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

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.

We build profiles of people as we do more interviews in the platform and then we can search and find the right person.

Okay, today we're sitting down with Alfred Walforce, founder and CEO of Listen Labs. Listen is an AI first

Listen Labs. Listen is an AI first customer research platform that can run thousands of voice interviews simultaneously. You launched about a

simultaneously. You launched about a year ago and you now serve 20% of the Fortune 500 including iconic brands like Microsoft, Anthropic, Sweet Green, NBC

and others. Um, and Constantine are very

and others. Um, and Constantine are very very excited to sit down with you today and talk about market research and how it's getting uh transformed with AI.

Yeah, 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. Uh, and the way we do that is

you can. Uh, 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 curses on boarding and then listen will create an interview

guide um 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

million participants. We can find pretty much anyone from an encologist 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 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.

Awesome. Do you have any favorite customer stories?

Um, yeah. So, Chubbies is one of our customers. They've been Yeah, they've

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

What they use you for?

They use us for everything. So, a lot of marketing testing for testing shirts to understand um what products perform well and what doesn't. And one of my favorite

examples is they discovered that chest hair interface 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 comfort comfortable. Um so, we saw you

comfort comfortable. Um so, we saw you know the small things to the big things.

Manscaped um changed their Super Bowl ad with insights from from Leen. So

never heard of that, but I'm not going to ask. Uh

to ask. Uh that's huge. So you you got the men's

that's huge. So you you got the men's hair market covered.

Yes, that's our niche.

From shaping to clothing.

That's right.

Wow.

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

You on trading.

Don't know what you're talking about, but I can I know context clues. So

that's awesome. Um I'd love to understand as and as you framed it as we get closer to this AGI future. Um one of the questions I have is you know traditionally I've always been very

skeptical actually of surveys because um people get paid to take surveys so you already got a selection bias issue. Um

the things that people say they would do uh or the 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 thoughts 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 I'm curious what you think of that and how you think

um AI or listen labs can help bridge that gap.

Yeah. And so we've done a lot of research on this. Um 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 like radically inconsistent. So even if you go back to

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. Um but

we did the same thing with listen when you have actually have to think and you have to really reason through your answer and then you're much more consistent um with at least how you

answer the same question. And then we're constantly tracking. For example, with

constantly tracking. For example, with with chubbies when we test their different charts, we 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 AB test is kind of

cases. I agree that AB 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. Um, and it's it's really useful

users. Um, and it's it's really useful to have some kind of input than no input at all. Does listen do uh voice to text

at all. Does listen do uh 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 as in it's a two-way conversation? What does listen start with and what does it finish with for the user experience?

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

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.

Um, and that's kind of much closer to how you actually behave in the real world.

And have you seen persona's point that actually having the person's face and their emotions and their voice and whatnot yields more uh engagement,

truthfulness?

have have we been able to have any studies or or at least data to point in that direction?

Yeah, specifically with advertising um it's a huge benefit because you might have people say on a like it scale which is like a you know five questions that

you click are you extremely likely to you know buy this product. um 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. Uh and we've seen that

much higher. Uh and we've seen that those ads then perform better in performance marketing, for example, on on Meta and and LinkedIn. 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?

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. Um so you

know that AI is not just hallucinating kind of where it's coming from.

That's awesome. Makes sense.

How did you come up with the idea to build this? Um, so my co-founder and I

build this? Um, so my co-founder and I actually built a consumer app u and that went viral. It was called um a be fake.

went viral. It was called um a be fake.

So you could create an AI avatar of yourself. It was an early version of uh

yourself. It was an early version of uh the chatbt images and you could fine-tune stable diffusion and put yourself in that world. And that ended up going super

world. And that ended up going super viral and overnight we had 20,000 users.

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 we had we had a ton of churn. So we

wanted to understand why um how they thought about our positioning different use cases and it was really useful for us.

Yeah.

And that's how we got started.

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 20,000 users. You don't understand how users are using the app, what they want next, why they're turnurning.

historically, how 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 Qualrix. Um, but

then there's also this very large services industry because it becomes harder and harder especially if you want to do uh 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

scale. So that's a multi-billion dollar industry and 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 um how someone actually will behave. You

can't just ask like how much are you willing to pay for this? Um 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, uh, 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.

Okay, so that's the old world of how this used to be done.

um maybe describe the new world and then it seems to me that there were 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 uh benefits um 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

yeah so most decisions that 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

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

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. Um and so that's one thing.

interview. Um and so that's one thing.

And because it's asynchronous, it's also much more affordable. 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.

Um why is that?

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

But then also we found yeah lower pressure. You can

kind of go on and off. Um 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 um 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 AI.

Okay. So historically for example if I was to do research on the kids market very very hard to access that market. Is

that a regulatory thing? Is that a scheduling thing?

Yeah it's you need parental consent. Um

you know kids are really busy they go to school. they have extracurricular

school. they have extracurricular activities. How do you find time with

activities. How do you find time with them? And you need to find the right

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

important. And that's actually where we spend 80% of our engineering resources.

Every company is driven by a power law and customer segmentation. So even a product like Sweet Green, 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 1% of the population does. And then you find that some people

does. And then you find that some people go to sweet cream every single day and that's 80% of their revenue. So if you can find that segment, um, the research

is so much more actionable. 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.

Yeah. Um, 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.

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 their new product line, you can offer that person up.

That's right.

That's amazing. And that was not possible to to do before um 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 these.

Yeah, they're terrible.

And one of the problems with that is that you then need to have a extensive screening process. So you have something

screening process. So you have something called an incidence rate which can be 10%. Which means only one in 10 people

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 greened out 10 times to even get paid the first time.

Why do these brands even need you for access if let's take Sweet Green. Sweet

Green knows who that 80% is. Can't they

just reach them? Don't they have direct relationships with them already?

Yeah. So, they can and we do that as well. We connect to their CRM and they

well. We connect to their CRM and they can um 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

those two? And then also what we found is that the CRM is typically really unorganized and sometimes there's also like reg regulatory issues. If you're at

Google, you can't just send emails to people who use Gmail and it it gets much easier to use an external third party and you run the risk of spam. Yeah.

Which can get you totally blocked. I 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.

Yeah, exactly.

It's cool.

What does this mean for the Mckenzies or whoevers of the world that are, you know, the people that are building the hundred slide decks that, you know, reach 3,000 people to reach some set of Did you do that, Sonia? Wasn't that a

former life? you know. No, Constantine,

former life? you know. No, Constantine,

but I'm glad that that's what you think of me.

Isn't that what banking does?

Um, we we hired consultants.

I used to hire these people.

Got it. Got it.

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

Okay. Got it.

I was even more redundant. Um, but what does it mean for all these people? Uh,

do they still have a role to play in this new future? Yeah, I think AI is changing all roles very quickly and we

work a lot with Bane for example. So

they use us to speed up their traditional processes and I think they still have a 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 to drop

and you have to make sure you kind of unbundle a lot of your services to maybe allow for AI agents to 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? 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 Bane 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?"

optimistic?" No, I 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.

I was thinking the chest hair shirt thing.

I'm glad that 20 minutes you're still thinking about chest hair. Constantine.

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 like even the way they laced these shoes. I'd love to give that feedback.

shoes. I'd love to give that feedback.

Yeah.

This is why you're a venture capitalist.

Details. Details.

Um, we hope to live in a world that finally works the way people want.

That would be great. Please.

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

AI than before AI. Um,

who who is getting the benefit of that economic surplus?

So, because you're able to do it faster, I would argue you 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 hundreds of thousands of dollars to speak to 20 doctors across eight countries. So maybe over the long

eight countries. So maybe over the long term like the individual interview will become more affordable. Um but I think you will be 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.

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. are

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 at any

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 life-saving use case for listen over time.

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 the if you find a bug for example they'll connect that to another

coding agent to go and solve the problem.

That's cool.

Let's talk about generative agent simulation. Like it seems like the

simulation. Like it seems like the entire industry has gone from market research 1.0 you know call call a 100 people one by one colleate them manually

to market research 2.0 So AI native or AI designs the question track is able to talk to thousands of people simultaneously um synthesize the answers. Um it seems

like we may we're maybe moving to market research 3.0 with generative agent simulation. What do you make of that?

simulation. What do you make of that?

And you know I both see the dream of it.

Um 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 and so I'd love to I'd love to hear your take on it and how you guys are taking on the uh 3.0

And maybe what is it too to start?

Yeah. So the way we are building simulation is by interviewing a single person. So if I interview Constantine

person. So if I interview Constantine for one hour, um I can probably start to predict your preferences to some degree.

Fascinating insights about chest hair.

Um and it turns out that LM 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 things are changing all the time and chaos theory tells us it's really hard to predict the future. Um otherwise we

would be on on Wall Street and and making a ton of money. Um, so what what we 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. So we have a very strong network effect. We've done a million interviews so far and that's grown exponentially since we reported that number.

Wow.

Um, and we're able to train audiences on 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 listen will say well I already talked to a thousand software engineers

this week let me predict how they're going to answer that question but the tricky part is knowing what things can you answer and what can't you answer um because

and how do you do that? Yeah, we try to be very explicit to the model of what is the domain of knowledge they have and then see how much can you expand that domain. That's kind of the fundamental

domain. That's kind of the fundamental idea and we can back test how well the simulation worked um with

what's in the kind of in our training data set. So we remove one of the

data set. So we remove one of the questions. Yeah. And then see like okay

questions. Yeah. 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.

That's really cool.

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

One of the most useful um things and is message testing. So that's the idea of

message testing. So that's the idea of like how what what's the tagline on the billboard or I was actually using it this weekend. Um, so I have created a

this weekend. Um, 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 inputed that into our simulation and then oh wow

the the top talk was like twice better than the next one and wow cool and I I like I don't know if it's correct um but it certainly felt correct and it was really helpful to have

guidance uh 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

and how does it compared to just asking chat GBT the same thing yeah so then I inputed the same questions into chat GBT and I actually had one I had another talk I did that

was not so successful and I had and I inputed a competitors or another talk that was more successful ful um and I showed both of them to chatbt and both

of them to our simulation and in chatbt 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 going to 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. And and just to push on this because I think it's so

fascinating like can't you kind of force the models into a specific niche or personality like hey Chacht you're a 35-year-old really grumpy software engineer that

likes using your terminal like and then it does then take on the preferences of of that niche is like is sort of my mental model at least and so I I'm

actually surprised that you know ChachiP wasn't able to write arrive at the right answer and then bootstrapping off real user data was um because ultimately it all is kind of a reflection of of real

user data, right? Uh and so actually what is the intuition for for why kind of sim only on pre-trained data isn't sufficient?

Yeah, so we've tried many different inputs. Um, and that certainly performs

inputs. Um, and that certainly performs a little bit better than just vanilla chatbt, but what performs much better is

we've tried credit card spend um kind of behavioral data, purchasing behavior um but what we found was the best data set

is interviews because it's more kind of allows you to go off tangents. It

understands you can ask behavioral questions. So also it can't just be any

questions. So also it can't just be any interview like the way you design the questions is also really important. Um

and the intuition I think is that the models don't have clean data on 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.

questions. That's why we're all here.

That's kind of the purpose of this type of format. And if you have enough people

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, you know, listen can fill that out because it has enough interviews. Yeah.

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 like they're

very different though. So extremely

different people. Um, 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 listens bot that then I can use instantaneously for any ad hoc question?

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

have. Okay.

Um, so that's that's what we call augmented responses. The cool part of

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 an human API where the agents are able to call the preferences of your users um 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.

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?

Yeah, we um are doing post-training typical rag as well. Um there's like a bunch of different techniques. Some of

them are proprietary, but um yeah.

All right. We'll do customer interviews and all your engineers report back.

I'm curious what you think of multi- aent systems and their role in helping us kind of iteratively use, you know, add inference time, iterate to a better

uh answer. Is that part of how you're

uh answer. Is that part of how you're doing simulation or or not?

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- aent, but you're not having those thousand people debate each other. That's what

I'm asking.

Oh yeah. No, we we don't have that yet.

But that's a good You think that would help potentially but there are these competi other competitors that are doing that approach more. I think the worry is that

approach more. I think the worry is that again chaos theory tells us it when things kind of compound it it becomes really hard to predict how the things

are going to interact with each other and um you know it's it's something we definitely should explore more but I'm I'm a little bit skeptical of the approach. Maybe the analogy I'd make is

approach. 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 LLMs and then have one LLM act as judge and synthesizing them. I do think on average you get a

them. I do think on average you get a slightly better response.

Yeah.

Cool. So, where else do you see yourself going from here then? You have you're you're going from market research 2.0 to market research 3.0 now with kind of generative simulation. Do you expect

generative simulation. Do you expect that 3.0 takes over as the majority of queries over time? Um, and then what else is ahead? 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. Um,

customer input. Um, 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? um if it's a small

your billboard? um if it's a small billboard then you can um use simulation to to answer that and I still think that there's a lot of alpha on the core

product as well uh to improve I mean when we started the the core idea was just making the interview 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 GPT4 sometimes we

would ask the same question 100 times.

Um and in the beginning that eval was like 20%. Now we've been able to climb

like 20%. Now we've been able to climb that eval to be 85%. Um

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

relevant anymore. And now we're back at like 20%. which I think is one of the

like 20%. which I think is one of the values that 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.

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

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

write code and talk to users.

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

Yeah.

Um 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. Um and the thing that you

these things. Um and the thing that you guys fundamentally are positioning yourselves to do as a company is the strategy part. Um from what to build to

strategy part. Um from what to build to what to say. Is that a fair synthesis?

Yeah. And u I think we'll when we have that one person billion dollar company we'll be part of that loop. Um so have a coding agent and listen and then run

that in the loop and we'll have these autonomous organizations. Um, and

autonomous organizations. Um, and and even 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 can talk to

us. But in software, if you can talk to a customer, figure out a bug, create a PR, have a coding agent, close it, ship it, customers happy, that seems like a

really important lefthand side of the equation. Find the bug from an actual

equation. Find the bug from an actual human. But I imagine it's the same thing

human. But I imagine it's the same thing in a big Adams company. Like if you're consumer packaged goods, if you're clothing, if you're any of those things, I imagine that's even more important

because you have to figure it out because once you actually do the thing, it's done.

Yeah, exactly. Like Proctor 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 listen. 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. I think Proctor and

software space. I think Proctor 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 um washing machine.

Yeah. pods 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 um easy to

use and through customer interviews um they found this insight made this new type pod and became really successful.

Um, another example which is in the acquired podcast when they talk about Mars, um, they did the first one of the first market research studies in the

1950s where M&M's were originally designed for being in the used in the army um, because they were like a sweet

treat. They don't melt in your pocket.

treat. They don't melt in your pocket.

And they discovered through market research that another great segment was young kids. And um they then decided to

young kids. And um 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.

As we progress towards this, you know, listen labs future future vision of the world. What are things that you're

world. What are things that you're confident will work and what are the things that you're still not sure about?

I'm confident that in the future you'll still need to have human input because even if you have a per perfect rational

being uh like AGI, humans are still irrational totally and they will still want kind of be chaotic in their nature where they all of a sudden get obsessed with a a new product, you know, a new

Tik Tok trend that shows up and you have to change your entire marketing strategy towards that. And so I think that will

towards that. And so I think that will remain a really huge part of how we do things. I think I'm still um uncertain

things. I think I'm still um uncertain of what level simulation will play and I'm I'm confident that it will work for

certain questions, but we'll see how like 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 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. Um,

Constantine, our resident humanist.

I I I'm a humanist. Absolutely. But if

companies are about serving people because that's what 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 asmtote then the delta in that asmtote which is what is in a human's mind that isn't in the AI's mind only becomes more important.

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 of these AI vertical AI companies and

yeah, what's your mode?

What is our mode?

We've got network effects and scale economies.

Yes, we those are nice.

Feel like we're on an episode of acquired right now.

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

right now. It's it's a good book. I

recommend it.

Um, seven powers.

Yeah, seven powers. Love seven powers.

Um yeah on 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

um the network affects the data mode that as we do more interviews you 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 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

Sher said one of our our partner was that founders want to build something that's complex X, but customers want something that's stupid

simple 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

custom software. And just one example of this is creating the interview guide is really difficult. It's actually an

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 LM models and the customers would create the interviews, they would get the data back and then they come back to us really frustrated saying like yeah

what is this? Like I can't use this data for anything. And we took the blame for

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 um best practices in in the work that you do.

So I want to go back to the concept of tide ponds that you had mentioned earlier. Um I think it's really

earlier. Um I think it's really interesting and so much of market research as I understand it today is almost more um 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 gonna see user research actually evolve into live product ideation? I could almost

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 the moment where AI can can create a tide pods idea in a market interview anytime soon?

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

interviews. But I think specifically also with simulation it becomes really powerful. So we now have an MCP as well

powerful. So we now have an MCP as well so that you can feed that into uh Claude and then you can tell Claude like hey

run listen in a loop and then come up with um a bunch of 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 can't I can't sound very portable for the AI to be, you know, live brainstorming with you solutions, not just 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 really love what you're building. Um, 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. Um, we really admire the business that uh you've built and thank you for your continued partnership.

Thank you so much.