Inside DuckDuckGo

In this episode, Gabriel (Founder) and Beah (Chief Product Officer) discuss our focus on delight, discovery, and dependability (the 3Ds), what they are, and how they shape our day-to-day work.
If you have feedback on Duck Tales, or episode ideas, email us at podcast@duckduckgo.com.
Show notes: Listen to our episode on Hack Days here.
Disclaimers: (1) The audio, video (above), and transcript (below) are unedited and may contain minor inaccuracies or transcription errors. (2) This website is operated by Substack. This is their privacy policy.
Beah: Hello and welcome to Duck Tales, where we go behind the scenes at DuckDuckGo and discuss the stories, technology, and people that help build privacy tools for everyone. In this episode, we are going to be talking about what we internally call the 3Ds: discoverability, dependability, and delight. And if we If you haven’t met me, I’m Beah. I’m on the product team and I’m here today with Gabriel, founder and CEO of DuckDuckGo. Anything anything you want to add to your introduction, Gabriel?
Gabriel: No, I do not. If you’ve watched this at all, you’ve already seen me a bunch. So and they’ve seen you as well, so
Beah: Yes. Cool. Okay. So yeah, let’s just jump in. 3Ds. What what’s the deal? What I mean, I said what each D stood for, but like contextualize this whole thing.
Gabriel: Yeah, so part of the way we run DuckDuckGo as a company, just like internally, like how we operate, is we have a what we call our top priority, which is like the main thing that we think we need to focus on as a company. And that’s changed every few years. But like right now we’re in the middle of and we’ve been doing it for I think around 18 months, something like that, this 3Ds focus on discovery, dependability, and delight as you pointed out. And those are product things. So it’s really a focus on making our product better in those kind of three areas. And just to walk through them briefly, discovery is we actually have a lot of stuff in our product that people do not know about. So there’s the basic thing that a lot of people still don’t even know DuckDuckGo exists and we have a product and we’re a privacy company. But when you get a little beyond that, you know, we’re a search engine, we’re a browser, we have a subscription now. In our browser, you can sync your passwords and bookmarks. There’s all sorts of features, and like the holistic product is a lot bigger than even most of our users realize. And so it’s kind of discovery of those features. Dependability is a little more straightforward. That’s like, okay, you’re a user, the product should be very dependable. But I mean that means like reliable. You can switch to it easily. All the workflows work well. There’s no rough edges in it. Obviously, that’s a tall order when you run a search engine and a browser and a VPN and all these other things we do. But we’ve been nail we’ve been working down the list of all the little issues that people you know complain about, rightly so, that might have been issues with their bugs in our product and we’re trying to fix them. And then the third one is delight, which is, you know. I think most intuitive, I guess, but even harder to actually articulate how we get there.
Beah: Yeah.
Gabriel: But it’s like you have a delightful product experience. It’s those products and experiences that you are unexpected, really bring you joy, like you really want to share with your friends and family. Like we want to bring that delight into using DuckDuckGo.
Beah: Yeah.
Gabriel: So the focus has been on all three of those, because we think we need to do all three to make a great product. And so we have projects and people working on, you know, basically all three at all at all times at the moment under our top priority.
Beah: Yeah, just to add to the discovery point, I mean we get feature requests and feedback. Feature requests for features we have. Like people just straight up asking for features that exist. Yeah. Private browsing. Yeah. Yeah.
Gabriel: Yes. I would argue that might be our top request. Things like, yeah. Yeah. This is a core discovery issue. Yeah, and even more fundamental than that is like a lot of a lot of search engine users, because that was our first product, and we have the most search engine users, don’t even know we have a browser. And our most browser users on mobile don’t realize we have a desktop browser.
Beah: Yeah.
Gabriel: So regardless of features within all those things, like just at the very basic level, people don’t even know we offer a browser. And they should. Yeah.
Beah: Yeah. And like sometimes it’s as simple as like changing like making something more visible and then other times I think it’s about the language we use or like the way we characterize something so that people know that it’s what they’re looking for. So yeah, there’s like that one’s yeah.
Gabriel: Yeah, one more point on that too. Like that one’s subtly hard as well, because like you don’t discovery means that you have to get people to know about it to your point. And you maybe think of something, which is that you have to get in front of people then with a message, but that can be annoying. Like it look can look like an ad, right?
Beah: Mm.
Gabriel: And so that’s a delicate balance. It’s like, yeah, you’re a cer you’re a private search user, you probably want our private browser because it’ll actually make the benefit of privacy much more you know, better in your life if you have them paired together. However, you don’t want a thousand messages of, hey, we have a browser downloaded now. So there’s there’s like this delicate balance of like trying to get awareness to get the people who will who want it who want it to get it without annoying people who don’t want it.
Beah: Mm. Totally. Yeah. And there’s just like it’s the competition of many things. Like you probably want our browser. When in our browser, you probably want your passwords and your bookmarks. And then you probably want those synced across devices. And you probably want this thing that’s opt in turned on. And like, yeah, you can just we have to I think we spend a lot of time trying to make sure that we’re not like bombarding people with too much messaging or promos and it’s but then but then again we get feedback requests for things that are sitting there somewhere behind the scenes. So yeah.
Gabriel: So just before we move on to that one practically, then what that means is we’re constantly running experiments basically of like how best to do these in product kind of promos. And and and I think to your point, like it’s better to do them. The naive approach is to shove them all in onboarding, which people then whiz by. But the better approach is like very contextual, like maybe like when you do a bookmark for the first time, ask people if they want to sync. And then the other part of discovery, just to end that part, is is just our external marketing. So like not in talking a lot of in-product stuff, but we also spend a lot of time just doing general marketing, especially in the US, about educating people about our different experiences.
Beah: Yeah. Yep. So we ended up talking a lot about discovery, but just another like zooming out question. How how did we land on these besides the fact that they all share a D, why did we pick why did we pick these three things?
Gabriel: That’s that’s clearly part of it. Yeah. Yeah. Yeah, I mean if you think about the whole product kind of marketing funnel, which is you need awareness in a product, then you need to know a little bit more about its benefits and value proposition to consider using it. They call that consideration. Then if you kind of are interested in that, like in our case, like privacy benefits, you may trial and then that initial trial point on like is kind of an activation in the funnel. And then you want to retain users and then ultimately a referral. You want users to be happy enough that they’re referring you. So that’s the traditional like marketing funnel. And different companies have issues you know, at different parts of the marketing funnel where they would want to focus. I think it was pretty clear that we had some gaps in in in kind of all parts of the funnel almost equally. And the 3Ds from a product perspective map onto that pretty well. So, like the discovery part is just we keep measuring these surveys, but most people still don’t know about us, and most people don’t know about our different products. So there’s that’s an obvious gap. The dependability one, we’re constantly running user surveys.
Beah: Mm.
Gabriel: And so once people are activated, you know, our retention has gotten a lot better in the last three years because we’ve really improved the dependability of all our products. But we could tell it was a little bit of a leaky bucket, like people had come in and really wanting to love us and try us, but having an issue switching, or you know, we didn’t have, you know, sync or, you know, with their bookmarks. And so we felt that that was also deserved a lot of a focus because we could see that people weren’t always staying when they were activating. And then the third part is word of mouth. So we had grown mainly through word of mouth. The most most of our users have come through word of mouth. But a lot of that was on the back of privacy being one of the biggest stories in tech. And I I hope that’s coming back, and it should, because AI has a lot of privacy issues. But you know, since the pandemic that has been less so. And so to get word of mouth without a big news cycle all the time, you need delight.
Beah: Mm-hmm.
Gabriel: And so it’s really mapping these to a funnel and making it a product focus of that funnel.
Beah: Yeah.
Gabriel: And then something that has either if you’re gonna pick names, you either have to rhyme or they have to have some alliteration. So this is alliteration.
Beah: Yeah. On the delight thing, I also think like for a I think can because we are not the default in many senses. I I think delight pre plays a pretty important role in retention too, because like there’s sort of this FOMO factor to retention where if you run into a little bit of friction on DuckDuckGo, I think you’re more likely to churn than if you run into a little bit of friction on like what people what is actually literally the default or people perceive as the default, you know, and I mean literally the default. Like if you have an Android phone and go, you know, Google provides your default browser, your default search engine, like you people run into friction or, you know, bad dependability like in any product. But I think like their tolerance for it can be lower initially in DuckDuckGo than in other than in Google, basically. So I think delight is an important antidote to that as well. Like you have to have a reason to be willing to endure occasional friction. I mean, we’re gonna keep trying to reduce that friction and make the product better and more dependable, but like it’s just never gonna be perfect. Like we could be, you know, yeah, no no product is ever perfect. There’s always rough edges. So I think delight also just helps on the retention front too.
Gabriel: Yeah, I mean I I think you’re making a good point. I presented it as mapping on different parts of the funnel, but I think you’re absolutely right. Delight really can map onto all parts of the funnel, even the top where you can have delightful marketing and people, you know, we hope that might be wanna be associated with our brand and our product just because not just values driven on privacy, but also we’re presenting that to the world in a way that resonates with them, you know, like they want to be associated with us because they like the way that we make our product and present our marketing and present our, you know, our app store page and stuff like that. I know you thought more about delight from the product side as probably anyone else in the company. Do you have like any deeper thoughts on like from the product side, like how to even think about going to going to incorporate, you know, that kind of experience?
Beah: Yeah. Yeah. I mean, the way I think about delight usually is is basically addressing the emotional needs of people, not just the functional needs. So you can have a feature or an interface that just that like has that works in some basic sense, but it’s not actually pleasurable to use. And like, you know, we’re humans, and emotional beings and pleasure is worth something. So yeah, I mean, I think that can take so many forms, and that’s part of what’s difficult about it. And sometimes the most like sort of the lowest hanging fruit stabs at delight are not in fact delightful at all. Like you can bombard people with confetti, you know. And I don’t think that’s usually delightful. I mean there might be moments where some kind of celebration in the product is is good and appropriate, but like there’s a lot of ways to be annoying too that that if you just try to inject some personality into the product or some delight like you that can take you down some some bad paths. So I think one of the tricky things about delight is that it like requires maybe several ingredients. One is a point of view. Like I think there has to be some consistency. Like it I do think personality matters and brand matters. And if like the product’s just like throwing all these different sort of personalities at you, that like that’s actually like disconcerting, not delightful. To another ingredient that I think is really important, maybe most important, is just like the amount of like rigor and finesse and discretion that has to go into delight features. Like sometimes it means obsessing over a single word, like way more than is obviously worthwhile. Or I don’t know, just like fine tuning the details until it feels just just right. The good news is I think you can usually test for it qualitatively pretty well. And we have a lot of good qualitative testing tools, and we can kind of like you know, put it and and the other thing is once you’ve spent so much rigor internally on something, it it is like awfully hard to look at it the way a new user would. And so I think qualitative testing is is important. But the bottom line of all of that is I think it is actually like often pretty expensive to do well in terms of people’s time and effort. And so it it requires like a real commitment, which is I think, you know, speaks to why it is in this company top priority is it is a statement of commitment to putting in the time to do this, do this well. The other thing I’ll say is that I also just think it’s like more fun to build products that are delightful. Like personally, even though it is so hard and time consuming, it is like one of my favorite ways to spend my time at DuckDuckGo. And I think people who are good at it and like it generally agree. So yeah, maybe that’s kind of a good segue actually to another question, which is how we actually operationalize the 3Ds. Like we say this is the company top priority. Like beyond that, how does it work its way into our day to day or week to week or month to month operating rhythms?
Gabriel: Yeah, no, that that’s a good segue. I mean, I think to some extent every product would like these three things, right? They’re like mapped onto the funnel and they need to be thought of to some extent pretty much by everybody making any product. But to your point about delight, despite wanting it in our product and knowing how important it was, I don’t think we were getting it until we set the top priority. And so one answer to that is and when they get to how to operationalize it, but like just saying we wanted it was not working, you know, like and even with dependability, it’s like, yeah, we have lots of bugs and we would want to fix them all, but we weren’t systematically, I think, addressing dependability in some areas as well. As we needed to, and we were seeing that in different metrics, say like crash reports and stuff like that. So I think operationalizing it could look different in different companies. For us, you know, we often operate as objectives, so we have like maybe 25 different objectives, and so we’re thinking about what are the top discovery dependability delight issues that we have, and then making an objective around that. Which each objective will have its own then set of projects to go solve that objective. And so we’ve been doing that for about eighteen months. And then within each pro
Beah: And you and like we’ve started in that time we’ve seen objectives that have started stating their problem to solve in terms of the the 3Ds, which is cool.
Gabriel: Exactly. And that and and and I think you’re right that it takes that amount of time to like get it in people’s heads to start thinking that way. And then even one below on the project level, you know, we’re tagging projects with the 3Ds. So is this a project about one of them or multiple of them in some cases? And even the criteria, I think to your last point, of individual projects are often related to one of these. So like dependability has KPIs depending on different contexts, but like crash reports is a good example. Like let’s get this crash number down in this way. Delight we’ve to your measurement point earlier, we’ve developed our own internal like KPI survey methodologies to to like actual test delight. And discovery, we can run surveys as well on our users and the general public to understand like is this working? Are we raising awareness of these different features? But essentially we’re operationalizing it by literally tying the company objectives and projects directly to these 3Ds and making the success criteria about.
Beah: Yeah. One other place that it shows up it is we just recently f had Hack Days, which is a convention at DuckDuckGo where everybody there’s a few days in a week that basically people can work on any project that they think would be interesting to work on, dedicate three days to outside of our normal like prioritization rigor. And at the end everybody who wants to demos in a company meeting the the project that they worked on. And we give out some awards and special mentions. And those are grouped in terms of like, you know, best of in those three categories dependability, delight, and discovery. So yeah, it feels like over the last year it’s really kind of woven its way into the the fabric of the company. And I think that’s been I think people really like it too. Like it just, you know. People come to DuckDuckGo from different functional backgrounds and skills and it like gives us some shared language to reason about product decisions and prioritization. I think it’s I think if you surveyed people it it would be a you would find that like it’s been a really rewarding shift for the company.
Gabriel: Yeah, I think people in particular like the permission, like you were saying earlier, for delight, you know, to be able to spend a little extra time and on polish and trying to do something really cool that they’d like to their friends and family to see that they’d be proud of. You know, like I think that’s where you get to when you even go beyond dependability into delight. Also just wanna say that we had an earlier Hack Days episode, so if that is any interest, you can go look at that in the archive.
Beah: We’ll put that in the show notes. I get to say that because I don’t write the show notes. So let’s see.
Gabriel: Yeah, well they’re just a transcript of the whole thing, so effectively effectively you’re right.
Beah: Cool. Okay, well that sounds like a good place to wrap. All right.
Gabriel: Cool. Well thanks for you.
Beah: Thanks, Gabriel. Later.


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In this episode, Zac (SVP, User Insights) and Mario (Marketing) discuss our study on the AI privacy problem: from stats and surprises, to AI hot takes, and how DuckDuckGo can help.
Show notes: Learn more about the study and our findings here
If you have feedback on Duck Tales, or episode ideas, email us at podcast@duckduckgo.com.
Disclaimers: (1) The audio, video (above), and transcript (below) are unedited and may contain minor inaccuracies or transcription errors. (2) This website is operated by Substack. This is their privacy policy.
Mario: Okay, there we go. Hey everyone. Welcome to Duck Tales, where we go behind the scenes at DuckDuckGo and discuss the stories, the technology, and the people that help build the privacy tools for everyone. In each episode, you will hear from employees about our vision, product updates, engineering, marketing, but also our approach to AI. Today you’re going to hear about some brand new research, very fresh, that we ran about what Americans actually think about privacy and AI. And I’m here today with someone who happens to have the exact same haircut as me. And that means luscious locks, of course. Zac, do you want to introduce yourself?
Zac: Yeah, hey everybody, I’m Zac Pappas, SVP of User Insights. I’ve been at DuckDuckGo for 14 wonderful years, and I’ll be talking to you about this study today.
Mario: Wow, fourteen years. Yeah, we’re we’re very lucky to to have you here. you’ve been, you know, working in Insights for a long time. You’re one of the first, very first DuckDuckGo employees, is this correct? Yeah.
Zac: Yeah, I think it was technically the second employee back when we were just a few people around the Paoli office here in Pennsylvania. So it’s yeah, it’s been a long fourteen years. but I think it’s been really exciting and things changed so so frequently that it doesn’t really feel like we’re doing the, you know, same insights or kind of same research over and over again.
Mario: Yeah. Well that’s that that’s pretty good. But it’s also useful because it means you also have about as long as a view as anyone on how people feel and think about privacy, you know, what they want protected, what they say versus what they do. So I think this is gonna be an interesting conversation. And yeah, by the way, you’ve introduced yourself. I’ve been talking, I have been rude, I haven’t introduced myself. My name is Mario, I’m in the on the marketing team. I’ve been here for almost four years now. a fun fact about myself is that I like to tell people I once came in fourth in the Eurovision Song Contest. For those who don’t know, it’s like it’s big music kind of song festival and competition in Europe. So yeah, I like to say I once came in fourth, but kind of. So not exactly. And I’ll just leave it at that. what about you, Zac? Do you have any fun facts you want to share? Or obnoxious facts? Also loud.
Zac: nothing that interesting or stand out. I guess y like most people, when I have the time I will go out to my garage, fire up the forge, and do a bit of blacksmithing. so pretty standard stuff.
Mario: Wow, okay, that’s that’s quite cool. I mean, this is this is Duck Tales, not Zac Tales. If it was Zac Tales, I would ask you a lot more questions, maybe put in a request. So
Zac: Next episode, yeah.
Mario: yeah, next time. Okay, let’s let’s jump in. So we as a company and you particularly, since you’ve been here for so long, we’ve been studying how people think about privacy for close to, I know, in this case like 15 years or more. You know, what’s what’s new is doing that specifically for AI. in June, our user insights team surveyed 1944 US adults, all quota balanced to census on age, gender, region, et cetera, about their AI privacy concerns, what they know, what they don’t know, what they actually do about it. you’ll find the methodology on the show notes if you’re interested. And this is this is what we want to talk about today. So, Zac, let’s let’s start with the juicy stuff. To you, what was the most surprising thing in this study?
Zac: I think given the fact that AI has been so pervasive and around for so long, most surprising thing was that I think forty-three percent of everyone knew none of six basic facts about how how AI chats are handled. So that includes a third of active AI users who can’t, you know, recall or kind of signal what basic data privacy and data handling practices that the AI services that they’re using are actually adherent to.
Mario: Well— well, can you tell us a little more like what facts you’re talking about?
Zac: Yeah, I actually wrote them down so I can get them correct for you. So the facts we asked people about were AI chats can be subpoenaed by courts and law enforcement. AI chats can be made public in a lawsuit. Employers can access work or enterprise AI accounts. Settings to delete or stop storage vary by provider. Free accounts often have weaker privacy than paid accounts, and humans can review your chats. Some of these maybe you would expect most people who haven’t dug too far into AI or privacy policies, they maybe wouldn’t think of those things first, but certainly human employees. Employees can review your chats. We’ve seen instances of this in the past with other companies in data collection when there’s a breach or when there’s a mishandling of data. Those are some pretty basic things that I would have assumed people at least had some suspicions about, but it’s surprising that they didn’t.
Mario: Yeah, this is this is a little yeah, it was a little unexpected the the level of of at least to me, you know, listening to this. I I was looking like looking a little bit here at the at the details. Like I saw, for example, on the that the AI chats can be subpoenaed by the government, for example, which means they could then made accessible in public because there were a lot of news about this, about some cases about AI companies. It was about only twenty five percent of people knew this. So why do you think people don’t know these facts given it has been you know, so much on the news. I know this isn’t in the study, but just curious about what you would you
Zac: Yeah, I I mean i it it’s definitely a different technology. I think it’s been a lot more opaque, the way that it’s been integrated and with the speed that it’s been rolled out, and the kind of the breadth that it’s been rolled out to most products that people are using. I think they get a different flavor and a different version of AI everywhere that they look with, you know, incomplete data practices, meaning they haven’t, you know, execute at the executive level, most companies haven’t really figured out how to deploy AI safely. So what they’re getting is say a rushed experience, but something that probably wasn’t built in the best interests of data privacy or kind of long-term sustainability in that regard. so here, yeah, it’s a it’s a maybe a combination of it being kind of a pervasive, like broad applic broad applied technology as well as how fast it has risen. and maybe my own personal experiences, there isn’t that that much useful information for a mainstream person, maybe who’s not a developer or a coder who’s you know gonna be really in the weeds of the technology. It’s kind of hard to find some of the better, you know, more mainstream or general approaches than how to use it safely.
Mario: Yeah, yeah, I can s I can see that. Thanks for that. So yeah, we know what’s the most surprising thing about the study for you, but like asking the bigger question, like, are people worried about privacy in AI in the AI context?
Zac: Yeah, absolutely. Seventy-five percent of people are worried about privacy when using AI. And that has been consistent for years as we’ve been running these studies. And we’ve seen this with external research as well. invasion of privacy, just the discomfort and uneasiness of using it and not knowing how the data’s being handled, and surveillance are usually around like 50% of what worries people the most when it comes to AI, and usually something privacy specific is in like the top two factors among those that are concerned about or like withholding use and not using it at all as a c it’s a contributing factor to why they’re not.
Mario: Yeah, and and yeah, and I I remember some of these studies that are published, like outside of our own, and it’s always like main concerns about AI and there’s usually one that’s like, you know, economic impact or job losses and then privacy is usually hanging there on the top. So I I definitely review myself.
Zac: Yeah, absolutely. And you know, I I think that gets lumped in with a lot of things. particularly the thing that I think most people do end up worrying about is that it’s going to have some impact on their financial situation down the line. That’s your personal details being involved in a breach, credit card details, somebody getting into or unfreezing your credit account and trying to open a new line of credit, like a lot of these things at the end of the day, I think, get back to the comfort and stability that people have with either their finances or their way of life. is it gonna jeopardize you know a job application that I have in the future to find something that you know I’ve put publicly online or didn’t know that I was putting publicly online through use of a service? So I think that we should expect that it’s going to be a top issue, especially as we kind of continue into the future as we integrate AI with all of these other services that are being used, whether it’s other apps or subscriptions.
Mario: Yeah. Yeah. And and I also remember other studies that that you guys have run and where it isn’t like there’s a one single thing about privacy that that people are worried about. There’s a broad concern about privacy. And and for some people it’s generic on like I don’t like the surveillance. I I just I don’t it’s an invasion of privacy. It can be a little a little more amorphous. For others it’s a bit more concrete, but it’s it’s the main thing is that there’s a broad general concern on this on this topic. And
Zac: Absolutely. Yeah.
Mario: So there’s a big concern about privacy. Seventy-five percent of people worry about it when using AI. What are they doing about it?
Zac: It’s a tough story to tell. not tough because we don’t have the data or that we don’t understand it. It’s just like kinda hard to to piece through. So about forty percent have taken no action at all to protect themselves. but what that means is sixty percent have, and about half of them of that sixty percent, have like just either, you know, avoided using AI or maybe toned down a question because of privacy concerns. And so that kind of nets out to about one in three of the US adults that we polled at least have held back from using AI in cases where they’ve had a privacy concern. You can imagine, given the volume, the number of people using AI, that is a lot of potential interactions every day that people are either withholding or kind of transforming into a way that they feel more safe in using it. Others are actually seeking out the privacy options that exist. So there are some things like temporary library chats, switching between multiple accounts or services so that you’re not, you know, contaminating, so to speak, one AI with some context that you might not want it to have. But where there aren’t strong defaults, we can expect that to be an uphill battle as we saw this with search privacy and browser privacy, is that people tend to default to whatever the first thing is that they see in the search that they’re using. So it’s no surprise that the number isn’t higher because it’s not easy for people to find, even though they are demonstrating a lot of pent-up demand and action on it.
Mario: Yeah, I I this is interesting. I I I remember once being in like a a qualitative study and I’m we’re just talking to people and you know they’re talking about their concerns. And and I remember there were a few people that they were saying they would, for example, they would often when they’re asking something to AI, they’ll even change what they’re writing. So they will not write what they want to ask, they will use some code words for example. So there’s there’s people are are are kind of editing themselves, even when they’re
Zac: Yeah, for sure. Another big part of this that we didn’t study specifically here, but that I can imagine plays a large part in the psychology behind this is not having a very clear policy even on the like work-administered AI or if there’s an organization that you’re a part of, if they’ve given you access to a you know company tool, usually there’s some concern there about what the admin of that tool can see, what you know, how the company is collecting all of that data even from their own employees. So it kind of goes back to the idea that they’re not given the the right instruction or the right success setup to get them started with AI in a way that’s actually going to help them long term and abate this issue of them having a concern about their privacy the whole time that they’re using it.
Mario: Mm-hmm. Yeah. Yeah, of course, of course. so yeah, people have these concerns. Do they trust the AI company?
Zac: Yeah. No, I knew you were gonna ask that. No, it’s fourteen percent of respondents say that they mostly or completely trust AI companies. I think it’s thirty-nine percent have no trust at all. and that’s been, you know, so one of the more consistent findings. especially I think it gets worse as companies continue to demonstrate some pretty poor handling of the rollout of AI, both from a privacy perspective and also just a UX perspective. I think it it contributes a lot to the mistrust that people have in the tool when they can’t really understand why it’s being integrated into their product experience. but if you look at something, you know, different than that, a place where AI hasn’t really been integrated that much, at least in a salient way, you can look at a stat, this US government trust in AI was even worse. It was 48% have no trust at all in the US government to mostly or completely or to to prep protect their data.
Mario: Hmm. Okay. well. Yeah. I’m I’m I can imagine it’s not just US government specific, I can see and being generalized even in other places, even though this is just a US study. Wow, okay.
Zac: Yeah, I mean maybe my o my my other personal take on this is there really aren’t a whole lot of good actors or, you know, role models in the space. I hope DuckDuckGo can can really stand for that and show people how to do it well. But if you think about people who are doing, you know, good AI rollouts, particularly the way that they’re handling, you know, user data or other types of data, there aren’t that many great examples. I can’t, you know, even think of any right now.
Mario: Yeah. Yeah. Okay. So we have three quarters of people that are, you know, of people that are concerned about privacy when using AI. We have about two-thirds of the US using AI in some way, shape, or form. So how do you kind of, you know, how do you how do you think about these two, you know, similarly contradicting, you know, stats if folks don’t trust AI with their data? Like how do you how do you explain that?
Zac: question. That’s the classic privacy paradox. But as with the past, I think it’s kind of a a load of BS in the sense that people still use things that they don’t trust, especially when they don’t have another choice. So a good example might be like your cable company. I don’t think most people you know have a really high affection for their cable company, yet they are forced to use it. They, you know, use it in mass. So oftentimes there aren’t other options. And if DuckDuckGo is an example, like people would rather use something that you know doesn’t feel like they’re paying for it with their data. And I think it was just the stat in our study that said 58% of AI users agree that like privacy keeps them from doing more with AI than they currently do. And goes back to that stat of the one third of Americans kind of like preventing themselves from putting something into AI in one of those cases. Like that’s a lot of this pent-up demand and uneasiness that shows you people are still using the tool, but they could be using it, say more more happily or in a more delightful way that doesn’t come with that that trade-off to their concern.
Mario: Yeah, yeah, yeah, totally. I I can see that. And so okay, clear. There’s concerns. you know, there’s a privacy paradox. Is there anything like when you looked at this broad data about concerns trust, is there any pattern when you look at specific segments? Is there you know, are these are these concerns like common across, you know, different segments or
Zac: Yeah, it’s funny, is this was a question that was asked a long time ago when I first started because we we used to look you know at the same thing with regards to search privacy or browser privacy. Is privacy you know more pernicious or a greater care for certain demographics or certain segments? and and no, at least here, like same thing with with broad privacy, it stretches across gender and political affiliation, urban versus rural, psychographics, demographics, and really something that I think everybody’s concerned about. If you you know apply some some basic rigor to that. I think you could say, you know, people who are less technically inclined, maybe older folks, or people who are less, you know, educated might have more of an aversion to it in certain cases or for certain applications. But broadly it is something that stretches across all walks of life that we that we’ve at least that we’ve studied.
Mario: Thank you. Thank you for that. So, okay, moving from this this this area. was there like when you go through the data in this study, was there any other facts that you think would like surprise people if they would hear about it, anything that came out of the study, any insight that would surprise anyone, you know, reading through it?
Zac: Yes, and we are gonna put something up on our Spread Privacy blog about this. the stat that I thought was most surprising was 32% of all AI users have shared something with AI that they have not shared with a family member, friend, or like a doctor. so that just kind of goes to tell you that there is this large contingent of people who are kind of giving away more to AI than they would even to the most trusted confidants that they have.
Mario: Yeah, so so in a way they are using AI as a confidant more than humans, sometimes. So
Zac: Yes.
Mario: yeah, it’s not surprising. I mean, you know, just you know, just the talking to people and and and seeing the news and I c I can see that, but is this something again I’m gonna ask you the segment question, is this something that is more prevalent on certain groups and less so on others?
Zac: well earlier you were asking about privacy concerns, or privacy concerns are are prevalent. Across pretty much every psychographic and demographic, and like I said, something I think everybody feels. On usage and the way that they’re using it, the interesting thing that we found was that parents are actually confiding in AI almost twice as much as non-parents. So among parents that use AI, 43% have told them something that they would have never shared with another person, and that number is only 25% for non-parents. So there’s this you know large gap, and you can kind of think through some of the reasons why that parents would versus non-parents would be kind of confiding more in it or giving it more you know lucrative information from them. partly I think because you know there’s a a lot of certain life stages where that level of expertise is both like expensive or inaccessible. So parents probably are asking a lot of questions that might be a little bit embarrassing or a little bit too difficult to reach out to somebody about, maybe requires a lot of context. And so here you see this behavior with parents kind of confiding more in AI compared to non-parents. And I do expect that if we— So,
Mario: Yeah, and and and that is a very interesting stat. And it reminds me once when I was talking with a few let’s let’s say AI users who were were parents from another study, and I remember them talking about the tension of sharing things with AI because it’s useful in many ways, but at the same time there’s also concern. So that’s it’s just interesting to have this one. Thank you. Thank you for this one. So you mentioned also before AI training, like AI chats being used to train AI. So i y you mentioned that. you mentioned that in general people didn’t know didn’t know about all o any of these facts about AI, but specifically about this one, about training. Like do people how many people know about it? How do people feel about it? Because this is something that’s talked about a lot.
Zac: Yeah. Yeah, so the earlier stat was, you know, forty percent of people can’t name even like one of the six basic stats about how the data is handled. but if you look specifically at the stat or the the the criteria of like whether or not people are aware that AI services can train based on your data, 53% of people didn’t know that. Or or they weren’t sure that their conversations were used for AI training. And when they learn about it, of that 58%, it’s, you know, about half of them or more than half of them are really uncomfortable with it. And I think 30% are very uncomfortable. So it’s something that I think as people do realize it or find out how it’s being applied, and learn that these, you know, that if you take that group of people who had no idea about any of these six concerns, the more exposure that they get to it, the more that they learn about it, the more concerned that they’re likely to develop.
Mario: Yeah, yeah, and my awareness of the of the issue and it’s it’s so important. Okay, that’s that’s interesting but also good good to to understand. so we’ve been talking a lot like about the study and like I think we can almost finish the start finishing the conversation here on on the study. But I’m just curious, especially with your you know, long experience studying privacy and technology, how is studying AI privacy different from studying privacy in other technology or studying the the consumers and users’ opinion about it. Like what’s what’s different from what you’ve been studying for for many, many years?
Zac: that’s such a great question, Mario. there’s a lot that’s different. There’s also a lot that’s similar. I guess one of the hard things is there’s all there’s always a background rate of people’s misunderstanding between where certain products and and others begin. So search to browse was historically one of the kind of boundaries that I think people had a hard time with. Where does Google Chrome stop and Google search start would be kind of something that we would kind of try to get our heads around. Here at the simplest level, people just give AI more. It is a, you know, chat box with a lot of text entry. It makes it very easy to just give it kind of more information. And they also do that through voice prompting and being able to use your microphone, connecting services through either MCP or other kind of connectors and just giving it tons and tons of context that I think lead people to providing more deeply personal information, a richer set of data. So if you think about what the AI company sees on the other side of that, they’re just getting a lot more. And so if you can kind of go back to the risk that people are rightly concerned about, they’re giving a ton of data. There’s a ton of interpolation on top of that about what that data means. It’s very opaque, if not completely unknown, how the the AI companies themselves are handling or storing that data. And then you come back to that risk. If there is a breach, if there is some bad actor or an employee who has access to this, what they have access to is a completely different level of detail and context than than prior. So in researching this, something that we try to take into account is one, this this kind of background rate of people who, you know, share either a deep cons like share a deep concern as well as a like low understanding of exactly— exactly what the concern should be about. And like that, sussing that out a little bit more, I think, is something that we’re gonna have to get a lot better at over time because the products are gonna get more complicated and, in my opinion, more homogenous. almost every app that I use today has some kind of a chat box in it. They all look very similar, they all act somewhat similar. and at the end of the day, I think what people are really trying to get back to is a solid understanding that their lives are getting better and they’re not trading off something extreme like their privacy or their— yeah, their wellbeing as a result of that.
Mario: Yeah, the the black box. you know, this is this reminds me of like I remember once watching Chanel, one of our researchers, and and she was talking to to someone and this person was was saying that with with AI, it’s it they were saying if if someone sees what I’m typing on a search box or on my browser, they’ll know what I do. But if they know what my AI chats are, they know how I think. So there is this feeling of a lot more intimacy and sensitivity and exposing your putting your brain on a paper, like it was even an expression I’ve heard someone use. That yeah, I think it it connects well to what you to what you just said.
Zac: Yeah. Yeah, exactly that.
Mario: Yeah. well, cool. Well, Zac, I think this ends the study discussion. You know, it’s good you give us this context, but you know, this is a little it’s a little it’s a little dire. Like where does this leave, you know, internet users and and people, you know, who just want to use it to, you know, improve their lives and and and you know and yeah, essentially. What’s do you have any words for us on that?
Zac: Yeah, hopefully these people end up finding out about DuckDuckGo. because we’ve made AI optional and private by default. So this is something that, you know, because this is our bread and butter, we’ve tried to understand it and really make a product that serves the interests of of you know mainstream people looking for useful AI that doesn’t come with all of these privacy trade-offs. More so though, like this is about trust. And we’ve built our products around raising the standard of trust online. And I think a lot of companies are going to have to meet users in the middle. Otherwise people, as they normally do, will vote with their dollars or kind of walk out of these products in search of something better.
Mario: Yeah, yeah. Well, okay, that’s it’s good to to end on this on on a trust and hope. And I’m glad we’re you know as a company that we’re trying to help in in making things better for for everyone. So okay, so my you know, I think like if I could summarize this, people are worried and check me and check if I’m saying something wrong. People are worried about AI and privacy. they’re worried, but they still use AI, even often as a confidant that they’ll say things that they don’t say to others. when you do inform people about the specifics of how they are at risk, they don’t like it at all. people that are already concerned about privacy, many are taking action. For example, by not asking certain questions or using AI less. and this is kind of like what I’m what I’m taking from it. And again, it’s good to know that, you know, companies like DuckDuckGo, but others as well are building tools, are building tools to solve this this issue. And this is a better place to be than, you know, nobody cares that there’s there’s people acting on it and that people are also taking action for it. and yeah, so Zac, I thank you for this, thank you for this overview, but I don’t wanna, you know, I don’t want to end on this on this note. So we’re gonna try to do something we have never done on Duck Tales. Are you willing?
Zac: yeah, this is this is not my first Duck Tales. I don’t know what you’re prepared for. So let’s let’s do it. I’m excited.
Mario: Okay. Well that’s okay, very good. So you’ve heard about Subway Takes, like popular online series. A lot of fun. Yeah, Subway Takes, hot takes. Yeah, yeah, or hot takes. Yeah, that
Zac: Like hot takes, set of things like hot takes. Okay. Yeah, yeah, yeah.
Mario: that works, that works well. So yeah, so we’re we’re gonna adapt this format a little bit. I’m gonna give you a topic. You’re gonna give me your hot take, and then I’ll tell you if I agree with it or I disagree with it. Okay. The most important that’s honestly the most relevant part. So we’re gonna
Zac: The most important part. Okay. Yes, yes, yes, yes.
Mario: call this Zac Takes. Okay, it’s a new segment. Very good. Ready? Okay,
Zac: Love it. Yes.
Mario: so what’s the most overrated thing in AI right now?
Zac: frontier models. Do I have to explain these by the or should I explain them? Lightly.
Mario: Slightly, just slightly.
Zac: Frontier, I just think if what you were using yesterday was now all of a sudden invalidated by something that was released today, then we kind of lost the point. And I understand this is a little bit of a loaded question and I don’t get enough time to respond to it, but I’m gonna say frontier models. I don’t think they’re as big of a deal in terms of my experience.
Mario: Yeah. Very good. Fifty percent agree. Fifty percent agree. That’s where I’ll leave it. What’s what’s the most underrated thing in AI right now?
Zac: Yeah. same this is where I was turning— task completion. I think that is the thing that we should be talking about and whether or not people are actually getting something better than they were getting before. Can they order pizza faster or better, more consistently? Can they converse with their family about things that they couldn’t do before? I think we kind of lost sight of the basics of building good product is satisfying task completion.
Mario: Well, I’m gonna give you a I’m gonna give you a another fifty percent agree on this one. I would counterpoint with I think giving people control of if, how and when they use AI. For me, that’s like an underrated feature, let’s put it like that, that I think we could all we could all benefit.
Zac: That is good. Yeah.
Mario: So in five years, another one. In five years, do you think private AI is gonna be its own category? It’s gonna be a checkbox inside every big AI product.
Zac: I I would say both. I think even I mean now it is a table stakes feature I think that people are are expecting, even though most companies aren’t delivering it or delivering it transparently. so I would say that it’s both. And I do think before long we’ll see a lot more vertical integrated AI where somebody’s gonna try to make a play either for privacy or some level of trust, which is the big differentiator.
Mario: Okay. I can’t not say a hundred percent agree on that one. So final two, what’s an everyday object that most deserves a privacy policy?
Zac: the easy answer would have been like mirror or something, but I’m gonna say hotel key card because and this is just a personal thing. Every time I have a hotel key card, it is I I don’t know if I should throw it away. Do I return it to the desk? Should I shred it? I’ve heard that there’s nothing on there. It’s just my I d I don’t know, but at this point I would just like to know when I receive one. Yeah, if there’s just maybe a little disclaimer on the back of the card that could just tell me exactly what’s on there. I’d feel a little bit better about how I can.
Mario: But information is important. You’re confusing. Yeah. Very good. Finally, last one. Last one of probably the last episode of Zac Takes Ever. What is the single most correct way to spell Zac?
Zac: There’s no way to know. That knowledge was lost centuries ago. I did in high school, I don’t know if if this this person’s gonna be watching this, but I did in high school try to make a play for Z-A-Q as the way to spell Zac. Yeah. Yeah.
Mario: Zac, I’m gonna say a hundred percent agree. That’s it. That’s it. We found the answer. We found the answer. No way I need it.
Zac: I haven’t seen anybody pull it off. I don’t think I am the type of person to pull it off, but if I if I would have started in the early two thousands, I think by this point it would have been fashionable.
Mario: There you go. Very good. Very good. Well, nicely done, Zac. Anything else you want to add before we wrap this?
Zac: Nothing for me. Thank you so much, Mario. This is great.
Mario: Very good. So Zac, thank you so much for joining Duck Tales. And to everyone listening, if you’ve got feedback on the show, episode idea, you want to complain about the host, want to know more about this study, for example, write to us at podcast at duckduckgo.com. That’s podcast at duckduckgo.com. We read all of them. We will listen to them if it’s an audio recording. We will see the video in case you’re sending us a video. We’re multimodal. So thanks, Zac, and see you around.
Zac: Thank you.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit insideduckduckgo.substack.com

In this episode, Beah (Chief Product Officer) and Chris (Partnerships) discuss the DuckDuckGo Subscription: the core protections, private AI, and how the new Pro-tier serves AI power users.
If you have feedback on Duck Tales, or episode ideas, email us at podcast@duckduckgo.com.
Disclaimers: (1) The audio, video (above), and transcript (below) are unedited and may contain minor inaccuracies or transcription errors. (2) This website is operated by Substack. This is their privacy policy.
Beah: Hello and welcome to Duck Tales, where we go behind the scenes at DuckDuckGo to discuss the stories, technology, and people that help build privacy tools for everyone. In this episode, you are going to hear about our Pro subscription plan. And my guest today is Chris Calvi. Chris, do you wanna briefly introduce yourself before we jump in?
Chris: I— I would love to. Thank you for having me back. I was on— a quick ad for people who have not listened to the original subscription overview Duck Tales episode. Gabriel and I did that six months— I don’t know, actually probably nine months ago at this point. So go find that. But I’m coming to you from sunny New Jersey. And I’m a member of the partnerships team here at DuckDuckGo. And I’ve been at DuckDuckGo for about five years. I love it here. And most recently helped run efforts to add this new Pro plan to the subscription, which we’ll be talking about on this episode.
Beah: Nice. I’d like to think of this episode as the Pro edition of the previous podcast episode that you recorded with Gabriel.
Chris: Okay, that’s fair enough.
Beah: Just to set the bar.
Chris: Yeah.
Beah: All right, and I’m Beah, I’m on the product team, if this is your first time meeting me. And yeah, I think we can kind of jump in. Like, let’s start with— can you just explain, Chris, what even is the subscription, what we’re talking about?
Chris: Yeah, and I always like to start with, like, why the subscription first, and then I’ll say what the subscription is. So the why: you know, DuckDuckGo has a ton of fully featured, excellent, free-to-use, no-login-required functionality. And we’ve had that for, like, what, fifth— 17 years? How— how long has DuckDuckGo been on? We’ve had it for a very long time. And— and so we, though, realize that there are— there’s extra functionality and features and protections that we would like to offer to— to our users, that you can’t offer— either can offer free, for free, because they have a material c—, like, a substantial cost to us, or it can’t be, like, ad-supported like the search. So— so two years, a little over two years ago, we launched the DuckDuckGo subscription. And— and that includes four protections— that are aligned with what I just mentioned, things that we sort of, like, can offer for free. And so there’s the identity theft restoration, which internally we call IDTR. And that is essentially, if you—
Beah: Not me. I call it identity theft restoration because I try to avoid—
Chris: You— you don’t like to use acronyms.
Beah: I’m anti-acronym, yes.
Chris: As a culture, we typically are. Though I like to break the rules. I break the rules.
Beah: I continue.
Chris: So in the event that your identity is ever— is ever compromised, your n—, your identity stolen and is used, for instance, to open up another credit card— for— that’s not— that you didn’t open, we have a phone number that you can dial into. Actually, let me share my screen and I can show you what I’m talking about.
Beah: You can do a live demo of calling a phone number and see what happens. Just don’t do that.
Chris: That would be risky. No, I’m just kidding. It would be good, but it would also be boring. So let me know— does this work? Do you see?
Beah: I see.
Chris: All right, so to get here— after— this is kind of like jumping ahead, but let’s say you have a subscription to DuckDuckGo already, you would log in, you’d— you’d come to your— if you have the app installed, you’d come in and you would select DuckDuckGo subscription. And in subscription settings, if you didn’t already have this device connected to your subscription, you would have to do that as a first step. But once it’s connected, then you have it and you can just come in here and go to the element of the subscription that you want. So let me walk you through those pieces of the subscription. The first one I was mentioning was identity theft restoration. And so what you would do is— in this example I mentioned where somebody opened a credit card in your name, and you’d want to re—, you wanna deal with that, you’d come in here and you would call our— this has some information for you about how it’s gonna go. You will click “talk to an advisor” and then you would make a simple phone call and they would help you make things right with your— your credit card companies, your banks, your credit bureaus, things like that. So that’s identity theft restoration. The— the subscription, as I mentioned, has four key pieces of functionality— we call it four protections in one. The second one is personal information removal. So this would also be available from your— your settings screen. You’d click on here and you’d click “open personal information removal.” And similar to the identity theft restoration, this would bring you to a page where it shows you what— what it’s going to do, what it is and how it’s going to help you and how you get started. And what personal information removal is: in the US, we have this insidious issue where data brokers essentially aggregate personal information about you, about everyone in the country, and they put it on the internet or they— they’ll sell it. Sorry, they’ll put part of it on— on the public web and then sell part of it. And this includes things like your name, your address, your phone number, your date of birth. And even relatives, like— that, like, lived in the household with you. I’m sure you’ve probably seen this if you’ve ever searched for yourself. And so with personal information removal, we go through and we opt you out of the listings on over 60 of those sites. And we’re adding more all the time. And so you would just come in here, you’d click “get started,” and this would ask you a few things, a few pieces of information which stays on your device and does not go to our servers. That’s important to mention here. And then goes through a process that’s run locally from your machine and takes some time. It takes, like, anywhere from a few hours to a few weeks or longer to get you fully removed from all of these sites. So some of the sites can act quicker than others, but this will go through and— and opt you out from being listed on those data broker websites. The— the— and interrupt me with any questions you have about this, any of these functionality, but we went through—
Beah: No, this is good, this good.
Chris: We went through the identity theft restoration, the second one being personal information removal, and then the third one is the VPN. And the VPN is— stands for, since we don’t like acronyms, virtual private network. And what it— yeah.
Beah: I guess I do allow VPN. I’m gonna allow it.
Chris: I mean, but— what it— what a VPN is, is it— is it, we set up a— anonymous secure server, and we have them in 30, or over 30, countries around the world. So wherever you’re at, you can connect to the closest one. And— and what it will do is securely— it encrypts your traffic and securely and privately tunnels it through these anonymous servers and then out to the resource that you’re requesting on the other end on the internet. So, like, all of your internet traffic is basically being tunneled through this— this private secure server and then out the other end. And so this— why— what, like, what this does is it protects you in— in— in two— two, like, key ways. One is your ISP, the internet service provider that you’re connected to, won’t be able to see the sites that you are accessing, like what sites you’re visiting. And that helps you because they’re not then able to— either it makes it more difficult for them to censor the content that you can access, and/or build an ad profile about you, for like an advertising profile, and then like resell that, for instance.
Beah: Okay.
Chris: And then on the other end, it— it protects you— the sites that you are visiting, they’re going to see the IP address, like, kind of like who you are, of the server. And so they don’t actually know who you are unless you were to, like, log in, for instance. So it gives you protection from both— in b—, on both of those cases. But, like, the— the— the use case that is, like, most common for— for the VPN, and I should have switched over to showing you this, is the— is when you’re at, like, a public Wi-Fi location, in an airport or a hotel or, like, a— a coffee shop, you don’t need those— you don’t need them seeing what websites you’re accessing. So VPN gives you that extra security. And I— as I had mentioned, you can change the location that you’re— the server, where the server is. I just keep it on “nearest location” and I’m on it right now. I keep it on all day. I use it all the time. And so I think that’s, like, the key thing about VPNs. I don’t know if the— if I did that justice.
Beah: You did. You’re doing such a good— I hate to rush you along, because you’re doing such a good job of explaining these features, but we have to get to the Pro plan. So I think you’re kind of getting to here.
Chris: The key thing. Okay, so let me just get you— yes, let me get you that last thing. So the—
Beah: Yes.
Chris: So the fourth feature is Duck.ai. And Duck.ai is free. But if you want higher limits and be able to do more on it, then— if you have a subscription, then you get higher limits, and that’s the fourth piece of functionality in the subscription. So there you go. That’s our subscription, those four things.
Beah: And explain the Pro plan versus the basic subscription versus free with regard to Duck.ai.
Chris: Okay, so free is— we don’t— we technically don’t ha—
Beah: And do you want to?
Chris: We call it a free plan. It’s not a free plan. It’s not, like, a tier in itself. DuckDuckGo— yeah, I should turn off— I could turn on— stop sharing.
Beah: Yeah.
Chris: So, okay, are we back?
Beah: I’m right back.
Chris: All right, so the— the f— DuckDuckGo, I mentioned before, it’s like everything is free on there until you get a subscription. So there’s— there isn’t really a free plan. You don’t log in. And then the— the— with the sub—, with the Plus tier, or the Plus plan, of our subscription— that was— that includes all of the functionality that I just mentioned. And— and then any— it— with Duck.ai you get increased limits over— and new models compared to what you would get on our— if you were just— just went to Duck.ai and didn’t have a— a subscription with us. The—
Beah: So when you say new models, you mean, like, basically fancier models that are more expensive to run.
Chris: Correct, yes, yeah, that’s exactly right. So just more capable, more f— more advanced models. And— and then the Pro— the Pro plan of our subscription is our newest plan. It just launched two months ago and is— is very much AI-oriented. It is a— it’s for the AI power user. And so for those who are using Duck.ai and want even more capabilities than you can get in the Plus plan, and higher limits to be able to be doing more work in there, then that’s— the Pro plan is for them. And so it includes everything in the Plus plan, but higher limits on AI and a—
Beah: But wait, there’s more.
Chris: —a Pro-exclusive model, which is Claude Opus, and better— like, better reasoning effort you can also enable in there, for when you need to— to kind of, like, go deeper on something. And I should mention the Plus— Plus plan is $9.99 a month or $99.99 a year. And the Pro plan is— is double that. So $19.99 a month or $199.99 a year.
Beah: Yeah, nice. Well, I am a daily user of our Pro plan features. I use Claude Opus via Duck.ai, I think, daily. So I can vouch for it. Well worth it. Can you tell me, Chris, were there any particular challenges with building the Pro plan?
Chris: Yeah, I mean, it was— like, kind— was like we started this in late fall, I would say, is when we started— the idea had been there, but in— in, like, mid-late fall is when we started actively working on this. And it was like adding a new floor to your home. So we— like, the home was built to be one way, and then we were like, well, we actually need to do this too. So kind of— just there was, like, the technical nuances of having that, right? This, like, all of the— every app version that we have, whether you’re using a mobile— you know, if you’re using iOS or Android or your desktop DuckDuckGo apps, all of them had to be updated to be able to recognize these new sub— the new subscription tier, and also to be able to help you get signed up to that, or to upgrade to that if you’re on the existing Plus plan and wanted to jump up to Pro. So I think that was sort of, like, the main challenge. But the— kind of the— to be more specific, the one— the one challenge that— is may surprise people f— is the— the actual subscrip— subscription flow— signing up or upgrading— is— is— is— was the biggest, I think, challenge. Because it’s— the subscription’s in 30— it’s in 30 countries. So we have to account for all of that. We have to account from where you’re coming from when you sign up, so that we can understand which features make sense for you to see, and, like, the— because we want it to— everything, all in this process, we want to be simple. And then adding that sort of second— second plan makes it complicated. You know, you want to be— be able to— to— to be clear to people: these are the things you’re going to get in each plan. And so you have to be very careful there when you’re— when you’re modifying things like that.
Beah: Yeah, you’re going from a “buy or not buy” decision to a “don’t buy, buy, which kind of buy” decision. And that’s going to vary a little bit— what you’re getting is going to vary a little bit by country— and me trying to be simultaneously transparent, extremely transparent, about all of these details and not overwhelm users with details they can’t absorb. Is that fair? Yeah.
Chris: Exactly. Yeah.
Beah: Okay, how about— were there surprises for you, like, after we’ve been live with the program for a couple months now? Like, any interesting feedback?
Chris: Yeah. Yeah. I mean, I think that the, like, biggest— there’s— there’s really two surprises. One is the pace of adoption of this— of this plan, the Pro plan, is much faster and gr— is growing quicker than I expected. And— and so that’s, like, on the— the plus side, the good news. On the other end—
Beah: Or on the Pro side.
Chris: On the Pro— the Pro, yes, to continue that— the Pro side. And then on the— I think, in terms of, like, the constructive feedback side of things— we launched this, and, you know, like, right out the gate when we launched this two months ago, you’d just— like, has been an issue before, I think— if you hit your limits, you would sort of hit a limit for your day and you’d be like, “no, like, I didn’t get any warnings, I didn’t get any, like, educational, what this means.” And so we received fair feedback on that. And so we’ve worked to remedy that. There’s now a usage— for, like, a progress bar in your settings when you have a subscription. So you can go in there and you can see, like, where you’re at compared to your limits. And we have also added some more proactive notifications. So it doesn’t just wait till the end, it gives you a few sort of, like, heads-up, like, you— you might want to consider switching to a— to another model that won’t home your— your budget as quickly. And so that’s— that’s kind of the area where we are spending a good amount of time at the moment, and trying to improve that, and how to be more— you know, it’s going back to the simplified thing, like how to be more simple and transparent with people about when they make decisions, like which models are good for everyday use and which you should be more using— more sparingly— when you really need that extra effort.
Beah: Yeah, yeah, like, limits are annoying on any plan. But if you’re— if you hit a limit and you’re, like, deep into a conversation and you just, like, lose all that context, that’s frustrating for anybody. But if you’re, like, paying for this thing, it’s like, that’s a new level of—
Chris: Yes.
Beah: —right? Like, your expectations are different.
Chris: Very ac— very astute point. And— and I— I think that, to be clear, we are— we are doing more there too. There’s some stuff that— hopefully— I’m not sure when this is gonna— when this is gonna air, but— but maybe by then there’ll ev— there’ll be even additional things that we’ve done to really improve the experience so that people don’t have those issues anymore.
Beah: That’s a good segue. Is there anything that you can share that’s upcoming for Pro that people have to look forward to?
Chris: Well, we are— across, like, both t— both plans, we are always looking to consider what new functionality should we be including there. Does it make sense? And, you know, I— in the— in the— for the Pro plan, I think the big thing at the min— at the moment are the— around limits, right? Around how we can just be— be— peep— make people’s workflow a little more efficient so that they don’t hit these stops, or they, you know, they can just work around them. So that’s— that’s kind of— I think that’s, in the short term, some of the stuff that we’ve got coming.
Beah: Gotcha. What if you are interested in all this but you do not know if Pro tier is for you? Can you try it for free?
Chris: Yes, so both the Plus and the Pro plan have a seven-day free trial, if you have not already used a trial. So if you’ve, like, tried Plus— the Plus prep plan already— and you want to try Pro, you’ll have to buy it for a month to just try it. Or if you— if you’re new, you can try either one for— for seven days for free.
Beah: Nice. Where does somebody go if they want to do that?
Chris: Yes, so you can go to duckduckgo.com/subscribe and you can see more details on everything I talked about today and— and sign up for a trial on either of those plans. But hopefully you’ll give Pro— Pro plan a— a try.
Beah: Yeah, I recommend it. Okay. Before we close, Chris, I want to ask you— this is an experiment. Haven’t done this before at the end of the Duck Tales episode, but we’re going to do it. You know how it’s, like, a popular thing at the end of podcast interviews to do a lightning round, like, fire off some quick questions? I have three questions for you.
Chris: Okay.
Beah: You get— you have to answer. My rule is going to be five words max. Can you it?
Chris: That’s gonna be challenge.
Beah: Okay. Let’s try it. Let’s see what happens. All right. What— model— we’ve been talking about model choice and Pro unlocking new models. What model do you personally use the most in your life, Chris?
Chris: I use Sonnet the most, and then I move— move up— this is really exceeding five words— but I move up to Opus as needed. So, like—
Beah: Okay.
Chris: —usually, like, at least once a day I’ll jump to Opus.
Beah: Sonnet, Opus as needed. I’m giving it to you.
Chris: There you go. Thank you. Yes.
Beah: It’s like a training cycle for the next question.
Chris: Yeah. We’ll see— yeah, let’s see if I can do this.
Beah: What was the location of your last functional team meetup? So, Chris, you mentioned you were on the partnerships team. We do— for those who don’t know, we do meetups where everybody gets together in a given location with their functional team. Also do that company-wide. Where was the last location of your partnership team meetup?
Chris: Rome, Italy.
Beah: Very good, very good. Okay,
Chris: Ha ha.
Beah: —this last one might stump you, but let’s try it. If you were Dax the Duck— Duck, Duck, Duck, DuckDuckGo’s mascot— what color bow tie would you wear?
Chris: Blue.
Beah: Okay, down to one word. You went from, like, 12 to four to three, I can’t remember, to one. It’s very good.
Chris: There is a— there is a, like, Haiku joke to be made here, ‘cause Haiku’s a model, but I’m not gonna— I’m not going—
Beah: That’s a very good audience.
Chris: —there.
Beah: I think you just did. You just nailed it. All right, well, thank you, Chris, so much for joining Duck Tales and talking about the Pro plan. And listeners, I hope you give it a try if you haven’t already. And yeah, see you around.
Chris: Thanks for having me.
Beah: All right, bye.
Chris: Bye.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit insideduckduckgo.substack.com

In this episode, Beah (Chief Product Officer) and Mihai (Design) discuss how the Duck.ai team prioritizes what to build, how we use research and user feedback, and a demo of recent UX updates.
If you have feedback on Duck Tales, or episode ideas, email us at podcast@duckduckgo.com.
Disclaimers: (1) The audio, video (above), and transcript (below) are unedited and may contain minor inaccuracies or transcription errors. (2) This website is operated by Substack. This is their privacy policy.
Beah: Hello, and welcome to Duck Tales, where we go behind the scenes at DuckDuckGo and discuss the stories, technology, and people that help build privacy tools for everyone. In each episode, you’ll hear from employees about our vision, product updates, engineering approach. And in this particular episode, you’re going to hear from Mihai, who is here to talk about the various UX improvements that we’ve made to Duck.ai recently. Mihai is on the design team. And do you want to just give your give a quick introduction of yourself, Mihai?
Mihai: Sure, thank thanks for having me. My name is Mihai. Originally from Moldova, lived across the world in many places, now based in Switzerland, joined DuckDuckGo maybe around three years ago, and I’ve been on the Duck.ai team almost from the very beginning, so I’ve seen the product evolve since then dramatically. So so yeah, we’ll talk about it today.
Beah: Nice. Well thanks for joining. For anyone who hasn’t met me, I’m Beah. I’m on the product team here. I’ve been around for a while. So yeah, I’m gonna go ahead and ask Mihai some questions about his work on Duck.ai and we’ll just dive on in. So I think I said we’re talking about improving Duck.ai UX, and I’m not sure I said that if you’re not familiar, UX is just a fancy abbreviation for user experience, even though we all know experience doesn’t start with an X. So this means like, you know, making the features that we have and the functionality that we have more usable, tweaking the functionality to better like suit people’s needs and desires, and just make the product more delightful and easier to use. You feel free to amend that definition. But just to kind of like set
Mihai: Feel free to amend definition.
Beah: the set the tone a little bit, Mihai, tell me a little bit about like how we even decide to work on something. Like how do we prioritize UX user experience changes in Duck.ai land?
Mihai: Yeah, as designers, we’re basically I I tend to think of myself as the diplomats that we we try to talk to as many people to get our work done. But we use various signals to inform these decisions. Some of some of them are more perfect, some of them are imperfect. There’s no There’s no reality where we’re working with perfect information. So sometimes we form assumptions, sometimes we rely on research. User feedback, internal feedback. Sometimes we have strategic bets that we place as an organization. We have dashboards with metrics. We have just no we know the competitive gaps that we have, and sometimes we just rely on instinct. So we
Beah: That’s good.
Mihai: we bundle all of these together in our decision-making process. Some of these signals they surface a problem,
Beah: Yeah.
Mihai: others validate the direction and it’s on us to use our own human judgment to determine the right trade-off for for these things. And in the end it narrows down to to looking at where the biggest overlap is with the user problems that we have and where we’re taking the product.
Beah: Yeah, or do all the hand and just then follow it.
Mihai: Yeah, maybe this is more kind of like general response, but no one decision was the same.
Beah: Yeah, right once. Yeah, yeah.
Mihai: So it’s it’s it’s all based on experienced people in the room making the the calls based on the information that we have.
Beah: Yeah, I mean one thing that I’m guessing people I I bet a lot of people would underestimate the degree to which we’re obsessed with user feedback. Like you can’t completely rely on user feedback to tell you how to build a product because, you know, hopefully the people building the product have additional insights about what’s possible and what good looks like that users can’t articulate or shouldn’t take the time to articulate. It’s not on them. But that aside, like we obsess pretty hard on feedback. Like we read an insane amount of feedback. We have systems for categorizing it and making sense of it. I mean, I’ve been caught up in like long discussions where we are really deeply trying to interpret a comment somebody made on Reddit or in the app store and like try to figure out the best way to address it. And so I’m just pointing that out because I think that might not be obvious. Like I think there’s probably a lot of, you know, for a tech company that serves millions and millions of users, I’m not sure that’s I’m guessing that’s not the norm. So that is a big part of it. But then like beyond that, you know, there’s a bunch of other signals we use, like Mihai said, and I know in Duck.ai on that team, like you guys have a rubric, right, that you’re using to actually like evaluate in a structured way a bunch of project ideas and feedback is one of the inputs to that rubric, but there are others as well. So also very maybe distinctive of DuckDuckGo is I think it’s a hyper structured and rational decision making process. Would you agree with that, Mihai?
Mihai: Yeah. It’s it’s quite unique and I think the the great thing is that a bunch of us, everyone here, they’re scoping their own projects, they have their own ideas, they uncover different things that they’re pitching in and that goes into that rubric. And at certain times we those categories of how how we score the rubric, they’re weighted. So sometimes certain elements are more important than the others. And that help us kind of look at what what’s important to us right now and what we’re getting from the users and all the projects that are strong candidates for us to tackle tackle in the next roadmap, for example.
Beah: Cool. Yeah, so let’s talk about something that did bubble to the top recently. Like, can you give me an example of an a UX improvement that you’ve worked on?
Mihai: Yes, so the first example is dictation. I’ll share my screen real quick. Cool, so this is our interface here. So users keep asking for dictation features, so it’s this microphone in the input field. There’s certain people that for them is they would use the input field. They’ll rather talk than type. It’s either a prompt is really long, or you they just want to do it hands free. And shipping this was actually great because it it got us in line with the competitors as well. But internally we we were kind of i it it was the group was split in terms of like if we wanna do it until that user feedback kept coming back, kept coming back. And we decided to to pull the trigger. For from the UX standpoint, what was exciting for me personally is voice interfaces, they have a big affordance problem. That it’s really hard sometimes to know what’s the system status for that and to avoid the jargon is basically you need to know what the what the app is doing at any point in time. And that was an interesting challenge on top of just delivering the feature. And if I if I trigger it, we we do three things here. We label things clearly for people to know what’s going on. You also get a visual cue, this waveform that provides feedback, and as you see me pause, that waveform reacts to my voice, but also the input field has changed its its shape not the height or anything but what’s inside it now is geared toward towards a different input which is not text and we’ve added this nice glow animation to it which is which is really cool so and then it’s trust transcribing and giving back the the text. Another one should should I should I give a few more? Alright.
Beah: Nice.
Beah: Yeah, yeah. Give me another example, please. That’s good.
Mihai: Alright. Another one is batch deletion, which is another feature that was requested by by users. This one is a little different because our we’re famous for our fire button. You you can basically burn all your data and stuff like that. But in Duck.ai specifically we had the reality that you could either delete one chat at a time or all of them. And nothing nothing in between. And we we decided to that we need to give people more precise control. To do it in just one step. Whenever you have 30 chats, for example, and you just want to delete seven at a time, it’s quite annoying to go one by one. So this one, it’s not groundbreaking or anything. It’s been a pattern on the web for quite some time. How you can batch select the different things. This is a great example, thinking back to the signals that we spoke. It’s a great example when you need to know where to follow the process for something when whenever you’re designing something that’s more unknown and whenever you need to trust your instincts more. And this is a project that was basically that where we we used our experience more and the project was short this was delivered and and shipped quite quickly because of that. And what the feature does you can go in and select many chats you want to delete. We have different entry points to how to do it. Usually
Beah: Yeah.
Mihai: we know people click the fire button to open it so we surface a button there for people to actually not delete everything but select multiple ones or for maybe power users whenever something’s selected you can shift click and enter this mode and delete delete your chats. So yeah, this one the reason I’m I’m asking is we spoke about all these signals and how complex things are, but this one was th three designers, we got in the room, we did a thirty minute thing, then I spent maybe two hours on it on edge cases, we put it to review to a wider group of stakeholders and it went straight into implementation, which is which is great because we have features that are a little bit more complex which require all all of that
Beah: That’ll be true.
Mihai: kind of digging, research and all those other things. But this is a great outlier maybe where we
Beah: Yeah.
Mihai: can just roll with differently a little bit.
Beah: Yeah, yeah. Also, there’s yeah, I j while you were talking, I was thinking about we ha we also added not too long ago another another way to delete chat level sort of at your cadence, which is like when you hover do you wanna show your screen again real quick, Mihai? When you hover over each chat in your chat history, you get a fire button. So you can also just tap that. So if you want to do one at a time, but you know, from a list, that’s another way to do it. I think that was a nice win. And then I don’t know, just for a little like real time inside baseball, well, truly an hour ago, Mihai and I were on a a thread where we were talking about with some other folks whether what the equivalent best equivalent of that on mobile is where you don’t have hover, you know. So we just have to if we want I I personally love and use those that per chat fire button. You’re like, I don’t need that, I don’t need that, I’m gonna clean it up real quick. On mobile we don’t have that. So yeah, like is it worth the real estate to just always show the fire button? Is that too noisy or people gonna misclick it? So we were kind of debating that this morning and gonna get it into an internal build and like kind of feel it out ourselves and then you know if it feels good we’ll we’ll release it. But we’ll watch any feedback coming in as well to help guide us on whether it’s working as we we hoped it would. So hot off the process. Alright, do you wanna do you wanna talk about one more we talked about maybe the the model picker might be interesting to show ‘cause that was a nice and nuanced recent UX improvement.
Mihai: Yes, yeah, let me share my screen again. Yeah, this one this one is not the one I designed, so I don’t want to upset the designer who actually done the hard work. But
Beah: Mm-hmm.
Mihai: I I advised this project so I stayed close to it. So I’ll it’s it’s Esteban. So thank you Esteban for
Beah: You wanna give him a you wanna name drop? Give him a shout out.
Mihai: a better model thinker. We’re all grateful for for that. Yes, it’s basically our our team has grown, so it’s it’s great that we we can cover more ground. And Esteban is not new on the team, but whenever whenever I started it was just me. So it’s great to see all of these things basically. Ship and all these amazing features that We can put out much faster. Now this one is basically speaking of old stuff, the old mode model picker had a whole lot more details in in in it, and through research we’ve uncovered that people just could not understand what’s in there. And they were stalling, trying to make sense of it a little bit. Power users have no problem, but I don’t know like general public, people who are actually not experts on just just want to use the tool. It it was a thing that was giving them pause. An another thing that we’ve uncovered which was useful insight is once they got past that confusion, they really valued it and it was the thing that they would recommend
Beah: Yeah.
Mihai: to friends and stuff. And on top of it we also as a company, Duck.ai specifically, we provide all these L LMs from different providers. So this is Some sort of a our own differentiator where you get the private versions of all of these. So we wanted to fix this old problem of people stalling and being a little bit confused about it. And the solution was basically again based on insights, we we realized that much people knew what they wanted to use or had some general idea and they they
Beah: Again, you know, I we like that people looking on a TV.
Mihai: just use what’s giving them a good response. They just stick to it and majority don’t really switch. There are use cases for some that that switch for different particular reasons, but for this particular model or now a drop-down they just needed something much simpler and the job of this is to show that there is choice because we provide all of these things, variety, but also we wanted to remove the guesswork for for people. So
Beah: And then you’re going to
Mihai: Esteban has done an amazing job of condensing this into showing that variety. But we switched what we are doing here to for the user is we’re providing now a recommendation of what we think and what we think that they’ll use to have a great time and a great experience on on the platform. For each subscription tier we have Free, Plus and Pro. We recommend two models, one always good for everyday use. Based on their tier and another one which will give even a maybe a stronger, more comprehensive response, but will use more limits or it’ll it’ll exhaust those limits faster basically. So yeah now it’s kind of it’s much simpler and much more enjoyable at least for for for me and
Beah: Yeah. I mean
Mihai: based on your the research for users to use.
Beah: Yeah, one detail is like before it was a button that you pressed and it opened a modal. Like this it opened a new screen effectively with the with your choices and then you select your choice and then click start new chat. And like even just it’s a small thing, but like the the difference between what feels like kind of a drop down and something that feels like a new screen where you have to pick and then say start new chat, like I I I knew how it worked, and it’s not like one click takes so so much time, but the sort of like cognitive friction of like picking and then and then clicking something again, like start new chat, and like usually I hadn’t even started a chat. Like it just it didn’t feel easy peasy to switch around, you know? It felt like higher friction than you needed to so like you everything else is that even just kind of moving that like changing the UX pattern, I think is a nice, a nice little win. Sweet. But that’s one that probably is like counter to your batch delete example. Like we really did kind of agonize over a bunch of nuances because like with any design decision there’s trade-offs and there’s different cases that you have to account for in different states and like it did it took a while to to reason through it all. Speaking of which, can you give me, Mihai, an example of like a difficult or non-obvious design decision that you’ve had to make recently?
Mihai: A recent one. We always I think as a designer here I o I’m always reminded of how little we know about our user in comparison
Beah: Yes.
Mihai: to everything else we’ve experienced. I remember like we just don’t know the count of how many users we have just because we don’t track them. And stuff like that. Everything is very much on those guesstimates and kind of like I’m people sometimes use phrases like I’m confident in this assumption, like eighty percent and stuff like that. They give a percentage and it’s it’s a wonderful constraint because it’s it’s why I joined. I’ve recently so
Beah: Because it just to like because it’s privacy respecting is what you’re getting at. Yeah, we don’t track.
Mihai: Yeah. Exactly. So and and that goes with everything we touch whenever we’re designing it. It’s you you design something and it then then it’s through that lens of of respecting the the data and and providing a a great experience without needing all of those other things which other companies do. And I maybe don’t have a very good example to give that is recent one that is maybe an older one is whenever you’re getting a response how you’re switching through different models which is something Which is currently being worked on and where you will be evolving that feature soon. But that one, what wasn’t too obvious about it is that switching models is not a new paradigm in user’s mind because this AI thing is somewhat new. That’s why it takes the shape of a chat interface because it’s familiar. And switching a model in other places we’ve seen very much is has different pagination approaches and stuff like that, which is now learned but not natural based on what we’ve uncovered. And every time when I spoke to people, like I’ve seen so many confused face faces about like the pagination of answers and stuff like that. And what
Beah: Button on the natural work is everything natural.
Mihai: What we did, which wasn’t obvious obvious, we basically w took a more linear approach to how those responses are getting surfaced, which feels more natural into a chat interface, a thing that we’ve been using for decades. So
Beah: The patterns are a thing that we have.
Mihai: we kind of went against the grain there. And it wasn’t obvious at first.
Beah: So what would a paginated response look like? Like if we went that route, we would have like what, opened a whole new chat or something?
Mihai: It it no it it creates the same response but it gives you like a counter, like a page for the responses and then you paginate and it gets forked in different streams, almost like a decision tree that you can paginate back and continue conversation in different so that that
Beah: Yeah.
Mihai: even like I spoke to engineers who use AI, like they use Cursor, they use all these like advanced tools. And for some it wasn’t apparent that to to some that this feature worked that s certain way. So even at advanced people who are used to these tools were a little confused by the conventional norms, so to speak. So it made me feel really proud a little a little bit because we kind of took a different approach. And I think we get more than 300K uses on week from that switcher, which which is cool to see.
Beah: And three... coming.
Beah: Nice. Alright. Last question, Mihai, before we sign off, can you preview any upcoming improvements that people should look forward to?
Mihai: It’s the project I just wrapped, which is still being worked on. So the future may still evolve, but one thing we’re trying to add to to the platform is are you seeing my Figma, Beah?
Beah: Yeah, I can see.
Mihai: Okay, perfect. So this is very very rough. I think my is hiding my some of it. Basically we wanna add the share functionality to your responses, something that we we don’t do currently and we wanna do this at the response level but also at the entire conversation level. And once you once you click share, you’re basically sharing a response. You create a public link. That we’ll we’ll leave on our encrypted server with the encryption keys and all those other things that engineers are working on to make that super secure and everything. And then you can copy and and send it to to to people to to get that that response. And you’ll be able to to see it’s as someone who opens the link, you’ll be able to see what was shared and you you’ll be able to delineate what’s yours if you choose to reply or if you choose to add it to your own chats. So this is how it was designed. Sometimes we evolve the work as we play with it live when it’s when it’s in code. But it’s something that yeah should help a lot of people send a link as opposed to send a bunch of text.
Beah: Nice. That eggshell white is looking pretty sharp. Is that what it’s called, eggshell?
Mihai: It’s actual actual TW10 specifically.
Beah: Ooh, eggshell 10. Nice. Awesome. Alright. Thank you, Mihai. Anything you want to add before we before we close this out?
Mihai: This is great. I yeah, this is great. Thank you for having me.
Beah: Yeah, thanks for being on Duck Tales. Alright. Thanks, Mihai. Thanks everyone.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit insideduckduckgo.substack.com

In this episode, Beah (Chief Product Officer) and Marc (VP, Engineering) discuss how we create, communicate, and challenge operational defaults to make better decisions at speed.
If you have feedback on Duck Tales, or episode ideas, email us at podcast@duckduckgo.com.
Disclaimers: (1) The audio, video (above), and transcript (below) are unedited and may contain minor inaccuracies or transcription errors. (2) This website is operated by Substack. This is their privacy policy.
Beah: Hello and welcome to Duck Tales, where we go behind the scenes at DuckDuckGo and discuss the stories, the technology, and the people who help build privacy tools for everyone. In each episode, you’ll hear from employees about our vision, product updates, engineering, or approach to AI. Today, you are going to hear about how we work, I guess, or one facet of how we work, which is using operational defaults and I have with me Marc, who is going to who I’m going to interview about operational defaults. Marc, do you wanna introduce yourself?
Marc: Yeah. Marc, I’m on the engineering team at DuckDuckGo.
Beah: Okay. That’s about all you need to know. and I’m Beah, I’m on the product team if if you haven’t seen me before. okay, cool. Well let’s jump in. What even is an operational default?
Marc: Yeah. Yeah, so defaults are I would say a deceptively simple concept. at at DuckDuckGo we’re always trying to leverage mental models to help us move more quickly and think more clearly and and things of that nature. in that context, the default effect is seen as a negative, right? And and I think we’ve all seen that in apps and in different places. Here we’re trying to leverage the the upside of that, where we’re basically taking any decision that you can make ahead of time that generally is true or has a right answer eighty percent of the time, we’re gonna call that a default. for that other twenty percent, we wanna leave room for our folks to be able to make exceptions, ideally very thoughtful exceptions.
Beah: When you say default is a negative in the app, you mean like, you know, if you get an Android phone and the default browser is Chrome and the default search engine is Google and it’s really hard for another player like DuckDuckGo to to break through th those defaults. Is that right? Yeah. Okay.
Marc: yeah, exactly. There’s there’s in Gabe’s book of mental models there’s the default effect and again it’s generally negative, but like I said, in this case we’re trying to use defaults as as something that we can leverage to help us go quicker.
Beah: Yeah, yeah. So how is that how is it different than a rule?
Marc: I mean it’s conceptually the same thing. I think rules are just more binary, right? Like this is something you do or you don’t do pretty much all the time. Where here we’re saying that these are things that are generally true. Like I said, eighty percent of the time is just the number that we made up. and that allows people to potentially make faster decisions. but we also want to include encourage people to think about what they’re doing and not just go on autopilot. And I think that’s the danger of catching something as a rule is that yeah, people will just kind of blindly follow it by default.
Beah: Yeah, yeah. I think that distinction is super important. Like, first of all, there are a lot of things where rules just have exceptions and therefore they’re not really good rules, you know? like you have to be pretty sure that there has to be a really good reason to make something a rule because it’s going to fit different situations differently. And yeah, it’s like once it becomes a rule, people like lose power to do anything differently, so they don’t think about it, they don’t feel empowered, and like with a default, there’s still a burden of judgment because you can overturn the default and choose a different path. but the idea, I think, would you care would you say this is a fair characterization is like A, don’t spend brain power thinking about like reinventing the wheel or thinking about something that doesn’t really warrant brain power, like there’s a good default, and B you know just to n to nudge people in the direction of the best average answer, you know.
Marc: Yeah, right. It’s leveraging decisions that we’ve deliberated over in the past and you know have come to a good decision on. and exactly what you said. We don’t want folks reinventing the wheel. That said, we do want I mean, we hire really smart people at DuckDuckGo and we wanna, you know, leverage and celebrate that. So yeah, everybody’s encouraged to challenge the norm, so to speak, or you know, pre existing defaults. And in fact that’s a big big part of this. whole concept is coming back and challenging these at times. But yeah, th they’re, you know, i in a lot of cases it’s it would just be wasted effort to be rethinking this from scratch. And as much as there are some that are that are rule or that you know might make better rules because they are more binary, there are others that really don’t make great defaults either. we had a decent example where it was like, yeah, there is something that was true, say fifty percent of the time. I don’t remember what the actual number was, but it just didn’t make a great default. I honestly don’t remember what it is, but yeah, d just to say that not everything c falls into that framework framework.
Beah: Yeah, that that’s a good segue. Yeah. T t give me some examples of things that we do think are good operational defaults or that we treat as such.
Marc: Sure, yeah. I mean we have a bunch I you know, one of the things that’s so powerful about this is that I think we all do this all the time. so for example at DuckDuckGo we try to prioritize quick wins, which I guess the other thing I should say about defaults is that they are somewhat self evident, like to the point where that’s why I said they’re kind of deceptively simple because there are things that you don’t necessarily feel like you should have to put a lot of time into, a a lot of thought into. But quick wins are one where, you know, we at the company we’ve been, you know, trying to push people toward that. Right. Like the idea of a quick win is something that is relatively low effort but has high impact. And I mean you would saying it out loud, you would say, of course that’s something we should prioritize. But I think it’s also important to acknowledge that people don’t think that way. instinctually in a lot of cases. They wanna sometimes they want to work on, you know, the hardest thing or what they see as the most impact when, yeah, maybe less would do. so that’s one. one of my favorite ones, and I think a lot of people at this company would agree is we do no meetings on Wednesdays. so that’s a very simple one that, yeah, you we we don’t I I I w wouldn’t call it a rule, whereas we don’t you know, you can have a meeting on a Wednesday and sometimes you have to, especially as if it’s with somebody external. But generally you shouldn’t be scheduling meetings on Wednesdays.
Beah: See, that’s interesting because I would have called that one a rule. We I specifically I would have called the rule no
Marc: I mean
Beah: If you want to hop on real time and talk to somebody internally about something as it comes up, fine. If you have like an external call, we’ve also accepted that. But I I try to enforce that one as a rule because I think that one, if it’s a default, people just don’t observe it. They break it way too much. That’s my opinion.
Marc: Well, I yeah, but I think you just gave a few exceptions that are good, right? Like I I’m not gonna necessarily schedule something ahead of time, but I will yeah, reach out to somebody and say, Hey, it would be quicker if we talked about this ad hoc. So maybe I mean, yeah, maybe it’s splitting hairs, but
Beah: Yeah, it’s interesting though, because I think of those as part of as like the rule is constrained. So I guess making a rule is is safer the more you constrain it. And you could you could arguably ha tackle one operational problem and either make a highly constrained rule that has exceptions and carve outs or a broader default and maybe sometimes both at the same time. So yeah, okay. All right, that’s a good one. give me you got another example.
Marc: Yeah, fair enough. Sure. Sure. Yeah, I I can give you another one. so this one we came up with recently and that’s working incrementally, which again, you would say and when I say work incrementally, I basically mean you know we’re we’re trying to ship smaller features more frequently or even larger features and smaller increments. you’re going back and you’re you know, making sure that you’re on the right track, etc. Which again sounds like obviously that’s something that you would want to do. But anyone who’s ever collaborated with anyone on anything, I think would would realize that, yeah, that’s I don’t think that’s a human default, right? Like I think we all, you know, want to have a task, go build whatever we’re supposed to build, and then show the awesome thing that we did. And yeah, that’s I think that’s a good instinct to have. But there are many cases where if you misunderstood something initially or new information pops up, yeah, it’s super important to make sure that you’re raising that up and collaborating with others on
Beah: Mm-hmm. So you s you said that we made that one recently. Like what does it mean to make an operational default? How do we codify or communicate these?
Marc: Yeah, that’s a great question. And I don’t think we’ve actually figured that out yet. We have a lot of defaults across the company. This one was was communicated to to several objectives. but yeah, it’s not I would say it’s written down, but it’s not there’s no like internal glossary or internal directory of of defaults or anything like that. It’s basically just a reminder for folks. And again, it seems like it’s something that you should obviously do, but I think it’s important to remind people of these things.
Beah: Yeah, so like we basically we write things down in the form of like messages and or or really I mean anyway, but and I think that can happen maybe one thing that’s worth saying is that that that can happen at different levels of the organization. Like sometimes you have a group of five people working on an objective and they might adopt their own defaults or ways of operating. And then on the other hand, once something once we like believe something to be
Marc: Sure.
Beah: Sorry, I don’t know if listeners can hear that, but my dog is drinking some water right now. So we’ll see if that comes through. you know, once we’re really confident in something and that it’s like broadly applicable across the organization, then we might communicate it at like a higher level and codify it a little bit more more strongly as a default.
Marc: Yeah, and that’s kind of where that’s kind of where we are with this right now. We’ve communicated it to a couple of objectives. You know, objectives end up having their own culture, intentionally or otherwise, and this is yeah, an attempt to kind of test it on a smaller care scale before we start communicating it more broadly. and I I have one other I can give. This one’s much more engineering centric, but so we’re all working on AI quite a bit these days.
Beah: Yeah. Okay. Yeah.
Marc: And I think anybody that’s ever worked with, well, anybody that’s ever used AI will tell you that or has observed that you don’t get the same answer twice, right? It’s it’s you know so-called non-deterministic output. so in order for us to build products leveraging AI, it’s really important that we have evaluations up front, right? That you start with, you know, say a list of if I have question why. or sorry, question X, I’m gonna get answer Y, or at least something in the general with the general gist of of answer y. and yeah, because you can’t always super accurately predict what’s gonna come out, it’s really important that before we start building something, you know, you you have this eval or evaluation data set so that you know when you’re doing a good job or or when you’re when you’re not. I think that one honestly that one’s fairly close to a rule in my book, in terms of the engineering expectations we have, but there are definitely cases where it, you know, it doesn’t make sense or it’s too much effort to start there.
Beah: Yeah. And so like because that’s a default, even if people most people kind of think it’s a best practice, codifying it as a default means that if somebody decides not to do it, they might get the question, why? Are you sure? And they have to like defend basically if you don’t use a default, you have to have a good answer.
Marc: Yeah, that’s exactly right. Yes, you have to have yeah, there’s there’s we encourage people to make exceptions, but it’s gotta be well reasoned.
Beah: Up there. Yeah. Yeah. Yeah, yeah. Cool. Can you think of any defaults that we’ve adopted as an organization or on a team and then like decided were the wrong default and changed them?
Marc: Yeah, I thought about this one a bit. I I can’t think of one where we actually decided this was the wrong default. But I can there there are several that we have you know, potentially evolved over time, and that continue to evolve. The one and and yeah, this one is also super engineering specific. But the one that that comes to mind is, you know, initially all of our back end engineers were the default for all of them was to use Perl for our back-end services, the Perl programming language. But we made exceptions there, you know, because no language is the right tool for every job. So, you know, if we were developing something that Perl just wasn’t the right tool for, we gave people the space to make a case to not use it. That and that was true for a number of years. I think now I wouldn’t call that a default anymore. I don’t think that that that is the starting place for most folks, like when we’re developing a new a new service. so maybe that’s one that as you would either say is evolved or or maybe retired is a better way to put it. But yeah, I I can’t think of one where I think we were actually outright wrong, but definitely new information and things come in and we change them.
Beah: Mm. Yeah. One that kinda one that comes to mind to me that maybe went from having a not really having defined a default intentionally, but let kind of the flow of the stream define itself was we we, you know, as some listeners may know, our company is organized into a a bunch of objectives, like teams working towards some important goal for the company. And those objectives are expected to hold what we call an assessment every so often to kind of like redefine the strategy in the roadmap. And we I think f for the first maybe f few years I was here, we didn’t have an explicit expectation on how often that happened, but I think the sort of adopted default was a pretty long period, like maybe on average, I don’t know, five months, six months. something like that and then at some point we what’s that?
Marc: Yeah, when you felt like it was important. I said when you felt like it was important, it was very I think very subjective.
Beah: Yeah. Well and it was like easy to procrastinate if you were leading an objective, you know, like ‘cause it felt like a time like a thing you’d have to put time into and put yourself out there. And so it just kind of was like getting long. But in reality we observed that when people ran assessments more often, we had better roadmaps and better decision making and, you know, sort of less leaving the track without, you know, the help to get back on the track and
Marc: Yeah.
Beah: And so at some point we I think maybe said like let’s default to every eight weeks and then we maybe t brought that back to six. Right now it’s it’s six weeks is the default. and I think that’s been a nice improvement to make that explicit.
Marc: It’s actually a it’s a good example of working incrementally over a you know, different horiz time horizon. But yeah, for sure.
Beah: Yeah. Cool. Anything else you want to add, Marc, before we wrap this?
Marc: I don’t think so. I think again, I feel like I have to keep saying that a lot of these are self evident because it just sounds so obvious in a lot of cases, but it is yeah, I think I think it leads to better results and and hopefully we’re gonna see that very soon in these objectives that where we’ve introduced it and yeah, r really important to challenge them as well.
Beah: Yeah, there’s probably a lot of obvious things that people don’t do on the regular because of some cognitive, you know, glitch or habit or, you know, your predictably irrational behavior. So yeah, just making it explicit I think can be powerful. Cool. All right. Well, thank you, Marc, and see you around.
Marc: Yeah, yeah. Yeah, thanks.


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