← The Peel with Turner Novak
The Peel with Turner Novak
Building Got Easy. This Startup Solves What to Build | Alfred Wahlforss, Listen Labs
01:13:32
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- [00:03]
A
Alfred, how's it going? Welcome to the show.
- [00:05]
B
Yeah, thank you for having me.
- [00:07]
A
Yeah, this will be fun. Really quick. For people who don't know what is Listen. How do you describe it to people?
- [00:13]
B
Yeah, so we built this AI agent that can understand what people want by talking to them. So marketers, PMs, user researchers will go to listen and ask a question like Microsoft is one of our customers. And they can go and ask like what do CIOs think of Azure versus GCP or AWS? Listen will go and find hundreds of CIOs. So we have a database of 30 million people. And then it will run interviews, sort of like zoom calls with hundreds of people in parallel and then give you recommendations of what you've learned. And then you build this repository of all of the interviews in one place. You can start to query that. Now we're also building simulation so you can actually use the interviews you've collected to simulate how people will answer questions in the future. We can talk about that later. We've raised $100 million we used by a large portion of the Fortune 100, including Microsoft Anthropic, Sweetgreen, P&G.
- [01:15]
A
Anthropic is considered Fortune 100 now. I guess they're pretty big. They've gotten pretty big pretty quick.
- [01:22]
B
I mean it probably would be up there. Yes. But we also are used by startups like Perplexity cursor, I think 20% of the Forbes AI 50 use listen as well. So it's really like every company that one understands their users better.
- [01:40]
A
So then how does it actually work? Like if I am a marketer and I'm like, hey, I want to know more about what someone thinks about Azure versus GCP versus whatever. Like am I, what, what kind of work do I have to do and what does it look like when I'm using the product? Just kind of like talk me through how it would actually work practically.
- [01:58]
B
Yeah. So you first start by telling listen what you want to find out and then it creates this interview guide. So it's a semi structured discussion guide. That's the technical term of it. It's basically allowing the AI to have some structure and while also be able to kind of ask follow up questions, go on tangents.
- [02:18]
A
The AI will go on a tangent.
- [02:20]
B
Yeah. Really, it won't tell you a good
- [02:24]
A
story or like
- [02:28]
B
it knows your business question like the context and then it's able to ask follow up questions so someone is giving you a bullshit answer or they're going off topic. It's able to Ask follow up questions. It's like, oh, that's interesting. Can you actually tell me a little bit more about that? And it learns across all of the interviews to dial into what is the core insight here. And then, yeah, it runs this. It's all over video. So the interviewer itself is actually text based. It can also speak. But we find that avatars are kind of janky right now. Expect that to be working at some point. But we pay people to answer the interviews. That's why they answer them. You can also interview your own users by just sending an email and then it writes these reports, slide decks. You have a chat so you can ask questions across the interviews. One example is sweetgreen. They launched their new protein bowl based on insights from Listen Manscaped. They tested their super bowl ad and radically changed their brand perception and positioning based on insights from Listen Hiabiz. We work a lot with apparel brands. They kind of interviewed kids using Listen to figure out AI is great for these slightly uncomfortable topics or if you want people to be able to share in an honest way. So they were able to interview kids who talked about how the liner is uncomfortable and they were able to launch a new product line that was really, really successful. And you can also use it to test products so you can have the AI, you share your screen to the AI agent so we can actually see what you do on the screen as well. That's a few of the examples.
- [04:19]
A
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- [06:27]
B
Yeah, so the way we've created that network is by partnering with over 200 different providers. And there are these API partners that can provide interviews that can be really niche. Like WebMD is one example where they have a unique way to access doctors. And so we can partner with them to find those doctors. Or you can have the expert networks like GLG and alphasites. But then we also have our own participant pool. And if you go and you go to listen, you ask a question. It's almost like a marketplace where we will bid on multiple partners, will bid on each query to see, hey, I can find 100 doctors for this price with this level of quality. And then we have something we call a quality guard. So it's able to check who someone is based on all the interviews we've done in listen to check kind of consistency. So a big problem in research. Overall, this is a financial transaction. There will be fraudulent actors. And so someone might show up as a software engineer in one interview and then they put on a hat and all of a sudden they're like a doctor or something. Or like a fake mustache.
- [07:47]
A
Yeah, I could see that being a problem.
- [07:48]
B
Yeah, yeah. I mean, it's a huge problem. And it was kind of shocking to us because we worked with one of the multibillion dollar revenue market research companies. They sent us participants and they were supposed to be B2B decision makers. And there were clearly people in sub Saharan Africa that could barely speak English because in surveys it looks really clean. You get these beautiful charts. And with us it's over Video, it's open ended. It's much harder to keep high level of quality. So we can actually check if you are who you claim you are. If you are not consistent across all of your interviews, you never get to do an interview with listen again.
- [08:29]
A
Oh really? So if I signed up as a participant and I was saying I was a doctor and I like do the first one, whatever, maybe I pull it off and then I sign up again with all the same information and it's for like a mechanic or like, you know, something that's completely unrelated, you'll start to flag, like wait a second, this guy's obviously not who he says he is.
- [08:48]
B
Exactly. And that's why we're kind of, since we're vertically integrated with the panel and the interview, we're able to do that, which none of our competitors typically are.
- [08:59]
A
And there's kind of this pretty big sort of customer research, customer discovery market. Right. Like I think it's, I think I saw the number was like 140 billion that people spend on doing these, you know, surveys essentially is what they are.
- [09:14]
B
Yeah, I mean it's an absolutely massive market. First the software spend is there. Like if you talk to Fortune 100, they typically spend about $10 million a year on Qualtrics, but then they will spend on top of that hundreds of millions to market research agencies. So there's this large services market because it's historically been really hard to find the right audience and to analyze the data and we can turn the services into software and automate a lot of the hard work. So when you think about it, every single company wants to understand their customers better and that's why it's such a large market.
- [09:59]
A
And who are some of the kind of the legacy larger players in the space have people listening, have maybe heard of them before. I feel like Nielsen is one. They do. The TV people might know them for TV ratings. Qualtrics, the software SurveyMonkey I think is another. I think those two were or are publicly traded.
- [10:16]
B
The Qualtrics went private for roughly 12 billion. And there are these services firms, Kantor and Ipsos, that have billions in revenue. That's another legacy player. And then there's a long tail of these small kind of agencies.
- [10:36]
A
So when you were kind of coming across the space, was there ever a thinking of, oh, they should make software to do this? Are they automating things?
- [10:47]
B
Yeah, we kind of got into this space by building listen for ourselves in the beginning. And then we learned more about this market over time and realized that
- [11:04]
A
it's
- [11:04]
B
Just really hard to adapt your technology if you, I mean first the services firms, they don't have the capability to build it in house. It's just really difficult to build. And then if you already have a working software like Qualtrics, which is a survey platform, they have millions of people running through their interviews. And so they have the problem that if you add an LLM, the gross margin becomes worse. They also have to change the deterministic flows that they already have, which some of the customers that are already running the flows will find that maybe frustrating if they're just switching it over overnight. So they kind of have to build an entire new product to do this. And large companies tend not to be so good at building new products from scratch.
- [11:55]
A
So then what does the traditional process of running a survey, like a customer survey kind of look like? Let's say I'm Microsoft, I want to do some research. What's my process generally look like? Maybe pre listen and then post listen. Like what does it look like before you guys. And then how did, how does it change when I'm using you?
- [12:12]
B
Yeah. So in the large enterprise you typically work with an agency, it will be this back and forth process where you might have a question but you don't know the methodology to answer the question. So for example, if you want to understand pricing, you can't just ask like how much are you going to pay for this? You have to use the right question methodology. And it's actually an academic subject. It's really hard to learn how to design market research studies.
- [12:41]
A
Well, my mother in law actually has a PhD in survey research methodology.
- [12:46]
B
She should come work at.
- [12:47]
A
Listen, she used to work at the University of Michigan. They kind of have this social research institute and then a company called Westat I think it's called, it is pretty big. They do a lot of like government research stuff. And then I forget the name of the company she works at now. DLH or something. DL dhl. But she literally designs and runs surveys all day, so.
- [13:09]
B
Right, yeah, you can ask her. It's like it's not easy to get this stuff right. And so you typically have to go to an agency. Then you have this process back and forth to design the guide and the discussion guide. So that means you write it by hand. Okay. Is it this question? No, it's that question. It's like a long discussion to get that right. Then you go and find the people and that can take weeks. Especially if you do it by like actual doing interviews. You can imagine all the scheduling that you have to do. If you want to do 50 interviews to make sure you have some kind of large scale, and then analyzing 50 transcripts is really difficult as well. And so the process can take eight weeks to do and hundreds of thousands of dollars. One of these agency projects can be like, 300, $500,000. And that's literally talking to. I mean, we talked to a pharmaceutical company that week who said, yeah, to talk to 20 doctors in eight markets, it's $300,000. You can think of international work. It, like, adds another layer of complexity where it's like, okay, now you have to find another agency that's like a layer on top that speaks this language, and that can translate to the other agency. And it's just, like, very inefficient. With Listen, you can get this done in 24 hours. You go to Listen, it's very opinionated with the questions. It finds the audience very quickly. In five minutes, you can get 10 interviews done, depending on the length of the interview. It's like, it's a really magical experience when you see people just show up answering your questions immediately. And then obviously, it analyzes the data very quickly as well.
- [14:55]
A
If I wake up one day and I'm just like, I wonder what people think of this podcast. I want to get some feedback on it. I spin up Listen, and I maybe set up a survey. It sounds like it's.
- [15:09]
B
It's not a survey. It's an interview.
- [15:11]
A
Oh, it's an interview. Okay, okay. Maybe that's something we should hit on in a second. So I tell Listen what I want to get, and then I will click a button. And I mean, maybe my podcast listeners aren't on the Listen network, but how do you go. And then you recruit people automatically, and then they click the link and they do it within 10 minutes. And then I get out of my next call and I have something sitting in front of me of like, here's all the data we collected.
- [15:40]
B
Yeah, exactly. And the way we find people, we essentially put everyone in this embedding space based on all of the interviews they've done on Listen. So, you know, like, what are the questions they can answer? What is their expertise? And over time, this gets smarter and smarter. And so we send them an email saying, like, hey, we think you would be a good fit for this interview. Do you want to take the question you can imagine in the future where we'll have a phone number that you can just call if you're ever feeling bored, or maybe you're driving and you can just answer market Research questions on demand and you get paid per minute.
- [16:17]
A
That could be good. Like you're driving to work every morning, you make 10 bucks, 20 bucks answering questions. Do you use Tide? What do you think about Pepsi? I know you did something with Sweetgreen,
- [16:29]
B
so I mean, maybe that's the last job for humans. It's a little bit dystopic, but as the models get better, as we get to AGI, I think the hard part will actually be knowing what to build, not how to build it. And that's what we want to do. And I think to do that right, you need human input. And humans are inherently irrational. So I think AGI will have a hard time predicting exactly how we're going to answer.
- [16:57]
A
Yeah, I've always had a really hard time with this. Just like AGI completely taking over the economy or whatever. Humans always need to do things. We will always be the reason that the computer and the software exists. Right. Even when you read those dystopian books where the world is a simulation, it's basically the computer is still serving humanity, keeping us safe, creating simulations to keep us going, basically. So I always have a really hard time with no one's going to work and AGI is going to take over everything. It's just a little far fetched in my opinion.
- [17:39]
B
Yeah. And I think whatever happens, we'll have to give input, asking what do we want the AI to do for us?
- [17:46]
A
When you can build anything, amplitude lets you know how to build the right thing. Use human language to get complex answers about your products. No more manually selecting events or building charts or dashboards. Just ask. Use agents to sense changes in customer behavior, decide what's causing them and ask you if it's okay to fix it. Continuously in the background while you work. Get the answers you need while building directly in the tools you are already in. Like Claude Cursor, lovable and more. And for the first time, understand if your agents actually work, measure quality, debug failures, experiment and measure their ROI with agent analytics, amplitude with AI analytics. All you have to do is ask. And so you mentioned specifically surveys versus interviews. So can you just explain why that's a big deal? Because I don't know, I think I might kind of get it, but I think maybe someone listening might be like, what's the point? Like, aren't they the same thing?
- [18:40]
B
Yeah, so you know, we live in a very divided world. There's wars going on, everyone likes different brands like Pepsi versus Coke. But there's one thing that we can all align on which is Everyone hates surveys because it's so boring to answer a survey. I've never met anyone who said, I love taking surveys. And you have to answer these multiple choice questions. And if you do that for more than three minutes, it just becomes super repetitive. And so you end up just clicking random buttons. And in fact, we've actually done research on this where we went back to the same person two weeks later asking survey questions. And they ended up being about 85% consistent per question. And if you then scale it up to 30 questions, the whole result becomes extremely noisy. So people are not even paying attention when they answer surveys. When you do that with listen, you have to actually think you have another entity that's kind of engaging with you. And so we find that people open up much more and they are much closer to how they actually behave. They answer much closer to how they actually behave in the real world.
- [20:02]
A
And this is because it's like they're having a conversation with someone versus sort of a one way, filling out a form, clicking buttons.
- [20:10]
B
Yeah, exactly. And it's much more engaging than doing that. We let people be human and surveys turn them into robots.
- [20:19]
A
Yeah, because I guess if there's like, if you ask someone, do you like Pepsi? Someone might say, yeah, right? Like, yeah, whatever, maybe that's like a 10 out of 10 or something in a survey. But if I answered it that way, like, I'm not very enthusiastic about it. But if I was like, oh, I love Pepsi. I drink it three times a day. And like, I don't even have blood. Like my blood is actually Pepsi because I drink so much Pepsi. That's a way different answer than just a yes.
- [20:47]
B
Exactly. And now these LLMs can also read your emotions. It can look at your video feed and say, yeah, this person said, yeah, this is great. I'd love to have this. I'd love to try this if I had more time. But they can tell that this is someone who's never going to try this product. Or maybe they even sarcastic. And so you can really kind of translate human emotion into action.
- [21:14]
A
That was a new feature you guys launched kind of recently, right? Like this emotional intelligence, I think you called it.
- [21:19]
B
Yeah, exactly. So we have this model of human emotion. We can read six different emotions and then we can use it for analyzing your responses. So one good example is advertising testing. So the holy grail of market research is to read someone's mind and see how did they actually react to this thing directly. And this is the next step in doing that.
- [21:48]
A
Interesting. So what exactly is it doing? What kind of things can you pick up on? Is it like a raised eyebrows? Is it like how their mouth moves to represent excitement or, like passion or something? Or disgust?
- [22:04]
B
Yeah, I mean, it's not perfect, but it's getting a lot better. I think it's around 60% in our eval and humans are around 80%. And it's both audio and video, so it will pick up on your intonation if you raise your eyebrows, it picks up on that. And we try to train it to avoid hallucinations as well. Sometimes it will read into too much of the video, but we use Gemini and a couple of other models to
- [22:41]
A
do that, and I think. So you mentioned that you work with Microsoft, you work with Suite Green. I think I saw that VCs are using listen to actually do diligence on companies. So how are people kind of using it? What are some things that people are getting out of it? I think you mentioned Chubby's earlier, too.
- [23:01]
B
Yeah. So it's things like ad campaigns, get product feedback, understand brand perception. Anthropic uses it for. If you churn from cloud code, listen will figure out why. And in some cases, if there is a bug, Listen can actually send that to another agent, which will create a ticket or coding agent that will actually solve the bug. VCs use it for diligence. So you'll have this whole process of talking to the customers of different products and understand, like, do they actually like it? So that's another use case. You can imagine Procter and Gamble. They're constantly launching new products in new markets. And to launch one of these products, it's tens of millions of dollars in ad spend and also retail shelf life. And if you can validate and understand how you should launch it, it can save a lot of money.
- [24:08]
A
Yeah, it's always interesting. You're proctoring Gamble and it's like, all right, we're coming up with a new chocolate. Do people like chocolate? Or should we add, you know, dark chocolate? Should we make it 70% instead of 60%? And they, like, do this whole research campaign. People they'll talk to this process of, like, hundreds of people to, like, make, like, change some food or, like, change the packaging. Like, it kind of seems a little bit silly, I guess, but there's just so much at stake that they definitely. They're like, all right, if we make the package green instead of blue, how will that change the perception and what's the ROI on that? So I guess it sounds a little bit ridiculous, but also it makes total sense that especially the more resources you have, the more you'd spend on this stuff.
- [24:51]
B
Yeah. And it can have a huge impact. The package that you choose, it can even be you constantly make decisions every day that in some way you're not fully aligned with your customers. You don't know exactly what your customer would want in one case. But I use lessen myself. We have created a simulation of our customer base so the way we can talk about that, but we built this ability to interview one person and then create the digital twin of them by doing essentially a one hour long form interview. And then you can scale it up to 1000 people so you have a representative sample. And the other day I was figuring out what's the title of my talk for a conference with our customer base. And it's a really small decision, but it actually does matter. Are 20 people going to show up, 50 people going to show up? And by iterating with this synthetic panel, I was able to get to a much better result than I initially had. And so I think that if you can help improve every single one of those small decisions, you will have meaningful change in a large company.
- [26:15]
A
And you said something interesting about these synthetic sort of Personas or data sets. How does that work and how is that useful? I'm just curious because I'm just thinking, do you run into different biases? It's not actually real customer data because it's synthetic or made up. How does that actually work?
- [26:37]
B
Yeah. So our core product is really focused on talking to real humans. And we realized that we've done more than a million interviews in the platform now and it's grown exponentially since we last reported it. And we said, what if we train digital twins based on all of those interviews? That would be really powerful. And you could think of this as if you have a partner, you spend a lot of time, then probably you can predict to some degree what they're going to like. And not like, like if they'll like
- [27:12]
A
a new food or if they'll like a movie or something like that.
- [27:14]
B
You think you can do that?
- [27:19]
A
I will say my wife can probably do that much better about me than I could about her. So I could give it like depending on what it is, I could probably call it. But she knows me so well, she'd be able to like anything. Like, oh yeah, Turner would or wouldn't like that.
- [27:34]
B
Okay, so your wife has a good model of you. But it turns out that LLMs can build this model quite successfully. So we have in some cases like 95% accuracy and we measure that by just removing one of the questions from the training set and testing like how well is AI able to predict the answer to this question? And so you can get very high accuracy. The problem is that obviously there are questions you can't predict. Right. And so the model needs to know what it can answer, what it can't answer, and what's the confidence interval. What the use cases are, I would say is like brainstorming the 99% of decisions where it's too difficult to talk to real people or you need answers really quickly, or it's really a small decision, but it still matters like the title of a talk or if there's hard to reach audiences, like high net worth individuals, really expensive to talk to. Now you can create these simulations of them. If you just talk to 100 of them, you can have some kind of simulation.
- [28:43]
A
So I'm trying to think of what something could be. So if I'm like Doritos, I mean I feel like, or like Taco Bell, like they always come up with these like crazy new products. So I can maybe say like, hey, should I make a strawberry flavored Dorito? And I could probably go into the Listen data set and like a bunch of people have maybe mentioned how they like strawberries or something or they don't. So I'd be able to maybe get like a little bit of feedback on like, hey, it looks like people may actually be interested in strawberry flavored Doritos. Or you have enough history to say, people probably won't like that.
- [29:19]
B
Yeah. One of the best use cases I think is message testing. So that is basically what is the title of this billboard? What should I name my product? These really difficult vague decisions that may or may not have some reference that you don't know about that went viral few weeks back and you'll be ridiculed by it or a specific set of framing. Also even aligning yourself and aligning other people. I've actually created a synthetic version of myself and sometimes when I have decision fatigue, I'll throw that in, what should I have for lunch? And I'll just let my synthetic AI choose for me because it's just easier to have someone else make the decision. So there's value in getting faster to decisions.
- [30:14]
A
One thing I maybe like relevant is for this podcast, when I'm trying to think of what do I title this thing, what should I put in the thumbnail? On YouTube, I always just basically copy and paste the transcript. And I have a clod skill that I'll just basically just Bang out a bunch of ideas and 9 out of 10 are pretty bad, but there's usually some in there that are pretty good. I'm like, oh, I did not think about like framing it this way. I haven't thought yet what the title this one could be. But even when I asked it before for prepping, part of it was like, oh, you should give it this immigrant to successful founder type of framing or you should give this AI unlocks the qualitative side of humanity, even though it's very quantitative or something. And I again, it was like I didn't feel like any of those really hit. So I'm going to see after this conversation, I'm literally going to throw it in, throw the transcript and be like, what are some ideas? But it, but it always comes up with. Usually there's a couple that are pretty good that I wasn't thinking of.
- [31:17]
B
So yeah, and what's interesting is that the taste of the models are trained on the average user. And so when we tried this, we asked Claude chatgpt, like, what do you think? Like, even if you tell it like, hey, you should act as a market researcher, whatever, it has different opinions than our synthetic or our digital twin panel and so it's not as aligned with your specific segment. So imagine if you had created a simulation of your user, like the people listening to this pod. You could then have that in as an MCP and let Claude kind of iterate together with the that simulation to come up with the perfect title.
- [32:04]
A
Interesting. I need to figure out a way to automate because it's all still kind of manual. I need to do probably some more cowork automations of when it notices that I've recorded an episode, it will automatically go and run. I haven't gone that far yet. I need to. I think this begs maybe an interesting question of if I'm a brand, couldn't I just go to ChatGPT or Claude and just be like, hey, here's what I'm thinking. What do you think? What's the value of using something like listen versus just a more general AI tool?
- [32:37]
B
Yeah, so the value for simulation is that the results are different. If you ask Claude, it has much worse taste than the simulation because it's not based on your specific subsegment. If you think of something like sweetgreen, you would think that, okay, that's a general audience, but actually it's high income, it's urban and by the way, they need to know what seed oils are. And all of a sudden it's A very small subset of the population, and it lacks a bunch of the nuance that you get from the interview. So we see a very meaningful lift in the accuracy. So when you look at pure Claude, accuracy is around like 40% and we get 95% accuracy in some cases.
- [33:28]
A
What is that accuracy? Like that 40%. What is 40%? And then what's that like, 95%. Is it like the success of an outcome?
- [33:37]
B
Yeah, it's the mean average error in answering a question. So we will remove 10 questions from the training set, which. The training set is basically the interview where we interview someone for one hour and we'll remove a bunch of questions and then predict how will we answer this question. And we let Claude do that and we have the real answer as well. And then we see what's the average error? We get about 5% of error.
- [34:13]
A
I think one of the things, you mentioned it a little bit earlier that you work with sweetgreen, I think it'd just be interesting. They actually developed a product using. Listen, what did sweetgreen use you for?
- [34:23]
B
Yeah. So they came to us and said that the menu has had issues with protein. And we did a study where we interviewed sweetgreen customers and ran hundreds of interviews, and Lyssen came out with an idea that they should create a new bowl should call the Max Protein bowl. And they ended up actually launching that and it became a huge viral hit and a lot of people are buying it now. So that's the kind of use cases that work really well when you're trying to do ideation or concept testing and you see how people react to these new ideas.
- [35:10]
A
Yeah, I feel like sweetgreen's really good at being on sort of the forefront of new technology that comes out. Actually, the very first guest of the podcast was Jonathan Niemann, the CEO of sweetgreen. And I think it was like at the time, they had just launched their robotic kitchen where they were like using these autonomous robot to automate some of the preparation. And I feel like they were pretty early on mobile takeout, mobile ordering and takeout, which obviously you can optimize the kitchen. I've looked at the stock price recently, but I know that I feel like just generally that category has been struggling a little bit. Just those pricing consumers are getting a little bit upset about the Chipotle slop bowl. Slop bowl memes. I'm sure you've seen those.
- [35:58]
B
Yeah, but they're kind of. They're really great at testing new things and they've been amazing partner from the beginning.
- [36:07]
A
So I know in just generally AI, there's kind of this like Jevons paradox thing where the better it gets, the more that you do. Is there a similar element going on with broadly customer research? Like are you finding that people are doing more and more, you know, talking to their customers because you make it easier and faster?
- [36:27]
B
I think there are these examples where there is no limit to how much you can get, how much value you can get out of a specific segment or of a specific task. And customer research is one of those. You can always perfect whatever you do to make sure that it's fully aligned. Are vision is to create a world that finally works the way people want. And there's so many small things that are misaligned with what people want. So we actually see that now that you can launch something like one of our customers, they used to do these things once a quarter. Now they do it every week.
- [37:10]
A
There's a new product or something or new event of some kind, like customer
- [37:15]
B
research, essentially, they used to work with one of these agencies once a quarter. And that means they can fundamentally launch more marketing campaigns, more products. They can iterate much faster and their products are more aligned with actually what their users want.
- [37:33]
A
You're basically just tightening the feedback loop, speeding them up. They're able to talk, I mean, really talking to your customers. If you go back to what is YC the advice to when you're starting your company, it's just talk to your customers, build a product that they'll pay you for. That's basically what you're helping people do at the end of the day.
- [37:51]
B
I mean, what I'm really excited about is when the coding models get really good. The YC model is write code, talk to users. And I think the coding models are almost good enough for this. We can essentially give a listen amount of capital and then go and talk to users, figure out what they want and build it and run that in a loop. And you have an autonomous organization.
- [38:16]
A
So that's pretty interesting. To what extent can you do that today? Are there certain points where it just doesn't quite work yet because the technology's not there yet?
- [38:25]
B
There is still the judgment of when to ask and when to build. And the models. The reliability is not quite there yet on the coding, but I think towards the end of this year there will be huge improvements. And especially with the simulation where you can get really quick feedback, I can see the way we develop will be quite different.
- [38:53]
A
So you can basically have somebody in product who is talking, using listen. It's Going out and talking to customers, they're getting feedback on it, and then they're like, okay, Devin, just go make it. And like, within the course of the day, maybe there's like, you know, time windows for all these things, but you're basically just kind of sitting there and you're talking to customers, and then that's. There's almost this, like, triangle of, like, product, customers, engineering. It's all in one maybe.
- [39:24]
B
Because today the preference model is you as the builder. You're building it for yourself. And you kind of have to think, okay, what would our customers actually want? What do they actually care about? But imagine if you could have a simulation of your real user, and that's just going to be so much more powerful.
- [39:42]
A
Is there people that are doing that well today? Do you feel like there's any companies that are the closest to that, or maybe how do you guys do it?
- [39:49]
B
I don't think anyone has cracked that yet. I think we have an edge because we're talking to real people all the time. So we have this extremely rich data set that we can train on. And that's why I'm excited for this direction. And we're hoping to launch this in a couple of months.
- [40:06]
A
Oh, so it's not out yet?
- [40:07]
B
Yeah, it's not out yet.
- [40:09]
A
Oh, interesting. Okay, what's the challenges in building this? What's been the hardest part of actually making it practical and usable, making it accurate?
- [40:17]
B
The models have a bunch of. They're super smart, right. So they will sometimes act in a way that's not in tune with how humans work. And a bunch of issues around that. Basically, that's the hardest part.
- [40:33]
A
Interesting. Yeah. Yeah. Because it's. It's sort of like with any. Anything like AI, it's like, how do you quantify everything? Like, everything needs to be a data point in a sense, but this is still a very qualitative thing. Like, how does something make someone feel? It's kind of like this weird balance of how. I don't know. How do you. How do you balance it? I don't know. I don't know if there's an answer.
- [40:54]
B
But you will not be able to replace all of the work we do with simulation. Because there is something about talking to real humans and seeing them react in ways that are just impossible to predict. And also being able to share highlight reels of how people actually feel when they see your product. Big value of research is aligning people, motivating them to actually go and fix the problems. Because sometimes, you know, all the problems it's just there's no one actually going and fixing them. But having real people react to how bad your experience is can be a really great catalyst to make that happen.
- [41:38]
A
Interesting. Yeah, because I feel like we're. And maybe an example of that happening right now is like a lot of people are now starting to build products and software that's kind of agent first instead of human first. Right. And like a year ago that probably wasn't necessary, but we've kind of, as more and more software moves to being more of like agents interfacing with other agents. Like the human first software is not quite built correctly or in the same way more efficiently. So it's like this new problem that emerges where like a year ago, nobody would have thought this was a thing. But then now, as an industry shifts, as behavior shift, demand, use cases shift, all of a sudden it's like, oh, there's actually a need for this to exist. That did that wasn't there six months ago.
- [42:24]
B
Yeah, but those agents will always be doing things on behalf of their humans. Right. So that's why it will always be very important to understand the humans behind the agents.
- [42:34]
A
Maybe speaking about humans, like selling to humans, I know you mentioned that Microsoft was a customer. I think they were kind of one of the biggest first customers that you have. How did you get them on board so early?
- [42:46]
B
Yeah, we were really lucky. We ended up hearing about this pitch competition in a niche conference around market research, and we decided to hop in and do our pitch. We ended up winning that competition. And in the audience there were a bunch of enterprises and the product was barely not working at the time. We were extremely early. It was like a couple of months in.
- [43:18]
A
So this was a startup pitch competition.
- [43:22]
B
Yeah, but for market research companies. And you would think, oh, if you race from Sequoia or whatever, you're too cool to go to those pitch competitions. And a lot of founders have that mentality. When we showed our giant check that we won, or some of my founder friends were like, oh, why did you do that? That must have been a waste of time. But it ended up validating us. And instead of having the typical advice for founders is to start mid markets and then go to enterprise sell to
- [43:55]
A
startups because they'll be much faster to convert, it's easier to identify the, the problem and who needs to buy. Usually it's the founder. Right. And they'll just make a decision right there.
- [44:05]
B
Exactly. But I think that that can be a huge mistake because you can just skip that step and sell to enterprise directly. Most of the revenue is in the enterprise and of course it depends on what you're building. But for us, we just built it enterprise ready from day one. And we're able to start out with Microsoft, Google P and G as one of our early customers. And a lot of very successful companies like Wiz have done that in the past because you just grow so much faster, especially in AI where the AI budgets are extremely large. In the enterprise, specifically traditional enterprise, that would be a piece of advice to go and build for them first.
- [44:50]
A
So then how do you convince Microsoft because it's still a big company, you got to prove the use case. How did you. They were in the audience, like, what happened next?
- [45:01]
B
We had printed out this traditional survey that I was sent by Ikea and I kind of had it as a prop when I gave the talk and I dropped it down and you see there's pages and pages of surveys and they just felt like, wow, this is how we understand our customers. We're not treating them well enough. And they were just really bought into that idea. And then we had to sprint and build really quickly. Luckily, my co founder is the national champion in competitive programming in Germany. So we're able to quickly recruit these amazing engineers from all around the world, get them into SF and build something that worked when we were ready. The procurement process took almost a year and so by then we actually had a working product that was pretty good.
- [45:59]
A
What would you say, how many total people at Microsoft did you like? Different people, like individuals did you interface with in that process?
- [46:08]
B
It was surprisingly simple to get the pilot done. It was just a couple, a handful of people. But now we're working with I think 30 teams and it's growing relatively quickly as well in the org. So it's like an infinite amount of people that can use listenicrosoft. So the key is land and then expand.
- [46:31]
A
And then I'm assuming they're probably giving you feedback on the product. You probably added features based on feedback you've gotten from them, all that kind of stuff.
- [46:39]
B
Yeah, and that allowed us to be kind of building for other enterprises as well. At the same time, it is important to have multiple enterprise customers and not just one, because then you can be kind of get stuck with them. But we always had, you know, a couple in the similar segment.
- [46:58]
A
So you're basically telling founders, don't try to get one big enterprise customer. Like, try to get like three or four. Like no big deal.
- [47:04]
B
That's easy, right?
- [47:06]
A
Was it like, were you able to use like, like logos to then Help you kind of like ladder up and like convince other people to take you seriously because you work with this other company. Like, is that maybe a benefit to doing the enterprise route?
- [47:19]
B
Yeah, if you have Microsoft, then all the other security and compliance, those procurements, you know, they have their own certification called SSPA. So like, forget about SOC 2 type 2. You have to kind of do get their own auditors to look at your stuff. It really needs to work. You can't use delve or anything like that. And so that was a huge validation. For the other enterprises that can be very slow moving. And then you use them as customer references as well.
- [47:52]
A
Oh yeah, that's got to be helpful. Plus it's probably like they have a friend who works in a similar role at another company, an old coworker or something like, hey, check these guys out. Yes. So speaking of advice for other founders, I know you had a pretty interesting process for fundraising, what you recommend other founders do. What's kind of the fundraising advice that you generally give people?
- [48:13]
B
Yeah, less about fundraising, but more about the psychology of VCs. So one thing I found is that VCs will work much harder before they invest than after they invest. And founders should really use that to their advantage, especially in these crazy times when fundraising is it's very hot market. So you should actually ask VCs to go and make a bunch of customer intros for you before they invest. And we systematized this so we created a leaderboard that we shared in our investor updates where you can see which VC is performing the best in terms of intros made. Not just like number of interests, but actually close one. And Ribit ended up leading our series B because they are like true workhorses. So their brand is not really well known, but there are these name brand VCs. They end up being a little bit complacent and they actually don't do the work that they promised that they can do. They're great at giving advice, but if you can get 10 customer enterprise intros, that can be worth a lot more. So ribbit closed almost $1 million in ARR for us before they let our series be. And you can actually get large amount of pipeline from this motion. A lot of VCs will probably get annoyed by this, but it does work. They also find it kind of fun and competitive because they're very competitive in nature.
- [49:52]
A
Yeah, hopefully the good ones. The good ones are probably competitive. How do you actually do that in practicality though, is it a part of the fundraise or is It a hey. Or did you mention, hey, we think we might be raising money in three months to plant the seeds and get them in the back of their head like, oh, I gotta start doing some work? Or do you say, hey, we're specifically picking our investor based on customer introductions? How do you actually tee that up in a way that lands correctly where the VCs will actually be motivated?
- [50:28]
B
Yeah, I mean you have to be careful to not be too arrogant. But you can also be pretty upfront and say like, hey, you'll get a lot of VC inbound if you do a Series A. And so for the. I think it only works like Series A and beyond because then it also becomes a very significant quantum of capital. And so a lot of people will try to fight to get into your deal, but they'll reach out and then you'll say, hey, I'm not fundraising right now. But when we do, we're basically going to look at this leaderboard and we're going to pick the top folks that perform the best. And so like, would love to kind of get to work. And you have to be, of course, when they do the work, you then have to show that you are building trust with them. And you can't just use people, of course, but I think they also enjoy like being competitive and helping out.
- [51:36]
A
So did you build some kind of custom thing or is it already just like a spreadsheet? It's like an extension of the pipeline.
- [51:42]
B
We have a vibe coded app that we share.
- [51:46]
A
So you raise money. I think you said you raised 100 million total. You're obviously trying to hire people. Now I'm assuming you're trying to ramp up the team. What are you looking for in terms of types of people roles you're trying to fill? How do you think about adding to the team?
- [52:02]
B
Yeah, so hiring is one of the most competitive things in this market, especially in San Francisco. I'm not from here, so I don't have a ton of friends. It's really been kind of a fist fight moved here from Sweden and one of the ways that we have tried to differentiate in general how I think about how you can get top tier talents if you're a small startup is by really having a distinct culture. So as I mentioned, my co founder, he is a competitive programmer. So we naturally have a bunch of engineers who are really into hard math problems and puzzles and kind of do problems on the weekends, IMO problems. And we wanted to communicate that. So we created this billboard that we put up in San Francisco that is just A string of random numbers. And that if you were able to understand what that was, which, by the way, alienated most people. No one had no idea. Most people had no idea. What do these numbers mean?
- [53:16]
A
Yeah, I wouldn't have known. It's literally like a URL, but it's all numbers in the URL. Like I was like, I don't know.
- [53:23]
B
But if you do know, it becomes this secret club and you feel like, wow, this is very interesting. Let me go and try to understand what this is. And you realize that it was AI tokens. So you could tokenize that. And you were put into this other URL where you had to act as a Berghain bouncer. So we actually had. One of the problems that you do in interviews is quotas. So it's this optimization problem where you have to figure out who should be interviewed. It needs to be representative of the world. And so that's actually quite similar to being a bouncer at a club. And we kind of reframed this internal problem as a fun puzzle. We spent months working on our fundraising announcement, but this ended up going much more viral than that, which was unfortunate. We just took a picture of it on iPhone, published it on X, and it got millions of views. We had 10,000 people actually do the puzzle and ended up. Now everyone who we interview knows about this thing. They don't know what our company does, but they know that we did the
- [54:38]
A
billboard at least, which, I mean, that's 10,000 people that applied as like a early stage startup at the time. I mean, that's pretty hard to do.
- [54:49]
B
Yeah, no, it was really cool to just see everyone trickle in. And we had people like, physically compete because if you won, you were able to. We would fly out to Berlin as well to go to Berghain.
- [55:01]
A
It's like this, like, pretty legendary nightclub, I think, in Berlin. Like an edm.
- [55:05]
B
Yeah, it's like. It's also really. It's famous for being extremely hard to get into because they're very picky about who they select. I don't think our engineer, in the end, he did not actually go to Berghain, but he did go to Berlin.
- [55:22]
A
And so typically, if you were just to not do that and you were to just say, hey, I want to hire a recruiting agency to help me out. What do you typically pay from the recruiting agency and what do you kind of get? So I think you paid about 25 grand for this billboard. You got 10,000 people that did the problem and applied. If you were to go the recruiting agency route, what would you have gotten?
- [55:43]
B
I mean, for one engineer, you can pay $50,000. So it's absurdly expensive using a recruiting agency. And the big problem is that they just reach out cold with 50 other companies. So then not only do you pay the recruiting agency, but you also end up being getting the most competitive candidates that are interviewing at Anthropic OpenAI and are getting million dollar salaries. So with this, we're able to get a bunch of folks that maybe the others don't know about, but they're just really excited about our culture and that's been an advantage.
- [56:25]
A
Yeah, that's why I think a lot of people don't always remember when you see some startup that's doing some crazy thing, they're just like, oh, why did they do that? That seems kind of like a waste of time or whatever. But if you're like, I'm assuming you're not paying the same salary as Anthropic, so you're not going to beat them by just, hey, we'll pay more money. You have to get people that are like, huh, this startup seems kind of interesting. Seems like a cool problem. Seems like it'd be fun to work there. I'll make the jump. Seems like an interesting place to work, seems like a cool problem to work on. Seems like a cool product. So I feel like a lot of people, they maybe kind of glaze over. That part is like, it's actually really hard to just give, to get people to give you the time of day even when you're trying to recruit your first 10, 50, even like sometimes first hundred, couple hundred employees because just no one cares about you. If you're a super early stage startup just getting started.
- [57:19]
B
Yeah, I mean you should. I always start to think of it from the position of the engineer. Right. Where they have no idea you exist. There's 50 other companies growing extremely quickly and how are they going to explain it when they talk to their friends? How can you make something that you give them a cool story to explain why they joined this company specifically?
- [57:45]
A
Yeah, because it's like their friends, but it's also their parents. Because it's like, okay, let's say you have someone, they went to a really prestigious school, they got a job at McKinsey or Goldman Sachs or Facebook, whatever, and you're trying to convince them to kind of make this slightly crazy jump of like, hey, you were like the top 1% your whole life. And you're like, you're obviously really ambitious, et cetera. And your parents are like, hey, why aren't you a doctor? Why are you doing this startup thing? There can be a lot of external things that you kind of have to help them solve for too.
- [58:21]
B
Yeah, 100% and yeah, being able to make it kind of high status and also clear why this is like a specific fit for them makes a huge difference. And we try to hire, when you think about hiring, we try to find people who are kind of a little bit obsessive. People who I find are great at something that could be even outside of work, they're just really passionate about often translates into being successful at lesen. So we have one person, she's a race car driver. She has eight race cars and does drifts in Tokyo. One of our engineers built a jet engine in high school. And I also look for this almost good version of arrogance where you take a lot of pride in your work, where whatever you put out in the world, it needs to meet a certain quality bar. I find that caring about what you do is the most important, especially as the models are just getting smarter and actually matters less about being smarter, more about kind of having agency, being ambitious, and just caring about every single detail.
- [59:47]
A
One interesting kind of like, thread along that is one of my first. Like, I did an internship at this big corporation in college and the CFO was just like talking about what he looks for and like, you know, they're early in your career. What do you do to stand out? And one of the things, it's like, you know, if you just spend that extra 10 minutes, like, relook at the thing you did, think of it from my perspective to, like, the colors look good or like, did you use the right font? Did you catch the last spelling error? Did you just spend the extra 10 or 15 or 20 minutes just like, giving a shit about the thing you were about to, like, the piece of work you're about to submit. And I think about that a lot. Just in everything, it's just like, okay, like, I just want this to look good. Like, spend an extra 10 minutes re looking at, like, maybe you redo something because you found a better way to do it. Super simple and like, I don't know, I won't tell you to do that. And maybe you'll think of a different, you know, lens of looking at something or framing something that wasn't there before. Help someone else understand it.
- [60:47]
B
So, yeah, I mean, I think a great documentary about this is called Jiro Dreams of Sushi. I don't know if you. Have you seen that one?
- [60:57]
A
I actually haven't seen it, but he's like a guy who runs a sushi restaurant or starts a sushi restaurant or something, and it's super successful.
- [61:05]
B
It's about this sushi chef who literally dreams of sushi and he's been doing it for 60 years and he's still obsessed with trying to refine every single part of the detail of how you cook the rice, how you make the omelette, and just has an insane quality bar. And I think with AI and you can generate AI slot now this becomes more and more important. We see this in our interviews as well. There's a bunch of folks that will be like, oh yeah, well, I generated this case study in 10 minutes with Claude. It's good, but they actually don't look at the details. And so loving the details. That's one of our values. It's more important than ever.
- [61:50]
A
Interesting. Well, speaking of films, I know you're really into old films. I think on your website you have a couple favorites that were released decades before we were both born, I think from what I saw. So what are some of your favorite movies and what do you like about them?
- [62:07]
B
I wanted to be a filmmaker growing up. And I think there's actually a lot of similarities with being a director, with being a startup founder because you have this kind of interdisciplinary group that you have to align on a mission and you have to learn the technical aspects of editing as well as the creative stuff like writing great scripts, directing the actors, sort of like your employees. You have to kind of align on your mission.
- [62:42]
A
So
- [62:46]
B
at one point I used to watch a film a day back in high school. I think one film I really like is called Toni Erdmann. It's actually newer, but it's a German film which is about a management consultant and her relationship with her dad. It's really funny. It has Sandra Muller in it, who was in Project Hail Mary I think was one of her like films when she became kind of famous. But I think the overall, like watching the classic films or reading fiction is a really good way of understanding what people want. And storytelling is a really important skill if you are a startup founder. So recommend everyone to watch. Dygma Bergman is a Swedish film director.
- [63:43]
A
Interesting. A movie about a management consultant. Okay, well I'll throw a link in the, in the description for people to find it. Slightly different topic, but I remember hearing that you guys have certain harness that you made for the agents at listen. So what exactly is that?
- [64:01]
B
Yeah, so an agent harness is the framework that the agent can use to do tool calling and the knowledge management. And what we found was that every other harness is built around a file system. So Claude code, for example, will use CloudMD. And that's how it kind of has memory and figures that out for us. That's the wrong architecture specifically for statistical analysis because we think that the right way of building a harness is a table because you can kind of operate on it as a Pandas data frame, which is a tool in Python. So you're able to. Every row is a response and then every column is a, a feature. So every row is like an interview. And then you can extract information for every single interview. So you can tell our agent to kind of, if you want to quantify something, it can run a sub agent for every single response and classify like, does this person like my product or not? Even if you collected like open ended interviews, does that make sense? And then you can easily do like aggregated stats, you can run correlations between columns. That's much harder in a file system. So that's one thing that I see that these vertical AI companies can do is essentially look at the job of the job that you're trying to do as an agent and really perfect the harness, perfect the workflow around that job and you can get much more juice out of the models than the vanilla model companies.
- [65:54]
A
Interesting. So it would basically be if I'm Doritos and I'm asking some questions about a new flavor, existing flavors, how I feel about the. You don't specifically ask me, turner, do you like Doritos? But you will be able to tell if I do like Doritos based on how I answered other questions?
- [66:12]
B
Essentially, yeah, it's all open ended. You feed all of that into the LLM and then it's able to predict how you like or not like something.
- [66:19]
A
I actually, I wanted to ask you something. So I know you do this fellowship where you bring and talk about it yourself. I know you're from Sweden, moved to the us. You actually run this fellowship program for other Swedes, helping them move to San Francisco. What's the program and what do you guys do?
- [66:35]
B
Yeah, I run this program called Velocity Fellows and I always struggled being kind of the only one obsessed with startups back in Stockholm and wanted to create a space where people like that can find other like minded founders and then bring them to SF to kind of scale their ambition. So the goal is not for them to move to SF because I don't want to increase brain drain, but hopefully to bring the SF spirit back to Sweden. So we had like max Unistrand, who's now the founder of Legora, used to be an intern at my company as well. He was part of the batch one. And we have a lot of them have now raised money and we connect them with, like, there's a bunch of Swedish folks in Silicon Valley as well, like Ali Gusti, who's the CEO and founder of Databricks. He's Swedish. Eric Barnard, who's at Modal, he's Swedish as well. And it's cool we're seeing resurgence of the Swedes.
- [67:43]
A
Nice. I was gonna say. Yeah. I had Eric on the podcast a couple months ago. He's really fun. Oh, great. Talking about clips and stuff. He had one of the most viral clips of the podcast. It was about CO2 levels in the office. The most random topic. But it got thousands of likes on Twitter, a couple hundred thousand views. I think it was close to a billion views. It was like people. And people were chiming in of like, yeah, CO2 levels. Like, you need to manage the CO2 level in your office. Like, it actually has a huge impact on your work productivity. And I was like, wow, did not know that this was such a big deal. But it's true. I guess. His. His. He's like, very big on, like, they have CO2 monitors in the office and make sure that CO2 levels don't get too high because it impacts your brain and makes you less productive. So it's like, huh. All right, interesting. One other fun fact that I remember hearing about you is your. I think your brother is the founder of SoundCloud.
- [68:37]
B
That's true. He's 16 years older than me.
- [68:41]
A
Okay. Yeah. I used to be a pretty heavy SoundCloud user. Just a lot of, like, EDM remixes and stuff. Less. So now there's just like, less People post on SoundCloud, I feel like. But I definitely, like, have fond memories of, like, my first job, I was an analyst at a bank, just listening to Chainsmokers remixes and avicii remixes on SoundCloud for 10 hours a day.
- [69:10]
B
No.
- [69:10]
A
Yeah.
- [69:10]
B
SoundCloud is obviously a big part of my childhood. Seeing him building that company, the things to do, the things not to do. Got to visit the office when I was very young. And I'm also know, competitive, and so I want to try to build something that's. That's bigger than my brother's company. But we're. He's moved now to the. The Bay Area as well, so we. We spend a lot of time together.
- [69:37]
A
Oh, cool. And I think one other thing. I heard you say You. You've mentioned before that you guys have a no shoes policy in the office. What's. That's a pretty. There's. There's a lot of. You go on the Internet, people have strong opinions of shoes versus no shoes. So what's the shoe policy?
- [69:53]
B
Yeah, so having no shoes makes it much more comfortable. It feels like you're at home. And that allows for more of an academic environment, I think, which is one of our values, that folks be able to have free discussions and you can sit in the sofas and be more open. We also have Lissn branded slippers. So if you do need some shoes when you come in, we help you swap from your sneakers to our slippers. But it seems to be a very controversial topic, which I don't fully understand why it's obviously much better to not have shoes in the office.
- [70:38]
A
Yeah. Well, I think. The thing that I think is kind of crazy is if you walk through San Francisco, not the cleanest city in the world, and then you go into an office, like you are walking the same shoes that were on the ground that people are partaking in, the external outside activities that happen on the streets in San Francisco that you then do in an office. Like, I can see the value behind it.
- [71:04]
B
Yeah. And it's from my high school. We didn't have any shoes on there as well. So this like, hippie high school in Sweden. Yeah.
- [71:12]
A
Whoa.
- [71:13]
B
We also only had vegetarian food,
- [71:17]
A
so
- [71:18]
B
it was a school that was controlled by the students. So if the students voted for something in the majority, it would happen. They had to stop that after a while because students ended up abolishing homework and things like that.
- [71:34]
A
But was that the crazy. What was the craziest thing that happened? Was it the no homework?
- [71:40]
B
I think that's when they had to pull it back. But the vegetarian food was also a big one, and it was amazing. It was so delicious. But that's the inspiration behind those shoes.
- [71:55]
A
Interesting. Okay, where can people find you? I think you post on Twitter. Are you pretty active on LinkedIn?
- [72:02]
B
Yeah. You can follow me on X and on LinkedIn Alfred Walforce, or go to ListenLabs AI and sign up for a demo. And we're also hiring for engineers, salespeople. We're around 60 people and want to be 150 by the end of the year. So trying to scale very quickly.
- [72:21]
A
Nice. Well, we'll throw links to all those in the description and people can find you. This is a lot of fun. Thanks for doing it.
- [72:28]
B
Yeah. Thank you so much.
- [72:30]
A
And thanks again to this episode's sponsors Upgrade your business to Flex with the link in the description. Put your sales tax on autopilot@numeral.com and for AI analytics, just ask Amplitude if you enjoy this conversation, please like comment, subscribe and share this episode with a friend who could use AI to do customer research. Make sure to check out the back catalog of over 100 episodes with the founders of companies like Robinhood, Sweetgreen and Mercury, and investors like Gary Tan at YC and Chathan and Eric at Benchmark. Tune in over the next few weeks for conversation with Hans, who started Secondaries from Industry Ventures, which was just acquired by Goldman Sachs, with Dan at Gutter Capital and a conversation I recorded at Allocates Beyond Summit, featuring observations from 15 GPs and LPs on what they're seeing on the ground of the venture capital market today. If you don't want to miss any of these, subscribe to my newsletter the Split linked in the description to get each episode plus a transcript emailed directly to your inbox every week. Thanks again for listening. See you next time. SA.