How micro1 grew from $4M to $200M revenue in a year | Ali Ansari

28:14And when we did that, we 30X in revenue. Um, 2025, we 30X in revenue. And so, you know, the decision worked from like a small base either. It's from a, we started the year with roughly four or 5 million run rates. And then we, um, You know, ended with like roughly 150. And so that was a decision that worked. And I think we try to make a few of these bold decisions each year. And if it doesn't work, it's OK. There's a lot of great learnings in it. And there's been a lot of those as well. So one of the things I've noticed throughout researching you is this idea of incentives. And we talked a little bit about this beforehand.

28:59But one of the biggest things that Warren Buffett has been very focused on is he's basically just this massive incentive aligner, and he tries to figure out how can we structure incentives at each individual company that we control so that everyone is basically creating long-term value for the company and then just funneling as much cash up to Berkshire to reallocate as possible. And if you have the wrong incentives for something like insurance, you know, you'll write a bunch of insurance that doesn't actually make sense And there's this big spike and you've grown a bunch but then the problem is is three years four years later You've lost all this money because of it But because there's some you know executives that will turn over so quickly You can have a situation where person grows the company a bunch leaves gets a bit massive like basically pay And then the results or issues happen only after they've left how do you think about basically designing incentives so that everyone wins on the right time horizon what i focus pretty much my entire time on or at least i try to is three things one is hiring to his product and three is aligning incentives like i literally sit down i think about the incentives that everyone has and their bonus structures and their equity and everything else and i try to keep optimizing them and sometimes i'm sitting there and like there's not many actions to take.

30:17It's almost like you have to come up with random actions. But I try to think about this every single week for hours a week. And I think, as you said, you have to align incentives very long-term as the main priority through equity. And I'm very grateful to be a sole founder. So I'm able to give a bit more equity to the founding team and really everyone that kind of joins even now. So that's definitely part of it. But I think another part of it is it's actually important to align short-term incentives as well. And I think perhaps this isn't thought about enough. I think when you're in a space that is growing so fast and three months matter a lot and like three months can literally double your run rate, you actually do need short-term incentives.

31:08And so like, you know, I'll give you like two random examples. which is, you know, we have our recruitment team sometimes gets these like absurd bonuses if they hire like a thousand people. And it's like a recruiter has never hired a thousand people in like two weeks. That's not something that you train for. Yeah. So like if this like completely outlier event is happening, they should get a pretty massive bonus. Like that's that's totally fine. I think the second thing is there's also these other outlier events, which is sometimes we have a customer that is about to sign a really massive contract, let's say, and we might align incentives with them in some other ways as well.

31:51And there's one person that's a key person that's leading this. I might tell them, hey, if this closes and therefore the company changes forever, you'll double your equity. and that i think you know our our general counsel our cfo is like you know what the hell is that like oh how do you do what do you do that but but i think this stuff is um there's like more short-term incentives are actually also very important and especially ones that are designed that that incentivize you very much so short-term but then also long-term in this case of like if you double your equity because the company has changed forever because of the deal that you're leading, you now have more equity for the long term.

32:35So I think this balance is quite important. That's kind of interesting. It's a little bit like you do get that short-term bonus, but it's in this form of a long-term instrument. Exactly. And the short-term bonus is also vested the same way that the rest gets vested. So you get this, you know that you're about to double your equity and change a lot about your life if we succeed, but you still have that vesting and the vesting actually restarts for that top up. So that's the, yeah, I think that that's the key part of it. I'd be curious to know, like, how did you come to this conclusion? Because that's something that I haven't heard almost anyone else do.

33:13I've seen people give like cash compensation and stuff to people for pulling something off or just going above and beyond. But I've never heard of a company, founder in particular specifically structuring individual comp so that it basically helps massively assuming deals go right it can help massively I'm not sure I actually came up with it I think it's a bit of an absurd idea and I also have not heard of anybody else doing it I heard I heard Jensen from NVIDIA I think he does something like this when I heard that it was like very reassuring that I'm not like completely like insane but you were doing this for months or like even over a year before you heard about this from Jensen.

33:54I actually was, I was thinking like maybe this is a bit of a too insane of a thing to do. So I was searching, like hoping somebody else does this. Like that's like a great founder, obviously like Jensen. And I couldn't find anyone other than Jensen. I think it was like something similar. It wasn't exactly the same. So that was a bit reassuring. But I don't know. I don't know how I came up with that. I think I just kind of thought like, hey, if this happens, we will, our probability of long-term success really materially increases, but also the short-term enterprise value that gets added is like pretty immense.

34:26And let's have this person get, you know, some portion of that. And I think that's the second thing is like, we're, we're in a very special space, um, that is, that has resulted in a lot of really fast growing companies. Of course, you know, it's not just us, We have a lot of great competitors and they're also growing incredibly fast. And it's because of the just the immense demand there is for data. And so, you know, I think we're when you're in this sort of outlier state, you should do these outlier things. Has there been any other things that you like other actions that you take that other companies and other founders don't take that you think are massive unlocks?

35:08I think this idea of actually caring for your team's happiness, similar to how we do with our experts. And the term we like to use is we want to get to this mode of being sustainably hardcore as soon as we can. Sustainably hardcore? Yes. Because right now it's not so sustainable right now. I mean, it's pretty much everyone on the team does 13, 14 hour days and, you know, leadership does even more than that. And it's not like basically if you're awake, you're thinking about it. Exactly. Like literally every waking hour for almost the entire core team is work. And it's a great place to be. And even I like I mean, I work pretty much every second of the day, but I sometimes feel like I'm not working as much as some other team members.

35:58And that's like I feel very honored to be in that place, you know. So we have to keep that going for a long time. But we also have to keep in the back of our mind that at some point, hopefully in the next few years, we'll get to a more sustainably hardcore structure. And I think a big part of that, even now, is we actually don't enforce working weekends. And it sounds a bit soft, but we actually have our team, I would bet we have our team work more weekends than most teams because we don't enforce it and we try to inspire it. And the leadership works basically every weekend, late nights, so forth.

36:51And there are pipelines that really need you in the weekend. and so you will be and the incentives are there for you to be inspired to work weekends and do so every weekend and so we don't put these like arbitrary constraints of like forcing folks to put put certain number of hours like this idea of like 996 and so forth I actually don't buy that I think that's like ridiculous and sounds really lame I think you instead should go until 12 a.m not till 9 p.m because you've inspired it and not because you've forced it and we've heard a lot of I've heard from a lot of my founder friends that are that know about some other founders that that force this sort of thing and it results in a lot of really unhappy team members and that doesn't last how do you design things so that you do create this culture like I this this reminds me of almost like sleeping with the troops you basically are in the same tent as everyone else or not, and you're sleeping on the ground.

37:47And I think the best form motivation is just seeing whoever the leader is working. And, you know, if they are going through hell, typically, you can you can kind of sign people up to do the Ernest Shackleton, let's go travel into the great unknown and be cold and be wet. And probably there might be success at the end of it, maybe. And if there is, there's glory and everything. But how do you kind of design your own life so that you are creating that natural feeling inside of people? The way I look at it is the hours that the founder puts in is sort of the max range that the company will put in broadly.

38:27And of course, again, very grateful to be in a place where there actually is some team members that put in more. But generally, the max is what the founder puts in. So if you put in 12 hours a day on average that that's certainly the max that people will put in so I think the first thing is you you just have to grind really hard the second thing is um you have to be in the details and now I'm going through this phase of uh and so is the rest of the kind of leadership going through this phase of of balancing being in the details and like doing actually useful things for the company. But if you're outside of the details too much, folks that are making the day-to-day decisions that really matter for any pipeline we're working on or any recruitment funnel or whatever it is, you actually won't have enough context to give the right opinion on when things get stuck.

39:24And so you cannot be too abstract away from details. So I try to stay in the details as as much as I can. And I think there's a, you know, there's, there's a sense of respect that everyone has for each other because everyone's hands-on. Like we have our CFO join us and he's running payroll himself and he's answering payroll questions to experts himself. And we have our general counsel join and there's no other lawyers. She's doing everything. So this, this idea of like every leader needs to be very hands-on. And of course, in the long run, you have to kind of converge to, you know, maybe 20 % of your time is hands on.

39:58You can't realistically be hands on your whole, whole, whole time. But, but, but having that 20 % really be there and not really go below that is, is really important in the way that we function. Yeah. There's this line that I love, which is if you're going through hell, just keep going. What's been the biggest moment where you and the team felt like you were going through hell? There's, there's this one time where we had a massive customer that, this was a couple of years ago, that was almost 50 % of our revenue. And I'll keep the details a little vague, but we essentially lost this customer like this.

40:41And it had to do with some external factors. And I remember the moment that we lost this customer. I was in the elevator going up to pitch some investors. Perfect time. I was literally, this was during one of our early rounds. And there's momentum. And we were about to close the round in a huge manner. And I'm going and pitching this great firm. And it's a partner's pitch. So a bit nervous. It's not really a conversation. You've got to just kind of pitch the whole time. and I remember I look at my phone and there's this um this email that we've lost this customer like literally as the elevator opens and it's like like why is this like why does it happen like this why does it have to be like a movie scene like just and so I you know I I read that email and then I go in and I um pitch anyways and I'm like about to like faint during the pitch because we just like lost the company and so um but I put on a I put on a face anyways and I you know do the pitch and the pitch goes well but obviously the revenue is not the same anymore so we have to correct that um but I remember the you know I kind of I leave that pitch and I go and kind of start walking endlessly and I'm in the middle of Palo Alto and I just uh put my laptop on the ground somewhere the laptop was like open and like it's like just like on the sidewalk and I just like I just like start walking and I like the laptop just like left and I'm like walking around and just doing like random stuff.

42:13And it was a very painful moment. And I, um, I mean, it was, it was, it was as, as painful as it gets. Um, and I remember we had to lay off a bunch of people. Um, and we thought that we probably, we, we might not survive this. And we had, um, you know, we were very close to not being able to make payroll and all, you know, all the rest. And I remember telling myself, if we get through this, this would be a great story to tell. Glad we're saying it now. And it will really allow the team to have something to bond on in a way that's just really intense. And now in retrospect, I'm glad that it happened this way.

43:11and I wouldn't really change a thing about it. And of course, there's been a lot of other moments since, but we did pull through, thankfully, and now it's a moment that we think back on a lot. This kind of reminds me, I think during COVID, Airbnb's revenue over the course of something like eight weeks dropped by 80%. And there was like the headlines about like, is Airbnb going to survive? and I remember before COVID, Brian Chesky and Airbnb were trying to go in all these different directions and what ended up happening was because of that existential crisis. It allowed, you know, Brian to basically say, no, we're just going to focus on this one thing, which is the stays.

43:52And that's what they did for three or four years after that. And it kind of eliminated all this distraction. Did you have anything come from that? Like you guys make different decisions going forward? Yes. And by the way, I know exactly what you're talking about. I watched that podcast that Brian Chesky was talking about this. And I think that actually, that podcast had the most impact on how I think about product. Really? And product roadmaps and this idea of like having one roadmap that Brian Chesky himself approves every module for. And we try to do that as much as we can. I think it's like the way he's explaining is that each of these features affect millions of people that are having these experiences.

44:38and me approving them for like 30 seconds, it actually does not take a lot of time. And the design and engineering and the impact that each of those features will have is so large that I should spend time approving every single one of the modules I'll go into Airbnb. And I would argue Airbnb is like one of the most delightful experiences in terms of like a user experience interface in terms of an app, but also like in terms of the product itself. So that had a lot of impact on me. But to your question, I think, you know, when the situation happened, we I got in this mode of actually not wanting to take a risk for for a few weeks.

45:15I was I started to be a bit risk averse. And was it just like a state of fear? It was a state of fear because not to get to the details of like what happened here, but part of it was because of an action that that that we took. But it was mostly external. and in retrospect, we found out that it was actually entirely external. But we'll tell that story later. But there's a – so I got in this mode of like for a few weeks of just being very scared to take actions and kind of – and I got very afraid that like, shit, this might change the way I operate because I like to be very risky and I like to – I like to kind of set up these initiatives and take a bunch of bold moves like the other day we set up this like really crazy incentive structure for if we hit this wild run rate by April the team gets like like 50 % of their uh total comp as like bonus like these like wild things and and like you know I should I should have like approved that by like a couple like you know Gerald Council maybe board and so forth but but I just like I just I just went with it and and I do a lot of things like this.

46:28And those, those few weeks, right after this, this thing happened, I, um, I felt like I was about to get into this mode of like never doing those things again and being very risk averse and like, kind of like a managerial style CEO. And I, uh, I remember very, very explicitly that I spent two days thinking about this and convincing myself that if I go into this mode and I don't change this now, this will be the worst possible thing for the company, for myself, for my career, for everything. And I decided that I will force myself to have the same level of risk as before. And in fact, even more than before right away, because this just simply can't happen.

47:06That was like the biggest pivotal moment that I had to make this decision. And I, you know, I felt the exact same thing. I remember there's been many moments in my life where I love risk, but I love risk that I can control. You know, so there's, there's basically, I think our brains are naturally designed to fear the loss, like fear loss twice as much as we anticipate gain. And so you kind of have to counteract that and correct for that, which means that in all likelihood, any decision that you're making, you're actually over-indexing on the risk of the decision when, in fact, there's probably a lot less risk than you actually think.

47:41And the risk may be long-term in not taking the call at all. How did you kind of see that manifest over that couple-week period where you were kind of going into a shell almost like a turtle and then figuring out no I can't do this I remember when I would make even small decisions within the company in that in that two-week period I felt the sense of maybe I'm actually not the best one to make these decisions and I started to think again very managerial like maybe I should start to hire these you know more professional folks to help me make people that know what they're doing. Exactly. And, and, you know, have, uh, have maybe more experienced folks join us.

48:23And cause they, you know, the, the strategy we've taken is, um, hire, you know, really kind of recent grads and inexperienced people and, uh, and put them, you know, put them to work. Um, and that has done really well for us. And so, but I started to kind of change this model a bit briefly. Um, and I think that the thing that helped me get out of this pretty fast is I came to the realization that the company depends on it. And the founder's job is to inject as much risk as they can into the company. Because really no one else will. Like outside risk? Yeah. I mean, like just bold moves that have a pretty bad downside also.

49:08But if it works, it works well. And I think the reason why naturally other folks in the company don't do this is because they don't want to get laid off. I mean, they want to continue working at the company. And so really the only folks that really can get laid off are the ones that can ingest the most risk into the company. And so if they don't, no one else will. And you'll just have a company that is not risky and doing some basic stuff. And And obviously, if you're not taking risks, you're not going to grow. So I came to that realization that it is my duty to ingest risk into the company. I have to do it.

49:41No one else will do it. The second thing was, if you think about risk taking and you think about what is the worst case scenario, anything that you do has the worst case scenario that is really bad. I mean, us driving here today, God forbid, has the worst case scenario of us not being here today. You can't think about that risk as you decide to drive here. So if you're making a business decision, in almost every case, the worst case scenario is you're going to go bankrupt. And so if you're assessing the worst case scenario every time, it's just like not a helpful data point. So you have to think about the expected value.

50:21And you have to think about this distribution of probability that exists within this decision that you're about to make. And there's obviously, again, a long tail of really bad outcomes, but they have very low probabilities. And if you can kind of if you can come with this, like just just intuition, there's no math that's involved here. But like just this intuition of like, what is the distribution of of risk taking here and and come to a decision that I think that framework allows you to be bold. Jeff Bezos even talked about this a little bit where he basically says like the role of especially an executive is to make like a few really good decisions a year.

50:54And then for the most part, you know, he even says like if there's a decision late at night that's going to have a massive impact on the company, he just will delay it until the next morning. And that's like a 10 a.m. meeting for him. Do you have the same kind of philosophy or framework where if there's something really big that you have to decide on, you hold off until the next morning? You know, maybe it's 12 a.m. at night and you've been up for 18 hours. how do you decide like what thing you're going to take action on versus a delay? Not to disagree with Bezos here, but I actually don't do that.

51:25If an urge to act on something comes about, I try to do it as fast as possible. Of course, partly to just move fast, but I think more importantly to not think about the action too much, frankly. Because a lot of the risks that we've taken, whether it's as simple as DMing a customer POC about some new pipeline that they may be interested in, or whether it's this crazy bonus structure we set for a milestone we have in April. Those things happen when I get the urge to do it, they happen best. And of course, I have to come up with the actual plan. And if it's something as simple as DMing a certain customer, if I decide to do it the next morning, I might kind of say, okay, maybe we do it in a week maybe they actually it's not the best time to reach them and so forth but what i realized is that like almost all those actions end up doing something good for the company and they often happen very late at night right away when i get the urge to do it i think this kind of comes back to like training your intuition and you have to figure out what are the right people and models to run through your own head so that when you do get this piece of information where it's like you can take this action right now and has some massive impact whether upside or downside how do you decide and how do you decide quickly?

52:40How have you kind of gone about training your intuition over time? It's funny you ask this. I actually, I think a lot of folks, even like newer folks at MicroOne, they think that I'm very analytical and actually I'm not at all. We have folks in the company that force us to kind of set quarterly KPIs and so forth. And we do a little bit of that just because there's a lot of, you know, there's a lot of roles that you do need that for like sales and and so forth but i actually like the way i do it is um i try to set structures that are that are a bit more qualitative and i i try to use my intuition as much as i can as i assess whether someone has had an exceptional outcome in the corner in the quarter or or not not an exceptional outcome and my my philosophy here is that when you dumb down someone's 12 hour days every single day for quarters straight to a few KPIs, you're really like, you may optimize for the wrong thing.

53:45And for a company that is growing fast, those KPIs will likely have to change pretty much right away, like within a week, you have to keep changing them. And you end up spending a lot of time just changing KPIs. And so one thing that I've done is we've added this, and this is not for everyone in the team, but for most folks where we have this, if the role is very directly related to like revenue, we have this like revenue override, which is if we hit our crazy ambitious goals for the end of the quarter, your KPIs actually don't matter and you should just do what's best to increase revenue and build a great product.

54:25And so I try to like distill these things into this idea of like being analytical and having these like very key KPIs because I think it actually results and people doing things that are most important at any given time for the company versus optimizing for their own kind of like arbitrary set KPIs. And I also look at it as like, if you, like the human brain, when it, this idea of like intuition is basically like a really large neural net that is considering many KPIs at once as it comes to a decision. But if you're looking at three KPIs, you're just like reducing the number of features by like so much.

55:06And why would you do that? Is the way I think about it. And of course, this is not like a, you need KPIs for a lot of roles and you cannot just like eliminate it completely. But like, I think jumping to structuring KPIs too quickly would be a mistake. Did this kind of evolve over time or did you initially try to go in the direction of being more analytical and then moved back from it? initially I wasn't in terms of determining who's performing and so forth. and then I wondered whether I should because that's how every company does it and then we did for some time and then we went back to not being so much like that.

55:44So it was kind of a, you know, I had this urge of like, this is the norm, everyone does it like this, I think maybe we're doing it wrong but then we went back and I think for the state that we're in right now, it works well but I think long-term, you do need to go back to KPIs because you'll have a lot more specialized roles where their KPIs actually don't really change quarter over quarter. And you can just quantify it with three or four numbers and leave it at that. There's not been very many companies in the history of anywhere that have grown kind of as fast as this, which means you don't really have, there's not exactly a playbook for how to react.

56:23How did that growth feel just internally? How did you make decisions during that 12-month period where you're growing 30X?

56:34It's very intense. I feel very stressed and grateful is I think the way to summarize it. And I have to be very flexible in the way that I work and be okay with the fact that my day-to-day will change very quickly. because if you think about a company that, you know, I think there's, you know, great companies, they 3X in a year, 4X, and sometimes they 10X, and these are like really exceptional growth rates. And those companies, they have, you know, let's say companies 3Xing year over year, they have three, four years, whatever it is to get to this like 30X. And so they have time to kind of iterate their work structure and their day-to-day but but when you 30x in a year you don't have time to iterate like every every month or two what I should spend time on changes and I'm always like man am I spending the right time on the right things like I'm constantly questioning this and it's like the most stressful thing because you know when I'm in a certain thread and I read the thread for like 10 minutes in Slack.

57:46And then I, at the end, I'm like, okay, a good decision was already made. And I just replied, great. Like nothing changed about the state of the world. I just, and like, I just do this to like many different threads within Slack. And I realized that like, okay, maybe I should actually abstract myself away slightly from some of these details. So things like this happen every few months. And I have to be in this mode of like very flexible with my work structure and being okay with like changing it very rapidly. And so does, I mean, so does everyone else in the company. Like there's folks that are very much like IC and hands-on and like they, obviously they have to be in the details and the team grows so fast under them that they have to become a leader like within like two weeks of joining.

58:33And like things like this are really abnormal. And so a lot of folks in the company, pretty much everyone has to constantly change the way they work. And it's really difficult, but it's also fun. How did you think about which areas were the highest leverage points that you could be focused on any given moment over the course of that change from going from a recruiting business to like an expert's business? Yeah, I think now, I mean, obviously in the early days, doing everything, being in the code base, doing sales and all the rest. And then, you know, within the first like few quarters into the company, kind of being a little bit less in the code base, hiring a good engineering team, but then continuing to do sales.

59:15So I think now what's the main focus for me are still being an account executive and, you know, doing sales because we don't have that many customers. So we need to, and it's obviously really, really fun to meet these like exceptional researchers and have these conversations with them. So a lot of my time right now is well spent doing sales and doing it in a very non-salesy way. I mean, we just like, we just hang out with these folks and like get dinner. The best sales is, it's not transactional. It's just like a relationship and it's an evolution of a relationship. Exactly. And we take a, our CRO said this actually yesterday.

59:51It was a good phrase. He said, you know, we take a very human first approach for our experts and we also do for our clients. I mean, we go to dinner with them and we don't talk about anything related to selling them anything. And sometimes they actually, it's funny, they message us after we've gone this a couple of times. They're like, hey, why don't you like sell anything like during that dinner? Like that was so nice. And that results in more expansion. So that's, I think, that's the approach we take for kind of both ends of the market. But what I spend time now on is mainly product roadmap, trying to, again, productize our operations as much as we can, fundraising.

1:00:32We'll do a little bit of that soon. And customer calls and then aligning incentives. And then, of course, being in the weeds with the team as much as I can because that is a very important part of what I do. How have you kind of thought about when the right time is to basically go into a new vertical and go take on some new challenge versus just making sure that the thing that you're currently doing is done kind of 11 star experience in the sense of Airbnb? It's a really hard balance, especially when you raise some money. There's a lot of there's a lot of experiments you could take. I mean, you can just assign some amount of funds to some random project, some random app.

1:01:15and it might go well. And you can kind of silo a team to work on it. And it's fun to do so because it's like a new idea. It's like a startup within a startup. But I think that's something that we try to avoid as much as we can. And in fact, the team tells me to avoid this. I think I do a bit too much of this. I try to go on these side experiments. And we have our CMO, Daniel. He pushes back on us a lot, which is a good balance. The other part to the kind of data infrastructure we're building is naturally we do have a few pillars to the product and it's less of a, I wish it was like a one roadmap Airbnb type, one application that kind of serves customers.

1:01:58It's more of a infrastructure play that has a lot of components to it. Of course, like the recruitment part, the data platform, the RL environments that we create. And so naturally we do have to have kind of a few different engineering teams that do have different roadmaps and so forth. but we try to limit the number of new engineering teams as much as we can and you know have have everything kind of in one platform to the extent that it's to the extent that it's possible and you know to kind of reduce the number of new subdomains that are that are created when you have a dinner with a customer and you just talk with them for you know a couple hours yeah have you been able to kind of figure out where the puck is you know skate to the puck where it's going beforehand and then just plan out a couple months in advance that you're going to have to get there you know to help them um let's say four months in advance or six months in advance the short the short answer is sort of um i mean we we're in this area that is like very frontier of course what what our customers do the research they do is um exceptional research that has a lot of very risky hypotheses that don't necessarily work out every time.

1:03:13And obviously these labs are spending many billions of dollars on this research. And so it's harder for a non-lab to kind of hypothesize these same things and let them and kind of predict what it will be. But we try to do as much as we can on these kind of proactive pipelines that we create. And one of them actually is the is the robotics one where we i think it's like four or five months ago i basically randomly guessed and there's like not much research into it that i think human demonstration kind of egocentric data will probably work my my intuition was um was like you you can't really scale up teleoperations teleoperated robots you need to figure out a way to map real humans doing things to robots learning from them.

1:04:10You have to figure that out. Like, and if, and if you don't, if the robotics labs don't figure that out, I don't think robots will work. And so, so we decided to just start this pipeline with like, you know, a hundred people to begin with that were doing these like household tasks and recording themselves to it. And then, and then a few months later, we saw, you know, this is obviously public. We saw physical intelligence came out with the paper around um egocentric data is actually like the most useful thing ever and we're like shit we also guessed that we don't know why but great and and we uh you know we we scaled the pipeline from 100 to 3 000 right away when that happened so we do make these guesses and i think a lot of it is based on like just like the intuition that we have um based on pipelines that we have but it's uh it's it's it's tough to get it right so there's a lot of like data that we create that becomes a bit stale because it's not actually useful.

1:05:03Yeah. How did you take that, you know, when you realize that you need to scale up the team by like 30x to collect this human data? How do you actually do that? Like, what did that look like internally? There's a lot of things to think about here in terms of scaling that up. The first is, can our recruitment engine be applied as well as it's applied to experts, to generalists. So we had to first kind of like build an environment that allows for generalists to be vetted well. And part of that was them recording a video that's similar to the tasks that they're going to do on the job and seeing if they do that well.

1:05:43Another part of it is that we, it's a, you know, obviously we really optimize for everyone that goes through funnel to be really happy, but there is going to be folks that are unhappy. be I mean for for for a massive pipeline like this and so when the pipeline is so large there's a kind of a branding thing that we have to think about as well like it's a how do we make sure that there isn't leaks that happen when there's 3 ,000 new people joining in this pipeline within two weeks so we have to kind of prepare on the on the marketing side basically so there's a bunch of things like this that we think about and we and you know the funny part is there's no there's no time to think about these things because the customer says, Hey, we want this much data.

1:06:28And it's like a very nice contract and we, somebody will do it and you know, we want it to be us. So we have to do it and we have to figure out all the rest that, that comes with it. You know, you talked about, there's been a couple, a couple of moments where you like promised speed that was just like, after the fact, realizing it's just impossible to, um, has it brendan any other moments where you kind of like wanted to move faster than was possible and you had to reevaluate things or even things where you thought you could go at you know get something done in like two or three weeks or a month and you're able to do it in like much less time yeah there's a lot of that and we um what we try to do is build kind of a prediction model for timelines now and we actually it's it's funny we um right after this mistake happened with this this person that I told you about that ended up being, that ended up kind of leaving this account as partly as a result of their mistake is we ended up building this like internal, pretty basic, but important model that predicts timelines.

1:07:34And that allows us to be much more accurate with our timelines. But it's still very messy because there's no, the data structures are very different in this space. There's some data points that take three minutes to create. and some sort of like preference labeling for image annotation or video annotation or whatever it is. But then there's also data points that create, that take like 50 hours to create. And it's maybe it's a tax expert that is simulating the full journey of filing someone's taxes in California, which is like a complex thing to do, especially with the new bills coming out. So the range is very high.

1:08:12And so we have to first determine what is the actual time per task, per data that's going to be created. And then from there extrapolates what is the timeline that the customer looks for and therefore how many experts do we need with that timeline. And then kind of work backwards on can we actually hire that many experts in the two days that we have. And then if it's feasible, then we say yes. As companies scale, typically what will happen, especially in extremely fast growing companies, is you'll experience this massive need to hire. And I think Airbnb had this problem where they just overhired and had a whole bunch of different people working on all these different functions to a point where Brian Chesky didn't even know like what everyone was doing at his company.

1:08:56And I think it's incredibly difficult to basically stop that natural short-term incentive and make sure that the people that you're hiring are all focused on the same objective. Over the last like 12 months, I believe you only scaled your team from like 45 to like 80 plus, you know not not that much doesn't track revenue growth at all pavel durov also had this example where he's built this billion plus person you know user company on the back of 30 people he's only got 30 people working at telegram yeah i think they generated like a billion dollars in revenue last year how do you think about deciding when to hire and when not to hire yeah so i think durov is obviously an incredible example of this and it's uh i think it's hard for any company to aspire to be even remotely close to that.

1:09:42I think it's just an exceptional outlier. But what we try to do at Micro One is, you know, we have this notion of keeping a lean team and this kind of lean team philosophy. And we, like I said, in 2025, we went from 35 people to about 60, 70, and then now we're about, you know, 80 or so. And the approach that we've taken since the beginning is hire when you absolutely need to. And really, you have to build this into the culture of the company because oftentimes hires don't come from, you know, me saying we need a hire or, you know, other execs saying we need a hire, but it's more so a recruitment manager or some operations team that thinks they need more bandwidth.

1:10:35And oftentimes we don't have enough, you know sufficient context on being able to argue one way or another like they they can get they can give a very good case on needing to hire and so if you build if you don't build this early on in the dna of the company everyone will will try to kind of uh trend towards hires every time there's problems but but if you build this in from the beginning and you really kind of make clear that like we basically can't hire at micro one unless we really need to it's like the last straw. Exactly. Then the cases are made when they are like truthful and when they just cannot do anything else.

1:11:14So that's one thing that we followed and we continue to do so till today. Um, I think the, uh, the caveat though, is that we, in the middle of 2025, maybe towards the end, we, I decided to actually loosen this just a bit and we decided to hire a bit more and we actually started to hire, you know, the majority of the kind of 40, 50 people that we added in 2025 were actually in the last like quarter or so of the year. And so, and this actually wasn't like a bad outcome either. We were able to scale up a lot faster and kind of sustain the 20, 30 % month over month growth that we were having. And so I think when the company's having this like breakthrough growth, being a little bit more flexible and allowing that to happen with a bit more hires is also an important part of this lean team philosophy.

1:12:12And so as long as the hires are not nearly close to proportional to revenue growth, then I think you'll be really fine. So that's kind of the approach that we take. On that same hiring point, what is the kind of step or what's the process in between someone thinking that they may, you know, they have some function that they need to fill and then they got to find someone to do that. But what is the process that you go through before you decide we're going to hire someone new versus just, you know, you have more leverage, figure out how to do it. Yeah. So, so the first thing is we first see if we can actually delete that thing that there's constraint by.

1:12:49Like the question of requirement. Yeah, exactly like there's a recent case of um there's like very frankly bad process of sending out contracts to experts and it's required a lot of human involvement and therefore one of the teams that does this had to kind of hire a bit faster than others and you know i i looked into like what why there's why is this team kind of growing pretty quickly and we realized that we actually just should not have this requirement of sending contracts in this way. Spent about a week with the engineering team to kind of automate this function, which of course freed up this team's time to do much better things.

1:13:30And we didn't need to hire for that at all. And we slowed it right away. And so I think there's a lot of kind of questioning the requirements as Elon puts it really well, that has to happen. And then I think the other thing is like, if that requirement must be there and that function must exist, really dive in and see if folks are actually not getting enough time to do it or if they're busy with some other coordination or something that exists and then they're not able to do the core function that they're supposed to do and try to delete those functions to allow them to actually focus on that one thing that must exist.

1:14:09When things are growing as fast as they are right now, how do you kind of plan a few months in advance or even like a year or two out and try to predict where the business will be and how can we build everything today so that you're basically ready for when that point hits? Honestly, we don't plan. We try to do a little bit of planning and then it just ends up being, you know, we have to kind of replan similar to how the KPIs have to be kind of readjusted so many times. And I think, you know eventually we'll get out of this state of like just complete outlier 30x year-over-year type growth and you know it will eventually normalize a bit more and that's that's the simple truth at that point there will be more of a typical planning and budgets and so forth but right now when you know when the when like when the board says like what's the the budget planning for the year i mean i haven't even looked at what that means like yeah we're uh actually don't even know how to like structure a budget yet like we'll look into that later we we are, you know, we've been profitable.

1:15:10So like the budget is fine. So, you know, once we become like not profitable and can plan a bit more, then I'll look into like what it means to set up a budget and we'll do it at that point. So there's not much planning that happens. Yeah. I also, I kind of love that. Not very many companies, especially this early on are able to get to a point where they are default, not only default alive, where you have enough cash in the bank to get to profitability, but you're already there. And what does that kind of enable you to do like just psychologically day to day when you're thinking about business decisions you know you have a great business and it's growing really fast how are you kind of thinking about growing it even further after you've reached that point yeah so i think it's a you know obviously we're very grateful to be in this position of profitability we um we've been profitable for a while now pretty much almost entirely 2025 was profitable and this actually resulted in us being um net profitable historically now, which means we've raised, we have not touched it and we've added to it.

1:16:14Um, so, you know, feel obviously feels really good to be in this position and be able to kind of determine our own, on our faith and not, not be able to, uh, if we, if we don't want to not have to raise money. Um, but, but I think at the same time, there's a lot of, uh, there's a lot of good stuff to spend on and, and we want to, you know, part of it is just this idea of like proactively building pipelines. And for us to have material revenue come from those pipelines, the cost basis needs to be high. I mean, if we want a pipeline to give us tens of millions of dollars in revenue, the cost basis needs to be some order max to that.

1:16:47It needs to be tens of millions of dollars. And so that sort of spend can only happen if we have a really nice cash cushion. so that's why we are going to raise but we um but but we'll try to be very capital efficient still and and basically the only line item on the pnl that'll grow really fast will be r &d which is again this like proactive data spend and everything else we'll try to keep as stable as possible and i think there's a there's a chance even post our next raise and post spending a lot on on these these pipelines, we may stay profitable and it won't be, it won't really be the goal.

1:17:31I mean, I think it's wrong to like fully optimize for profitability right now, but I think it will be the byproduct of just having discipline within the company and having this like insane growth. So, you know, we'll, we'll sort of, we'll look at it as a bit of a bit of a side aim, but, but not like a full focus. Is this something where it's a little bit like Google where DeepMind could not do what they're doing if Google didn't just have this massive cash cow, which is search and ads. And because of that, they're able to take all these other bets. Like I look at DeepMind and they're working on completely different things than other AI labs are.

1:18:07They're working on like protein folding and stuff like that. And part of the reason that they're able to do that is because they've got this just massive cash generation engine, which they know is going to be there pretty much forever. How is that kind of allowed you to take or is going to allow you to take different bets? Yeah, I think obviously DMI is an incredible company. No way that we can compare ourselves in any way. But I think there's this idea of just having a large cash cushion gives you the flexibility to take on big, bold bets. And we believe that this market will be a multi-trillion dollar a year spend market on human data over the long run.

1:18:49But of course, there's a chance that that's wrong. And there's a chance that like, maybe the data business actually doesn't work. And if we have that cash cushion, we'll do everything we can to make it work and be the biggest winner in the space. But if it doesn't, we also have time to make sure that the current state of our product gets applied to something else. So that kind of, you know, comfort is not the right word, but that cash cushion in general allows us to just really take the bold bets that are required to hopefully build a multi-hundred billion dollar company. You posted a blog post probably like two weeks ago now, and you basically said here's why training data and human data is just going to continue to be valuable over time.

1:19:29Do you want to walk through what that actually looks like? Why can human data just be like a trillion dollar industry long term? Yes. I have a lot of thoughts on this, so cut me off if I'm talking too much here. But I, so there's a bunch of things. First is there's this notion of last mile in AI. And I think the first thing here is that the last mile in AI just doesn't exist. The reason for it is, you know, right now, the strategy for labs, what people perceive that the strategy is is that they're kind of trying to automate and optimize the last 10 % of capabilities. But in some ways, that's true.

1:20:16But in other ways, the capabilities that exist currently won't be the same ones that exist in the future. The function space of the economy will expand very largely and rapidly. With every technological revolution, the function space of the economy expands rapidly. But especially with this one, it will expand because humanity will have its time freed up on hopefully all of the current functions that they do, which means all of the current functions of the economy will be automated over time. And of course, this won't be an instantaneous thing. It'll be a very long iteration over time, which means as that happens in a continuous manner, human time gets freed up in every domain and they get to spend time on things that are more creative, more fun.

1:21:09And what that results in is net new functions that get created within those domains. So the example I'll give you that makes this concrete is obviously an area that we're very familiar with, which is recruitment. We believe that Micro One has the most powerful recruiters on the planet currently because of the agent that we built. And they're able to hire hundreds of people every single day. And their functions as recruiters does not look the same as any other recruiter at all. They're still recruiters. That job still is there and it's now way more impactful, but the tasks they do looks fundamentally different.

1:21:43And it's, it's ones that it's a lot more fun for, for, for, for these recruiters to work on tasks that they do versus a task that typical recruiters do. It's almost like the farmer pre and post industrial revolution where you're like actually on a farm versus you're just controlling a bunch of tractors. Yeah. And you can hundred X that across every function in the economy. If, if intelligence actually does become quote unquote commoditized. So now take this recruitment example and take what's happening literally today at Micro One, which is our recruiters are coming up with new things to do that are really impactful, that are now part of their function space, if you will.

1:22:22And now we're going after and automating those functions and this loop will continue. And so I'll give you one specific example in terms of this recruitment function space is that our recruiters, because they don't do any interviews anymore and our agent does all of it, they're able to spend time on this creative sourcing strategies where they create these fun, almost like marketing campaigns. And they're almost like doing kind of a marketing job in a recruitment context where recruiters would not do that before. And so this is a net new function that hasn't been created. And it's a lot more fun for humans that will automate also, but then there'll be nuance.

1:22:57So that's the first thing, which is there is no last mile. And so as humanity comes up with new functions, we will have to create, we would have to get structured human judgment on those net new functions to then automate those functions. So that's first. The second thing is, which is like, you know, maybe even a bigger reason, which is the labs and everyone just broadly in the US and pretty much all around the world is spending a lot of money on computes build-outs and of course algorithm efficiencies like hiring researchers and so forth but mainly compute build-outs like hundreds of billions of dollars maybe like trillion dollars at this point and they're they're betting on future inference and as jensen says a lot inference is going to million x or billion x or something and for that to happen and for the for the for like for the economy to not collapse entirely because of all these build-outs we need to unlock a lot of new capabilities for models and and the inference will the current state of models that inference will not be used at all like very small portions of what the future bet inferences will be used and so we must unlock a lot of new capabilities and and the way to do so again is structured human judgment in each of those domains that we're trying to unlock uh ai uh capabilities in and And there's no other route.

1:24:23The third thing is as synthetic data becomes more and more relevant and useful, what that results in is every human data point becomes a lot more valuable. Because if you think about the current pipelines, what happens in pretty much every pipeline is you take some amount of human structure judgment and you extrapolate that by a lot with synthetic data. Of course, it's not in every pipeline, but like in most pipelines, there's some notion of increasing the data points by orders of magnitude, sometimes like 1 ,000x with synthetic data. And if synthetic data generation becomes even better and you're able to like, you know, millionx that or something, and like the model can train on way less human data points, that's actually the greatest thing that can happen to our business and this human data market, which like is maybe the most basic economic principle, which is if something is more valuable, a lot more of it will be, there will be demand for.

1:25:25And so the spend will increase by orders of magnitude. And so we hope synthetic data continues to be, continues to accelerate in capabilities. And we actually want to like contribute to that as well. And so these are kind of the three main things that will result in this massive market. And actually, sorry, last thing I'll say is there is, if you think about like a, let's take one example of lawyers. Lawyers, what they do in their job is they create basically unstructured data for their law firm all day, right? They redline some random contract, they get paid for it. They do some M &A, they get paid for it.

1:26:03And it's a lot of unstructured work happening, which obviously is very useful for the economy. I mean, clearly. But you have to wonder why lawyers are, as just one example, are getting paid more to work at Micro One than their law firm. They're getting roughly 20 % more. And it's not, I mean, of course it's us paying them more, but it's like the economy that like allows for that. The value that they're creating is just literally higher. Exactly. And specifically the structured red linings that they do, the structured M &A tasks that they create are, the economy has determined are more valuable than the unstructured work they do for the law firm and so so one natural argument is like okay so why don't they just spend their whole time then like clearly but but but but you can't do that because there needs to be some percentage to to actually run the economy until we automate it um and so there needs to be some like equilibrium point of spending some portion of their time on structured value creation and then and then unstructured but but you could infer from this uh argument that if if the current state of basically every domain is some percentage of their time is spent on structured human judgment and human data creation, then over time, basically the entire economy will spend some small percentage of their time on this idea of human data.

1:27:23And so you could take a percentage of the entire labor market and put it as human data spent. So even if you take 5%, and which we have, you know, I did some math on like, well, I think 5 % is reasonable. If you take 5 % of 50 trillion a year in spend, that's$2.5 trillion a year. And then you just discount it by a lot for whatever reason, you know, maybe not all of it will be kind of like recognized in terms of like spend and a lot of it will happen in like less formal ways. Then you can make a pretty clear argument for$1 trillion a year in human data spend over the long run. This almost reminds me of kind of like the safe and when the safe was invented.

1:28:02There was all these very complex instruments that startups used to be funded on. And then I think, what was it? Carolyn Levy invented the safe and it suddenly enabled founders to basically raise a huge amount of money in a very short period of time, simply, simply because there wasn't necessarily as many steps in the process. It didn't cost as much. It wasn't as complex. Yeah. I think in that case, you would maybe argue that because it's so much easier to raise and maybe a little bit easier to also invest, that somehow there'll actually be less VCs? I mean, certainly not. There's a lot more. And that, you know, I think that has a good impact on the economy.

1:28:43So yeah, I think it's similar arguments. The kind of the principle of Jevons paradox. I want to spend a little time on, you mentioned, I think that you're basically getting more and more data points that are like long horizon tasks. And you're trying to have a single person maybe spend a week or weeks on a single data point that, you know, is fed into a model. How has that kind of evolved and how did you kind of come to that conclusion? If you think about the current state of models, they're very good at answering complex questions in pretty much every domain, no matter how complex the domain is, how niche it is, they will answer your question.

1:29:22And you could put some broad accuracy on the answers of like, let's say 90 % just generally of accuracy. And they continue to get better in those as well. And I think we'll approach even higher accuracy just broadly. But then if you think about models doing tasks and what it means to do a task, I think for a human, what it means to do a task is like, you essentially answer a bunch of questions, right? You first determine, you know, the first step of a task is you kind of planning it. So the question is like, what is the plan for this task, right? And then like you take an action and you're just like answering the question of what the next action should be.

1:30:03And like, you're essentially just answering a series of questions. And then obviously you're making some movements to actually act on it. So what the models have to do is basically just answer a bunch of questions in a row to do tasks. And answering a question with 90 % accuracy is a good outcome. But if you have to answer 20 questions in a row to do a task and you do 0.9 to the power of 20, you're going to get like, I don't know, close to zero, like 0.15 or something like that. Some very low number of accuracy, which means you're going to have that task be done correctly like literally 10 % of the time.

1:30:38And that's obviously horrible. And so this idea of like compounding errors is why models are not yet good at doing much. And even for applications that are like coding and they're obviously making a lot of great impact, if you if you ask cursor to kind of go back in the conversation and like check something you asked a few turns ago there's some struggles right and this so this idea of like multi-step tasks and very long horizon tasks is what models continue to struggle with and and so the way to get them to not struggle is by um not creating tasks that are questions and answers but creating tasks that are actual tasks that are very long horizon.

1:31:25So one example would be if you look at the domain of taxes, and currently if you ask anything about W2 California taxes, you'll get great responses. But there isn't really an agent that will file your taxes. So what we're trying to do is one of the RL environments that we're building is essentially simulating the full end-to-end process of filing one's taxes, which is certainly complex. And it's not just about the final tax form that you fill out. It's about first getting the right information from the customer. The customer is going to probably tell to you like, Hey, can you do my taxes and not send you any information?

1:32:04And then you're gonna say, Hey, like, can you send your income for the year, send your bank statements and a few other things? They'll send you half of what you asked. And then you'll have to ask again. And then you'll, uh, you know, you, you'll probably need some more information because it had some capital gains and so forth. So the first step is like a bunch of tasks to actually gather the right info. And then in the context of, again, in the context of taxes, you then have to kind of have a conversation with the customer about like optimizing their taxes. You certainly don't want to get the information and then file a tax.

1:32:31You got to like say, Hey, maybe, um, maybe go buy a car or something and like reduce your, uh, or increase your expenses and you know, whatever, maybe sell some stocks and have some realized losses to, to, uh, match, uh, to reduce to realize gains or whatever it is. And, and then, so, so there's like a bunch of questions to be answered in that, in that conversation. And then, and then there's a bunch of other steps. And then ultimately you file the taxes, which is you take all that previous States that exist, and then you, you, you fill out the form that is sent to the government. And so if you don't have a very long horizon task that kind of gives you rewards that are, that are partial rewards for each of those States before the final action of filing one's PD, filling out one's PDF, you will fail quite badly.

1:33:15So that's kind of the approach we're taking now, which is these very long horizon tasks that simulates end-to-end workflows. A lot of people kind of thought that 2025 was going to be the year of agents, and that didn't really happen, like you said, because they're very long horizon tasks and they're complex. It's very difficult for things to really understand what all the steps are to actually execute and make something happen. How do you think agents are going to happen to the point where we have super useful entities that are able to go execute your will? Yeah, I think you're exactly right. I mean, there's a lot of, there's a couple of really good use cases, coding, customer support, and a few others that have been working very well.

1:33:55But I think realistically, there hasn't been yet a huge adoption within enterprises or just broadly. And I think part of that is part of what people argue is the fact that enterprises, there's just like this lag in the economy to adopt new technology generally. And I think that's partly true, but I would say it's actually mainly because of this notion of evals is not yet built into enterprises. So the first part is, of course, you have to improve the foundational models in these long horizon tasks. So that's kind of the first step. But then the second is when you actually are using a foundational model to build any agent in any given context for an enterprise, you need to actually really further evaluate that within the workflows, within the very niche workflows of that one enterprise.

1:34:47And so this is like this notion of like contextual evaluations, which I think enterprises have not yet really thought about or implemented. And I think the way that this adoption speed changes is if enterprises start to treat evaluations as core as engineering in their full-on product buildouts. So basically a very large portion of the product budgets within these companies has to be spent on evaluations where they're kind of looking at each function that this agent should have needs to be qualitatively assessed versus like, does it work or not? But how well does it work? Final question. What's the hardest thing you've overcome?

1:35:29them realizing in retrospect how much my parents gave up to come to the u.s when they were when they had a pretty good life in iran they they had to um they had to give up pretty much everything and kind of restart their life and of course u.s is definitely the greatest country to be in but if someone has spent decades in one country and has to reset their life entirely it's an incredibly difficult thing to do and you know I remember the early days of my parents like really struggling when they came to the U.S. and us having to kind of live in a single bedroom with a family of four for a long time and all the rest that I'm now kind of appreciative of in retrospect of how hard they had to work for me and my sister to basically be able to live here and have a good education and have the opportunity to build companies and so forth.

1:36:34And so I think this is part of my, maybe the main part of my drive is really making sure that my parents get a great outcome with this move. And hopefully I can contribute to that outcome. Thank you.