The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell (co-founder and CEO)

24:41So when we started building cursor, we were a fairly paranoid about spinning for a while without releasing to the world. And so to begin with, too, we actually, the first version of cursor was hand -rolled. We now we use the s code kind of as a base like many browsers use Chromium as a base in his fortoff of that. To begin with, we didn't and built the prototype of cursor from scratch. And that involved a lot of work. We had to build our own, you know, there were a lot of things that go into, you know, a longer code editor, including, you know, support for many different languages and navigation support for moving amongst the language, you know, error checking support for things.

25:26There's, you know, things like, you know, an integrated command line, the ability to use, like, remote servers to, you know, to the ability to capture remote servers to view and run code. And so we kind of just like went on this blitz of building things incredibly quickly, building kind of our own editor from scratch and then also the AI components. And it was after like a couple of months that we just, you know, it was after maybe five weeks that we were living on the editor full time. And you know, I had to run away our previous editor and we're using new one. And then once it got to a point where we found it a bit useful, then we put in another people's hands and had this like very short beta period.

26:05And then we launched it up to the world within a couple of months from the first line of code. I think it was probably probably two months. And it was definitely a like, you know, let's just get this out to people and build in public quickly. The thing that took us by surprise is we thought we would be building for a couple hundred people for a long time. And you know, from the get -go, there was kind of an immediate crusher of interests and a lot of feedback too. And you know, that was super helpful. We learned from that. And that's actually, you know, why we switched to being based off of VS Code instead of just, you know, this headrolled thing.

26:36A lot of that was motivated by kind of the initial user feedback. And, you know, and then have been iterating in public from there. I like how you understated the attraction that you got. I think you guys went from $0 to $100 million ARR in like a year, year and a half or something like that, which is historic. What do you think was the key to success of something like this? You're talking about dock footing being a big part of it. Like you build it in three months. That's insane. What do you think was the secret to your success? The first version was not the three of the version wasn't very good.

27:14And so I think it's been, you know, a sustained paranoia about, you know, there are all of these ways in which this thing could get better. You know, the end goal is really to invent a very new form of programming that involves automating a lot of coding as we know, no, no, today. And no matter, you know, where we are with cursor, it feels like we're very, very far away from that end goal. And so there's, there's always a lot to do. But I think it's been kind of a lot of it hasn't been overrooted on kind of that initial push. But instead is like the continued evolution of the tool and just making the tool consistently better.

27:47Was there an inflection point after those three months where things just started to really take off? To be honest, they felt fairly slow to begin with. And, you know, maybe maybe comes from some impatience on our part. But one, what I think, you know, there's that the overall speed of the growth, which is, you know, continuous to take us by surprise, I think one of the things that has been most surprising, Q, is that the growth has been fairly just consistent on an exponential of just consistent month over month growth accelerated at times by launches on our part and other things. But, you know, an exponential to begin with feels fairly slow and the numbers are really low.

28:32And to me, this sounds like building and they will come actually working. You guys just built an awesome product that you loved yourselves as engineers. You put it out. People just loved it, told everyone about it. It being essentially all just us, you know, the team working on the product and making the product good in the lieu of, you know, other things one could spend one's time on. You have wheat. We definitely spent time on tons of other things. For instance, building the team was incredibly important. And, you know, doing things like, you know, support rotations, very important. But some of the normal things that people would maybe reach for in building the company early on, we really let those fires burn for a long time, especially when it came to things like like sales and marketing.

29:15And so just working on the products and building a product that you like yours, your team likes, and then, you know, also then adjusting it for some set of users that can kind of sound simple. But then it's, you know, it's hard to do that well. And there are a bunch of different directions one could have run in a bunch of different product directions. And I think that, you know, one of the difficult things, you know, I think focus and kind of strategically picking the right things to build and prioritizing effectively as tricky. I think another thing that's tricky about this this domain is it's kind of the new very interesting area in that we are something in between a normal software company.

29:56And then, in between a normal software company and then a foundation model company in that, you know, we want to develop a, you know, we're developing a product from millions of people. And that, you know, that side of things has to be excellent. Then also one important dimension of product quality is doing more and more on the science and doing more and more on the model side of things in places where it makes sense. And so that element of things doing that well too has been tricky. But yeah, the overall thing would notice, you know, maybe, you know, some of these things sound simple to specify, but I'm like doing them well is hard in their rough to always you run.

30:30And I'm excited to have Andrew Luo joining us today. Andrew is CEO of one schema, one of our long time podcast sponsors, welcome, Andrew. Thanks for having me, Lenny. Great to be here. So what is new with one schema? I know that you work with some of my favorite companies like Ram and Vanta and Watershed. I heard you guys launched a new data intake product that automates the hours of manual work that teams spent importing and mapping and integrating CSV and Excel files. Yes. So we just launched the 2 .0 of one schema file feeds. We've rebuilt it from the ground up with AI. We saw so many customers coming to us with teams of data engineers that struggled with the manual work required to clean messy spreadsheets.

31:10File feeds 2 .0 allows non technical teams to automate the process of transforming CSV and Excel files with just a simple prompt. We support all the trickiest file integrations, SFTP, S3, and even email. I can tell you that if my team had to build integrations like this, how nice would it be to take this off our roadmap and instead use something like one schema? Absolutely, Lenny. We've heard so many horror stories of outages from even just a single bad record. In transactions, employee files, purchase orders, you name it. Debugging these issues is often like finding a needle in a haystack. One schema stops any bad data from entering your system and automatically validates your files, generating error reports with the exact issues in all bad files.

31:51I know that importing incorrect data can cause all kinds of pain for your customers and quickly lose their trust. Andrew, thank you so much for joining me. If you want to learn more, head on over to oncegeema .co. That's oncegeema .co. What is the most counter -intuitive thing you've learned so far about building cursor building AI products? I think one thing that's been counter -intuitive for us hinted at it a little bit before. We definitely didn't expect to be doing any of our own model development when we started. As mentioned, when we got into this, there were companies that were immediately from the get -go going and just focusing on training model from scratch.

32:30We had done the calculation for it to change it before and just knew that that was not convincingly we were going to be able to do. It also felt a little bit like focusing one's attention in the wrong area because there were lots of amazing models out there. Why do all this work to replicate what other players had done? Especially in the pre -training side of things, taking a neural net worth of the notes, nothing, and then teaching it the whole internet. We thought we were working on doing that at all. It seemed clear to us from the start that the existing models, there were lots of things that they could be doing for us.

33:05They weren't doing because there wasn't the right tool build for them. In fact, though, we do a ton of model development. Internally, it's a big focus for us in the hiring front and have assembled a fantastic team there. It's also been a big win on the product quality side of things for us. At this point, every natural -commoning cursor involves a custom model in some way. That was definitely counterintuitive and surprising. It's been a gradual thing, where there was an initial use case for a training run model where really didn't make sense to use any of the biggest foundation models. That was incredibly successful, moved to another use case that worked really well and had been going from there.

33:47One of the helpful things in doing this sort of model development is picking your spots carefully, not trying to reinvent the wheel, not trying to focus some places, and maybe where the best foundation models are excellent, but instead focusing on their weaknesses and how you can compliment them. I think this is going to be surprising to a lot of people hearing that you have your own models. When people talk about cursor and all the focus in the space, they would call them GPT wrappers. They're just sitting on top of chat -chapity or sonnet. What you're saying is that you have your own models.

34:19Talk about just the stack behind the scenes. We definitely use the biggest generation models a bunch of different ways. They're really important components of bringing the cursor experience to people. The place is where we use our own models. Sometimes it's to survey use case that a foundation model wouldn't be able to serve at all for cost or speed reasons. One example of that is the auto -comboly side of things. This can be a little bit tricky for people who don't code to understand, but code is this weird, pharma work, where sometimes really the next 5, 10, 20, 30 minutes of your work is entirely great from looking over your shoulder.

35:02I would contrast this with writing. Writing, a lot of people are familiar with Gmail's auto -complete and the different forms of auto -comboly that you're trying to get. Most tech messages or emails or things like that. They can only be so helpful because often it's just really not clear what you're going to be writing just by looking at what you've written before. But in code sometimes when you edit a part of a code base, it's just you're going to need to change things in other parts of a code base. It's entirely clear how you're going to need to change things. One core part of cursor is really suit the bottom complete experience where it predicts the next set of things you're going to be doing across multiple files across multiple places for a file.

35:42Making models good at that use case. One, there's the speed component of those models need to be really fast. They need to give you a completion within 300 milliseconds. There's also this cost component of running tons and tons and tons of molecules. Every keystroke we need to be changing our prediction from what you're going to do next. Then it's also this specialty use case of you need models that are really good not at completing the next token, just a generic text sequence. But are really good at autocompleteing a series of diffs, looking at what's changed within the code base and then creating the next set of things that are going to change, both deleted and added and all of that.

36:18We found a ton of success in training models specifically for that task. That's a place where no foundation models are involved. It's our own thing. We don't have a lot of labeling or branding about this in the app that cores of power is a very core part of cursor. Then another set of places where we're using our models are to help things like Sonnet or Gemini or GPT. Those sit both on the input of those big models and on the output. On the input side of things, those models are searching to write a code base, trying to figure out that parts of a code base to show to one of these big models. You can kind of think about this as like a mini Google search that's specifically built for finding the relevant parts of the code base to show one of these big models.

37:02And then on the output side of things, we take the sketches of the changes that these models are suggesting you make with that code base. Then we have models that then fill in the details of like the high level thinking, it's done by the smartest models. They spend a few tokens on doing that. Then these smaller specialty, incredibly jazz models coupled with some inference tricks, then take those high level changes and turn them actually into a full code disk. And so it's been super helpful for pushing on quality in places where you need specialty task. And it's been super helpful for pushing on speed, which is such an important dimension of product quality for us too.

37:39This is so interesting. I just said Kevin, Wheel on the podcast, CPO of OpenAI, and he calls this the ensemble of models. That's the same way they work. To use the best feature of each one and to your point, the cost, advantages of using cheaper models. These other models are based on like Lama and things like that, just open source models that you guys plug into and build on. Yeah, so again, we try to be very pragmatic about the place that we're going to do this work, and we don't want to reinvent the wheel. Starting from the very best pre -trained models that exist out there, often open source ones, sometimes in collaboration with these big model providers that don't share their weights out into the world.

38:21Because the thing we carve out last is the ability to read, bind by line, the matrix of weights that then go to give you your swimming output, and we just carve out the ability to train these things, to post train them. By and large, by and large, yes, open source models, sometimes working with the close source providers to you to tune things. This leads to a discussion that a lot of AI founders always think about in investors, which is MOATs and defensibility in AI. So it feels like one is custom models is a MOAT in the space. How do you just think about long -term defensibility in the space knowing there's other folks, as you said, launching constantly trying to take you, try to eat your lunch?

39:03I think that there are ways to build in inertia and traditional modes. But I think by and large, we're in a space where it is incumbent on us to continue to try to build the best thing and everyone in this industry. I truly just think that the ceiling is so high that no matter what entrenchments you build, you can be leap -wrought. And I think that this resembles markets that are maybe a little bit different from normal software markets, normal enterprise markets have passed. I think one that comes to mind is the market for search engines at the end of 1999, or at the end of the 90s and beginning of the 2000s.

39:51I think another market that comes to mind that resembles this market in many ways, is actually just like the development of the peripheral computer and many computers in the 70s, 80s, and I think that yes, in each of those markets, the ceiling was incredibly high. It was possible to switch. You could keep getting value for the incremental hour of a smart person's time, the incremental R &D dollar for a really long time. You wouldn't run out of useful things to build. And then in search in particular, not in the computer case, adding distribution was helpful for making the product better too, in that you could tune the algorithms, you could tune the learning based off of the data and the feedback you're getting from users.

40:35And I think that all of those dynamics exist in our market too. And so I think that maybe the sad truth for people like us, but then the amazing truth for the world is, I think that there are many leads to dogs that exist. There's many more useful things to build. We're a long way away from where we can be in 5, 10 years, and it's kind of incumbent on us to keep that engine going. So I'm hearing this sounds like a lot more like a consumer sort of moat, where it's just be the best thing consistently, so that people stick with the versus creating lock -in and things like that, where they're just sales force, where it's just contracted into our company and you have to use this product.

41:10Yeah, and I think the important thing to note is, if you're in a space where you kind of run out of useful things to do very quickly, then that's my great situation to begin. But if you're in a place where big investments and having more and more great people working on the right path can keep giving you value, then you can get these economies as caliber and D, and you can kind of have deeply work on the technology in the right direction and get to a place where that is defensible. But yes, I think there's a consumer like Tendency to it, and I really think it's just about building the best thing possible.

41:48Do you think in the future there's one winner in the space, or do you think it's going to be a world of a number of products like this? I think the market is just so very big, and this is also one thing that you ask about the IDE thing early on, and one thing that I think a trip of some people that were thinking about the space is they looked at the IDE market the past 10 years, and they said, who's making money off of that? Or is there a sell piece? It's this super fragment, it's a space where everyone kind of has the wrong thing, but the wrong configuration, and there's one company that commercially actually makes money off of making create editors, but that company is only so big.

42:32The conclusion was it was going to look like that in the future, and I think that the thing that people missed was that there was only so much you could do, building an editor in the 2010s for coders, and the company that made money off of editors was doing things like making it easy to navigate around a codebase, and doing some air checking and type checking for things, and having good debudging tools, which were very useful, but I think that the set of things you can build for programmers, I think the set of things you can build for knowledge workers in many different areas just goes very far and very deep, and I think that really the problem in front of all of us is the automation of a lot of busy work and knowledge work, and really changing all the areas in knowledge work in front of us to be much reliable and more productive.

43:19That was a long, long way to say, I think the market's really big that we're in, and it's much bigger than people have realized than other building tools for developers in the past, and I think that there will be a bunch of different solutions. I think that there will be one company, and to be determined if it's going to be us, but I do think there will be one company that builds the general tool that builds almost all the world's software, and that will be a very, very generationally big business. But I think that there will be kind of niches you can occupy in doing something for a particular segment of the market, or for a very particular part of the software development lifecycle, but the general programming shifts from just writing formal programming languages to something way higher level.

44:01This is the application you purchase and use to do that. I think that there will be generally one winner at there, and it will be a very big business. Juicy, along those lines, it's interesting that Microsoft was actually right at this, like at the center of this first, with an amazing product, amazing distribution co -pilot, you said, it was like the thing that got you over the hump of like, wow, there could be something really big here, and it doesn't feel like they're winning, feels like they're falling behind. What do you think, what do you think happened there? I think that there are specific historical reasons why co -pilot by none have lived up right so far, have kind of lived up to the expectations that some people have for it, and then I think that there are structural reasons.

44:47I think the structural reason is, and to be clear, in the co -pilot case, obviously, big inspiration for our work, in general, I think they do lots of awesome things, and we're users of many Microsoft products. But I think that this is a market that's not super friendly to incumbents, in that a market that's friendly to incumbents might be one where there's only so much to do, it kind of gets commoditized fairly quickly, and you can bundle that in with other products. And where the ROI between different products is quite small. And in that case, perhaps it doesn't make sense to buy the innovative solution, it makes sense to just kind of buy the thing spumbled in with other stuff.

45:31Another market that might be particularly helpful for incumbents is one where there's from the get -go, it's just like you have your stuff in one place, and it's really, really speculatingly hard to switch. And for better for worse, I think in our case, you can try out different tools, you can decide which product you think is better. And so that's not super friendly to join comments, and that's more friendly to whoever you think is going to have the most innovative product. And then the specific historical reasons, like as I understand them, are the group of people that worked on the first version of Copilot have by and large gone on to do other things at other places.

46:07I think it's been a little hard to kind of coordinate them on all the different departments and parties that might be involved in making something like this. I want to come back to CurseR. A question I like to ask everyone that's building a tool like this. If you could sit next to every new user that uses CurseR for the first time, just whisper a couple of tips in their ear to be more successful, most successful with CurseR. What would be like one or two tips? I think right now, and we'd want to fix this at a product level, a lot of being successful with CurseR is kind of having a taste for what the models can do, both what complexity of a task they can handle and how much you need to specify things to that model.

46:50But having a taste for the quality of the model and where it's gaps exists and what it can do and what it can't. Right now, we don't do a good job in the product of educating people around that, and maybe giving people some swim lanes, giving people some guidelines. But to develop that taste, we'd give two tips. One is, as mentioned before, we'd bias less toward like trying to have the model like, trying in one go to tell the model, hate yours exactly what I want you to do, then seeing the output and then either be disappointed or accepting the entire thing for an entire big task. Instead, what I would do is I would chop things up into bits and you can spend basically the same amount of time specifying things overall, but chopped up more.

47:36So you're specifying a little bit, you're getting a little bit of work, you're specifying a little bit, getting a little bit of work and not doing as much the like, let's write a giant thing, telling them all exactly what to do. I think that will be a little bit of a recipe for disaster right now. And so, by is saying toward chopping things up, at the same time, and it might make sense to do this on a side project and not on your professional work, I would encourage people to, especially, you know, developers who are kind of used to existing workflows for building software, I would encourage people to explicitly try to fall on their face and try to discover the limits of what these models can do by being ambitious and like kind of a safe environment, like perhaps a side project and trying to kind of go on to and you say, I have to the fullest because sometimes we do run, or a lot of the time we run into people who haven't even the AI yet, a fair shake and are kind of underestimating these abilities.

48:31So generally, by isom towards chopping things up and making things smaller, but like to discover the limits of what you can do there, like explicitly just kind of try to go for broke and a safe environment, you know, get a taste for it. You might be surprised in some of the places where the model doesn't break. What I'm essentially hearing is kind of build a gut feeling of what the model can do and how far it can take an idea versus just kind of guiding it along. And I bet that you need to rebuild this gut every time there's a new model launch, like when it's on it, I don't know, 4 .0 comes out, you have to kind of do this again, is that generally right?

49:04Yes, it's not, you know, for the past few years, it hasn't been as big as like I think the first kind of experience people have had with some of these big models. But yeah, you know, this is also a problem we would hope to solve much better just for users and take the brand off of them. But yeah, each of these things have slightly different quirks and different personalities. Kind of along these lines, something that people are always debating, tools like cursor, they more helpful to junior engineers or they more how faulted senior engineers, that they make senior engineers 10x better, do they make junior engineers more like senior engineers?

49:39What do you think most of who do you think it benefits most today from cursor? I think across the board, both of these cohorts benefit in big ways. It's a little hard to say on the relative ranking. I will say the fall into different anti -patterns. So I would, the junior engineers we see going a little to wholesale relying on AI for everything. And we're not yet in a place where you can kind of do that antenna and not a professional tool, working with tens hundreds of other people within a long -lived good base. And then the senior engineers, for many folks, it's not true for all. And we actually often, one of the ways these tools are adopted is there's developer experience, teams with and companies, often those are step -byte, incredibly senior people.

50:28Because often those are people who are building tools to make the rest of the engineers within an organization more productive. And we've seen some very, very boundary -pushing kind of, yeah, we've seen people who are on the front lines of really trying to adopt the technologies much as possible there. The buy and larger would say on average as a group, the senior engineers underrate what AI can do for them and stick to their existing workflows. And so the relative rankings a little hard, I think they fall into different anti -patterns, but they both buy and large yet get big benefits from these tools.

51:03That makes absolute sense. I love that it's like two ends of the spectrum, like expect too much, don't expect enough. And it's like the, about three bears, is that the allegory? Yeah. Yeah, okay. Yeah, maybe the sort of senior, but not staff, you know, it's bright and bright in the middle. Interesting. Okay, just a couple more questions. What's something that you wish you knew before you got into this role? If you could go back to Michael at the beginning of cursor, which was not that long ago, and you could give them some advice. What's something that you would tell them? The tough thing with this is she feels like so much of the, the hard one knowledge is tacit and a bit hard to communicate for bleed.

51:48And the sad fact of life feels like for, you know, for some areas of human endeavor, like you kind of do you need to fall on your face to either, either need to fall on your face to learn the correct thing or you need to be kind of around someone who's a great example of kind of excellence in the thing. And one area where we felt this is, is, is hiring. I think that we actually were, so we tried to be incredibly occasion on the hiring front. It was really important to us that, you know, both for personal reasons and also for, I can actually for the company strategy, having a world -class group of engineers and researchers to work on on cursor with us, was going to be incredibly important.

52:35Also getting people who sit, you know, a certain mix of, you know, intellectual curiosity and experimentation because there can be so many new things we need to build. And then also kind of an intellectual honesty. And maybe micro pessimism and bluntness, because you know, with all the noise and, you know, especially as the company's grown and the businesses grown, you know, keeping a level ahead, I think is an incredibly important issue. But getting the right group of people into the company, you know, was, you know, the thing that made me more than anything else, apart from, apart from building the product, we really, really, you know, fussed over.

53:12And, you know, we actually waited a long time to grow the team because of that. And I think that most, you know, many people you hear are higher too fast. I think we actually are too slow to begin with. I think it could have been remedied. I think we could have been better at it. And, you know, the method of recruiting that we ended up eventually falling into and working really well for us, which isn't that novel of like going after people that we think are really world -class and like recruiting them over the course of, in some cases, many years, I ended up working for us in the end, but I don't think we were very good at it to begin with.

53:49And so I think that there were hard -won lessons around both who was the right profile, like who actually meets on the team, like what did greatness look like, and then how to, you know, talk with someone about the opportunity and, you know, get them excited if they really weren't looking for anything. There were lots of kind of learnings there about how to do that well, and that's a good subject of time. What are some of those learnings, folks that are, you know, firing right now, what's something you missed or learned? I think, you know, to start with, we maybe, we actually biased a little bit too much towards looking for people who fit the archetype of well -known school very young, had done the things that were like, you know, high credential in those well -known school environments.

54:37And actually, like, you know, I think, Sean, we're lucky early on to find a lot of, you know, to find fantastic people who are willing to, you know, to do this with us, who were later careered. And so, yeah, I think we should kind of spend a bunch of time on maybe a little bit of the wrong profile to begin with, and part of that was a seniority thing. Part of that was like, you know, kind of an interest and experience thing too. We have hired people who are excellent, excellent, excellent, and very young, but they maybe look, in some cases, slightly different from, you know, being straight out of central casting.

55:11You know, another lesson is just like, we very much evolved our interview loop. And so, now we, you know, we have like a hand -rolled set of interview questions and then, you know, kind of core to our, um, core to how we interview too is actually we have people on site for two days, and do, do a project with us, a work test project. And, um, that has worked really well, but increasingly, you're finding that. And then, yeah, I think how to, to learn about what people are interested in and, you know, put our best foot forward and, and letting them know about the opportunity when they're really not looking for anything and have those conversations.

55:49There's definitely been, you know, got in, got in better at that over time. The other favorite interview question they like to ask? I think this two -day work test, which we thought would not scale past a few people has been, has had surprising staying power. And the great thing about it is, it lets someone go end to end on it like a, a real project. It's, it's not, you know, work to we use as kind of a, kind of a, a can to list a project. Um, but it gives you two days of seeing, like, a real work product. And, um, it doesn't have to be incredibly time and has another chance for, uh, time, you know, you can take the time you spend in like a half day or one day on site and you kind of spread it out over those two days and give someone a lot of time to do, to do work on their projects.

56:32And so that can actually help it, help it scale. And then it really helps you, it helps you enforce, you know, do you want to be around this person type test? Because you are around this person, uh, you know, for two days and so the, you know, a bunch of meals with them. And, uh, so that one, we didn't expect that one to stick around, but that has been really, really important to our value to process. And then also important to getting people excited at the, especially the very early stages of the company, because before people are using the product and know about it, and you know, when the, the product is comparatively like not very good, really the only thing you have going for you is, you know, a team of people all that, you know, some, some people find spefel and want to be around.

57:13And, you know, the two days, you would, would give us a chance to just like, you know, have this person, uh, meet us and, uh, in some cases, hopefully get gifts and events that they want to throw in with us. And so yeah, that one, that one was unexpected, not exactly an interview question, but kind of like, you know, a forward interview. The ultimate interview question. So just to be very clear about we're describing, it's that you give them an assignment, like build this feature in our actual code base, work with the team to, uh, code it and ship it. That roughly right. Uh, yes, not, not like, so we don't use the IP, not should end.

57:44But yeah, it's like a mock, like, yeah, very often in our code base. Here's a real mini two day project. You're going to do it at hand, largely being left alone, you know, there's, there's collaboration too. Uh, and then, you know, we're, we're a pretty impressive company. So, and almost all cases, yeah, it's actually just sitting in office with us too. And you've been saying that this has scaled to even today's, how, how big are you guys at this point? Uh, so we are going on 60 people. So small for the scale and impact that I was, I was thinking it'd be a lot larger than that. Yeah. And I imagine the largest percentages engineers.

58:19Yeah, the thing that's more than anything, and to be clear, you know, a big part of the work ahead of us is, is, is building a group of people that is, is bigger and awesome and can continue to make the, the product better in the service we give to customer's better. And so you don't plan to stay that small for longer, we wouldn't, we didn't hope so. But, uh, yeah, part, part of the reason that that number is, is small, is the percentage of, of engineering and research and design is very high within the company. And so, uh, many software companies when they have, you know, rustley, four engineers would be over a hundred people because there's lots of operational work and often they're very, very sales led from the get go.

58:58Uh, and that's just quite labor intensive. And, you know, we started from a place of being like incredibly lean in product led and like, you know, we now serve lots of well -market customers and I built that out, but you know, there's much more to do there. Question I wanted to ask you, there's so much happening in AI, there's things launching every, there's like newsletters, like many newsletters whose entire function is to tell you what is happening in AI every single day, running a company that's at the center, kind of the White Hot Center of the space. How do you stay focused and how do you help your team stay focused and headstown and just build and not get distracted by all these shiny things?

59:34You know, I think hiring is a big part of it. And if you get people with the right attitude, um, and you know, all of this should be asked for it in like, you know, I think we're doing well there. I think that like, you know, we'd probably be doing better there too. And, um, you know, it's something that we should probably talk even more about as a company. But I think that, you know, hiring people with the right disposition, you know, people who are less focused on external validation and more focused on building something really great, more focused on doing really high quality work. And people who are just generally kind of level, level headed and maybe like the high turn very high and lows aren't very low.

1:00:11I think hiring can get you through a lot here. And I think that's, that's actually like, you know, a learning throughout the company is that, you know, for any, you need process, you need hierarchy, you need lots of things. But for, for any kind of organizational tool that you're introducing into a company, you know, the result you're looking to get from that tool. Also, you know, you can go pretty far on like hiring people with the right behaviors that you want, like, you know, to result from that for organizational thing. And, you know, the specific example that comes to mind is we've been able to get away with not a ton of process yet on the engineering front.

1:00:47I think we need a little bit more process, but for our size not a ton of process, by hiring people who I think are really excellent. You know, one is, you know, hiring people are a level headed. I think two is just talking about it a lot. I think three is hopefully leading by example. And yeah, for us personally, you know, we've been out since 2021, 2022 and professionally working on on this and working on AI. And we've just seen a sea change of the coming and goings of various technologies and ideas of, you know, if you're to transport yourself back to end of 2021 beginning of 2022. This is GPT -3, you know, in Stratux GPT as it exists, there's no Dali, there's no stable diffusion.

1:01:25And then, you know, we've gone through all of those image technologies existing, Stratux GPT in that rise. And, you know, G4, all of these new models, all these different modalities, all the video stuff. And only, you know, a very small number of these things really kind of affect the business. So I think we've kind of just built up a little bit of an immune system and kind of know when an event comes around that actually is really going to matter for us. And this is, you know, this dynamic too of there being lots and lots and lots of chatter, but then maybe only a few things that really matter.

1:02:02I think it's been meered in AI over the last decade where there have been so many papers on deep learning in academia. So many people are on AI in academia. But then the amazing thing is there are really a lot of, I mean, a lot of the progress of AI can be attributed to some very simple, elegant ideas that have stayed around. And the vast majority of ideas that have been put out there haven't had to end hard and haven't added a ton. And so the data was a little bit mirrored in kind of the evolution deep learning as a field of real. Last question. What do you think people still most misunderstand or maybe don't fully grasp about where things are heading with AI and building in the way the world will change?

1:02:45People are still a little bit, you know, occupied too much either end of a spectrum of, you know, it's all going to happen very fast. And, this is all bluster and type and so forth. Snake well. And, you know, I think we're in the middle of a technology shift that's going to be incredibly consequential. You need to be more consequential in the internet. And it's going to be more consequential in, you know, any shift intact that we've seen since the advent of computers. And I think it's going to take a while. And I think it's going to be a multi decade thing. And I think many different groups will be consequential in pushing it forward.

1:03:24And, you know, to get to a world where computers can increasingly do more and more and more for us, there's all of these independent problems that need to be not down and progress needs to be made on them. And some of those are on the side side of things of getting these models to understand different types of data, disaster, cheaper, smarter, you know, conform to the, yeah, the modalities that we care about, you know, take actions in the real world. And then some of it's on the like, how we're going to work with them. And, you know, what's the, you know, what's the experience a human should actually be seeing and controlling on a computer and working with these things.

1:03:57But I think it's going to, you know, it's going to take decades. I think that there's going to be lots of amazing work to do. I think that also, you know, one of the most, like, a pattern of a group that I think will be especially important here, you know, not to talk or a book. But I think it's like, you know, the company that works on automating and augmenting a particular area of knowledge work builds the both the technology under, you know, under the surface for that, integrating the best parts from providers, sometimes doing it in -house. And then also builds the, you know, the product experience for that.

1:04:30I think people who do that and, you know, we're doing it and trying to do it in software, people who do that in other areas. I think those folks will be really, really, really consequential, not just for, like, you know, the end value that you use to see. But then I think as they get to scale, they'll be really important for pushing forward, you know, the technology. Because I think they'll be able to build, you know, the most testable of them will be able to build very, very big businesses. And, yeah, so excited to see the rise of, you know, other companies like that in other areas. And then you guys are hiring for folks that are interested in, hey, I want to go work here and build a sort of stuff.

1:05:05What kind of roles are you looking for? Now anyone, specifically you're trying to, any roles you're most excited about filling ASAP, what should people now? If they're curious? There are so many things that this group of people need to do that, like, we are not get equipped to do. And so, you know, kind of generic across the board, first of all. And so, if you don't think we have a role for something, maybe if you reach out, that won't actually be the case. And then we can actually learn from you and kind of decide that we need something that we weren't yet aware of. But, you know, by and large, I think that, you know, two of the most important things for us to do this year are have the best product in the space and then grow it.

1:05:43And we're kind of in this land grab mode where almost everyone in the world is either using no tool like ours or they're using one that's maybe developing less quickly. And so, growing, growing, Kershichiro is a big goal. And I would say, yeah, especially always on the hunt for folks who excellent engineers, designers, researchers, but then folks in all across the business side too. I can help but ask this question that you talk about engineers. There's kind of this question of just like, you know, codes are going to write up all our code. AI is going to write all our code. But everyone's still hiring engineers like crazy, all the foundational models so many.

1:06:24We're not searching the, you know, the horn of, do you think there's going to be an inflection point of like engineering role start to kind of slow down? I know this is like a big question, but just it's, do you see engineers being more and more needed across all these companies? Or do you think at some point there's all these cursor agents running building for us? Again, we kind of have the view that like there's this, you know, both long day just like you step back and you ask for all your stuff to be done and you have your engineering to prevent. And you know, very much like you want to evolve from programming as it exists today.

1:07:05We want humans to be in the driver's seat. And you know, we think even in the end state, like that's, you know, giving folks control over everything is really important. You will need professionals to do that and kind of decide what the software looks like. So both, both I think that yes, like, you know, like, you know, engineers are definitely needed. I think that engineers will be able to do much more. I think the demand for software is very lasting, which is, you know, not the most novel thing, but I think it's, it's kind of crazy to think about how expensive and labor intensive it is to build things that are pretty simple and easy to specify or would look like it to the outside of server.

1:07:45And, you know, just how far those things are to do right now. And so if you can, you know, all of the stuff that exists right now that's, you know, justified by the cost and demand that we have now, if you could bring that down by order so I should, I think you would have tons and tons and tons of more stuff that we could do our computers, tons more tools. And, you know, I felt this where, you know, one of my early jobs actually was working for a biotechnology company. And it was building internal tools for them. And the off the shelf tools that existed were horrible. And did not fit their use case at all.

1:08:15And then the internal tools I was building, there was definitely a ton of demand there for things that could be built. And, you know, that far outstrip, just the things that I could build in the time that I was with them. But yes, I think that it's still so, you know, the physics of working on computers are so great. It should be able to, you should be able to kind of basically just move everything around. Do everything that you want to do. There's still so much friction. I think there's much more demand for the software than for software than what we can build today with, you know, things costing like a blockbuster movies make kind of simple productivity software.

1:08:47And so I think long into the future, yes, there will actually be more demand for engineers. Is there anything that we didn't cover that you wanted to mention in the last negative was them you wanted to leave listeners with? You could also say no because we've got a lot. We think a lot about how you set up a team to be able to make new stuff in addition to like continuing to improve the stuff that you have right now. And I think if we were to be successful, like, yeah, IDE is going to have to change a ton. What for like looks like it's going to have to change a time going into the future. And, you know, if you look around the companies we respect, there are definitely examples of companies that have continued to really, like, you know, ride the wave of many lead frogs and continue to kind of actually push the frontier.

1:09:35But, you know, they're kind of rare too. Like it's a hard thing to do. And so, you know, part of that is just kind of thinking about the thing and trying to reflect on it, you know, in our air days and, you know, the first principle side of things. But it is also, you know, trying to get in and study past examples of greatness here. And, you know, that's something that we think about a lot too. Yeah, but what you just told is we were before we started recording all these books behind you. And I was like, what's that over there? It's like the history of some old computer company that was influential in a lot of ways that I've never heard of.

1:10:11And I think that says a lot about you, where a lot of this innovation comes from, studying the past and studying history and what's worked in, what hasn't. Okay. Working folks find it online. If they want to reach out and maybe apply, you said that there may be roles you, they may not even be aware of. Where do they go find that? And then how could listeners be useful to you? Yeah, I, you know, if folks are, you know, interested in working on this stuff would love to speak. And they can find, if they get a stock home, they can kind of both find the product and find a, how to reach us. So easy.

1:10:42Michael, thank you so much for being here. This was incredible. It was wonderful. Thank you. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's podcast .com. See you in the next episode.