Dhruv Mahatra (3:48)

Yeah. So I started to learn how to program computers like after college. I basically was sick of working at coffee shops and I saw it as like a good opportunity to not work in service anymore and get a job doing programming work. So I kind of like 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. It was a sex toy actually. It was like, wow, that's amazing.

John Scott-Railton (4:32)

So that's so funny because of course Sam obviously covers Internet connected sex toys all the time. We covered them motherboard, like 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. What year are we talking? If you can remember to get an idea.

Dhruv Mahatra (4:53)

That was like 2012, maybe 2012, 2013. And I truly did not know what I was doing. It was like a four person company and I think they hired me because I met them at a bar in New York City and they're like, oh, you seem cool, like let's see what you can do. So that was the first thing 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. That was sort of my weird segue to journalism. I was doing like these community run GSM networks in Kenya and Nicaragua using software to find radios. And I was basically a systems engineer. And through some of that work I was introduced to reporters and generally kind of that launched my career working in newsrooms.

John Scott-Railton (5:45)

That's really, really interesting. Yeah, let's definitely slow 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 when you say here you were Working on networks in Kenya and other places. What was that exactly? That setting up mobile phone networks, setting up mesh networks. What was that exactly? And was it volunteer based?

Dhruv Mahatra (6:15)

Yeah, so it was largely grant funded and these were community run GSM networks. So they were cell phone networks that was built with low cost hardware and they were basically the goal was to make communications in these areas that are 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. So 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.

John Scott-Railton (6:59)

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. Like how do you learn to do that?

Dhruv Mahatra (7:12)

A lot of self learning and you know, a lot of these places where I worked initially, everyone is self taught. You sort of like you just kind of do it and we're using a lot of like out of the box software that we just have to learn kind of to run on the equipment that we have. I did a master's program at NYU that was called itp and that's kind of how I got connected to the world of open source cell phone network. So everyone is 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 this. These types of cell phone networks.

John Scott-Railton (7:54)

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 way back in 2013 or 2012 or whatever, I basically didn't really consume journalism. I did a bit but obviously nothing now. And I didn't really fully understand frankly what the job was or what its role was in society, to be honest. What were you like then? Were you engaging with or at least reading journalism or what was the deal there?

Dhruv Mahatra (8:45)

Yeah, I mean I was always sort of politically active, I guess. Like I, I, you know, I, during like Occupy Wall street and stuff, I was, I also had been working similarly or like working with friends and building like tech, tech tools and I don't know, like journalism was always sort of adjacent to that. Like, you know, we had People we, I knew people who 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. 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. 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 using technology or using technology to interrogate technology, or being a network engineer can be really useful for stories. That's how I learned about the world of tech journalism.

John Scott-Railton (9:55)

Yeah. And back then 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 it's fully ingrained and integrated with the reporting process, choosing what to publish, interrogating data sets, all of that sort of thing. It was really embryonic back then. And I think you and others were really leaders in this and making it like 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?

Dhruv Mahatra (10:40)

Yeah, so I guess there's two different stories here. The first sort of like act of journalism I had done was it was. I was still doing. I was still doing these open source cell phone networks. And I had gone to Standing Rock and 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 to increase signal there. And as I was there, I like, noticed, you know, there were all these surveillance airplanes that were kind of constantly hovering around low in the middle of the night or throughout the day really. And 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, you know, I pulled ADSV signals 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. And 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, like, expressed any interest in it, I was like, oh, you know what? Maybe. Maybe I should start, like, doing more of this type of work. But, you know, 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. So I built her a VPN that she could connect all of her devices to that would just drop traffic to the various tech giants. And then she just kind of wanted to write a story about how her life fell apart without the tech giants. So that was my first real published work of journalism that I did.

John Scott-Railton (12:43)

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, like, 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. So, 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 journalists 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. I'm just curious for you, is it one of those. Both of those. Neither of those, sort of what comes first for you, the technique or the question?

Dhruv Mahatra (14:24)

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. It's the opposite. Some of that is just, you know, basic survival in a newsroom. Like, I'll explain what I mean. Like, you had mentioned that, you know, Cash was. Was unceremoniously fired from Gizmodo. Well, she was unceremoniously fired from Gizmodo, like, a week after I was hired. And I was kind of put on a desk with her and the special project desk. Immediately 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's my first time in the 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, like, the skills that I knew how to do to find new data sets or to compile new data sets. And I think, like, my work at Gizmodo is sort of unique in that. Like, it's not traditional data reporting. And maybe it is now. Maybe this is what you call data reporting. But back then, like, you know, you have someone like me in a newsroom, and no one really knows what to do with you.

John Scott-Railton (15:53)

Yeah, I mean, back then, data journalism was seen more as well. We got this big set of spreadsheets, maybe financial earnings or something, or maybe a leak, something like that. And 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? You said there in Gizmodo, you're almost like, put in the corner for a bit, and maybe people don't really know what to do with you. What. What has it been like more broadly in newsrooms?

Dhruv Mahatra (16:51)

Yeah, I mean, it's. 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 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. You need your computer restarted? I don't know, like, basic.

John Scott-Railton (17:41)

Has that happened?

Dhruv Mahatra (17:43)

Not exactly. I mean, I've definitely had to do.

John Scott-Railton (17:46)

But along those lines. Yes. Yeah.

Dhruv Mahatra (17:47)

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 just show. You 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. Like, he's not just a data reporter. He's not just a computer guy. He's something kind of different.

John Scott-Railton (18:21)

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 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. And my question is, how do you develop that? Is it just a muscle that comes over time? Like, how does that develop?

Dhruv Mahatra (19:11)

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. 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 Gizmodo, because of the, the metabolism and the pace of stories was so kind of high and fast. Like I just was always hunting for stories and just like trying to figure out how I could insert myself into the news cycle or find something that, you know, created a news cycle. And you know, that's like really like working at a digital publication like that, like a blog that's constantly publishing, like 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? Like, you know, what's a tweet? That took a lot of, a lot of time, but I think it was, it was useful to have to fight to do it.

John Scott-Railton (20:09)

Yeah, yeah. I mean, not to the same extent as you obviously, but also similar, of course, advice as well where you are kind of expected to just fucking get something out. You 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, exactly. 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, but do you just want to run us through some of your favorite stories there because you were frankly churning them out. There was the Epstein stuff, there was other location data stuff. Towards the end there was the ICE911 calls. I think you choose maybe just run us through a couple and sort of what you did there.

Dhruv Mahatra (21:17)

Yeah, well, first a brief correction between Gizmodo and Wired. I worked at the center for Investigative Reporting Reveal for two quiet years where I was working on long term investigations or maybe published one or two things there.

John Scott-Railton (21:32)

Well, that's actually perfect. I'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?

Dhruv Mahatra (21:42)

Yeah, that was shocking. I, I hadn't, you know, I hadn't worked at a news. I, you know, I actually had frankly hadn't worked on a, 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 it, what that meant. I kind of had an idea of it from watching, you know, television or something from. But like I didn't know the work that went into investigation until I was at Reveal. So yeah, it was tough to kind of turn your brain off, turn it, turn it off of like the sort of daily news cycle and doom 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. And you know, at Reveal, like my niche there was policing. I worked on for basically two years there on a story about the D.C. 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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John Scott-Railton (25:54)

I 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, like, difficult for you? I would just say personally for me, like, I don't have to be in the news cycle specifically, but I have to publish. You know, I mean, like, I don't want to be sitting on stuff. And like, writing a book, for example, was like, Jesus Christ, this has taken literally years. Can I just like, get it out? Like, what was the feeling there?

Dhruv Mahatra (26:35)

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, the mandate here is to break news. Yeah, no, it's definitely. It's definitely tough. And I feel that now because now I'm at Bloomberg and I'm going back to sort of the, the longer term investigative work. And yes, 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.

John Scott-Railton (27:14)

Yeah, yeah. And, and 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, you know, for example, will, will churn out stuff about mobile fortify, the ICE facial recognition app. Because I just want to, I 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.

Dhruv Mahatra (27:50)

Right.

John Scott-Railton (27:51)

So we've Wired, give us a couple of like your favorite stories that you worked on there, either for technical reasons or reporting reasons.

Dhruv Mahatra (28:01)

I think my favorite story at Wired, there's two. I mean you had mentioned the ICE911 calls, which was a really difficult story to report. But a story that's really near and dear to my heart from Wired is a story where I went to the DNC in Chicago with a backpack full of radios essentially and technology to try to find a stingray, which is an mc, a cell phone, a fake cell tower essentially, which is a kind of like, it's a surveillance technology that has like a mythical mythological status for a lot of people. They'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'd gone with, to the DNC in Chicago with like a backpack full of some tech and some MC catching mc

John Scott-Railton (28:53)

fake cell phone tower, IMSI catcher detector tech.

Dhruv Mahatra (28:59)

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. And we were able, 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 McCatcher. So it was just like great moment for me because I had been thinking about McCatchers for years. I used to work on cell phone towers and I'm interested in surveillance technology. It's like the perfect blend of all the things I'm interested in. And I happened to catch one. So that was a really story near and dear to my heart.

John Scott-Railton (29:57)

Yeah. And 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 IMSI catchers. 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. Now 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 incredibly alarming for fourth Amendment rights and et cetera, et cetera, et cetera. 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 Nimsy catcher at the dnc?

Dhruv Mahatra (31:27)

Yeah, I mean, I think it's for the reasons that you said, right? Like I think having been to CUP both as a participant and also covering protests for years, there's so much speculation about whether or not your phone gets hot. So you think you're connected to NZ catcher, 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 catcher is super valuable because you don't want to self censor if you think because you think you're connected to a tower or connected to the fake tower when you're not. Right. Like, I think it's super useful to know that actually the main use case is not monitoring people at protest, it's location finding for a specific target or something like that. So for me, 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 we captured that the device captured the signal. So it wasn't used at a protest as far as I know. It was something different.

John Scott-Railton (32:38)

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. Like I'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?

Dhruv Mahatra (33:01)

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.

John Scott-Railton (33:26)

Yeah, yeah. Before we move on to what you're doing now at Bloomberg, I kind of wanted to slot this in, which 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. I 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, is it finding a way in at all?

Dhruv Mahatra (34:35)

For me, yeah, it is. I think a lot of what I use AI for is coding as a coding assistant essentially. And I think about this a lot. I'll never have AI write a full write all of my code for a story because you can't do that. You have to trust that if you're scraping a big data set that the scrape is complete or things like that. But I will use it for 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 as a reporter, especially at Wired, where every second kind of counted for some stories being able to. To either use AI to check my work, check the code itself. Right. Or even just like I can't remember how to, you know, parse a CSV in a certain way. Like, what exactly is the. Is the, is the pandas function? I need to. I need to call here, you know?

John Scott-Railton (35:37)

Yeah. So rather than going to stack overflow now you go to Gemini or something?

Dhruv Mahatra (35:41)

Yeah, yeah. Regrettably, it's. It's sad. It is sad,

John Scott-Railton (35:46)

but I think, at least in my head, thinking it like that is like, it's almost just a different version of that, right? Where, 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 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? And I mean both editorially with your stories, because you'd be 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 editorially first. What are you doing? Is it more of the same stuff?

Dhruv Mahatra (36:59)

Well, you know, full disclosure is that I've been on parentally for six months essentially. So I basically started right now, immediately left. So I haven't been doing much. But, you know, the, the role I was hired for at Bloomberg is on a desk actually with Surya. So it's kind of full circle here. The idea behind the desk is to like, use tech to tell stories that couldn't otherwise be told. Right. And we kind of think about it as like journalism, skunk works. So like, instead of starting with a data set or a beat, we start with a capability like you were talking about. Right. And we think about like, what can we Build what can we collect and what can we observe at a scale that like no one else is looking at.

John Scott-Railton (37:40)

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.

Dhruv Mahatra (37:48)

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. I'm going to be focusing more on building large scale technical things. But again, like, can't talk too much about it because frankly haven't been there very long. But yeah, I mean my first kind of set of stories there were is actually prior to the big Epstein dumps from the doj, but it was. We had gotten access to email inbox that previously wasn't public and did some analysis on that.

John Scott-Railton (38:21)

Yeah, yeah, really interesting stuff. I guess actually just my last question is because we covered so much there and perspectives have changed 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? And I'm not asking what would you tell a young journalist entering the field what to do? Not that sort of thing. I just mean everybody in journalism, from editors to reporters, what would you hope that they keep in mind about the capabilities that you or people like you can bring into stories? What would you like them to know or think about?

Dhruv Mahatra (39:07)

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 like it's having some technical skills and putting them on a story. By 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 data set to do data reporting. Right. Like 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.

John Scott-Railton (39:58)

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. I'm. 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 like the biggest thing in the world as in data size, but it ended up being a good amount of information. And you're right in that 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?

Dhruv Mahatra (40:55)

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 like, what the flaws are with, like, checking names against, you know, the database of names that they had. And that wasn't, you know, a story that required a data set at all, but it just required some, like, clever engineering. And, you know, Wired is ultimately able to tell a pretty interesting story about that, about that tool, you know, and that that again, isn't necessarily data reporting, but it is, you know, computer assisted reporting.

John Scott-Railton (41:53)

Yeah, absolutely. Montru, 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. But thank you so much for joining us. Really, really appreciate it.

Dhruv Mahatra (42:22)

Yeah, thanks for having me. I appreciate it.

John Scott-Railton (42:27)

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