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The AI Daily Brief: Artificial Intelligence News and Analysis
Why 2026 Is the Year of the AI Builder with Lovable CEO Anton Osika
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- [00:01]
A
Today on the AI Daily Brief, we are joined by lovable CEO Antoine Oeca to discuss the evolution of AI coding and why 2026 is the year of the builder. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG Robots and Pencils, Blitzy and Super Intelligent. To get an ad free version of the show, go to patreon.com aidaily brief or you can subscribe on Apple Podcasts. Ad free is just three bucks a month. To learn more about sponsoring the show or pretty much anything else about it, go to aidailybrief AI. If you want to learn more about our recent benchmarking survey, you can get that information@aidbintel.com and for today's episode, I'm excited to be joined by someone who has been about as deep in the vibe coding revolution as anyone can be. In this episode we discuss everything from the earliest origins of what would become lovable back in 2023, to which I actually covered in the first couple months of the show, to where the market was at the end of last year, to how what people are doing with Vibe coding has changed over the course of 2025 to what Anton thinks is coming in 2026. All right, Anton, welcome to the AI Daily Brief. How are you doing?
- [01:12]
B
Great to see you, Nathaniel.
- [01:13]
A
Yeah, it's great. Great to have you here. So as I was just sharing this conversation is part of this series of end of year episodes that are a little bit about looking back and a little bit about looking forward and. And for me, undisputedly, the most important kind of AI theme of the last year has been the rise of vibe coding, AI coding, agenta coding, whatever you want to call it, AI assisted coding. And I think it's poised to be extremely important heading into next year as well. And I was actually looking back because so I started this show in April of 2023 and I remembered very early on doing a show where I thought I had done a show about GPT Engineer. And sure enough I just went back and looked and it was July 19, 2023 that I did the first show covering that. And so obviously you've been on this journey for a minute and I kind of wanted to talk about. Clearly the idea of using AI to produce real functional code was something that you got interested in very early. But what was the sort of journey from those earliest experiments and people on GitHub getting super excited about GPT engineer up to kind of when Lovable became a thing towards the end of last year.
- [02:19]
B
Yes, it's been a wonderful year, 2025, I have to say. And I think we're still just getting started and the coming months and years are going to be about scaling the impact you have when you create something with AI. And going back to like before, Lovable wasn't even an idea to me. It was clear in 2022 that we would see this model just getting smarter and smarter and. And in 2023 they started being able to reason. So what I had been doing is just like showing people, look, these things can actually reason. So that means you can give them a task and then they break it down. And they're especially pretty good at coding and it's going to completely change how we create software. And back then like people were like super skeptical and like, no, no, it's AI. AI stupid. So the tool that I put together over a few weekends was this open source command line interface. It's like similar to Claude code, but it was spring of 2023 where I recorded the video Create me a Snake game and it went out and I wrote all the files for the Snake game and then started it on my computer. And from there that got super popular. I think that inspired like dozens or hundreds startups to even start in the first place before Lovable. And what I started thinking about was, hey, what's the bigger implication of this? Is it the implication that we as developers are going to have more tools to move faster? Yes, that's one implication. But the one that caught my attention after a few days of thinking about this was, look, the biggest change is it's going to change who can create software. And for me as a builder, it's very clear that just being able to create software, like shape some, take an idea and shape it into something that you can interact with is like super rewarding. So that's when I decided I'm going to start a company. It's going to reimagine like what tools we use, what the interface is to create software. And when I realized this, I went on my bicycle and I biked over to Fabian, my co founder's place now and I called him up and I said, hey, look Fabian, we're going to change how software is created and who gets to create it. And we took a walk and decided to start the company. So that's the backstory. And then since then there's as you know, like if you're building an AI product. There's a lot of hard work that goes into making it not just be a cool demo, but actually really create a lot of value and be as reliable as it can be. And that's what we've been hard at work with since then. And just listening to what our users use Lovable for and how can we improve the tool.
- [04:57]
A
So it's super interesting. Everything on AI is accelerated time, but I think in particular the shift in how people are thinking about AI for coding over the last calendar year is enormous. I remember going into sort of the end of 2024, heading into 2025, you know, we do at Superintelligent, we do a ton of work with enterprises and it's all about kind of figuring out where they could be applying AI and what they could be applying agents to. And I remember up to November, December of last year, there was still such incredible resistance among software engineers to using AI for code. It was sort of like the most surprising holdout in some way. Now, not every organization, some organizations were a little bit more forward thinking, but in many that we found, you know, you would have expected them to sort of be on the front lines and they weren't. When you started Lovable, did you guys face sort of that type of skepticism either from a consumer audience of existing developers or from kind of like an enterprise audience? You know, I mean, obviously there was rapid uptake really quickly, so some portion of people got it. But you know, were, were there, were there still a lot of skeptics, I guess, you know, back in the end of 2024?
- [06:08]
B
End of 24, yeah, there were, most were skeptics, I'd say. And then it's when people actually tried, tried out Lovable that they were like you very quickly get this wow, aha moment that it can move so much faster and do things that humans cannot do. As you're building something out, as you're building out like a first version of a product and the development over the year has been like, it's been very, very impressive to us as well because the things like 90% of the things you couldn't do a year ago, you can now do with a tool or even more so. And there is now really the case that Lovable has been pulled into enterprises. Microsoft, Uber, they use Lovable to move faster as a team. And they're of course asking for like, okay, how do we bridge this, what we are having in production today? How do you, should we be using Lovable as infrastructure? And that's the next natural evolution and how Fast this has gone is just tremendous from skeptics to enterprises saying okay, we want to use this more. Many of large enterprises are now building their rebuilding their workflows on top of our tool and using it as infrastructure.
- [07:19]
A
Yeah, I want to come back to that sort of organizational redesign because I think that's a key part of what's happening right now with coding. But one of the things that I was really interested in that you maybe have unique insight into is how the patterns of what people are building with a tool like Lovable have changed over the course of the year. You know, like what did your early adopters look like? What are the types of things that they were building end of last year, beginning of this year and how has that changed kind of throughout the year coming into to where we are now?
- [07:45]
B
Sure, I mean so it's kind of developed into new uptakes over the year and it's just being used by more and more for more and more use cases. But some of the like the upticks that I can point out is that in the beginning Lovable was like a super impressive tool for early adopters that are like a bit technical, technically inclined. They were often helping clients deliver projects. And they weren't engineers full, they were formally trained engineers themselves. They realized that if they were using Lovable they could actually build custom fully working applications. So that was this like a huge unlock for that full crowd. And then when Lovel started to get rich more of a mainstream, that's when you actually saw like anyone out there in the inside of a business for themselves creating a website for their event. I saw wedding proposals being made with Lovable so that's like even more normal to the consumer side. And for the second adoption was throw away prototypes by product managers and designers who because you could get to a high fidelity design much faster than you can with any other tool. And that is still the case and this is still like a huge use case for product and engineering organizations where in companies like Deutsche Telekom, like the most old school German enterprise is 2000 people are being accelerated by using Lovable. And now the last big uptick is how companies know that a big bottleneck, how work gets done is the software that, that they are relying on and the limitations in the AI of that software. So there are hundreds of companies that are like established that are using Lavable to reimagine their workflows and replacing what they used before as SaaS but building it customly for how their organization works and adding AI on top of that. And this is like going from lovable being this entry point for creation to load bearing infrastructure for where you run your business on top of. And, and that's what we are leaning into and scaling the impact as a platform.
- [09:49]
A
Yeah, it's interesting. So this kind of mirrors my. And our usage of lovable specifically. So towards the beginning of the year, probably end of Q1, beginning of Q2, we shifted to soft banning product ideas with words. It's just like just vibe code it right show, show off what it is. Because two things, one is going to be way easier to show than to tell and two, the process of, you know, actually articulating what you're trying to do is going to refine your idea further. So we did that. But that was fully in that prototype zone. And then over the course of the year and especially on like my. More the personal side and the podcast side as you guys added additional features, particularly sort of like the ability to go end to end and actually deploy the thing and even buying domains from within it which is like not a big barrier but just keeping it all contained. Then all of a sudden I just started building my. I rebuilt my entire sort of personal stack, the podcast website, everything on the actual sort of end production things and now that's where I am. You know, I was just saying to you, I built something for one of these end of year episodes just before we were, we were on this call.
- [10:51]
B
What did you build?
- [10:53]
A
It's so the last episode of the year on December 31st will be. It's like a 10 week self guided AI resolution. And so the idea is not, it's not like a course, it's just 10 weekend projects that you can do to give yourself a real full expanse of what's available out there. And I saw last year there was this massive increase in downloads of the show between. Between December and January. There's very clearly a comeback to work. You know, I'm going to get AI fied this year and I expect that to be the same this year. I think that we'll, I'll see another kind of big uptick. And so I wanted to give people something that they could. Some tangible thing that they could go do. And so I built basically a. I started to build a website for the community who is doing that thing to share what they're doing kind of week by week. So you know when they create their personal project tracker, which is week one, they can share the link to that with anyone who wants to see it. When they do an infographic on like week Six. They can share that. So that was built with Lovable in, I don't know, an hour before this.
- [11:54]
B
Okay, well, how did it go?
- [11:56]
A
It's great. So I think that the, for me, a big change was sort of the full end to end suite where you can sort of build the backend and connect it and deploy it live. And then the second part was the addition of the sort of the chat mode. Right. So you can chat and plan before you actually execute. That has made a major difference in how quickly things come together and how well it works right out of the gate. Right. There's a lot less guessing and go back and rebuild the whole thing over and over again. It kind of used to be like you run the same prompt a few times until you get one that's closer to it, whereas now you can actually sort of plan and execute. And actually this brings up a question for me though, which is, as you guys are designing this product, I think most people's expectations would be that the increase in, in the value of the product is going to be largely connected to models. But I think both of the examples that I just gave are actually not exclusively model specific. They're sort of features and the experience set and the product around it. So how do you guys figure out how to prioritize different sort of project and usability features while also thinking about improvements in the models that exist underneath?
- [13:01]
B
Yeah, so as you point out, there is a lot of capabilities that we are adding to loveable, like the agent that is lovable. And a lot of like, what's the right interface for us humans? Like, oh, sometimes we want to be more thoughtful. And when you're working with a human software engineer, talk about the problem, talk about what's the right solution. And then you also say, okay, okay, let's do this. Right. So both of those are things that are going to add value irrespectively of how intelligent the model is. Right. So generally that's how we want to prioritize and just bet on that. The models are getting much, much more intelligent over time they're getting faster. And there is still a big bottleneck in like how the models are not as smart as we want them to be. But over time it's the capabilities of the models and the thing that we've been betting on since Lovable started seeing production use cases, which is security, security and like data governance, making sure that you, as someone that builds it, are fully confident about where does my who has access to my data and that the UX of the product and number of capabilities are the things that I see as the main areas that are completely timeless where we generally invest most of our time.
- [14:16]
A
How do you balance the different needs of different audience segments? Right, so if you, if you think about independent builders who are technical as one group and non technical vibe coders who are speaking in code for the first time as another group, and then enterprises who are actually, you know, as you were kind of intimating at the beginning, starting to redesign their workflows around this, how do you think about prioritizing the balance of what those different groups might need or want out of the product?
- [14:46]
B
That's a very good question. Since we're seeing so many different users, we just expect the AI to take care of a lot of the onboarding for our users. If you don't know how to do something lovable, you have an existing production system and you won't understand how they should go together, you just ask the AI, which is one of the strengths in having a product that does so many different things like creating your slide presentation. A huge use case in Loveable today, creating internal tools, creating like AI applications themselves on Lovel, all very different use cases, quite different audiences. But fortunately AI can kind of take care of helping users understand how they are successful with the product. Instead what we focus on and breaking it down and saying like okay, this is the type of user is what are the capabilities that unlock completely new capabilities? And those are things like ensuring that if you change the product it doesn't break. There's nothing that used to work that stops working and that's something that all users want. Right? Then there's some bucket of work that we put into only for, that's only valuable for teams like how you collaborate, how you set up access and so on for your team. And there we say like, oh, we put X percent of all our work into that area and that's changing over month to month. Or how much time do we put into the collaboration team use case? And then we have a bucket which is for founders that are building new companies that we want to empower to build successful companies. And we put like Y percent of all our work into that use case building a full like we want Lovable to be the best place to build your company on for any founder. And that's another one of those buckets that we put in some extra work to make sure you can do everything, accepting payments and so on as a founder.
- [16:35]
A
So it sounds like it is actually like as simple, simple to say if, if hard to do as balancing the needs of different groups that all have kind of slightly different things that they want out of it.
- [16:45]
B
Yes, yes. And then like we could break it down further, but that's how far we break it down.
- [16:54]
A
All right, let's talk about the signal versus the noise. In enterprise AI, the challenge right now isn't just about what's possible, it's about what's practical. That's the entire focus of the youe Can With AI podcast I host for kpmg. Season one Cut through the hype to focus on deployment and responsible scaling. Season two goes a level deeper. We're bringing together panels of AI builders, clients and KPMG leaders to debate the strategic questions that will define what's next for AI in the enterprise. Six episodes packed with frameworks you can actually use. Find you can with AI wherever you get your podcasts. Subscribe now so you don't miss the new season. AI isn't a one off project. It's a partnership that has to evolve as the technology does. Robots and pencils work side by side with clients to bring practical AI into every phase. Automation, personalization, decision support and optimization. They prove what works through applied experimentation and build systems that amplify human potential. As an AWS Certified Partner with Global Delivery Centers, Robots and Pencils combines reach with high touch service where others hand off. They stay engaged because partnership isn't a project plan, it's a commitment. As AI advances, so will their solutions. That's long term value. Progress starts with the right partner. Start with robots and pencils@ropotsandpencils.com aidaily Brief this episode is brought to you by Blitzi, the Enterprise autonomous software development platform with infinite code context. Blitzi uses thousands of specialized AI agents that think for hours to understand enterprise scale code bases with millions of lines of code. Enterprise engineering leaders start every development sprint with the blitzi platform, bringing in their development requirements. The blitzi platform provides a plan, then generates and pre compiles code for each task. Blitzi delivers 80% plus of the development work autonomously while providing a guide for the final 20% of human development work required to complete the sprint. Public Companies are achieving a 5x engineering velocity increase when incorporating Blitzi as their pre IDE development tool, pairing it with their coding pilot of choice. To bring an AI native SDLC into their org, visit blitzi.com and press get a demo to learn how Blitzy transforms your SDLC from AI assisted to AI native. Today's episode is brought to you by my company, Superintelligent. Superintelligent is an AI planning platform. And right now, as we head into 2026, the big theme that we're seeing among the enterprises that we work with is a real determination to make 2026 a year of scaled AI deployments, not just more pilots and experiments. However, many of our partners are stuck on some AI plateau. It might be issues of governance, it might be issues of data readiness, it might be issues of process mapping. Whatever the case, we're launching a new type of assessment called Plateau Breaker that, as you probably guess from that name, is about breaking through AI plateaus. We'll deploy voice agents to collect information and diagnose what the real bottlenecks are that are keeping you on that plateau. From there, we put together a blueprint and an action plan that helps you move right through that plateau into full scale deployment and real roi. If you're interested in learning more about Plateau Breaker, shoot us a note. ContacteeSuper AI with plateau in the subject line. One of the things that was interesting coming towards, call it the last two or three months, is that on the one hand, for those non technical users, I have a thesis that we're kind of using the same language right now to describe like wildly different sets of users and wildly different sets of uses. Right. We're still talking about kind of vibe coding as though it's one thing. And I think that the agentic and AI coding for actual technical people and for software engineering departments is wildly different than vibe coding for non technical users. And I, I kind of think that they're going to be more divergent in the year to come. But among the, the software engineers, I feel like the last few months has seen more and more people who are technical reconciling with what they can't do with AI and agent decoding and trying to figure out basically better organizations and better systems for taking advantage of what AI is good at with coding and redesigning around what it can't do. How do you see the sort of the professional side of this? Right. Actual software engineering organizations, how are they adopting or adapting to rather this toolset and these capabilities? And what are the things where you still think AI enabled coding or AI assisted coding really struggles with that you'd like to make Progress on in 26?
- [21:20]
B
Yeah, sure. I mean it started to become clear to more people. Like, I mean this has been obvious for a while that when you have like an old system that's distributed across many code bases and there are many teams, like a huge, huge bottling becomes how you coordinate between the humans in those Teams. And if you rather use for example lovable then it will be more opinionated about doing things in a certain way. So that this handover and alignment between different teams is not required anymore. And you can go to one, you can all collaborate in the same tool. Designers can collaborate there, product managers, business stakeholders, like the CEO comes in and collaborates in the same tool where you don't have to run around different engineering teams and like spend most of the time on planning and aligning. And companies that have like this are in this complex current situation should be doing is something, I mean I'd love to be able to help on that. We have a customer facing function now that does guide large companies into like how do you adopt flavable the best. But the one thing we do see is that companies that are started from scratch with just like one founder that's great at using AI can move so much faster than companies that have a lot of built up legacy systems that might not be talking to each other where it's hard to integrate AI into the tools that they are using. So this rebuilding things from scratch is something that people are considering much more and more seriously. Even though that in some cases you cannot rebuild everything from scratch.
- [22:56]
A
Yeah, no, I guess maybe to take the question at sort of a slightly different angle, how do you think the debate is going to evolve around folks who are concerned that. Well, so the most common critique that you'll hear of vibe coding is that you're basically just shifting the balance of your time to sort of like reviewing and editing. And this is really interesting because it demonstrates this very kind of clear culture divide, I think between coders who have done things for a certain way in a long time and kind of neophytes who are just doing things from the ground up where sort of the new folks and the non technical folks just don't care. They're like if there's a problem I'll just ask the AI to solve it. Whereas the folks who have been building for a long time, who maybe have to be fair more complex workloads are kind of, you know, worried about the sort of new technical debt that it creates. They're worried about even skills atrophy. Do you think that this is just a transitional period where the way we build things is changing so radically that the older way of doing is going to inevitably go by the wayside and people have to just get on board or you know, or is there sort of more more to that there?
- [23:57]
B
I mean I, I can start by phrasing it like so Skills atrophy. I mean that sounds bad, right? But what's even worse than skills atrophy is not being fast enough at acquiring super valuable skills and understanding what are the possibilities that are like sometimes completely new possibilities. What are and what are the limitations and what's the best practices in using new tools that are of course going to be the way that things get done in the future? Increasingly what I tell everyone who is thinking about where do I fit in in the future workplace is just spend as much time as possible using new tools. Like try to break the tools, try to do impossible things with the tools. Ask the tools, ask lovable, like why can't I, why did I not failing to do this? What should I do if I try again from scratch? And even the things that you can't do today, they are going to be possible very, very soon. I think that's this recommendation of doing instead of talking about it that I think everyone in the business and considering starting a company is going to benefit a lot from.
- [25:06]
A
What do you think the difference in I don't know what the right timescale is? 3 years, 5 years, 10 years time? What's the difference going to be between a software engineer and someone who just uses Vibe coding tools and, or you know, whatever we call them by then, and what's going to be the difference between like a 10x engineer that we consider now and just sort of your average run of the mill software engineer?
- [25:27]
B
I think it's always been important to be able to like quickly learn new things in technical fields and I imagine that's becoming more and more valuable. That's becoming more and more valuable. The other thing is like how big of a complex systems can you like reason about together with AI, like with the help of AI and you get more leverage with using AI, Right. So knowing with the many steps ahead of thinking, like what will happen if I do this large change with AI, what are going to be the positive and downside of those trade offs? And what questions should I ask the AI to know that ahead of time? I think that skill set has always been a part of software engineering and it's becoming more valuable as the decisions we take are having much larger implications because we're just clicking one button and then boom, there's huge implications. So both of those things are getting very valuable. I do think also for many fields like human creativity or like, oh, is this a good thing? Is another human going to like this thing which is one part of creativity? Right? Or it's like, is there some very Creative solution in leveraging AI effectively that has never in a way that is not been done in other software products like can we change the UX completely? With AI, that type of creativity is going to be super, super important in creating the best user experiences like the best human experience, which is ultimately what software does for you. So creativity and great judgment is getting more valuable as AI becomes more impactful.
- [27:02]
A
Yeah, it's interesting. So I agree with you wholeheartedly. I think things like taste, creativity, I think management and decision making, I think planning, being able to see the horizon, these things all intuitively make sense as the set of skills that are going to be extremely valuable when we all become kind of agent managers. I'm looking forward to, you know, that is increasingly we're actually starting to see that not just speculating around it. So I think it gets clearer over the next couple of years what that actually looks like in practice. Right now you can tell right now we're in this weird in between where just by looking at like the courses that are available or the upskilling things that are resources that are available to people. It's, it's very in between. It's still kind of like prompt engineering type courses rather than whatever, agent management. But I think that that's going to change as we wrap up. I wanted to ask you just about a couple kind of themes that I've started to see emerge that I'm interested to get your take on whether they actually become a big use case or set of use cases going forward. So one is what some people have called ephemeral software or personal software. Basically people creating little one off apps that are useful for them in a small kind of discreet way that they at some point disc. Is that something that you guys are seeing? Is that something you think will be an increasing part of the landscape?
- [28:14]
B
Yeah, I definitely think so. So this with Lovable, there's this ecosystem of people that create small apps for themselves and that app that gets remixed and spreads. We're going to continue to see that these apps are getting more powerful as the AI is becoming better at making the apps better, of course and something we're working very hard on it which is to connect the applications to anything else like one prompt live with Lovable and then you can make the app generate voice, talk to like be agentic, talk back to you, generate pictures. And as more of those things that we take for granted when we're building software ourselves, like it can integrate to anywhere that's making this take off more. It also puts less pressure on like a single app being able to do everything because you have multiple small apps for yourself for different things. And they are. And that you can make sure they talk to the same underlying data with the connections that they have.
- [29:13]
A
Yeah. So this is something that I'm seeing. I'm also super interested to see if this kind of begets a new, not really a new category, but a slight variation on entrepreneurship where right now the economics of building an app are such that you have to think in subscription terms.
- [29:29]
B
Right.
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A
And how much can you get? Like how much, how can you increase the lifetime value of an individual user? And I wouldn't be surprised if we start to see more things that are like, hey, this was useful to me. I'm going to sort of get it ready where it might be useful to other people and I'm going to charge you $2 once for it, but then never again and just have a different relationship with it. One thing that's interesting is the number of apps submitted to app stores this year was up 24% from last year. And that's the first time that it's gone up meaningfully since like 2015. So I don't know if that's directly attributable to Vibe coding, but it's the only thing that would make sense to me as sort of like a natural cause of that. Okay, so the other trend that or the other phenomenon is one that has been speculated about around AI coding forever and has made some headlines with companies like Klarna ripping out Salesforce and Workforce. But what do you think about companies actually replacing SaaS with their own custom built tools? Does that happen? Do you imagine that happening at all? Do you imagine it happening on a small scale but not sort of big replacement? How do you see that kind of disruption potential to SaaS from companies who are deciding to just kind of roll their own.
- [30:39]
B
Yep. So there's, there are good reasons to do it. You can make it work perfectly for your company and you can save money. The one of the things that you really want though is that someone owns that this is fully secure, fully reliable, you know, who has access to which data. And I mentioned before that we're focusing on secure Vibe coding. And this goes far deeper than just that the code is secure, which is something like we're running multiple checks on all the code generated. It really goes deep into how do you fundamentally architect how software is built so that it's almost like there's something called provably correct software. And that's something that this entire field of that if you create your SaaS yourself, it's going to be more secure than some third party that you don't know runs it for you. Like, once that is truly provably the case, then I definitely see this happening right now. I think the answer is that it depends. I think there is a lot of SaaS that it's just easier to buy than build. That is the case. But it's quickly shifting. And in many cases when the tool is super simple and when you can just with one prompt build the same tool with Lovable and it has AI built in as well, then that's where we're seeing the biggest shift based on the users, what users are building on Lovable today. Like out of the 100,000 new projects built every day.
- [32:05]
A
Interesting. All right, so last question is, what sort of use case or trend are you seeing that you're most excited about for 2026 in terms of what people are building?
- [32:16]
B
I think the, the exciting thing for me is when people have like new ideas and they create something new. So the personal software trend and that turning into actual small businesses that make money is something I'm hearing about every single week. Someone who like entrepreneur being passionately sharing what they built with me and especially when it's something creative, that's what I'm most excited about.
- [32:39]
A
Awesome. Well, Anton, so great to have you here on the show. Very excited to see where you guys take AI coding in the next year. And all the best of luck for the new year.
- [32:49]
B
Thank you. Nathaniel, do you have any. Anything final on what you would want us to do more of?
- [32:56]
A
I think continuing to build out for me, just continuing to build out the full suite so that it can happen sort of end to end. And, you know, so here, here's the way that I would describe it. It used to be that when I had some stupid idea for something that I really shouldn't distract myself with, I would buy a URL, right? Like you would buy the URL and you would post it as like, I'm going to get to that someday. Maybe you'd even like go into Photoshop or Canva or whatever and like design some interface for it. Now I just launch that crap, right? So I just, you know, when I, when I think of it, I do it. And so anything that accelerates the time to me distracting myself with some stupid idea that may actually become a thing, that's what I want more of. But so far, so far you guys have done a great job of delivering on that.
- [33:38]
B
Yeah, Nathaniel, it's really the age of the builder. I think it's like I love the shift that you just explained in how you're moving in this world. Great to chat. I look forward to see soon.