Former Intel CEO on What Went Wrong, What's Next + Lovable CEO on the Real Promise of Vibe Coding

spent a long time at Intel and only 34 years. 34 years. Yeah. Probably one of the greatest American companies ever. And then absolutely went off the rails and got absolutely demolished by Nvidia, TSMC, and I guess Apple to a certain extent. So you had this incredible Intel inside moment. We bought our computers based on, you know, hey, the Pentium and that sound. Intel inside, baby. Tell inside. Dum dum dum. Yeah, it's done. And so let's talk about how things went wrong. What went right? And then how did it and you were there for a long time? You took a break and then you came back. But there seemed to be have been some critical mistakes that we can learn from. So let's just embrace it and go right into it. And tremendous success as an American company coming back now, I think, reasonably. But when you look back on it and we do our post mortem, what were the mistakes and what would we change in terms of the direction of that company? If you were building a global financial system from first principles today, you wouldn't build it on 50 year old legacy rails. You'd build air wallets. One AI native platform for global accounts, cards, and payments is designed to make the entire world feel like a local market. Others are bolting AI onto broken infrastructure. But air wallets was built for the intelligent era from day one. Stop paying the legacy tax and start building the future at air wallets.com/all-in air wallets. Built for the future. Having spent so much of my life there, you know, I view it. I joined when I was 18. I went through puberty at Intel, right? I joke right, you know, I was like, I am so early, grove, noise, bear, bear it, right? And they were the people I grew up at, right? So when they were my mentors, they were the people I adored for it. And they were deeply technical. Andy Grove. Andy Grove, Gordon Moore, Bob Nois, co-inventor. These were deeply technical leaders. I remember when I joined the executive staff for the first time. There was probably 15 of the 20 people that were in the room were PhDs, right? It was just that technical. And I view one of the things that went off the rail was when it started to be run by business people. Supposed to technical people. The being counters, the finance people. Yeah. And when I became CEO in 2001, that was the first technical leader in essentially 15 years, right? You know, associated with it. If you have a business leader, who does he promote? Business leaders. And, you know, so I think one of the fundamental things is, and you know, as you look at the great technology companies today, they're deeply technical. And founder led typically. And even if they're not, you know, such as not a founder, right? Yes, and are. So, $1.00 is not a founder as well. But they're deeply technical individuals. And when you're making these hard core technical decisions that affect billions of dollars, you don't do that through a spreadsheet. That's a lousy investment, right? Unless the technology trends make it the right investment. And I think that's one of the fundamental things. And obviously, you know, in the five years, five, six years before I came back, you know, Intel gave $100 billion to shareholders. Oh, the dividends. And my stock buybacks. Well, 100, what I wouldn't have done for another $100 billion on the, well, I mean, what would you have done? You probably would have made chips for iPhone, which Intel passed on, yeah? Yeah. But, you know, it had built a new factory in a decade when I got there. It's like, you know, how can you not be building? How could you not buy EV machines? You know, there's just all of these things, you know, that you would only do as a technologist because the economics behind them by themselves were not good. So you know, it was getting back to the core of technology to me that was, you know, the fundamental thing. You know, and you make good decisions. You make bad decisions as leaders at every business does that as they go along. But you know, fundamentally, this is a technology business. And you need technologists running technology that then hires technologists that are sitting at the staff that then hire the best technologist, you know, you know, and take big swings at, you know, categories that could matter in the future, like skating to where the puck's going. If you look at Apple, they did the same thing for the past 15 years, buying back the stock, tremendous amount of dividends to the largest holder of capital of any company I believe to this date. And what have, what companies did they buy? They buy little tiny acquisitions on the margins. I think the largest ones was, was beats because they wanted to get inroads into, you know, certain demographic segments like in the Android space that they couldn't get into. But my God, what a colossal waste of time. Like you said, they could have done so many amazing things. Tell me about Steve Jobs in 2008, 2009 deciding, I think we're going to make our own silicon and that impact because was that a covert product project? Did you guys know he was doing that? Did he inform you? Well, that seemed to be another one of those forks in the road, yeah? Yeah. Steve was an incredible leader. Yeah. He was also a ruthless leader, right? Very difficult, you know, read Walter Eiders' Isaacson's book on him as well. I had many, many conversations with Steve over the years, you know, for it. But when they moved to Intel in this ventrino chip, it was a big deal. Yeah. Right? And they were putting extraordinary demands on Intel. You know, make the chip smaller, drive lower power. They're demanding a customer. And when he was no longer convinced that we could continue to do that, he started the project, right? You know, and if you remember, I was it, you know, you know, P semi, you know, they acquired some small company started to build some competency. But you know, they did a few little chips internally. It wasn't a big deal. And then the little chips got a little bit bigger. You know, and Steve was a master of this. You know, just starting, you know, these small efforts to build core competence inside the company. I remember when we had the first conversation with Steve about porting the operating system to the Intel chip from the power chip that they were running on before they moved to Intel. And we were quite proud of the silicon software competencies that we had in compilers and operating systems. You know, so Steve will help you port the operating system to the X86. And I remember that Steve said, I've been working on that the last four releases. He had been preparing the core technologies inside of Apple for something that might happen in the future. You know, and he was already, you know, to me, I just remember I was a shocked. You know, I've ported the last four releases to the X86. I think we got this. Yeah. Right. And that's how they got into the semiconductor, you know, doing their own semiconductor. Hmm. I'm not sure I can rely on Intel to be that much ahead of the industry. And I can start optimizing the system design with the silicon design as opposed to relying on one that's been somewhat optimized for a Windows environment versus an iOS environment, you know, in their operating system. And, you know, it was just, you know, it was never that kind of thing that he said, you know, you're right. You failed as a supplier. No, I can supply myself better. Yeah. And Jensen decides, hey, he's going to go all into making these video cards and talk about just incredible serendipity that these happen to be also very applicable for cryptocurrency and running these AI jobs. Was that luck or skill or combination of both there? Well, you know, when you think about that progression, you know, Jensen, he was just building high performance computer, you know, throughput machines. You know, when we were at the height of our strength on CPUs at Intel, we sort of scoffed at his machines. Yeah. Right. So I was like, oh, it's a graphic machine. You know, there's some gamers when he used that kind of stuff. Right. It was always the big CPU and those little GPUs. But when they started to build a real software stack, yes with it. Right. You know, sort of, okay, this kuda thing and SIMT is a technology, you know, you know, multi-threading and so on. And it just sort of kept getting a little bit better and a little bit better. And it was a little bit jobs like in that way. Yeah. We're just making it better every release and it's becoming more robust and all of a sudden, you know, the crazy, you know, Japanese HPC guy said, hey, we could take those graphics cards and maybe start using them in HPC. Right. You know, that was sort of defining moment where it wasn't just about doing graphics anymore. This was a more computationally dense platform to start attacking some of the world's most interesting workloads. And I think Jensen would agree that was a defining moment and then sort of saying, oh, these aren't just graphics cards anymore. You know, these are general purpose computing devices that can start applying to these other workloads. And you know, AI was, you know, had gone through what? It's fifth nuclear winter by that point. Yeah. Or it's like, man, you know, this is never going to matter. Right. We're never going to, you know, get the breakthroughs. But the community around it was continuing to develop. Yeah. you know, for it. And the kudos software kept getting better generation by generation. And, you know, I had a project at Intel Lariby, right? Where we were trying to take the X86 and essentially do the same thing, right? You know, for it. And, you know, in my first departure from Intel, the project was killed a week after I left. And the world would have been so much different, right? I mean, it really, I think it's a luxury of illustrative of what continuous innovation, taking some risks and doing that fundamental research and the compounding power of technology, because I think it was William Gibson who said the street finds its own use for technology. Like, Nvidia did not predict that this Bitcoin project would take over and that this would be the best way to do those computations. Or did they anticipate, I think, you know, that AI would take off, but because it was the best solution, the hacker community could kind of figure that out. Well, as we wrap on the Intel portion of your career, okay, Apple Silicon, that's one. And then you have Nvidia. And then you have this Taiwanese company that starts making, you know, really great at fabricating these chips and Intel misstatus, well, yeah. And maybe you talk a little bit about TSMC and they're surging and we can even get into a little bit of the politics of it now. And then we'll get into some of these AI chips and venture investing. The thing with TSMC was they started with a vision of foundry, right? You know, they were going to become the factory for the industry. And again, these factories are so expensive. 20 billion, 30 billion and the engineering and the continuous investment required to do it. And you know, it was a stunning, you know, vision at that point in time. Intel was an IDM, as we called it, the integrated design and manufacturing. You know, we never worked to make our process and our factories available for third parties. Hmm. Right. It was always this thing. Hey, it's, you know, we do enough CPUs ourselves. So we reuse it for chip sets and some of the other things that we're doing, but it was never standardized in a way that it could be made available for a broad ecosystem, you know, using PDKs and all the design tools. You know, we did a lot of our own EDA tools ourselves. You know, one of the projects that I started earlier in my career was the foundations of EDA, right? As well, the first place in route, you know, the first standard cells, the first high level description language, you know, it was so proprietary and TSMC basically cut that in half and says, I don't care who's chip it is. I don't care what you're designing. I'll be your manufacturing partner. Yeah. And at the time, that was such a trivial piece of the business and tell it didn't even care. Hmm. Right. You know, so on. And then over steady progress over a long period of time and Apple as a customer driving them to become really meaningful, you know, obviously the world changed. And when I came back to Intel in 2001, TSMC was producing 5X the wafers of Intel. Wow. Right? Not 10% more 5X. And all of a sudden that model of Foundry became the model of the semiconductor industry with two exceptions, Intel and memory. You know, memory deize design and manufacture, right? That is uniquely different. And obviously, you know, we're seeing the $3 trillion memory company, it's just extraordinary. Yeah. And trillion dollar Foundry company in TSMC, you know, the industry has said, I want a lot of wafers. I want a lot of innovation of different designs. I have a layer of standardization and EDA tools and the world change. And obviously, as I came back to Intel, that was one of the core thesis of the new strategy. Yeah. We must become a Foundry as well. 5 to 1. And that was more like 7 to 1 in terms of wafers, you know, to TSMC to and are we going to be able to ensure that? Obviously, we had the chips act and just give us broad strokes. What you think is going to happen here in terms of obviously Taiwan is in play. Some people in the administration believe it's going to happen the year after Trump's out unless he takes his third term, other people believe like it was going to happen as early as 27 or maybe going into 28. So are we going to be able to replicate that here in America in a reasonable amount of time where it's just like truly could be a cataclysmic event if, you know, got for a bit trying to decide, hey, we're going to blockade Taiwan and then the Taiwanese decide, yeah, we're going to burn the fabs and we're going to fly out all of the engineers and ship them to America. Well, there's a lot in that question. Yeah. Do we have an hour to talk about this question? Well, I mean, we have six minutes, but, yeah, do the best you can. Okay. I was going to talk also about the AI bubble. So super, you know, three things about this super quick. You know, one is the chips act is having benefit. Yeah. Right. You know, when we started the chips act in, you know, when 2001, when I came back, the US was building about 12% of leading edge today, that number is more like 18%. Okay. Yeah, we're making progress. It's not 50%. We have a long way to go, right? You know, Intel is starting to be a real foundry. Okay. That's real progress. And TSMC's factories are up and operating at scale. Right. We have Samsung and go as well, but you know, I'd say the Intel and the TSMC progress. Okay. That's meaningful. We get ugly for a second. The island of Taiwan has less than three weeks, a big article in the Wall Street Journal two weeks ago on this, less than three weeks of energy reserves. Wow. Okay. That should just put a chill in everybody's spine, right? Because the blockade, after three weeks, the island browns out. When you turn off a fab, it doesn't come back on for 90 days. Right. The economic impact of a brown out of Taiwan is greater than the Great Depression, right? In the world. Never do you need to do anything, a shot to be fired. You just need to say, great. No energy for three weeks. No oil. Right. No LNG. Right. That's how the island run. That is scary. You know what I mean? We need more resilient supply chains associated with it. And I don't think this is an alternative for the world because if it really does become a risk, you know, and I'm, you know, I, you know, I don't sit in the situation room and get all the data and so on. But let's remind each other that I think China has blockaded the Taiwan Straits seven times over the last four years. Yeah. This isn't a theory. No, no, they're running exercises. They're being prenicious and pretty provocative in terms of 2027. Is that 2030? Is that 2035? Their intentions have been clear over a sustained period of time. We need more resilient supply chains for it. So something, you know, I put a lot of my time and energy into and we're making progress, but we need to go faster. Need to go more meaningful. Yeah. And let's talk a little bit about the AI build out. I mean, you watched the PC revolution servers, the internet, these were all extraordinary buildouts. And then this is the build out to end all buildouts. The amount of data centers, the amount of chips, the amount of inference needed. Do you think it's a bubble? I think I've heard just say like it's obviously a bubble. But what's the risk factor here that we build too much or that the technology doesn't solve enough problems and we are swimming in tokens? What worries you about what you're seeing now? The valuations of these companies has gotten quite extraordinary. And you know, if they build too much and they spend too much money and they don't make enough money, well, based on New York, their experience with running a company, a public fund, that's a lot of tension on it. When you don't make as much money as you're spending, people tend to fall out of love with these stocks. Yeah. Well, I do think there is a silver lining here that guarantees we don't get too far ahead of ourself in terms of bubble. And that is energy capacity. Right. Right. The world is expanding 4 or 5 percent. In the US, we had a decade at 1 percent. Right. I mean, it's just hideous what we did to our energy grid over about a decade and a half. But now that's getting built out. But essentially nobody's going to build and buy GPUs and build data centers if they don't have energy. So essentially, you have an upper bound on how aggressive and how hyped and bubbled that we get. So I take a lot of solace in that. Yeah. I think that's a lot more important because what then is the incremental value of a token. And if it's a measure of intelligence, it's somewhat infinite. Right. In the sense, if I have more intelligence, I will do better supply chain. I will do better finance. I will do more efficient logistics. I will all of those things. So to me, the potential value that we unleash in a token economic world is somewhat infinite. And particularly with labor shortages and so on that we see in developed countries, I am an optimist that we are in a couple of decade build out. Wow. Right. Not a couple of years, a couple of decades. One of the big objectives I've said is that I have to make AI 10,000X better. It's way too expensive today. We want to drop by five orders of magnitude, the cost per token, the energy per token, so that we really do have Jevons law that we just explode the access to AI and much more economic ways. - Which it does seem like Jevons paradox has been at play over the last year. Oh my Lord, these tokens are so cheap and the tools are getting so good. - Yeah, I'm just gonna start using these tools all day long until the bill comes in and you're like, "Okay, yeah, maybe I need to get some ROI out of this, but you do have these incredible companies, cerebris, groc, et cetera, making inference." - DeMate Tricks. - Yeah, just to look in and so, you know, if we accomplish, right, these orders of magnitude, improving and token economics, availability, reduction, and energy costs associated with it, you know, we just have a fantastic couple of decades in front of us. There has not been a time in human history where it's been better to be a technologist than the one we're in right now. We will solve chemistry, we will solve language, we will invent new materials, we'll new forms of interaction, killing cancer, lifting people out of poverty. There is not a better time to be alive than the one that we're in right now. And as technologists, we get to sit in the driver seat of it. - Pretty amazing and you're investing in that your passion now. - What do you think of these valuations? It's quite, seems, you know, if you live through the Dockham bubble, we did see a disconnect there. These companies slightly different. We just had 11 labs up, 600 million in revenue. Lovable. I think they're at five or 600 million. So that's quite different than the Dockham speculation, yeah? - Yeah, well, fundamentally we have real revenues, you know, real margins coming out of these businesses as well. You know, that said, anytime the multiples get too high, okay, some corrections. You know, and to me, periodic corrections that keep the multiple, you know, earnings multiples and you're so on in reasonable things is good because this will not be a smooth curve. You know, I'm predicting two decades of goodness and there's gonna be lots of disruptions along the way. It's not gonna be a smooth curve and every time we have one of those corrections, say thank you, right? We're not letting the bubble get ahead of itself, right? You know, hey, we have the SaaS apocalypse. There's gonna be other apocalypsees on that journey when industries get impacted by the capabilities that will be unleashed. And that's even before it gets exciting and what I call the trinity of computing. Classical computing, AI computing and quantum computing. And when those three come together, okay, that's what things get really exciting. - Hey, it quadms been about five years away for 25 years. When is it actually gonna do anything mean? - This decade. - This decade, so by 2030. - Yep. - What should we expect in terms of its impact in 2030? - You know, you're gonna be able to start doing things that cannot be computed today. Chemistry, biology, there will be things that can't be computed today. Some of the easy things will be some of the logistics where I will compute the best answer to get this thing to you, right? - Provincetion, Salisman problem. - Yeah, all of a sudden, all of those problems. Obviously it's probably gonna be, you know, 2032, 2033 when we solve things like encryption, right? Where you'll have the fundamental Q-day kind of implications. But this decade, we will see quantum supremacy to results across multiple industries. You know, we know how to build qubits. We know how to error correct qubits. We now have algorithmics, right, against quantum. And now it's just about the engineering scale. - Who's gonna win? - Well, obviously I'm a side quantum guy, right? So that's one of our portfolio companies. But the thing that you're seeing is that you now have like four, five, six modalities of quantum that are demonstrating pretty good results, right? You know, across trapped ions, across, you know, photonic approaches, spin approaches. So you now say modality is not an issue. Air correction's been proven across them. And, you know, I think the race will be on and my prediction is meaningful results before 2030. - Wow, you realize that's about 40 months from now. - Yeah, okay, meaningful results. Thanks so much Pat for sharing. - Yeah, thanks to all this incredible information and knowledge, great to see you. - Very good. ♪ I'm doing all you ♪ - Your most valuable conversations rarely happen at a desk. The hallway sink, the dinner, the quick founder call, applaud, no pin-esque, clips on, and captures all of it, hands-free. Afterward, applaud intelligence turns the recording into clean notes and clear next steps. You stay in the room, applaud handles the rest. For people who live in meetings, that's real average. Where are you applaud at applaud.ai? ♪ I'm doing all you ♪ - Oseeka is one of my favorite founders. He's the founder of Lovable. Why do I love this founder? Well, he's built a product that people are addicted to. Primarily, Anton, the people who work for me. And I love talking to you because as the founder, you have a North Star, you're incredibly laser focused on enabling anyone to build great software. Yeah, it's the mission of the company I'm paraphrasing here, but essentially, that's the mission of Lovable. - Mission I talk about, empowering humans. - Empowering humans. - And the first gap is to build the products. The second gap is to build a business around the products. - Right. - And everyone at Lovable, we're working on both of these two gaps. - Right. - The first one, we've got to very far. We're seeing a million new products built every single week on the battle. - Incredible. - And on the second one, we're investing a lot in making it easier to run your business and to get people to care, people to discover what you build and the entire business of whatever you're doing as a small business. As if you're a large business, we're also getting a lot of traction. And we're actually seeing as a proof of that, more than 700 million visits to the applications every month. So every month there's extreme growth in the surface area of the entire, more than 50 million apps built on the platform today. - How many years has Lovable been in market or how many months now? - 20 months, 20 months. - 20 months, yeah. And again, we're seeing people who are first time founders, we're seeing enterprise leaders move much faster together with the teams on this platform that has a lot of opinionated pieces in how you should create software and how to operate that software and how the different applications in your company connect to each other over time. So that's why we're seeing so much growth also on the enterprise side, where we're actually growing fastest right now. - This is really interesting because 10 years ago, people were doing Wizzy Wig software. What was the name for it? Before Vyco? - No code. - No code, low code. Yes. And when I saw that 10 years ago in my incubator, every 20th company, somebody would come in who was an MBA or not a developer and they had Vybe coded something and not Vybe coded, they had no code. And they were using these different software platforms and the software didn't look good, it didn't work perfectly well, it was slow, but the promise was there. And I guess it took LLMs and this new intelligence to make actually good software. So maybe you could talk a little bit about who is the customer because developers, do developers use Loveable or is it the other 95% of society that are your customers? How do you think about who your ideal customer profile is? - Yeah, we're seeing people use Loveable both with a technical background. About 20% are technical or some type of engineer. And they love that we're quite opinionated, we put all the best practices into how the software is architected and we make it seamless to, we want from get payments set up in a very secure way and do things like run security scans after every change, even now in the background monitoring the projects. So it's actually quite appreciated by the engineers in the technical community. Also because it's a great bridge from the non-technical people, which is four out of five are non-technical. And they're building often first to figure out what is the right thing to build, which is where Loveable has always been an exception to good. And now what we're seeing is that people are running business as making more than million dollars of revenue on this platform. So it's this building for everyone, it's this entire spectrum. And what's exciting to see is often that if someone who discovers Loveable from their colleagues at the large company, they go out and then run a side hustle. And some of those side hustle's really work. They make hundreds of thousands of dollars and then they become a founder after that. So this is cross pollination from both. - Yeah, and this is like the really interesting thing about vibe coding. If we were sitting here last year, people would look at it and say, it's a great way to make a mock up. Like you said, a great way to think about product and maybe create wire frames or a workable prototype. All of that's out the window now. The whole concept of building wire frames and building a mock up, well, you can just go right to building the product in a day or two days. And what people I think don't appreciate about what you're doing at Loveable is, after you've made a product that you're proud of and that has some product market fit, there are many more steps that are required. You mentioned payments. You met? in security, making sure that the data isn't lost or that it's not leaked. That's changed dramatically over the last 12 months, yeah. Very much so. So, I would say many engineers, they don't look at the code, they don't write code anymore, and that means that you don't need to be an engineer to create software, right? But the thing that Labable does for anyone, also the non-technical people, is that it takes, it's a structure for the architecture of the software that you build. And it makes sure that you don't go off a cliff and that things like setting up payments, emails, things like getting discovered by other AI chat engines and by Google search. Those things are kind of taking care of. You don't have to know how all these things work in the details. You can trust the platform to take care of data security, connecting to other tools that you might be using in a secure way. And that's really where, of being opinionated from day one and being focused on making this for the 99%. It's a vast market, right? From day one, it's what made us very successful. Yeah, and I can tell you internally, I gave my team all the different tools they could possibly want to use. And somebody had started with Labable. I think I told you the story when you were on this week and started up a year ago. And they made some interesting websites and they were trying to make an internet. They couldn't quite get it done. Then I had some people who started using, you know, cursor or clawed code. They started vibe coding stuff, but they couldn't finish the product. And then people tried to solve some problems with code work. I really like Proplexia Computer. And then my team came to me and for one of our projects, I was talking to you about Founder University, our pre-accelerator, they wanted to make an internet. Now, this is something I would have never okayed because it would have cost $500,000, 10 years ago to make it. And we don't have that kind of budget. You know, we would rather put that towards the founders in the program and getting more people into the program. And in four to eight hours, they made the whole internet and they made a bunch of things I had an ask for. And it was the person running this Founder University who made it. And she did it on her own without permission, in loveable. I said, "Whoa, how did you build this?" She said, "Loveable." I was like, "Oh, we still have loveable." And they were like, "I just put it on my corporate card to your point." She made it. Now that software is driving the program and the reason people do the program in their country, we have an in Saudi and in Japan is because it has economic impact. So I said, "Hey, I have an idea. Can you make for me an economic impact of the 50 companies that are in the program?" She asked Loveable to do it. I gave her some, you know, prompting, human prompting, boss, to. Now it has the economic impact in there. And it considered, you know, with our prompting, "Well, how many people work at each company? What are they paying taxes? Well, how much do they rent their home for? What is their average salary?" And it built something that I would have never been able to afford to build. And Loveable is $50 a month, I think. I don't know how much you charge, but it's far too little. Like $50 a month, I think? Yeah, that's if you're on a business plan. Yeah, it starts on 25. Yeah. So, the economic impact of what you're building is I would equate for what you built to us. It would have cost me $500,000 two years ago. So we've built in four hours by an employee, which if you just put employees at 50, 60, whatever, $70, plus the cost of your software it got made for less than $2,000. In a year, it's extraordinary. I'd love to hear more about the progress of the internet. Anything that you ask for that you want to forward direct it to me? Well, right now, you know, my concern was security and making sure that data didn't leak and they talked to your team and they went through it and it's secure. So we feel good about it. Look, I'm now asking people who do penetration testing to say, I want you to compare all the tools and make sure that there's all the work that we're doing that's not visible on security and trust. There's a lot of a lot of other things where we invest and spend money on that every day. We also free users get a lot of security scanning running in the background that that actually translates to something that security experts can see. And a year ago, we were at mockups, now we're at functionality and secure and super viable for deployment. Where will you be in a year? Yeah, so what we're seeing is that there's a gap in being able to build a product right and you built an entire internet on the platform. That's great. What we've done since then is to have a new product line basically, the hosting part, which is both the AI and all the normal hosting and that's product line has been going faster than the building thing. I mentioned AWS competitor. Let's go. Let's run all your software and then we're working with companies like AWS and other hood as well. But what you also want to have is to use loveable, we're seeing by our customers as an AI co-founder. And larger that you talked about everything in your business. And if you're running your apps, your tools are on the platform, then just talking to loveable has access to all the data that you might want to know about your company, how it's doing. So we're working with some of our customers in pre-release to give them access to a co-founder that works for you even when you're sleeping and comes back to you in the morning and says, here are some strategic directions you could go. Here are some optimizations you can go in terms of growing your business faster, serving your customers better, faster. And that's that evolution towards operation and intelligence towards driving towards outcome in your business. So you can come to build the software, but you stay to build the business. Yes. To operate your business. And what we're already doing, I've been doing for a very long time, is to compound from everything we're learning. Every time loveable makes a mistake, it goes to a agentic system with our engineers in it, improving it. That compounding intelligence is of course applicable to our customers. Our users running their business on our platform as well. Is software going to become a hundred percent bespoke, even like the internal tools I was looking at Slack, and our bill for Slack, even on the highest version, is maybe $10,000 a year. It's not a lot of money. It's well worth it. But I was starting to think, well, maybe I should vibe code my own Slack. So it's integrated into everything we do at a deeper level. So how do you think the future will look like in terms of some of these foundational pieces of software that every startup, every enterprise uses, sells for us, hubspot, Slack, the Google suite, Microsoft Office, will bespoke software, start to replace those? Do you believe? I like this question. Let me ask Hansley, but I'll just give you a story about someone I recently heard who's going on this journey. They're quite advanced. So they're not, he works at a pretty large company in the US, NERSA, and he came to our platform because he wanted to build out the new product lines, NERSA study for educating more nurses. And he built out all the admin tools for the program, the scheduling for the nurses getting their licenses and their certification management. And he was able to build that into a product and to take it to market because they have all that access to nurses wanting their certification. What he also did was he took it into their back office internally and they've now replaced more than 10 tools that they had. Oh wow. It used to bespoke applications. And in terms of your question, you can do that for multiple reasons. In their case, they're saving more than a million dollars per year. Right. So that's huge, right? But it's also the case that in some cases, you have specific requirements where the tools that you've been using today, they aren't suited for those requirements exactly. And in those cases, I think yes, you will have more bespoke solutions. But I also expect us to see that lovable continues to interoperate with all of those tools. And I'm not sure if you try this. If you ask for connecting to anything in the Google suite, or anything in the Microsoft suite, or Slack, lovable guides you through all the steps to do that in a way where you can get a very good overview of exactly how the data flows, which is of course very important that you don't give access to the wrong person to the wrong data. And you can continue to use Salesforce, HubSpot and all the tools that you'd like to use under the hood, but with a bespoke interface on top of it. How have these new frontier models? They're in some ways competitive, but in some ways you can use them to power lovable. So how do you think about the competition with them, open source, and the future of lovable? Because people have announced that lovable's dead. every six months since you started and then every six months you go from 100 to 200 to 300 I think you're at 400 million in revenue something crazy. We reached 500 in May. Okay. Growth is a phenomenon. So you're dying again by another 100 million in annual revenue. Exactly. But underneath the hood you're using some of these. Yeah, let me explain. Yeah. So we've always had this strategy that we do whatever is best for our customers and in terms of intelligence that means that we're using multiple models. And so if you ask the available now it's actually routed to the model that's most suitable to whatever you want to do and that's both the commercial frontier models. Yeah, from multiple vendors and increasingly it's open weight models where our team, whenever it gets routed to our own model, that model becomes more intelligent for our agent harness. Yeah, especially on the mistakes that it might be making. In some cases on which tool to call, which integration to create and how to guide you through success for your business. Right. So you're all in on open source. You believe that's the future of loveable. I'm reading into it. So we have multiple partnerships and we're investing heavily to be close with those partners. Right. It's the big, the big labs. And it's also to make sure that we get the fastest performance at the lowest cost for our customers when we know that we can do that with our own models. Right. And we have a really, really strong research team Appys.com who is working on what's called post training. Sure. And we're applying all the best practices to do that and scaling up that team quite significantly. Since we also believe it's a part of the European ecosystem to have that capability in Europe specifically. Are you doing or are you using any the data labeling data training companies to help you understand the most common businesses and build that proprietary data. So what we're doing is that we're looking at the mistakes that any of the models do right now. And then we prioritize them by what drives most in by for our customers. And then we make the models. We create data sets where we do something called reinforcement learning. Sure. Specifically for the problems where the frontier models are making mistakes for us right now. And we have this enormous token distribution right from a million new projects being built every every single week. You're burning a lot of tokens. We are yes. Yeah. And that's a lot of signals for making the system both the agent harness and what we've been refining over the last two years, which is the skills that we have to have is like internal type of skills that the agent knows when to remember the facts from our software engineers that know how to build really really good software. Someone told me we're modifying both of those on every every single week. Make total sense. And somebody told me some companies are doing token dumping. They're selling $100 worth of tokens for $50. Basically become token resellers in some ways. And they're money losing businesses. You have to you're a month you're profitable I believe now or close to it. We always monitor our margins. But again, we're doing those best for our customers. And that means that often is more intelligence. So we're not we're not looking at all. Let's use it. We've never have the decision to say we're let's use a cheaper model here. If it's measurably worse for our customers. And we can measure that. What's best for us? Is it unlimited for the 50 or you have caps? We have caps. Yeah, we have caps. Overgising caps. Are people starting to hit them? Yeah, our customers definitely hit caps and then you can top up. You can have a we have multiple subscription tiers. I'm just curious like what percentage of people need to top up. They're so addicted to it that they're blowing past the. So from the lowest subscription tier. Yeah. I think it's something like 60% of our customers. Yeah, and I'm hearing that more and more often that people are willing to pay the overages because they're getting so much value. And I think that's the future of the business is people are looking at it going like I am. Well, if I'm paying $600 and if you token max to 6,000 a year. But this is a $500,000 piece of software. I don't care. I'm still paying somewhere between 0.1% and 1% of what I would have paid three years ago. Who cares? Go for it. So yeah, what we're seeing is everything is about moving fast. Yeah. And they have more AI usually lets you move much faster. So this spend is usually worth it. Do your customers a final question for you? Because I'm starting to see this now where multiple people in the organization try to solve the same software problem and they're competing with each other. So like this intro net I'm talking about, we built one for Japan. Yeah. But somebody built the US one. So now I have two pieces of software. So I said to the two different people. Do we have, did you guys fork each others code or they're like, no, we just built two different lovable projects. And I'm like, is that the right thing to do because you went faster and I had two swings. I bet two different intelligent brilliant people making their version of the software. But you would never have done that in the previous way of building software. You would have one track of software and you would be building Frank and software where you'd be trying to get all the needs into it from the two different groups. Yeah, I'm actually a huge fan of very rapid experimentation. Yeah. And I have a story where for a while I worked at a place called CERN where they do tropical physics. It's pretty, here's a group right. Yeah. And that's where I was introduced to this concept of co-opitation where they have two actually quite isolated teams working on the same particle accelerator but different places on it. And then they don't share their results until they publish. And that's nice. They can kind of over time learn what's working west in the different organizations. But you don't get stuck in a local minimum. And it's free markets work extremely well because of competition. And they do that in academia as well. And now since the engineering is less of the bottleneck, it's more the question of what is the right thing to build. I think it's a great thing to have, if you have as sufficiently many humans right, to do, to try to attempt solving the same problem in different ways. And then if you do that unlovable, what I like to do is I bring up a new project or one of the projects and I say, hey, can you go and check out this other one and take these three things that I really like and bring them over here and maybe even run and split steps, run and experiment to see if it's improving the metrics for our customers we're trying to serve. Did you see somebody used Fable to build Fortnite? And I see in the 3D games, yeah. Yeah. What is your take on, you know, this latest version from Anthropic Fable? I know they're a part, or I assume they're a partner. I don't know that. Yeah, but use Fable as well. Is it one of the models that's unlovable? What do you think of it in terms of compared to the last generation? Faster, better, both? Yeah. Is it a massive stand function? Yeah. What I've seen is that it can, in the first attempt, create very sophisticated things that look really good. Then, as you're evolving, right, it's still the same thing where you as a human, you have to think, you often should be planning together with your agent about what is the right thing to do. And that's more of that's again, more of the bottleneck, whereas more intelligence is on some tasks, it's great. Yeah. Like it creates really beautiful things, 3D games, for example. But figuring out what to build, figuring out what are the right strategic directions or experiments you should run to improve the outcomes for your business. That's not changing as fast. It's the humans knowing how to use the tool to get the end to plug in all the right data, to be able to take the right decisions for taking your product forward and to take your business forward. Listen, I love the product, but even more than I love the product in USFounder, I love the outcome. The outcome for business is extraordinary. So anybody who's listening, lovable is absolutely worth your time. Don't wait. Just put it on your corporate card and start building. That's my message. Just start building with lovable. It's an incredible product. And congratulations on being reborn six times. Because every six months, you had 100 million in revenue it seems. And then everybody says lovable is dead because the new foundation model is so good. But you keep studying your customer and you keep somehow surviving and thriving. So congratulations, that's an entrepreneur. Thank you so much, Jason. I enjoyed this. I hope you enjoyed the rest of you stay here in the past. It's pretty great. And the palace of Versailles is so impressive. Someday we'll be building this with lovable and optimist robots. I'm going to do it. [Music]