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 customer research platform that can run thousands of voice interviews simultaneously you launched about a year ago and you now serve 20 percent of the fortune 500 including iconic brands like microsoft anthropic sweetgreen mbc and others and constantine they're very very excited to sit down with you today and talk about market research and how it's getting 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 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 onboarding and then listen we'll 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 much anyone from an oncologist to a software engineer and we'll go and actually talk to them and have hundreds of those interviews and then analyze the data give you recommendations and now the final step that we're just launching in a couple 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 like this yeah they've been like one of our early customers what did 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 comfortable comfortable um so we saw you know the small things to the big things manscaped and changed their super bowl ad uh with insights from from lisanne so never heard of that but i'm not gonna ask uh that's huge so you you got the men's hair yes that's our niche shaping to clothing that's right wow we do other things skims is one of our customers 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 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 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 uh matters so much more than asking people about what they would do and so i'm curious what you think of that and how you think 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 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 actually have to think and you have to really reason through your answer and then you're much more consistent um with kind of at least how you answer the same question and then we're constantly tracking for example with with chubbies when we test their different shirts we a couple months later look back and see how did that perform with the actual sales data and i think it depends on different use cases i agree that a b test is kind of the 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 to have some kind of input than no input at all does listen do a voice to text as in the actual customer who is answering the survey can speak their answer and then you guys transcribe it does it also do text to voice it's an 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 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 personia's point that actually having the person's face and their emotions and their voice and whatnot yields more engagement truthfulness have we been able to have any studies or or at least data to point in that direction 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 and we've seen that those ads then perform better in performance marketing for example on meta 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 and so you know that ai is not just hallucinating kind of where it's coming from that's awesome how'd you come up with the idea to build this um so my co-founder and i actually built a consumer app um and that did what that went viral it was called um a beef fake so you could create an ai avatar of yourself it was an early version of the chat gpt images and you could fine-tune stable diffusion and put yourself in that world and that ended up going super viral and overnight we had 20 000 users and we're also kind of experimenting with different ways of using ai so we built this ai interview for ourselves because we had a bunch of questions of how we had we had a ton of churn so we wanted to understand why 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 turning historically how did people go about doing that yeah so what we discovered was that there are these survey tools that are pretty old school like qualtrics but then there's also this very large services industry because it becomes harder and harder especially if you want to do market research where you want to talk to your prospective customers not your current customers it becomes harder and harder to do that as you scale so that's a multi-billion dollar industry and 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 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 it's probably much more scalable probably much more cost effective but there's probably also less obvious 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 marketer customer research yeah so most decisions that gets made are not based on the customer input right and the reason for that is it's just a lot of friction to even talk to customers so when you can lower the barriers of talking to customers you end up making much smarter decisions so the speed advantage is actually huge for us you can get input within five minutes from real people and it's a really magical experience when you see hundreds of people populate in your like interview and so that's one thing and because it's asynchronous it's also much more affordable so you can pay people much less than if you would have to run like synchronous interviews so actually that's an interesting thing that people often ask us like 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 why is that I think it's mostly because it's asynchronous and it's a little bit more expensive and it's a little bit more expensive than people are very busy but then also lower we found yeah lower pressure you can kind of go on and off and we've also found that people are more honest talking to an AI we've had people really open up it's a very therapeutic experience because it's a non-judgmental entity that's really interested in you and we can also have sensitive conversations like interviewing kids how they react to different products and and so I think that's another advantage as well that people can be brutally honest talking to the AI okay so historically for example if I do research on the kids market very very hard to access that market is that regulatory things the scheduling thing yeah it's you need parental consent and you know kids are really busy they go to school they have extracurricular activities how do you find time with them and you need to find the right kind of kids like one of the things we realized is the audience is very busy and they don't have time to talk to each other and they don't have time to talk to each other and they don't have time to talk to each other and they don't have time to talk to each other and so I think that's one of the things that we've found is that the audience is extremely important and that's actually where we spend 80% of our engineering resources every company is driven by a power law in 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 go to sweet green every single day and that's when you find that the audience is really important and that's 80% of their revenue so if you can find that segment and 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 looking for and 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 these different types of interviews that we do so we build profiles of people as we do more interviews in the platform and then we can search and find the right person so someone might say in a totally unrelated interview I'm a total sneakerhead and you can keep that in the database on that person and then when Nike or what have you is launching a new product line you can offer that person up that's right that's amazing and that was not possible to do before and because it was usually like separate entities and it would be a very manual process where you would have an email list and you would just spam email I've been on the receiving end yeah 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 called an incidence rate which can be ten percent which means only one in ten people gets qualified to even take the interview and that causes significant churn on these databases yeah because it's really annoying to be screened out ten 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 connected their CRM and they can send that out but then the really interesting part is how do you talk to prospective customers people who also may not be kind of current power you like you know law users and how do you compare those two and then also what we found is that the CRM is typically really unorganized and sometimes there's also like regulatory issues if you're at Google you can't just send emails to people who use Gmail and it gets much easier to use an external third party and you run the risk of spam yeah which can get you totally blocked I've 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 McKinsey's or whoever is 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 you know no Constantine but I'm glad that that's what you think of me isn't that what Bank does we hired consultants so I was you know I was a layer on top of the layer on top I was even more redundant but what does it mean for all these people I do they still have a role to play in this new future yeah I think AI is changing all roles yeah very quickly and we work a lot with Bain for example so they use us to speed up their traditional processes and I think they still have a role to play I think traditional services and being able to then implement these changes is still extremely valuable but a lot of margins are going to drop and you have to make sure you kind of unbundle a lot of your services to maybe allow for AI agents to you know to be able to do that and I think that's a big part of it I think traditional services and being able to then implement these changes is still extremely valuable but a lot of margins are going to drop and you have to make sure you kind of unbundle a lot of your services to maybe allow for AI agents to help solve some of the problems that you would go to traditional consulting firms before maybe I'm an optimist here but why wouldn't it be more why wouldn't I if I'm running a business say oh great I want to find five new areas to expand to now that I have AI and these tools and I will pay you Bain or what-have-you the same dollars you use Lyssen and just explore those new areas and tell me where to where to build is that overly optimistic no I think it's one of those areas where the ceiling is very high you can kind of learn more about your customers and you can build more things and so I think I think you're it I was thinking the chest hair shirt thing there's so many little things that I'd love to tell the companies that I have a consumer of like the smallest little like even the way they laced these shoes I'd love to give that feedback yeah this is why you're a venture capitalist details we hope to live in a world that finally works the way people are you seeing any price and compression already hit the industry like I would imagine if I am Bain's customer I'm thinking well you're able to do this survey a lot more efficiently now with AI than before AI who who's 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 term like the individual interview but will become more affordable and but I think you will be you'll be doing kind of two orders of magnitude more of research and I think what's really exciting is also simulation which is something we're building now 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 they're busy important people but it feels like the companies have not even invested the time in figuring out where all those paper cuts are and the doctors are really busy so they're not going to go schedule an appointment and have some long conversation and meet with some group but if they could do it at any time like in an app on their phone as part of the normal home page 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 them if you 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 entire industry has gone from market research 1.0 you know call call 100 people one by one collate them manually to market research 2.0 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 be moving to market research 3.0 with generative agent simulation what do you make of that and you know i both see the dream of it 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 3.0 yeah and maybe what is it too to start yeah so the way we are building simulation is by interviewing a single person say if i interview constantin for one hour um i can probably start to predict your preferences to something fascinating insights about chest hair and it turns out that lms are quite good at this as well so you can essentially try to feed in as much information as possible on a single individual and then in some cases we're able to get 95 accuracy to predict how they will answer certain questions 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 like how what what's the tagline on the billboard or i was actually using it 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 inputted that into our simulation and then oh wow the the top talk was like twice uh better than the next one and wow cool and 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 compare to just asking chat gpt the same thing yeah so then i inputted the same questions into chat gpt and i actually had one i had another talk i did that was not so successful and i had and i inputted a competitor or another talk that was more successful and and i showed both of them to chat gpt and both of them to our simulation and in chat gpt it picked the wrong one and in our simulation it picked the right one so the you know it's early for us we're gonna release this in a couple of months but it seems like it's it's performing better than the general models and the models are trained on the average person yeah and you want to build for a very specific niche and and that's how we can kind of essentially train the models of all 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 chat gpt 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 it's sort of my mental model at least and so i i'm actually surprised that you know chat gp 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 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 a little bit better than just vanilla chat gpt but what performs much better is we try credit card spend and kind of behavioral data purchasing behavior um but what we found was the best data set is interviews because it's more kind of it allows you to go off tangents it understands you can ask behavioral questions so also it can't just be any interview like the way you design the questions is also really important 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 i guess kind of the purpose of this type of format and if you have enough people that follow a certain group as opposed to the average that can tell you a lot about other things that they might not have explicitly said you know all of ai is this generalization of some sort of compressed data of some sort and so if you have this compression in a slightly different part of this hyperspace that you say now complete this orbital of what everybody is thinking in this category of person 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 i they're very different though so extremely different people if i wanted to understand my customer could you do active interviews the normal listen lab interviews have a thousand or ten thousand cumulatively and then offer a little special purpose listen labs bot that then i can use instantaneously for any ad hoc question yeah that's exactly what we what we have okay um so that's that's what we call augmented responses the cool part of this as well is that it can also live in your coding agents or your other agents so i think in the future you will want to have almost an human api where the agents are able to call the preferences of your users and to be able to know like what to build how to do it or who to invest in or how to help them the best 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 on all your engineers report back i'm curious what you think of multi-agent systems and their role in helping us kind of iteratively use you know at inference time iterate to a better answer 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-agent but you're not having those thousand people debate each other that's what i'm asking oh yeah no we don't have that yet but that's you think that would help potentially but there are these other competitors that are doing that approach more i think the worry is that again chaos theory tells us when things kind of compound it it becomes really hard to predict how the things are going to interact with each other and um you know 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 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 in synthesizing them i do think on average it's 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 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 will be many more use cases that are now opened up where you can get customer input um so for the large decisions if you're doing a super bowl ad or things like that you'll still need to run real interviews but for the smaller things like what what should be the tagline for your billboard and if it's a small billboard then you can use simulation to 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 more accessible to the customer so we wanted to make it a little bit 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 yeah um and in the beginning that eval was like 20 now we've been able to climb that eval to be 85 percent um but now we created a new eval that's much more advanced what are you doing on your screen when you're screen recording or can you skip questions that are not relevant anymore and now we're back at like 20 percent 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 eval and that's your advantage as a vertical AI company. Keep pushing forward, better data, harder problems, better data. It seems to me like you're in the middle of a very interesting infinity loop, right? Because fundamentally a company is figure out what to build, build it, figure out what to build, build it. Write code and talk to users. Exactly. And the build it is coming up rapidly up in exponential. And the figure out what to build is the thing that you are pushing forward. Totally, Sonia. Yeah. And then it's not only even outside of product and engineering, the broader loop is actually strategy execution, strategy execution. And so much of what AI is enabling us to do is it's making execution faster, cheaper, better, all these things. And the thing that you guys fundamentally are positioning yourselves to do as a company is the strategy part from what to build to what to say. Is that a fair synthesis? Yeah. Yeah. And I think when we have that one person billion dollar company, we'll be part of that loop. So we'll have a coding agent and listen and then run that in a loop. And we'll have these autonomous organizations and be excited for that. Even the big companies still, like back to this idea of you can implement things faster. Let's say you have an agent. I mean, if you can be a big company and we're talking in software because software is native to us, but in software, if you could talk to a customer, figure out a bug, create a PR, have a coding agent, close it, ship it, customer's happy, that seems like a really important left-hand side of the equation. Find the bug from an actual human. But I imagine it's the same thing in a big atoms company. Like if you're a 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, Procter & Gamble, when they're launching in a new market, that can be tens of millions of dollars, if not more. And you have to make sure that that is right when you launch. And that's one of the reasons why they're the customers of listen. Who has done this historically really well? Who are the companies that are admired in history, have done a great job of listening to their customers, either in the consumer space or in the software space? I think Procter & Gamble is kind of the archetype of best market research organization, where they're essentially marketing companies that are trying to figure out what are niches that people really care about and then build specific brands to solve those problems. I mean, one example is the Tide washing machine and pods, that they were able to figure out that it was really uncomfortable to use the washing machine, but they were able to figure out that it was really uncomfortable to use the washing machine and pods that they were able to figure out that it was really uncomfortable to use the washing liquid. And they discovered that people wanted something that was much more easy to use. And through customer interviews, they found this insight, made this new Tide pod and became really successful. Another example, which is in the Acquired podcast when they talk about Mars, they did one of the first market research studies in the 1950s, where M&Ms were originally designed for being used in the army, because they were like a sweet treat that don't melt in your pocket. And they discovered through market research that another great segment was young kids. And they then decided to pivot the entire advertising strategy to focus on this, because it doesn't melt and ruins your furniture, for example, and things like that. As we progress towards this, you know, listening to this podcast, I'm really excited to hear what you guys have to say about this. that shows up and you have to change your entire marketing strategy towards that. And so I think that will remain a really huge part of how we do things. I think I'm still uncertain of what level simulation will play and I'm confident that it will work for certain questions, but we'll see how good the models get to predicting human behavior. 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. Constantine, a resident humanist. I'm a humanist, absolutely. But if companies are about serving people, because that's why we're all working is to help someone else in some way, and intelligence gets better and better and better. And you have what the human tier and the intelligence is approaching that asymptote, then the delta in that asymptote, which is what is in a human's mind that isn't in the AI's mind, only becomes more important. Yeah. And one of the things that we've also realized is that there's a lot of talk around, what's the mode 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. I feel like we're on an episode of Acquired right now. Hey, I'm feeling it. I'm feeling it right now. It's a good book, I recommend it. Seven Powers. Yeah, Seven Powers, love Seven Powers. Yeah, on the modes, I mean, we have the clear modes, which are the network effects on the panel where you have supply and demand dynamic. We also have the network effects, the data mode, that as we do more interviews, you get better simulation. And then the product is very sticky because you have all these interviews in your platform and you don't want to lose that, you want to track things over time. But even the simplest things, I think in terms of product advantages, one of the first things that Brian Shire said, one of our, our Sequoia partner, was that founders want to build something that's complex, 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 this is creating the interview guide is really difficult. It's actually an academic subject. And it's one of the reasons why you have services firms, because they know what methodology to use if you want to understand pricing or brand perception, these kinds of things. 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 LLM models, and the customers would create the interviews, they would get the data back, and then they'd come back to us really frustrated saying like, "What is this? I can't use this data for anything." And we took the blame for that. Now, we've trained it to follow the best practices so that you always get good data out of the interviews. And I think that's the advantage you have as a vertical AI company, that you can essentially train this agent to follow best practices in the work that you do. So I want to go back to the concept of Tide Ponds that you had mentioned earlier. I think it's really interesting. And so much of market research as I understand it today is almost more inviting people to pass judgment on ideas that you feed them. But it seems to me that hallucinations can be a bug. They can also be a feature with generative AI. And do you think we're going to see user research actually evolve into live product ideation? I could almost imagine 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 create a Tide Pods idea in a market interview anytime soon? Yeah, I think that's really exciting. Today, they do that manually, use AI to generate images, have different concepts, and feed that into the interviews. But I think specifically also with simulation, it becomes really powerful. So we now have an MCP as well, so that you can feed that into Claude, and then you can tell Claude like, "Hey, run, listen in a loop," and then come up with a bunch of ideas for how to market something or different concepts. And then you can have it run like that. I think it's really exciting. I'm even thinking in the course of it, an interview, as somebody is complaining about the Tide, it's not very portable for the AI to be live brainstorming with you solutions, not just listening. This is what it could look like, an image generator too. That'd be cool, Sonia. Yeah, I think it's a good idea. You should be on our product team. Awesome. Well, Alfred, we really love what you're building. Thank you for taking the time to share insights both on the broader market, which I think is just so fascinating, and also what it takes to be building in the application layer right now. We really admire the business that you've built, and thank you for your continued partnership. Thank you so much. ♪ ♪