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

[00:00] Our goal is to get to a billion people [00:02] in our audience. [00:04] and then to be able to stratify and know [00:06] what exactly is this person an expert on? [00:10] And it might be... [00:11] Even something like sneakers, [00:14] 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. [00:34] Thank you. [00:50] Okay, today we're sitting down with Alfred Walfors, 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 Constantine, I'm very excited to sit down with you today and talk about market research and how it's getting transformed with AI. Yeah. [01:19] Thank you for having me. Maybe just to get started. So you are building an AI-enabled platform that scales market research. What does that mean? 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 cursors onboarding? And then Listen will create an interview guide, which is an instructions for the agent to make the interviews.

[01:46] 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. [02:00] And now the final step that we're just launching in a couple months is simulation. So after you've done tens of thousands of interviews in the platform, 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. [02:22] Awesome. Do you have any favorite customer stories? [02:25] Yeah, so Chubbies is one of our customers. Like the sports brands? Yeah, they've been like one of our early customers. What do they use you for? 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... [02:44] 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, comfortable, [02:57] So we saw the small things, the big things, Manscaped changed their Super Bowl ad with insights from Lesanne. [03:07] Never heard of that, but I'm not going to ask. That's huge. So you got the men's hair covered. Yes, that's our niche. From shaping to clothing.

[03:17] That's right. Wow. We do other things. Skims is one of our customers is one. You heard it here. You heard it on Trader. [03:23] Don't know what you're talking about, but 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 – [03:39] 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 – [03:54] 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. Yeah. And so... [04:07] We've done a lot of research on this. One of the things we've done with surveys, for example, is [04:13] 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 [04:33] really reason through your answer and then you're much more consistent with [04:39] At least how you answer the same question. [04:41] And then we're constantly tracking, for example, with Chubbies, when we test their different charts, we...

[04:49] couple of months later look back and see how did that perform with the actual sales data [04:54] and [04:55] I think it depends on the different use cases. I agree that A/B test is kind of the holy grail, but in practice it becomes really difficult to get right because you need a very large volume of users. [05:08] And it's really useful to have some kind of input than no input at all. [05:14] 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? [05:31] 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 that's – [05:51] kind of much closer to how you actually behave in the real world. And have you seen Personya's point that actually having the person's face and their emotions and their voice and whatnot yields more [06:03] uh, [06:04] Engagement, truthfulness, have we been able to [06:08] have any studies or at least data to point in that direction? Yeah, specifically with advertising, it's a huge benefit because you might have people say, on a likert scale, which is like five questions that you click, "Are you extremely likely to buy this product?"

[06:29] versus [06:30] You might have very high scores on a survey question like that. [06:34] But when someone... [06:36] also reacts very enthusiastically, it's going to be like perform. [06:40] much higher. And we've seen that those ads then perform better in performance marketing, for example, on Meta and LinkedIn. [06:48] And can you, if you're the customer and you commission 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? [06:58] Yeah, so we built a platform around traceability so that for every data point you can always click and then... [07:05] look at the video or see the quote. So you know that AI is not just hallucinating kind of where it's coming from. [07:12] That's awesome. [07:13] How did you come up with the idea to build this? [07:16] So, my co-founder and I actually built a consumer app and… That did what? That went viral. It was called… [07:24] and be fake so [07:26] 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 20,000 users. [07:41] And we were 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 [07:50] We had a ton of churn, so we wanted to understand why. [07:54] how they thought about our positioning, different use cases, and it was really useful for us. Yeah.

[07:59] That's how we got started. [08:00] Maybe just walk us through how the industry is changing before and after ListenLabs. Historically, let's say you're somebody with an app with 20,000 users. You don't understand how users are using the app, what they want next, why they're turning. Historically, how did people go about doing that? Yeah, so what we discovered was that there are these survey tools that are pretty old school, like Qualtrics. [08:30] 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. [08:39] So that's a multi-billion dollar industry and [08:44] It's... [08:45] What they do is come up with questions to ask, which is an academic subject in and of itself. It's actually really hard to know, like, how do you ask questions to your point that get to... [08:55] how someone actually will behave. You can't just ask, how much are you willing to pay for this? There's different methodologies that work better than others. To finding the audience, how do you source the participants? [09:07] to then analyze hundreds of these calls and [09:11] 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, [09:21] and interview them and [09:23] we can help speed that up much faster. [09:26] 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 are obvious kind of first-order benefits, like there's probably much more scalable, probably much more cost-effective, but there's probably also less obvious benefits. Maybe just talk about some of those benefits of what is it like when you actually do AI-first market or customer research?

[09:51] Most decisions, [09:53] that gets made are not based on the customer input. [09:57] Right. [09:59] The reason for that is... [10:01] it's just a lot of friction to even talk to customers. [10:05] When you can lower the barriers of talking to customers, you end up making much smarter decisions. So the speed advantage is actually huge. [10:14] 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 interview. [10:25] So that's one thing. [10:27] and [10:28] Because it's asynchronous, it's also... [10:30] much more affordable so you can pay people much less than if you would have to run like synchronous interviews so actually that's an interesting thing that people often ask us like is [10:42] 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 an actual interviewer. Why is that? [10:54] Okay. [10:55] I think it's mostly because it's asynchronous and people are very busy, but then also... [11:00] Lower pressure. What we found, yeah, lower pressure. You can kind of go on and off. [11:05] and [11:07] 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 non-judgmental entity that's really interested in you. And we can also have sensitive conversations like interviewing kids, how they react to different products.

[11:27] I think that's another advantage as well, that people can be brutally honest talking to the AI. Okay, so historically, for example, if I was doing research on the kids' market, [11:35] Very, very hard to access that market. Is it a regulatory thing? Is it a scheduling thing? [11:39] Yeah. [11:40] Yeah, it's... [11:42] You need parental consent. Kids are really busy, they go to school, they have extracurricular activities, how do you find time with them? You need to find [11:52] the right kind of kids. One of the things we realized is the audience is extremely important, and that's actually where we spend 80% of our engineering resources. Every company is driven by... [12:05] a power law in customer segmentation. So even a product like Sweetgreen, which you would think is for everyone, the right audience is typically... [12:13] urban [12:14] high household income, mostly female. And by the way, they need to know what seed oils are, which only like 1%. [12:21] of the population does. And [12:24] then you find that some people go to Sweetgreen every single day and that's 80% of their revenue. So if you can find that segment, [12:32] And [12:32] the research is so much more actionable. [12:35] Yeah, there's probably a network effect to it as well, where when you get a certain scale and people use it, you can access the same kind of person that otherwise... [12:44] might be really difficult to access or [12:46] Maybe it's a scale... [12:48] economy, something along the lines of accessing those really [12:51] really specific people that are really valuable for the type of product that you're trying to introduce. [12:56] Yeah. Um,

[12:58] It's really about... [13:00] Our goal is to get to a billion people [13:03] in our audience. [13:05] And then to be able to stratify and know... [13:07] what exactly [13:09] is this person an expert on? [13:11] And it might be... [13:13] Even something like sneakers, [13:15] 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... [13:27] 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. So someone might say in a totally unrelated interview, I'm a total sneakerhead. [13:40] And you can keep that in the database on that person. And then when Nike or what have you is launching a new product line, you can offer that person up. [13:47] That's right. That's amazing. [13:48] And that was not possible to do before 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. I've been on the receiving end of those. Yeah, they're terrible. [14:02] And one of the problems with that is, [14:04] that you then need to have an extensive screening process [14:08] So [14:10] 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. Yeah. Because it's really annoying to be screened out 10 times to even get paid the first time. Why do these brands even need you for access if, let's take Sweetgreen?

[14:34] Sweetgreen knows who that 80% is. [14:36] Can't they just reach them? Don't they have direct relationships with them already? [14:39] Yeah, so... [14:41] they can [14:43] And we do that as well. We connect to their CRM and they can... [14:47] send that out but then the [14:49] really interesting part is how do you talk to [14:51] 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 [15:03] the CRM is typically really unorganized. And sometimes there's also 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. 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... [15:24] You know, you do outbound and then eventually you're in the Google filter. And next thing you know, you're in Microsoft purgatory. [15:30] I guess going through you guys, you don't have to deal with that. Yeah, exactly. That's cool. What does this mean for the McKinsey's or whoever's in the world that are [15:39] The people that are building the 100 slide decks that reach 3,000 people to reach some set of insights. Didn't you do that, Sonia? Wasn't that a former life? No, Constantine, but I'm glad that that's what you think of me. Isn't that what banking does? [15:54] We hired consultants. I used to hire these people. Got it, got it. So I was a layer on top of the layer on top of the layer. Okay, got it, okay. I was even more redundant. But what does it mean for all these people?

[16:09] have a role to play in this new future [16:11] Yeah, I think AI is changing all roles very quickly and [16:17] We work a lot with Bane. [16:19] 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 [16:30] being able to then implement these changes is still extremely valuable. But [16:36] a lot of margins are going to drop. And [16:39] No. [16:40] 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. Maybe I'm an optimist here, but why wouldn't it be more? [16:54] 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. [17:04] Bain or what have you, the same dollars you use, listen, listen, [17:09] and just explore those new areas and tell me where to where to build. Is that [17:14] overly optimistic. 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, [17:25] I think you're right. [17:26] I was thinking the chest hair shirt thing. [17:29] I'm glad that 20 minutes in you're still thinking about chest hair. 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 lace these shoes. I'd love to give that feedback.

[17:45] This is why you're a venture capitalist. Details. Details. [17:51] We hope to live in a world that finally works the way people want. That would be great. Please. Are you seeing any price and 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 [18:03] survey a lot more efficiently now with AI than before AI. [18:09] Who is getting the benefit of that economic surplus? [18:12] So – [18:13] Because you're able to do it faster, I would argue you should be able to charge more for it. And is that what's actually playing out? We have done some studies where we're able to charge... [18:23] hundreds of thousands of dollars to speak to 20 doctors across eight countries. So [18:29] Maybe over the long term, like the individual interview will become more affordable. But I think you'll be doing kind of two orders of magnitude more of research. And I think what's really exciting is also simulation, which is something we're building now. [18:45] were... [18:47] you're able to unlock the 99% of use cases where you would never have time to talk to real people. [18:54] I think that's so awesome in part because there are so many areas where they don't even listen to the customer. [18:58] like medicine, there are a million little problems [19:03] with [19:04] the medical system. [19:05] I hear about it all the time, and these are [19:08] you know the doctors are busy important people [19:10] But [19:11] 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 at any time, 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 lines, that seems like a lifesaving use case for listen over time.

[19:40] Yeah, I think... [19:42] 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 this direction by some of our customers where they will have a churn interview later. [19:56] And then they will... [19:58] connect the, if you find a bug, for example, they'll connect that to another coding agent to go and solve the problem. [20:04] That's cool. Let's talk about generative agent simulation. It seems like the entire industry has gone from market research 1.0... [20:12] Call 100 people one by one, collate them manually. [20:16] 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, previously. [20:39] 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? And so I'd love to, I'd love to hear your take on it and how you guys are taking on a 3.0. Yeah. And maybe what is it to, to start? Yeah. Yeah. [20:58] So. [20:59] The way we are building simulation is by... [21:02] interviewing a single person, say if I interview Konstantin for one hour, [21:07] I can probably start to predict your preferences to some degree. Fascinating insights about chest hair.

[21:15] And... [21:16] 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. Now the problem becomes... [21:32] things are changing all the time. And chaos theory tells us it's really hard to predict the future. [21:39] Otherwise, we would be on Wall Street and making a ton of money. [21:44] So what we, [21:46] How we think about it is you need to hydrate these audiences. And the way we do that is by all of the interviews that are running through Listen. [21:54] So we have a very strong network effect. We've done a million interviews so far, and that's [21:59] grown exponentially since we reported that number. And we're able to train [22:05] Audiences on those interviews. So you can imagine a future. Where you can ask a question and listen. Like how do software engineers. Think about cloud code. And then. [22:15] Listen will say, well, I already talked to a thousand software engineers this week. [22:20] Let me predict how they're going to answer that question. But the tricky part is knowing... [22:25] What things can you answer and what can't you answer? [22:29] And how do you do that? Yeah. We try to be very explicit to the model of... [22:34] what is the domain of knowledge they have and then [22:37] see how much can you expand that domain. That's kind of the fundamental [22:41] idea and [22:43] we can backtest

[22:46] how well the simulation worked, [22:49] with what's in our training dataset. So we remove one of the questions. - Hold that set, yeah. [22:54] and then see like, okay, how accurately did you predict that? And then you can add in nonsensical things like, what's the name of their dog or something like that. And then you can say like, is the model able to understand that you can't predict that. [23:07] That's really cool. What sorts of things are you finding that you can predict well versus can't? [23:13] One of the most useful things and [23:17] is, [23:18] Message testing. So [23:20] 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 100 different titles for my talk. [23:44] and inputted that into our simulation. And then-- - Oh, wow. [23:47] The top talk was like twice better than the next one. Wow, cool. And I like... [23:54] I don't know if it's correct, but it certainly felt correct, and it was really helpful to have guidance in making that decision. [24:02] And I also think like even if it's [24:04] It's just nice to have some help in making a decision. It's also nice to outsource your decisions. And how does it compare to just asking ChatGPT the same thing? Yeah, so then I inputted the same questions into ChatGPT. And I actually had one...

[24:18] I had another talk I did that was not so successful, and I inputted a competitor or another talk that was more successful. [24:27] And I showed both of them to ChachiPT and both of them to our simulation. [24:32] And in Chattoot, it picked the wrong one. [24:35] and in our simulation, I picked the right one. [24:38] The [24:39] It's early for us, we're going to release this in a couple of months, but... [24:44] it seems like it's performing better than the general models. And the models are trained on the average person. Yeah. And... [24:51] you want to build for a very specific niche. [24:54] And that's how we can kind of essentially train the models to follow that niche. And just to push on this, because I think it's so fascinating, [25:02] Like, [25:03] Can't you kind of force the models into a specific niche or personality? Like, hey, ChachiPT, you're a 35-year-old, really grumpy software engineer that likes using your terminal. And then it does then take on the preferences of... [25:16] of that niche is like it's sort of my mental model at least and so I'm actually surprised that [25:23] You know, ChatGP wasn't able to arrive at the right answer and then bootstrapping off real user data was – [25:29] Because ultimately it all is a reflection of real user data. [25:33] And so actually, what is the intuition for why... [25:38] kind of SIM only on pre-trained data isn't sufficient. [25:41] Yeah, so we've tried many different inputs and... [25:46] that certainly performs a little bit better than just vanilla ChatTBT, but

[25:52] what performs much better is we try credit card spend and kind of behavioral data [25:58] purchasing behavior. But what we found was the best data set is interviews because it's [26:04] more [26:05] kind of 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 the [26:23] how a specific persona acts and how they think. 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. 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, [26:44] 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. [26:52] And so if you have this compression in a slightly different... [26:55] part of this hyperspace that you say now complete this orbital, uh, [27:00] of what everybody is thinking in this category of person. [27:05] you know, Listen can fill that out because it has enough interviews. [27:08] Yeah. [27:09] Do you think that you will... [27:11] offer that package as a product. As in, if I wanted to understand my customer, [27:15] And for me, for us, our customers, founders, [27:18] I they're very different though. So extremely different people. Um,

[27:24] if I wanted to understand my customer, could you do... [27:26] 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, [27:38] that then I can use instantaneously. [27:40] for any [27:41] ad hoc question? Yeah, that's exactly what we have. So that's what we call augmented responses. [27:48] The cool part of this as well is that it can also live in your coding agents or your other agents. [27:55] I think in the future, you will want to have... [27:58] almost a human API where the agents are able to call the preferences of your users 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. [28:09] today, is it all rag? Is it fine-tuned? Is it something else? How do you take those conversations and then combine them with [28:17] you know, the models that you're doing the rest of the LIST and LAPS with. Yeah, we [28:22] are doing post-training, typical RAG as well. There's a bunch of different techniques. Some of them are proprietary, but... [28:33] All right, we'll do customer interviews on all your engineers. Report back. [28:37] I'm curious what you think of multi-agent systems and their role in helping us kind of iteratively use, you know, at inference time, iterate to a better answer. [28:48] Is that part of how you're doing simulation or not? Yeah, like the way we do simulation is essentially

[28:56] You have one person... [28:57] the demodell [28:58] really well and then you scale it up with a thousand people so you have [29:02] a representative sample. [29:05] And it's essentially multi-agent. But you're not having those thousand people debate each other. That's what I'm asking. Oh, yeah. No, we don't have that yet, but that's a good question. Do you think that would help? Potentially, but there are these other competitors that are doing that approach more. I think the worry is that... [29:22] Again, chaos theory tells us that when things kind of compound, it becomes really hard to predict how the things are going to interact with each other. And... [29:33] It's something we definitely... [29:36] We should explore more, but I'm a little bit skeptical of the approach. Maybe the analogy I'd make is like the AI council approach of, you know, send the same query out to three different LLMs. [29:46] and then have one LLM act as judge and synthesizing them, I do think on average you get a slightly better response. [29:52] Cool. So where else do you see yourself going from here then? You have... [29:56] You're going from market research 2.0 to market research 3.0 now with kind of generative... [30:02] Do you expect that the 3.0 takes over as the majority of queries over time... [30:06] And then what else is ahead? [30:08] Yeah, I think you'll still need human input, but I think there will be many more use cases that are now opened up where you can get customer input. [30:19] So for the large decisions, if you're doing a Super Bowl ad or things like that, you will still need to...

[30:26] run real interviews [30:28] But for the smaller things, like what should be the tagline for your billboard, you're [30:35] If it's a small billboard, then you can... [30:37] use simulation to answer that. [30:40] And I still think that there's a lot of [30:42] alpha on the core product as well to improve. [30:47] I mean, when we started... [30:49] The core idea was just making... [30:52] that [30:53] interview [30:54] less annoying to go through like we had an eval that looked at repetitive questions or looked at [31:02] Is the AI even able to follow the instructions and... [31:06] With GPT-4 sometimes we would ask the same question a hundred times. [31:11] In the beginning, that evil was like 20%. [31:14] Now we've been able to climb that eval to be 85%. [31:18] But now we created a new eval that's much more advanced [31:21] So, it's able to understand what are you doing on your screen when you're [31:25] Screen recording or... [31:28] can you skip questions that are not relevant anymore? And now we're back at like 20%, which I think is one of the values that vertical AI companies use [31:36] can have is that they have this proprietary eval that they can use and essentially climb that [31:42] climb that eval and that's your advantage as a vertical AI company. [31:46] keep pushing forward, better data, harder problems, better data. Yeah. [31:50] It seems to me like you're in the middle of a very interesting... [31:53] Infinity loop, right? Because I mean fundamentally a company is figure out what to build.

[31:58] Build it. Figure out what to build. [32:00] Write code and talk to users. Exactly. And the build it is coming up rapidly up in exponential. [32:08] and the figure out what to build is the thing that you are pushing forward. Totally, Sonia, yeah. 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, [32:23] enabling us to do is it's making execution work. [32:25] 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. [32:33] from what to build to what to say. Is that a fair synthesis? Yeah. And I think when we have that one person billion dollar company, we'll be part of that loop. [32:44] So I have a coding agent and listen and then run that in the loop. [32:49] We'll have these autonomous organizations, um, [32:53] And 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, you can talk to a customer, figure out a bug, create a PR, have a coding agent, close it. [33:12] Ship it. [33:14] customers happy. [33:15] That seems like a really important left-hand side of the equation. Find the bug from an actual human. [33:21] 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,

[33:30] Imagine that's even... [33:31] more important because you have to figure it out because once you actually do the thing, [33:35] It's done. [33:36] Yeah, exactly. Like Procter & 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 the customers have listened. [33:50] 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 and [34:01] Either... [34:02] in [34:03] the, you know, consumer space or in the software space. [34:09] I think Procter & Gamble 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. [34:27] I mean, one example is the tide. [34:30] washing machine and pods that they were able to figure out that it was really uncomfortable to use the washing liquid and they [34:40] discovered that people wanted something that was much more easy to use. And through customer interviews, they found this insight, made this new iPod and became really successful. Another example, which is in the Acquired podcast, when they talk about Mars, they did one of the first market research studies in the 1950s, where...

[35:04] M&Ms were originally designed for being used in the army. [35:10] and [35:11] because they were like a sweet treat they don't melt in pocket. [35:15] And [35:15] They discovered through market research that another great segment was young kids. And they then decided to pivot the entire advertising strategy to focus on this because it doesn't melt and ruins your furniture, for example, and things like that. [35:32] as we progress towards this... [35:35] you know, Listen Labs, [35:36] future vision of the world? What are things that you're confident will work? And what are the things that you're still not sure about? [35:42] I'm confident that... [35:45] In the future, you'll still need to have human input. [35:49] Because even if... [35:51] you have a perfect rational being [35:54] like AGI, [35:56] humans are still irrational and they will still kind of be chaotic in their nature where they all of a sudden get obsessed with a new product, a new TikTok trend that shows up and you have to change your entire marketing strategy towards that. [36:11] And so I think that will remain a really huge part of how we do things. I think I'm still [36:18] uncertain of what level simulation will play [36:22] I'm confident that it will work for... [36:26] certain questions, but... [36:29] We'll see how good the models get to predicting human behavior. I mean, I'd imagine that it's actually even more important the better AI gets.

[36:37] to have the Delta because [36:39] the competition [36:41] 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. [36:48] I'm a humanist, absolutely. But if companies are about serving people, because that's what [36:55] That's why we're all working is to help someone else in some way. And intelligence gets better and better and better. [37:01] And you kind of have what the human wants here and the intelligence is approaching that asymptote. [37:05] then the delta in that asymptote, which is what is in a human's mind that isn't in the AI's mind, [37:11] only becomes more important. [37:12] Yeah [37:13] And... [37:14] One of the things that we've also realized is that [37:18] There's a lot of talk around what's the motive of these vertical AI companies. [37:24] Yeah, what's your motive? What is our motive? We've got network effects and scale economies. Yes. Those are nice. I feel like we're on an episode of Acquired right now. Hey, I'm feeling it. I'm feeling it right now. It's a good book. Recommended. Seven Powers. [37:38] Yeah, Seven Powers. Love Seven Powers. Yeah, on the moats, I mean, we have... [37:43] the clear modes, which are the network effects on [37:46] the panel where you have supply and demand dynamic. We also have the network effects, the data moat, that as we do more interviews, [37:55] You [37:56] gets better simulation and then the product is very sticky because you have all these interviews in your platform and you don't want to [38:03] lose that. You want to track things over time. But even the simplest things, I think, in terms of product advantages, one of the first things that

[38:11] Brian Shire said, one of our Sequoia partner, [38:14] was that... [38:16] Founders want to build something that's complex. [38:19] but customers want something that's stupid, simple. [38:23] And it just works. [38:24] They don't want to... [38:27] configure their own workflow. They don't want to sit and build a custom software. And just one example of this is [38:34] creating the interview guide, [38:36] is... [38:37] really difficult. It's actually an academic subject [38:40] And it's one of the reasons why you have services firms, because they know [38:46] what methodology to use if you want to understand pricing or brand perception, [38:50] These kind of things. You don't want to lead the witness. You don't want to lead the witness. [38:55] And it's really hard to get that right. In the beginning, we just used the vanilla LLM models. And the customers... [39:03] would create the interviews, they would get the data back, and then they'd come back to us really frustrated, saying like, what is this? Like, I can't use this data for anything. And we took the blame for that. [39:14] Now, [39:15] We've trained it to follow the best practices so that you always get good data out of the interviews. [39:22] And I think that's the advantage you have as a vertical AI company, that you can essentially train this agent to follow [39:29] best practices in the work that you do. [39:32] So I want to go back to the concept of tide 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... [39:41] more

[39:42] inviting people to pass judgment on ideas that you feed them. But it seems to me that one of the [39:47] you know. [39:48] hallucinations can be a bug. They can also be a feature with generative AI. And... [39:53] you know, do you think we're going to see [39:56] user research actually evolve into... [39:59] 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 Tide Pods idea in a market interview anytime soon? [40:19] 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 [40:46] ideas for how to market something or different concepts and then you can have it run like that i'm even thinking in the course of an interview as somebody's complaining about you know the tide yeah i can't it's not very portable for the ai to be you know live brainstorming with you solutions not just yeah this is what it could look like an image generator too yeah that'd be cool sonia yeah i think it's a good idea you should be on our product team awesome well alfred uh we

[41:16] taking the time to share insights both on the brother 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. Thank you so much. [41:30] Thank you.