Hinge CPTO on Building an App Designed to Be Deleted | Ben Celebicic | E312

I can see a world in the future, where if I am able to provide a perfect query input to let's say a cloud core or cursor or codex or any of these tools, I will probably get a perfect output. >> It's kind of an anti-metric. You want people to stop using your product because you want them to find success as soon as possible. As looking at the market cap of your holding company, Match Group, it's around 25% up year-to-date. You look at SaaS companies, most of them are down. >> So, Hinge is on the mission to achieve $1 billion in revenue in 2027, next year. >> Ben Chellal Betcheek, Chief Product and Technology Officer at Hinge. >> You're making me think about the dark side of AI now. People creating fake images, trying to look better. People who can fake conversations to try to get more dates. >> Trust and safety lives under me, and it's the third of our Hinge organization is trust and safety. All of that has been done through AI for ages. >> Do you see a future where the lines are truly blurring and we can have more non-technical people really shipping into production? Hey, this is Carlos, CEO at Product School, and your host on the Product Podcast. My guest today is Ben Chellal Betcheek, Chief Product and Technology Officer at Hinge. He has been there 11 years since the 2015 relaunch, and he got the product title added to the tech one. So, now he runs a team of about 300 and negotiates the CPO versus CTO tension inside his own head. Hinge is on track for a billion dollars in revenue next year, and their North Star metric is getting you to leave. Here are the things we'll cover. The app designed to be deleted and why great dates is the metric. They only charge for what they cannot give away free. A third of the whole company is trust and safety. His conservative take on vibe coding and where he draws the line. The perfect query problem and why he thinks it's 2 years out. Let's get into it. Welcome to the Product Podcast, Ben. >> Thank you, Carlos. Thank you for having me. >> Ben, you've been at Hinge for almost 11 years. You're a real OG, right? And then, most recently, you got promoted from Chief Technology Officer to Chief Product and Technology Officer. So, I'm very curious about that transition that seems to be a trend for other companies as well. >> Yeah, 100%. So, you're right. I've been with Hinge for 11 years now, pretty much since the beginning when we rebooted Hinge in 2015, and I led our technology work from from the get-go. But, I was in the room, you know, with the exact team when any product decisions or any marketing decision for that matter, any company decisions were made. So, it's very very close to product. And when our old chief product officer, Stefan, left about 2 and 1/2 years ago, it was actually an idea from Angel, who was our chief operating officer at the time, you know, why why don't we let Ben do this? It wasn't my idea at all. >> [laughter] >> And and you know, Justin, who was our founder and CEO at the time, said like, "Look, you've been with me for all these years. You've been in these rooms. Let's let's try see how it goes." And honestly, we never looked back. It's been, you know, I'll be honest, it's been really tough in the in the beginning, especially, you know, for a company that is a product company like Hinge is, to take additional responsibilities beyond just building technology. But, I think there are like some bright spots around. You know, there's always a tension between CPO and CTO around resourcing, around like what to concentrate on, what to build, where to spend time. CPO is always kind of tendency is to concentrate on product and and innovation and build as as as quickly as possible on that front. While the chief technology officer always wants to make sure that, you know, high quality, there is we pay debt on the tech side, we build infrastructure that will help us and enable us to to build cool things in the future. So, there's always this tension between the two, and I think it's a very healthy tension to have. So, I had to have that tension with myself since then. And but, you know, I would say luckily for me, you know, I have a really strong team under me, and uh we still debate a lot of these decisions when we kind of prioritize work. Uh so, it's been really tough in the beginning, as I said. It's a lot of responsibilities to to handle both, but I think with time and team building that I made, uh I'm now at a place where I feel this was the right decision. >> What is the the total size of the the team that you oversee? >> Yeah, so so Hinge is about overall, I think about 360 or so. My team is very close to 300. Uh most of those are, you know, on the tech side, and the rest is product, design, and research. >> So, other than the obvious benefit of having one person that consolidates both technology and and product, what are other decisions that you have to make around the org to make sure that you still have that healthy friction and that, you know, the the teams collaborate even without you in the room? >> Yeah, 100%. So, you know, I I I I think for me, org structure heavily depends on the problem you're trying to solve and the strategy you have in place. Like, org structure is always a reflection of those things, and and it doesn't really work in the opposite direction. So, what you want to have is a very clear vision what you're trying to achieve, a clear mission for the company, and some kind of a strategy that is helping you moving towards that vision and mission, and then put a team in place to uh that will help you achieve that. Uh so, every year, you know, as we build our strategy for the following year, we really look at the org and how it's really organized and what makes sense, what doesn't make sense, and adjust from there on. From this tension perspective, I think, you know, having uh leaders on engineering and AI and data, and then also having leaders on product and design and research in the room when we have these discussions is tremendously helpful. You know, when there's really no debate around resourcing or on prioritization, whether we spend time doing more of a product work or more of a tech work, that it of lights a bulb in my head that something is wrong. So, I really prompt the team to really think deeper about it. So, you have to have a team underneath you that is going to have perspective and is going to push you and is going to make sure that like nothing really falls through cracks. Especially now in the age of AI, I would say where we're still all trying to figure out what this technology is going to enable in the long run. I think having infrastructure built and ready once we kind of decided what makes us to build is tremendously important. So, having people really pushing for spending time building infrastructure, building, preparing for the future is very, very necessary. On the other hand, you know, we also have to do good by our users and build features and and and and build improvements to the product in a way that is going to, you know, get them achieve success on our platform. So, that tension >> Let's talk about that then because to be honest, I mean, I'm married, so I cannot speak as a user, but the reality is that the online dating space, I think when when when Tinder came out, like they pioneered this new behavior, right? Swipe left, swipe right. But I haven't seen much more innovation since then. Like it feels like an industry that hasn't produced any new of those type of breakthroughs. I'm curious to know from your own perspective, like what is it that you are trying to do to maintain that level of high innovation? >> Yeah. Yeah, I think that's very interesting question. You know, in the very beginning, the industry tried to solve for access. The industry was born to kind of get you in contact and connect you with people who you otherwise would not meet in real life. And that problem was solved, but the problem that was created by solving the problem was problem of access. All of a sudden, you have access to too many people. You have this paradox of choice and it's really hard to make progress when you always think there's like something else out there that I can look through. So, Hinge, you know, purposely decided to concentrate on one thing, which is what actually happens when people leave Hinge. So, we are measuring our success uh and our North Star is great dates. So, we don't really try to keep you engaged with a platform. We really try for users to spend time. We really do everything we can to push them off of the platform and really spend time in real life. So, I think when you optimize for something like that, that's where innovation is born because it really makes you think hard. Like, you know, it's counterintuitive that we're trying to actually get rid of people. And and you know, Hinge is known as the app that is designed to be deleted. We really want people to come to us, find success, and leave Hinge. And when you kind of optimize like that, it hasn't been done before. So, you really have to think outside of the box and innovate quite a bit together. >> I I like that. And it's You're right. It's kind of an anti-metric. Like, you want people to stop using your product because you want them to find success as soon as possible. Uh but that also leads me to the next point, which is your business model, right? Like, usually in like in social media world or other other types of products, you want to keep people as engaged as possible because then you monetize through ads and other mechanisms. So, in your case, how did you actually make money? >> Yeah, it's a great question. So, most of people on Hinge don't actually pay. I think probably less than 15% of users on Hinge pay for something. And our core principle from monetization perspective is the free experience is sacred. You really want the free experience to be built in a way that everyone can achieve success. And then you charge for things that either break constraints or help you achieve success a little bit faster. So, the second principle we have is we only charge for things that we cannot give away because if we gave them away for free, either they would mess up the ecosystem and I'll explain what it means or they would mean nothing. You know, where scarcity is actually a value that you provide. So, if you think about things like boost on Hinge or roast kind of helps you stand out in the crowd, those things would, you know, mean nothing if we gave away those for free for everyone. On the other hand, you know, for for dating app to really work well, think about it as like this great party. You're this great party. Everyone is excited to be there. Everyone wants to be there. Everyone wants to speak with with with other people. And the more people you have in the room, the better the party is. So, like providing access to to this party comes down to not charging as well. And then if you really want to break some constraints, you want two people to go and talk with each other separately or or you want to you know have a leg up in how quickly achieve success, those constraints can be broken through through subscription or maybe paying some ala carte things that you can stand up stand out with. So, it's our monetization model is very simple. You know, we only charge for things we otherwise cannot give away for free. And our free product is sacred. We want this great party to continue going. >> As you think about the volume of users that are already on your platform, the age of those users, you have Gen Z's who are probably starting to date for the first time. You probably have a ton of millennials. And so, I'm curious to know how do you go about segmenting users and what type of experiences you think are different for different types of populations? >> Yeah. Yeah, you can look at it from that perspective. You can also look at it from a perspective of like I'm a brand new to dating and I need some, you know, help figuring things out or I just fresh out of divorce and maybe I need maybe a little bit different type of of dating experience. You know, the way Hinge operates is we always look at the dating culture. So, we have a team that we call culture and consumer insights. They're constantly out there talking with daters, understanding their problems, understanding where the needs are, whether those are brand new daters coming onto the scene now or daters who have been using dating apps for a while or not finding success. And then deeply understanding, you know, what problems we need to solve for those. Once we kind of align what problems we need to solve for those, then we have hypothesis. Then we utilize to really provide solution to those problems. The the key thing to to kind of mention here is, as I said originally, Hinge is really measured through getting people off of the app. So, if anything that we build here does not increase the chance of two people leaving Hinge and meeting in real life, we don't ship it. So, everything goes through that lens. So, we kind of prioritize all the work we have, all the problems that we hear from from people through the lens of like, is this increasing the chance of vast majority of people meeting in real life or not? And that's what also helps us keep the product very simple and with very clean. >> I think that's part of the the magic yet the challenge, right? It's a clean, simple product that has a lot of complexity because you want to personalize the user experience depending on the on the situation. You mentioned someone who is divorced versus someone who is newly new to date. So, how do you create that type of one-to-one personal personal experience so someone gets to that aha moment as soon as possible? >> That's a great question. I think, you know, that is what AI is enabling us to do recently. That wasn't really possible if you look like two, three years ago. It wasn't really possible to provide a personalized experience to everyone. You might have maybe provided an experience that would fit one or two different needs, but experience that is truly personalized for for individual needs, and those can be many, is was really impossible to do without AI. So, if you think about like what AI can do is can really quickly recognize based on some behavioral patterns and how people behave on the product what would be the next best step for them and help them achieve that. So, think about you know, as you create your profile, you might be really quick about either selecting your photos or choosing prompts you want to answer about yourself, and you might be really good about it. Or if you're new to dating, Hinge might actually, you know, push you to think a little bit deeper or go a little bit deeper as you answer things about yourself. And we kind of recognize that through how you answer questions on on the profile. And as I said, that was really not possible before generative AI. >> You're making me think about the dark side of AI now. Applied to dating, right? Like people creating fake images, trying to look better than they are, or like, I don't know, these are clickers, or people who can fake conversations to try to get more dates. So, how do you go about identifying those type of situations and kind of prevent them to ensure the health of the ecosystem? >> Yeah, that's a really great question. You know, I think first of all, trust and safety. So, trust and safety lives under me, and it's the third of entire Hinge organization is trust and safety. So, we deeply care about trust and safety of users, and spend a lot of effort and resources finding those fake users or bad actors on the platform. So, all of that has been done through AI for ages. You know, it's not that generative AI, the recent development in generative AI have been game-changing in this space. What has been game-changing is AI generally, and a simple, traditional machine learning models that we use for for forever. Now, if you really think about, you know, people coming onto our platform, and purpose of Hinge of getting them off of the platform, you really want to make sure that you are as authentic as you can be as a user, so when you meet other person real life, you know, you show up as they expect you to show up. You're not going to achieve success if you show up as something completely different. So, what we're really seeing is people are presenting themselves in in in true light and authentic light. Otherwise, success is almost impossible to achieve. >> Totally. And Lucas, speaking of AI, I was looking at the market cap of your holding company, Match Group. It has It's around 25% up year-to-date. If you look at SaaS companies, most of them are down, right? So, there's clearly an AI narrative that is working for you. Right? So, I'm curious to know, what is it What do you think it is that is helping you be so successful in the public markets. >> Yeah, I mean, it's probably hard for me to speak generally about Match Group and and and what they're doing. I can speak specifically about Hinge. So, Hinge is on the way on the mission to achieve $1 billion in revenue in 2027 next year. And we're well, you know, on our way to do it so. We more or less grown double digits year over year over the last you know, 10 years, more or less. And I think it's very much tied to our obsession about really talking with users, understanding their core needs and core problems, looking at the next generation of data all the time, and building features and products that are solving their core needs. That that is like the very simple, I would say, formula for success. If you're like obsessive about really constantly thinking about your users and solving their problems, you're just going to your business is going to have this flywheel of getting better and better and better. And that that is from Hinge perspective. Match Group, as I said, cannot really speak more broadly because it includes Tinder and many many other apps under the same umbrella. >> Yeah, it's really fascinating. I was looking at this around. Match Match Group has 45 global dating social discovery brands all under the same umbrella. It seems like Hinge is number two in terms of market share. Number one is Tinder. Plus, I was just curious to know from your own perspective, like how do you go about positioning your own product to make sure there's a clear value prop that is not competing against other companies even within your same group. >> Yeah, I mean, if you look at most of those, you know, 45 you mentioned dating products out there, some of them are very specialized. So, so you have products that are specialized for maybe older generation of users or maybe for people looking for Black Love, BLK, or or Chispa. So, so they they have multiple specialized products. And then you have probably out of the big ones you have Tinder and Hinge. Hinge is I would describe Hinge as a product that is more intentional. So, people coming to us are looking for really intentional connections and getting off of the app as quickly as possible. So, our positioning is different than any of these other dating apps, and therefore, you know, we have our lane to kind of play in. Other apps have their other positionings, and of course, it's impossible to achieve very clear swim lanes. Everyone is kind kind of going to play in each other's, but it's interesting also to understand that this category is multi-use product category. So, users don't necessarily use a single product. They're going to use Tinder and Hinge and maybe some others as well, and and I think that that works in our favor. >> That's a super interesting story to me. I That's one I keep looking at closely. A friend of mine that was the the CPO at Tinder, and he told me that he actually joined the company because he's he now is in the nature of starting to date, and he wanted to figure out, you know, how the new generation of people go over the product. And and he told me that yes, one of the things was that they were multi-product, right? Like the same way they could be using that app, they could be using others, and and that seems to be like an acceptable behavior. >> Yeah, 100%, you know, most users use multiple apps, and and it's similar, you know, in other cases, you know, if you if you're streaming videos or movies or whatever, like most people will use Netflix and maybe Hulu or some other products as well. So, this is not special to the dating category. It is just the nature of how people utilize these digital products. >> So, as we look now under the hood in terms of how you're building, how your team is shipping now fast, better, prettier, right? Like tell me more about what is it fundamentally different today that you maybe weren't doing a few months ago. >> And I'm assuming you're looking for how AI is actually helping us do so, right? >> But for real. >> For real, for real. So So, when I think about AI, you know, as I CPO product, as I CPO product, I talk with individuals in New York that we're all kind of really asking the same question, like, how is this really helping us achieve success? And everyone is, you know, still questioning the the value. What I can tell you at Hinge specifically, the biggest value I see is the feedback loop. How quickly can I learn whether something is valuable to invest in or not. So, what does that actually mean on the ground? I can really, without any engineering support, prototype an idea that is as close as possible to final product. So, it like really works with the APIs, it connects to our back end, you know, and I can put it in the hands of users and really gain either conviction that this is valuable building and putting in product, or this is not worth pursuing and leave it behind. So, like this feedback loop of how quickly I really learn about whether something is worth building or not is real. I really learn much, much quicker. That's one. The second thing is, you know, product managers now can wipe codes a quick, you know, visualization of something they're trying to to create. So, instead of, you know, having this like product spec of 20 pages describing what this feature is supposed to do, me as a product manager can say, "Okay, please you are use our design system and build something for me that I can actually put in front of people and show them what I'm really thinking about without them having to read all the product spec to understand what the idea is. So, like that kind of thing is much, much easier than doing research. So, user research has become also a little bit easier. You can, through some AI tools out there, reach more people, number one, and also do more user research. You You do do more interviews with users. And then I would say last piece, we're startly starting to slowly move towards what I would describe as mobile engineering. So, hinges a native product, so we have iOS and Android built separately, natively. And some people on the ground are picking up some of these tools, you know, are already starting to say like, "Look, I'm building this feature on iOS. Why don't I just tell the tool to build it for me on Android?" And then all of a sudden, you know, I've done work of two people in in one sitting. So, those kind of things are already seeing happening on the ground. So, there's like in multiple pockets we see a lot of improvements and and help of how these AI tools are make our life easier. But, I think if I really look holistically, it does not translate to what probably the narrative in the media out there is. We are moving faster, we're learning faster, we have more tools that at our disposal. But, I believe, you know, we still haven't achieved the full potential. So, there's probably a couple of years left, I think, before we really start to see true benefits of how quickly we move, how better we build, how higher quality the products we build are, and things like those. >> Curious to know where you draw the line for what PMs can do, given that you come from an engineering background, right? And I can see two camps now. We see a lot of AI-native companies saying, "Hey, everybody's a builder. PMs, designers, basically anyone any non-engineer now is empowered to go and ship into production." Uh but, I also see sometimes engineers saying, "Hey, like we have a background in engineering, there's other things that we need to take into consideration." So, like what is your What is your take on this dichotomy? >> I I am taking a little bit more conservative approach to this, personally. And And not because I'm coming from engineering background, but because I really want to see the proof that product managers or designers can actually provide and build technology that is going to be scalable and maintainable in the long run. Uh And in order for me to really maintain that, it I I draw the line at the build. So, for us, engineers are still building the product and they're still those who are designing the architecture around it and and setting up standards for the quality of our product. I haven't really made that leap of having product managers or designers actually contribute to the code base. They do actually, you know, use these tools to uh as I said before, to to create prototypes, to to learn quicker whether these things are worth pursuing or not, to to convince others through visualizing their ideas in a sense. So, like we are learning much much quicker. But, we're not in a place where non-engineers are building products. >> Got it. I think you are the first CPO CPO that actually shares that. Everybody else seems to be more on the everybody can shift, right? So, it's refreshing to hear that. But, I think also curious to know like kind of as you mentioned like there's a lot to be realized in the upcoming years. So, like do you see a future where like the lines are truly blurring and we can have more non-technical people really shipping into production? >> So, I I can see a world in the future uh where if I am able to provide a perfect query input to let's say Cloud Coder, Cursor, or Codex, or any of these tools, I will probably get a perfect output. So, if I work backwards from like what does it actually take to provide this perfect query, it probably starts with having a really high-quality bulletproof product spec in some sense. And design and design system built in a way that is really repeatable. So, as this query is generated, it can pull out from the design system design elements that are necessary to build a feature. From product spec, it will have all the cases covered, all the edge cases covered in a way. So, I can see that future. We're not there yet. And that's what I said when I said before, probably 2 years at least away from like having non-engineers build products for us because I haven't personally been able to achieve this perfect query myself. I always have to go and like adjust something or change something or tweak something on the output side. And I think you need engineering skills. So, I'm hopeful that we're going to get there and some people that I'm talking with, which are taking a little bit less conservative approach, more, you know, aggressive approach, uh believe that like even if you create some tech that now and even if you feel lower your quality right now in exchange for moving faster, these tools are going to get better. They're going to be able to fix all of those things for you. I tend not to trust that at this point. So, I'm like really, you know, let me take slower, more more conservative approach. And then if the tools do achieve that level of of quality and perfection, then I'm going to jump on the train. So, we're not there yet. Let's see what happens. >> Ben, it's been a pleasure to have you on the pod. Thanks so much for joining us and for your honest takes. I think it's also very refreshing to have someone who hasn't been doing the rounds in the podcast who who's really grounded in reality. >> Yeah, of course. Thank you for having me.