Hi everyone, thanks for joining us today for the CEO connection this quarter. Before we get started, I want to note that today's discussion may include some forward-looking statements and the risks related to those are listed here and also in our filings with the Securities and Exchange Commission.
So, today we're going to go inside Tinder's product and engineering teams to discuss something that you've heard us discuss quite a bit, which is our product velocity. It's a very important part of the Tinder story and something that investors are keenly focused on and I get asked about a lot for good reason.
So today I'm joined by Mark Caner. Mark is our chief product officer at Tinder. He leads our cross-functional teams of designers and product managers who help determine what we build into our product. Mark has more than 20 years of experience as a product builder and an entrepreneur and a founder, including roles at Zinga and his own startups.
Also with us is Venet Kuravilla, CTO of Tinder. Venet leads engineering, AI and product innovation at Tinder and also leads our central AI teams at Match Group. Venet has 20 years of engineering and product leadership experience at companies like Amazon, Venmo, and Brightitewheel.
Before we get into that discussion, we're going to start with a quick look at how much the Tinder experience has evolved over just the past 18 months. Many of you probably aren't active Tinder users, so we wanted to bring the product to you by highlighting where it was, where it is today, and just how much has changed in a relatively short period of time. Let's take a look.
It's pretty cool to see a lot of blood, sweat, and tears went into 18 months. Hopefully the video gives you a more tangible sense of what we mean when we talk about product velocity. I've been a tech executive for 28 years and I have never seen a company change its core product so much in such a short period of time.
What you just saw is the output, which I'm very proud of, and it's impacting our users and our business results every day. What I want to spend today focusing on is how we get it done. So, Mark, I'm going to start with you. We just showed how different Tinder looks today than 18 months ago. Let's start with the obvious question. What's changed and why are we able to move at this pace today?
You know, as we just saw in that video, nearly every part of Tinder has improved. But not only does it look different and it feels better, but more importantly, it actually works better for our users. We've had major strides in trust and safety, bringing down the prevalence of bots and bad actors by more than 60%. We've greatly improved recommendations, driving better outcomes and sparks for our users. And we've built new ways to connect like double date and events.
And there are a few things that are really driving that change. The first is the org. The org has changed dramatically. We have smaller, more autonomous teams making much faster decisions. We've really sharpened our understanding of who we're building for and the problems we're solving with the use of new personas and a lot more time and focus directly talking to our users. And then lastly, we have reoriented the entire team around one north star metric, sparks, which is our term for a multi-way, six-way conversation that really aligns everyone around driving better outcomes. And the result of that is not only a product that works better, but a team that moves a lot faster.
Yeah, we just finished a three-day offsite with product and engineering and marketing leadership from around the world coming together here in LA. And it was amazing how often personas and sparks came up, like in every conversation people were talking about the personas. So these are the archetypes of whom we're building for, and then sparks obviously is the KPI that we focus on. So Venet, take us under the hood from an engineering standpoint. How does Tinder actually build differently today?
Yeah, so Tinder's entire operating model around how we build has completely changed. We've got fewer linear handoffs, really tight collaboration between product design, engineering, faster experimentation and iteration. Our engineering team is actually shipping at a velocity that's twice as high as it was a year ago. And faster shipping matters because it creates faster feedback loops where we can build, test, learn from our users and improve based on the data and the metrics that we're seeing.
Now, we've made a number of investments in our technology stack, and we've, for instance, we've rearchitected parts of our codebase which had a lot of technical debt and were slowing us down, like our chat system. We've invested in our infrastructure so teams like our recommendations team and our machine learning team can move a lot faster, and we've invested in our experimentation platform so that running and reading the results of experiments can be very cheap and fast.
Venet, you mentioned tech debt, and this is a term that I know investors are familiar with because other public companies talk about it from time to time. What is our philosophy for paying down tech debt and maintaining pace of innovation even as we're kind of retrofitting old parts of the product?
It's a great question. Yeah, our philosophy has been to incrementally eliminate the tech debt and looking at the areas of the codebase where the teams are getting slowed down the most, as well as the components in the products where we have the most potential to improve user outcomes. And so chat was a great example, right? Our chat team was really getting slowed down because of tech debt in the codebase, and there was so much potential to modernize chat, add a lot of new features. And so we rewrote and rearchitected our chat system, and we did it in a way so that every other part of our ecosystem could move forward while we were rearchitecting our chat system. So we're going to continue to do that. Our next step could be onboarding, but that's our approach to tech.
So one for both of you. How does AI fit into all this from a product ideation standpoint and then from an engineering standpoint?
You know, AI plays a critical role both within the product, how it benefits our users, but also how we build those products within the product. We use it to reduce friction during onboarding, helping people make better profiles, selecting better photos. We use it to improve trust and safety through features like 'Are you sure?' and 'Does this bother you?' in chat, as well as Face Check. And we use it to improve our recommendations.
But we also use it in terms of, you know, how we actually build better. And we've seen that AI essentially accelerates the entire product development life cycle from research and ideation, rapid prototyping to development. And it essentially compresses the work that used to take months now into weeks and sometimes from weeks into days. And the result of that is it essentially has let our teams have far more high-quality shots on goal.
You know, we could take an example of events, which we're all very excited about. This was an idea of Spencer's back in January. We had our first meeting in January. We had rapid prototypes days later. And then in March, we launched our first MVP publicly to the people of LA just eight weeks later, which is amazing to go from first meeting in January to public launch just, you know, a couple months later.
And Venet, how does AI impact your world?
Yeah, and like Mark said, you know, this is all possible. This pace is all possible because every engineer is using AI coding tools every day. Nearly every aspect of our product development life cycle has been completely rebuilt with AI at the center. Over 90% of all the new code at Tinder is AI-generated. And we've built agents that now write our tests and verify our code. We have agents that can fix simpler bugs with human verification and oversight. And so we're prototyping and developing much faster because AI is compressing the entire software development life cycle.
So sometimes people use the term AI slop to sort of derisively note like low-quality product coming from AI. So how do we guard against that? You know, are Mark, are the robots just coming up with all of our ideas? Venet, is the AI just writing bad code that makes things worse? How do we guard against quality issues?
Yeah, we have a policy in place where every engineer has to, you know, carefully review the code that AI has generated, right? In fact, it's reviewed not just by one engineer but two engineers, right? And then we spend a lot of time codifying what it means to write high-quality code within our codebase so that, you know, agents have like really clear instructions and they're producing high-quality code and not AI slop. We're also spending a lot of time on verification and testing, which are again done by AI agents, when we know that the features we produce are high quality and, you know, don't have bugs in them.
I think on the product design side, you know, we've got a very talented team that I'd say our ideas today are coming from them. But really, you know, also from talking to our users, we collect a lot of user feedback, a lot of research, and we've used AI to help us synthesize that big corpus of data into really actionable insights.
So, we're definitely changing the way we work because of AI. Has it also changed our talent recruitment, retention, and engagement strategy? Are we looking for different things from people?
Yeah, we're leaning very heavily into early-career AI-fluent talent. We're very deliberate about evaluating AI fluency in our interview loops. This is something we take very, very seriously. We ask every engineering interview candidate, you know, to solve multiple tasks with AI and then we talk to them about exactly how they used AI to, you know, solve those problems. And we're having a lot of success with this strategy, right? And we're pairing this early-career AI-fluent talent with some of our very deep domain experts in recommendations and machine learning, and that combination is proving to be very powerful.
Have you changed what you're looking for in a designer and a product manager?
Yeah, you know, I think I'm really looking for curiosity, hustle, and initiative with these tools. Now, there is no reason for people not to be, you know, at home prototyping and building their own things. So, the first thing I ask is like, what are your personal projects? I think if they don't have anything, then, you know, it probably means they're not going to be that creative here. So, I really want to see what it is that gets them excited and make sure that they're able to do it.
It's interesting how the world has changed. I mean, it used to be like that litmus test of what are you tinkering with on your own nights and weekends. Like, now that's a positive sign of somebody that's intellectually curious and pushing the envelope of how they can use AI to solve problems that they're fascinated with. It used to be like, well, hold on, why are you distracted on other things? So that might be the most important question now.
Yeah, totally. What are you playing with? What are you tinkering with? How are you learning?
