Duck Tales: How we use AI at DuckDuckGo while staying true to our principles (Ep.45)

In this episode, Marc (Engineering) and Zach (Data Science) discuss how we use AI internally: our most common uses, how we use it privately, and why our principles matter more when using AI, not less.

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Marc: Hi, 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, or approach to AI. My name is Marc and I’m on the engineering team. I have with me Zach. Zach, you want to introduce yourself?

Zach: hi, yeah. I’m Zach Deane-Mayer on our data science team here at DuckDuckGo. I am still a Duckling, so I’ve been here for about ten months. and I work on building our internal AI systems.

Marc: Great. yeah, so today we’re gonna talk even more about AI, which is Zach’s specialty. I think today we’re gonna talk specifically about how we use Duck or sorry, how we use AI internally at DuckDuckGo. so yeah, I guess to start out, as a company, you know, we’ve adopted AI pretty pretty heavily internally as a tool to help us move faster. can you talk a little bit, Zach, about the the approach that we’ve taken here? Given that I think this you were in on the ground floor with this, right?

Zach: Yeah, yeah. So DuckDuckGo as a company takes I I think a somewhat unique approach to AI as we take a unique approach to everything we do. We very much have a culture of of things like first principles thinking and and you know our own spin on things.

Marc: Right.

Zach: And a very important part of what we do are our privacy and security. so before we rolled out many of these tools, we spent quite a bit of time internally getting comfortable with their failure modes and the blast radius potential for for those failures. One of the first places we started using AI a lot was with coding tools. So right off the bat with Cursor and then Claude Code. And you know, one, the models are very, very good at writing code. But then two, if you are effectively using tools like git and GitHub, an agent that’s limited to writing code can only do so much damage if your code base is backed up in GitH