spent a long time at intel yeah and uh only 34 years 34 years yeah probably one of the greatest american companies uh 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 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 seem 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 uh reasonably but when you when we 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 airwallex 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 airwallex was built for the intelligent era from day one stop paying the legacy tax and start building the future at airwallex.com all in airwallex built for the future having spent so much of my life there uh you know i view it uh i joined when i was 18 i went through puberty at intel right my joke right you know just like you know i am so early uh grove noise bear uh barrett right uh and uh uh you know they they were the people i grew up at right you know so on they were my mentors they were the people i adored uh for it and they were deeply technical andy grove andy grove gordon moore 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 you know 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 yeah opposed to technical the bean counters the finance people yeah and you know when i became uh ceo uh in 2001 that was the first technical leader in 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 as you look at the great technology companies uh today you know they're deeply technical and founder led typically you know and even if they're not you know satch is not a founder right you know sundar is not a founder uh 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 to investment and i think that's one of the fundamental things and obviously you know in the five years uh five six years before i came back you know intel gave a hundred billion dollars to shareholders oh the dividends and my stock buybacks a hundred what i wouldn't have done for another hundred billion dollars on the uh well i mean what what would you have done you probably would have made well chips for iphone which intel passed on yeah yeah you know but you know it hadn't 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 euv 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's getting back to the core of technology to me that was you know the fundamental thing you know you make good decisions you make bad decisions as leaders every business does that uh as they go along but uh you know fundamentally this is a technology business and you need technologists running technology uh that uh then hires technologists that are sitting at the staff but then hire the best technologists 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 they're 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 because they wanted to get inroads into you know certain uh 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 you know steve was an incredible leader yeah you know he was also a ruthless leader right you know very difficult you know read walter isaacson's book on him uh as well i had many many conversations with steve over the years uh you know for it um but you know when they moved to intel and 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 uh customer and when he was no longer convinced that we could continue to do that you know he started the project right you know and if you remember uh was it you know you know uh 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 porting 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 and 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 just shocked you know i've ported the last four releases to the x86 i think we got this yeah right you know it was that kind of thing and that's how they got into the semiconductor you know doing their own semiconductor boom 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 uh operating system and you know it was just you know uh you know it was never that kind of thing that he sort of said you know all right you failed as a supplier no i can supply myself better yeah and jensen uh decides hey he's going to go all into making these video cards and talk about just incredible uh serendipity that these happen to be also very applicable for cryptocurrency and running these ai jobs yeah was that luck or skill or a combination of both there well you know when you think about that progression you know jensen he was just building high performance computers 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 like oh it's a graphic machine you know okay you know there's some gamers who want to use that kind of stuff right you know 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 cuda thing and sim t as 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 it 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 them in hpc right you know and 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 uh workloads and you know ai was you know had gone through what its fifth nuclear winter by that point we're 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 yeah uh you know for it and the cuda software kept getting better uh generation by generation and uh you know i had a project at intel larabee 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 illustrative 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 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 uh that starts making you know really great at fabricating the these chips um and intel missed that as well yeah and maybe you talk a little bit about tsmc and their surging and we can even get into a little bit of the 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 uh 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 you know it was always this thing hey it's you know we do enough cpus ourself you know we reuse it for chipsets 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 uh 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 and says i don't care whose chip it is i don't care what you're designing i'll be your manufacturing partner and at the time that was such a trivial piece of the business intel didn't even care right you know 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 changed and when i came back to intel in 2001 tsmc was producing 5x the wafers of intel wow right not 10 more 5x 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 di's design and manufacture right for you know that is uniquely different and obviously you know we're seeing them you know three trillion dollar memory companies just extraordinary you know and you know trillion dollar foundry company uh 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 changed 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 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 um 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 china decides hey we're going to blockade um 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 you know do we have an hour to talk about this well i mean we have six minutes but oh okay yeah do the best you can okay i'll 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 you know 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 uh and tsmc's factories are up and operating at scale right we have samsung and you know uh 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 uh 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 brownout of taiwan is greater than the great depression right uh 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 yes right right no lng right you know that's how the island run that is scary you know to me we need more resilient supply chains uh associated you you know 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 yep this isn't a theory no no they're running exercises they're being pernicious and right 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 you know 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 build outs and then this is the build out to end all build out so 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 you say like it's it's obviously a bubble but what what's the risk factor here that we build too much uh or that the technology doesn't solve enough problems and we are swimming in tokens what 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 you know there is a silver lining here that guarantees we don't get too far ahead of ourselves in terms of bubble you know and that is energy capacity right 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 i mean 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 solace 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 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 the potential value that we unleash in a token economic world is somewhat infinite right and particularly with labor shortages and so on that we see right in uh 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 000x 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 jevin's law that we just explode the access to ai right in much more economic uh ways which it does seem like jevin's uh paradox has been at 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 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 cerebrus grok etc making inference dematrix yeah just silicon and so 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 re you know new forms of you know uh interaction you know uh 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 as technologists we get to sit in the driver's seat of it pretty amazing and you're investing uh and that's your passion now what do you think of these valuations it's quite seems you know if you live through the dot-com 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 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 know so on and 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 sas apocalypse there's going to be other apocalypses 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 quantum's been about five years away for 25 years um when is it actually going to do anything this decade this decade so by 2030. yep it'll be meaningful what should we expect in terms of its impact in 2030 like 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 salesman problem right you know all of a sudden all those problems uh obviously it's probably going to be you know 20 20 20 32 20 33 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 to uh 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 uh quantum and you know now it's just about engineering scale who's gonna win well obviously i'm a psy quantum guy right since 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 uh approaches spin uh approaches so you now say modality is not an issue error correction's been proven uh 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 all this incredible uh information and knowledge great to see him very good your most valuable conversations rarely happen at a desk the hallway sink the dinner the quick founder call plod no pin s clips on and captures all of it hands-free afterward plod 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 you're plod at plod.ai osika is one of my favorite founders he's the founder of lovable why do i love this founder uh well he's built a product that people are addicted to primarily anton the people who work for me and i uh love talking to you because as a 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 yeah essentially that's the mission of lovable 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 that product right and now we're at everyone at lovable we're working on both of these two gaps right the first one we've gotten very far we're seeing a million new projects built every single week on the incredible and on the 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 what you're whatever you're doing as a small business as if you're a large business we're also getting a lot of traction um 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 um extreme growth in in the surface area of the entire more than 50 million apps built on the platform to date how many years has lovable been in market or how many months now 20 months 20 months and again we're seeing people who are first-time founders we're seeing enterprise leaders move much faster together with their 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 which where we're actually growing fastest right now this is really interesting because 10 years ago uh people were doing WYSIWYG software um what was the name for it before vibe 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 um 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 how do you think about who is the customer because developers uh 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 that's about 20 are technical or some type of engineer and they 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 um we want from get payments set 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 uh appreciated by the engineers in the technical community um also because it's a great bridge from the non-technical people which is four out of five are not technical and they're building uh often first to figure out what is the right thing to build which is where lovable has always been exceptionally good and now what we're seeing is that people are running uh businesses making more than million dollars of revenue on the on this platform and so it's it's this we're building for everyone it's this entire spectrum and what's it what's exciting to see is often that if someone who discovers lovable from their colleagues at a large company they go out and then run a side hustle and some of those those idols hustles 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 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 uh 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 um 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 um but the thing that lovable does for any anyone also the non-technical people is that it it takes a greater structure for the architecture of the software that you build and it makes sure that you don't go off a cliff um 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 taken care of yeah you don't have to know how all these things work in the details you trust you can trust the platform to take care of data security uh connecting to other tools that you might be using in a secure way and and that's really where um us being opinionated from day one and being focused on making this for the 99 it's a vast market right right from the 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 in startups a year ago like and they made some interesting websites and uh they were trying to make an intranet they couldn't quite get it done then i had some people who started using you know cursor or claude code they started vibe coding stuff but they couldn't finish the product and then people tried to solve some problems with co-work i really like 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-accelerator they wanted to make it intranet um now this is something i would have never okayed because it would have cost five hundred thousand dollars ten 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 intranet and they made a bunch of things i hadn't asked for and it was the person running the um this founder university who made it and she did it on her own without uh permission in lovable i said whoa wait how did you build this she said lovable i was like oh we still have lovable 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 uh the the program in their country we have it 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 lovable 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 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 lovable 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 if you're on a business plan yeah it starts at 25. yeah so uh 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 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 it got made for less than two thousand dollars in a year it's extraordinary i'd love to hear more about the progress of the of the internet anything that you asks for that you want to forward directly to me uh 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 now asking people who do penetration testing to say i want you to compare all the tools yeah and make sure that there's all the work that we're doing that's not visible on security and trust yeah there's a lot of a lot of things um 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 um translates to something that security experts can can see and a year ago we were at mock-ups 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 build being able to build the product rights and and that you built an entire intranets on the platform that's great um what we've done since then is to have a new product line basically the hosting part which is both the ai and you know all the normal hosting and that product line has been growing faster than the building uh thing i i mentioned aws competitor it's uh let's let's you run all your software and then we're working with companies like aws under the hood as well but but what you also want to have is um to use lovable we're seeing by our customers as an ai co-founder a partner that you talk to about everything in your business and if you're running your apps your tools are on the platform then just talking to lovable has access to all the data that you might want to know about your about your company how it's doing so we're 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 it comes 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 serving your customers better uh faster and and and that's um that evolution towards operation and intelligence for towards driving towards outcome right your business so you come to build the software but you stay to build the business yes to operate your business right and um 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 uh it goes to a genetic system with our engineers in it improving it that compounding intelligence is of course applicable to our 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 ten thousand dollars 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 few what do you think the future will look like in terms of some of these you know uh foundational pieces of software that every startup every enterprise uses salesforce hubspot slack uh the google suite microsoft office will bespoke software start to replace those do you believe i like this question let me ask answer but i'll just give you a story about someone i recently heard who's going on this journey they're quite advanced so ninad he works at a pretty large company in the us nurse and he came to our platform because he wanted to build out the new product lines nurse a study for educating more nurses right and and he built out all the admin tools for the program the scheduling for the nurses getting getting uh 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 they have had all that access to nurses wanting their certification what he also did was he took it into the back office internally and they've now replaced more than 10 tools that they had oh wow bespoke applications and um i think 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 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 to date they aren't suited for those requirements exactly and in those cases i think yes you will have more more bespoke solutions yeah but we're all i will i also expect us to see that lovable continues to interoperate with all of those tools and uh i'm not sure if you tried this if you ask for connecting to anything in the google suite suite yeah or now so anything in the microsoft suite or 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 um hubspot and all the tools that you kind of 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 met we reached 500 in may okay growth is phenomenal so you're dying again by another 100 million in annual revenue exactly so but underneath the hood you're using some of these yeah let me explain yeah yeah so we've always had the strategy that we do whatever is best for our customers and in terms of the intelligence that means that we're using multiple models and so if you ask lovable 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 so 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 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 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 um 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 so and we're applying all the best practices to do that and scaling up that team uh quite significantly since we also believe it's a it's a part of the european ecosystem to have that capability in europe specifically are you doing or are you using any of the data labeling data training companies to help you understand the most common businesses and build that proprietary data so so what we're doing is that we're looking at the mistakes that any of the models do right now and then we we prioritize them by what drives most impact for our customers and then we make the models we create data sets we um we did something called reinforcement learning specifically for the problems where the frontier models are making mistakes for us right now and um we have this enormous token distribution right from um a million new projects being built every every single week yeah 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 this 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 it makes total sense and somebody told me some companies are doing token dumping they're you know selling a hundred dollars worth of tokens for fifty dollars um you know basically become token resellers in some ways and they're money losing businesses you have to your money 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 means more intelligence so we're not we're not looking at oh let's use it we've never had the decision to say we let's use a cheaper model here right if it's uh measurably worse for our customers and we can measure that what's best for what are they is it unlimited for the 50 or you have caps now we have caps yeah you have to have over just in caps are people starting to hit them yeah our customers definitely hit gaps and then you can top up you can yeah we have multiple subscription tiers what number 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 uh some of the from the lowest subscription tier yeah um i i think it's um uh much it's something like 60 percent of our customers 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 percent and one percent 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 and they are more ai usually lets you move much faster so the spend is usually worth it all right 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 intranet 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 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 and i had two swings at bat 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 franken software where you would be trying to get all the needs into it from the two different groups i yeah i'm actually a huge fan of very rapid experimentation yeah and i i have a story where for a while i worked at a place called cern where they do particle physics it's pretty here in central europe right and that's where i was introduced to this concept of co-opetition where they have two actually quite isolated teams working on the same um particle accelerator but different places on it and then they don't share their results until they publish and that way they uh they can kind of over time learn what's working west in the different organizations yeah 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 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 um if you have the sufficiently many humans right to do to try to attempt the 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 bring them over here and maybe even run a split test run an experiment to see if it's if it's improves improving the metrics for for our customers we're trying to serve did you see somebody used fable to build fortnite and uh i've seen this in 3d some of 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 we use fable as well as one of the models yeah so what do you think of it in terms of compared to the last generation faster better both yeah is it a massive step function yeah what i've seen is that it can in the first attempt create very sophisticated things that look really good then when as you're evolving right it's 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 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 on figuring what to what to build figuring out figuring out what are the right strategic directions or experiments you should run to improve the outcomes for your business that's the uh that's not changing as fast is the humans knowing how to use the tool to get the 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 uh 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 uh congratulations on being reborn six times because every six months you add 100 million in revenue it seems and then everybody says lovable is dead because uh the new foundation model is so good but you keep studying your customer and you keep somehow surviving and thriving so congratulations as an entrepreneur thank you so much jason i enjoyed this chat i hope you enjoyed the rest of you your stay here in paris it's pretty great and the palace of versailles is so impressive huh uh someday we'll be building this with lovable and optimist robots i'm looking forward to it