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

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 in that sound, Intel inside, baby, tell inside, do them, do them, do them, do them, do them, and so let's talk about how things went wrong, and 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, tremendous success as an American

“company, coming back now, I think, reasonably, but when you, when we look back on it and we do”

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intelligent era from day one, stop paying the legacy tax and start building the future at airwlocks.com/all-in airwlocks, built for the future. Having spent so much of a life there, you know, I view it, I joined when I was 18, I went through puberty, it didn't tell, right? I joke right, you know, it's like, you know, I am so early, grove, noise, bear it, right? And, you know, they were the people like grew up at, right? You know, so on, they were my mentors, they were the people I adored

for, and they were deeply technical. And you grow, and you grow, of boredom more, Bob noise, you know, co-inventor, you know, these were deeply technical leaders. I remember when I joined the

executive staff for the first time, there was, you know, probably 15 of the 20 people that were in the

room were PhDs, right? You know, it was just that technical. And, you know, I view one of the things that went off the rail was when it started to be run by business people, the being counters, the finance people. And, you know, when I became CEO in 2001, that was the first technical leader and essentially 15 years, right, you know, associated with it. You know, and if you have a business

“leader, who does he promote business leaders and, you know, right, you know, so I think one of the”

fundamental things is, and, you know, so you look at the great technology companies today, you know, they're deeply technical. And founder led typically, you know, and even if they're not, you know, such as not a founder, right, you know, so it's not a founder as well, but they're deeply technical individuals. And when you're making these hardcore technical, you know, decisions that affect billions of dollars, you don't do that through a spreadsheet, right? 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 a hundred billion dollars to share holders. Oh, the dividends and my

stock buybacks. Well, 100, what I wouldn't have done for another hundred billion dollars on the, well, I mean, what, what would you have done? You probably would have made, well, I tips for iPhone, which Intel passed on, yeah, yeah, you know, but, you know, it had a built-in 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 UV 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. 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 hires the best

technologist, you know, you know, and 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 if what companies do they buy?”

They buy little tiny acquisitions on the margins. I think the largest ones was, was beats

intro, it's based that they couldn't get into. But I got 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, you know, he was also a ruthless leader, right, you know, very difficult, you know, Reed Walber-Ider, Isaacson's book on him as well. I had many, many conversations with Steve over the years, you know, for it. But, you know, when they moved to Intel in the Centrino 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, go, he started the project, right, you know, and if you remember, was it, you know, you know, the P, semi, you know, they acquired some small companies, 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. Uh, I remember when we had the first conversation with Steve about, uh, forwarding the, uh, 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,

it's so Steve will help you port the operating system to the X86. And I remember this 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, and to me, I just remember I was just shocked. You know, I've ported the last four releases

“to the X86. I think we got this. Yeah. Right. You know, was that kind of thing? And that's how they got”

into the semiconductor, you know, doing their own semiconductor. 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, and their operating system. And you know, it was just, you know,

it was never that kind of thing that you said, you know, you failed 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. Yeah. Yeah. Was that law core 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, uh, at Intel, we sort of scoffed at his machines. Yeah. Right. You know, so I was a graphic machine, you know, okay. Yeah. There's some gamers who want to use 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 coulda thing and SIMT is a technology, you know, uh, 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. You know, we're just making the better, every release and it's becoming more robust and all of a sudden, you know, the crazy, you know, uh, Japanese HPC guys said, hey, we could take those graphics cards and maybe start using

'em an 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 or just like, man, you know,

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, you know, forward. And the Kuda software kept getting better, uh, generation by generation. And, you know, I had a project that intel therapy,

forward. And, you know, in my first departure from intel, the project was killed a week after I left.

Huh. And the world would have been so much different, right? I mean, it really, I think it's 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. Nor 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, uh, career, um, okay, Apple Silicon, that's one, uh, and then you have Nvidia. And then you have this Taiwanese company, that starts making, you know, really great at fabricating these chips, um, and Intel missed that as well, yeah. And then 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. You know, 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, uh, at that point in time. Intel was 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, right? It was always this thing. Hey, it's,

you know, we do enough CPUs ourselves. You know, 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 herself. You know, one of the projects that I started earlier in my career was the foundations of EDA, right, as well. The first place and 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 that says, I don't care who's chip it is. I don't care what you're designing. I'll be your manufacturing partner. You know, at the time, that was a trivial piece of the business Intel didn't even care, right over the so on. And then over steady progress over a long period of time and Apple, as a customer driving them to be good, become really meaningful, you know, obviously the world change.

And when I came back to Intel in 2001, TSMC was producing five X the way first of Intel.

Wow. Right. Not 10% more five X. Yeah. 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 device design and manufacturer, right, for, you know, that is uniquely different. And obviously, you know, we're seeing the, you know, $3 trillion memory company. It's just extraordinary. You know, and you know, trillion dollar foundry company in TSMC. You know, the industry has said, I want a lot of

way first. 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. Five to one. And now it's more like seven to one in terms of wait first, you know, two TSMC two. And are we going to be able to unsure of that? Obviously, we had the chips act and just give us broad strokes what you think's

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?”

Or is this like truly could be a cataclysmic event if, you know, God forbid trying to decide, hey, we're going to block a Taiwan and then the Taiwanese decide, yeah, we're going to burn the phabs 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, you know, do we have an hour to talk about this question? Well, I mean, we have six minutes, but oh, um, yeah, do the best you can. Okay.

one is the chips act is having benefit. Yeah. Right. You know, when we started the chips act

“and, 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, you know, as well, but you know, I'd say the Intel and the TSMC progress. Okay. That's meaningful. Now, let's make it 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 all on for 90 days, right? The economic

impact of a brownout 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. Yeah. Great. No LNG. Right. That's how the island run. That is scary. You know, to me, 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 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. They go forward. 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 buildout to end or 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 your 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 ourselves in terms of bubble. And that is energy capacity. Right. You know, energy capacity in the world is expanding four or five percent. You know, in the US, we had a decade at one percent. Right. You know, let me, it's just hideous what we did to our energy grid. You know, 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 solos in that. Yeah. Right. You know, for it, because what then is the incremental value of a token? And if it's a measure of intelligence, but it's somewhat infinite. Right. You know, in the sense, if I have more intelligence,

I will do, you know, better supply chain. I will do better finance. I will do more, you know, efficient logistics. I will, you know, all of those things. So to me, the potential value that we unleash in a token economic world is somewhat infinite. Right. And particularly with labor shortages, the so on that we see right in developed countries. I am an optimist, you know, that we're 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 10,000 X better. Right. You know, it's way too expensive today. You know, we want to drop, you know, by five orders of magnitude, the cost per token, you know, the energy, you know, per token, so that we really do have

Which it does seem like Japanese paradox has been a play over the last year. Like, oh, my Lord, these tokens are so cheap and the tools are getting so good. Yeah. I'm just going to 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, cerebrouss, grock, et cetera, making inference.

Dematrix. Yeah. Just a silicon. You know, and, you know, if we accomplish, right, you know, 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, you know,

invent new materials. We, you know, new forms of, you know, interaction, you know, killing cancer, right, lifting people out of poverty. There is not a better time to be alive than the one that we're in right now. And this technologist, we get to sit in the driver's seat of it.

Pretty amazing. And you're investing and that's your passion now. What do you think of these

valuations? It's quite, it seems, you know, if you live through the dot com bubble, we did see a disconnect there. These companies slightly different. We just have 11 labs up, 600 million in revenue,

“lovable. I think they're at five or 600 million. So that's quite different than the dot com”

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 going to be

lots of disruptions along the way. It's not going to 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 going to be other opocylipses on that journey when 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 cotton's been about five years away for 25 years. When is it actually going to do anything this decade? This decade. So by 2030. Yep. You don't have to be meaningful. What should we expect in terms of its impact in 2030? You know, you're going to be able to start doing things that cannot be computed today. You know, chemistry, you know, biology, there will be things that can't be computed today. You know, some of the easy things will be some of like the logistics

where I will compute the best answer to get this thing to you, right? Traveling salesmen problem. Right. You know, all of a sudden, all of those problems. Obviously, it's probably going to be, you know, 2028, 2032, 2033 when we solve, you know, things like encryption. Right. You know, where, you know, you'll have the fundamental Q day, you know, kind of implications. But this decade, we will see quantum supremacy results across multiple industries. You know, we know how to build

qubits. We know how to error correct qubits. We now have algorithms, right, against quantum. And, you know, now it's just about the engineering scale. Who's going to 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 know, same modality is not an issue. Air corrections have 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. Excellent. All this incredible information and knowledge. Great to see.

There you go. I'm doing all of you. Your most valuable conversations rarely happen at a desk. The hallway sink, the dinner, the quick founder call. Plod, no pines, clips on and captures all of it hands-free. Afterwards, plot intelligence turns the recording into clean notes and clear next steps. You stay in the room. Plod handles the rest. For people who live in meetings, that's real leverage. Where are your plots at plot.ai? Oh, Sica is one of my favorite founders.

He's the founder of Loveable. Why do I love this founder? Well, he's built a product that people are addicted to. Primarily, and the people who work for me. And I love talking to you

anyone to build great software. Yeah. It's the mission of the company. I'm paraphrasing here,

“but essentially, that's the mission of loveable. Mission I talk about, empowering humans.”

Empowering humans. And the first gap is to build a product. The second gap is to build a business around the product. Right. And then we're at every one at loveable. We're working on both of these two gaps.

Right. The first one we've gotten very, very far. We're seeing a million new projects built

every single week. 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 or 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 is an extreme growth in the surface

area of the entire, more than 50 million apps built on the platform today. How many years has loveable been in market or how many months now? 20 months. Yeah. And 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. We're we're actually growing fastest right now. This is really interesting because 10 years ago people were doing wizy wigs software. What was the name for it? Before five code. No code, low code. Yes. And when I saw that 10 years ago in my incubator, you know, every 20th company somebody would come in who was an

MBA or not a developer and they had vibe coded something. And not vibe coded. They had no coded. 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 lovable 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 lovable 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 that up in a very secure way. And do things like run security scans after every change, even not

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 lovable has always been an extra new good. And now what we're

seeing is that people are running the business is making more than $1 million of revenue on the on this platform. So it's this is a building for everyone. It's this entire spectrum. And what's exciting to see is often that if someone who discovers lovable from their colleagues at the large company, they go out and then run a side hustle. And some of those side hustle is really work. They make hundreds of thousands of dollars and then they become a founder after that. So this is

just a donation from both. Yeah, this is like the really interesting thing about vibe coding. If we were sitting here last year, people would look at 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, you could 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. A lovable 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 mentioned 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

lovable does for anyone, also the non-technical people, is that it takes a great 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 the 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 I'll be opinionated from day one and being focused on making this for the 99%. It's a vast market, right, from day one is 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 lovable, 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 cursor or clawed code, they started live coding stuff, but they couldn't finish the product. And then people tried to solve some problems with coworker. I really liked perplexity computer. And then my team came to me. And for one of our projects, I was talking to you about founder, university,

our pre-excelerator. They wanted to make an internet. Now, this is something I would have never

arcade 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 asked 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're like, she's 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 the program in their country, we have an insanity 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 it. Now it has the economic impact in there. And it considered, you know, with our prompting, well, how many people work in each company? What are they paying taxes? How much 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 bucks 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 even 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 would have cost me $500,000, two years ago. It was built in four hours by an employee, which if you just put employees at 50, 60, whatever, $70, plus the cost of your software

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 directed 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, um, I'm, no, asking people who do penetration testing to say, I want you to compare all the tools. Yeah. And make sure that there's some all the work that we're doing that's not visible. On security and trust. Yeah. There's a lot of a lot of other things where we, we invest and spend

money on that every also free users get a lot of security scanning running in the background that that actually translates to something that the security experts can see. And here we go. We were at mockups. Now we're at functionality and secure and super viable for deployment. Well, you'd be in a year. Yeah. So what we're seeing is that there's a gap in build being able to build the product rights. And you build 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.

It's a, let's say it's let you run all your software. Then we're working with companies like AWS

“out of the 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 talk to about everything in your business. And if you're running your apps, your tools are on the platform, then you're just talking to loveable has access to all the data that you might want to know about your company, how you're doing. So we're working with some of our customers in previous days to give them access to a co-founder that works for you even when you're sleeping. And come back to you in the morning and says,

like, here are some strategic directions you could go. Here's some optimizations you can go in terms of growing your business faster, serve your customers better, faster. And that's that evolution towards operation and intelligence towards driving towards outcome. Right, your business. You 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 lovable makes a mistake. It goes to a gigantic 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, you know, foundational pieces of software that every startup, every enterprise uses Salesforce, HubSpot, Slack, the Google Suite, Microsoft

Office will bespoke software, start to replace those. Do you believe? I like this question. Let me answer it, 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 pre-large company in the US, Narsha. And he came to our platform because he wanted to build out the new product lines, Narsha study for educating more nurses. And he built out all the admin tools for the program,

the scheduling for the Narsha's getting their license as under certification management. And he was able to build that into product and to take it to market because they have, they have had all that access to Narsha's wanting their certification. What he also did was he took it into their back office internally. And they've not replaced more than 10 tools that they had announced 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 and more bespoke solutions. Yeah, but we're all, I also expect”

us to see that lovable continues to interoperate with all of those tools. And I'm not sure if you tried this, if you ask for connecting to anything in the Google so it's sweet. Yeah. We're now so anything in the Microsoft screen or Slack, it 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, HobbSpot, all the tools that you're going to 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 a 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. It's growth is a phenomenal. So you're dying again by another 100 million in annual revenue.

So, but underneath the hood, you're using some of these, yeah, that makes many. Yeah. So we've

that means that we're using multiple models. And so if you ask lovable now, it's actually

“routed to the model that's both suitable to whatever you want to do. And that's both the commercial”

frontier models. So 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 an open source. You believe that's the future of lovable.

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 up in Stockholm, who is working on what's called post training. And we're playing all the best practices to do that.

And scaling up that's in 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 they're right now. And then we prioritize them by what drives most impact

for our customers. And then we make the models, we create data sets. There's something called reinforcement learning, specifically for the problems where the frontier models are making mistakes for our

right now. And we have this enormous token distribution. Right. From a million new projects

being built every single week, you're learning a lot of tokens. We are. Yes. 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. How is it internal type of skills that the agent knows went to remember the facts from our software engineers that know how to build really really good software. So much, we're modifying both of those on every single week. It makes total sense. And somebody

told me some companies are doing token dumping. They're, you know, selling a hundred dollars worth the tokens for $50. You know, basically become token resellers in some ways. And there are

“money-losing businesses. You have to, you're profitable. I believe now or close to it. We,”

we always monitor our margins. But again, we're doing what's 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 never have a decision to say, let's use a tea per model here. If it's measureably worse for our customers. And we can measure that. What's best for it? But are they, is it unlimited for the 50 or you have caps? We have caps. Yeah, we have caps. We're reducing caps. Are people starting to hit them? Yeah, our customers definitely hit caps and then you can't 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, some of the, from the lowest subscription tier. Yeah. I think it's, um, much, I something like 60% of our customers out. Yeah, 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 businesses. 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. Um, so yeah, what we're seeing is everything is about moving, moving fast. Yeah. And they are more AI, usually let's you move much faster. So this, this man is usually worth it. Do your customers of 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 internet 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 sent to the two different people. Or do we have, did you guys fork each other's 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

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 Franken software where you would 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 Surn, where they do geographical physics. It's pretty short here in Surn. We know it, right? Yeah. And that's where I was introduced to this concept of co-optician, where they have two

actually quite isolated teams working on the same particular exaggerated about different places on it. And then they don't share their results until they publish. And that's where 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, you know, 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 of the question of, what is the right thing to build? I think it's a great thing to have,”

if you have the, as sufficiently many humans, right, to do, to try to attempt to solving the same problem in different ways. And then if you do that on lovable, what I like to do is I, I take, 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 this, these three things that I really like and bring them over here. And maybe even run an explicit test, run an experiment to see if it's

improved, improving the metrics for our customers, we're trying to serve. Did you see somebody used Fable to build Fortnite? And I think this is 3D, someone that's 3D games, yeah. Yeah. What is your take on, you know, this latest version from Anthropics Fable, I know they're a part, or I assume they're a partner, I don't know that. Yeah, but who's Fable as well? It's one of the models. Yeah, I see one of them. What do you

think of it in terms of compared to the last generation? Faster, better, both.

Yeah, what I've seen is that it can in the first attempt create very sophisticated things that

look really good. Then when you're revolving, right, it's still the same thing where you as a human,

“you have to think, you're often too planning together with your agent about what is the right”

thing to do. And that's more of that again, more of the bottleneck, whereas more intelligence is on some tasks, it's great. Yeah, it creates really beautiful things, 3D games, for example. But on figuring 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, is the humans knowing how to use the tool to get and 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 and you as a founder, 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 a hundred million in revenue, it seems, and then everybody says lovable's 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 the tough. 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, huh?”

Someday we'll be building this with lovable and optimist robots. I'm doing all of you.