areas that are like pretty hard to reach for traditional cell phone networks to make communications cheaper and locally controlled, just in places where access is incredibly expensive and unreliable.

6:57So a lot of this was just software-defined radios, climbing up high towers and using this open source software to route calls and do general systems engineering work. Huh. Fascinating. How did you learn to do that? Do you just rock up and you're told? Or I imagine you have to do a ton of self-learning. How do you learn to do that? A lot of self-learning. And a lot of these places where I worked initially, everyone is self-taught. You just kind of do it. And we're using a lot of out-of-the-box software that we just have to learn to run on the equipment that we have. I did a master's program at NYU that's called ITP.

7:46And that's kind of how I got connected to the world of open source cell phone network. So everyone was sort of like learning and using tech in these kinds of interesting, creative ways to do like non-traditional, I don't know, work. And that's kind of how I taught myself and learned in that environment to do these types of cell phone networks. Yeah, really, really interesting. As you say, around that time when you're doing these networks, that's when you start to get introduced to journalists and journalism. I'm just curious, before that, were you consuming a lot of journalism? And I will be vulnerable and transparent in mine in that when I applied for an internship at Vice, you know, way back in 2013 or 2012 or whatever, I basically didn't really consume journalism you know like I did a bit but like obviously nothing like now and I didn't really fully understand frankly what the job was or what its role was in society to be honest like what were you like then were you engaging with or at least reading journalism or what was the deal there yeah I mean I was always sort of politically active I guess Like, you know, during like Occupy Wall Street and stuff, I also had been working similarly or like working with friends and building like tech tools.

9:13And I don't know, like journalism was always sort of adjacent to that. Like, you know, we had people, I knew people were talking to reporters and, you know, I was consuming like the big stories, obviously. But I wasn't like, I didn't know what being a reporter really was. it wasn't until you know i had met people like surya matu and ingrid burrington who are two you know reporters and engineers and artists um it wasn't until i had met sort of that crew that i realized that there was like a place for people like me in journalism um so i think that's kind of what like piqued my interest in the world of journalism right like the fact that like oh you can use there is a place for like using technology or using technology to interrogate technology or like being a network engineer can be really useful for stories right um so that's kind of how i learned about the world of of tech journalism yeah and back then i mean there was still a lot you could do with technology of course and applying it to journalism but in my opinion it's not like it is today where like it's fully ingrained and integrated with like the reporting process choosing what to publish interrogating um data sets all of that sort of thing it was really embryonic back then and you know you i think you and others were really leaders in this and making it like a an established routine tool that people turn to so you do that you get introduced to journalism how do you first get into journalism then yeah um so i guess there's two different stories here uh the first sort of like act of journalism i had done was um it was i was still doing i was still doing these open source cell phone networks and uh i had gone to standing rock in north dakota where the dakota access pipeline protests were and i had gone to do to essentially to sort of build, not build a cell phone network, but to like set up cell phone repeaters and increase signal there.

11:23And as I was there, I like noticed, you know, there were all these surveillance airplanes that were kind of constantly hovering around low and then middle of the night or throughout the day, really. And, you know, I started doing what I kind of knew, the only thing I knew how to do was just collect data. So I had my software on radio and I pulled ADSV signals and And I tracked these flights and I logged tail numbers. And I was just trying to understand who was in the air and why. And with that data, I ended up reaching out to several reporters. And I was like, hey, guys, I have this data set. Like, I don't know if this is useful to you, but, you know, here, have at it.

11:58And that was like the first kind of act of journalism. You know, once you start like documenting state behavior with evidence, you're sort of doing journalism, whether or not you call it that. once those reporters expressed any interest in it, I was like, oh, you know what? Maybe I should start doing more of this type of work. But my first paid journalism gig was for Gizmodo, working on a series with Kashmir Hill called Goodbye Big Five. Basically, she wanted a system that could help her block Amazon, Facebook, Google, Microsoft, and Apple from getting her time and attention. And so, you know, I built her like a VPN that she could connect all of her devices to that would just drop traffic to the various tech giants.

12:44And then she just kind of wanted to write a story about like how her life fell apart without the tech giants. So that was my first real, you know, published work of journalism that I did. Yeah. And her life did fall apart from what I can remember. It was a massive pain in the ass where, of course, we know this now. But at the time, this was really the first piece of work that put in front of you that, holy shit, these monopolies have real tangible control over our lives. And if you try to escape them, it's like, oh, no, you can't escape Amazon because they have AWS and everything runs on AWS and we're completely screwed, that sort of thing.

13:24So, yeah, I absolutely remember that. And of course, now, Kashmir, I think after being laid off by Gizmodo, immediately got picked up by the New York Times. And now is, of course, one of the most respected technology journals in the world. I mean, good stuff comes out of Gizmodo, even if they dropped the ball there. Kind of going back, not really in time, but just to that idea of the plane data. I'm curious, kind of related to the learning question, there's sort of two different ways to go about it, right? sometimes I'll come across a new technique like oh I've learned or I want to learn how to reverse engineer Android apps for example and I just do it because it's interesting and then it allows you to do other stories like the location data stuff that I've done and that sort of thing and of course there's a flip side of well I have a question that I would like to explore and maybe get an answer to, then I go and find a technique to exploit and then try to get an answer out of that.

14:27I'm just curious for you, is it one of those, both of those, neither of those? What comes first for you? The technique or the question? Yeah, I mean, it's definitely a little bit of both. And I think when I first started my career, it was the technique came first and the story came second. And now as I've gotten older and I've grown in my career, it's the opposite. Some of that is just basic survival in a newsroom. I'll explain what I mean. You had mentioned that Cash was unceremoniously fired from Gizmodo. Well, she was unceremoniously fired from Gizmodo a week after I was hired. And I was put on a desk with her and the special project desk.

15:12Immediately after getting hired, they gutted the entire desk and fired everyone on it except for me because I think I had data in my job title and they were like, oh, he's probably useful for something. But when I was there, like I didn't, that was my first time in a newsroom. I didn't know how to write a story, report a story or do anything. All I knew how to do was like tech stuff and like not even like the tech stuff that was traditional in a newsroom. Like, I don't know, data analysis. Like I knew how to decompile apps and I knew how to like do network engineering. so I started to use the skills that I knew how to do to find new data sets or to compile new data sets and I think my work at Gizmodo is sort of unique in that it's not traditional data reporting and maybe it is now, maybe this is what you call data reporting but back then you have someone like me in a newsroom and no one really knows what to do with you Yeah, I mean back then data journalism was seen more as where we got this big set of spreadsheets, maybe financial earnings or something, or maybe a leak, something like that.

16:19And we need somebody who knows how to do pivot tables in Excel. And that was data journalism, essentially, which, yes, definitely has its place, still has a place today and is still useful. But there's this whole world that is completely inaccessible to you if you don't have the skills that you and others do. and you may not even think that it's possible. And of course, we'll get to your other roles as well, but I'm just curious sort of building on that. What has it been like for you in newsrooms with your unique role? Like you said there in Gizmodo, you almost like put in the corner for a bit and maybe people don't really know what to do with you.

16:59What has it been like more broadly in newsrooms? Yeah, I mean, it's in i mean i'm an interesting kind of creature for a newsroom because you know i have these technical skills but i'm most interested actually in doing the reporting um and actually like you know talking to people doing a lot of traditional kind of reporting tasks traveling going places and like trying to learn what was happening on the ground so i think for a while i had to fight to not be seen as tech support for a newsroom. I think, you know, the first instinct for a lot of newsrooms when you have someone like me on staff is to reach out to them when you need a data set scraped or you need, I don't know, anything.

17:50You need your computer restarted. I don't know, like basic tech support. Has that happened? Not exactly. I mean, I've definitely had to do... But along those lines, yes. Yes. Yeah. so I think like breaking out of that box of just being computer boy was really difficult at first and I think as I was able to publish more and people were able to see sort of the types of stories I can do because it's hard to explain a lot of the work I do you kind of have to just you should kind of show it so I think after I had a body of work people were like okay this is I see what he can do I see how this is useful to a newsroom he's not just a data reporter He's not just a computer guy.

18:32He's something kind of different. Yeah, absolutely. And I mean, we'll move on to Wired now where like from following your work, I really think you came into your own, but you definitely there, and before I think as well, kind of what you're getting at, you have this very rare ability to be able to do the tech stuff, but also know what a story is. You know, like you can see a story or you can ask the correct question or you know what an interesting answer is. Whereas a lot of tech people who I come across are fantastic, technically speaking, but they may not understand what a journalistic story is.

19:14And my question is, how do you develop that? Is it just a muscle that comes over time? Like, how does that develop? Honestly, I think it was survival at Gizmodo, right? Like, I had to be able to pitch a story that an editor was interested in. And that was a lot of throwing stuff at a wall and seeing what stuck. And I think, yeah, I think that's what makes my background unique is like you said, right? There's a lot of technical people in newsrooms, but not a lot of people who necessarily know what a story is. And at Gizmodo, because of the metabolism and the pace of stories was so kind of high and fast.

19:51I just was always hunting for stories and just trying to figure out how I could insert myself into the news cycle or find something that created a new cycle. And that's really like working at a digital publication like that, like a blog that's constantly publishing, really helps to hone that muscle of like, what is a story? What is 800 words? What is 2 ,000 words? What's 5 ,000 words? What's a tweet? That took a lot of time, but I think it was useful to have to fight to do it. Yeah, yeah. I mean, not to the same extent as you, obviously, but also similar, of course, advice as well, where you kind of expected to just fucking get something out.

20:32You know, maybe not in the later years when Motherboard became a little bit more separate, but definitely early on, it's like, it is your job to find something for 400 words and write it and get on the internet, basically. Yeah. A different time. We were all chasing advertising dollars and banner ads from clicks and whatever. And thankfully, we're both working for primarily subscription-driven media outlets now. I'll say. So we don't have to do that. But then if I'm remembering correctly, the timeline, you then move, as I said, to Wired. I will have just mentioned some of the stories again in the intro.

21:09But like, do you just want to run us through some of like your favorite stories there? Because you were, I mean, you were frankly churning them out. There was the Epstein stuff. There was other location data stuff. Towards the end, there was the ICE 911 calls, I think. you choose. Maybe just run us through a couple and what you did there. Yeah. Well, first, a brief correction. Between Gizmodo and Wired, I worked at the Center for Investigative Reporting and Reveal for two quiet years where I was working on long-term investigations. I maybe published one or two things there. Well, that's actually perfect.

21:46I'll just ask on that then because, as you say, you publish a lot less. The cadence there is completely different. So how was that different? Yeah, that was shocking. I hadn't, you know, I hadn't worked at a news... You know, I actually, frankly, hadn't worked on a long-term investigation, investigative story until I got to Reveal. And I didn't know what it took. I didn't know what that meant. I kind of had an idea of it from watching, you know, television or something. But, like, I didn't know the work that went into the investigation until it was at Reveal. So yeah, it was tough to kind of turn your brain off, turn it off of like the sort of daily news cycle and doing scrolling and looking for a way to insert yourself into the news cycle or find a story to just focusing on this one kind of particular niche.

22:36and you know at reveal like my niche there was policing um i worked on for for basically two years there on a story um about the dc police and their disciplinary process and then i moved from there to wired where i'm doing i did some i guess a mix of both

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26:06I guess just before going to some of your work from Wired again, when it came to the longer cadence at Reveal, Whereas you say you're just doing maybe one large thing a year or something like that. And you have to hesitate and be disciplined about not trying to jump into the news cycle. Was that difficult for you? I would just say personally for me, I don't have to be in the news cycle specifically, but I have to publish. You know what I mean? I don't want to be sitting on stuff and writing a book, for example, was like, Jesus Christ, this has taken literally years. Can I just get it out? like what was the feeling there that was exactly the feeling i really enjoy publishing i really enjoy getting news out there right even if it's 400 words if you're breaking a story you're breaking a story and that's important so it was really tough and that's actually the reason why i was so interested in wired because that was the sort of promise of the desk i was going to at wired was like okay let's you know the mandate here is to break news yeah no it's definitely tough and I feel that now because now I'm at Bloomberg and I'm going back to the longer term investigative work and I definitely have seen news cycles pass I'm like, I have something but I can't, you know, I'm out of place for it.

27:28And to be clear, it's not like one is better than the other or worse or anything like that. It's just a different cadence, a different rhythm and they all have their own trade-offs where, for example, will churn out stuff about Mobile Fortify, the ice facial recognition app, because I just want to get stuff out so people have information. But then let's say in six months, the New York Times or Bloomberg, for example, will probably come out with a 3 ,000 word incredible piece that's like the definitive one, you know? And that's just how it is. It's just a different rhythm, right? So with Wired, give us a couple of like your favorite stories that you you worked on there either for technical reasons or reporting reasons or you know i think my favorite story at wire there's two there's two i mean you had mentioned the ice 9-1-1 calls which was like a really difficult story to report but a story that's like really near and dear to my heart from wired is is a story right where i went to um the dnc in chicago with like a backpack full of radios essentially and technology to try to find a stingray, which is a fake cell tower, essentially, which is kind of like, it's a surveillance technology that has like a mythological status for a lot of people.

28:47They're hard to find. There's a lot of people who think they're connected to them all the time or, you know, it's hard to know exactly when you're connected to a fake cell tower or if you even are in the first place. So I had gone to the DNC in Chicago with like a backpack full of some tech and some MC catching, MC fake cell phone tower. IMSI catcher detector. Yeah, exactly. And, you know, as I was there, I was kind of walking around collecting signals, not just cell signals, but Bluetooth and stuff. And I did a story initially when I got back about how, you know, you can eavesdrop on Bluetooth signals from police body cameras.

29:27And I was able to sort of count how many police officers were at specific protests at the DNC based on the unique Mac addresses of the Bluetooth signals. But a couple months later, I had given the data I collected to the EFF to analyze and they came back and they were like, hey, actually, while you were there, you actually connected to an MC catcher. So it was just like a great moment for me because, you know, I had been thinking about MC catchers for years. I used to work on cell phone towers, and I'm interested in surveillance technology. It's the perfect blend of all the things I'm interested in.

30:04And I happened to catch one. So that story is near and dear to my heart. Yeah, I'll just say on that, I'm not entirely sure when this episode will come out because we do record them in advance. But if I'm correct about the schedule in my head, before this, there will be an interview with Cooper Quinton at the EFF, who of course developed Ray Hunter and worked on that, this tool for detecting Indycatchers. And that conversation was really, really fascinating. I feel like you did your story slightly before Ray Hunter came out, or maybe there was a new version or something like that. But Cooper, very interestingly, and I hope I'm not spoiling it, because again, I'm pretty sure this comes out after, but he said, the data they've collected shows that they're not finding IMSI catchers at protests.

30:58Now that is fascinating. And the reason I bring that up is because, as you say, IMSI catchers, stingrays, whatever, they almost have this like mythological presence where people see them as this all-knowing, omnipresent, everywhere surveillance technology. And they're absolutely controversial. Police using them without warrants is, you know, incredibly alarming for Fourth Amendment rights and etc, etc, etc. But you need that data to then actually learn stuff like that like huh they're actually not deploying it in that way and i mean why was it so interesting to you that you found what looks like a nimsicatcher at the dnc yeah i mean i think it's for the reasons that you that you said right like i think having been to cut um both as a participant and also um covering protests for years there's so much speculation about whether or not, you know, your phone gets hot.

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31:55So you think you're connected to an MC capture, but really it's just the cell network that's, you know, that's down or something like that. And I think having the tools to be able to definitively say like, Hey, no, actually, it's not an MC capture is super valuable because you don't want to self-censor if you think, because you think you're connected to a tower when you're connected to a fake tower when you're not, right? Like, I think it's, it's super useful to know that actually the main use case is not monitoring people at protests. It's, you know, location finding for a specific target or something like that.

32:25So for me, like just even having, getting some sense of, of how these things are used is really interesting. And I will say the thing about the DNC is that the data that I collected didn't show that it was used at a protest. It was actually when I was biking around after a protest, like in downtown Chicago, that, that we captured that, that the device captured the signal. So it wasn't used at a protest as far as we, as far as I know. Um, yeah, It was something different. Yeah. I mean, I don't want to speculate too much, but that's probably more a protective mission because there's high-level officials there and that sort of thing.

33:03I'm just imagining what it was. I can't recall if you got maybe comment from the organizers or anything, but did you ever get a response from the authorities about that? Chicago police says that it wasn't them. So that means likely it was federal and they never responded to my request for comment. So, you know, in national security situations, you can get an emergency. Federal agencies can use these things in an emergency without a warrant. So perhaps that's what it was. But again, we just don't know. Yeah, yeah. Before we move on to what you're doing now at Bloomberg, I kind of wanted to slot this in.

33:47which is that, again, data journalism or computational journalism, back when you started, very, very different beast to what it is today, in part because, of course, more and more people are turning to AI. We do have these coding agents that can whip out a tool incredibly quickly. Typically, the way that we use AI is, well, two ways. either basically to just transcribe non-sensitive audio, which I think every journalist does. They use Otter or Slack or whatever. And the other one, frankly, for us is just that when we're trying to fuck with the AIs, because of course, Emmanuel, Jason and Sam, they do a lot more than me, but they are trying to find some sort of issue with Gemini or whatever.

34:31I think they're basically the only times we use AI realistically. But I'm curious if AI has proven useful at all for you when it comes to journalism and technology? Like, is it finding a way in the talk? For me, yeah, it is. I think a lot of it, a lot of what I use AI for is coding, as a coding assistant, essentially. And, you know, I think about this a lot. Like, I don't, I'll never have AI write a full, like, write all of my code for a story because you just, you can't do that. You have to trust that, you know, if you're scraping a big data set that the scrape is complete or things like that.

35:14But I will use it for sort of discrete tasks or to help me debug my own code. And I find it incredibly useful for that. And it's definitely made me faster, faster as a reporter, especially at Wired where like every second kind of counted for some stories, being able to either use AI to check my work, check the code itself, right? or even just like, I can't remember how to parse a CSV in a certain way. Like, what exactly is the Pandas function I need to call here? Yeah, so rather than going to Stack Overflow, now you go to Gemini or something. Yeah, yeah, regrettably. It's sad, it is sad. But I think, at least in my head, to think of it like that is like it's almost just a different version of that, right?

36:08Where, look, whenever anyone writes code, they are going to stack overflow for something because they can't fucking remember it. I've done it a million times when I can't remember how to use a particular Python library or something like that. This is similar to that, putting, you know, obviously all the caveats in of how it's working. and it's built on the labor of other people and probably scraping stack overflow en masse, all of that sort of thing. But I think it's interesting that you are using it to be more efficient at journalism. So I guess just the last thing really is what are you doing now at Bloomberg?

36:53And I mean both editorially with your stories because you've been doing stuff, especially on Epstein, I believe, and then more, well, I was going to say organizationally, but not really. Sort of, I guess, in a role sort of way. So I guess auditorially first, what are you doing? Is it more of the same stuff? Well, you know, full disclosure is that I've been on parental leave for six months, essentially. So I basically started at Bloomberg and then immediately left. So I haven't been doing much. But, you know, the role I was hired for at Bloomberg is on a desk actually with Syria. So it's kind of full circle here.

37:32The idea behind the desk is to use tech to tell stories that couldn't otherwise be told. And we kind of think about it as journalism skunkworks. So instead of starting with a data set or a beat, we start with a capability like you were talking about. And we think about what can we build, what can we collect, and what can we observe at a scale that no one else is looking at. Which is interesting, because when I asked you that earlier, you said it almost been the way around. Now it's gone back to having a technique almost. Yeah, and I think part of it is because my role at Bloomberg is going to be less as a sort of reporter day-to-day than it was at Wired.

38:10I'm going to be focusing more on building large-scale technical things. But again, like, I can't talk too much about it because I frankly haven't been there very long. But yeah, I mean, my first kind of set of stories there was actually prior to the big Epstein dumps from the DOJ, but we had gotten access to an email inbox that previously wasn't public and did some analysis on that. Yeah, really interesting stuff. I guess actually just my last question is, because we covered so much there and perspectives of change and the industry has changed and technology and AI has changed, I guess, what would you like journalists or even people interested in journalism to think a bit more about today.

38:56And I'm not asking, what would you tell some young journalist entering the field what to do? Like not that sort of thing. I just mean everybody in journalism from editors to reporters, like what would you hope that they keep in mind about sort of the capabilities that you or people like you can bring into stories? What would you like them to know or think about? Someone like me in a newsroom doesn't... what's the best way to put this? You don't need to have a big data set to use someone like me in a newsroom to tell a story. Really, it's having some technical skills and putting them on a story.

39:34By doing that, you can actually tell the story differently or get access to the story in a different way. So you don't have to have this big data set to do data reporting, right? You can use your computer in a clever way to tell a story that a more traditional reporter might do, right? Especially when there are sort of silos where there's data desks and things like that, you often get put into a box where if the story doesn't have a big data set behind it, why is it on our desk in the first place? And I think that's not the right way to really approach this type of reporting. You can find a way in regardless of what your beat is.

40:11Yeah, yeah, yeah. I totally agree. And of course I'm going to agree because I'm much better at handling smaller data sets because when it gets large, I'm like, oh my God, I can't do this. It's like, I'm not as hybrid as you are, but I'm more of a traditional reporter who can do a bit of computational stuff on the side. And we've worked on stories together and we did one about apps and real-time bidding and location data. And that was just the right size where it was this list of apps and showing that they can be used or were being used to get people's location. And it wasn't the biggest thing in the world, as in data size, but it ended up being, you know, a good amount of information.

40:54And you're right in that you don't need, you don't need like a 300 gigabyte Panama Papers sort of dump for it to be really, really useful investigative computational journalism. Am I interpreting that correctly? Yeah, yeah. Or like, you know, I'll give you another example. Like at Wired, I had done a story about this group called True the Vote. They're a, you know, quote unquote, election fraud monitoring nonprofit. They're trying to find fraud that doesn't exist, essentially. And they had released an app that, you know, is meant for people to sort of check voter rolls. And I, you know, this story didn't involve a data set, but I basically found the app and I decompiled it and I looked at what it was doing under the hood and I wrote a story about how it works and what the flaws are with checking names against the database of names that they had.

41:49And that wasn't a story that required a data set at all, but it just required some clever engineering. And Wired is ultimately to tell a pretty interesting story about that tool. And that, again, isn't necessarily data reporting, but it is computer-assisted reporting. Yeah, absolutely. Madhruf, thank you so much for joining us. I've been really, really looking forward to this conversation for a long time. I'm really, really glad we could have it and give people a little bit of a peek behind the curtain of all the work that goes into computational journalism. I'll definitely put links in the show notes to your previous Wired byline where people can see your previous work, but then also your Bloomberg one where people can see your work when they come back.

42:33But thank you so much for joining us. Really, really appreciate it. Yeah, thanks for having me. I appreciate it. As a reminder, 404 Media is germless 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 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.

43:10That stuff really, really does help us out. This has been 404 Media. We'll see you again next time.

From the publisher

This week Joseph talks to Dhruv Mehrotra, a journalist and technologist at Bloomberg. Before that, Dhruv was at WIRED, where you probably saw a ton of his interesting work. Dhruv sits in a very unusual space in journalism: he is able to both write technical tools to dig through data, or collect information, or really anything else, and is also able to just write a damn good story. That is a very unique blend. The pair chat about Dhruv’s entry into journalism, how computational journalism has changed over the years, and how Dhruv uses AI too.

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