Knowing What Your Customers Want, All the Time: Listen Labs' Alfred Wahlforss

Alfred Wahlforss, co-founder and CEO of Listen Labs, is building an AI agent that interviews your customers at a scale no focus group ever could—thousands of voice conversations at once, drawn from an audience of 30 million people. A year after launch, Listen serves hundreds of Fortune 100s to Startups including Microsoft, Google, NBC Universal, P&G, Anthropic, Cursor, and Cognition. Alfred explains the counterintuitive finding underneath it all: people are often more honest with an AI than a human interviewer, opening up to a non-judgmental entity that costs less and never makes them feel rushed. He walks through why interview transcripts—not credit card data or behavioral logs—turn out to be the richest fuel for predicting how customers will behave, how Listen back-tests its simulations to know which questions it can and can't answer, and why 80% of the company's engineering goes into building the right audience. As AGI makes building trivial, Alfred argues the scarce resource becomes knowing what to build. That's the loop Listen wants to own.

• 0:00Speaker 00

  Our goal is to get to a billion people in our audience. And then to be able to stratify and know what exactly is this person an expert on? And it might be even something like sneakers. You have some people who are influencers and kind of early adopters. And if you're able to find that audience and interview them first, the insights are much more valuable. And we can learn across all of the interviews that we do. We build profiles of people as we do more interviews in the platform. And then we can search and find the right person.

• 0:51Speaker 01

  Okay, today we're sitting down with Alfred Wahlforss, founder and CEO of Listen Labs. Listen is an AI-first customer research platform that can run thousands of voice interviews simultaneously. You launched about a year ago and you now serve 20% of the Fortune 500, including iconic brands like Microsoft, Anthropic, Sweetgreen, NBC and others. And Konstantin, I'm very excited to sit down with you today and talk about market research and how it's getting transformed with AI.

• 1:19Speaker 00

  Yeah, thank you for having me.

• 1:20Speaker 01

  Maybe just to get started, so you are building an AI-enabled platform that scales market research. What does that mean?

• 1:25Speaker 00

  Yeah, so we have this AI agent that can understand your customers better than you can. And the way we do that is by talking to them. So to give you an example, you can ask a question like, how can you improve courses onboarding? And then Listen will create an interview guide, which is an instructions for the agent to make the interviews. And then we have an audience, we have 30 million participants. We can find pretty much anyone from an oncologist to a software engineer. And we'll go and actually talk to them and have hundreds of those interviews. And then analyze the data, give you recommendations. And now the final step that we're just launching in a couple of months is simulation. So after you've done tens of thousands of interviews in the platform,

• 2:08Speaker 00

  can you predict how your customers will answer questions in the future? Put it another way, as we get closer to AGI, it will be easier to build things, but the hard part will know what to build. And that's what we're building at Listen.

• 2:22Speaker 01

  Awesome. Do you have any favorite customer stories?

• 2:25Speaker 00

  And yeah, so Chubbies is one of our customers.

• 2:29Speaker 01

  Like the shortbread?

• 2:30Speaker 00

  Yeah, they've been like one of our early customers.

• 2:32Speaker 01

  What'd they use you for?

• 2:33Speaker 00

  They use us for everything. So a lot of marketing testing, for testing shirts to understand what products perform well and what doesn't. And one of my favorite examples is they discovered that chest hair interferes really poorly with one of the materials they have. So it's like really uncomfortable to wear one of their shirts and they changed the shirt and it became like radically more comfortable.

• 2:58Speaker 00

  So we solve, you know, the small things to the big things, Manscaped, and changed their Super Bowl ad with insights from Listen, so.

• 3:07Speaker 01

  Never heard of that, but I'm not gonna ask.

• 3:10Speaker 02

  So you, you got the men's hair market covered.

• 3:13Speaker 00

  Yes, that's our niche.

• 3:14Speaker 02

  From shaping to clothing.

• 3:17Speaker 00

  That's right.

• 3:17Speaker 02

  Wow.

• 3:18Speaker 00

  We do other things. Skims is one of our customers as well.

• 3:23Speaker 01

  Don't know what you're talking about, but I can, I know context clues, so. That's awesome. I'd love to understand, as you framed it, as we get closer to this AGI future, one of the questions I have is, you know, traditionally I've always been very skeptical, actually, of surveys because people get paid to take surveys, so you already got a selection bias issue. The things that people say they would do, or the way that they describe how they would behave is different from how they actually behave in practice. And so I guess I come from the school of thought where, like, actual just telemetry in the real world matters so much more than asking people about what they would do. And so I'm curious what you think of that and how you think AI or Listen Labs can help bridge that gap.

• 4:05Speaker 00

  Yeah, and so we've done a lot of research on this. One of the things we've done with surveys, for example, is we went back to the same person and asked them a multiple choice survey again, and they were radically inconsistent. So even if you go back to the same person and ask them a survey question in a multiple choice fashion, they're much more inconsistent. But we did the same thing with Listen, when you actually have to think and you have to really reason through your answer, and then you're much more consistent with, at least how you answer the same question. And then we're constantly tracking, for example, with with Shabbies.

• 4:45Speaker 00

  When we test their different shirts, we, a couple of months later, look back and see how did that perform with the actual sales data? And I think it depends on different use cases. I agree that A B test is kind of the the holy grail. But in practice, it becomes really difficult to get right because you need a very large volume of users. And it's it's really useful to have some kind of input than no input at all.

• 5:12Speaker 02

  Does Listen do a voice to text as in the actual customer who's answering the survey can speak their answer and then you guys transcribe it? Does it also do text to voice? It's a two way conversation. What does Listen start with and what does it finish with for the user experience?

• 5:31Speaker 00

  Yeah, so it's essentially a Zoom call that you have with the agent. So you're on video and you can also detect their emotions. So that's another way to bridge the gap between what they say and how they actually think and feel. So it looks at your eyes, the way you say it. And and that's kind of much closer to how you actually behave in the real world.

• 5:54Speaker 02

  And have you seen Personia's point that actually having the person's face and their emotions and their voice and whatnot yields more engagement, truthfulness? Have we been able to have any studies or or at least data to point in that direction?

• 6:12Speaker 00

  Yeah, specifically with advertising. It's a huge benefit because you might have people say on a Likert scale, which is like a, you know, five questions that you click, are you extremely likely to, you know, buy this product? Versus when you you might have very high scores on a survey question like that. But when someone also reacts very enthusiastically, it's going to be like perform much higher. And we've seen that those ads then perform better in performance marketing, for example, on on Meta and LinkedIn.

• 6:48Speaker 02

  And can you, if you're the customer and you commissioned this and you get all this response, can you actually click in and if you ever wanted to watch the interview to get that level of granularity?

• 6:58Speaker 00

  Yeah, so we built the platform around traceability so that for every data point, you can always click and then look at the video or see the quote. And so you know that AI is not just hallucinating kind of where it's coming from.

• 7:12Speaker 02

  That's awesome.

• 7:13Speaker 01

  How'd you come up with the idea to build this?

• 7:16Speaker 00

  And so my co-founder and I actually built a consumer app and

• 7:22Speaker 01

  That did what?

• 7:22Speaker 00

  That went viral. It was called BeFake, so you could create an AI avatar of yourself. It was an early version of the ChatGPT images and you could fine tune, stable diffusion and put yourself in that world. And that ended up going super viral and overnight we had twenty thousand users. And we're also kind of experimenting with different ways of using AI. So we built this AI interview for ourselves because we had a bunch of questions of how we had, we had a ton of churn, so we wanted to understand why, how they thought about our positioning, different use cases, and it was really useful for us.

• 7:58Speaker 01

  Yeah.

• 7:58Speaker 00

  And that's how we got started.

• 8:00Speaker 01

  Maybe just walk us through how the, how the industry is changing before and after Listen Labs. Like historically, let's say you're somebody with an app with twenty thousand users. You understand how users are using the app, what they want next, why they're churning. Historically, how, how did people go about doing that?

• 8:16Speaker 00

  Yeah, so what we discovered was that there are these survey tools that are pretty old school, like Qualtrics, but then there's also this very large services industry because it becomes harder and harder, especially if you want to do market research, where you want to talk to your prospective customers, not your current customers. It becomes harder and harder to do that as you scale. So that's a multi-billion dollar industry and

• 8:44Speaker 00

  It's what they do is come up with questions to ask, which is an academic subject in of itself. It's actually really hard to know, like, how do you ask questions to your point? That get to how someone actually will behave. You can't just ask, like, how much are you willing to pay for this? And there's different methodologies that work better than others. To finding the audience, how do you source the participants? To then like analyze hundreds of these calls. And in the traditional industries like CPG, even in Microsoft, they spend tens of millions of dollars on focus groups to bring people in a room and interview them. And we can help speed that up much faster.

• 9:26Speaker 01

  Okay, so that's the old world of how this used to be done. Maybe describe the new world and then it seems to me that there were obvious kind of first order benefits. Like it's probably much more scalable, probably much more cost effective. But there's probably also less obvious benefits. Maybe just talk about, talk about some of those benefits of, you know, what is it like when you actually do AI first market or customer research?

• 9:50Speaker 00

  Yeah, so most decisions that gets made are not based on the customer input. Right? And the reason for that is it's just a lot of friction to even talk to customers. So when you can lower the barriers of talking to customers, you end up making much smarter decisions. So the speed advantage is actually huge. For us, you can get input within five minutes from real people. And it's a really magical experience when you see hundreds of people populate in your like interview. And so that's one thing. And because it's asynchronous, it's also much more affordable.

• 10:32Speaker 00

  So you can pay people much less than if you would had to run like synchronous interviews. So actually, that's an interesting thing that people often ask us, like, do people even like being interviewed by an AI? And the objective answer is yes, because you can pay them less to talk to an AI than to talk to a actual interviewer.

• 10:53Speaker 01

  Why is that?

• 10:55Speaker 00

  I think it's mostly because it's asynchronous and people are very busy. But then also.

• 11:00Speaker 02

  Lower pressure.

• 11:01Speaker 00

  Yeah, lower pressure. You can kind of go on and off. And we've also found that people are more honest talking to an AI. We've had people really open up. It's a very therapeutic experience because it's a nonjudgmental entity that's really interested in you. And we can also have sensitive conversations like interviewing kids. And how they react to different products. And and so I think that's another advantage as well, that people can be brutally honest talking to the AI.

• 11:32Speaker 01

  Okay, so historically, for example, if I was to do research on the kids market, very, very hard to access that market. Is that regulatory thing? Is that a scheduling thing?

• 11:40Speaker 00

  Yeah, it's you need parental consent. And you know, kids are really busy. They go to school, they have extracurricular activities. How do you find time with them? And you need to find the right kind of kids. Like, one of the things we realized is the audience is extremely important. And that's actually where we spend eighty percent of our engineering resources. Every company is driven by a power law in customer segmentation. So even a product like Sweetgreen, which you would think is for everyone. The right audience is typically urban, high household income, mostly female. And by the way, they need to know what seed oils are, which only like one percent of the population does.

• 12:23Speaker 00

  And then you find that some people go to Sweetgreen every single day, and that's eighty percent of their revenue. So if you can find that segment, and the research is so much more actionable.

• 12:35Speaker 02

  Yeah, there's probably a network effect to it as well, where when you get of a certain scale and people use it, you can access the same kind of person that otherwise might be really difficult to access. Or maybe it's a scale economy, something along the lines of accessing those really, really specific people that are really valuable for the type of product that you're trying to introduce.

• 12:56Speaker 00

  Yeah, it's really about, you know, we have, our goal is to get to a billion people in our audience and then to be able to stratify and know what exactly is this person an expert on? And it might be, you know, even something like sneakers. You have some people who are influencers and kind of early adopters. And if you're able to find that audience and interview them first, the insights are much more valuable. And we can learn across all of the interviews that we do. So we build profiles of people as we do more interviews in the platform. And then we can search and find the right person.

• 13:36Speaker 02

  So someone might say in a totally unrelated interview, I'm a total sneaker head. And you can keep that in the database on that person. And then when Nike or what have you is launching the new product line, you can offer that person up.

• 13:47Speaker 00

  That's right.

• 13:48Speaker 02

  That's amazing.

• 13:49Speaker 00

  And that was not possible to do before. And because it was usually like separate entities and it would be a very manual process where you would have an email list and you would just spam email.

• 13:59Speaker 02

  I've been on the receiving end of those. Yeah, they're terrible.

• 14:02Speaker 00

  And one of the problems with that is that you then need to have a extensive screening process. So you have something called an incidence rate, which can be 10%, which means only one in 10 people gets qualified to even take the interview. And that causes significant churn on these databases.

• 14:23Speaker 02

  Yeah.

• 14:23Speaker 00

  Because it's really annoying to be screened out 10 times to even get paid the first time.

• 14:29Speaker 01

  Why do these brands even need you for access if, let's take Sweetgreen. Sweetgreen knows who that 80% is. Can't they just reach them? Don't they have direct relationships with them already?

• 14:39Speaker 00

  Yeah, so they can. And we do that as well. We connect to their CRM and they can send that out. But then the really interesting part is how do you talk to prospective customers? People who also may not be kind of current power law users. And how do you compare those two? And then also what we found is that The CRM is typically really unorganized. And sometimes there's also like regulatory issues. If you're at Google, you can't just send emails to people who use Gmail. And it gets much easier to use an external third party.

• 15:17Speaker 02

  And you run the risk of spam, which can get you totally blocked. I have seen that at some of our companies over the years where, you know, you do outbound and then eventually you're in the Google filter. And next thing you know, you're in Microsoft purgatory. I guess going through you guys, you don't have to deal with that.

• 15:33Speaker 00

  Yeah, exactly.

• 15:34Speaker 02

  That's cool.

• 15:35Speaker 01

  What does this mean for the McKinsey's or whoever's of the world that are, you know, the people that are building the hundred slide decks that, you know, reached 3,000 people to reach some set of insights.

• 15:45Speaker 02

  Didn't you do that, Sonia? Wasn't that a former life?

• 15:47Speaker 01

  You know, no, Konstantin, but I'm glad that that's what you think of me.

• 15:51Speaker 02

  Isn't that what banking does?

• 15:54Speaker 01

  We hired consultants. We used to hire these people.

• 15:58Speaker 02

  Got it, got it.

• 16:00Speaker 01

  So I was, you know, I was a layer on top of the layer on top of layer.

• 16:03Speaker 02

  Okay, got it, okay.

• 16:03Speaker 01

  I was even more redundant. But what does it mean for all these people? Do they still have a role to play in this new future?

• 16:11Speaker 00

  Yeah, I think AI is changing all roles very quickly. And we work a lot with Bain, for example. So they use us to speed up their traditional processes. And I think they still have a role to play. I think traditional services and being able to then implement these changes is still extremely valuable. But a lot of margins are going to drop. And you have to make sure you kind of unbundle a lot of your services to maybe allow for AI agents to help solve some of the problems that you would go to traditional consulting firms before.

• 16:52Speaker 02

  Maybe I'm an optimist here, but why wouldn't it be more? Why wouldn't I, if I'm running a business, say, oh, great. I want to find five new areas to expand to now that I have AI and these tools. And I will pay you, Bain or what have you, the same dollars. You use Listen and just explore those new areas and tell me where to, where to build. Is that overly optimistic?

• 17:15Speaker 00

  No, I think it's one of those areas where the ceiling is very high. You can kind of learn more about your customers and you can build more things. And so I think, I think you're right.

• 17:26Speaker 02

  I was thinking the chest hair shirt thing. Not a problem that I have.

• 17:31Speaker 01

  I'm glad that 20 minutes in, you're still thinking about chest hair. That's insane.

• 17:34Speaker 02

  There's so many little things that I'd love to tell the companies that I am a consumer of. Like the smallest little thing, even the way they laced these shoes. I'd love to give that feedback.

• 17:45Speaker 01

  This is why you're a venture capitalist.

• 17:47Speaker 02

  Details, details.

• 17:51Speaker 00

  We hope to live in a world that finally works the way people want.

• 17:53Speaker 02

  That would be great, please.

• 17:55Speaker 01

  Are you seeing any pricing compression already hit the industry? Like, I would imagine if I am Bain's customer, I'm thinking, well, you're able to do this survey a lot more efficiently now with AI than before AI.

• 18:09Speaker 01

  Who is getting the benefit of that economic surplus?

• 18:12Speaker 00

  So because you're able to do it faster, I would argue you should be able to charge more for it.

• 18:18Speaker 01

  And is that what's actually playing out?

• 18:19Speaker 00

  We have done some studies where we're able to charge hundreds of thousands of dollars to speak to 20 doctors across eight countries. So maybe over the long term, like the individual interview will become more affordable. But I think you'll be doing kind of two orders magnitude more of research. And I think what's really exciting is also simulation, which is something we're building now, where you're able to unlock the 99% of use cases where you would never have time to talk to real people.

• 18:54Speaker 02

  I think that's so awesome in part because there are so many areas where they don't even listen to the customer, like medicine. There are a million little problems with the medical system. I hear about it all the time. And these are, you know, they're doctors, they're busy, important people. But it feels like the companies haven't even invested the time in figuring out where all those paper cuts are. And the doctors are really busy, so they're not going to go schedule an appointment and have some long conversation and meet with some group. But if they could do it anytime, like in an app on their phone as part of the normal homepage app and give feedback on their EHR or something in the operating room or something along those

• 19:36Speaker 02

  lines, that seems like a life-saving use case for listen over time.

• 19:40Speaker 00

  Yeah, I think what I'm really excited about as well is taking all those small things and then telling another agent to go and solve that problem. And we're getting pulled in in this direction by some of our customers, where they will have a churn interview and then they will connect them. If you find a bug, for example, they'll connect that to another coding agent to go and solve the problem.

• 20:04Speaker 02

  That's cool.

• 20:05Speaker 01

  Let's talk about generative agent simulation. Like, it seems like the entire industry has gone from market research 1.0, you know, call a hundred people one by one, collate them manually, to market research 2.0, AI native, where AI designs the question track, is able to talk to thousands of people simultaneously, synthesize the answers. It seems like we're maybe moving to market research 3.0 with generative agent simulation. What do you make of that? And, you know, I both see the dream of it. I see how synthetic data has changed, for example, self-driving cars. And then I also am inherently skeptical of it, like, is a bunch of synthetic data just remixing what's already in the pre-training sets? And are you actually learning anything useful or with alpha there?

• 20:50Speaker 01

  And so I'd love to, I'd love to hear your take on it and how you guys are taking on the 3.0.

• 20:54Speaker 02

  And maybe what is it too, to start?

• 20:57Speaker 00

  Yeah, so the way we are building simulation is by interviewing a single person. Say if I interview Konstantin for one hour, and I can probably start to predict your preferences to some degree.

• 21:10Speaker 02

  Fascinating insights about chest hair.

• 21:14Speaker 00

  And it turns out that LMs are quite good at this as well. So you can essentially try to feed in as much information as possible on a single individual. And then in some cases, we're able to get 95% accuracy to predict how they will answer certain questions.

• 21:30Speaker 01

  What sorts of things can you, are you finding that you can predict well versus can't?

• 21:34Speaker 00

  One of the most useful things is message testing. So that's the idea of like, what's the tagline on the billboard? Or I was actually using it this weekend. So I have created a panel of our customer base. And I had to come up with the title for a talk at a conference. And it's like a small thing, but it actually does matter because it will increase conversion if people show up. And I came up with a hundred different titles for my talk and inputted that into our simulation. Oh, wow. The the top talk was like twice better than the next one. Wow, cool. And I like, I don't know if it's correct, but it certainly felt correct.

• 22:18Speaker 00

  And it was really helpful to have guidance in making that decision. And I also think like, even if it's wrong, it's just nice to make to have some help in making a decision. It's also nice to outsource your decisions.

• 22:31Speaker 01

  And how does it compare to just asking ChatGPT the same thing?

• 22:34Speaker 00

  Yeah, so then I inputted the same questions into ChatGPT. And I actually had one, I had another talk I did that was not so successful. And I had and I inputted a competitors or another talk that was more successful. And and I showed both of them to ChatGPT and both of them to our simulation. And in ChatGPT, it picked the wrong one. And in our simulation, it picked the right one. So the, you know, it's early for us, we're gonna release this in a couple of months. But it seems like it's it's performing better than the general models. And the models are trained on the average person. Yeah. And you want to build for a very specific niche. And and that's how we can kind of essentially train the models to follow that niche.

• 23:20Speaker 01

  And and just to push on this, because I think it's so fascinating. Like, can't you? Kind of forced the models into a specific niche or personality like, hey, ChatGPT, you're a thirty-five-year-old, really grumpy software engineer that likes using your terminal. Like, and then it does then take on the preferences of that niche. It's like, it's sort of my mental model, at least. And so I'm actually surprised that, you know, ChatGPT wasn't able to arrive at the right answer, and then bootstrapping off real user data was. Because ultimately, it all is kind of a reflection of real user data, right? And so, actually, what is the intuition for why kind of sim-only on pre-trained data isn't sufficient?

• 24:03Speaker 00

  Yeah, so we've tried many different inputs. And that certainly performs a little bit better than just vanilla ChatGPT. But what performs much better is we tried credit card spend, kind of behavioral data, purchasing behavior. But what we found was the best dataset is interviews because it's more, kind of, it allows you to go off tangents. It understands, you can ask behavioral questions. So also, it can't just be any interview. Like, the way you design the questions is also really important. And the intuition, I think, is that the models don't have clean data on how a specific persona acts and how they think.

• 24:48Speaker 02

  It's anecdotal, but it makes perfect sense. Because if you want to understand someone, what better way to understand them than asking them a lot of questions? That's why we're all here. I guess it's kind of the purpose of this type of format. And if you have enough people that follow a certain group as opposed to the average, that can tell you a lot about other things that they might not have explicitly said. You know, all of AI is this generalization of some sort of compressed data of some sort. And so if you have this compression in a slightly different part of this hyperspace that you say, now complete this orbital of what everybody is thinking in this category of person.

• 25:27Speaker 02

  You know, Listen can fill that out because it has enough interviews.

• 25:29Speaker 00

  Yeah.

• 25:30Speaker 02

  Do you think that you will offer that package as a product? As in, if I wanted to understand my customer, and for me, for us, our customers, founders, I, they're very different, though. So extremely different people. If I wanted to understand my customer, could you do active interviews? The normal Listen Lab interviews? Have a thousand or 10,000 cumulatively? And then offer a little special purpose Listen Labs bot? That then I can use instantaneously for any ad hoc question?

• 26:04Speaker 00

  Yeah, that's exactly what we, what we have.

• 26:06Speaker 02

  Okay.

• 26:07Speaker 00

  So that's, that's what we call augmented responses. The cool part of this as well is that it can also live in your coding agents or your other agents. So I think in the future, you will want to have almost a human API, where the agents are able to call the preferences of your users. And to be able to know like what to build, how to do it, or who to invest in, or how to help them the best.

• 26:30Speaker 02

  Today, is it all RAG? Is it fine tuned? Is it something else? How do you take those conversations and then combine them with, you know, the models that you're doing the rest of the Listen Labs with?

• 26:42Speaker 00

  Yeah, we are doing post-training, typical RAG as well. There's like a bunch of different techniques. Some of them are proprietary, but yeah.

• 26:54Speaker 02

  All right, we'll do customer interviews on all your engineers. Report back.

• 26:58Speaker 01

  I'm curious what you think of multi-agent systems? And their role in helping us kind of iteratively use, you know, ad inference time, iterate to a better answer? Is that part of how you're doing simulation or not?

• 27:13Speaker 00

  Yeah, like the way we do simulation is essentially, you have one person that you model really, really well. And then you scale it up with a thousand people. So you have. A representative sample. Um, and it's essentially multi-agent.

• 27:29Speaker 01

  But you're not having those thousand people debate each other. That's what I'm asking.

• 27:31Speaker 00

  Oh, yeah. No, we don't have that yet, but that's a good question.

• 27:34Speaker 01

  Do you think that would help?

• 27:35Speaker 00

  Potentially, but there are these other competitors that are doing that approach more. I think the worry is that, again, chaos theory tells us when things kind of compound, it it becomes really hard to predict how the things are going to interact with each other. And, you know, it's it's something we definitely should explore more, but I'm I'm a little bit skeptical of that approach.

• 28:01Speaker 01

  Maybe the analogy I'd make is like, like the AI council approach of, you know, send send the same query out to three different L L Ms. And then have one L L M act as judge in synthesizing them. I do think on average it gets a slightly better response.

• 28:13Speaker 00

  Yeah.

• 28:14Speaker 01

  Cool. So where else do you see yourself going from here then? You have, you're you're going from market research two dot O to market research three dot O now with kind of generative simulation. Do you expect that three dot O takes over as the majority of queries over time? Um, and then what what else is ahead?

• 28:30Speaker 00

  Yeah, I think you'll still need human input. But I think there would be many more use cases that are now opened up where you can get customer input. And so for the large decisions, if you're doing a Super Bowl ad or things like that, you will still need to run real interviews. But for the smaller things, like what what should be the tagline for your billboard? And if it's a small billboard, then you can use simulation to to answer that. And I still think that there's a lot of alpha on the core product as well to improve. I mean, when we started, the the core idea was just making the interview

• 29:16Speaker 00

  less annoying to go through, like we had an eval that looked at repetitive questions or looked at, is the AI even able to follow the instructions? And with G P T four, sometimes we would ask the same question a hundred times.

• 29:30Speaker 01

  Yeah.

• 29:30Speaker 00

  Um, and.

• 29:33Speaker 00

  In the beginning, that eval was like twenty percent. Now we've been able to climb that eval to be eighty-five percent. But now we created a new eval that's much more advanced. So it's able to understand what are you doing on your screen when you're screen recording? Or can you skip questions that are not relevant anymore? And now we're back at like twenty percent, which I think is one of the values that

• 29:56Speaker 02

  That's great, yeah.

• 29:56Speaker 00

  Vertical AI companies can have is that they have this proprietary eval that they can use and essentially climb that, climb that eval. And that's your advantage as a vertical AI company.

• 30:07Speaker 02

  Keep pushing forward, better data, harder problems, better data, repeat.

• 30:11Speaker 00

  Yeah.

• 30:12Speaker 01

  It seems to me like you're in the middle of a very interesting infinity loop, right? Because fundamentally, a company is figure out what to build. Build it. Figure out what to build. Build it.

• 30:22Speaker 00

  Write code and talk to users.

• 30:24Speaker 01

  Exactly. And, and the, and the build it is coming up rapidly up in exponential. And the figure out what to build is the thing that you are pushing forward.

• 30:33Speaker 02

  Totally, Sonia, yeah.

• 30:34Speaker 01

  And then it's not only, even outside of product and engineering, the broader loop is actually strategy execution, strategy execution. And so much of what AI is enabling us to do is it's making execution faster, cheaper, better, all these things. And the thing that you guys fundamentally are positioning yourselves to do as a company is the strategy part, from what to build to what to say. Is that a fair synthesis?

• 30:59Speaker 00

  Yeah. And I think we'll, when we have that one person billion dollar company, we'll be part of that loop. So I have a coding agent and listen and then run that in a loop. And we'll have these autonomous organizations. And be excited for that.

• 31:15Speaker 02

  And even the, even the big companies still, like back to this idea of you can implement things faster, let's say you have an agent. I mean, if you can be a big company, and we're talking in software because software is native to us, but in software, if you could talk to a customer, figure out a bug, create a PR, have a coding agent, close it. Chip it, customers happy, that seems like a really important left-hand side of the equation. Find the bug from an actual human. But I imagine it's the same thing in a big Adams company. Like if you're a consumer packaged goods, if you're clothing, if you're any of those things. Imagine that's even more important because you have to figure it out because once you actually do the thing, it's done.

• 31:57Speaker 00

  Yeah, exactly. Like Procter and Gamble, when they're launching in a new market, that can be tens of millions of dollars, if not more. And you have to make sure that that is right when you launch. And that's one of the reasons why they're the customers of Listen.

• 32:12Speaker 02

  Who has done this historically really well? Like, who are the companies that are admired in history have done a great job of listening to their customers? Either in the, you know, consumer space or in the software space?

• 32:30Speaker 00

  I think Procter and Gamble is is kind of the archetype of best market research organization. Where they're essentially marketing companies that are trying to figure out what are niches? That people really care about and then build specific brands to solve those problems. I mean, one example is the Tide washing machine.

• 32:53Speaker 02

  Yeah.

• 32:53Speaker 00

  And pods that they were able to figure out that it was really uncomfortable to use the washing liquid. And they discovered that people wanted something that was much more easy to use. And through customer interviews, they found this insight, made this new Tide Pod and became really successful. Another example, which is in the acquired podcast, when they talk about Mars. And they did the first, one of the first market research studies in the 1950s. Where M and M's were originally designed for being in the, used in the army. Cause they were like a sweet treat, they don't melt in pocket.

• 33:35Speaker 00

  And they discovered through market research that another great segment was young kids. And they then decided to pivot the entire like advertising strategy to focus on on this. Because it doesn't melt and ruins your furniture, for example, and things like that.

• 33:54Speaker 01

  As we progress towards this, you know, Listen Labs, future, future vision of the world. What are the things that you're confident will work? And what are the things that you're still not sure about?

• 34:04Speaker 00

  I'm confident that in the future, you'll still need to have human input. Because even if you have a perfect rational being, like AGI, humans are still irrational.

• 34:19Speaker 02

  Totally.

• 34:19Speaker 00

  And they will still want to be chaotic in their nature, where they all of a sudden get obsessed with a new product, you know, a new TikTok trend that shows up and you have to change your entire marketing strategy towards that. And so I think that will remain a really huge part of how we do things. I think I'm still uncertain of what level simulation will play. And I'm confident that it will work for certain questions. But we'll see how good the models get to predicting human behavior.

• 34:54Speaker 02

  I mean, I'd imagine that it's actually even more important, the better AI gets, to have the delta. Because the competition, if companies are about serving people, which I think we can all agree on, like at the end of the day, every company is about serving humans.

• 35:09Speaker 01

  Konstantin, our resident humanist.

• 35:12Speaker 02

  I'm a humanist, absolutely. But if companies are about serving people, because that's why we're all working, is to help someone else in some way. And intelligence gets better and better and better. And you kind of have what the human wants here, and the intelligence is approaching that asymptote. Then the delta in that asymptote, which is what is in a human's mind that isn't in the AI's mind, only becomes more important.

• 35:34Speaker 00

  Yeah. And yeah, one of the things that we've also realized is that there's, you know, a lot of talk around, what's the mode of these AI, virtual AI companies? And

• 35:46Speaker 01

  Yeah, what's your mode?

• 35:47Speaker 00

  What is our mode?

• 35:48Speaker 02

  They've got network effects and scale economies.

• 35:50Speaker 00

  Yes.

• 35:51Speaker 02

  Those are nice.

• 35:52Speaker 01

  I feel like we're on an episode of Acquired right now.

• 35:53Speaker 02

  Hey, I'm feeling it. I'm feeling it right now. It's a good book. Recommended. Seven Powers.

• 35:59Speaker 00

  Yeah, Seven Powers. Love Seven Powers. Yeah, on the modes, I mean, we have the clear modes, which are the network effects on the panel, where you have supply and demand dynamic. We also have the network effects, the data mode, that as we do more interviews, you get better simulation. And then the product is very sticky, because you have all these interviews in your platform, and you don't want to lose that. You want to track things over time. But even like the simplest things, I think, in terms of product advantages, like one of the first things that Brian Shire said, one of our Sequoia partner, was that founders want to build something that's complex, but customers want something that's stupid, simple.

• 36:44Speaker 00

  And it just works. They don't want to configure their own workflow. They don't want to sit and build a custom software. And just one example of this is creating the interview guide is really difficult. It's actually an academic subject. And it's one of the reasons why you have services firms, because they know what methodology to use if you want to understand pricing or brand perception, these kind of things. You don't want to lead the witness. You don't want to lead the witness. And it's really hard to get that right. In the beginning, we just used the vanilla L M models. And the customers would create the interviews. They would get the data back and then they'd come back to us really frustrated saying like,

• 37:29Speaker 00

  what is this? Like, I can't use this data for anything. And we took the blame for that. Now, we've trained it to follow the best practices so that you always get good data out of the interviews. And I think that's the advantage you have as a vertical AI company, that you can essentially train this agent to follow best practices in the work that you do.

• 37:54Speaker 01

  So I want to go back to the concept of tied ponds that you had mentioned earlier. I think it's really interesting. And so much of market research, as I understand it today, is almost more inviting people to pass judgment on ideas that you feed them. But it seems to me that one of the, you know, hallucinations can be a bug. They can also be a feature with generative AI. And, you know, do you think we're going to see? User research actually evolve into live product ideation. I could almost imagine, you know, AI inventing solutions as customers are going about their interview process, even helping visualize those solutions. Are your customers doing that already? Or do you think we're going to have a moment where AI can create a TidePod idea in a market

• 38:39Speaker 01

  interview anytime soon?

• 38:40Speaker 00

  Yeah, I think that's really exciting. Today, they do that manually, you know, use AI to generate images of different concepts and feed that into the interviews. But I think specifically also with simulation, it becomes really powerful. So we now have an MCP as well, so that you can feed that into Claude. And then you can tell Claude like, hey, run listen in a loop and then come up with a bunch of ideas for how to market something or different concepts. And then you can have it run like that.

• 39:15Speaker 01

  I'm even thinking in the course of an interview, as somebody's complaining about, you know, the Tide is, can't, it's not very portable for the AI to be, you know, live brainstorming with you solutions, not just listening.

• 39:26Speaker 02

  This is what it could look like, an image generator too.

• 39:28Speaker 01

  Yeah.

• 39:28Speaker 02

  That'd be cool, Sonia.

• 39:29Speaker 00

  Yeah, I think it's a good idea. You should be on our product team.

• 39:34Speaker 01

  Awesome. Well, Alfred, we really love what you're building. Thank you for taking the time to share insights both on the broader market, which I think is just so fascinating, and also what it takes to be building in the application layer right now. We really admire the business that you've built and thank you for your continued partnership.

• 39:50Speaker 00

  Thank you so much.