The Exchanges

Q Do you mind if devs go to Lovable, get 60% of the code from there, and then fine tune it? How do you feel about that?

A I don't, like from the get go two years ago, I decided that I'm gonna build Lovable, uh, we know for a world where humans don't write code anymore. And we're quickly moving there. We're very, very quickly moving there. Uh, today, I don't mind at all. Like, it should be flexible. Some humans have their way of doing things, and, um, it's, I think it's good if you have, like, an ecosystem where you can use many, many, many different tools on, on the product. Over time, I think it will converge towards the very opinionated platform, like the ones we're building towards, and everyone's just going to look at what's the cost benefit of doing it with some other tool as well. And only using lava ball is going to be the obvious, uh, most, like, velocity, high velocity, and high quality driving, uh, choice. That's, that's the future.

AI assessment note: “today, I don't mind at all.”

Q So you're saying it's more secure than humans?

A Not yet. For, I mean, I, I said this at some point, which is if, um, you take your average developer who Like, normally works in a large team where they have a lot of support, and then they, they, that human goes out and builds an application, they are going to create software that has security holes on average. Um, and when you build an application with Lovable, it's going to tell you to go through a bunch of security reviews, and the AI is going to do a bunch of security reviews, and finally it's going to give you green light, like, we haven't found any security vulnerabilities. So if you compare those two, that like average, really average developer, With Lovable, Lovable is going to, uh, have a lower chance of having a vulnerability, and so we want them to put that to zero percent. We need to put that to zero percent chance of vulnerability.

AI assessment note: “Not yet.”

Q What question, we, we spoke about kind of people being, um, threatened, large incumbents. What question should, Large CEOs , business leaders be asking today about the future of AI and their companies and how they use it that they're not asking, do you think?

A I think, um, one of the biggest bottlenecks for these companies is going to be some kind of change management for the humans in the organization, um, and I think they should be asking how have similar companies to ours done change management very, very, very rapidly, uh, and get that conversation into the leadership room, and then maybe across the entire organization to start to study those examples of where change management has been very successful, And then look at specifically, um, which AI tools should we be using? Should we be adopting, like, building our product on top of Lavable a hundred percent because then everyone can collaborate or should we be doing some, should we hire some new type of people that come in and upskill everyone?

AI assessment note: “I think they should be asking how have similar companies to ours done change management”

Q So you're saying it's more secure than humans?

A Not yet. For, I mean, I, I said this at some point, which is if, um, you take your average developer who Like, normally works in a large team where they have a lot of support, and then they, they, that human goes out and builds an application, they are going to create software that has security holes on average. Um, and when you build an application with Lovable, it's going to tell you to go through a bunch of security reviews, and the AI is going to do a bunch of security reviews, and finally it's going to give you green light, like, we haven't found any security vulnerabilities. So if you compare those two, that like average, really average developer, With Lovable, Lovable is going to, uh, have a lower chance of having a vulnerability, and so we want them to put that to zero percent. We need to put that to zero percent chance of vulnerability.

AI assessment note: “Not yet.”

Q joys of this show and being friends is Because we can have a discussion, not like a one back and forth interview. Do you think you actually need that? We've had founder mode be so, uh, propagated and praised, and being close to the metal, Jensen having 52 direct reports. I would say structure and that middle layer is where slowness and apathy comes. Do you think you need that?

A That's a good question. I think I'm going to always operate with this, with most of my impact coming from, like, founder mode, but I do need, uh, given that there's so many things thrown at me and coming in from all the different directions, to have Um, kind of a protective layer that introduces a lot of order in, like, how do we prioritize all these incoming things? And, and that comes down to a well-running organization. Um, if I'm, and then you, for a well-running organization, you need a very organized manager, uh, somewhere at the top. And I'm not planning to be that, like, you don't have that manager myself, but surround myself with great leaders who, who do more of the,

AI assessment note: “I do need, uh, given that there's so many things... kind of a protective layer”

Q What question, we, we spoke about kind of people being, um, threatened, large incumbents. What question should, Large CEOs , business leaders be asking today about the future of AI and their companies and how they use it that they're not asking, do you think?

A I think, um, one of the biggest bottlenecks for these companies is going to be some kind of change management for the humans in the organization, um, and I think they should be asking how have similar companies to ours done change management very, very, very rapidly, uh, and get that conversation into the leadership room, and then maybe across the entire organization to start to study those examples of where change management has been very successful, And then look at specifically, um, which AI tools should we be using? Should we be adopting, like, building our product on top of Lavable a hundred percent because then everyone can collaborate or should we be doing some, should we hire some new type of people that come in and upskill everyone?

AI assessment note: “they should be asking how have similar companies to ours done change management very, very, very rapidly”

Q So take me to that. So we have this weekend, we released the product. What happens then, dude?

A Yeah. So it wasn't clear to me that I would build a business on this at all. Thought it was fun that there was like, and this was an open source project. So I, I just started nourishing like a community that went on to work on this open source project while I went to my co-founder and said like, okay, so this thing is absolutely huge. Um, I've been thinking of doing something else to be honest. And here's a pretty, like, this is a bit of a wake up call for me that it's, it might be time to find a good replacement for me as a CTO at, at the PICT. That's what happened next. And then within a few months passed.

AI assessment note: “That's what happened next.”

Q Yeah. So November, twenty-twenty-four, you launch. From day one, is it just nuts? How does it go?

A Um, so, I mean, we had users, paying users on like an earlier version that was called something else. Um, and then our launch, I think it wasn't one of these like, wow launches. I, I think we could have, uh, Gotten like 10 times more press on the launch, a hundred percent. Um, but people start noticing like, whoa, this is really good. And, and we continue to quickly improve things in the products, cheap things very rapidly. Um, so it ramps, like growth starts ramping up after we launch. And we were like, wow, we're growing one million ARR per week at some point. And, and that just keeps going. So the accelerating into, that was in December and then it just keeps accelerating. We, uh, uh, it was a bit on the technical side. It was a bit frustrating because we have, we have some, a lot of scaling issues that we run into and the team is like, okay, we can continue to patch this, but they say like, we will, let's rewrite everything while we're seeing this explosive growth. And there's so many quick fixes that we want to do on the product side.

AI assessment note: “I think it wasn't one of these like, wow launches.”

Q I don't, I don't really respectfully agree or get you because like you give prompts under the prompt box. So you say like, build me a SaaS app or build me a dog website or whatever the prompt is. Dude, all you need to do is click it and you see the code being written and then you see it being created. Like the aha moment is pretty obvious, no?

A So there are many aha moments for, or like education moments to get the most value, get the full value out of using it. And the, the most important of those are with regards to when the, when you feel like you're getting stuck and the AI doesn't understand you. And there's a, there are many things in how to get around, around that, that you can pick up as a user. It is how you, about how you prompt It's about how to understand what doesn't work and explain or explain clearly what, what the, what the problems you're seeing, what, what you find could be the problem when you're building a more complex feature. And it's about that you can actually onboard an engineer to do small changes to the code base. Like those are things our users should know and not everyone knows them.

AI assessment note: “there are many aha moments for, or like education moments to get the most value”

Q fit, like, all the characteristics of, like, successful founders that I have on the show. It's like, number one, made money early, number two, excelling gaming, uh, both very, very clear, um, archetypes. I want to move to Lovable. So GPT Engineer starts as a side project. Where does the idea come from? We've left a pit. GPT Engineer starts as a side project. Where does the idea come from?

A This was the spring after ChatGPT came out, and I've been playing with the precursors of that as well. And I, from already like a year before then, I felt there's an, there's a massive wave coming from scaling up these models with more data. And the PICT is not set up for, or currently not set up for leveraging that. After, like I was actually traveling with my now wife, uh, sorry, engagement trip. And when you're traveling, I get extra creative. And I, there, um, I don't think there was anyone who was talking about like AI agents at the time, but I, uh, during those, like sitting on an airplane, I was, I started writing a lot of like, okay, you can, you should Hook them up and make, uh, you basically put the large language model in a for loop and then you can have it do a lot of agentic things. And then, uh, when I'm back in Sweden, I'm like, okay, where do I apply this? Obviously on software engineering. And I've been talking to people about this and I felt no one was really sufficiently imaginative to what I was thinking about. And I had to prove a point in that this is already now with the current, the first versions of ChatGPT APIs, you can build an agent that writes code. And then I put together that or two, I drank a lot of coffee, and then I just crammed away, and, um, I got the first version that really impressed people. You write, create a snake game, and then you get…

AI assessment note: “during those, like sitting on an airplane, I was, I started writing”

Q You mentioned, like, people took a bit of time to find their aha moment. How important is the time to aha moment?

A I think it's very important. And it's funny because you asked me this question and I, I think we are very bad at making the time to aha moment super short. I think we could like double our conversion rates if we become better at, uh, like speed to aha moment. But I can say something we're doing. Let me take credit for something. When you come to Lovable, you just see a prompt box. It's very inviting. Instead of getting to a landing page, you see a prompt box. And then for the ones that do enter a prompt box, you get a quite a quick aha of the first aha moment. And there has to be many aha moments in Lovable. It's quite a complex, a software engineer is a very complex feature, but that, that we are doing well, and that's what I would recommend. Just give the user something interactive with instant reward.

AI assessment note: “I think it's very important. And it's funny because you asked me this question”

Q Yeah. So November, twenty-twenty-four, you launch. From day one, is it just nuts? How does it go?

A Um, so, I mean, we had users, paying users on like an earlier version that was called something else. Um, and then our launch, I think it wasn't one of these like, wow launches. I, I think we could have, uh, Gotten like 10 times more press on the launch, a hundred percent. Um, but people start noticing like, whoa, this is really good. And, and we continue to quickly improve things in the products, cheap things very rapidly. Um, so it ramps, like growth starts ramping up after we launch. And we were like, wow, we're growing one million ARR per week at some point. And, and that just keeps going. So the accelerating into, that was in December and then it just keeps accelerating. We, uh, uh, it was a bit on the technical side. It was a bit frustrating because we have, we have some, a lot of scaling issues that we run into and the team is like, okay, we can continue to patch this, but they say like, we will, let's rewrite everything while we're seeing this explosive growth. And there's so many quick fixes that we want to do on the product side.

AI assessment note: “growth starts ramping up after we launch. And we were like, wow, we're growing”

Q I mean by that is like, if you look at delivery, you know, it's margins of shit in the early days. And over time they get better and better as you have more and more people use it and more density, more orders in small areas. You've got to be patient, so to speak. Same with OpenAI, same with Lovable. How long does one think before you're thinking margin optimization?

A Um, I, I have these two conflicting pieces of perspectives on it. Uh, and one is I speak to Nick who built Revolut, and he just tells me like, Anton, you need to compute the payback time and then you need to do Super hard performance optimization on acquiring new users. Uh, and then you of course need to have a good, like payback, payback times in terms of profits for per user, uh, which makes sense. And then if you can do small changes in on margins, it, it actually affects a lot on how much, how fast you can grow. Uh, but, but the other perspective, which I indexed a bit more on right now, right now is you just want to have as much mind share and as many users who just love the brand as possible right now. And then you can think about that later. So Exactly how I trade those two perspectives off is, um, I mean, you need to look at the weights in my neural network, but it's some combination of the two.

AI assessment note: “you just want to have as much mind share... And then you can think about that later.”

Q a way that most people aren't, and then it's led by you and your voice. And the two combinations of like cult of personality, you and Anton, you being Anton, not you in the third person, uh, and then you and ARR growth is what really drives the success in that way. So that's kind of what's harder. What's better? Like why, why should everyone build their company in Europe?

A I mean, we can be, we are the biggest talent magnet in Stockholm right now. Which is amazing. You can't, it's much, much more difficult to be that in San Francisco or New York. So we can really pick up all the, um, underutilized talent and shape their, like, 10 X their performance by being in a 10 X better culture or ways of working and, like, with amazing colleagues. So being able to be that, like, top one is, I think, the biggest one. Um, there's, uh, A culture of like humility and low ego and working really, really well together as a team that I think is stronger in Europe and like this, uh, way of thinking in terms of efficiency and doing much, much more with less, um, that's also stronger here.

AI assessment note: “A culture of like humility and low ego... is stronger in Europe”

Q What would you most like to do now? That you're not doing or can't do.

A So I would like to rethink how the applications are built. Like what is the best way to build an application, right? What Lovable does now is it takes all the best practices from decades of how great software products we've built, but that's not how the future is going to look like. They're going, all software applications are going to have some type of AI. They're going, they're going to have extremely seamless payment and checkout flows. And that's something I'd love to Uh, for us to spend time on figuring out and making possible for our users, not to just have like a superhuman AI engineer, but to have a AI engineer that builds like the future of applications.

AI assessment note: “I would like to rethink how the applications are built.”

Q What is the bottleneck that you'll be discussing in the board tomorrow?

A Um, I think the bottleneck for our long-term future is how we identify the, uh, technical product, like, so engineers that will take the product to its next phase and, um, Innovate on many, many fronts at the same time. Um, that's in the long term. If you think about the product today, um, it's giving our AI more capabilities that are really like, make it really polished user experience and give it more of those capabilities so that you can build out your full company, uh, grow your business on, on top of lovable. And, um, then I think a bottleneck is for how we, Serve all this extreme amount of enterprise customers love and pull from them at the same time as we, if we focus, like probably one is for the, for founders building on Lovable.

AI assessment note: “bottleneck for our long-term future is how we identify the, uh, technical product”

Q I'm being serious. I think not enough people are opinionated. You said brand is important. Great brands are opinionated. People love them or hate them. Lovable. Good example. Um, But what is yours that is non-obvious about hiring or talent assessment?

A So I like to think a little about slope and that, like, if I talk to someone and I learn a lot of things talking from them, and I, I notice that my conversation is like very dynamic and exciting, that, that is usually feels like a very good indicator that they're going to adapt to the organization and their slope will be very high. Um, I, otherwise, I think There are good ways to just understand how did they perform? Like if I could be there with a video camera when they worked in the past, uh, that gives me a lot of signals. So that's, that's usually what I spend a lot of time, uh, when I talking to new candidates.

AI assessment note: “I like to think a little about slope and that, like, if I talk”

Q I mean by that is like, if you look at delivery, you know, it's margins of shit in the early days. And over time they get better and better as you have more and more people use it and more density, more orders in small areas. You've got to be patient, so to speak. Same with OpenAI, same with Lovable. How long does one think before you're thinking margin optimization?

A Um, I, I have these two conflicting pieces of perspectives on it. Uh, and one is I speak to Nick who built Revolut, and he just tells me like, Anton, you need to compute the payback time and then you need to do Super hard performance optimization on acquiring new users. Uh, and then you of course need to have a good, like payback, payback times in terms of profits for per user, uh, which makes sense. And then if you can do small changes in on margins, it, it actually affects a lot on how much, how fast you can grow. Uh, but, but the other perspective, which I indexed a bit more on right now, right now is you just want to have as much mind share and as many users who just love the brand as possible right now. And then you can think about that later. So Exactly how I trade those two perspectives off is, um, I mean, you need to look at the weights in my neural network, but it's some combination of the two.

AI assessment note: “you just want to have as much mind share... and then you can think about that later.”

Q was like zero to ten million in two years. Was like the gold standard. That's what I was brought up on, which makes me feel really old, which is amazing to see. My question to you is, when you look at revenue breakdown, of the hundred million, just kind of guesstimate, what is split between hobbyists, pro devs, Kind of normal people. How does it fit between the different segments?

A You're right. So, uh, people do everything with Lovable. They come with their idea to build a software business and product. And then there's a lot of people in large companies that, that use it as like, okay, now I can prove, show what I actually think we should build in the business. And they, they build a working, uh, product that then they can like decide, are we going to put this, this into our, uh, give it to our, our engineering team and they actually implement it. Um, and then it's everyone else who build, like, their personal website, their small business websites, like, in a few minutes, and 80% of people are, are in the first category. They're building real complex applications.

AI assessment note: “80% of people are, are in the first category. They're building real complex applications.”

Q we spoke about it with regards to the company. Dude, I love your social media presence, because you're also opinionated in your social media presence, and I think it's respectfully also just quite blunt, and you've been opinionated in how you talk about competition, and specifically like a rapplet of the world. How do you think about whether or not to engage in an opinionated stance against competition or not?

A Um, look, I don't think so much about competition. Uh, I, what the, the only thing that matters is that we make our product the best product and we continue to deliver on like our value promises to our customers. Uh, if there is something that happens, so there, there was a competitor that like found a lot of apps that have been poorly made. Um, and they, they said, oh, this is a vulnerability. And I spoke to a lot of security professionals. That wasn't really like how you would normally Uh, and also vulnerability. So then I went in and bashed them, uh, as a, as an outcome of that. I, I think that was a very reactive thing, and I think it was, like, uh, something I'd be happy to share in person to that competitor, like, face to face.

AI assessment note: “the only thing that matters is that we make our product the best product”

Q What do you think is the biggest secret to a successful co-founding pair scaling at the speed of Lovable scaling?

A I think the most important thing is just the, the raw horsepower and adaptability of the founders, um, and then Like, if those are maxed out, or those are high, um, I mean, you must be able to, um, work together with, like, if you have sufficiently low ego, it's going to work, but if, if you really want to work extremely well together, what's, I'll take an example, which is Fabian and me. He's, like, uh, not very, um, big on doing some weird new way of doing things. He's just, like, Simplify it as much as possible. Um, he's quite introvert and quiet until he's like, has really shaped an opinion about what's the most important thing. And I'm on the polar side of the spectrum and saying, Father, we should use this new crazy thing. And, and that's like polarity is actually very productive for both of us.

AI assessment note: “the most important thing is just the, the raw horsepower and adaptability of the founders”

Q Okay, so we're building the first version of Lovable. You've got your co-founder. Talk to me about that time. When did we release, and how did the official release go with Lovable as a product and company?

A So the launch of Lovable, like the product, it was one year after we started building, and, um, in the meantime, we launched, like, wait-listed preview versions called GPT Engineer App. That was, I mean, that's the getting user feedback cycle, and Um, building, building up, I guess, some, some excitement about what we were working on and employer branding as well. And the first versions, I, I think they were very good as well. They were good. They were not very good. And we had some people really liking it, but the, like the, the harm aha moments didn't click for sufficiently many people like, okay, this is how I get these real value from this. When we, when we went on to iterate over the coming year, we packaged together all of these things so that you can ask today lovable, I want to build a basically a SaaS business, and then people have built their entire SaaS companies and get made money by just prompting our AI.

AI assessment note: “The launch of Lovable, like the product, it was one year after we started building”

Q fit, like, all the characteristics of, like, successful founders that I have on the show. It's like, number one, made money early, number two, excelling gaming, uh, both very, very clear, um, archetypes. I want to move to Lovable. So GPT Engineer starts as a side project. Where does the idea come from? We've left a pit. GPT Engineer starts as a side project. Where does the idea come from?

A This was the spring after ChatGPT came out, and I've been playing with the precursors of that as well. And I, from already like a year before then, I felt there's an, there's a massive wave coming from scaling up these models with more data. And the PICT is not set up for, or currently not set up for leveraging that. After, like I was actually traveling with my now wife, uh, sorry, engagement trip. And when you're traveling, I get extra creative. And I, there, um, I don't think there was anyone who was talking about like AI agents at the time, but I, uh, during those, like sitting on an airplane, I was, I started writing a lot of like, okay, you can, you should Hook them up and make, uh, you basically put the large language model in a for loop and then you can have it do a lot of agentic things. And then, uh, when I'm back in Sweden, I'm like, okay, where do I apply this? Obviously on software engineering. And I've been talking to people about this and I felt no one was really sufficiently imaginative to what I was thinking about. And I had to prove a point in that this is already now with the current, the first versions of ChatGPT APIs, you can build an agent that writes code. And then I put together that or two, I drank a lot of coffee, and then I just crammed away, and, um, I got the first version that really impressed people. You write, create a snake game, and then you get…

AI assessment note: “where do I apply this? Obviously on software engineering.”

Q That is very, very kind of you. It's the British accent, but I want to start actually pre-Lovable. And I spoke to a couple of your investors in your first company, Depict. And so I just want to start there. What are your biggest takeaways from Depict that shaped how you think about Lovable?

A We scaled super, super fast at the PICT as well. And, and we did like moving just very fast, scrappy. We did that. We nailed that really well. I think we did really well on this high potential talent as well, quite junior with high potential talent. You can see that from all the companies coming out from the PICT, the PICT mafia is absolutely real. Um, but what I think we did, what works in the beginning Is to say yes onto a lot of opportunities and try out what works. Once you become more people and you have to follow up and maintain everything that you start, you, you have to be much more focused. So we said yes to too many things at, at the PICT and, uh, we didn't like take this one thing that we could do 10 times better than anyone else. Uh, and then at, as the economics, uh, Macro wise turned worse. We, we didn't continue the scaling trajectory that we were on initially.

AI assessment note: “we said yes to too many things at, at the PICT”

Q Okay, so we're building the first version of Lovable. You've got your co-founder. Talk to me about that time. When did we release, and how did the official release go with Lovable as a product and company?

A So the launch of Lovable, like the product, it was one year after we started building, and, um, in the meantime, we launched, like, wait-listed preview versions called GPT Engineer App. That was, I mean, that's the getting user feedback cycle, and Um, building, building up, I guess, some, some excitement about what we were working on and employer branding as well. And the first versions, I, I think they were very good as well. They were good. They were not very good. And we had some people really liking it, but the, like the, the harm aha moments didn't click for sufficiently many people like, okay, this is how I get these real value from this. When we, when we went on to iterate over the coming year, we packaged together all of these things so that you can ask today lovable, I want to build a basically a SaaS business, and then people have built their entire SaaS companies and get made money by just prompting our AI.

AI assessment note: “the launch of Lovable, like the product, it was one year after we started building”

Q Okay. Let's just unpack talent because it's been a call. I spoke to Frederick at, uh, Creandum before this, and he said we had to chat about this too. You favor talent over experience, which sounds kind of obvious. Respectfully, you're going to go for like an it, like an experienced, untalented person. But how, how do you think about your hiring lessons around experience? Experience versus talent.

A Um, so I think experience can be a negative thing in some cases. You often want people who are super ambitious, they have a lot to prove, and they are more open-minded towards how, how you should work together in the team. Uh, so, um, I mean, for many roles, I, I think, uh, junior talent is, is first of all, super easy to, to get them into the company. They don't, they're not, Uh, already committed to some, like many of their projects, the best people that you can hire at a young age, they would go on and become founders, and then you can't hire them anymore, right? So, so that's why junior, uh, people are often quite good.

AI assessment note: “I think experience can be a negative thing in some cases.”

Q When you do the user interviews and user feedback sessions, what are your big lessons or piece of advice on how to do them well? What questions are good? What questions are bad? Any lessons?

A Uh, so they, they, I think for us, there are two different types of user interviews. We, we have this, this type where we just see them use the product and see, like, where do they, do they understand the product? And so that's more of a user experience interview. Um, and the other one is, uh, to understand, like, if they have just tried the product a bit, we ask them, okay, so you try the product a bit, what's, uh, why are you even interested in this? And ask them, like, what problems they're facing in their business. Try to identify What's the biggest pain point that you, that they're actually looking to solve? It might be, oh, I want to get more customers, and I think I can get more customers if I can show to my customers that I can get the first version out with AI more quickly. So those are the questions.

AI assessment note: “there are two different types of user interviews. We, we have this, this type”

Q Mm-hmm. How did you analyze GPT-V? And when you look at performance post, Are you more or less bullish on OpenAI?

A So we looked a lot about how does, sorry, we looked a lot at how GPT-V would impact our users before we decided, okay, let's put this into the product, and we looked at how long time it took to get responses, we looked at our qualitative evals, and, and then we just vibed, checked it in many different ways, and what we concluded was that it's Oftentimes too, like, ambitious for our users, and that's why we decided, hey, um, this is very smart, so let's give it to all our users and see what they, what they tell us in terms of how, like, what's good and what's bad. Um, what we found was that, um, it's, like, for the use cases when you have to solve a really, really hard problem, it's great. And in terms of, is OpenAI doing a great job? I think this was a really smart, uh, obvious choice for them to say, like, we have these five different models that you have to select in ChatGPT. Let's just bring it down into one model, GPT-V. Um, but, so they should, they should definitely should have done that, but it, but it comes with a lot of trade-offs, and so far I'd say they executed pretty well on it. The model is still too ambitious.

AI assessment note: “I'd say they executed pretty well on it. The model is still too ambitious.”

Q What do you think is the biggest secret to a successful co-founding pair scaling at the speed of Lovable scaling?

A I think the most important thing is just the, the raw horsepower and adaptability of the founders, um, and then Like, if those are maxed out, or those are high, um, I mean, you must be able to, um, work together with, like, if you have sufficiently low ego, it's going to work, but if, if you really want to work extremely well together, what's, I'll take an example, which is Fabian and me. He's, like, uh, not very, um, big on doing some weird new way of doing things. He's just, like, Simplify it as much as possible. Um, he's quite introvert and quiet until he's like, has really shaped an opinion about what's the most important thing. And I'm on the polar side of the spectrum and saying, Father, we should use this new crazy thing. And, and that's like polarity is actually very productive for both of us.

AI assessment note: “the most important thing is just the, the raw horsepower and adaptability of the founders”

Q I'm being serious. I think not enough people are opinionated. You said brand is important. Great brands are opinionated. People love them or hate them. Lovable. Good example. Um, But what is yours that is non-obvious about hiring or talent assessment?

A So I like to think a little about slope and that, like, if I talk to someone and I learn a lot of things talking from them, and I, I notice that my conversation is like very dynamic and exciting, that, that is usually feels like a very good indicator that they're going to adapt to the organization and their slope will be very high. Um, I, otherwise, I think There are good ways to just understand how did they perform? Like if I could be there with a video camera when they worked in the past, uh, that gives me a lot of signals. So that's, that's usually what I spend a lot of time, uh, when I talking to new candidates.

AI assessment note: “I like to think a little about slope and that”